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Luo AC, Sydnor VJ, Pines A, Larsen B, Alexander-Bloch AF, Cieslak M, Covitz S, Chen AA, Esper NB, Feczko E, Franco AR, Gur RE, Gur RC, Houghton A, Hu F, Keller AS, Kiar G, Mehta K, Salum GA, Tapera T, Xu T, Zhao C, Salo T, Fair DA, Shinohara RT, Milham MP, Satterthwaite TD. Functional connectivity development along the sensorimotor-association axis enhances the cortical hierarchy. Nat Commun 2024; 15:3511. [PMID: 38664387 PMCID: PMC11045762 DOI: 10.1038/s41467-024-47748-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
Human cortical maturation has been posited to be organized along the sensorimotor-association axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the sensorimotor-association axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3355; ages 5-23 years): the Philadelphia Neurodevelopmental Cohort (n = 1207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1126). Across datasets, the development of functional connectivity systematically varied along the sensorimotor-association axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These consistent and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.
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Affiliation(s)
- Audrey C Luo
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Adam Pines
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305, USA
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, 55455, USA
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sydney Covitz
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Andrew A Chen
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA
| | | | - Eric Feczko
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
| | - Alexandre R Franco
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, 10962, USA
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Fengling Hu
- Penn Statistics in Imaging and Visualization Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arielle S Keller
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Gregory Kiar
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
| | - Kahini Mehta
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Giovanni A Salum
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Tinashe Tapera
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, 02115, USA
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
| | - Chenying Zhao
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Taylor Salo
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, 55455, USA
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
- Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, 10962, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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2
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Kang K, Seidlitz J, Bethlehem RA, Xiong J, Jones MT, Mehta K, Keller AS, Tao R, Randolph A, Larsen B, Tervo-Clemmens B, Feczko E, Dominguez OM, Nelson S, Schildcrout J, Fair D, Satterthwaite TD, Alexander-Bloch A, Vandekar S. Study design features that improve effect sizes in cross-sectional and longitudinal brain-wide association studies. bioRxiv 2024:2023.05.29.542742. [PMID: 37398345 PMCID: PMC10312450 DOI: 10.1101/2023.05.29.542742] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Brain-wide association studies (BWAS) are a fundamental tool in discovering brain-behavior associations. Several recent studies showed that thousands of study participants are required to improve the replicability of BWAS because actual effect sizes are much smaller than those reported in smaller studies. Here, we perform analyses and meta-analyses of a robust effect size index (RESI) using 63 longitudinal and cross-sectional magnetic resonance imaging studies from the Lifespan Brain Chart Consortium (77,695 total scans) to demonstrate that optimizing study design is critical for improving standardized effect sizes and replicability in BWAS. A meta-analysis of brain volume associations with age indicates that BWAS with larger covariate variance have larger effect size estimates and that the longitudinal studies we examined have systematically larger standardized effect sizes than cross-sectional studies. We propose a cross-sectional RESI to adjust for the systematic difference in effect sizes between cross-sectional and longitudinal studies that allows investigators to quantify the benefit of conducting their study longitudinally. Analyzing age effects on global and regional brain measures from the United Kingdom Biobank and the Alzheimer's Disease Neuroimaging Initiative, we show that modifying longitudinal study design through sampling schemes to increase between-subject variability and adding a single additional longitudinal measurement per subject can improve effect sizes. However, evaluating these longitudinal sampling schemes on cognitive, psychopathology, and demographic associations with structural and functional brain outcome measures in the Adolescent Brain and Cognitive Development dataset shows that commonly used longitudinal models can, counterintuitively, reduce effect sizes. We demonstrate that the benefit of conducting longitudinal studies depends on the strengths of the between- and within-subject associations of the brain and non-brain measures. Explicitly modeling between- and within-subject effects avoids conflating the effects and allows optimizing effect sizes for them separately. These findings underscore the importance of considering study design features to improve the replicability of BWAS.
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Affiliation(s)
- Kaidi Kang
- Department of Biostatistics, Vanderbilt University Medical Center
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, The Children’s Hospital of Philadelphia
- Department of Psychiatry, University of Pennsylvania
- Lifespan Brain Institute of The Children’s Hospital of Philadelphia and Penn Medicine
| | | | - Jiangmei Xiong
- Department of Biostatistics, Vanderbilt University Medical Center
| | - Megan T. Jones
- Department of Biostatistics, Vanderbilt University Medical Center
| | - Kahini Mehta
- Department of Psychiatry, University of Pennsylvania
- Lifespan Brain Institute of The Children’s Hospital of Philadelphia and Penn Medicine
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania
| | - Arielle S. Keller
- Department of Psychiatry, University of Pennsylvania
- Lifespan Brain Institute of The Children’s Hospital of Philadelphia and Penn Medicine
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center
| | - Anita Randolph
- Department of Pediatrics, University of Minnesota Medical School
| | - Bart Larsen
- Department of Pediatrics, University of Minnesota Medical School
| | - Brenden Tervo-Clemmens
- Department of Department of Psychiatry & Behavioral Sciences, University of Minnesota Medical School
| | - Eric Feczko
- Department of Pediatrics, University of Minnesota Medical School
| | | | - Steve Nelson
- Department of Pediatrics, University of Minnesota Medical School
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Damien Fair
- Department of Pediatrics, University of Minnesota Medical School
| | - Theodore D. Satterthwaite
- Department of Psychiatry, University of Pennsylvania
- Lifespan Brain Institute of The Children’s Hospital of Philadelphia and Penn Medicine
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania
| | - Aaron Alexander-Bloch
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, The Children’s Hospital of Philadelphia
- Department of Psychiatry, University of Pennsylvania
- Lifespan Brain Institute of The Children’s Hospital of Philadelphia and Penn Medicine
| | - Simon Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center
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3
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Keller AS, Moore TM, Luo A, Visoki E, Gataviņš MM, Shetty A, Cui Z, Fan Y, Feczko E, Houghton A, Li H, Mackey AP, Miranda-Dominguez O, Pines A, Shinohara RT, Sun KY, Fair DA, Satterthwaite TD, Barzilay R. A general exposome factor explains individual differences in functional brain network topography and cognition in youth. Dev Cogn Neurosci 2024; 66:101370. [PMID: 38583301 PMCID: PMC11004064 DOI: 10.1016/j.dcn.2024.101370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 03/19/2024] [Accepted: 03/26/2024] [Indexed: 04/09/2024] Open
Abstract
Childhood environments are critical in shaping cognitive neurodevelopment. With the increasing availability of large-scale neuroimaging datasets with deep phenotyping of childhood environments, we can now build upon prior studies that have considered relationships between one or a handful of environmental and neuroimaging features at a time. Here, we characterize the combined effects of hundreds of inter-connected and co-occurring features of a child's environment ("exposome") and investigate associations with each child's unique, multidimensional pattern of functional brain network organization ("functional topography") and cognition. We apply data-driven computational models to measure the exposome and define personalized functional brain networks in pre-registered analyses. Across matched discovery (n=5139, 48.5% female) and replication (n=5137, 47.1% female) samples from the Adolescent Brain Cognitive Development study, the exposome was associated with current (ages 9-10) and future (ages 11-12) cognition. Changes in the exposome were also associated with changes in cognition after accounting for baseline scores. Cross-validated ridge regressions revealed that the exposome is reflected in functional topography and can predict performance across cognitive domains. Importantly, a single measure capturing a child's exposome could more accurately and parsimoniously predict cognition than a wealth of personalized neuroimaging data, highlighting the importance of children's complex, multidimensional environments in cognitive neurodevelopment.
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Affiliation(s)
- Arielle S Keller
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Tyler M Moore
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA; Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Audrey Luo
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Elina Visoki
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Mārtiņš M Gataviņš
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA; Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alisha Shetty
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing, China
| | - Yong Fan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Hongming Li
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Allyson P Mackey
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Adam Pines
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Kevin Y Sun
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA; Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Ran Barzilay
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA; Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA
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4
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Radhakrishnan H, Zhao C, Sydnor VJ, Baller EB, Cook PA, Fair DA, Giesbrecht B, Larsen B, Murtha K, Roalf DR, Rush‐Goebel S, Shinohara RT, Shou H, Tisdall MD, Vettel JM, Grafton ST, Cieslak M, Satterthwaite TD. A practical evaluation of measures derived from compressed sensing diffusion spectrum imaging. Hum Brain Mapp 2024; 45:e26580. [PMID: 38520359 PMCID: PMC10960521 DOI: 10.1002/hbm.26580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 12/13/2023] [Accepted: 12/20/2023] [Indexed: 03/25/2024] Open
Abstract
Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of 26 participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n = 20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.
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Affiliation(s)
- Hamsanandini Radhakrishnan
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Chenying Zhao
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Brain Institute, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Bioengineering, School of Engineering and Applied ScienceUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Valerie J. Sydnor
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Erica B. Baller
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Philip A. Cook
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Damien A. Fair
- Masonic Institute for the Developing BrainUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Barry Giesbrecht
- Department of Psychological and Brain SciencesUniversity of CaliforniaSanta BarbaraCaliforniaUSA
| | - Bart Larsen
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Kristin Murtha
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David R. Roalf
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Brain Institute, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sage Rush‐Goebel
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Russell T. Shinohara
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing & AnalyticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing & AnalyticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - M. Dylan Tisdall
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Jean M. Vettel
- Department of Psychological and Brain SciencesUniversity of CaliforniaSanta BarbaraCaliforniaUSA
- U.S. Army Research LaboratoryAberdeen Proving GroundAberdeenMarylandUSA
| | - Scott T. Grafton
- Department of Psychological and Brain SciencesUniversity of CaliforniaSanta BarbaraCaliforniaUSA
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Theodore D. Satterthwaite
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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5
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Zhang S, Larsen B, Sydnor VJ, Zeng T, An L, Yan X, Kong R, Kong X, Gur RC, Gur RE, Moore TM, Wolf DH, Holmes AJ, Xie Y, Zhou JH, Fortier MV, Tan AP, Gluckman P, Chong YS, Meaney MJ, Deco G, Satterthwaite TD, Yeo BT. In-vivo whole-cortex marker of excitation-inhibition ratio indexes cortical maturation and cognitive ability in youth. bioRxiv 2024:2023.06.22.546023. [PMID: 38586012 PMCID: PMC10996460 DOI: 10.1101/2023.06.22.546023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
A balanced excitation-inhibition ratio (E/I ratio) is critical for healthy brain function. Normative development of cortex-wide E/I ratio remains unknown. Here we non-invasively estimate a putative marker of whole-cortex E/I ratio by fitting a large-scale biophysically-plausible circuit model to resting-state functional MRI (fMRI) data. We first confirm that our model generates realistic brain dynamics in the Human Connectome Project. Next, we show that the estimated E/I ratio marker is sensitive to the GABA-agonist benzodiazepine alprazolam during fMRI. Alprazolam-induced E/I changes are spatially consistent with positron emission tomography measurement of benzodiazepine receptor density. We then investigate the relationship between the E/I ratio marker and neurodevelopment. We find that the E/I ratio marker declines heterogeneously across the cerebral cortex during youth, with the greatest reduction occurring in sensorimotor systems relative to association systems. Importantly, among children with the same chronological age, a lower E/I ratio marker (especially in association cortex) is linked to better cognitive performance. This result is replicated across North American (8.2 to 23.0 years old) and Asian (7.2 to 7.9 years old) cohorts, suggesting that a more mature E/I ratio indexes improved cognition during normative development. Overall, our findings open the door to studying how disrupted E/I trajectories may lead to cognitive dysfunction in psychopathology that emerges during youth.
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Affiliation(s)
- Shaoshi Zhang
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National Univeristy of Singapore, Signapore
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Valerie J. Sydnor
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tianchu Zeng
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National Univeristy of Singapore, Signapore
| | - Lijun An
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National Univeristy of Singapore, Signapore
| | - Xiaoxuan Yan
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National Univeristy of Singapore, Signapore
| | - Ru Kong
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National Univeristy of Singapore, Signapore
| | - Xiaolu Kong
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National Univeristy of Singapore, Signapore
- ByteDance, Singapore
| | - Ruben C. Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E. Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tyler M. Moore
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daniel H. Wolf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Avram J Holmes
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, United States
- Wu Tsai Institute, Yale University, New Haven, CT, United States
| | - Yapei Xie
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National Univeristy of Singapore, Signapore
| | - Juan Helen Zhou
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National Univeristy of Singapore, Signapore
| | - Marielle V Fortier
- Department of Diagnostic and Interventional Imaging, KK Women’s and Children’s Hospital, Singapore
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore
| | - Ai Peng Tan
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Peter Gluckman
- UK Centre for Human Evolution, Adaptation and Disease, Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Yap Seng Chong
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Michael J Meaney
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Gustavo Deco
- Center for Brain and Cognition, Department of Technology and Information, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats, Universitat Barcelona, Barcelona, Spain
| | - Theodore D. Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - B.T. Thomas Yeo
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National Univeristy of Singapore, Signapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hopstial, Charlestown, MA, USA
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6
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Hermosillo RJM, Moore LA, Feczko E, Miranda-Domínguez Ó, Pines A, Dworetsky A, Conan G, Mooney MA, Randolph A, Graham A, Adeyemo B, Earl E, Perrone A, Carrasco CM, Uriarte-Lopez J, Snider K, Doyle O, Cordova M, Koirala S, Grimsrud GJ, Byington N, Nelson SM, Gratton C, Petersen S, Feldstein Ewing SW, Nagel BJ, Dosenbach NUF, Satterthwaite TD, Fair DA. A precision functional atlas of personalized network topography and probabilities. Nat Neurosci 2024:10.1038/s41593-024-01596-5. [PMID: 38532024 DOI: 10.1038/s41593-024-01596-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 02/08/2024] [Indexed: 03/28/2024]
Abstract
Although the general location of functional neural networks is similar across individuals, there is vast person-to-person topographic variability. To capture this, we implemented precision brain mapping functional magnetic resonance imaging methods to establish an open-source, method-flexible set of precision functional network atlases-the Masonic Institute for the Developing Brain (MIDB) Precision Brain Atlas. This atlas is an evolving resource comprising 53,273 individual-specific network maps, from more than 9,900 individuals, across ages and cohorts, including the Adolescent Brain Cognitive Development study, the Developmental Human Connectome Project and others. We also generated probabilistic network maps across multiple ages and integration zones (using a new overlapping mapping technique, Overlapping MultiNetwork Imaging). Using regions of high network invariance improved the reproducibility of executive function statistical maps in brain-wide associations compared to group average-based parcellations. Finally, we provide a potential use case for probabilistic maps for targeted neuromodulation. The atlas is expandable to alternative datasets with an online interface encouraging the scientific community to explore and contribute to understanding the human brain function more precisely.
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Affiliation(s)
- Robert J M Hermosillo
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA.
| | - Lucille A Moore
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Eric Feczko
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Óscar Miranda-Domínguez
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Adam Pines
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Ally Dworetsky
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Department of Psychology, Florida State University, Tallahassee, FL, USA
| | - Gregory Conan
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Michael A Mooney
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Center for Mental Health Innovation, Oregon Health and Science University, Portland, OR, USA
| | - Anita Randolph
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Alice Graham
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Eric Earl
- Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD, USA
| | - Anders Perrone
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Cristian Morales Carrasco
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | | | - Kathy Snider
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Olivia Doyle
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Michaela Cordova
- Joint Doctoral Program in Clinical Psychology, San Diego State University, San Diego, CA, USA
- Joint Doctoral Program in Clinical Psychology, University of California San Diego, San Diego, CA, USA
| | - Sanju Koirala
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Gracie J Grimsrud
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Nora Byington
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Steven M Nelson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Department of Psychology, Florida State University, Tallahassee, FL, USA
- Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | - Steven Petersen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Bonnie J Nagel
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
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7
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Shafiei G, Keller AS, Bertolero M, Shanmugan S, Bassett DS, Chen AA, Covitz S, Houghton A, Luo A, Mehta K, Salo T, Shinohara RT, Fair D, Hallquist MN, Satterthwaite TD. Generalizable Links Between Borderline Personality Traits and Functional Connectivity. Biol Psychiatry 2024:S0006-3223(24)01140-5. [PMID: 38460580 DOI: 10.1016/j.biopsych.2024.02.1016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 02/02/2024] [Accepted: 02/29/2024] [Indexed: 03/11/2024]
Abstract
BACKGROUND Symptoms of borderline personality disorder (BPD) often manifest during adolescence, but the underlying relationship between these debilitating symptoms and the development of functional brain networks is not well understood. Here, we aimed to investigate how multivariate patterns of functional connectivity are associated with borderline personality traits in large samples of young adults and adolescents. METHODS We used functional magnetic resonance imaging data from young adults and adolescents from the HCP-YA (Human Connectome Project Young Adult) (n = 870, ages 22-37 years, 457 female) and the HCP-D (Human Connectome Project Development) (n = 223, ages 16-21 years, 121 female). A previously validated BPD proxy score was derived from the NEO Five-Factor Inventory. A ridge regression model with cross-validation and nested hyperparameter tuning was trained and tested in HCP-YA to predict BPD scores in unseen data from regional functional connectivity. The trained model was further tested on data from HCP-D without further tuning. Finally, we tested how the connectivity patterns associated with BPD aligned with age-related changes in connectivity. RESULTS Multivariate functional connectivity patterns significantly predicted out-of-sample BPD scores in unseen data in young adults (HCP-YA ppermuted = .001) and older adolescents (HCP-D ppermuted = .001). Regional predictive capacity was heterogeneous; the most predictive regions were found in functional systems relevant for emotion regulation and executive function, including the ventral attention network. Finally, regional functional connectivity patterns that predicted BPD scores aligned with those associated with development in youth. CONCLUSIONS Individual differences in functional connectivity in developmentally sensitive regions are associated with borderline personality traits.
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Affiliation(s)
- Golia Shafiei
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute of Perelman School of Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Arielle S Keller
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute of Perelman School of Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Maxwell Bertolero
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute of Perelman School of Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Sheila Shanmugan
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute of Perelman School of Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Dani S Bassett
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, Pennsylvania; Santa Fe Institute, Santa Fe, New Mexico
| | - Andrew A Chen
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Sydney Covitz
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute of Perelman School of Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, Minnesota
| | - Audrey Luo
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute of Perelman School of Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Kahini Mehta
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute of Perelman School of Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Taylor Salo
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute of Perelman School of Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Damien Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, Minnesota; Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, Minnesota
| | - Michael N Hallquist
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute of Perelman School of Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania.
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8
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Wang X, Zhao K, Yao L, Fonzo GA, Satterthwaite TD, Rekik I, Zhang Y. Delineating Transdiagnostic Subtypes in Neurodevelopmental Disorders via Contrastive Graph Machine Learning of Brain Connectivity Patterns. bioRxiv 2024:2024.02.29.582790. [PMID: 38496573 PMCID: PMC10942316 DOI: 10.1101/2024.02.29.582790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Neurodevelopmental disorders, such as Attention Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD), are characterized by comorbidity and heterogeneity. Identifying distinct subtypes within these disorders can illuminate the underlying neurobiological and clinical characteristics, paving the way for more tailored treatments. We adopted a novel transdiagnostic approach across ADHD and ASD, using cutting-edge contrastive graph machine learning to determine subtypes based on brain network connectivity as revealed by resting-state functional magnetic resonance imaging. Our approach identified two generalizable subtypes characterized by robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the somatomotor network. These subtypes exhibited pronounced differences in major cognitive and behavioural measures. We further demonstrated the generalizability of these subtypes using data collected from independent study sites. Our data-driven approach provides a novel solution for parsing biological heterogeneity in neurodevelopmental disorders.
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Affiliation(s)
- Xuesong Wang
- Data 61, Commonwealth Scientific and Industrial Research Organisation, New South Wales, Australia
| | - Kanhao Zhao
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Lina Yao
- Data 61, Commonwealth Scientific and Industrial Research Organisation, New South Wales, Australia
- School of Computer Science and Engineering, University of New South Wales, New South Wales, Australia
| | - Gregory A Fonzo
- Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
| | | | - Islem Rekik
- BASIRA Lab, Imperial-X and Department of Computing, Imperial College London, London, UK
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA
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9
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Satterthwaite TD, Bagautdinova J. Q&A with Ted Satterthwaite and Joëlle Bagautdinova. Cell Rep 2024; 43:113659. [PMID: 38219148 DOI: 10.1016/j.celrep.2023.113659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 12/20/2023] [Accepted: 12/20/2023] [Indexed: 01/16/2024] Open
Abstract
We at Cell Reports discuss the work, interests, and mentoring experiences of Theodore Satterthwaite (TS) and his graduate student and co-author, Joëlle Bagautdinova (JB). They share with us their recent findings highlighting the relationship between the development of cognitive function and white matter and also talk about the challenges and technical advances in cognitive neuroscience and neuroimaging.
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10
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Tu D, Wrobel J, Satterthwaite TD, Goldsmith J, Gur RC, Gur RE, Gertheiss J, Bassett DS, Shinohara RT. Regression and Alignment for Functional Data and Network Topology. bioRxiv 2024:2023.07.13.548836. [PMID: 37503017 PMCID: PMC10370026 DOI: 10.1101/2023.07.13.548836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of pre-processing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales-from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures.
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Affiliation(s)
- Danni Tu
- The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Julia Wrobel
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA
| | - Theodore D. Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA USA
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, PA, USA
| | - Jeff Goldsmith
- Department of Biostatistics, Columbia University, New York, NY, USA
| | - Ruben C. Gur
- Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA USA
- The Penn Medicine-CHOP Lifespan Brain Institute, Philadelphia, PA, USA
| | - Raquel E. Gur
- Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA USA
- The Penn Medicine-CHOP Lifespan Brain Institute, Philadelphia, PA, USA
| | - Jan Gertheiss
- Department of Mathematics and Statistics, School of Economics and Social Sciences, Helmut Schmidt University, Hamburg, Germany
| | - Dani S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T. Shinohara
- The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
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11
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Georgiadis F, Larivière S, Glahn D, Hong LE, Kochunov P, Mowry B, Loughland C, Pantelis C, Henskens FA, Green MJ, Cairns MJ, Michie PT, Rasser PE, Catts S, Tooney P, Scott RJ, Schall U, Carr V, Quidé Y, Krug A, Stein F, Nenadić I, Brosch K, Kircher T, Gur R, Gur R, Satterthwaite TD, Karuk A, Pomarol-Clotet E, Radua J, Fuentes-Claramonte P, Salvador R, Spalletta G, Voineskos A, Sim K, Crespo-Facorro B, Tordesillas Gutiérrez D, Ehrlich S, Crossley N, Grotegerd D, Repple J, Lencer R, Dannlowski U, Calhoun V, Rootes-Murdy K, Demro C, Ramsay IS, Sponheim SR, Schmidt A, Borgwardt S, Tomyshev A, Lebedeva I, Höschl C, Spaniel F, Preda A, Nguyen D, Uhlmann A, Stein DJ, Howells F, Temmingh HS, Diaz Zuluaga AM, López Jaramillo C, Iasevoli F, Ji E, Homan S, Omlor W, Homan P, Kaiser S, Seifritz E, Misic B, Valk SL, Thompson P, van Erp TGM, Turner JA, Bernhardt B, Kirschner M. Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study. Mol Psychiatry 2024:10.1038/s41380-024-02442-7. [PMID: 38336840 DOI: 10.1038/s41380-024-02442-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 01/08/2024] [Accepted: 01/18/2024] [Indexed: 02/12/2024]
Abstract
Schizophrenia is a prototypical network disorder with widespread brain-morphological alterations, yet it remains unclear whether these distributed alterations robustly reflect the underlying network layout. We tested whether large-scale structural alterations in schizophrenia relate to normative structural and functional connectome architecture, and systematically evaluated robustness and generalizability of these network-level alterations. Leveraging anatomical MRI scans from 2439 adults with schizophrenia and 2867 healthy controls from 26 ENIGMA sites and normative data from the Human Connectome Project (n = 207), we evaluated structural alterations of schizophrenia against two network susceptibility models: (i) hub vulnerability, which examines associations between regional network centrality and magnitude of disease-related alterations; (ii) epicenter mapping, which identifies regions whose typical connectivity profile most closely resembles the disease-related morphological alterations. To assess generalizability and specificity, we contextualized the influence of site, disease stages, and individual clinical factors and compared network associations of schizophrenia with that found in affective disorders. Our findings show schizophrenia-related cortical thinning is spatially associated with functional and structural hubs, suggesting that highly interconnected regions are more vulnerable to morphological alterations. Predominantly temporo-paralimbic and frontal regions emerged as epicenters with connectivity profiles linked to schizophrenia's alteration patterns. Findings were robust across sites, disease stages, and related to individual symptoms. Moreover, transdiagnostic comparisons revealed overlapping epicenters in schizophrenia and bipolar, but not major depressive disorder, suggestive of a pathophysiological continuity within the schizophrenia-bipolar-spectrum. In sum, cortical alterations over the course of schizophrenia robustly follow brain network architecture, emphasizing marked hub susceptibility and temporo-frontal epicenters at both the level of the group and the individual. Subtle variations of epicenters across disease stages suggest interacting pathological processes, while associations with patient-specific symptoms support additional inter-individual variability of hub vulnerability and epicenters in schizophrenia. Our work outlines potential pathways to better understand macroscale structural alterations, and inter- individual variability in schizophrenia.
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Affiliation(s)
- Foivos Georgiadis
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland.
| | - Sara Larivière
- McGill University, Montreal Neurological Institute, Montreal, QC, Canada
| | - David Glahn
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, US
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, US
| | - Bryan Mowry
- Queensland Brain Institute, The University of Queensland, St Lucia, QLD, Australia
| | - Carmel Loughland
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, USA
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Carlton South, VIC, Australia
| | - Frans A Henskens
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
| | - Melissa J Green
- School of Clinical Medicine, Discipline of Psychiatry and Mental Health, UNSW Sydney, Sydney, NSW, Australia
| | - Murray J Cairns
- School of Biomedical Science and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
| | - Patricia T Michie
- School of Psychological Sciences, University of Newcastle, Newcastle, NSW, Australia
| | - Paul E Rasser
- School of Medicine and Public Health, College of Health, Medicine, and Wellbeing, The University of Newcastle, Callaghan, NSW, Australia
| | - Stanley Catts
- Faculty of Medicine, University of Queensland, St Lucia, QLD, Australia
| | - Paul Tooney
- School of Biomedical Science and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Rodney J Scott
- School of Biomedical Science and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
| | - Ulrich Schall
- Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Vaughan Carr
- School of Clinical Medicine, Discipline of Psychiatry, UNSW Sydney, Sydney, NSW, Australia
| | - Yann Quidé
- School of Clinical Medicine, Discipline of Psychiatry and Mental Health, UNSW Sydney, Sydney, NSW, Australia
| | - Axel Krug
- University Hospital Bonn, Department of Psychiatry and Psychotherapy, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Frederike Stein
- Department of Psychiatry, University of Marburg, Rudolf Bultmann Str. 8, 35039, Marburg, Germany
| | - Igor Nenadić
- Department. of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry, University of Marburg, Rudolf Bultmann Str. 8, 35039, Marburg, Germany
| | - Tilo Kircher
- Department of Psychiatry, University of Marburg, Rudolf Bultmann Str. 8, 35039, Marburg, Germany
| | - Raquel Gur
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ruben Gur
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Andriana Karuk
- FIDMAG Germanes Hospitalàries Research Foundation & CIBERSAM, ISCIII, Barcelona, Spain
| | - Edith Pomarol-Clotet
- FIDMAG Germanes Hospitalàries Research Foundation & CIBERSAM, ISCIII, Barcelona, Spain
| | - Joaquim Radua
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | | | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research Foundation & CIBERSAM, ISCIII, Barcelona, Spain
| | | | - Aristotle Voineskos
- School of Biomedical Science and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
| | - Kang Sim
- West Region, Institute of Mental Health, Singapore, Singapore
| | | | - Diana Tordesillas Gutiérrez
- Department of Radiology, Marqués de Valdecilla University Hospital, Valdecilla Biomedical Research Institute IDIVAL, Santander, Spain
| | - Stefan Ehrlich
- Division of Psychological & Social Medicine and Developmental Neurosciences, Technischen Universität Dresden, Faculty of Medicine, University Hospital C.G. Carus, Dresden, Germany
| | - Nicolas Crossley
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Vince Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - Kelly Rootes-Murdy
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - Caroline Demro
- University of Minnesota Department of Psychology, Minneapolis, MN, USA
- Minneapolis VA Health Care System, Minneapolis, MN, USA
| | - Ian S Ramsay
- University of Minnesota Department of Psychiatry & Behavioral Sciences, Minneapolis, MN, USA
| | - Scott R Sponheim
- Minneapolis VA Health Care System, Minneapolis, MN, USA
- University of Minnesota Department of Psychiatry & Behavioral Sciences, Minneapolis, MN, USA
| | - Andre Schmidt
- University of Basel, Department of Psychiatry, Basel, Switzerland
| | | | | | - Irina Lebedeva
- Mental Health Research Center, Moscow, Russian Federation
| | - Cyril Höschl
- National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic
| | - Filip Spaniel
- National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Dana Nguyen
- Department of Pediatric Neurology, University of California Irvine, Irvine, CA, USA
| | - Anne Uhlmann
- Department of child and adolescent psychiatry, TU Dresden, Dresden, Germany
| | - Dan J Stein
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Fleur Howells
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Henk S Temmingh
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Ana M Diaz Zuluaga
- Research Group in Psychiatry, Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Colombia
| | - Carlos López Jaramillo
- Research Group in Psychiatry, Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Colombia
| | - Felice Iasevoli
- University of Naples, Department of Neuroscience, Naples, Italy
| | - Ellen Ji
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Stephanie Homan
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Wolfgang Omlor
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Philipp Homan
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Stefan Kaiser
- Division of Adult Psychiatry, Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland
| | - Erich Seifritz
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Bratislav Misic
- McGill University, Montreal Neurological Institute, Montreal, QC, Canada
| | - Sofie L Valk
- Forschungszentrum Jülich, Jülich, Germany
- Max Planck Institute for Cognitive and Brain Sciences, Leipzig, Germany
| | - Paul Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, the Ohio State University, Columbus, OH, USA
| | - Boris Bernhardt
- McGill University, Montreal Neurological Institute, Montreal, QC, Canada
| | - Matthias Kirschner
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland.
- Division of Adult Psychiatry, Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland.
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12
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Wagstyl K, Adler S, Seidlitz J, Vandekar S, Mallard TT, Dear R, DeCasien AR, Satterthwaite TD, Liu S, Vértes PE, Shinohara RT, Alexander-Bloch A, Geschwind DH, Raznahan A. Transcriptional cartography integrates multiscale biology of the human cortex. eLife 2024; 12:RP86933. [PMID: 38324465 PMCID: PMC10945526 DOI: 10.7554/elife.86933] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024] Open
Abstract
The cerebral cortex underlies many of our unique strengths and vulnerabilities, but efforts to understand human cortical organization are challenged by reliance on incompatible measurement methods at different spatial scales. Macroscale features such as cortical folding and functional activation are accessed through spatially dense neuroimaging maps, whereas microscale cellular and molecular features are typically measured with sparse postmortem sampling. Here, we integrate these distinct windows on brain organization by building upon existing postmortem data to impute, validate, and analyze a library of spatially dense neuroimaging-like maps of human cortical gene expression. These maps allow spatially unbiased discovery of cortical zones with extreme transcriptional profiles or unusually rapid transcriptional change which index distinct microstructure and predict neuroimaging measures of cortical folding and functional activation. Modules of spatially coexpressed genes define a family of canonical expression maps that integrate diverse spatial scales and temporal epochs of human brain organization - ranging from protein-protein interactions to large-scale systems for cognitive processing. These module maps also parse neuropsychiatric risk genes into subsets which tag distinct cyto-laminar features and differentially predict the location of altered cortical anatomy and gene expression in patients. Taken together, the methods, resources, and findings described here advance our understanding of human cortical organization and offer flexible bridges to connect scientific fields operating at different spatial scales of human brain research.
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Affiliation(s)
- Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, University College LondonLondonUnited Kingdom
| | - Sophie Adler
- UCL Great Ormond Street Institute for Child HealthHolbornUnited Kingdom
| | - Jakob Seidlitz
- Department of Psychiatry, University of PennsylvaniaPhiladelphiaUnited States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Simon Vandekar
- Department of Biostatistics, Vanderbilt UniversityNashvilleUnited States
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General HospitalBostonUnited States
- Department of Psychiatry, Harvard Medical SchoolBostonUnited States
| | - Richard Dear
- Department of Psychiatry, University of CambridgeCambridgeUnited Kingdom
| | - Alex R DeCasien
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental HealthBethesdaUnited States
| | - Theodore D Satterthwaite
- Department of Psychiatry, University of PennsylvaniaPhiladelphiaUnited States
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania School of MedicinePhiladelphiaUnited States
| | - Siyuan Liu
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental HealthBethesdaUnited States
| | - Petra E Vértes
- Department of Psychiatry, University of CambridgeCambridgeUnited Kingdom
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Aaron Alexander-Bloch
- Department of Psychiatry, University of PennsylvaniaPhiladelphiaUnited States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Daniel H Geschwind
- Center for Autism Research and Treatment, Semel Institute, Program in Neurogenetics, Department of Neurology and Department of Human Genetics, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Armin Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental HealthBethesdaUnited States
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13
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Cieslak M, Cook PA, Shafiei G, Tapera TM, Radhakrishnan H, Elliott M, Roalf DR, Oathes DJ, Bassett DS, Tisdall MD, Rokem A, Grafton ST, Satterthwaite TD. Diffusion MRI head motion correction methods are highly accurate but impacted by denoising and sampling scheme. Hum Brain Mapp 2024; 45:e26570. [PMID: 38339908 PMCID: PMC10826632 DOI: 10.1002/hbm.26570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/22/2023] [Accepted: 12/04/2023] [Indexed: 02/12/2024] Open
Abstract
Head motion correction is particularly challenging in diffusion-weighted MRI (dMRI) scans due to the dramatic changes in image contrast at different gradient strengths and directions. Head motion correction is typically performed using a Gaussian Process model implemented in FSL's Eddy. Recently, the 3dSHORE-based SHORELine method was introduced that does not require shell-based acquisitions, but it has not been previously benchmarked. Here we perform a comprehensive evaluation of both methods on realistic simulations of a software fiber phantom that provides known ground-truth head motion. We demonstrate that both methods perform remarkably well, but that performance can be impacted by sampling scheme and the extent of head motion and the denoising strategy applied before head motion correction. Furthermore, we find Eddy benefits from denoising the data first with MP-PCA. In sum, we provide the most extensive known benchmarking of dMRI head motion correction, together with extensive simulation data and a reproducible workflow. PRACTITIONER POINTS: Both Eddy and SHORELine head motion correction methods performed quite well on a large variety of simulated data. Denoising with MP-PCA can improve head motion correction performance when Eddy is used. SHORELine effectively corrects motion in non-shelled diffusion spectrum imaging data.
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Affiliation(s)
- Matthew Cieslak
- Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Philip A. Cook
- Department of RadiologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Golia Shafiei
- Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Tinashe M. Tapera
- Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Hamsanandini Radhakrishnan
- Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Mark Elliott
- Department of RadiologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - David R. Roalf
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Desmond J. Oathes
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Dani S. Bassett
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of BioengineeringUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of Physics and AstronomyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of Electrical and Systems EngineeringUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Sante Fe InstituteSanta FeNew MexicoUnited States
| | - M. Dylan Tisdall
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
| | - Ariel Rokem
- Department of Psychology and the eScience InstituteUniversity of WashingtonSeattleWashingtonUnited States
| | - Scott T. Grafton
- Department of Psychological and Brain SciencesUniversity of California Santa BarbaraSanta BarbaraCaliforniaUnited States
| | - Theodore D. Satterthwaite
- Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUnited States
- Penn‐CHOP Lifespan Brain InstitutePhiladelphiaPennsylvaniaUnited States
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14
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Bagautdinova J, Bourque J, Sydnor VJ, Cieslak M, Alexander-Bloch AF, Bertolero MA, Cook PA, Gur RE, Gur RC, Hu F, Larsen B, Moore TM, Radhakrishnan H, Roalf DR, Shinohara RT, Tapera TM, Zhao C, Sotiras A, Davatzikos C, Satterthwaite TD. Development of white matter fiber covariance networks supports executive function in youth. Cell Rep 2023; 42:113487. [PMID: 37995188 PMCID: PMC10795769 DOI: 10.1016/j.celrep.2023.113487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 10/05/2023] [Accepted: 11/09/2023] [Indexed: 11/25/2023] Open
Abstract
During adolescence, the brain undergoes extensive changes in white matter structure that support cognition. Data-driven approaches applied to cortical surface properties have led the field to understand brain development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. Although white matter development also appears asynchronous, previous studies have relied largely on anatomical tract-based atlases, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Harnessing advances in diffusion modeling and machine learning, we identified 14 data-driven patterns of covarying white matter structure in a large sample of youth. Fiber covariance networks aligned with known major tracts, while also capturing distinct patterns of spatial covariance across distributed white matter locations. Most networks showed age-related increases in fiber network properties, which were also related to developmental changes in executive function. This study delineates data-driven patterns of white matter development that support cognition.
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Affiliation(s)
- Joëlle Bagautdinova
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Josiane Bourque
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Maxwell A Bertolero
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Philip A Cook
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Fengling Hu
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hamsanandini Radhakrishnan
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russel T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tinashe M Tapera
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Chenying Zhao
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aristeidis Sotiras
- Department of Radiology and Institute for Informatics, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63130, USA
| | - Christos Davatzikos
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) of Penn Medicine and Children's Hospital of Philadelphia (CHOP), University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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15
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Wen J, Nasrallah IM, Abdulkadir A, Satterthwaite TD, Yang Z, Erus G, Robert-Fitzgerald T, Singh A, Sotiras A, Boquet-Pujadas A, Mamourian E, Doshi J, Cui Y, Srinivasan D, Skampardoni I, Chen J, Hwang G, Bergman M, Bao J, Veturi Y, Zhou Z, Yang S, Dazzan P, Kahn RS, Schnack HG, Zanetti MV, Meisenzahl E, Busatto GF, Crespo-Facorro B, Pantelis C, Wood SJ, Zhuo C, Shinohara RT, Gur RC, Gur RE, Koutsouleris N, Wolf DH, Saykin AJ, Ritchie MD, Shen L, Thompson PM, Colliot O, Wittfeld K, Grabe HJ, Tosun D, Bilgel M, An Y, Marcus DS, LaMontagne P, Heckbert SR, Austin TR, Launer LJ, Espeland M, Masters CL, Maruff P, Fripp J, Johnson SC, Morris JC, Albert MS, Bryan RN, Resnick SM, Fan Y, Habes M, Wolk D, Shou H, Davatzikos C. Genomic loci influence patterns of structural covariance in the human brain. Proc Natl Acad Sci U S A 2023; 120:e2300842120. [PMID: 38127979 PMCID: PMC10756284 DOI: 10.1073/pnas.2300842120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 10/31/2023] [Indexed: 12/23/2023] Open
Abstract
Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science, Department of Neurology, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ilya M. Nasrallah
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Radiology, University of Pennsylvania, Philadelphia, PA19104
| | - Ahmed Abdulkadir
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Theodore D. Satterthwaite
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Zhijian Yang
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Guray Erus
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Timothy Robert-Fitzgerald
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ashish Singh
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine, St. Louis, MO63110
| | - Aleix Boquet-Pujadas
- Biomedical Imaging Group, Department of Biomedical Engineering, École Polytechnique Fédérale de Lausanne, Lausanne1015, Switzerland
| | - Elizabeth Mamourian
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Jimit Doshi
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Yuhan Cui
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Dhivya Srinivasan
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ioanna Skampardoni
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Jiong Chen
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Gyujoon Hwang
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Mark Bergman
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA19104
| | - Yogasudha Veturi
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Zhen Zhou
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Shu Yang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA19104
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, LondonWC2R 2LS, United Kingdom
| | - Rene S. Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Hugo G. Schnack
- Department of Psychiatry, University Medical Center Utrecht, Utrecht 3584 CX Ut, Netherlands
| | - Marcus V. Zanetti
- Institute of Psychiatry, Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo05508-070, Brazil
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Heinrich Heine University, Düsseldorf40204, Germany
| | - Geraldo F. Busatto
- Institute of Psychiatry, Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo05508-070, Brazil
| | - Benedicto Crespo-Facorro
- Hospital Universitario Virgen del Rocio, School of Medicine, University of Sevilla,Sevilla41004, Spain
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Stephen J. Wood
- Orygen and the Centre for Youth Mental Health, Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Chuanjun Zhuo
- Key Laboratory of Real Tine Tracing of Brain Circuits in Psychiatry and Neurology, Department of Psychiatry, Tianjin Medical University, Tianjin300070, China
| | - Russell T. Shinohara
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ruben C. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Raquel E. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich 80539, Germany
| | - Daniel H. Wolf
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Andrew J. Saykin
- Indiana Alzheimer’s Disease Research Center, Department of Radiology, Indiana University School of Medicine, Indianapolis, IN46202-3082
| | - Marylyn D. Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA19104
| | - Paul M. Thompson
- Imaging Genetics Center, Department of Neurology, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
| | - Olivier Colliot
- Institut du Cerveau, Sorbonne Université, Paris75013, France
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, German Center for Neurodegenerative Diseases, University Medicine Greifswald, Greifswald17475, Germany
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, German Center for Neurodegenerative Diseases, University Medicine Greifswald, Greifswald17475, Germany
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94143
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore21224, MD
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore21224, MD
| | - Daniel S. Marcus
- Department of Radiology, Washington University School of Medicine, St. Louis, MO63110
| | - Pamela LaMontagne
- Department of Radiology, Washington University School of Medicine, St. Louis, MO63110
| | - Susan R. Heckbert
- Department of Epidemiology, University of Washington, Seattle, WA98195
| | - Thomas R. Austin
- Department of Epidemiology, University of Washington, Seattle, WA98195
| | - Lenore J. Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Washington, MD20817
| | - Mark Espeland
- Sticht Center for Healthy Aging and Alzheimer’s Prevention, Divisions of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC27101
| | - Colin L. Masters
- Florey Institute of Neuroscience and Mental Health, Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC3010, Australia
| | - Paul Maruff
- Florey Institute of Neuroscience and Mental Health, Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC3010, Australia
| | - Jurgen Fripp
- Health and Biosecurity, Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD4029, Australia
| | - Sterling C. Johnson
- Wisconsin Alzheimer's Institute, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI53792
| | - John C. Morris
- Knight Alzheimer Disease Research Center, Department of Neurology, Washington University in St. Louis, St. Louis, MO63110
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD21205
| | - R. Nick Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA19104
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore21224, MD
| | - Yong Fan
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX78229
| | - David Wolk
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Neurology, University of Pennsylvania, Philadelphia, PA19104
| | - Haochang Shou
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Christos Davatzikos
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
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16
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Keller AS, Pines AR, Shanmugan S, Sydnor VJ, Cui Z, Bertolero MA, Barzilay R, Alexander-Bloch AF, Byington N, Chen A, Conan GM, Davatzikos C, Feczko E, Hendrickson TJ, Houghton A, Larsen B, Li H, Miranda-Dominguez O, Roalf DR, Perrone A, Shetty A, Shinohara RT, Fan Y, Fair DA, Satterthwaite TD. Personalized functional brain network topography is associated with individual differences in youth cognition. Nat Commun 2023; 14:8411. [PMID: 38110396 PMCID: PMC10728159 DOI: 10.1038/s41467-023-44087-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 11/29/2023] [Indexed: 12/20/2023] Open
Abstract
Individual differences in cognition during childhood are associated with important social, physical, and mental health outcomes in adolescence and adulthood. Given that cortical surface arealization during development reflects the brain's functional prioritization, quantifying variation in the topography of functional brain networks across the developing cortex may provide insight regarding individual differences in cognition. We test this idea by defining personalized functional networks (PFNs) that account for interindividual heterogeneity in functional brain network topography in 9-10 year olds from the Adolescent Brain Cognitive Development℠ Study. Across matched discovery (n = 3525) and replication (n = 3447) samples, the total cortical representation of fronto-parietal PFNs positively correlates with general cognition. Cross-validated ridge regressions trained on PFN topography predict cognition in unseen data across domains, with prediction accuracy increasing along the cortex's sensorimotor-association organizational axis. These results establish that functional network topography heterogeneity is associated with individual differences in cognition before the critical transition into adolescence.
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Affiliation(s)
- Arielle S Keller
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Adam R Pines
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sheila Shanmugan
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing, China
| | - Maxwell A Bertolero
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ran Barzilay
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Nora Byington
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN, 55414, USA
| | - Andrew Chen
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Gregory M Conan
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN, 55414, USA
| | - Christos Davatzikos
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Eric Feczko
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN, 55414, USA
| | - Timothy J Hendrickson
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN, 55414, USA
- University of Minnesota Informatics Institute, University of Minnesota, Minneapolis, MN, 55414, USA
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN, 55414, USA
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Hongming Li
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Oscar Miranda-Dominguez
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN, 55414, USA
| | - David R Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Anders Perrone
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN, 55414, USA
| | - Alisha Shetty
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Yong Fan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN, 55414, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
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17
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Tu D, Mahony B, Moore TM, Bertolero MA, Alexander-Bloch AF, Gur R, Bassett DS, Satterthwaite TD, Raznahan A, Shinohara RT. CoCoA: conditional correlation models with association size. Biostatistics 2023; 25:154-170. [PMID: 35939558 PMCID: PMC10724258 DOI: 10.1093/biostatistics/kxac032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 07/14/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Many scientific questions can be formulated as hypotheses about conditional correlations. For instance, in tests of cognitive and physical performance, the trade-off between speed and accuracy motivates study of the two variables together. A natural question is whether speed-accuracy coupling depends on other variables, such as sustained attention. Classical regression techniques, which posit models in terms of covariates and outcomes, are insufficient to investigate the effect of a third variable on the symmetric relationship between speed and accuracy. In response, we propose a conditional correlation model with association size, a likelihood-based statistical framework to estimate the conditional correlation between speed and accuracy as a function of additional variables. We propose novel measures of the association size, which are analogous to effect sizes on the correlation scale while adjusting for confound variables. In simulation studies, we compare likelihood-based estimators of conditional correlation to semiparametric estimators adapted from genomic studies and find that the former achieves lower bias and variance under both ideal settings and model assumption misspecification. Using neurocognitive data from the Philadelphia Neurodevelopmental Cohort, we demonstrate that greater sustained attention is associated with stronger speed-accuracy coupling in a complex reasoning task while controlling for age. By highlighting conditional correlations as the outcome of interest, our model provides complementary insights to traditional regression modeling and partitioned correlation analyses.
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Affiliation(s)
- Danni Tu
- The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA, 19104, USA
| | - Bridget Mahony
- Section on Developmental Neurogenomics, National Institutes of Mental Health, 10 Center Drive, Bethesda, MD, 20892, USA
| | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Maxwell A Bertolero
- Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, USA and Penn Lifespan Informatics and Neuroimaging Center, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | | | - Ruben Gur
- Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, 209 South 33rd Street, Philadelphia, PA, 19104, USA, Department of Physics and Astronomy, University of Pennsylvania, 209 South 33rd Street, Philadelphia, PA, 19104, USA, Department of Electrical and Systems Engineering, University of Pennsylvania, 200 South 33rd Street, Philadelphia, PA, 19104, USA and Department of Neurology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, USA and Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, PA, USA
| | - Armin Raznahan
- Section on Developmental Neurogenomics, National Institutes of Mental Health, Bethesda, MD, USA
| | - Russell T Shinohara
- The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
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18
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Chen AA, Weinstein SM, Adebimpe A, Gur RC, Gur RE, Merikangas KR, Satterthwaite TD, Shinohara RT, Shou H. Similarity-based multimodal regression. Biostatistics 2023:kxad033. [PMID: 38058018 DOI: 10.1093/biostatistics/kxad033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 10/07/2023] [Accepted: 11/06/2023] [Indexed: 12/08/2023] Open
Abstract
To better understand complex human phenotypes, large-scale studies have increasingly collected multiple data modalities across domains such as imaging, mobile health, and physical activity. The properties of each data type often differ substantially and require either separate analyses or extensive processing to obtain comparable features for a combined analysis. Multimodal data fusion enables certain analyses on matrix-valued and vector-valued data, but it generally cannot integrate modalities of different dimensions and data structures. For a single data modality, multivariate distance matrix regression provides a distance-based framework for regression accommodating a wide range of data types. However, no distance-based method exists to handle multiple complementary types of data. We propose a novel distance-based regression model, which we refer to as Similarity-based Multimodal Regression (SiMMR), that enables simultaneous regression of multiple modalities through their distance profiles. We demonstrate through simulation, imaging studies, and longitudinal mobile health analyses that our proposed method can detect associations between clinical variables and multimodal data of differing properties and dimensionalities, even with modest sample sizes. We perform experiments to evaluate several different test statistics and provide recommendations for applying our method across a broad range of scenarios.
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Affiliation(s)
- Andrew A Chen
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Sarah M Weinstein
- Department of Epidemiology and Biostatistics, Temple University College of Public Health, Philadelphia, PA 19122, USA
| | - Azeez Adebimpe
- Penn Lifespan Informatics & Neuroimaging Center, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kathleen R Merikangas
- Genetic Epidemiology Research Branch, Intramural Research Program, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics & Neuroimaging Center, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
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19
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Ge R, Yu Y, Qi YX, Fan YV, Chen S, Gao C, Haas SS, Modabbernia A, New F, Agartz I, Asherson P, Ayesa-Arriola R, Banaj N, Banaschewski T, Baumeister S, Bertolino A, Boomsma DI, Borgwardt S, Bourque J, Brandeis D, Breier A, Brodaty H, Brouwer RM, Buckner R, Buitelaar JK, Cannon DM, Caseras X, Cervenka S, Conrod PJ, Crespo-Facorro B, Crivello F, Crone EA, de Haan L, de Zubicaray GI, Di Giorgio A, Erk S, Fisher SE, Franke B, Frodl T, Glahn DC, Grotegerd D, Gruber O, Gruner P, Gur RE, Gur RC, Harrison BJ, Hatton SN, Hickie I, Howells FM, Pol HEH, Huyser C, Jernigan TL, Jiang J, Joska JA, Kahn RS, Kalnin AJ, Kochan NA, Koops S, Kuntsi J, Lagopoulos J, Lazaro L, Lebedeva IS, Lochner C, Martin NG, Mazoyer B, McDonald BC, McDonald C, McMahon KL, Nakao T, Nyberg L, Piras F, Portella MJ, Qiu J, Roffman JL, Sachdev PS, Sanford N, Satterthwaite TD, Saykin AJ, Schumann G, Sellgren CM, Sim K, Smoller JW, Soares J, Sommer IE, Spalletta G, Stein DJ, Tamnes CK, Thomopolous SI, Tomyshev AS, Tordesillas-Gutiérrez D, Trollor JN, van ’t Ent D, van den Heuvel OA, van Erp TGM, van Haren NEM, Vecchio D, Veltman DJ, Walter H, Wang Y, Weber B, Wei D, Wen W, Westlye LT, Wierenga LM, Williams SCR, Wright MJ, Medland S, Wu MJ, Yu K, Jahanshad N, Thompson PM, Frangou S. Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain: Algorithm Benchmarking and Model Optimization. bioRxiv 2023:2023.01.30.523509. [PMID: 38076938 PMCID: PMC10705253 DOI: 10.1101/2023.01.30.523509] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/22/2023]
Abstract
We present an empirically benchmarked framework for sex-specific normative modeling of brain morphometry that can inform about the biological and behavioral significance of deviations from typical age-related neuroanatomical changes and support future study designs. This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The Multivariate Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins, and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3,000 study participants. The model and scripts described here are freely available through CentileBrain (https://centilebrain.org/).
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Affiliation(s)
- Ruiyang Ge
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Yuetong Yu
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Yi Xuan Qi
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Yunan Vera Fan
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Shiyu Chen
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Chuntong Gao
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Faye New
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
| | - Philip Asherson
- Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry Center, King's College London, London, UK
| | - Rosa Ayesa-Arriola
- Department of Psychiatry, Marqués de Valdecilla University Hospital, Valdecilla Biomedical Research Institute (IDIVAL), Santander, Spain
| | - Nerisa Banaj
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Germany
| | - Sarah Baumeister
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Germany
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Stefan Borgwardt
- Translational Psychiatry Unit, Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Josiane Bourque
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Daniel Brandeis
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Germany
- Department of Child and Adolescent Psychiatry, University of Zürich, Zurich, Switzerland
| | - Alan Breier
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Henry Brodaty
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Rachel M Brouwer
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Randy Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jan K Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Dara M Cannon
- Clinical Neuroimaging Laboratory, National University of Ireland Galway, Galway, Ireland
| | - Xavier Caseras
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Simon Cervenka
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
- Department of Medical Sciences, Psychiatry, Uppsala University, Uppsala, Sweden
| | - Patricia J Conrod
- Department of Psychiatry and Addiction, Université de Montréal, CHU Ste Justine, Montréal, Canada
| | - Benedicto Crespo-Facorro
- University Hospital Virgen del Rocio, Seville, Spain; Department of Psychiatry, University of Seville, Institute of Biomedicine of Seville (IBIS), Seville, Spain
- Mental Health Research Networking Center (CIBERSAM), Madrid, Spain
| | - Fabrice Crivello
- Institut des Maladies Neurodégénératives, Université de Bordeaux, Bordeaux, France
| | - Eveline A Crone
- Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Institute of Psychology, Leiden University, Leiden, The Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Liewe de Haan
- Department of Psychiatry, Amsterdam UMC, Amsterdam, The Netherlands
| | - Greig I de Zubicaray
- School of Psychology & Counselling, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Annabella Di Giorgio
- Laboratory of Biological Psychiatry, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Susanne Erk
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Simon E Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Barbara Franke
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Thomas Frodl
- University Clinics and Clinics for Psychiatry, Psychotherapy and Psychosomatic Medicine, RWTH Aachen University, Aachen, Germany
| | - David C Glahn
- Department of Psychiatry, Tommy Fuss Center for Neuropsychiatric Disease Research Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Dominik Grotegerd
- Department of Psychiatry and Psychotherapy, University of Muenster, Muenster, Germany
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
| | - Patricia Gruner
- Department of Psychiatry, Yale University, New Haven, Connecticut, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ben J Harrison
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Melbourne, Australia
| | - Sean N Hatton
- Center for Multimodal Imaging and Genetics, University of California San Diego, La jolla, California, USA
| | - Ian Hickie
- Brain and Mind Centre, University of Sydney, Sydney, Australia
| | - Fleur M Howells
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Hilleke E Hulshoff Pol
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Psychology, Utrecht University, Utrecht, The Netherlands
| | - Chaim Huyser
- Department of Child and Adolescent Psychiatry, Academic Medical Centre/De Bascule, Amsterdam, The Netherlands
| | - Terry L Jernigan
- Center for Human Development, Departments of Cognitive Science, Psychiatry, and Radiology, University of California, San Diego, USA
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - John A Joska
- Department of Neuropsychiatry, University of Cape Town, Cape Town, South Africa
| | - René S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Andrew J Kalnin
- Department of Radiology, The Ohio State University College of Medicine, Columbus, Ohio, USA
| | - Nicole A Kochan
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Sanne Koops
- Department of Psychiatry and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jonna Kuntsi
- Institute of Psychiatry, Psychology and Neuroscience, Social, Genetic and Developmental Psychiatry Center, King's College London, London, UK
| | - Jim Lagopoulos
- Sunshine Coast Mind and Neuroscience - Thompson Institute, University of the Sunshine Coast, Queensland, Australia
| | - Luisa Lazaro
- Department of Child and Adolescent Psychiatry and Psychology, Hospital Clínic Barcelona, Barcelona, Spain
| | | | - Christine Lochner
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
| | - Nicholas G Martin
- Queensland Institute of Medical Research, Berghofer Medical Research Institute, Brisbane, Australia
| | - Bernard Mazoyer
- Institut des Maladies Neurodégénératives, Université de Bordeaux, Bordeaux, France
| | - Brenna C McDonald
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Colm McDonald
- Centre for Neuroimaging & Cognitive Genomics (NICOG), NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
| | - Katie L McMahon
- School of Clinical Sciences, Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, Australia
| | - Tomohiro Nakao
- Department of Neuropsychiatry, Kyushu University, Fukuoka, Japan
| | - Lars Nyberg
- Department of Radiation Sciences, Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden; Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Maria J Portella
- Mental Health Research Networking Center (CIBERSAM), Madrid, Spain
- Department of Psychiatry, Hospital de la Santa Creu iSant Pau, Institutd' Investigació Biomèdica SantPau, Universitat Autònomade Barcelona (UAB), Barcelona, Spain
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality, Southwest University, Ministry of Education, Chongqing, PR China
- Faculty of Psychology, Southwest University, Chongqing, PR China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, PR China
| | - Joshua L Roffman
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Nicole Sanford
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Gunter Schumann
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology, and Neuroscience, Social, Genetic & Developmental Psychiatry Centre, King's College London, London, UK; Institute for Science and Technology of Brain-inspired Intelligence, Fudan University, Shanghai, PR China; Centre for Population Neuroscience and Stratified Medicine (PONS), Charite Mental Health, Department of Psychiatry and Psychotherapy, CCM, Charite Universitätsmedizin Berlin, Berlin, Germany
| | - Carl M Sellgren
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
- Department of Physiology and Pharmacology, Karolinska Institute, Stockholm, Sweden
| | - Kang Sim
- Institute of Mental Health, Singapore
| | - Jordan W Smoller
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jair Soares
- University of Texas Health Harris County Psychiatric Center, Houston, Texas, USA
| | - Iris E Sommer
- Department of Biomedical Sciences of Cells and Systems, Rijksuniversiteit Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | | | - Dan J Stein
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Christian K Tamnes
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Sophia I Thomopolous
- Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck USC School of Medicine, Marina del Rey, California, USA
| | | | - Diana Tordesillas-Gutiérrez
- Department of Radiology, Marqués de Valdecilla University Hospital, Valdecilla Biomedical Research Institute (IDIVAL), Santander, Spain; Advanced Computing and e-Science, Instituto de Física de Cantabria (UC-CSIC), Santander, Spain
| | - Julian N Trollor
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
- Department of Developmental Disability Neuropsychiatry, School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Dennis van ’t Ent
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Odile A van den Heuvel
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Anatomy & Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Theo GM van Erp
- Department of Psychiatry and Human Behavior, University of California, Irvine, California, USA
| | - Neeltje EM van Haren
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Daniela Vecchio
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Dick J Veltman
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Henrik Walter
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Yang Wang
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Bernd Weber
- Institute for Experimental Epileptology and Cognition Research, University of Bonn Germany, Bonn, Germany; University Hospital Bonn, Bonn, Germany
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality, Southwest University, Ministry of Education, Chongqing, PR China
- Faculty of Psychology, Southwest University, Chongqing, PR China
| | - Wei Wen
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Lara M Wierenga
- Institute of Psychology, Leiden University, Leiden, The Netherlands; Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Steven CR Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
| | - Sarah Medland
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
| | - Mon-Ju Wu
- Department of Psychiatry and Behavioral Science, University of Texas Health Science Center, Houston, Texas, USA
| | - Kevin Yu
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Neda Jahanshad
- Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck USC School of Medicine, Marina del Rey, California, USA
| | - Paul M Thompson
- Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck USC School of Medicine, Marina del Rey, California, USA
| | - Sophia Frangou
- Djavad Mowafagian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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20
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Van AN, Montez DF, Laumann TO, Suljic V, Madison T, Baden NJ, Ramirez-Perez N, Scheidter KM, Monk JS, Whiting FI, Adeyemo B, Chauvin RJ, Krimmel SR, Metoki A, Rajesh A, Roland JL, Salo T, Wang A, Weldon KB, Sotiras A, Shimony JS, Kay BP, Nelson SM, Tervo-Clemmens B, Marek SA, Vizioli L, Yacoub E, Satterthwaite TD, Gordon EM, Fair DA, Tisdall MD, Dosenbach NU. Framewise multi-echo distortion correction for superior functional MRI. bioRxiv 2023:2023.11.28.568744. [PMID: 38077010 PMCID: PMC10705259 DOI: 10.1101/2023.11.28.568744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Functional MRI (fMRI) data are severely distorted by magnetic field (B0) inhomogeneities which currently must be corrected using separately acquired field map data. However, changes in the head position of a scanning participant across fMRI frames can cause changes in the B0 field, preventing accurate correction of geometric distortions. Additionally, field maps can be corrupted by movement during their acquisition, preventing distortion correction altogether. In this study, we use phase information from multi-echo (ME) fMRI data to dynamically sample distortion due to fluctuating B0 field inhomogeneity across frames by acquiring multiple echoes during a single EPI readout. Our distortion correction approach, MEDIC (Multi-Echo DIstortion Correction), accurately estimates B0 related distortions for each frame of multi-echo fMRI data. Here, we demonstrate that MEDIC's framewise distortion correction produces improved alignment to anatomy and decreases the impact of head motion on resting-state functional connectivity (RSFC) maps, in higher motion data, when compared to the prior gold standard approach (i.e., TOPUP). Enhanced framewise distortion correction with MEDIC, without the requirement for field map collection, furthers the advantage of multi-echo over single-echo fMRI.
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Affiliation(s)
- Andrew N. Van
- Department of Biomedical Engineering, Washington University in St. Louis, MO 63130
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - David F. Montez
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110
| | - Timothy O. Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110
| | - Vahdeta Suljic
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Thomas Madison
- Institute of Child Development, University of Minnesota Medical School, Minneapolis, MN 55455
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Noah J. Baden
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | | | - Kristen M. Scheidter
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Julia S. Monk
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Forrest I. Whiting
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Roselyne J. Chauvin
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Samuel R. Krimmel
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Athanasia Metoki
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Aishwarya Rajesh
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Jarod L. Roland
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO 63110
| | - Taylor Salo
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104
| | - Anxu Wang
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Division of Computation and Data Science, Washington University School of Medicine, St. Louis, MO 63110
| | - Kimberly B. Weldon
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine, St. Louis, MO 63130
| | - Joshua S. Shimony
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Benjamin P. Kay
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
| | - Steven M. Nelson
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Brenden Tervo-Clemmens
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
- Department of Psychiatry & Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Scott A. Marek
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Luca Vizioli
- Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN 55455
| | - Theodore D. Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104
| | - Evan M. Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Damien A. Fair
- Institute of Child Development, University of Minnesota Medical School, Minneapolis, MN 55455
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN 55455
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455
| | - M. Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Nico U.F. Dosenbach
- Department of Biomedical Engineering, Washington University in St. Louis, MO 63130
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110
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21
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Seidlitz J, Mallard TT, Vogel JW, Lee YH, Warrier V, Ball G, Hansson O, Hernandez LM, Mandal AS, Wagstyl K, Lombardo MV, Courchesne E, Glessner JT, Satterthwaite TD, Bethlehem RAI, Bernstock JD, Tasaki S, Ng B, Gaiteri C, Smoller JW, Ge T, Gur RE, Gandal MJ, Alexander-Bloch AF. The molecular genetic landscape of human brain size variation. Cell Rep 2023; 42:113439. [PMID: 37963017 DOI: 10.1016/j.celrep.2023.113439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 06/13/2023] [Accepted: 10/27/2023] [Indexed: 11/16/2023] Open
Abstract
Human brain size changes dynamically through early development, peaks in adolescence, and varies up to 2-fold among adults. However, the molecular genetic underpinnings of interindividual variation in brain size remain unknown. Here, we leveraged postmortem brain RNA sequencing and measurements of brain weight (BW) in 2,531 individuals across three independent datasets to identify 928 genome-wide significant associations with BW. Genes associated with higher or lower BW showed distinct neurodevelopmental trajectories and spatial patterns that mapped onto functional and cellular axes of brain organization. Expression of BW genes was predictive of interspecies differences in brain size, and bioinformatic annotation revealed enrichment for neurogenesis and cell-cell communication. Genome-wide, transcriptome-wide, and phenome-wide association analyses linked BW gene sets to neuroimaging measurements of brain size and brain-related clinical traits. Cumulatively, these results represent a major step toward delineating the molecular pathways underlying human brain size variation in health and disease.
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Affiliation(s)
- Jakob Seidlitz
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA
| | - Jacob W Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Informatics and Neuroimaging Center, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Younga H Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA
| | - Varun Warrier
- Department of Psychiatry, University of Cambridge, Cambridge CB2 1TN, UK; Department of Psychology, University of Cambridge, Cambridge CB2 1TN, UK
| | - Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, VIC 3052, Australia; Department of Paediatrics, University of Melbourne, Melbourne, Melbourne, VIC 3052, Australia
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö P663+Q9, Sweden; Memory Clinic, Skåne University Hospital, Malmö P663+Q9, Sweden
| | - Leanna M Hernandez
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90024, USA
| | - Ayan S Mandal
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
| | - Michael V Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, 38068 Rovereto, Italy
| | - Eric Courchesne
- Department of Neuroscience, University of California, San Diego, San Diego, CA 92093, USA; Autism Center of Excellence, University of California, San Diego, San Diego, CA 92093, USA
| | - Joseph T Glessner
- The Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Informatics and Neuroimaging Center, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | | | - Joshua D Bernstock
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard University, Boston, MA 02115, USA; Department of Neurosurgery, Boston Children's Hospital, Harvard University, Boston, MA 02115, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Shinya Tasaki
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Bernard Ng
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA; Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA; Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Raquel E Gur
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael J Gandal
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aaron F Alexander-Bloch
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
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22
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Mehta K, Salo T, Madison T, Adebimpe A, Bassett DS, Bertolero M, Cieslak M, Covitz S, Houghton A, Keller AS, Luo A, Miranda-Dominguez O, Nelson SM, Shafiei G, Shanmugan S, Shinohara RT, Sydnor VJ, Feczko E, Fair DA, Satterthwaite TD. XCP-D: A Robust Pipeline for the post-processing of fMRI data. bioRxiv 2023:2023.11.20.567926. [PMID: 38045258 PMCID: PMC10690221 DOI: 10.1101/2023.11.20.567926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Functional neuroimaging is an essential tool for neuroscience research. Pre-processing pipelines produce standardized, minimally pre-processed data to support a range of potential analyses. However, post-processing is not similarly standardized. While several options for post-processing exist, they tend not to support output from disparate pre-processing pipelines, may have limited documentation, and may not follow BIDS best practices. Here we present XCP-D, which presents a solution to these issues. XCP-D is a collaborative effort between PennLINC at the University of Pennsylvania and the DCAN lab at the University at Minnesota. XCP-D uses an open development model on GitHub and incorporates continuous integration testing; it is distributed as a Docker container or Singularity image. XCP-D generates denoised BOLD images and functional derivatives from resting-state data in either NifTI or CIFTI files, following pre-processing with fMRIPrep, HCP, and ABCD-BIDS pipelines. Even prior to its official release, XCP-D has been downloaded >3,000 times from DockerHub. Together, XCP-D facilitates robust, scalable, and reproducible post-processing of fMRI data.
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Affiliation(s)
- Kahini Mehta
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Taylor Salo
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Thomas Madison
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, 55454, USA
| | - Azeez Adebimpe
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, PA 19104, USA
- Department of Electrical & Systems Engineering, University of Pennsylvania, PA 19104, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Santa Fe Institute, Santa Fe, NM, 87051, USA
| | - Max Bertolero
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Arielle S Keller
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Audrey Luo
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Oscar Miranda-Dominguez
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, 55454, USA
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Steve M Nelson
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, 55454, USA
| | - Golia Shafiei
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sheila Shanmugan
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Valerie J Sydnor
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Eric Feczko
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, 55454, USA
| | - Damien A Fair
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, 55454, USA
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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23
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Baller EB, Sweeney EM, Cieslak M, Robert-Fitzgerald T, Covitz SC, Martin ML, Schindler MK, Bar-Or A, Elahi A, Larsen BS, Manning AR, Markowitz CE, Perrone CM, Rautman V, Seitz MM, Detre JA, Fox MD, Shinohara RT, Satterthwaite TD. Mapping the Relationship of White Matter Lesions to Depression in Multiple Sclerosis. Biol Psychiatry 2023:S0006-3223(23)01722-5. [PMID: 37981178 DOI: 10.1016/j.biopsych.2023.11.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/27/2023] [Accepted: 11/11/2023] [Indexed: 11/21/2023]
Abstract
BACKGROUND Multiple sclerosis (MS) is an immune-mediated neurological disorder, and up to 50% of patients experience depression. We investigated how white matter network disruption is related to depression in MS. METHODS Using electronic health records, 380 participants with MS were identified. Depressed individuals (MS+Depression group; n = 232) included persons who had an ICD-10 depression diagnosis, had a prescription for antidepressant medication, or screened positive via Patient Health Questionnaire (PHQ)-2 or PHQ-9. Age- and sex-matched nondepressed individuals with MS (MS-Depression group; n = 148) included persons who had no prior depression diagnosis, had no psychiatric medication prescriptions, and were asymptomatic on PHQ-2 or PHQ-9. Research-quality 3T structural magnetic resonance imaging was obtained as part of routine care. We first evaluated whether lesions were preferentially located within the depression network compared with other brain regions. Next, we examined if MS+Depression patients had greater lesion burden and if this was driven by lesions in the depression network. Primary outcome measures were the burden of lesions (e.g., impacted fascicles) within a network and across the brain. RESULTS MS lesions preferentially affected fascicles within versus outside the depression network (β = 0.09, 95% CI = 0.08 to 0.10, p < .001). MS+Depression patients had more lesion burden (β = 0.06, 95% CI = 0.01 to 0.10, p = .015); this was driven by lesions within the depression network (β = 0.02, 95% CI = 0.003 to 0.040, p = .020). CONCLUSIONS We demonstrated that lesion location and burden may contribute to depression comorbidity in MS. MS lesions disproportionately impacted fascicles in the depression network. MS+Depression patients had more disease than MS-Depression patients, which was driven by disease within the depression network. Future studies relating lesion location to personalized depression interventions are warranted.
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Affiliation(s)
- Erica B Baller
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Elizabeth M Sweeney
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Timothy Robert-Fitzgerald
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Sydney C Covitz
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Melissa L Martin
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Matthew K Schindler
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Amit Bar-Or
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ameena Elahi
- Department of Information Services, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Bart S Larsen
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Abigail R Manning
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Clyde E Markowitz
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Christopher M Perrone
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Victoria Rautman
- Department of Information Services, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Madeleine M Seitz
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - John A Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania.
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24
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Weinstein SM, Vandekar SN, Alexander-Bloch AF, Raznahan A, Li M, Gur RE, Gur RC, Roalf DR, Park MTM, Chakravarty M, Baller EB, Linn KA, Satterthwaite TD, Shinohara RT. Network Enrichment Significance Testing in Brain-Phenotype Association Studies. bioRxiv 2023:2023.11.10.566593. [PMID: 38014137 PMCID: PMC10680593 DOI: 10.1101/2023.11.10.566593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Functional networks often guide our interpretation of spatial maps of brain-phenotype associations. However, methods for assessing enrichment of associations within networks of interest have varied in terms of both scientific rigor and underlying assumptions. While some approaches have relied on subjective interpretations, others have made unrealistic assumptions about the spatial structure of imaging data, leading to inflated false positive rates. We seek to address this gap in existing methodology by borrowing insight from a method widely used in genomics research for testing enrichment of associations between a set of genes and a phenotype of interest. We propose Network Enrichment Significance Testing (NEST), a flexible framework for testing the specificity of brain-phenotype associations to functional networks or other sub-regions of the brain. We apply NEST to study phenotype associations with structural and functional brain imaging data from a large-scale neurodevelopmental cohort study.
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Affiliation(s)
- Sarah M. Weinstein
- Department of Epidemiology and Biostatistics, Temple University College of Public Health, Philadelphia, PA, USA
| | - Simon N. Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Aaron F. Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Armin Raznahan
- Section on Developmental Neurogenomics, National Institute of Mental Health Intramural Research Program, Bethesda, MD, USA
| | - Mingyao Li
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Raquel E. Gur
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Ruben C. Gur
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - David R. Roalf
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Min Tae M. Park
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Mallar Chakravarty
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Cerebral Imaging Centre, Douglas Research Centre, McGill University, Montreal, QC, Canada
| | - Erica B. Baller
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Kristin A. Linn
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Theodore D. Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Russell T. Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
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25
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Linguiti S, Vogel JW, Sydnor VJ, Pines A, Wellman N, Basbaum A, Eickhoff CR, Eickhoff SB, Edwards RR, Larsen B, McKinstry-Wu A, Scott JC, Roalf DR, Sharma V, Strain EC, Corder G, Dworkin RH, Satterthwaite TD. Functional imaging studies of acute administration of classic psychedelics, ketamine, and MDMA: Methodological limitations and convergent results. Neurosci Biobehav Rev 2023; 154:105421. [PMID: 37802267 DOI: 10.1016/j.neubiorev.2023.105421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/13/2023] [Accepted: 10/02/2023] [Indexed: 10/08/2023]
Abstract
Functional magnetic resonance imaging (fMRI) is increasingly used to non-invasively study the acute impact of psychedelics on the human brain. While fMRI is a promising tool for measuring brain function in response to psychedelics, it also has known methodological challenges. We conducted a systematic review of fMRI studies examining acute responses to experimentally administered psychedelics in order to identify convergent findings and characterize heterogeneity in the literature. We reviewed 91 full-text papers; these studies were notable for substantial heterogeneity in design, task, dosage, drug timing, and statistical approach. Data recycling was common, with 51 unique samples across 91 studies. Fifty-seven studies (54%) did not meet contemporary standards for Type I error correction or control of motion artifact. Psilocybin and LSD were consistently reported to moderate the connectivity architecture of the sensorimotor-association cortical axis. Studies also consistently reported that ketamine administration increased activation in the dorsomedial prefrontal cortex. Moving forward, use of best practices such as pre-registration, standardized image processing and statistical testing, and data sharing will be important in this rapidly developing field.
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Affiliation(s)
- Sophia Linguiti
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Jacob W Vogel
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States; Department of Clinical Sciences, Malmö, SciLifeLab, Lund University, Lund, Sweden
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Adam Pines
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States; Department of Psychiatry, Stanford University, Stanford, CA, United States
| | - Nick Wellman
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Allan Basbaum
- Department of Anatomy, University of California, San Francisco, United States
| | - Claudia R Eickhoff
- Institute of Neuroscience and Medicine, (INM-1, INM-7), Research Centre Jülich, Jülich, Germany; Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-University, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, (INM-1, INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - Robert R Edwards
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Andrew McKinstry-Wu
- Department of Anesthesiology and Critical Care, Neuroscience of Unconsciousness and Reanimation Research Alliance (NEURRAL), University of Pennsylvania, Philadelphia, United States
| | - J Cobb Scott
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States; VISN4 Mental Illness Research, Education, and Clinical Center at the Corporal Michael J. Crescenz VA (Veterans Affairs) Medical Center, Philadelphia, PA, United States
| | - David R Roalf
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Vaishnavi Sharma
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Eric C Strain
- Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, 5510 Nathan Shock Drive, Baltimore, MD, United States
| | - Gregory Corder
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Robert H Dworkin
- Department of Anesthesiology and Perioperative Medicine, University of Rochester School of Medicine and Dentistry, Rochester, NY, United States
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States.
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26
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Frahm L, Satterthwaite TD, Fox PT, Langner R, Eickhoff SB. ALE meta-analyses of voxel-based morphometry studies: Parameter validation via large-scale simulations. Neuroimage 2023; 281:120383. [PMID: 37734477 PMCID: PMC10686967 DOI: 10.1016/j.neuroimage.2023.120383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 09/15/2023] [Accepted: 09/19/2023] [Indexed: 09/23/2023] Open
Abstract
Activation likelihood estimation (ALE) meta-analysis has been applied to structural neuroimaging data since long, but up to now, any systematic assessment of the algorithm's behavior, power and sensitivity has been based on simulations using functional neuroimaging databases as their foundation. Here, we aimed to determine whether the guidelines offered by previous evaluations can be generalized to ALE meta-analyses of voxel-based morphometry (VBM) studies. We ran 365000 distinct ALE analyses filled with simulated experiments, randomly sampling parameters from BrainMap's VBM experiment database. We then examined the algorithm's sensitivity, its susceptibility to spurious convergence, and its susceptibility to excessive contributions by individual experiments. In general, the performance of the ALE algorithm was highly comparable between imaging modalities, with the algorithm's sensitivity and specificity reaching similar levels with structural data as previously observed with functional data. Because of the lower number of foci reported and the higher number of participants usually included in structural experiments, individual studies had, on average, a higher impact towards significant clusters. To prevent significant clusters from being driven by single experiments, we recommend that researchers include at least 23 experiments in a VBM ALE dataset, instead of the previously recommended minimum of n = 17. While these recommendations do not constitute hard borders, running ALE analyses on smaller datasets would require special diligence in assessing and reporting the contributions of experiments to individual clusters.
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Affiliation(s)
- Lennart Frahm
- Department of Psychiatry, Psychotherapy and Psychosomatics, School of Medicine, RWTH Aachen University, Aachen, Germany; Institute of Neuroscience and Medicine (INM7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany.
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA; Penn Lifespan Informatics and Neuroimaging Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center, San Antonio, USA; Departments of Radiology, Neurology, Psychiatry and Behavioral Sciences, and Physiology, University of Texas Health Science Center, San Antonio, USA
| | - Robert Langner
- Institute of Neuroscience and Medicine (INM7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
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27
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Scott JC, Moore TM, Roalf DR, Satterthwaite TD, Wolf DH, Port AM, Butler ER, Ruparel K, Nievergelt CM, Risbrough VB, Baker DG, Gur RE, Gur RC. Development and application of novel performance validity metrics for computerized neurocognitive batteries. J Int Neuropsychol Soc 2023; 29:789-797. [PMID: 36503573 PMCID: PMC10258222 DOI: 10.1017/s1355617722000893] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVES Data from neurocognitive assessments may not be accurate in the context of factors impacting validity, such as disengagement, unmotivated responding, or intentional underperformance. Performance validity tests (PVTs) were developed to address these phenomena and assess underperformance on neurocognitive tests. However, PVTs can be burdensome, rely on cutoff scores that reduce information, do not examine potential variations in task engagement across a battery, and are typically not well-suited to acquisition of large cognitive datasets. Here we describe the development of novel performance validity measures that could address some of these limitations by leveraging psychometric concepts using data embedded within the Penn Computerized Neurocognitive Battery (PennCNB). METHODS We first developed these validity measures using simulations of invalid response patterns with parameters drawn from real data. Next, we examined their application in two large, independent samples: 1) children and adolescents from the Philadelphia Neurodevelopmental Cohort (n = 9498); and 2) adult servicemembers from the Marine Resiliency Study-II (n = 1444). RESULTS Our performance validity metrics detected patterns of invalid responding in simulated data, even at subtle levels. Furthermore, a combination of these metrics significantly predicted previously established validity rules for these tests in both developmental and adult datasets. Moreover, most clinical diagnostic groups did not show reduced validity estimates. CONCLUSIONS These results provide proof-of-concept evidence for multivariate, data-driven performance validity metrics. These metrics offer a novel method for determining the performance validity for individual neurocognitive tests that is scalable, applicable across different tests, less burdensome, and dimensional. However, more research is needed into their application.
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Affiliation(s)
- J. Cobb Scott
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- VISN4 Mental Illness Research, Education, and Clinical Center at the Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Tyler M. Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David R. Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D. Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel H. Wolf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Allison M. Port
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ellyn R. Butler
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kosha Ruparel
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Caroline M. Nievergelt
- Center for Excellent in Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Psychiatry, University of California (UCSD), San Diego, CA, USA
| | - Victoria B. Risbrough
- Center for Excellent in Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Psychiatry, University of California (UCSD), San Diego, CA, USA
| | - Dewleen G. Baker
- Center for Excellent in Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Psychiatry, University of California (UCSD), San Diego, CA, USA
| | - Raquel E. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute, Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ruben C. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- VISN4 Mental Illness Research, Education, and Clinical Center at the Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Lifespan Brain Institute, Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
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28
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Larsen B, Sydnor VJ, Keller AS, Yeo BTT, Satterthwaite TD. A critical period plasticity framework for the sensorimotor-association axis of cortical neurodevelopment. Trends Neurosci 2023; 46:847-862. [PMID: 37643932 PMCID: PMC10530452 DOI: 10.1016/j.tins.2023.07.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 06/23/2023] [Accepted: 07/25/2023] [Indexed: 08/31/2023]
Abstract
To understand human brain development it is necessary to describe not only the spatiotemporal patterns of neurodevelopment but also the neurobiological mechanisms that underlie them. Human neuroimaging studies have provided evidence for a hierarchical sensorimotor-to-association (S-A) axis of cortical neurodevelopment. Understanding the biological mechanisms that underlie this program of development using traditional neuroimaging approaches has been challenging. Animal models have been used to identify periods of enhanced experience-dependent plasticity - 'critical periods' - that progress along cortical hierarchies and are governed by a conserved set of neurobiological mechanisms that promote and then restrict plasticity. In this review we hypothesize that the S-A axis of cortical development in humans is partly driven by the cascading maturation of critical period plasticity mechanisms. We then describe how recent advances in in vivo neuroimaging approaches provide a promising path toward testing this hypothesis by linking signals derived from non-invasive imaging to critical period mechanisms.
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Affiliation(s)
- Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arielle S Keller
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - B T Thomas Yeo
- Centre for Sleep and Cognition (CSC), and Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn-CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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29
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Schabdach JM, Schmitt JE, Sotardi S, Vossough A, Andronikou S, Roberts TP, Huang H, Padmanabhan V, Ortiz-Rosa A, Gardner M, Covitz S, Bedford SA, Mandal AS, Chaiyachati BH, White SR, Bullmore E, Bethlehem RAI, Shinohara RT, Billot B, Iglesias JE, Ghosh S, Gur RE, Satterthwaite TD, Roalf D, Seidlitz J, Alexander-Bloch A. Brain Growth Charts for Quantitative Analysis of Pediatric Clinical Brain MRI Scans with Limited Imaging Pathology. Radiology 2023; 309:e230096. [PMID: 37906015 PMCID: PMC10623207 DOI: 10.1148/radiol.230096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 08/21/2023] [Accepted: 09/12/2023] [Indexed: 11/02/2023]
Abstract
Background Clinically acquired brain MRI scans represent a valuable but underused resource for investigating neurodevelopment due to their technical heterogeneity and lack of appropriate controls. These barriers have curtailed retrospective studies of clinical brain MRI scans compared with more costly prospectively acquired research-quality brain MRI scans. Purpose To provide a benchmark for neuroanatomic variability in clinically acquired brain MRI scans with limited imaging pathology (SLIPs) and to evaluate if growth charts from curated clinical MRI scans differed from research-quality MRI scans or were influenced by clinical indication for the scan. Materials and Methods In this secondary analysis of preexisting data, clinical brain MRI SLIPs from an urban pediatric health care system (individuals aged ≤22 years) were scanned across nine 3.0-T MRI scanners. The curation process included manual review of signed radiology reports and automated and manual quality review of images without gross pathology. Global and regional volumetric imaging phenotypes were measured using two image segmentation pipelines, and clinical brain growth charts were quantitatively compared with charts derived from a large set of research controls in the same age range by means of Pearson correlation and age at peak volume. Results The curated clinical data set included 532 patients (277 male; median age, 10 years [IQR, 5-14 years]; age range, 28 days after birth to 22 years) scanned between 2005 and 2020. Clinical brain growth charts were highly correlated with growth charts derived from research data sets (22 studies, 8346 individuals [4947 male]; age range, 152 days after birth to 22 years) in terms of normative developmental trajectories predicted by the models (median r = 0.979). Conclusion The clinical indication of the scans did not significantly bias the output of clinical brain charts. Brain growth charts derived from clinical controls with limited imaging pathology were highly correlated with brain charts from research controls, suggesting the potential of curated clinical MRI scans to supplement research data sets. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Ertl-Wagner and Pai in this issue.
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Affiliation(s)
- Jenna M. Schabdach
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - J. Eric Schmitt
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Susan Sotardi
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Arastoo Vossough
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Savvas Andronikou
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Timothy P. Roberts
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Hao Huang
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Viveknarayanan Padmanabhan
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Alfredo Ortiz-Rosa
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Margaret Gardner
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Sydney Covitz
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Saashi A. Bedford
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Ayan S. Mandal
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Barbara H. Chaiyachati
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Simon R. White
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Edward Bullmore
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Richard A. I. Bethlehem
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Russell T. Shinohara
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Benjamin Billot
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - J. Eugenio Iglesias
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Satrajit Ghosh
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Raquel E. Gur
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Theodore D. Satterthwaite
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - David Roalf
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Jakob Seidlitz
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - Aaron Alexander-Bloch
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
| | - for the Lifespan Brain Chart Consortium
- From the Lifespan Brain Institute (LiBI) of the Children’s
Hospital of Philadelphia (CHOP) and Penn Medicine, Philadelphia, Pa (J.M.S.,
A.O.R., M.G., A.S.M., B.H.C., R.E.G., T.D.S., J.S., A.A.B.); Department of Child
and Adolescent Psychiatry and Behavioral Science (J.M.S., J.S., A.A.B.),
Department of Radiology (S.S., A.V., S.A., T.P.R., H.H.), PolicyLab and Clinical
Futures, CHOP Research Institute (B.H.C.), and Department of Biomedical and
Health Informatics (J.E.S., S.S., V.P.), Children’s Hospital of
Philadelphia, Philadelphia, Pa; Department of Psychiatry (J.E.S., R.E.G.,
T.D.S., D.R., J.S., A.A.B.), Department of Radiology (J.E.S., S.S., A.V., S.A.,
T.P.R., H.H.), Lifespan Informatics and Neuroimaging Center (PennLINC),
Department of Psychiatry (S.C., T.D.S.), and Department of Pediatrics (B.H.C.),
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa;
Departments of Psychiatry (S.A.B., S.R.W., E.B., R.A.I.B.) and Psychology
(R.A.I.B.), University of Cambridge, Cambridge, United Kingdom; Center for
Biomedical Image Computation and Analytics (R.T.S.), Penn Statistics in Imaging
and Visualization Center, Department of Biostatistics, Epidemiology and
Informatics (R.T.S.), and Lifespan Brain Chart Consortium (S.R.W., E.B.,
R.A.I.B., R.T.S., T.D.S., J.S., A.A.B.), University of Pennsylvania,
Philadelphia, Pa; Centre for Medical Image Computing, Department of Medical
Physics and Biomedical Engineering, University College London, London, United
Kingdom (B.B., J.E.I.); Martinos Center for Biomedical Imaging and Department of
Radiology (J.E.I.) and Department of Otolaryngology–Head and Neck Surgery
(S.G.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass;
and McGovern Institute for Brain Research (S.G.) and Computer Science &
Artificial Intelligence Laboratory (B.B., J.E.I.), Massachusetts Institute of
Technology, Cambridge, Mass
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Fotiadis P, Cieslak M, He X, Caciagli L, Ouellet M, Satterthwaite TD, Shinohara RT, Bassett DS. Myelination and excitation-inhibition balance synergistically shape structure-function coupling across the human cortex. Nat Commun 2023; 14:6115. [PMID: 37777569 PMCID: PMC10542365 DOI: 10.1038/s41467-023-41686-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 09/08/2023] [Indexed: 10/02/2023] Open
Abstract
Recent work has demonstrated that the relationship between structural and functional connectivity varies regionally across the human brain, with reduced coupling emerging along the sensory-association cortical hierarchy. The biological underpinnings driving this expression, however, remain largely unknown. Here, we postulate that intracortical myelination and excitation-inhibition (EI) balance mediate the heterogeneous expression of structure-function coupling (SFC) and its temporal variance across the cortical hierarchy. We employ atlas- and voxel-based connectivity approaches to analyze neuroimaging data acquired from two groups of healthy participants. Our findings are consistent across six complementary processing pipelines: 1) SFC and its temporal variance respectively decrease and increase across the unimodal-transmodal and granular-agranular gradients; 2) increased myelination and lower EI-ratio are associated with more rigid SFC and restricted moment-to-moment SFC fluctuations; 3) a gradual shift from EI-ratio to myelination as the principal predictor of SFC occurs when traversing from granular to agranular cortical regions. Collectively, our work delivers a framework to conceptualize structure-function relationships in the human brain, paving the way for an improved understanding of how demyelination and/or EI-imbalances induce reorganization in brain disorders.
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Affiliation(s)
- Panagiotis Fotiadis
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | - Matthew Cieslak
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Xiaosong He
- Department of Psychology, University of Science and Technology of China, Hefei, Anhui, 230026, China
| | - Lorenzo Caciagli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Mathieu Ouellet
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Santa Fe Institute, Santa Fe, NM, 87501, USA.
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31
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Shafiei G, Fulcher BD, Voytek B, Satterthwaite TD, Baillet S, Misic B. Neurophysiological signatures of cortical micro-architecture. Nat Commun 2023; 14:6000. [PMID: 37752115 PMCID: PMC10522715 DOI: 10.1038/s41467-023-41689-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 09/11/2023] [Indexed: 09/28/2023] Open
Abstract
Systematic spatial variation in micro-architecture is observed across the cortex. These micro-architectural gradients are reflected in neural activity, which can be captured by neurophysiological time-series. How spontaneous neurophysiological dynamics are organized across the cortex and how they arise from heterogeneous cortical micro-architecture remains unknown. Here we extensively profile regional neurophysiological dynamics across the human brain by estimating over 6800 time-series features from the resting state magnetoencephalography (MEG) signal. We then map regional time-series profiles to a comprehensive multi-modal, multi-scale atlas of cortical micro-architecture, including microstructure, metabolism, neurotransmitter receptors, cell types and laminar differentiation. We find that the dominant axis of neurophysiological dynamics reflects characteristics of power spectrum density and linear correlation structure of the signal, emphasizing the importance of conventional features of electromagnetic dynamics while identifying additional informative features that have traditionally received less attention. Moreover, spatial variation in neurophysiological dynamics is co-localized with multiple micro-architectural features, including gene expression gradients, intracortical myelin, neurotransmitter receptors and transporters, and oxygen and glucose metabolism. Collectively, this work opens new avenues for studying the anatomical basis of neural activity.
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Affiliation(s)
- Golia Shafiei
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ben D Fulcher
- School of Physics, The University of Sydney, Camperdown, NSW, 2006, Australia
| | - Bradley Voytek
- Department of Cognitive Science, Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada.
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Tu D, Goyal MS, Dworkin JD, Kampondeni S, Vidal L, Biondo-Savin E, Juvvadi S, Raghavan P, Nicholas J, Chetcuti K, Clark K, Robert-Fitzgerald T, Satterthwaite TD, Yushkevich P, Davatzikos C, Erus G, Tustison NJ, Postels DG, Taylor TE, Small DS, Shinohara RT. Automated analysis of low-field brain MRI in cerebral malaria. Biometrics 2023; 79:2417-2429. [PMID: 35731973 PMCID: PMC10267853 DOI: 10.1111/biom.13708] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Accepted: 06/08/2022] [Indexed: 11/26/2022]
Abstract
A central challenge of medical imaging studies is to extract biomarkers that characterize disease pathology or outcomes. Modern automated approaches have found tremendous success in high-resolution, high-quality magnetic resonance images. These methods, however, may not translate to low-resolution images acquired on magnetic resonance imaging (MRI) scanners with lower magnetic field strength. In low-resource settings where low-field scanners are more common and there is a shortage of radiologists to manually interpret MRI scans, it is critical to develop automated methods that can augment or replace manual interpretation, while accommodating reduced image quality. We present a fully automated framework for translating radiological diagnostic criteria into image-based biomarkers, inspired by a project in which children with cerebral malaria (CM) were imaged using low-field 0.35 Tesla MRI. We integrate multiatlas label fusion, which leverages high-resolution images from another sample as prior spatial information, with parametric Gaussian hidden Markov models based on image intensities, to create a robust method for determining ventricular cerebrospinal fluid volume. We also propose normalized image intensity and texture measurements to determine the loss of gray-to-white matter tissue differentiation and sulcal effacement. These integrated biomarkers have excellent classification performance for determining severe brain swelling due to CM.
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Affiliation(s)
- Danni Tu
- The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania
| | - Manu S. Goyal
- Mallinckrodt Institute of Radiology, Washington University in St. Louis
| | | | | | - Lorenna Vidal
- Department of Radiology, Children’s Hospital of Philadelphia
| | | | | | - Prashant Raghavan
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine
| | - Jennifer Nicholas
- University Hospitals Cleveland Medical Center, Department of Radiology, Case Western Reserve University
| | - Karen Chetcuti
- Department of Paediatrics and Child Health, Kamuzu University of Health Sciences
| | - Kelly Clark
- The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania
| | - Timothy Robert-Fitzgerald
- The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania
| | | | | | | | - Guray Erus
- Center for Biomedical Image Computing and Analysis (CBICA), Department of Radiology, University of Pennsylvania
| | | | - Douglas G. Postels
- Division of Neurology, George Washington University/Children’s National Medical Center
| | - Terrie E. Taylor
- Blantyre Malaria Project, Kamuzu University of Health Sciences
- College of Osteopathic Medicine, Michigan State University
| | | | - Russell T. Shinohara
- The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania
- Center for Biomedical Image Computing and Analysis (CBICA), Department of Radiology, University of Pennsylvania
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33
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Borges MS, Hoffmann MS, Simioni A, Axelrud LK, Teixeira DS, Zugman A, Jackowski A, Pan PM, Bressan RA, Parker N, Germann J, Bado PP, Satterthwaite TD, Milham MP, Chakravarty MM, Paim Rohde LA, Constantino Miguel E, Paus T, Salum GA. Deviations from a typical development of the cerebellum in youth are associated with psychopathology, executive functions and educational outcomes. Psychol Med 2023; 53:5698-5708. [PMID: 36226568 DOI: 10.1017/s0033291722002926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Understanding deviations from typical brain development is a promising approach to comprehend pathophysiology in childhood and adolescence. We investigated if cerebellar volumes different than expected for age and sex could predict psychopathology, executive functions and academic achievement. METHODS Children and adolescents aged 6-17 years from the Brazilian High-Risk Cohort Study for Mental Conditions had their cerebellar volume estimated using Multiple Automatically Generated Templates from T1-weighted images at baseline (n = 677) and at 3-year follow-up (n = 447). Outcomes were assessed using the Child Behavior Checklist and standardized measures of executive functions and school achievement. Models of typically developing cerebellum were based on a subsample not exposed to risk factors and without mental-health conditions (n = 216). Deviations from this model were constructed for the remaining individuals (n = 461) and standardized variation from age and sex trajectory model was used to predict outcomes in cross-sectional, longitudinal and mediation analyses. RESULTS Cerebellar volumes higher than expected for age and sex were associated with lower externalizing specific factor and higher executive functions. In a longitudinal analysis, deviations from typical development at baseline predicted inhibitory control at follow-up, and cerebellar deviation changes from baseline to follow-up predicted changes in reading and writing abilities. The association between deviations in cerebellar volume and academic achievement was mediated by inhibitory control. CONCLUSIONS Deviations in the cerebellar typical development are associated with outcomes in youth that have long-lasting consequences. This study highlights both the potential of typical developing models and the important role of the cerebellum in mental health, cognition and education.
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Affiliation(s)
- Marina S Borges
- Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, rua Ramiro Barcelos 2350, Porto Alegre, 90035-003, Brazil
| | - Maurício S Hoffmann
- Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, rua Ramiro Barcelos 2350, Porto Alegre, 90035-003, Brazil
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- Department of Neuropsychiatry, Universidade Federal de Santa Maria, Avenida Roraima 1000, Santa Maria, 97105-900, Brazil
| | - André Simioni
- Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, rua Ramiro Barcelos 2350, Porto Alegre, 90035-003, Brazil
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Luiza K Axelrud
- Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, rua Ramiro Barcelos 2350, Porto Alegre, 90035-003, Brazil
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Danielle S Teixeira
- Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, rua Ramiro Barcelos 2350, Porto Alegre, 90035-003, Brazil
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - André Zugman
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
- Laboratório Interdisciplinar de Neurociências Integrativas (LiNC), Departamento de Psiquiatria, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Andrea Jackowski
- Laboratório Interdisciplinar de Neurociências Integrativas (LiNC), Departamento de Psiquiatria, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Pedro M Pan
- Laboratório Interdisciplinar de Neurociências Integrativas (LiNC), Departamento de Psiquiatria, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Rodrigo A Bressan
- Laboratório Interdisciplinar de Neurociências Integrativas (LiNC), Departamento de Psiquiatria, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Nadine Parker
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Jurgen Germann
- University Health Network, Toronto, ON, Canada
- Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Patrícia P Bado
- Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, rua Ramiro Barcelos 2350, Porto Alegre, 90035-003, Brazil
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | | | - Michael P Milham
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Luis Augusto Paim Rohde
- Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, rua Ramiro Barcelos 2350, Porto Alegre, 90035-003, Brazil
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil
| | - Eurípedes Constantino Miguel
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil
- Universidade de São Paulo (USP), São Paulo, Brazil
| | - Tomas Paus
- Departments of Psychology and Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
- Centre hospitalier universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada
| | - Giovanni A Salum
- Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul, rua Ramiro Barcelos 2350, Porto Alegre, 90035-003, Brazil
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil
- Department of Psychiatry and Legal Medicine, Universidade Federal do Rio Grande do Sul, Rua Ramiro Barcelos 2350, Porto Alegre, 90035-003, Brazil
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Parkes L, Kim JZ, Stiso J, Brynildsen JK, Cieslak M, Covitz S, Gur RE, Gur RC, Pasqualetti F, Shinohara RT, Zhou D, Satterthwaite TD, Bassett DS. Using network control theory to study the dynamics of the structural connectome. bioRxiv 2023:2023.08.23.554519. [PMID: 37662395 PMCID: PMC10473719 DOI: 10.1101/2023.08.23.554519] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains dynamics. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter dynamics in a desired way. We have extensively developed and validated the application of NCT to the human structural connectome. Through these efforts, we have studied (i) how different aspects of connectome topology affect neural dynamics, (ii) whether NCT outputs cohere with empirical data on brain function and stimulation, and (iii) how NCT outputs vary across development and correlate with behavior and mental health symptoms. In this protocol, we introduce a framework for applying NCT to structural connectomes following two main pathways. Our primary pathway focuses on computing the control energy associated with transitioning between specific neural activity states. Our second pathway focuses on computing average controllability, which indexes nodes' general capacity to control dynamics. We also provide recommendations for comparing NCT outputs against null network models. Finally, we support this protocol with a Python-based software package called network control theory for python (nctpy).
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Affiliation(s)
- Linden Parkes
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Rutgers University, Piscataway, NJ 08854, USA
| | - Jason Z Kim
- Department of Physics, Cornell University, Ithaca, NY 14853, USA
| | - Jennifer Stiso
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
| | | | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, Riverside, CA 92521, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dale Zhou
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, Philadelphia, PA 19104, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, PA 19104, USA
- Department of Physics and Astronomy, University of Pennsylvania, PA 19104, USA
- Santa Fe Institute, Santa Fe, NM 87501, USA
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Shafiei G, Keller AS, Bertolero M, Shanmugan S, Bassett DS, Chen AA, Covitz S, Houghton A, Luo A, Mehta K, Salo T, Shinohara RT, Fair D, Hallquist MN, Satterthwaite TD. Generalizable links between symptoms of borderline personality disorder and functional connectivity. bioRxiv 2023:2023.08.03.551534. [PMID: 37662311 PMCID: PMC10473667 DOI: 10.1101/2023.08.03.551534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Background | Symptoms of borderline personality disorder (BPD) often manifest in adolescence, yet the underlying relationship between these debilitating symptoms and the development of functional brain networks is not well understood. Here we aimed to investigate how multivariate patterns of functional connectivity are associated with symptoms of BPD in a large sample of young adults and adolescents. Methods | We used high-quality functional Magnetic Resonance Imaging (fMRI) data from young adults from the Human Connectome Project: Young Adults (HCP-YA; N = 870, ages 22-37 years, 457 female) and youth from the Human Connectome Project: Development (HCP-D; N = 223, age range 16-21 years, 121 female). A previously validated BPD proxy score was derived from the NEO Five Factor Inventory (NEO-FFI). A ridge regression model with 10-fold cross-validation and nested hyperparameter tuning was trained and tested in HCP-YA to predict BPD scores in unseen data from regional functional connectivity, while controlling for in-scanner motion, age, and sex. The trained model was further tested on data from HCP-D without further tuning. Finally, we tested how the connectivity patterns associated with BPD aligned with age-related changes in connectivity. Results | Multivariate functional connectivity patterns significantly predicted out-of-sample BPD proxy scores in unseen data in both young adults (HCP-YA; pperm = 0.001) and older adolescents (HCP-D; pperm = 0.001). Predictive capacity of regions was heterogeneous; the most predictive regions were found in functional systems relevant for emotion regulation and executive function, including the ventral attention network. Finally, regional functional connectivity patterns that predicted BPD proxy scores aligned with those associated with development in youth. Conclusion | Individual differences in functional connectivity in developmentally-sensitive regions are associated with the symptoms of BPD.
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Affiliation(s)
- Golia Shafiei
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Arielle S. Keller
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Maxwell Bertolero
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sheila Shanmugan
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Dani S. Bassett
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104
- Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104
- Santa Fe Institute, Santa Fe, NM 87501
| | - Andrew A. Chen
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics,Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sydney Covitz
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, USA
| | - Audrey Luo
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Kahini Mehta
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Taylor Salo
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Russell T. Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics,Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Damien Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, USA
- Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Michael N. Hallquist
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Theodore D. Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
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Zhao C, Jarecka D, Covitz S, Chen Y, Eickhoff SB, Fair DA, Franco AR, Halchenko YO, Hendrickson TJ, Hoffstaedter F, Houghton A, Kiar G, Macdonald A, Mehta K, Milham MP, Salo T, Hanke M, Ghosh SS, Cieslak M, Satterthwaite TD. A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps. bioRxiv 2023:2023.08.16.552472. [PMID: 37645999 PMCID: PMC10461987 DOI: 10.1101/2023.08.16.552472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Neuroimaging research faces a crisis of reproducibility. With massive sample sizes and greater data complexity, this problem becomes more acute. Software that operates on imaging data defined using the Brain Imaging Data Structure (BIDS) - BIDS Apps - have provided a substantial advance. However, even using BIDS Apps, a full audit trail of data processing is a necessary prerequisite for fully reproducible research. Obtaining a faithful record of the audit trail is challenging - especially for large datasets. Recently, the FAIRly big framework was introduced as a way to facilitate reproducible processing of large-scale data by leveraging DataLad - a version control system for data management. However, the current implementation of this framework was more of a proof of concept, and could not be immediately reused by other investigators for different use cases. Here we introduce the BIDS App Bootstrap (BABS), a user-friendly and generalizable Python package for reproducible image processing at scale. BABS facilitates the reproducible application of BIDS Apps to large-scale datasets. Leveraging DataLad and the FAIRly big framework, BABS tracks the full audit trail of data processing in a scalable way by automatically preparing all scripts necessary for data processing and version tracking on high performance computing (HPC) systems. Currently, BABS supports jobs submissions and audits on Sun Grid Engine (SGE) and Slurm HPCs with a parsimonious set of programs. To demonstrate its scalability, we applied BABS to data from the Healthy Brain Network (HBN; n=2,565). Taken together, BABS allows reproducible and scalable image processing and is broadly extensible via an open-source development model.
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Affiliation(s)
- Chenying Zhao
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children’s Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dorota Jarecka
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children’s Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yibei Chen
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Damien A. Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota Medical School, University of Minnesota, Minneapolis, MN, USA
| | - Alexandre R. Franco
- Child Mind Institute, New York, NY, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
| | | | - Timothy J. Hendrickson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | | | - Austin Macdonald
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Kahini Mehta
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children’s Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael P. Milham
- Child Mind Institute, New York, NY, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Taylor Salo
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children’s Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Hanke
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Satrajit S. Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Otolaryngology, Harvard Medical School, Boston, MA, USA
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children’s Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D. Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children’s Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA
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Mehta K, Pines A, Adebimpe A, Larsen B, Bassett DS, Calkins ME, Baller EB, Gell M, Patrick LM, Shafiei G, Gur RE, Gur RC, Roalf DR, Romer D, Wolf DH, Kable JW, Satterthwaite TD. Individual differences in delay discounting are associated with dorsal prefrontal cortex connectivity in children, adolescents, and adults. Dev Cogn Neurosci 2023; 62:101265. [PMID: 37327696 PMCID: PMC10285090 DOI: 10.1016/j.dcn.2023.101265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 05/24/2023] [Accepted: 06/11/2023] [Indexed: 06/18/2023] Open
Abstract
Delay discounting is a measure of impulsive choice relevant in adolescence as it predicts many real-life outcomes, including obesity and academic achievement. However, resting-state functional networks underlying individual differences in delay discounting during youth remain incompletely described. Here we investigate the association between multivariate patterns of functional connectivity and individual differences in impulsive choice in a large sample of children, adolescents, and adults. A total of 293 participants (9-23 years) completed a delay discounting task and underwent 3T resting-state fMRI. A connectome-wide analysis using multivariate distance-based matrix regression was used to examine whole-brain relationships between delay discounting and functional connectivity. These analyses revealed that individual differences in delay discounting were associated with patterns of connectivity emanating from the left dorsal prefrontal cortex, a default mode network hub. Greater delay discounting was associated with greater functional connectivity between the dorsal prefrontal cortex and other default mode network regions, but reduced connectivity with regions in the dorsal and ventral attention networks. These results suggest delay discounting in children, adolescents, and adults is associated with individual differences in relationships both within the default mode network and between the default mode and networks involved in attentional and cognitive control.
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Affiliation(s)
- Kahini Mehta
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Adam Pines
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Azeez Adebimpe
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Bart Larsen
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA; Santa Fe Institute, Santa Fe, NM, 87051, USA
| | - Monica E Calkins
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Erica B Baller
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Martin Gell
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany; Institute of Neuroscience and Medicine (INM-7: Brain & Behaviour), Research Centre Jülich, Jülich, Germany
| | - Lauren M Patrick
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Golia Shafiei
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - David R Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Daniel Romer
- Annenberg Public Policy Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daniel H Wolf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Joseph W Kable
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA.
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38
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Mahadevan AS, Cornblath EJ, Lydon-Staley DM, Zhou D, Parkes L, Larsen B, Adebimpe A, Kahn AE, Gur RC, Gur RE, Satterthwaite TD, Wolf DH, Bassett DS. Alprazolam modulates persistence energy during emotion processing in first-degree relatives of individuals with schizophrenia: a network control study. Mol Psychiatry 2023; 28:3314-3323. [PMID: 37353585 PMCID: PMC10618098 DOI: 10.1038/s41380-023-02121-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 04/28/2023] [Accepted: 06/06/2023] [Indexed: 06/25/2023]
Abstract
Schizophrenia is marked by deficits in facial affect processing associated with abnormalities in GABAergic circuitry, deficits also found in first-degree relatives. Facial affect processing involves a distributed network of brain regions including limbic regions like amygdala and visual processing areas like fusiform cortex. Pharmacological modulation of GABAergic circuitry using benzodiazepines like alprazolam can be useful for studying this facial affect processing network and associated GABAergic abnormalities in schizophrenia. Here, we use pharmacological modulation and computational modeling to study the contribution of GABAergic abnormalities toward emotion processing deficits in schizophrenia. Specifically, we apply principles from network control theory to model persistence energy - the control energy required to maintain brain activation states - during emotion identification and recall tasks, with and without administration of alprazolam, in a sample of first-degree relatives and healthy controls. Here, persistence energy quantifies the magnitude of theoretical external inputs during the task. We find that alprazolam increases persistence energy in relatives but not in controls during threatening face processing, suggesting a compensatory mechanism given the relative absence of behavioral abnormalities in this sample of unaffected relatives. Further, we demonstrate that regions in the fusiform and occipital cortices are important for facilitating state transitions during facial affect processing. Finally, we uncover spatial relationships (i) between regional variation in differential control energy (alprazolam versus placebo) and (ii) both serotonin and dopamine neurotransmitter systems, indicating that alprazolam may exert its effects by altering neuromodulatory systems. Together, these findings provide a new perspective on the distributed emotion processing network and the effect of GABAergic modulation on this network, in addition to identifying an association between schizophrenia risk and abnormal GABAergic effects on persistence energy during threat processing.
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Affiliation(s)
- Arun S Mahadevan
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Eli J Cornblath
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA, 19104, USA
| | - David M Lydon-Staley
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Dale Zhou
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA, 19104, USA
| | - Linden Parkes
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Bart Larsen
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Azeez Adebimpe
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ari E Kahn
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA, 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA, 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA, 19104, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Daniel H Wolf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM, 87501, USA.
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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Hu F, Chen AA, Horng H, Bashyam V, Davatzikos C, Alexander-Bloch A, Li M, Shou H, Satterthwaite TD, Yu M, Shinohara RT. Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization. Neuroimage 2023; 274:120125. [PMID: 37084926 PMCID: PMC10257347 DOI: 10.1016/j.neuroimage.2023.120125] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/12/2023] [Accepted: 04/19/2023] [Indexed: 04/23/2023] Open
Abstract
Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.
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Affiliation(s)
- Fengling Hu
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States.
| | - Andrew A Chen
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Hannah Horng
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Vishnu Bashyam
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Aaron Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, United States
| | - Mingyao Li
- Statistical Center for Single-Cell and Spatial Genomics, Perelman School of Medicine, University of Pennsylvania, United States
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; The Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, United States
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
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Kiar G, Clucas J, Feczko E, Goncalves M, Jarecka D, Markiewicz CJ, Halchenko YO, Hermosillo R, Li X, Miranda-Dominguez O, Ghosh S, Poldrack RA, Satterthwaite TD, Milham MP, Fair D. Align with the NMIND consortium for better neuroimaging. Nat Hum Behav 2023; 7:1027-1028. [PMID: 37386112 PMCID: PMC11024722 DOI: 10.1038/s41562-023-01647-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Affiliation(s)
- Gregory Kiar
- Center for Data Analytics, Innovation, and Rigor, Child Mind Institute, New York, NY, USA.
| | - Jon Clucas
- Center for Data Analytics, Innovation, and Rigor, Child Mind Institute, New York, NY, USA
| | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | | | - Dorota Jarecka
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | | | - Yaroslav O Halchenko
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Robert Hermosillo
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Xinhui Li
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | | | - Satrajit Ghosh
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | | | - Theodore D Satterthwaite
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Michael P Milham
- Center for Data Analytics, Innovation, and Rigor, Child Mind Institute, New York, NY, USA
| | - Damien Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
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Larsen B, Baller EB, Boucher AA, Calkins ME, Laney N, Moore TM, Roalf DR, Ruparel K, Gur RC, Gur RE, Georgieff MK, Satterthwaite TD. Development of Iron Status Measures during Youth: Associations with Sex, Neighborhood Socioeconomic Status, Cognitive Performance, and Brain Structure. Am J Clin Nutr 2023; 118:121-131. [PMID: 37146760 PMCID: PMC10375461 DOI: 10.1016/j.ajcnut.2023.05.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 04/24/2023] [Accepted: 05/01/2023] [Indexed: 05/07/2023] Open
Abstract
BACKGROUND Iron is essential to brain function, and iron deficiency during youth may adversely impact neurodevelopment. Understanding the developmental time course of iron status and its association with neurocognitive functioning is important for identifying windows for intervention. OBJECTIVES This study aimed to characterize developmental change in iron status and understand its association with cognitive performance and brain structure during adolescence using data from a large pediatric health network. METHODS This study included a cross-sectional sample of 4899 participants (2178 males; aged 8-22 y at the time of participation, M [SD] = 14.24 [3.7]) who were recruited from the Children's Hospital of Philadelphia network. Prospectively collected research data were enriched with electronic medical record data that included hematological measures related to iron status, including serum hemoglobin, ferritin, and transferrin (33,015 total samples). At the time of participation, cognitive performance was assessed using the Penn Computerized Neurocognitive Battery, and brain white matter integrity was assessed using diffusion-weighted MRI in a subset of individuals. RESULTS Developmental trajectories were characterized for all metrics and revealed that sex differences emerged after menarche such that females had reduced iron status relative to males [all R2partial > 0.008; all false discovery rates (FDRs) < 0.05]. Higher socioeconomic status was associated with higher hemoglobin concentrations throughout development (R2partial = 0.005; FDR < 0.001), and the association was greatest during adolescence. Higher hemoglobin concentrations were associated with better cognitive performance during adolescence (R2partial = 0.02; FDR < 0.001) and mediated the association between sex and cognition (mediation effect = -0.107; 95% CI: -0.191, -0.02). Higher hemoglobin concentration was also associated with greater brain white matter integrity in the neuroimaging subsample (R2partial = 0.06, FDR = 0.028). CONCLUSIONS Iron status evolves during youth and is lowest in females and individuals of low socioeconomic status during adolescence. Diminished iron status during adolescence has consequences for neurocognition, suggesting that this critical period of neurodevelopment may be an important window for intervention that has the potential to reduce health disparities in at-risk populations.
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Affiliation(s)
- Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States; Penn/Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, United States.
| | - Erica B Baller
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States; Penn/Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, United States
| | - Alexander A Boucher
- Department of Pediatrics, Division of Pediatric Hematology/Oncology, University of Minnesota, Minneapolis, MN, United States
| | - Monica E Calkins
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States; Penn/Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, United States
| | - Nina Laney
- Penn/Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, United States
| | - Tyler M Moore
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States; Penn/Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, United States
| | - David R Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States; Penn/Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, United States
| | - Kosha Ruparel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States; Penn/Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, United States
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States; Penn/Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, United States; Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States; Penn/Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, United States; Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Michael K Georgieff
- Department of Pediatrics, Division of Neonatology, University of Minnesota Medical School, Minneapolis, MN, United States
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States; Penn/Children's Hospital of Philadelphia Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA, United States
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Baller EB, Sweeney EM, Cieslak MC, Robert-Fitzgerald T, Covitz SC, Martin ML, Schindler MK, Bar-Or A, Elahi A, Larsen BS, Manning AR, Markowitz CE, Perrone CM, Rautman V, Seitz MM, Detre JA, Fox MD, Shinohara RT, Satterthwaite TD. Mapping the relationship of white matter lesions to depression in multiple sclerosis. medRxiv 2023:2023.06.09.23291080. [PMID: 37398183 PMCID: PMC10312888 DOI: 10.1101/2023.06.09.23291080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Importance Multiple sclerosis (MS) is an immune-mediated neurological disorder that affects nearly one million people in the United States. Up to 50% of patients with MS experience depression. Objective To investigate how white matter network disruption is related to depression in MS. Design Retrospective case-control study of participants who received research-quality 3-tesla neuroimaging as part of MS clinical care from 2010-2018. Analyses were performed from May 1 to September 30, 2022. Setting Single-center academic medical specialty MS clinic. Participants Participants with MS were identified via the electronic health record (EHR). All participants were diagnosed by an MS specialist and completed research-quality MRI at 3T. After excluding participants with poor image quality, 783 were included. Inclusion in the depression group (MS+Depression) required either: 1) ICD-10 depression diagnosis (F32-F34.*); 2) prescription of antidepressant medication; or 3) screening positive via Patient Health Questionnaire-2 (PHQ-2) or -9 (PHQ-9). Age- and sex-matched nondepressed comparators (MS-Depression) included persons with no depression diagnosis, no psychiatric medications, and were asymptomatic on PHQ-2/9. Exposure Depression diagnosis. Main Outcomes and Measures We first evaluated if lesions were preferentially located within the depression network compared to other brain regions. Next, we examined if MS+Depression patients had greater lesion burden, and if this was driven by lesions specifically in the depression network. Outcome measures were the burden of lesions (e.g., impacted fascicles) within a network and across the brain. Secondary measures included between-diagnosis lesion burden, stratified by brain network. Linear mixed-effects models were employed. Results Three hundred-eighty participants met inclusion criteria, (232 MS+Depression: age[SD]=49[12], %females=86; 148 MS-Depression: age[SD]=47[13], %females=79). MS lesions preferentially affected fascicles within versus outside the depression network (β=0.09, 95% CI=0.08-0.10, P<0.001). MS+Depression had more white matter lesion burden (β=0.06, 95% CI=0.01-0.10, P=0.015); this was driven by lesions within the depression network (β=0.02, 95% CI 0.003-0.040, P=0.020). Conclusions and Relevance We provide new evidence supporting a relationship between white matter lesions and depression in MS. MS lesions disproportionately impacted fascicles in the depression network. MS+Depression had more disease than MS-Depression, which was driven by disease within the depression network. Future studies relating lesion location to personalized depression interventions are warranted.
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Affiliation(s)
- Erica B Baller
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
| | - Elizabeth M Sweeney
- Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA
| | - Matthew C Cieslak
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
| | - Timothy Robert-Fitzgerald
- Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA
| | - Sydney C Covitz
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
| | - Melissa L Martin
- Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA
| | - Matthew K Schindler
- Department of Neurology, University of Pennsylvania, Philadelphia, PA USA
- Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, PA USA
| | - Amit Bar-Or
- Department of Neurology, University of Pennsylvania, Philadelphia, PA USA
- Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, PA USA
| | - Ameena Elahi
- Department of Information Services, University of Pennsylvania, Philadelphia, PA USA
| | - Bart S Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
| | - Abigail R Manning
- Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA
| | - Clyde E Markowitz
- Department of Neurology, University of Pennsylvania, Philadelphia, PA USA
- Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, PA USA
| | - Christopher M Perrone
- Department of Neurology, University of Pennsylvania, Philadelphia, PA USA
- Center for Neuroinflammation and Neurotherapeutics, University of Pennsylvania, Philadelphia, PA USA
| | - Victoria Rautman
- Department of Information Services, University of Pennsylvania, Philadelphia, PA USA
| | - Madeleine M Seitz
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
- Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA
| | - John A Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, PA USA
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, and Radiology, Brigham and Women's Hospital, Harvard Medical School
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA USA
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA USA
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Tong X, Xie H, Fonzo GA, Zhao K, Satterthwaite TD, Carlisle N, Zhang Y. Dissecting Symptom-linked Dimensions of Resting-State Electroencephalographic Functional Connectivity in Autism with Contrastive Learning. bioRxiv 2023:2023.05.22.541841. [PMID: 37292736 PMCID: PMC10245871 DOI: 10.1101/2023.05.22.541841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by social interaction deficits, communication difficulties, and restricted/repetitive behaviors or fixated interests. Despite its high prevalence, development of effective therapy for ASD is hindered by its symptomatic and neurophysiological heterogeneities. To collectively dissect the ASD heterogeneity in neurophysiology and symptoms, we develop a new analytical framework combining contrastive learning and sparse canonical correlation analysis to identify resting-state EEG connectivity dimensions linked to ASD behavioral symptoms within 392 ASD samples. Two dimensions are successfully identified, showing significant correlations with social/communication deficits (r = 0.70) and restricted/repetitive behaviors (r = 0.45), respectively. We confirm the robustness of these dimensions through cross-validation and further demonstrate their generalizability using an independent dataset of 223 ASD samples. Our results reveal that the right inferior parietal lobe is the core region displaying EEG activity associated with restricted/repetitive behaviors, and functional connectivity between the left angular gyrus and the right middle temporal gyrus is a promising biomarker of social/communication deficits. Overall, these findings provide a promising avenue to parse ASD heterogeneity with high clinical translatability, paving the way for treatment development and precision medicine for ASD.
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Affiliation(s)
- Xiaoyu Tong
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Hua Xie
- Center for Neuroscience Research, Children’s National Hospital, Washington, DC, USA
| | - Gregory A. Fonzo
- Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, TX, USA
| | - Kanhao Zhao
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Theodore D. Satterthwaite
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Nancy Carlisle
- Department of Psychology, Lehigh University, Bethlehem, PA, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA
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Keller AS, Mackey AP, Pines A, Fair D, Feczko E, Hoffmann MS, Salum GA, Barzilay R, Satterthwaite TD. Caregiver monitoring, but not caregiver warmth, is associated with general cognition in two large sub-samples of youth. Dev Sci 2023; 26:e13337. [PMID: 36305770 DOI: 10.1111/desc.13337] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/30/2022] [Accepted: 10/13/2022] [Indexed: 11/29/2022]
Abstract
Individual differences in cognitive abilities emerge early during development, and children with poorer cognition are at increased risk for adverse outcomes as they enter adolescence. Caregiving plays an important role in supporting cognitive development, yet it remains unclear how specific types of caregiving behaviors may shape cognition, highlighting the need for large-scale studies. In the present study, we characterized replicable yet specific associations between caregiving behaviors and cognition in two large sub-samples of children ages 9-10 years old from the Adolescent Brain Cognitive Development Study® (ABCD). Across both discovery and replication sub-samples, we found that child reports of caregiver monitoring (supervision or regular knowledge of the child's whereabouts) were positively associated with general cognition abilities, after covarying for age, sex, household income, neighborhood deprivation, and parental education. This association was specific to the type of caregiving behavior (caregiver monitoring, but not caregiver warmth), and was most strongly associated with a broad domain of general cognition (but not executive function or learning/memory). Additionally, we found that caregiver monitoring partially mediated the association between household income and cognition, furthering our understanding of how socioeconomic disparities may contribute to disadvantages in cognitive development. Together, these findings underscore the influence of differences in caregiving behavior in shaping youth cognition. RESEARCH HIGHLIGHTS: Caregiver monitoring, but not caregiver warmth, is associated with cognitive performance in youth Caregiver monitoring partially mediates the association between household income and cognition Results replicated across two large matched samples from the Adolescent Brain Cognitive Development Study® (ABCD).
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Affiliation(s)
- Arielle S Keller
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Allyson P Mackey
- Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Adam Pines
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Damien Fair
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, Minnesota, USA
| | - Eric Feczko
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, Minnesota, USA
| | - Mauricio S Hoffmann
- Care Policy and Evaluation Centre, London School of Economics and Political Science, London, UK
- Department of Neuropsychiatry, Universidade Federal de Santa Maria, Santa Maria, RS, Brazil
- Graduation Program in Psychiatry and Behavioural Sciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Giovanni A Salum
- Graduation Program in Psychiatry and Behavioural Sciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil
| | - Ran Barzilay
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Levinson T, Prettyman G, Savage C, White L, Moore TM, Calkins ME, Ruparel K, Gur RE, Gur RC, Satterthwaite TD, Wolf DH. Activation of Internal Correctness Monitoring Circuitry in Youths With Psychosis Spectrum Symptoms. Biol Psychiatry Cogn Neurosci Neuroimaging 2023; 8:542-550. [PMID: 37019760 PMCID: PMC10164703 DOI: 10.1016/j.bpsc.2023.01.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 01/23/2023] [Accepted: 01/24/2023] [Indexed: 02/05/2023]
Abstract
BACKGROUND Self-directed performance monitoring is a critical contributor to cognitive performance and general functioning and is impacted by psychiatric symptoms and personality traits, but has been understudied in psychosis-risk states. We have shown that ventral striatum (VS) responds to correctness during cognitive tasks where no explicit feedback is required, and this intrinsic reinforcement response is reduced in schizophrenia. METHODS Here, we examined this phenomenon in youths (n = 796, age range 11-22 years) from the Philadelphia Neurodevelopmental Cohort (PNC) performing a working memory functional magnetic resonance imaging task. We hypothesized that VS would respond to internal correctness monitoring, while classic salience network regions, such as dorsal anterior cingulate cortex and anterior insular cortex, would reflect internal error monitoring and that these responses would increase with age. We expected that neurobehavioral measures of performance monitoring would be reduced in youths with subclinical psychosis spectrum features and would correlate with amotivation severity. RESULTS Supporting these hypotheses, we found correct>incorrect activation in VS and incorrect>correct activation in anterior cingulate cortex and anterior insular cortex. Furthermore, VS activation was positively correlated with age, reduced in youths with psychosis spectrum features, and inversely correlated with amotivation. However, these patterns were not significant in anterior cingulate cortex and anterior insular cortex. CONCLUSIONS These findings advance our understanding of the neural underpinnings of performance monitoring and its impairment in adolescents with psychosis spectrum features. Such understanding can facilitate investigation of the developmental trajectory of normative and aberrant performance monitoring; contribute to early identification of youths at elevated risk for poor academic, occupational, or psychiatric outcomes; and provide potential targets for therapeutic development.
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Affiliation(s)
- Tess Levinson
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Lynch School of Education and Human Development, Boston College, Chestnut Hill, Massachusetts
| | - Greer Prettyman
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Chloe Savage
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Lauren White
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Penn-CHOP Lifespan Brain Institute, University of Pennsylvania, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Tyler M Moore
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Penn-CHOP Lifespan Brain Institute, University of Pennsylvania, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Monica E Calkins
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Penn-CHOP Lifespan Brain Institute, University of Pennsylvania, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Kosha Ruparel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Penn-CHOP Lifespan Brain Institute, University of Pennsylvania, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Penn-CHOP Lifespan Brain Institute, University of Pennsylvania, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Theodore D Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Penn-CHOP Lifespan Brain Institute, University of Pennsylvania, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Daniel H Wolf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania.
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Zhao C, Tapera TM, Bagautdinova J, Bourque J, Covitz S, Gur RE, Gur RC, Larsen B, Mehta K, Meisler SL, Murtha K, Muschelli J, Roalf DR, Sydnor VJ, Valcarcel AM, Shinohara RT, Cieslak M, Satterthwaite TD. ModelArray: An R package for statistical analysis of fixel-wise data. Neuroimage 2023; 271:120037. [PMID: 36931330 PMCID: PMC10119782 DOI: 10.1016/j.neuroimage.2023.120037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 03/08/2023] [Accepted: 03/14/2023] [Indexed: 03/17/2023] Open
Abstract
Diffusion MRI is the dominant non-invasive imaging method used to characterize white matter organization in health and disease. Increasingly, fiber-specific properties within a voxel are analyzed using fixels. While tools for conducting statistical analyses of fixel-wise data exist, currently available tools support only a limited number of statistical models. Here we introduce ModelArray, an R package for mass-univariate statistical analysis of fixel-wise data. At present, ModelArray supports linear models as well as generalized additive models (GAMs), which are particularly useful for studying nonlinear effects in lifespan data. In addition, ModelArray also aims for scalable analysis. With only several lines of code, even large fixel-wise datasets can be analyzed using a standard personal computer. Detailed memory profiling revealed that ModelArray required only limited memory even for large datasets. As an example, we applied ModelArray to fixel-wise data derived from diffusion images acquired as part of the Philadelphia Neurodevelopmental Cohort (n = 938). ModelArray revealed anticipated nonlinear developmental effects in white matter. Moving forward, ModelArray is supported by an open-source software development model that can incorporate additional statistical models and other imaging data types. Taken together, ModelArray provides a flexible and efficient platform for statistical analysis of fixel-wise data.
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Affiliation(s)
- Chenying Zhao
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tinashe M Tapera
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joëlle Bagautdinova
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Josiane Bourque
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bart Larsen
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kahini Mehta
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Steven L Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA 02139, USA
| | - Kristin Murtha
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - John Muschelli
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - David R Roalf
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Valerie J Sydnor
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alessandra M Valcarcel
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA.
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Dwyer DB, Chand GB, Pigoni A, Khuntia A, Wen J, Antoniades M, Hwang G, Erus G, Doshi J, Srinivasan D, Varol E, Kahn RS, Schnack HG, Meisenzahl E, Wood SJ, Zhuo C, Sotiras A, Shinohara RT, Shou H, Fan Y, Schaulfelberger M, Rosa P, Lalousis PA, Upthegrove R, Kaczkurkin AN, Moore TM, Nelson B, Gur RE, Gur RC, Ritchie MD, Satterthwaite TD, Murray RM, Di Forti M, Ciufolini S, Zanetti MV, Wolf DH, Pantelis C, Crespo-Facorro B, Busatto GF, Davatzikos C, Koutsouleris N, Dazzan P. Psychosis brain subtypes validated in first-episode cohorts and related to illness remission: results from the PHENOM consortium. Mol Psychiatry 2023; 28:2008-2017. [PMID: 37147389 PMCID: PMC10575777 DOI: 10.1038/s41380-023-02069-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 03/15/2023] [Accepted: 04/05/2023] [Indexed: 05/07/2023]
Abstract
Using machine learning, we recently decomposed the neuroanatomical heterogeneity of established schizophrenia to discover two volumetric subgroups-a 'lower brain volume' subgroup (SG1) and an 'higher striatal volume' subgroup (SG2) with otherwise normal brain structure. In this study, we investigated whether the MRI signatures of these subgroups were also already present at the time of the first-episode of psychosis (FEP) and whether they were related to clinical presentation and clinical remission over 1-, 3-, and 5-years. We included 572 FEP and 424 healthy controls (HC) from 4 sites (Sao Paulo, Santander, London, Melbourne) of the PHENOM consortium. Our prior MRI subgrouping models (671 participants; USA, Germany, and China) were applied to both FEP and HC. Participants were assigned into 1 of 4 categories: subgroup 1 (SG1), subgroup 2 (SG2), no subgroup membership ('None'), and mixed SG1 + SG2 subgroups ('Mixed'). Voxel-wise analyses characterized SG1 and SG2 subgroups. Supervised machine learning analyses characterized baseline and remission signatures related to SG1 and SG2 membership. The two dominant patterns of 'lower brain volume' in SG1 and 'higher striatal volume' (with otherwise normal neuromorphology) in SG2 were identified already at the first episode of psychosis. SG1 had a significantly higher proportion of FEP (32%) vs. HC (19%) than SG2 (FEP, 21%; HC, 23%). Clinical multivariate signatures separated the SG1 and SG2 subgroups (balanced accuracy = 64%; p < 0.0001), with SG2 showing higher education but also greater positive psychosis symptoms at first presentation, and an association with symptom remission at 1-year, 5-year, and when timepoints were combined. Neuromorphological subtypes of schizophrenia are already evident at illness onset, separated by distinct clinical presentations, and differentially associated with subsequent remission. These results suggest that the subgroups may be underlying risk phenotypes that could be targeted in future treatment trials and are critical to consider when interpreting neuroimaging literature.
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Affiliation(s)
- Dominic B Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany.
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia.
- Orygen, Melbourne, VIC, Australia.
| | - Ganesh B Chand
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Alessandro Pigoni
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- Social and Affective Neuroscience Group, MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Adyasha Khuntia
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
- Max-Planck Institute of Psychiatry, Munich, Germany
| | - Junhao Wen
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mathilde Antoniades
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gyujoon Hwang
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dhivya Srinivasan
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Erdem Varol
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Statistics, Zuckerman Institute, Columbia University, New York, NY, USA
| | - Rene S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hugo G Schnack
- Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Eva Meisenzahl
- LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany
| | - Stephen J Wood
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia
- Orygen, Melbourne, VIC, Australia
- University of Birmingham, Edgbaston, UK
| | - Chuanjun Zhuo
- Department of Psychiatric-Neuroimaging-Genetics and Co-morbidity Laboratory (PNGC-Lab), Nankai University Affiliated Tianjin Anding Hospital; Department of Psychiatry, Tianjin Medical University, Tianjin, China
| | - Aristeidis Sotiras
- Department of Radiology and Institute for Informatics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Russell T Shinohara
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Pedro Rosa
- Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
| | - Paris A Lalousis
- Institute for Mental Health and Centre for Brain Health, University of Birmingham, Birmingham, UK
| | - Rachel Upthegrove
- Institute for Mental Health and Centre for Brain Health, University of Birmingham, Birmingham, UK
- Early Intervention Service, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
| | | | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Barnaby Nelson
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia
- Orygen, Melbourne, VIC, Australia
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Robin M Murray
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Marta Di Forti
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Simone Ciufolini
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Marcus V Zanetti
- Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
- Hospital Sírio-Libanês, São Paulo, Brazil
| | - Daniel H Wolf
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Benedicto Crespo-Facorro
- Mental Health Service, Hospital Universitario Virgen del Rocío, Seville, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III (CIBERSAM), Madrid, Spain
- Instituto de Biomedicina de Sevilla (IBiS), Seville, Spain
- Department of Psychiatry, Universidad de Sevilla, Seville, Spain
| | - Geraldo F Busatto
- Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany.
- Max-Planck Institute of Psychiatry, Munich, Germany.
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.
| | - Paola Dazzan
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.
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Richie-Halford A, Cieslak M, Ai L, Caffarra S, Covitz S, Franco AR, Karipidis II, Kruper J, Milham M, Avelar-Pereira B, Roy E, Sydnor VJ, Yeatman JD, Satterthwaite TD, Rokem A. Author Correction: An analysis-ready and quality controlled resource for pediatric brain white-matter research. Sci Data 2023; 10:247. [PMID: 37117243 PMCID: PMC10147723 DOI: 10.1038/s41597-023-02137-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/30/2023] Open
Affiliation(s)
- Adam Richie-Halford
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA.
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA.
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.
| | - Lei Ai
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
| | - Sendy Caffarra
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
- University of Modena and Reggio Emilia, Department of Biomedical, Metabolic and Neural Sciences, 41125, Modena, Italy
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Alexandre R Franco
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
- Nathan Kline Institute for Psychiatric Research, Center for Biomedical Imaging and Neuromodulation, Orangeburg, New York, 10962, USA
| | - Iliana I Karipidis
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
- Stanford University, Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford, California, 94305, USA
- University of Zurich, Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, Zurich, 8032, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, 8057, Switzerland
| | - John Kruper
- University of Washington, Department of Psychology, Seattle, Washington, 98195, USA
| | - Michael Milham
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
- Nathan Kline Institute for Psychiatric Research, Center for Biomedical Imaging and Neuromodulation, Orangeburg, New York, 10962, USA
| | - Bárbara Avelar-Pereira
- Stanford University, Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford, California, 94305, USA
| | - Ethan Roy
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
| | - Valerie J Sydnor
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Jason D Yeatman
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Ariel Rokem
- University of Washington, Department of Psychology, Seattle, Washington, 98195, USA
- University of Washington, eScience Institute, Seattle, Washington, 98195, USA
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49
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Pines A, Keller AS, Larsen B, Bertolero M, Ashourvan A, Bassett DS, Cieslak M, Covitz S, Fan Y, Feczko E, Houghton A, Rueter AR, Saggar M, Shafiei G, Tapera TM, Vogel J, Weinstein SM, Shinohara RT, Williams LM, Fair DA, Satterthwaite TD. Development of top-down cortical propagations in youth. Neuron 2023; 111:1316-1330.e5. [PMID: 36803653 PMCID: PMC10121821 DOI: 10.1016/j.neuron.2023.01.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 12/08/2022] [Accepted: 01/18/2023] [Indexed: 02/19/2023]
Abstract
Hierarchical processing requires activity propagating between higher- and lower-order cortical areas. However, functional neuroimaging studies have chiefly quantified fluctuations within regions over time rather than propagations occurring over space. Here, we leverage advances in neuroimaging and computer vision to track cortical activity propagations in a large sample of youth (n = 388). We delineate cortical propagations that systematically ascend and descend a cortical hierarchy in all individuals in our developmental cohort, as well as in an independent dataset of densely sampled adults. Further, we demonstrate that top-down, descending hierarchical propagations become more prevalent with greater demands for cognitive control as well as with development in youth. These findings emphasize that hierarchical processing is reflected in the directionality of propagating cortical activity and suggest top-down propagations as a potential mechanism of neurocognitive maturation in youth.
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Affiliation(s)
- Adam Pines
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA; The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Arielle S Keller
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Bart Larsen
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Maxwell Bertolero
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Arian Ashourvan
- Department of Psychology, The University of Kansas, Lawrence, KS 66045, USA
| | - Dani S Bassett
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA; Departments of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics & Astronomy, The University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, Santa Fe, NM 87051, USA
| | - Matthew Cieslak
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney Covitz
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Fan
- Department of Radiology, The University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Eric Feczko
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Amanda R Rueter
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Manish Saggar
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA
| | - Golia Shafiei
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Tinashe M Tapera
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Jacob Vogel
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah M Weinstein
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Theodore D Satterthwaite
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA.
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Hwang G, Wen J, Sotardi S, Brodkin ES, Chand GB, Dwyer DB, Erus G, Doshi J, Singhal P, Srinivasan D, Varol E, Sotiras A, Dazzan P, Kahn RS, Schnack HG, Zanetti MV, Meisenzahl E, Busatto GF, Crespo-Facorro B, Pantelis C, Wood SJ, Zhuo C, Shinohara RT, Shou H, Fan Y, Di Martino A, Koutsouleris N, Gur RE, Gur RC, Satterthwaite TD, Wolf DH, Davatzikos C. Assessment of Neuroanatomical Endophenotypes of Autism Spectrum Disorder and Association With Characteristics of Individuals With Schizophrenia and the General Population. JAMA Psychiatry 2023; 80:498-507. [PMID: 37017948 PMCID: PMC10157419 DOI: 10.1001/jamapsychiatry.2023.0409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/06/2023]
Abstract
Importance Autism spectrum disorder (ASD) is associated with significant clinical, neuroanatomical, and genetic heterogeneity that limits precision diagnostics and treatment. Objective To assess distinct neuroanatomical dimensions of ASD using novel semisupervised machine learning methods and to test whether the dimensions can serve as endophenotypes also in non-ASD populations. Design, Setting, and Participants This cross-sectional study used imaging data from the publicly available Autism Brain Imaging Data Exchange (ABIDE) repositories as the discovery cohort. The ABIDE sample included individuals diagnosed with ASD aged between 16 and 64 years and age- and sex-match typically developing individuals. Validation cohorts included individuals with schizophrenia from the Psychosis Heterogeneity Evaluated via Dimensional Neuroimaging (PHENOM) consortium and individuals from the UK Biobank to represent the general population. The multisite discovery cohort included 16 internationally distributed imaging sites. Analyses were performed between March 2021 and March 2022. Main Outcomes and Measures The trained semisupervised heterogeneity through discriminative analysis models were tested for reproducibility using extensive cross-validations. It was then applied to individuals from the PHENOM and the UK Biobank. It was hypothesized that neuroanatomical dimensions of ASD would display distinct clinical and genetic profiles and would be prominent also in non-ASD populations. Results Heterogeneity through discriminative analysis models trained on T1-weighted brain magnetic resonance images of 307 individuals with ASD (mean [SD] age, 25.4 [9.8] years; 273 [88.9%] male) and 362 typically developing control individuals (mean [SD] age, 25.8 [8.9] years; 309 [85.4%] male) revealed that a 3-dimensional scheme was optimal to capture the ASD neuroanatomy. The first dimension (A1: aginglike) was associated with smaller brain volume, lower cognitive function, and aging-related genetic variants (FOXO3; Z = 4.65; P = 1.62 × 10-6). The second dimension (A2: schizophrenialike) was characterized by enlarged subcortical volumes, antipsychotic medication use (Cohen d = 0.65; false discovery rate-adjusted P = .048), partially overlapping genetic, neuroanatomical characteristics to schizophrenia (n = 307), and significant genetic heritability estimates in the general population (n = 14 786; mean [SD] h2, 0.71 [0.04]; P < 1 × 10-4). The third dimension (A3: typical ASD) was distinguished by enlarged cortical volumes, high nonverbal cognitive performance, and biological pathways implicating brain development and abnormal apoptosis (mean [SD] β, 0.83 [0.02]; P = 4.22 × 10-6). Conclusions and Relevance This cross-sectional study discovered 3-dimensional endophenotypic representation that may elucidate the heterogeneous neurobiological underpinnings of ASD to support precision diagnostics. The significant correspondence between A2 and schizophrenia indicates a possibility of identifying common biological mechanisms across the 2 mental health diagnoses.
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Affiliation(s)
- Gyujoon Hwang
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Junhao Wen
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Laboratory of AI & Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey
| | - Susan Sotardi
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Edward S Brodkin
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Ganesh B Chand
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Department of Radiology, School of Medicine, Washington University in St Louis, St Louis, Missouri
| | - Dominic B Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Guray Erus
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Jimit Doshi
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Pankhuri Singhal
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Dhivya Srinivasan
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Erdem Varol
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Department of Statistics, Zuckerman Institute, Columbia University, New York, New York
| | - Aristeidis Sotiras
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Department of Radiology, School of Medicine, Washington University in St Louis, St Louis, Missouri
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Rene S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Hugo G Schnack
- Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Marcus V Zanetti
- Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
- Hospital Sírio-Libanês, São Paulo, Brazil
| | - Eva Meisenzahl
- LVR-Klinikum Düsseldorf, Kliniken der Heinrich-Heine-Universität, Düsseldorf, Germany
| | - Geraldo F Busatto
- Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
| | - Benedicto Crespo-Facorro
- University Hospital Virgen del Rocio, Department of Psychiatry, School of Medicine, IBiS-CIBERSAM, University of Sevilla, Seville, Spain
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
| | - Stephen J Wood
- Orygen, Melbourne, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- School of Psychology, University of Birmingham, Edgbaston, UK
| | - Chuanjun Zhuo
- Department of Psychiatric-Neuroimaging-Genetics and Co-morbidity Laboratory, Tianjin Anding Hospital, Tianjin, China
- Department of Psychiatry, Tianjin Medical University, Tianjin, China
| | - Russell T Shinohara
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Haochang Shou
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Yong Fan
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Adriana Di Martino
- Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience at the New York University Child Study Center, New York
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Theodore D Satterthwaite
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Daniel H Wolf
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Christos Davatzikos
- AI 2 D Center for Data Science for Integrated Diagnostics, and Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
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