1
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Paus T. Development and Maturation of the Human Brain, from Infancy to Adolescence. Curr Top Behav Neurosci 2024. [PMID: 39138744 DOI: 10.1007/7854_2024_514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2024]
Abstract
This chapter describes basic principles and key findings regarding the development and maturation of the human brain, the former referring to the pre-natal and early post-natal periods and the latter concerning childhood and adolescence. In both cases, we focus on brain structure as revealed in vivo with multi-modal magnetic resonance imaging (MRI). We begin with a few numbers about the human brain and its cellular composition and a brief overview of a number of MRI-based metrics used to characterize age-related variations in grey and white matter. We then proceed with synthesizing current knowledge about developmental and maturational changes in the cerebral cortex (its thickness, surface area, and intra-cortical myelination) and the underlying white matter (volume and structural properties). To facilitate biological interpretations of MRI-derived metrics, we introduce the concept of virtual histology. We conclude the chapter with a few notes about future directions in the study of factors shaping the human brain from conception onwards.
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Affiliation(s)
- Tomáš Paus
- Departments of Psychiatry and Neuroscience, Faculty of Medicine and Centre Hospitalier Universitaire, University of Montréal, Montreal, QC, Canada.
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2
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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. Biostatistics 2024:kxae026. [PMID: 39140988 DOI: 10.1093/biostatistics/kxae026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 07/05/2024] [Accepted: 07/08/2024] [Indexed: 08/15/2024] Open
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 preprocessing 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, 423 Guardian Drive, University of Pennsylvania, Philadelphia, PA, 19104, United States
| | - Julia Wrobel
- Department of Biostatistics and Bioinformatics, 1518 Clifton Rd. NE, Emory University, Atlanta, GA, 30322, United States
| | - Theodore D Satterthwaite
- Department of Psychiatry, 3700 Hamilton Walk, Perelman School of Medicine, Philadelphia, PA, 19104, United States
- Penn Lifespan Informatics and Neuroimaging Center, 3700 Hamilton Walk, Philadelphia, PA, 19104, United States
| | - Jeff Goldsmith
- Department of Biostatistics, 722 W. 168th St, Columbia University, New York, NY, 10032, United States
| | - Ruben C Gur
- Department of Psychiatry, 3700 Hamilton Walk, Perelman School of Medicine, Philadelphia, PA, 19104, United States
- The Penn Medicine-CHOP Lifespan Brain Institute, 3700 Hamilton Walk, Philadelphia, PA, 19104, United States
| | - Raquel E Gur
- Department of Psychiatry, 3700 Hamilton Walk, Perelman School of Medicine, Philadelphia, PA, 19104, United States
- The Penn Medicine-CHOP Lifespan Brain Institute, 3700 Hamilton Walk, Philadelphia, PA, 19104, United States
| | - Jan Gertheiss
- Department of Mathematics and Statistics, School of Economics and Social Sciences, Holstenhofweg 85, Helmut Schmidt University, 22043 Hamburg, Germany
| | - Dani S Bassett
- Department of Bioengineering, 210 S 33rd St, University of Pennsylvania, Philadelphia, PA, 19104, United States
- Department of Physics and Astronomy, 209 S 33rd St, University of Pennsylvania, Philadelphia, PA, 19104, United States
- Department of Electrical and Systems Engineering, 200 S 33rd St, University of Pennsylvania, Philadelphia, PA, 19104, United States
- Department of Neurology, 3400 Spruce St, University of Pennsylvania, Philadelphia, PA, 19104, United States
| | - Russell T Shinohara
- The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, 423 Guardian Drive, University of Pennsylvania, Philadelphia, PA, 19104, United States
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3
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Chen L, Qiao C, Ren K, Qu G, Calhoun VD, Stephen JM, Wilson TW, Wang YP. Explainable spatio-temporal graph evolution learning with applications to dynamic brain network analysis during development. Neuroimage 2024; 298:120771. [PMID: 39111376 DOI: 10.1016/j.neuroimage.2024.120771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 07/23/2024] [Accepted: 08/02/2024] [Indexed: 08/17/2024] Open
Abstract
Modeling dynamic interactions among network components is crucial to uncovering the evolution mechanisms of complex networks. Recently, spatio-temporal graph learning methods have achieved noteworthy results in characterizing the dynamic changes of inter-node relations (INRs). However, challenges remain: The spatial neighborhood of an INR is underexploited, and the spatio-temporal dependencies in INRs' dynamic changes are overlooked, ignoring the influence of historical states and local information. In addition, the model's explainability has been understudied. To address these issues, we propose an explainable spatio-temporal graph evolution learning (ESTGEL) model to model the dynamic evolution of INRs. Specifically, an edge attention module is proposed to utilize the spatial neighborhood of an INR at multi-level, i.e., a hierarchy of nested subgraphs derived from decomposing the initial node-relation graph. Subsequently, a dynamic relation learning module is proposed to capture the spatio-temporal dependencies of INRs. The INRs are then used as adjacent information to improve the node representation, resulting in comprehensive delineation of dynamic evolution of the network. Finally, the approach is validated with real data on brain development study. Experimental results on dynamic brain networks analysis reveal that brain functional networks transition from dispersed to more convergent and modular structures throughout development. Significant changes are observed in the dynamic functional connectivity (dFC) associated with functions including emotional control, decision-making, and language processing.
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Affiliation(s)
- Longyun Chen
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Chen Qiao
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Kai Ren
- Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, China.
| | - Gang Qu
- Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118, USA.
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA.
| | | | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, USA.
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118, USA.
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4
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Karl V, Engen H, Beck D, Norbom LB, Ferschmann L, Aksnes ER, Kjelkenes R, Voldsbekk I, Andreassen OA, Alnæs D, Ladouceur CD, Westlye LT, Tamnes CK. The role of functional emotion circuits in distinct dimensions of psychopathology in youth. Transl Psychiatry 2024; 14:317. [PMID: 39095355 PMCID: PMC11297301 DOI: 10.1038/s41398-024-03036-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 07/17/2024] [Accepted: 07/23/2024] [Indexed: 08/04/2024] Open
Abstract
Several mental disorders emerge during childhood or adolescence and are often characterized by socioemotional difficulties, including alterations in emotion perception. Emotional facial expressions are processed in discrete functional brain modules whose connectivity patterns encode emotion categories, but the involvement of these neural circuits in psychopathology in youth is poorly understood. This study examined the associations between activation and functional connectivity patterns in emotion circuits and psychopathology during development. We used task-based fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC, N = 1221, 8-23 years) and conducted generalized psycho-physiological interaction (gPPI) analyses. Measures of psychopathology were derived from an independent component analysis of questionnaire data. The results showed positive associations between identifying fearful, sad, and angry faces and depressive symptoms, and a negative relationship between sadness recognition and positive psychosis symptoms. We found a positive main effect of depressive symptoms on BOLD activation in regions overlapping with the default mode network, while individuals reporting higher levels of norm-violating behavior exhibited emotion-specific lower functional connectivity within regions of the salience network and between modules that overlapped with the salience and default mode network. Our findings illustrate the relevance of functional connectivity patterns underlying emotion processing for behavioral problems in children and adolescents.
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Affiliation(s)
- Valerie Karl
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway.
| | - Haakon Engen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Institute of Military Psychiatry Norwegian Armed Forces Joint Medical Services, Oslo, Norway
| | - Dani Beck
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Linn B Norbom
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Lia Ferschmann
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Eira R Aksnes
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Rikka Kjelkenes
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Irene Voldsbekk
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Dag Alnæs
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Cecile D Ladouceur
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lars T Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Christian K Tamnes
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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5
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Kurth F, Schijven D, van den Heuvel OA, Hoogman M, van Rooij D, Stein DJ, Buitelaar JK, Bölte S, Auzias G, Kushki A, Venkatasubramanian G, Rubia K, Bollmann S, Isaksson J, Jaspers‐Fayer F, Marsh R, Batistuzzo MC, Arnold PD, Bressan RA, Stewart SE, Gruner P, Sorensen L, Pan PM, Silk TJ, Gur RC, Cubillo AI, Haavik J, O'Gorman Tuura RL, Hartman CA, Calvo R, McGrath J, Calderoni S, Jackowski A, Chantiluke KC, Satterthwaite TD, Busatto GF, Nigg JT, Gur RE, Retico A, Tosetti M, Gallagher L, Szeszko PR, Neufeld J, Ortiz AE, Ghisleni C, Lazaro L, Hoekstra PJ, Anagnostou E, Hoekstra L, Simpson B, Plessen JK, Deruelle C, Soreni N, James A, Narayanaswamy J, Reddy JY, Fitzgerald J, Bellgrove MA, Salum GA, Janssen J, Muratori F, Vila M, Giral MG, Ameis SH, Bosco P, Remnélius KL, Huyser C, Pariente JC, Jalbrzikowski M, Rosa PG, O'Hearn KM, Ehrlich S, Mollon J, Zugman A, Christakou A, Arango C, Fisher SE, Kong X, Franke B, Medland SE, Thomopoulos SI, Jahanshad N, Glahn DC, Thompson PM, Francks C, Luders E. Large-scale analysis of structural brain asymmetries during neurodevelopment: Associations with age and sex in 4265 children and adolescents. Hum Brain Mapp 2024; 45:e26754. [PMID: 39046031 PMCID: PMC11267452 DOI: 10.1002/hbm.26754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 04/29/2024] [Accepted: 05/23/2024] [Indexed: 07/25/2024] Open
Abstract
Only a small number of studies have assessed structural differences between the two hemispheres during childhood and adolescence. However, the existing findings lack consistency or are restricted to a particular brain region, a specific brain feature, or a relatively narrow age range. Here, we investigated associations between brain asymmetry and age as well as sex in one of the largest pediatric samples to date (n = 4265), aged 1-18 years, scanned at 69 sites participating in the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. Our study revealed that significant brain asymmetries already exist in childhood, but their magnitude and direction depend on the brain region examined and the morphometric measurement used (cortical volume or thickness, regional surface area, or subcortical volume). With respect to effects of age, some asymmetries became weaker over time while others became stronger; sometimes they even reversed direction. With respect to sex differences, the total number of regions exhibiting significant asymmetries was larger in females than in males, while the total number of measurements indicating significant asymmetries was larger in males (as we obtained more than one measurement per cortical region). The magnitude of the significant asymmetries was also greater in males. However, effect sizes for both age effects and sex differences were small. Taken together, these findings suggest that cerebral asymmetries are an inherent organizational pattern of the brain that manifests early in life. Overall, brain asymmetry appears to be relatively stable throughout childhood and adolescence, with some differential effects in males and females.
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Affiliation(s)
- F. Kurth
- School of PsychologyUniversity of AucklandAucklandNew Zealand
- Institute of Diagnostic and Interventional Radiology, Jena University HospitalJenaGermany
| | - D. Schijven
- Language and Genetics DepartmentMax Planck Institute for PsycholinguisticsNijmegenThe Netherlands
| | - O. A. van den Heuvel
- Department of PsychiatryAmsterdam University Medical CenterAmsterdamThe Netherlands
| | - M. Hoogman
- Department of PsychiatryRadboud University Medical CenterNijmegenThe Netherlands
- Department of Human GeneticsRadboud University Medical CenterNijmegenThe Netherlands
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
| | - D. van Rooij
- Donders Institute for Brain, Cognition and Behavior, Department of Cognitive NeuroscienceRadboud University Medical CenterNijmegenThe Netherlands
| | - D. J. Stein
- SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | - J. K. Buitelaar
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
- Department of Cognitive NeuroscienceRadboudumcNijmegenThe Netherlands
| | - S. Bölte
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's HealthKarolinska Institutet & Stockholm Health Care Services, Region StockholmStockholmSweden
- Curtin Autism Research Group, Curtin School of Allied HealthCurtin UniversityPerthAustralia
| | - G. Auzias
- Institut de neurosciences de la Timone UMR 7289, Aix‐Marseille Université & CNRSMarseilleFrance
| | - A. Kushki
- Holland Bloorview Kids Rehabilitation Hospital, Institute for Biomedical EngineeringUniversity of TorontoTorontoCanada
| | - G. Venkatasubramanian
- National Institute of Mental Health and Neuro Sciences (NIMHANS)BengaluruIndia
- Department of Psychiatry, Temerty Faculty of MedicineUniversity of TorontoTorontoCanada
| | - K. Rubia
- Institute of Psychiatry, Psychology & NeuroscienceKing's College LondonLondonUK
| | - S. Bollmann
- School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia
| | - J. Isaksson
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's HealthKarolinska Institutet & Stockholm Health Care Services, Region StockholmStockholmSweden
- Child and Adolescent Psychiatry Unit, Department of Medical SciencesUppsala UniversityUppsalaSweden
| | - F. Jaspers‐Fayer
- BC Children's Research Institute and the University of British ColumbiaVancouverCanada
| | - R. Marsh
- Department of PsychiatryColumbia University Irving Medical Center and the New York State Psychiatric InstituteNew YorkNew YorkUSA
| | - M. C. Batistuzzo
- Department & Institute of PsychiatryUniversity of Sao Paulo, Medical SchoolSao PauloBrazil
- Department of Methods and Techniques in PsychologyPontifical Catholic UniversitySao PauloBrazil
| | - P. D. Arnold
- The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain InstituteUniversity of CalgaryCalgaryCanada
| | - R. A. Bressan
- Federal University of São PauloSão PauloBrazil
- Instituto Ame Sua MenteSão PauloBrazil
| | - S. E. Stewart
- British Columbia Children's Hospital, British Columbia Mental Health and Substance Use ServicesUniversity of British ColumbiaVancouverCanada
| | - P. Gruner
- Department of PsychiatryYale UniversityNew HavenConnecticutUSA
| | - L. Sorensen
- Department of Biological and Medical PsychologyUniversity of BergenBergenNorway
| | - P. M. Pan
- Laboratório de Neurociências Integrativas (LINC), Departamento de PsiquiatriaUniversidade Federal de São Paulo (UNIFESP)São PauloBrazil
- Instituto Nacional de siquiatria do Desenvolvimento (INPD)São PauloBrazil
| | - T. J. Silk
- Centre for Social and Early Emotional Development and School of PsychologyDeakin UniversityGeelongAustralia
- Murdoch Children's Research InstituteMelbourneAustralia
| | - R. C. Gur
- Department of Psychiatry, Section on Neurodevelopment and Psychosis and the Lifespan Brain Institute, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - A. I. Cubillo
- Institute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | - J. Haavik
- Department of BiomedicineUniversity of BergenBergenNorway
- Division of PsychiatryHaukeland University HospitalBergenNorway
| | - R. L. O'Gorman Tuura
- Center for MR Research, University Children's Hospital ZurichUniversity of ZurichZurichSwitzerland
| | - C. A. Hartman
- Interdisciplinary Center Psychopathology and Emotion Regulation, Department of Psychiatry, University of GroningenUniversity Medical Center GroningenGroningenThe Netherlands
| | - R. Calvo
- Department of Child and Adolescent Psychiatry and Psychology, Neuroscience InstituteHospital ClinicBarcelonaSpain
- School of MedicineUniversity of BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red Salud Mental (CIBERSAM)BarcelonaSpain
- Institute d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain
| | - J. McGrath
- Department of PsychiatryTrinity College DublinDublinIreland
| | - S. Calderoni
- IRCCS Stella Maris FoundationPisaItaly
- Department of Clinical and Experimental MedicineUniversity of PisaPisaItaly
| | - A. Jackowski
- Department of PsychiatryUNIFESPSão PauloBrazil
- Department of EducationICT and Learning, Østfold University CollegeHaldenNorway
| | - K. C. Chantiluke
- Institute of Psychiatry, Psychology & NeuroscienceKing's College LondonLondonUK
| | - T. D. Satterthwaite
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Brain InstituteUniversity of Pennsylvania & Children's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing and Analytics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - G. F. Busatto
- Department of Psychiatry, Faculty of MedicineUniversity of São PauloSão PauloBrazil
| | - J. T. Nigg
- Department of Psychiatry and Center for ADHD ResearchOregon Health & Science UniversityPortlandOregonUSA
| | - R. E. Gur
- Department of Psychiatry, The Penn‐CHOP Lifespan Brain InstituteUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - A. Retico
- Pisa DivisionNational Institute for Nuclear Physics (INFN)PisaItaly
| | | | - L. Gallagher
- Department of PsychiatryTrinity College DublinDublinIreland
- The Hospital for Sick childrenTorontoCanada
- The Centre for Addiction and Mental Health TorontoTorontoCanada
- Department of PsychiatryUniversity of TorontoTorontoCanada
| | - P. R. Szeszko
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mental Illness Research, Education and Clinical Center (MIRECC)James J. Peters VA Medical CenterNew YorkNew YorkUSA
| | - J. Neufeld
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's HealthKarolinska Institutet & Stockholm Health Care Services, Region StockholmStockholmSweden
- Swedish Collegium for Advanced Study (SCAS)UppsalaSweden
| | - A. E. Ortiz
- Department of Child and Adolescent Psychiatry and Psychology, Neuroscience InstituteHospital ClinicBarcelonaSpain
- Institute d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain
| | - C. Ghisleni
- Center for MR Research, University Children's Hospital ZurichUniversity of ZurichZurichSwitzerland
| | - L. Lazaro
- Department of Child and Adolescent Psychiatry and Psychology, Neuroscience InstituteHospital ClinicBarcelonaSpain
- School of MedicineUniversity of BarcelonaBarcelonaSpain
- Centro de Investigación Biomédica en Red Salud Mental (CIBERSAM)BarcelonaSpain
- Institute d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain
| | - P. J. Hoekstra
- Department of Child and Adolescent Psychiatry & Accare Child Study CenterUniversity of Groningen, University Medical Center GroningenGroningenThe Netherlands
| | - E. Anagnostou
- Holland Bloorview Kids Rehabilitation Hospital, Department of Pediatrics, Temetry School of MedicineUniversity of TorontoTorontoCanada
| | - L. Hoekstra
- Karakter University Center for Child and Adolescent PsychiatryNijmegenThe Netherlands
- Donders Center for Cognitive NeuroimagingNijmegenThe Netherlands
- Radboud University Medical CenterNijmegenThe Netherlands
| | - B. Simpson
- New York State Psychiatric Institute/CUIMCNew YorkNew YorkUSA
| | - J. K. Plessen
- Division of Child and Adolescent Psychiatry, Department of PsychiatryUniversity Hospital LausanneLausanneSwitzerland
| | - C. Deruelle
- Institut de neurosciences de la Timone UMR 7289, Aix‐Marseille Université & CNRSMarseilleFrance
| | - N. Soreni
- Pediatric OCD Consultation ClinicSJHHamiltonCanada
- Department of Psychiatry and Behavioral Neurosciences and Offord Child StudiesMcMaster UniversityHamiltonCanada
| | - A. James
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - J. Narayanaswamy
- National Institute of Mental Health and Neuro Sciences (NIMHANS)BengaluruIndia
| | - J. Y. Reddy
- National Institute of Mental Health and Neuro Sciences (NIMHANS)BengaluruIndia
| | | | - M. A. Bellgrove
- School of Psychological Sciences and Turner Institute for Brain and Mental HealthMonash UniversityMelbourneAustralia
| | - G. A. Salum
- Graduate Program of Psychiatry and Behavioral SciencesUniversidade Federal do Rio Grande do Sul, Hospital de Clínicas de Porto AlegrePorto AlegreBrazil
- Child Mind InstituteNew YorkNew YorkUSA
| | - J. Janssen
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental HealthHospital General Universitario Gregorio Marañón, IiSGM, CIBERSAMMadridSpain
| | | | - M. Vila
- Department of Child and Adolescent Psychiatry and Psychology, Neuroscience InstituteHospital ClinicBarcelonaSpain
| | - M. Garcia Giral
- Department of Child and Adolescent Psychiatry and Psychology, Neuroscience InstituteHospital ClinicBarcelonaSpain
| | - S. H. Ameis
- Campbell Family Mental Health Research InstituteCentre for Addiction and Mental HealthTorontoCanada
- Temerty Faculty of Medicine, Department of PsychiatryUniversity of TorontoTorontoCanada
| | - P. Bosco
- IRCCS Stella Maris FoundationPisaItaly
| | - K. Lundin Remnélius
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's HealthKarolinska Institutet & Stockholm Health Care Services, Region StockholmStockholmSweden
| | - C. Huyser
- Academic Center Child and Youth PsychiatryLevvelAmsterdamThe Netherlands
- Department of Child and Adolescent PsychiatryAmsterdamUMCAmsterdamThe Netherlands
| | - J. C. Pariente
- Magnetic Resonance Image Core FacilityInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain
| | - M. Jalbrzikowski
- Department of Psychiatry and Behavioral SciencesBoston Children's HospitalBostonMassachusettsUSA
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
| | - P. G. Rosa
- Laboratory of Psychiatric Neuroimaging (LIM‐21), Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de MedicinaUniversidade de Sao PauloSao PauloBrazil
| | - K. M. O'Hearn
- Atrium Health Wake Forest Baptist Medical CenterWinston‐SalemNorth CarolinaUSA
| | - S. Ehrlich
- Division of Psychological and Social Medicine and Developmental Neurosciences & Department of Child and Adolescent PsychiatryFaculty of Medicine, TU DresdenDresdenGermany
| | - J. Mollon
- Boston Children's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - A. Zugman
- National Institutes of Health/National Institute of Mental HealthBethesdaMarylandUSA
| | - A. Christakou
- Institute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
- Centre for Integrative Neuroscience and Neurodynamics, School of Psychology and Clinical Language SciencesUniversity of ReadingReadingUK
| | - C. Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, School of MedicineUniversidad Complutense, CIBERSAMMadridSpain
| | - S. E. Fisher
- Language and Genetics DepartmentMax Planck Institute for PsycholinguisticsNijmegenThe Netherlands
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
| | - X. Kong
- Department of Psychology and Behavioral SciencesZhejiang UniversityHangzhouChina
- Department of Psychiatry of Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouChina
| | - B. Franke
- Department of PsychiatryRadboud University Medical CenterNijmegenThe Netherlands
- Department of Human GeneticsRadboud University Medical CenterNijmegenThe Netherlands
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
| | - S. E. Medland
- QIMR Berghofer Medical Research InstituteHerstonAustralia
| | - S. I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - N. Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - D. C. Glahn
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
- Tommy Fuss Center for Neuropsychiatric Disease Research, Boston Children's HospitalBostonMassachusettsUSA
| | - P. M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - C. Francks
- Language and Genetics DepartmentMax Planck Institute for PsycholinguisticsNijmegenThe Netherlands
- Department of Human GeneticsRadboud University Medical CenterNijmegenThe Netherlands
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
| | - E. Luders
- School of PsychologyUniversity of AucklandAucklandNew Zealand
- Swedish Collegium for Advanced Study (SCAS)UppsalaSweden
- Department of Women's and Children's HealthUppsala UniversityUppsalaSweden
- Laboratory of Neuro Imaging, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
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6
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Rosenblatt M, Tejavibulya L, Sun H, Camp CC, Khaitova M, Adkinson BD, Jiang R, Westwater ML, Noble S, Scheinost D. Power and reproducibility in the external validation of brain-phenotype predictions. Nat Hum Behav 2024:10.1038/s41562-024-01931-7. [PMID: 39085406 DOI: 10.1038/s41562-024-01931-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 06/18/2024] [Indexed: 08/02/2024]
Abstract
Brain-phenotype predictive models seek to identify reproducible and generalizable brain-phenotype associations. External validation, or the evaluation of a model in external datasets, is the gold standard in evaluating the generalizability of models in neuroimaging. Unlike typical studies, external validation involves two sample sizes: the training and the external sample sizes. Thus, traditional power calculations may not be appropriate. Here we ran over 900 million resampling-based simulations in functional and structural connectivity data to investigate the relationship between training sample size, external sample size, phenotype effect size, theoretical power and simulated power. Our analysis included a wide range of datasets: the Healthy Brain Network, the Adolescent Brain Cognitive Development Study, the Human Connectome Project (Development and Young Adult), the Philadelphia Neurodevelopmental Cohort, the Queensland Twin Adolescent Brain Project, and the Chinese Human Connectome Project; and phenotypes: age, body mass index, matrix reasoning, working memory, attention problems, anxiety/depression symptoms and relational processing. High effect size predictions achieved adequate power with training and external sample sizes of a few hundred individuals, whereas low and medium effect size predictions required hundreds to thousands of training and external samples. In addition, most previous external validation studies used sample sizes prone to low power, and theoretical power curves should be adjusted for the training sample size. Furthermore, model performance in internal validation often informed subsequent external validation performance (Pearson's r difference <0.2), particularly for well-harmonized datasets. These results could help decide how to power future external validation studies.
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Affiliation(s)
- Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Huili Sun
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Chris C Camp
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Milana Khaitova
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Brendan D Adkinson
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Margaret L Westwater
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Psychology, Northeastern University, Boston, MA, USA
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
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7
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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. A network control theory pipeline for studying the dynamics of the structural connectome. Nat Protoc 2024:10.1038/s41596-024-01023-w. [PMID: 39075309 DOI: 10.1038/s41596-024-01023-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 05/16/2024] [Indexed: 07/31/2024]
Abstract
Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. 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 the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms. To date, NCT has been validated to study different aspects of the human structural connectome. NCT outputs can be monitored throughout developmental stages to study the effects of connectome topology on neural dynamics and, separately, to test the coherence of empirical datasets with brain function and stimulation. Here, we provide a comprehensive pipeline for applying NCT to structural connectomes by following two procedures. The main procedure focuses on computing the control energy associated with the transitions between specific neural activity states. The second procedure focuses on computing average controllability, which indexes nodes' general capacity to control the dynamics of the system. We provide recommendations for comparing NCT outputs against null network models, and we further support this approach with a Python-based software package called 'network control theory for python'. The procedures in this protocol are appropriate for users with a background in network neuroscience and experience in dynamical systems theory.
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Affiliation(s)
- Linden Parkes
- Department of Psychiatry, Rutgers University, Piscataway, NJ, USA.
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Jason Z Kim
- Department of Physics, Cornell University, Ithaca, NY, USA
| | - Jennifer Stiso
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Julia K Brynildsen
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 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
| | - 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
| | - Raquel E Gur
- 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
| | - Ruben C Gur
- 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
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, Riverside, CA, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Philadelphia, PA, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dale Zhou
- Department of Bioengineering, 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
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
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8
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Saberi A, Wischnewski KJ, Jung K, Lotter LD, Schaare HL, Banaschewski T, Barker GJ, Bokde ALW, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Brühl R, Martinot JL, Martinot MLP, Artiges E, Nees F, Orfanos DP, Lemaitre H, Poustka L, Hohmann S, Holz N, Baeuchl C, Smolka MN, Vaidya N, Walter H, Whelan R, Schumann G, Paus T, Dukart J, Bernhardt BC, Popovych OV, Eickhoff SB, Valk SL. Adolescent maturation of cortical excitation-inhibition balance based on individualized biophysical network modeling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.18.599509. [PMID: 38948771 PMCID: PMC11213014 DOI: 10.1101/2024.06.18.599509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
The balance of excitation and inhibition is a key functional property of cortical microcircuits which changes through the lifespan. Adolescence is considered a crucial period for the maturation of excitation-inhibition balance. This has been primarily observed in animal studies, yet human in vivo evidence on adolescent maturation of the excitation-inhibition balance at the individual level is limited. Here, we developed an individualized in vivo marker of regional excitation-inhibition balance in human adolescents, estimated using large-scale simulations of biophysical network models fitted to resting-state functional magnetic resonance imaging data from two independent cross-sectional (N = 752) and longitudinal (N = 149) cohorts. We found a widespread relative increase of inhibition in association cortices paralleled by a relative age-related increase of excitation, or lack of change, in sensorimotor areas across both datasets. This developmental pattern co-aligned with multiscale markers of sensorimotor-association differentiation. The spatial pattern of excitation-inhibition development in adolescence was robust to inter-individual variability of structural connectomes and modeling configurations. Notably, we found that alternative simulation-based markers of excitation-inhibition balance show a variable sensitivity to maturational change. Taken together, our study highlights an increase of inhibition during adolescence in association areas using cross sectional and longitudinal data, and provides a robust computational framework to estimate microcircuit maturation in vivo at the individual level.
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Affiliation(s)
- Amin Saberi
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Kevin J Wischnewski
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Mathematics, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf, Dusseldorf, Germany
| | - Kyesam Jung
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Leon D Lotter
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Max Planck School of Cognition, Stephanstrasse 1A, 04103 Leipzig, Germany
| | - H Lina Schaare
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Sylvane Desrivières
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, 05405 Burlington, Vermont, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- German Center for Mental Health (DZPG), site Berlin-Potsdam, Germany
| | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie"; Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli; Gif-sur-Yvette, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie"; Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli; Gif-sur-Yvette, France
- AP-HP. Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie"; Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli; Gif-sur-Yvette, France
- Psychiatry Department, EPS Barthélémy Durand, Etampes, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
| | | | - Herve Lemaitre
- NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
- Institut des Maladies Neurodégénératives, UMR 5293, CNRS, CEA, Université de Bordeaux, 33076 Bordeaux, France
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry, Center for Psychosocial Medicine, University Hospital Heidelberg, Heidelberg, Germany
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Nathalie Holz
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Christian Baeuchl
- Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany
| | - Michael N Smolka
- Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany
| | - Nilakshi Vaidya
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Germany
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Tomáš Paus
- Departments of Psychiatry and Neuroscience, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada
| | - Juergen Dukart
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Oleksandr V Popovych
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sofie L Valk
- Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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9
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Jones JD, Boyd RC, Sandro AD, Calkins ME, Los Reyes AD, Barzilay R, Young JF, Benton TD, Gur RC, Moore TM, Gur RE. The General Psychopathology 'p' Factor in Adolescence: Multi-Informant Assessment and Computerized Adaptive Testing. Res Child Adolesc Psychopathol 2024:10.1007/s10802-024-01223-8. [PMID: 38869751 DOI: 10.1007/s10802-024-01223-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/06/2024] [Indexed: 06/14/2024]
Abstract
Accumulating evidence supports the presence of a general psychopathology dimension, the p factor ('p'). Despite growing interest in the p factor, questions remain about how p is assessed. Although multi-informant assessment of psychopathology is commonplace in clinical research and practice with children and adolescents, almost no research has taken a multi-informant approach to studying youth p or has examined the degree of concordance between parent and youth reports. Further, estimating p requires assessment of a large number of symptoms, resulting in high reporter burden that may not be feasible in many clinical and research settings. In the present study, we used bifactor multidimensional item response theory models to estimate parent- and adolescent-reported p in a large community sample of youth (11-17 years) and parents (N = 5,060 dyads). We examined agreement between parent and youth p scores and associations with assessor-rated youth global functioning. We also applied computerized adaptive testing (CAT) simulations to parent and youth reports to determine whether adaptive testing substantially alters agreement on p or associations with youth global functioning. Parent-youth agreement on p was moderate (r =.44) and both reports were negatively associated with youth global functioning. Notably, 7 out of 10 of the highest loading items were common across reporters. CAT reduced the average number of items administered by 57%. Agreement between CAT-derived p scores was similar to the full form (r =.40) and CAT scores were negatively correlated with youth functioning. These novel results highlight the promise and potential clinical utility of a multi-informant p factor approach.
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Affiliation(s)
- Jason D Jones
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children's Hospital of Philadelphia, Roberts Center for Pediatric Research, 2716 South Street, Philadelphia, PA 19146, USA.
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA.
| | - Rhonda C Boyd
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children's Hospital of Philadelphia, Roberts Center for Pediatric Research, 2716 South Street, Philadelphia, PA 19146, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Akira Di Sandro
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Monica E Calkins
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Andres De Los Reyes
- Department of Psychology, University of Maryland, College Park, Maryland, USA
| | - Ran Barzilay
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children's Hospital of Philadelphia, Roberts Center for Pediatric Research, 2716 South Street, Philadelphia, PA 19146, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Jami F Young
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children's Hospital of Philadelphia, Roberts Center for Pediatric Research, 2716 South Street, Philadelphia, PA 19146, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Tami D Benton
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children's Hospital of Philadelphia, Roberts Center for Pediatric Research, 2716 South Street, Philadelphia, PA 19146, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Tyler M Moore
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Raquel E Gur
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children's Hospital of Philadelphia, Roberts Center for Pediatric Research, 2716 South Street, Philadelphia, PA 19146, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
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10
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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 BTT. In vivo whole-cortex marker of excitation-inhibition ratio indexes cortical maturation and cognitive ability in youth. Proc Natl Acad Sci U S A 2024; 121:e2318641121. [PMID: 38814872 PMCID: PMC11161789 DOI: 10.1073/pnas.2318641121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 04/04/2024] [Indexed: 06/01/2024] Open
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 noninvasively 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 gamma-aminobutyric acid (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 the association cortex) is linked to better cognitive performance. This result is replicated across North American (8.2 to 23.0 y old) and Asian (7.2 to 7.9 y 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 and Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore117594, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore117583, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore117456, Singapore
- Integrative Sciences and Engineering Programme, National University of Singapore, Singapore119077, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Signapore117456, Signapore
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA19104
- Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA19104
- Department of Pediatrics, University of Minnesota, Minneapolis, MN55455
| | - Valerie J. Sydnor
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA19104
- Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA19104
| | - Tianchu Zeng
- Centre for Sleep and Cognition and Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore117594, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore117583, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore117456, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Signapore117456, Signapore
| | - Lijun An
- Centre for Sleep and Cognition and Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore117594, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore117583, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore117456, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Signapore117456, Signapore
| | - Xiaoxuan Yan
- Centre for Sleep and Cognition and Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore117594, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore117583, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore117456, Singapore
- Integrative Sciences and Engineering Programme, National University of Singapore, Singapore119077, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Signapore117456, Signapore
| | - Ru Kong
- Centre for Sleep and Cognition and Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore117594, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore117583, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore117456, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Signapore117456, Signapore
| | - Xiaolu Kong
- Centre for Sleep and Cognition and Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore117594, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore117583, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore117456, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Signapore117456, Signapore
- ByteDance, Singapore048583, Singapore
| | - Ruben C. Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA19104
- Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA19104
- Department of Radiology, University of Pennsylvania, Philadelphia, PA19104
| | - Raquel E. Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA19104
- Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA19104
- Department of Radiology, University of Pennsylvania, Philadelphia, PA19104
| | - Tyler M. Moore
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA19104
- Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA19104
| | - Daniel H. Wolf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA19104
| | - Avram J. Holmes
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ07103
- Wu Tsai Institute, Yale University, New Haven, CT06520
| | - Yapei Xie
- Centre for Sleep and Cognition and Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore117594, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore117583, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore117456, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Signapore117456, Signapore
| | - Juan Helen Zhou
- Centre for Sleep and Cognition and Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore117594, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore117583, Singapore
- Integrative Sciences and Engineering Programme, National University of Singapore, Singapore119077, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Signapore117456, Signapore
| | - Marielle V. Fortier
- Department of Diagnostic and Interventional Imaging, Kandang Kerbau Women’s and Children’s Hospital, Singapore229899, Singapore
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore138632, Singapore
| | - Ai Peng Tan
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore138632, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore119074, Singapore
| | - Peter Gluckman
- Centre for Human Evolution, Adaptation and Disease, Liggins Institute, University of Auckland, Auckland1142, New Zealand
| | - Yap Seng Chong
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore138632, Singapore
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore119228, Singapore
| | - Michael J. Meaney
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore138632, Singapore
- Department of Neurology and Neurosurgery, McGill University, Montreal, QCH3A1A1, Canada
| | - Gustavo Deco
- Center for Brain and Cognition, Department of Technology and Information, Universitat Pompeu Fabra, Barcelona08002, Spain
- Institució Catalana de la Recerca i Estudis Avançats, Universitat Barcelona, Barcelona08010, Spain
| | - Theodore D. Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA19104
- Lifespan Brain Institute of Penn Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA19104
| | - B. T. Thomas Yeo
- Centre for Sleep and Cognition and Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore117594, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore117583, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore117456, Singapore
- Integrative Sciences and Engineering Programme, National University of Singapore, Singapore119077, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Signapore117456, Signapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hopstial, Charlestown, MA02129
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11
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Zoupou E, Moore TM, Calkins ME, Gur RE, Gur RC, Scott JC. Domain-specific associations between psychopathology and neurocognitive functioning. Psychol Med 2024:1-11. [PMID: 38828712 DOI: 10.1017/s0033291724001302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
BACKGROUND Neurocognitive dysfunction is a transdiagnostic finding in psychopathology, but relationships among cognitive domains and general and specific psychopathology dimensions remain unclear. This study aimed to examine associations between cognition and psychopathology dimensions in a large youth cohort. METHOD The sample (N = 9350; age 8-21 years) was drawn from the Philadelphia Neurodevelopmental Cohort. Data from structured clinical interviews were modeled using bifactor confirmatory factor analysis (CFA), resulting in an overall psychopathology ('p') factor score and six orthogonal psychopathology dimensions: dysphoria/distress, obsessive-compulsive, behavioral/externalizing, attention-deficit/hyperactivity, phobias, and psychosis. Neurocognitive data were aggregated using correlated-traits CFA into five factors: executive functioning, memory, complex cognition, social cognition, and sensorimotor speed. We examined relationships among specific and general psychopathology dimensions and neurocognitive factors. RESULTS The final model showed both overall and specific associations between cognitive functioning and psychopathology, with acceptable fit (CFI = 0.91; TLI = 0.90; RMSEA = 0.024; SRMR = 0.054). Overall psychopathology and most psychopathology dimensions were negatively associated with neurocognitive functioning (phobias [p < 0.0005], behavioral/externalizing [p < 0.0005], attention-deficit/hyperactivity [p < 0.0005], psychosis [p < 0.0005 to p < 0.05]), except for dysphoria/distress and obsessive-compulsive symptoms, which were positively associated with complex cognition (p < 0.05 and p < 0.01, respectively). CONCLUSION By modeling a broad range of cognitive and psychopathology domains in a large, diverse sample of youth, we found aspects of neurocognitive functioning shared across clinical phenotypes, as well as domain-specific patterns. Findings support transdiagnostic examination of cognitive performance to parse variability in the link between neurocognitive functioning and clinical phenotypes.
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Affiliation(s)
- Eirini Zoupou
- Department of Psychiatry, Neurodevelopment and Psychosis Section, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tyler M Moore
- Department of Psychiatry, Neurodevelopment and Psychosis Section, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Monica E Calkins
- Department of Psychiatry, Neurodevelopment and Psychosis Section, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, Neurodevelopment and Psychosis Section, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI), Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, Neurodevelopment and Psychosis Section, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI), Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA
| | - J Cobb Scott
- Department of Psychiatry, Neurodevelopment and Psychosis Section, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- VISN4 MIRECC, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA 19104, USA
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12
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Weinstein SM, Vandekar SN, Li B, 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. Hum Brain Mapp 2024; 45:e26714. [PMID: 38878300 PMCID: PMC11179683 DOI: 10.1002/hbm.26714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 04/08/2024] [Accepted: 05/04/2024] [Indexed: 06/19/2024] Open
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 spatial properties 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 genetics 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 enrichment of 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 BiostatisticsTemple University College of Public HealthPhiladelphiaPennsylvaniaUSA
| | - Simon N. Vandekar
- Department of BiostatisticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Bin Li
- Department of Computer and Information SciencesTemple University College of Science and TechnologyPhiladelphiaPennsylvaniaUSA
| | - Aaron F. Alexander‐Bloch
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
- Department of Child and Adolescent Psychiatry and Behavioral ScienceChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Armin Raznahan
- Section on Developmental NeurogenomicsNational Institute of Mental Health Intramural Research ProgramBethesdaMarylandUSA
| | - Mingyao Li
- Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Raquel E. Gur
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Ruben C. Gur
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - David R. Roalf
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Min Tae M. Park
- Department of Psychiatry, Temerty Faculty of MedicineUniversity of TorontoTorontoOntarioCanada
- Integrated Program in NeuroscienceMcGill UniversityQCCanada
| | - Mallar Chakravarty
- Department of PsychiatryMcGill UniversityQCCanada
- Cerebral Imaging Centre, Douglas Research Centre, McGill UniversityQCCanada
| | - Erica B. Baller
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Kristin A. Linn
- Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Theodore D. Satterthwaite
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Russell T. Shinohara
- Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
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13
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Orlichenko A, Qu G, Zhou Z, Liu A, Deng HW, Ding Z, Stephen JM, Wilson TW, Calhoun VD, Wang YP. A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.16.594528. [PMID: 38798580 PMCID: PMC11118390 DOI: 10.1101/2024.05.16.594528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Objective fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevel-opmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.
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14
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Orlichenko A, Qu G, Zhou Z, Liu A, Deng HW, Ding Z, Stephen JM, Wilson TW, Calhoun VD, Wang YP. A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds. ARXIV 2024:arXiv:2405.07977v1. [PMID: 38800653 PMCID: PMC11118598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Objective fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevelopmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.
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Affiliation(s)
- Anton Orlichenko
- Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118
| | - Gang Qu
- Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118
| | - Ziyu Zhou
- Department of Computer Science, Tulane University, New Orleans, LA 70118
| | - Anqi Liu
- Center for Biomedical Informatics and Genomics, Tulane Integrated Institute of Data & Health Sciences, Tulane University, New Orleans, LA 70112
| | - Hong-Wen Deng
- Center for Biomedical Informatics and Genomics, Tulane Integrated Institute of Data & Health Sciences, Tulane University, New Orleans, LA 70112
| | - Zhengming Ding
- Department of Computer Science, Tulane University, New Orleans, LA 70118
| | | | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118
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15
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Ma Y, Li H, Zhou Z, Chen X, Ma L, Guray E, Balderston NL, Oathes DJ, Shinohara RT, Wolf DH, Nasrallah IM, Shou H, Satterthwaite TD, Davatzikos C, Fan Y. pNet: A toolbox for personalized functional networks modeling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.591367. [PMID: 38746228 PMCID: PMC11092457 DOI: 10.1101/2024.04.26.591367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Personalized functional networks (FNs) derived from functional magnetic resonance imaging (fMRI) data are useful for characterizing individual variations in the brain functional topography associated with the brain development, aging, and disorders. To facilitate applications of the personalized FNs with enhanced reliability and reproducibility, we develop an open-source toolbox that is user-friendly, extendable, and includes rigorous quality control (QC), featuring multiple user interfaces (graphics, command line, and a step-by-step guideline) and job-scheduling for high performance computing (HPC) clusters. Particularly, the toolbox, named personalized functional network modeling (pNet), takes fMRI inputs in either volumetric or surface type, ensuring compatibility with multiple fMRI data formats, and computes personalized FNs using two distinct modeling methods: one method optimizes the functional coherence of FNs, while the other enhances their independence. Additionally, the toolbox provides HTML-based reports for QC and visualization of personalized FNs. The toolbox is developed in both MATLAB and Python platforms with a modular design to facilitate extension and modification by users familiar with either programming language. We have evaluated the toolbox on two fMRI datasets and demonstrated its effectiveness and user-friendliness with interactive and scripting examples. pNet is publicly available at https://github.com/MLDataAnalytics/pNet.
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Affiliation(s)
- Yuncong Ma
- Center for Biomedical Image Computing and Analytics (CBICA), the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Radiology, the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Hongming Li
- Center for Biomedical Image Computing and Analytics (CBICA), the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Radiology, the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Zhen Zhou
- Center for Biomedical Image Computing and Analytics (CBICA), the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Radiology, the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Xiaoyang Chen
- Center for Biomedical Image Computing and Analytics (CBICA), the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Radiology, the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Liang Ma
- Center for Biomedical Image Computing and Analytics (CBICA), the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Radiology, the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Erus Guray
- Center for Biomedical Image Computing and Analytics (CBICA), the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Radiology, the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Nicholas L Balderston
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Desmond J Oathes
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Center for Biomedical Image Computing and Analytics (CBICA), the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Radiology, the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
- Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology and Informatics, the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel H Wolf
- Center for Biomedical Image Computing and Analytics (CBICA), the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Psychiatry, the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Ilya M Nasrallah
- Center for Biomedical Image Computing and Analytics (CBICA), the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Radiology, the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Haochang Shou
- Center for Biomedical Image Computing and Analytics (CBICA), the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104 USA
- Center for Clinical Epidemiology (CCEB), the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Center for Statistics in Big Data (CSBD), Department of Biostatistics, Epidemiology and Informatics, 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
| | - Theodore D Satterthwaite
- Center for Biomedical Image Computing and Analytics (CBICA), the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Psychiatry, the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
- 9. Penn Lifespan Informatics and Neuroimaging Center (PennLINC), the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Radiology, the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics (CBICA), the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Radiology, the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
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16
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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] [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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17
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Qu G, Orlichenko A, Wang J, Zhang G, Xiao L, Zhang K, Wilson TW, Stephen JM, Calhoun VD, Wang YP. Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1568-1578. [PMID: 38109241 PMCID: PMC11090410 DOI: 10.1109/tmi.2023.3343365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Graph convolutional deep learning has emerged as a promising method to explore the functional organization of the human brain in neuroscience research. This paper presents a novel framework that utilizes the gated graph transformer (GGT) model to predict individuals' cognitive ability based on functional connectivity (FC) derived from fMRI. Our framework incorporates prior spatial knowledge and uses a random-walk diffusion strategy that captures the intricate structural and functional relationships between different brain regions. Specifically, our approach employs learnable structural and positional encodings (LSPE) in conjunction with a gating mechanism to efficiently disentangle the learning of positional encoding (PE) and graph embeddings. Additionally, we utilize the attention mechanism to derive multi-view node feature embeddings and dynamically distribute propagation weights between each node and its neighbors, which facilitates the identification of significant biomarkers from functional brain networks and thus enhances the interpretability of the findings. To evaluate our proposed model in cognitive ability prediction, we conduct experiments on two large-scale brain imaging datasets: the Philadelphia Neurodevelopmental Cohort (PNC) and the Human Connectome Project (HCP). The results show that our approach not only outperforms existing methods in prediction accuracy but also provides superior explainability, which can be used to identify important FCs underlying cognitive behaviors.
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18
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Chang JR, Yao ZF, Hsieh S, Nordling TEM. Age Prediction Using Resting-State Functional MRI. Neuroinformatics 2024; 22:119-134. [PMID: 38341830 DOI: 10.1007/s12021-024-09653-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/21/2023] [Indexed: 02/13/2024]
Abstract
The increasing lifespan and large individual differences in cognitive capability highlight the importance of comprehending the aging process of the brain. Contrary to visible signs of bodily ageing, like greying of hair and loss of muscle mass, the internal changes that occur within our brains remain less apparent until they impair function. Brain age, distinct from chronological age, reflects our brain's health status and may deviate from our actual chronological age. Notably, brain age has been associated with mortality and depression. The brain is plastic and can compensate even for severe structural damage by rewiring. Functional characterization offers insights that structural cannot provide. Contrary to the multitude of studies relying on structural magnetic resonance imaging (MRI), we utilize resting-state functional MRI (rsfMRI). We also address the issue of inclusion of subjects with abnormal brain ageing through outlier removal. In this study, we employ the Least Absolute Shrinkage and Selection Operator (LASSO) to identify the 39 most predictive correlations derived from the rsfMRI data. The data is from a cohort of 176 healthy right-handed volunteers, aged 18-78 years (95/81 male/female, mean age 48, SD 17) collected at the Mind Research Imaging Center at the National Cheng Kung University. We establish a normal reference model by excluding 68 outliers, which achieves a leave-one-out mean absolute error of 2.48 years. By asking which additional features that are needed to predict the chronological age of the outliers with a smaller error, we identify correlations predictive of abnormal aging. These are associated with the Default Mode Network (DMN). Our normal reference model has the lowest prediction error among published models evaluated on adult subjects of almost all ages and is thus a candidate for screening for abnormal brain aging that has not yet manifested in cognitive decline. This study advances our ability to predict brain aging and provides insights into potential biomarkers for assessing brain age, suggesting that the role of DMN in brain aging should be studied further.
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Affiliation(s)
- Jose Ramon Chang
- Department of Mechanical Engineering, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan
| | - Zai-Fu Yao
- College of Education, National Tsing Hua University, Hsinchu, 30013, Taiwan
- Research Center for Education and Mind Sciences, National Tsing Hua University, Hsinchu, 30013, Taiwan
- Department of Kinesiology, National Tsing Hua University, Hsinchu, 30013, Taiwan
- Basic Psychology Group, Department of Educational Psychology and Counseling, National Tsing Hua University, Hsinchu, 30013, Taiwan
| | - Shulan Hsieh
- Department of Psychology, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan
- Institute of Allied Health Sciences, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan
- Department of Public Health, College of Medicine, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan
| | - Torbjörn E M Nordling
- Department of Mechanical Engineering, National Cheng Kung University, No. 1 University Rd., Tainan, 701, Taiwan.
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19
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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 : THE PREPRINT SERVER FOR BIOLOGY 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] [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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20
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Orlichenko A, Su KJ, Shen H, Deng HW, Wang YP. Somatomotor-visual resting state functional connectivity increases after 2 years in the UK Biobank longitudinal cohort. J Med Imaging (Bellingham) 2024; 11:024010. [PMID: 38618171 PMCID: PMC11009525 DOI: 10.1117/1.jmi.11.2.024010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 01/26/2024] [Accepted: 03/29/2024] [Indexed: 04/16/2024] Open
Abstract
Purpose Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, in which high connectivity among all brain regions changes to a more modular structure with maturation. We examine FC changes in older adults after 2 years of aging in the UK Biobank (UKB) longitudinal cohort. Approach We process fMRI connectivity data using the Power264 atlas and then test whether the average internetwork FC changes in the 2722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected t -test. We also compare the ability of Power264 and UKB-provided, independent component analysis (ICA)-based FC to determine which of a longitudinal scan pair is older. Finally, we investigate cross-sectional FC changes as well as differences due to differing scanner tasks in the UKB, Philadelphia Neurodevelopmental Cohort, and Alzheimer's Disease Neuroimaging Initiative datasets. Results We find a 6.8% average increase in somatomotor network (SMT)-visual network (VIS) connectivity from younger to older scans (corrected p < 10 - 15 ) that occurs in male, female, older subject (> 65 years old), and younger subject (< 55 years old) groups. Among all internetwork connections, the average SMT-VIS connectivity is the best predictor of relative scan age. Using the full FC and a training set of 2000 subjects, one is able to predict which scan is older 82.5% of the time using either the full Power264 FC or the UKB-provided ICA-based FC. Conclusions We conclude that SMT-VIS connectivity increases with age in the UKB longitudinal cohort and that resting state FC increases with age in the UKB cross-sectional cohort.
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Affiliation(s)
- Anton Orlichenko
- Tulane University, Department of Biomedical Engineering, New Orleans, Louisiana, United States
| | - Kuan-Jui Su
- Tulane University, School of Medicine, Center for Biomedical Informatics and Genomics, New Orleans, Louisiana, United States
| | - Hui Shen
- Tulane University, School of Medicine, Center for Biomedical Informatics and Genomics, New Orleans, Louisiana, United States
| | - Hong-Wen Deng
- Tulane University, School of Medicine, Center for Biomedical Informatics and Genomics, New Orleans, Louisiana, United States
| | - Yu-Ping Wang
- Tulane University, Department of Biomedical Engineering, New Orleans, Louisiana, United States
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21
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Rosenblatt M, Tejavibulya L, Jiang R, Noble S, Scheinost D. Data leakage inflates prediction performance in connectome-based machine learning models. Nat Commun 2024; 15:1829. [PMID: 38418819 PMCID: PMC10901797 DOI: 10.1038/s41467-024-46150-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 02/15/2024] [Indexed: 03/02/2024] Open
Abstract
Predictive modeling is a central technique in neuroimaging to identify brain-behavior relationships and test their generalizability to unseen data. However, data leakage undermines the validity of predictive models by breaching the separation between training and test data. Leakage is always an incorrect practice but still pervasive in machine learning. Understanding its effects on neuroimaging predictive models can inform how leakage affects existing literature. Here, we investigate the effects of five forms of leakage-involving feature selection, covariate correction, and dependence between subjects-on functional and structural connectome-based machine learning models across four datasets and three phenotypes. Leakage via feature selection and repeated subjects drastically inflates prediction performance, whereas other forms of leakage have minor effects. Furthermore, small datasets exacerbate the effects of leakage. Overall, our results illustrate the variable effects of leakage and underscore the importance of avoiding data leakage to improve the validity and reproducibility of predictive modeling.
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Affiliation(s)
- Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Rongtao Jiang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Stephanie Noble
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Psychology, Northeastern University, Boston, MA, USA
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT, USA
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22
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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 : THE PREPRINT SERVER FOR BIOLOGY 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] [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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23
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Adkinson BD, Rosenblatt M, Dadashkarimi J, Tejavibulya L, Jiang R, Noble S, Scheinost D. Brain-phenotype predictions can survive across diverse real-world data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.23.576916. [PMID: 38328100 PMCID: PMC10849571 DOI: 10.1101/2024.01.23.576916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Recent work suggests that machine learning models predicting psychiatric treatment outcomes based on clinical data may fail when applied to unharmonized samples. Neuroimaging predictive models offer the opportunity to incorporate neurobiological information, which may be more robust to dataset shifts. Yet, among the minority of neuroimaging studies that undertake any form of external validation, there is a notable lack of attention to generalization across dataset-specific idiosyncrasies. Research settings, by design, remove the between-site variations that real-world and, eventually, clinical applications demand. Here, we rigorously test the ability of a range of predictive models to generalize across three diverse, unharmonized samples: the Philadelphia Neurodevelopmental Cohort (n=1291), the Healthy Brain Network (n=1110), and the Human Connectome Project in Development (n=428). These datasets have high inter-dataset heterogeneity, encompassing substantial variations in age distribution, sex, racial and ethnic minority representation, recruitment geography, clinical symptom burdens, fMRI tasks, sequences, and behavioral measures. We demonstrate that reproducible and generalizable brain-behavior associations can be realized across diverse dataset features with sample sizes in the hundreds. Results indicate the potential of functional connectivity-based predictive models to be robust despite substantial inter-dataset variability. Notably, for the HCPD and HBN datasets, the best predictions were not from training and testing in the same dataset (i.e., cross-validation) but across datasets. This result suggests that training on diverse data may improve prediction in specific cases. Overall, this work provides a critical foundation for future work evaluating the generalizability of neuroimaging predictive models in real-world scenarios and clinical settings.
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Affiliation(s)
- Brendan D Adkinson
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Javid Dadashkarimi
- Department of Radiology, Athinoula. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA, 02129, USA
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Rongtao Jiang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Stephanie Noble
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Bioengineering, Northeastern University, Boston, MA, 02120, USA
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, 06510, USA
- Wu Tsai Institute, Yale University, New Haven, CT, 06510, USA
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24
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Rosenblatt M, Tejavibulya L, Jiang R, Noble S, Scheinost D. The effects of data leakage on connectome-based machine learning models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.09.544383. [PMID: 38234740 PMCID: PMC10793416 DOI: 10.1101/2023.06.09.544383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Predictive modeling has now become a central technique in neuroimaging to identify complex brain-behavior relationships and test their generalizability to unseen data. However, data leakage, which unintentionally breaches the separation between data used to train and test the model, undermines the validity of predictive models. Previous literature suggests that leakage is generally pervasive in machine learning, but few studies have empirically evaluated the effects of leakage in neuroimaging data. Although leakage is always an incorrect practice, understanding the effects of leakage on neuroimaging predictive models provides insight into the extent to which leakage may affect the literature. Here, we investigated the effects of leakage on machine learning models in two common neuroimaging modalities, functional and structural connectomes. Using over 400 different pipelines spanning four large datasets and three phenotypes, we evaluated five forms of leakage fitting into three broad categories: feature selection, covariate correction, and lack of independence between subjects. As expected, leakage via feature selection and repeated subjects drastically inflated prediction performance. Notably, other forms of leakage had only minor effects (e.g., leaky site correction) or even decreased prediction performance (e.g., leaky covariate regression). In some cases, leakage affected not only prediction performance, but also model coefficients, and thus neurobiological interpretations. Finally, we found that predictive models using small datasets were more sensitive to leakage. Overall, our results illustrate the variable effects of leakage on prediction pipelines and underscore the importance of avoiding data leakage to improve the validity and reproducibility of predictive modeling.
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Affiliation(s)
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT
| | - Rongtao Jiang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Stephanie Noble
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
- Department of Bioengineering, Northeastern University, Boston, MA
- Department of Psychology, Northeastern University, Boston, MA
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
- Child Study Center, Yale School of Medicine, New Haven, CT
- Department of Statistics & Data Science, Yale University, New Haven, CT
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25
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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] [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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Lewis L, Corcoran M, Cho KIK, Kwak Y, Hayes RA, Larsen B, Jalbrzikowski M. Age-associated alterations in thalamocortical structural connectivity in youths with a psychosis-spectrum disorder. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:86. [PMID: 38081873 PMCID: PMC10713597 DOI: 10.1038/s41537-023-00411-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 10/30/2023] [Indexed: 12/23/2023]
Abstract
Psychotic symptoms typically emerge in adolescence. Age-associated thalamocortical connectivity differences in psychosis remain unclear. We analyzed diffusion-weighted imaging data from 1254 participants 8-23 years old (typically developing (TD):N = 626, psychosis-spectrum (PS): N = 329, other psychopathology (OP): N = 299) from the Philadelphia Neurodevelopmental Cohort. We modeled thalamocortical tracts using deterministic fiber tractography, extracted Q-Space Diffeomorphic Reconstruction (QSDR) and diffusion tensor imaging (DTI) measures, and then used generalized additive models to determine group and age-associated thalamocortical connectivity differences. Compared to other groups, PS exhibited thalamocortical reductions in QSDR global fractional anisotropy (GFA, p-values range = 3.0 × 10-6-0.05) and DTI fractional anisotropy (FA, p-values range = 4.2 × 10-4-0.03). Compared to TD, PS exhibited shallower thalamus-prefrontal age-associated increases in GFA and FA during mid-childhood, but steeper age-associated increases during adolescence. TD and OP exhibited decreases in thalamus-frontal mean and radial diffusivities during adolescence; PS did not. Altered developmental trajectories of thalamocortical connectivity may contribute to the disruptions observed in adults with psychosis.
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Affiliation(s)
- Lydia Lewis
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Mary Corcoran
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, USA
| | - Kang Ik K Cho
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - YooBin Kwak
- Department of Brain and Cognitive Sciences, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea
| | - Rebecca A Hayes
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, USA
| | - Bart Larsen
- Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Maria Jalbrzikowski
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
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27
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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] [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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28
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Orlichenko A, Daly G, Zhou Z, Liu A, Shen H, Deng HW, Wang YP. ImageNomer: Description of a functional connectivity and omics analysis tool and case study identifying a race confound. NEUROIMAGE. REPORTS 2023; 3:100191. [PMID: 38125823 PMCID: PMC10732473 DOI: 10.1016/j.ynirp.2023.100191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Most packages for the analysis of fMRI-based functional connectivity (FC) and genomic data are used with a programming language interface, lacking an easy-to-navigate GUI frontend. This exacerbates two problems found in these types of data: demographic confounds and quality control in the face of high dimensionality of features. The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset. FC in particular usually contains tens of thousands of features per subject, and can only be summarized and efficiently explored using visualizations. To remedy this situation, we have developed ImageNomer, a data visualization and analysis tool that allows inspection of both subject-level and cohort-level demographic, genomic, and imaging features. The software is Python-based, runs in a self-contained Docker image, and contains a browser-based GUI frontend. We demonstrate the usefulness of ImageNomer by identifying an unexpected race confound when predicting achievement scores in the Philadelphia Neurodevelopmental Cohort (PNC) dataset, which contains multitask fMRI and single nucleotide polymorphism (SNP) data of healthy adolescents. In the past, many studies have attempted to use FC to identify achievement-related features in fMRI. Using ImageNomer to visualize trends in achievement scores between races, we find a clear potential for confounding effects if race can be predicted using FC. Using correlation analysis in the ImageNomer software, we show that FCs correlated with Wide Range Achievement Test (WRAT) score are in fact more highly correlated with race. Investigating further, we find that whereas both FC and SNP (genomic) features can account for 10-15% of WRAT score variation, this predictive ability disappears when controlling for race. We also use ImageNomer to investigate race-FC correlation in the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP) dataset. In this work, we demonstrate the advantage of our ImageNomer GUI tool in data exploration and confound detection. Additionally, this work identifies race as a strong confound in FC data and casts doubt on the possibility of finding unbiased achievement-related features in fMRI and SNP data of healthy adolescents.
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Affiliation(s)
- Anton Orlichenko
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
| | - Grant Daly
- College of Medicine, University of South Alabama, Mobile, AL, USA
| | - Ziyu Zhou
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
| | - Anqi Liu
- School of Medicine, Tulane University, New Orleans, LA, USA
| | - Hui Shen
- School of Medicine, Tulane University, New Orleans, LA, USA
| | - Hong-Wen Deng
- School of Medicine, Tulane University, New Orleans, LA, USA
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
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29
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Thomas M, Whittle S, Tian YE, van Rheenen TE, Zalesky A, Cropley VL. Pathways from threat exposure to psychotic symptoms in youth: The role of emotion recognition bias and brain structure. Schizophr Res 2023; 261:304-313. [PMID: 37898031 DOI: 10.1016/j.schres.2023.10.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 08/21/2023] [Accepted: 10/11/2023] [Indexed: 10/30/2023]
Abstract
BACKGROUND Research supports an association between threatening experiences in childhood and psychosis. It is possible that early threat exposure disrupts the development of emotion recognition (specifically, producing a bias for facial expressions relating to threat) and the brain structures subserving it, contributing to psychosis development. METHODS Using data from the Philadelphia Neurodevelopmental Cohort, we examined associations between threat exposure and both the misattribution of facial expressions to fear/anger in an emotion recognition task, and gray matter volumes in key emotion processing regions. Our sample comprised youth with psychosis spectrum symptoms (N = 304), control youth (N = 787), and to evaluate specificity, youth with internalizing symptoms (N = 92). The moderating effects of group and sex were examined. RESULTS Both the psychosis spectrum and internalizing groups had higher levels of threat exposure than controls. In the total sample, threat exposure was associated with lower left medial prefrontal cortex (mPFC) volume but not misattributions to fear/anger. The effects of threat exposure did not significantly differ by group or sex. CONCLUSIONS The findings of this study provide evidence for an effect of threat exposure on mPFC morphology, but do not support an association between threat exposure and a recognition bias for threat-related expressions, that is particularly pronounced in psychosis. Future research should investigate factors linking transdiagnostic alterations related to threat exposure with psychotic symptoms, and attempt to clarify the mechanisms underpinning emotion recognition misattributions in threat-exposed youth.
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Affiliation(s)
- Megan Thomas
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Melbourne, Australia.
| | - Sarah Whittle
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Melbourne, Australia
| | - Ye E Tian
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Melbourne, Australia
| | - Tamsyn E van Rheenen
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Melbourne, Australia; Centre for Mental Health, School of Health Sciences, Swinburne University, Melbourne, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Melbourne, Australia
| | - Vanessa L Cropley
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Melbourne, Australia
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30
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Rosenblatt M, Tejavibulya L, Camp CC, Jiang R, Westwater ML, Noble S, Scheinost D. Power and reproducibility in the external validation of brain-phenotype predictions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.25.563971. [PMID: 37961654 PMCID: PMC10634903 DOI: 10.1101/2023.10.25.563971] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Identifying reproducible and generalizable brain-phenotype associations is a central goal of neuroimaging. Consistent with this goal, prediction frameworks evaluate brain-phenotype models in unseen data. Most prediction studies train and evaluate a model in the same dataset. However, external validation, or the evaluation of a model in an external dataset, provides a better assessment of robustness and generalizability. Despite the promise of external validation and calls for its usage, the statistical power of such studies has yet to be investigated. In this work, we ran over 60 million simulations across several datasets, phenotypes, and sample sizes to better understand how the sizes of the training and external datasets affect statistical power. We found that prior external validation studies used sample sizes prone to low power, which may lead to false negatives and effect size inflation. Furthermore, increases in the external sample size led to increased simulated power directly following theoretical power curves, whereas changes in the training dataset size offset the simulated power curves. Finally, we compared the performance of a model within a dataset to the external performance. The within-dataset performance was typically within r=0.2 of the cross-dataset performance, which could help decide how to power future external validation studies. Overall, our results illustrate the importance of considering the sample sizes of both the training and external datasets when performing external validation.
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Affiliation(s)
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT
| | - Chris C. Camp
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT
| | - Rongtao Jiang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Margaret L. Westwater
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Stephanie Noble
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
- Department of Bioengineering, Northeastern University, Boston, MA
- Department of Psychology, Northeastern University, Boston, MA
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
- Child Study Center, Yale School of Medicine, New Haven, CT
- Department of Statistics & Data Science, Yale University, New Haven, CT
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31
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Zhu J, Margulies D, Qiu A. White matter functional gradients and their formation in adolescence. Cereb Cortex 2023; 33:10770-10783. [PMID: 37727985 DOI: 10.1093/cercor/bhad319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 09/21/2023] Open
Abstract
It is well known that functional magnetic resonance imaging (fMRI) is a widely used tool for studying brain activity. Recent research has shown that fluctuations in fMRI data can reflect functionally meaningful patterns of brain activity within the white matter. We leveraged resting-state fMRI from an adolescent population to characterize large-scale white matter functional gradients and their formation during adolescence. The white matter showed gray-matter-like unimodal-to-transmodal and sensorimotor-to-visual gradients with specific cognitive associations and a unique superficial-to-deep gradient with nonspecific cognitive associations. We propose two mechanisms for their formation in adolescence. One is a "function-molded" mechanism that may mediate the maturation of the transmodal white matter via the transmodal gray matter. The other is a "structure-root" mechanism that may support the mutual mediation roles of the unimodal and transmodal white matter maturation during adolescence. Thus, the spatial layout of the white matter functional gradients is in concert with the gray matter functional organization. The formation of the white matter functional gradients may be driven by brain anatomical wiring and functional needs.
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Affiliation(s)
- Jingwen Zhu
- Department of Biomedical Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
| | - Daniel Margulies
- Integrative Neuroscience and Cognition Center, Centre National de la Recherche Scientifique (CNRS) and Université de Paris, 45 Rue des Saint-Pères, 75006 Paris, France
| | - Anqi Qiu
- Department of Biomedical Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
- NUS (Suzhou) Research Institute, National University of Singapore, No. 377 Linquan Street, Suzhou 215000, China
- The N.1 Institute for Health, National University of Singapore, 28 Medical Dr, Singapore 117456, Singapore
- Institute of Data Science, National University of Singapore, 3 Research Link, #04-06, Singapore 117602, Singapore
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Kowloon, Hong Kong
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States
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32
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Zugman A, Jett L, Antonacci C, Winkler AM, Pine DS. A systematic review and meta-analysis of resting-state fMRI in anxiety disorders: Need for data sharing to move the field forward. J Anxiety Disord 2023; 99:102773. [PMID: 37741177 PMCID: PMC10753861 DOI: 10.1016/j.janxdis.2023.102773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 09/11/2023] [Accepted: 09/12/2023] [Indexed: 09/25/2023]
Abstract
Anxiety disorders are among the most prevalent psychiatric disorders. Neuroimaging findings remain uncertain, and resting state functional magnetic resonance (rs-fMRI) connectivity is of particular interest since it is a scalable functional imaging modality. Given heterogeneous past findings for rs-fMRI in anxious individuals, we characterize patterns across anxiety disorders by conducting a systematic review and meta-analysis. Studies were included if they contained at the time of scanning both a healthy group and a patient group. Due to insufficient study numbers, the quantitative meta-analysis only included seed-based studies. We performed an activation likelihood estimation (ALE) analysis that compared patients and healthy volunteers. All analyses were corrected for family-wise error with a cluster-level threshold of p < .05. Patients exhibited hypo-connectivity between the amygdala and the medial frontal gyrus, anterior cingulate cortex, and cingulate gyrus. This finding, however, was not robust to potential file-drawer effects. Though limited by strict inclusion criteria, our results highlight the heterogeneous nature of reported findings. This underscores the need for data sharing when attempting to detect reliable patterns of disruption in brain activity across anxiety disorders.
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Affiliation(s)
- André Zugman
- Section on Development and Affective Neuroscience (SDAN), Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States.
| | - Laura Jett
- Section on Development and Affective Neuroscience (SDAN), Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States; Child Emotion Lab, University of Wisconsin, Madison, Madison, WI, United States.
| | - Chase Antonacci
- Section on Development and Affective Neuroscience (SDAN), Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States; Department of Psychology, Stanford University, Stanford, CA, United States.
| | - Anderson M Winkler
- Section on Development and Affective Neuroscience (SDAN), Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States; Division of Human Genetics, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, Texas, United States.
| | - Daniel S Pine
- Section on Development and Affective Neuroscience (SDAN), Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States.
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Matsudaira I, Yamaguchi R, Taki Y. Transmit Radiant Individuality to Offspring (TRIO) study: investigating intergenerational transmission effects on brain development. Front Psychiatry 2023; 14:1150973. [PMID: 37840799 PMCID: PMC10568142 DOI: 10.3389/fpsyt.2023.1150973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 09/07/2023] [Indexed: 10/17/2023] Open
Abstract
Intergenerational transmission is a crucial aspect of human development. Although prior studies have demonstrated the continuity of psychopathology and maladaptive upbringing environments between parents and offspring, the underlying neurobiological mechanisms remain unclear. We have begun a novel neuroimaging research project, the Transmit Radiant Individuality to Offspring (TRIO) study, which focuses on biological parent-offspring trios. The participants of the TRIO study were Japanese parent-offspring trios consisting of offspring aged 10-40 and their biological mother and father. Structural and functional brain images of all participants were acquired using magnetic resonance imaging (MRI). Saliva samples were collected for DNA analysis. We obtained psychosocial information, such as intelligence, mental health problems, personality traits, and experiences during the developmental period from each parent and offspring in the same manner as much as possible. By April 2023, we completed data acquisition from 174 trios consisting of fathers, mothers, and offspring. The target sample size was 310 trios. However, we plan to conduct genetic and epigenetic analyses, and the sample size is expected to be expanded further while developing this project into a multi-site collaborative study in the future. The TRIO study can challenge the elucidation of the mechanism of intergenerational transmission effects on human development by collecting diverse information from parents and offspring at the molecular, neural, and behavioral levels. Our study provides interdisciplinary insights into how individuals' lives are involved in the construction of the lives of their descendants in the subsequent generation.
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Affiliation(s)
- Izumi Matsudaira
- Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, Sendai, Japan
- Smart-Aging Research Center, Tohoku University, Sendai, Japan
| | - Ryo Yamaguchi
- Japan Society for the Promotion of Science, Tokyo, Japan
- Department of Medical Sciences, Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Yasuyuki Taki
- Smart-Aging Research Center, Tohoku University, Sendai, Japan
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Kippenhan JS, Gregory MD, Nash T, Kohn P, Mervis CB, Eisenberg DP, Garvey MH, Roe K, Morris CA, Kolachana B, Pani AM, Sorcher L, Berman KF. Dorsal visual stream and LIMK1: hemideletion, haplotype, and enduring effects in children with Williams syndrome. J Neurodev Disord 2023; 15:29. [PMID: 37633900 PMCID: PMC10464045 DOI: 10.1186/s11689-023-09493-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 07/04/2023] [Indexed: 08/28/2023] Open
Abstract
BACKGROUND Williams syndrome (WS), a rare neurodevelopmental disorder caused by hemizygous deletion of ~ 25 genes from chromosomal band 7q11.23, affords an exceptional opportunity to study associations between a well-delineated genetic abnormality and a well-characterized neurobehavioral profile. Clinically, WS is typified by increased social drive (often termed "hypersociability") and severe visuospatial construction deficits. Previous studies have linked visuospatial problems in WS with alterations in the dorsal visual processing stream. We investigated the impacts of hemideletion and haplotype variation of LIMK1, a gene hemideleted in WS and linked to neuronal maturation and migration, on the structure and function of the dorsal stream, specifically the intraparietal sulcus (IPS), a region known to be altered in adults with WS. METHODS We tested for IPS structural and functional changes using longitudinal MRI in a developing cohort of children with WS (76 visits from 33 participants, compared to 280 visits from 94 typically developing age- and sex-matched participants) over the age range of 5-22. We also performed MRI studies of 12 individuals with rare, shorter hemideletions at 7q11.23, all of which included LIMK1. Finally, we tested for effects of LIMK1 variation on IPS structure and imputed LIMK1 expression in two independent cohorts of healthy individuals from the general population. RESULTS IPS structural (p < 10-4 FDR corrected) and functional (p < .05 FDR corrected) anomalies previously reported in adults were confirmed in children with WS, and, consistent with an enduring genetic mechanism, were stable from early childhood into adulthood. In the short hemideletion cohort, IPS deficits similar to those in WS were found, although effect sizes were smaller than those found in WS for both structural and functional findings. Finally, in each of the two general population cohorts stratified by LIMK1 haplotype, IPS gray matter volume (pdiscovery < 0.05 SVC, preplication = 0.0015) and imputed LIMK1 expression (pdiscovery = 10-15, preplication = 10-23) varied according to LIMK1 haplotype. CONCLUSIONS This work offers insight into neurobiological and genetic mechanisms responsible for the WS phenotype and also more generally provides a striking example of the mechanisms by which genetic variation, acting by means of molecular effects on a neural intermediary, can influence human cognition and, in some cases, lead to neurocognitive disorders.
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Affiliation(s)
- J Shane Kippenhan
- Section on Integrative Neuroimaging, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA.
- Clinical and Translational Neuroscience Branch, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA.
| | - Michael D Gregory
- Section on Integrative Neuroimaging, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
- Clinical and Translational Neuroscience Branch, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Tiffany Nash
- Section on Integrative Neuroimaging, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
- Clinical and Translational Neuroscience Branch, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Philip Kohn
- Section on Integrative Neuroimaging, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
- Clinical and Translational Neuroscience Branch, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Carolyn B Mervis
- Neurodevelopmental Sciences Laboratory, Department of Psychological and Brain Sciences, University of Louisville, Louisville, KY, 40202, USA
| | - Daniel P Eisenberg
- Section on Integrative Neuroimaging, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
- Clinical and Translational Neuroscience Branch, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Madeline H Garvey
- Section on Integrative Neuroimaging, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
- Clinical and Translational Neuroscience Branch, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Katherine Roe
- Section on Integrative Neuroimaging, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
- Clinical and Translational Neuroscience Branch, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Colleen A Morris
- Department of Pediatrics, Kirk Kerkorian School of Medicine at UNLV, Las Vegas, NV, 89102, USA
| | - Bhaskar Kolachana
- Clinical and Translational Neuroscience Branch, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Ariel M Pani
- Department of Biology, University of Virginia, Charlottesville, VA, 22903, USA
| | - Leah Sorcher
- Section on Integrative Neuroimaging, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
- Clinical and Translational Neuroscience Branch, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Karen F Berman
- Section on Integrative Neuroimaging, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA.
- Clinical and Translational Neuroscience Branch, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA.
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Orlichenko A, Su KJ, Tian Q, Shen H, Deng HW, Wang YP. Somatomotor-Visual Resting State Functional Connectivity Increases After Two Years in the UK Biobank Longitudinal Cohort. ARXIV 2023:arXiv:2308.07992v2. [PMID: 37645050 PMCID: PMC10462162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Purpose Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, where high connectivity among all brain regions changes to a more modular structure with maturation. In this work, we examine changes in FC in older adults after two years of aging in the UK Biobank longitudinal cohort. Approach We process data using the Power264 atlas, then test whether FC changes in the 2,722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected t-test. We also compare the ability of Power264 and UKB-provided, ICA-based FC to determine which of a longitudinal scan pair is older. Results We find a 6.8% average increase in SMT-VIS connectivity from younger to older scan (from ρ = 0.39 to ρ = 0.42) that occurs in male, female, older subject (> 65 years old), and younger subject (< 55 years old) groups. Among all inter-network connections, this average SMT-VIS connectivity is the best predictor of relative scan age, accurately predicting which scan is older 57% of the time. Using the full FC and a training set of 2,000 subjects, one is able to predict which scan is older 82.5% of the time using either the full Power264 FC or the UKB-provided ICA-based FC. Conclusions We conclude that SMT-VIS connectivity increases in the longitudinal cohort, while resting state FC increases generally with age in the cross-sectional cohort. However, we consider the possibility of a change in resting state scanner task between UKB longitudinal data acquisitions.
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Affiliation(s)
- Anton Orlichenko
- Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118
| | - Kuan-Jui Su
- Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA 70118
| | - Qing Tian
- Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA 70118
| | - Hui Shen
- Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA 70118
| | - Hong-Wen Deng
- Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA 70118
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118
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Orlichenko A, Su KJ, Tian Q, Shen H, Deng HW, Wang YP. Somatomotor-Visual Resting State Functional Connectivity Increases After Two Years in the UK Biobank Longitudinal Cohort. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.15.23294133. [PMID: 37645791 PMCID: PMC10462217 DOI: 10.1101/2023.08.15.23294133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Purpose Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, where high connectivity among all brain regions changes to a more modular structure with maturation. In this work, we examine changes in FC in older adults after two years of aging in the UK Biobank longitudinal cohort. Approach We process data using the Power264 atlas, then test whether FC changes in the 2,722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected t-test. We also compare the ability of Power264 and UKB-provided, ICA-based FC to determine which of a longitudinal scan pair is older. Results We find a 6.8% average increase in SMT-VIS connectivity from younger to older scan (from ρ = 0.39 to ρ = 0.42 ) that occurs in male, female, older subject (> 65 years old), and younger subject (< 55 years old) groups. Among all inter-network connections, this average SMT-VIS connectivity is the best predictor of relative scan age, accurately predicting which scan is older 57% of the time. Using the full FC and a training set of 2,000 subjects, one is able to predict which scan is older 82.5% of the time using either the full Power264 FC or the UKB-provided ICA-based FC. Conclusions We conclude that SMT-VIS connectivity increases in the longitudinal cohort, while resting state FC increases generally with age in the cross-sectional cohort. However, we consider the possibility of a change in resting state scanner task between UKB longitudinal data acquisitions.
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Affiliation(s)
- Anton Orlichenko
- Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118
| | - Kuan-Jui Su
- Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA 70118
| | - Qing Tian
- Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA 70118
| | - Hui Shen
- Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA 70118
| | - Hong-Wen Deng
- Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA 70118
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118
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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 : THE PREPRINT SERVER FOR BIOLOGY 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] [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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Orlichenko A, Qu G, Su KJ, Liu A, Shen H, Deng HW, Wang YP. Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results. ARXIV 2023:arXiv:2308.01451v1. [PMID: 37576121 PMCID: PMC10418521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Functional magnetic resonance (fMRI) is an invaluable tool in studying cognitive processes in vivo. Many recent studies use functional connectivity (FC), partial correlation connectivity (PC), or fMRI-derived brain networks to predict phenotypes with results that sometimes cannot be replicated. At the same time, FC can be used to identify the same subject from different scans with great accuracy. In this paper, we show a method by which one can unknowingly inflate classification results from 61% accuracy to 86% accuracy by treating longitudinal or contemporaneous scans of the same subject as independent data points. Using the UK Biobank dataset, we find one can achieve the same level of variance explained with 50 training subjects by exploiting identifiability as with 10,000 training subjects without double-dipping. We replicate this effect in four different datasets: the UK Biobank (UKB), the Philadelphia Neurodevelopmental Cohort (PNC), the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP), and an OpenNeuro Fibromyalgia dataset (Fibro). The unintentional improvement ranges between 7% and 25% in the four datasets. Additionally, we find that by using dynamic functional connectivity (dFC), one can apply this method even when one is limited to a single scan per subject. One major problem is that features such as ROIs or connectivities that are reported alongside inflated results may confuse future work. This article hopes to shed light on how even minor pipeline anomalies may lead to unexpectedly superb results.
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Affiliation(s)
- Anton Orlichenko
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
| | - Gang Qu
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
| | - Kuan-Jui Su
- School of Medicine, Tulane University, New Orleans, LA, USA
| | - Anqi Liu
- School of Medicine, Tulane University, New Orleans, LA, USA
| | - Hui Shen
- School of Medicine, Tulane University, New Orleans, LA, USA
| | - Hong-Wen Deng
- School of Medicine, Tulane University, New Orleans, LA, USA
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
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Kan X, Li Z, Cui H, Yu Y, Xu R, Yu S, Zhang Z, Guo Y, Yang C. R-Mixup: Riemannian Mixup for Biological Networks. KDD : PROCEEDINGS. INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING 2023; 2023:1073-1085. [PMID: 38343707 PMCID: PMC10853987 DOI: 10.1145/3580305.3599483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
Biological networks are commonly used in biomedical and healthcare domains to effectively model the structure of complex biological systems with interactions linking biological entities. However, due to their characteristics of high dimensionality and low sample size, directly applying deep learning models on biological networks usually faces severe overfitting. In this work, we propose R-Mixup, a Mixup-based data augmentation technique that suits the symmetric positive definite (SPD) property of adjacency matrices from biological networks with optimized training efficiency. The interpolation process in R-Mixup leverages the log-Euclidean distance metrics from the Riemannian manifold, effectively addressing the swelling effect and arbitrarily incorrect label issues of vanilla Mixup. We demonstrate the effectiveness of R-Mixup with five real-world biological network datasets on both regression and classification tasks. Besides, we derive a commonly ignored necessary condition for identifying the SPD matrices of biological networks and empirically study its influence on the model performance. The code implementation can be found in Appendix E.
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Affiliation(s)
- Xuan Kan
- Department of Computer Science, Emory University, Atlanta, GA, USA
| | - Zimu Li
- Pritzker School of Molecular, Engineering, University of Chicago, Chicago, IL, USA
| | - Hejie Cui
- Department of Computer Science, Emory University, Atlanta, GA, USA
| | - Yue Yu
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Ran Xu
- Department of Computer Science, Emory University, Atlanta, GA, USA
| | - Shaojun Yu
- Department of Computer Science, Emory University, Atlanta, GA, USA
| | - Zilong Zhang
- School of Statistics, University of International Business and Economics, Beijing, China
| | - Ying Guo
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA
| | - Carl Yang
- Department of Computer Science, Emory University, Atlanta, GA, USA
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Yang L, Qiao C, Zhou H, Calhoun VD, Stephen JM, Wilson TW, Wang Y. Explainable Multimodal Deep Dictionary Learning to Capture Developmental Differences From Three fMRI Paradigms. IEEE Trans Biomed Eng 2023; 70:2404-2415. [PMID: 37022875 PMCID: PMC11045007 DOI: 10.1109/tbme.2023.3244921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
OBJECTIVE Multimodal-based methods show great potential for neuroscience studies by integrating complementary information. There has been less multimodal work focussed on brain developmental changes. METHODS We propose an explainable multimodal deep dictionary learning method to uncover both the commonality and specificity of different modalities, which learns the shared dictionary and the modality-specific sparse representations based on the multimodal data and their encodings of a sparse deep autoencoder. RESULTS By regarding three fMRI paradigms collected during two tasks and resting state as modalities, we apply the proposed method on multimodal data to identify the brain developmental differences. The results show that the proposed model can not only achieve better performance in reconstruction, but also yield age-related differences in reoccurring patterns. Specifically, both children and young adults prefer to switch among states during two tasks while staying within a particular state during rest, but the difference is that children possess more diffuse functional connectivity patterns while young adults have more focused functional connectivity patterns. CONCLUSION AND SIGNIFICANCE To uncover the commonality and specificity of three fMRI paradigms to developmental differences, multimodal data and their encodings are used to train the shared dictionary and the modality-specific sparse representations. Identifying brain network differences helps to understand how the neural circuits and brain networks form and develop with age.
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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: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [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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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: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [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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Als TD, Kurki MI, Grove J, Voloudakis G, Therrien K, Tasanko E, Nielsen TT, Naamanka J, Veerapen K, Levey DF, Bendl J, Bybjerg-Grauholm J, Zeng B, Demontis D, Rosengren A, Athanasiadis G, Bækved-Hansen M, Qvist P, Bragi Walters G, Thorgeirsson T, Stefánsson H, Musliner KL, Rajagopal VM, Farajzadeh L, Thirstrup J, Vilhjálmsson BJ, McGrath JJ, Mattheisen M, Meier S, Agerbo E, Stefánsson K, Nordentoft M, Werge T, Hougaard DM, Mortensen PB, Stein MB, Gelernter J, Hovatta I, Roussos P, Daly MJ, Mors O, Palotie A, Børglum AD. Depression pathophysiology, risk prediction of recurrence and comorbid psychiatric disorders using genome-wide analyses. Nat Med 2023; 29:1832-1844. [PMID: 37464041 PMCID: PMC10839245 DOI: 10.1038/s41591-023-02352-1] [Citation(s) in RCA: 44] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 04/17/2023] [Indexed: 07/20/2023]
Abstract
Depression is a common psychiatric disorder and a leading cause of disability worldwide. Here we conducted a genome-wide association study meta-analysis of six datasets, including >1.3 million individuals (371,184 with depression) and identified 243 risk loci. Overall, 64 loci were new, including genes encoding glutamate and GABA receptors, which are targets for antidepressant drugs. Intersection with functional genomics data prioritized likely causal genes and revealed new enrichment of prenatal GABAergic neurons, astrocytes and oligodendrocyte lineages. We found depression to be highly polygenic, with ~11,700 variants explaining 90% of the single-nucleotide polymorphism heritability, estimating that >95% of risk variants for other psychiatric disorders (anxiety, schizophrenia, bipolar disorder and attention deficit hyperactivity disorder) were influencing depression risk when both concordant and discordant variants were considered, and nearly all depression risk variants influenced educational attainment. Additionally, depression genetic risk was associated with impaired complex cognition domains. We dissected the genetic and clinical heterogeneity, revealing distinct polygenic architectures across subgroups of depression and demonstrating significantly increased absolute risks for recurrence and psychiatric comorbidity among cases of depression with the highest polygenic burden, with considerable sex differences. The risks were up to 5- and 32-fold higher than cases with the lowest polygenic burden and the background population, respectively. These results deepen the understanding of the biology underlying depression, its disease progression and inform precision medicine approaches to treatment.
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Affiliation(s)
- Thomas D Als
- Department of Biomedicine, Aarhus University, Aarhus, Denmark.
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark.
- Center for Genomics and Personalized Medicine, Aarhus, Denmark.
| | - Mitja I Kurki
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jakob Grove
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Georgios Voloudakis
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J Peters VA Medical Center, Bronx, NY, USA
| | - Karen Therrien
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J Peters VA Medical Center, Bronx, NY, USA
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Elisa Tasanko
- Department of Psychology and Logopedics, SleepWell Research Program, University of Helsinki, Helsinki, Finland
| | - Trine Tollerup Nielsen
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Joonas Naamanka
- Department of Psychology and Logopedics, SleepWell Research Program, University of Helsinki, Helsinki, Finland
| | - Kumar Veerapen
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Daniel F Levey
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jonas Bybjerg-Grauholm
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Biao Zeng
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ditte Demontis
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Anders Rosengren
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Mental Health Centre Sct. Hans, Capital Region of Denmark, Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen, Denmark
| | - Georgios Athanasiadis
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Mental Health Centre Sct. Hans, Capital Region of Denmark, Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Evolutionary Biology, Ecology and Environmental Sciences, University of Barcelona, Barcelona, Spain
| | - Marie Bækved-Hansen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Per Qvist
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | | | | | | | - Katherine L Musliner
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- National Centre for Register-Based Research (NCRR), Business and Social Sciences, Aarhus University, Aarhus, Denmark
- Department of Affective Disorders, Aarhus University Hospital-Psychiatry, Aarhus, Denmark
- The Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Veera M Rajagopal
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Leila Farajzadeh
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Janne Thirstrup
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Bjarni J Vilhjálmsson
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
| | - John J McGrath
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Brisbane, Queensland, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
| | - Manuel Mattheisen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
- Department of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada
- Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Sandra Meier
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Department of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada
- Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada
- Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Esben Agerbo
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- National Centre for Register-Based Research (NCRR), Business and Social Sciences, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, CIRRAU, Aarhus University, Aarhus, Denmark
| | | | - Merete Nordentoft
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Mental Health Centre Copenhagen, Capital Region of Denmark, Copenhagen University Hospital, Copenhagen, Denmark
| | - Thomas Werge
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Mental Health Centre Sct. Hans, Capital Region of Denmark, Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen, Denmark
- Institute of Clinical Sciences and GLOBE Institute, LF Center for GeoGenetics, University of Copenhagen, Copenhagen, Denmark
| | - David M Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Preben B Mortensen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- National Centre for Register-Based Research (NCRR), Business and Social Sciences, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, CIRRAU, Aarhus University, Aarhus, Denmark
| | - Murray B Stein
- Psychiatry Service, VA San Diego Healthcare System, San Diego, CA, USA
- Departments of Psychiatry and Herbert Wertheim School of Public Health, University of California, San Diego, La Jolla, CA, USA
| | - Joel Gelernter
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Iiris Hovatta
- Department of Psychology and Logopedics, SleepWell Research Program, University of Helsinki, Helsinki, Finland
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J Peters VA Medical Center, Bronx, NY, USA
- Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Mark J Daly
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Ole Mors
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Psychosis Research Unit, Aarhus University Hospital-Psychiatry, Aarhus, Denmark
| | - Aarno Palotie
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Anders D Børglum
- Department of Biomedicine, Aarhus University, Aarhus, Denmark.
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark.
- Center for Genomics and Personalized Medicine, Aarhus, Denmark.
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Wang J, Li H, Qu G, Cecil KM, Dillman JR, Parikh NA, He L. Dynamic weighted hypergraph convolutional network for brain functional connectome analysis. Med Image Anal 2023; 87:102828. [PMID: 37130507 PMCID: PMC10247416 DOI: 10.1016/j.media.2023.102828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 04/14/2023] [Accepted: 04/18/2023] [Indexed: 05/04/2023]
Abstract
The hypergraph structure has been utilized to characterize the brain functional connectome (FC) by capturing the high order relationships among multiple brain regions of interest (ROIs) compared with a simple graph. Accordingly, hypergraph neural network (HGNN) models have emerged and provided efficient tools for hypergraph embedding learning. However, most existing HGNN models can only be applied to pre-constructed hypergraphs with a static structure during model training, which might not be a sufficient representation of the complex brain networks. In this study, we propose a dynamic weighted hypergraph convolutional network (dwHGCN) framework to consider a dynamic hypergraph with learnable hyperedge weights. Specifically, we generate hyperedges based on sparse representation and calculate the hyper similarity as node features. The hypergraph and node features are fed into a neural network model, where the hyperedge weights are updated adaptively during training. The dwHGCN facilitates the learning of brain FC features by assigning larger weights to hyperedges with higher discriminative power. The weighting strategy also improves the interpretability of the model by identifying the highly active interactions among ROIs shared by a common hyperedge. We validate the performance of the proposed model on two classification tasks with three paradigms functional magnetic resonance imaging (fMRI) data from Philadelphia Neurodevelopmental Cohort. Experimental results demonstrate the superiority of our proposed method over existing hypergraph neural networks. We believe our model can be applied to other applications in neuroimaging for its strength in representation learning and interpretation.
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Affiliation(s)
- Junqi Wang
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Hailong Li
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Gang Qu
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
| | - Kim M Cecil
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Jonathan R Dillman
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Nehal A Parikh
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Lili He
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, 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] [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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Plachti A, Latzman RD, Balajoo SM, Hoffstaedter F, Madsen KS, Baare W, Siebner HR, Eickhoff SB, Genon S. Hippocampal anterior- posterior shift in childhood and adolescence. Prog Neurobiol 2023; 225:102447. [PMID: 36967075 PMCID: PMC10185869 DOI: 10.1016/j.pneurobio.2023.102447] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 03/14/2023] [Accepted: 03/23/2023] [Indexed: 04/07/2023]
Abstract
Hippocampal-cortical networks play an important role in neurocognitive development. Applying the method of Connectivity-Based Parcellation (CBP) on hippocampal-cortical structural covariance (SC) networks computed from T1-weighted magnetic resonance images, we examined how the hippocampus differentiates into subregions during childhood and adolescence (N = 1105, 6-18 years). In late childhood, the hippocampus mainly differentiated along the anterior-posterior axis similar to previous reported functional differentiation patterns of the hippocampus. In contrast, in adolescence a differentiation along the medial-lateral axis was evident, reminiscent of the cytoarchitectonic division into cornu ammonis and subiculum. Further meta-analytical characterization of hippocampal subregions in terms of related structural co-maturation networks, behavioural and gene profiling suggested that the hippocampal head is related to higher order functions (e.g. language, theory of mind, autobiographical memory) in late childhood morphologically co-varying with almost the whole brain. In early adolescence but not in childhood, posterior subicular SC networks were associated with action-oriented and reward systems. The findings point to late childhood as an important developmental period for hippocampal head morphology and to early adolescence as a crucial period for hippocampal integration into action- and reward-oriented cognition. The latter may constitute a developmental feature that conveys increased propensity for addictive disorders.
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Affiliation(s)
- Anna Plachti
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital -Amager and Hvidovre, Copenhagen, Denmark
| | - Robert D Latzman
- Data Sciences Institute, Takeda Pharmaceutical, Cambridge, MA, USA
| | | | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
| | - Kathrine Skak Madsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital -Amager and Hvidovre, Copenhagen, Denmark; Radiography, Department of Technology, University College Copenhagen, Copenhagen, Denmark
| | - William Baare
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital -Amager and Hvidovre, Copenhagen, Denmark
| | - Hartwig R Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital -Amager and Hvidovre, Copenhagen, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sarah Genon
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; GIGA-CRC In vivo Imaging, University of Liege, Liege, Belgium.
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Orlichenko A, Qu G, Zhang G, Patel B, Wilson TW, Stephen JM, Calhoun VD, Wang YP. Latent Similarity Identifies Important Functional Connections for Phenotype Prediction. IEEE Trans Biomed Eng 2023; 70:1979-1989. [PMID: 37015625 PMCID: PMC10284019 DOI: 10.1109/tbme.2022.3232964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
OBJECTIVE Endophenotypes such as brain age and fluid intelligence are important biomarkers of disease status. However, brain imaging studies to identify these biomarkers often encounter limited numbers of subjects but high dimensional imaging features, hindering reproducibility. Therefore, we develop an interpretable, multivariate classification/regression algorithm, called Latent Similarity (LatSim), suitable for small sample size but high feature dimension datasets. METHODS LatSim combines metric learning with a kernel similarity function and softmax aggregation to identify task-related similarities between subjects. Inter-subject similarity is utilized to improve performance on three prediction tasks using multi-paradigm fMRI data. A greedy selection algorithm, made possible by LatSim's computational efficiency, is developed as an interpretability method. RESULTS LatSim achieved significantly higher predictive accuracy at small sample sizes on the Philadelphia Neurodevelopmental Cohort (PNC) dataset. Connections identified by LatSim gave superior discriminative power compared to those identified by other methods. We identified 4 functional brain networks enriched in connections for predicting brain age, sex, and intelligence. CONCLUSION We find that most information for a predictive task comes from only a few (1-5) connections. Additionally, we find that the default mode network is over-represented in the top connections of all predictive tasks. SIGNIFICANCE We propose a novel prediction algorithm for small sample, high feature dimension datasets and use it to identify connections in task fMRI data. Our work can lead to new insights in both algorithm design and neuroscience research.
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Li MT, Sun JW, Zhan LL, Antwi CO, Lv YT, Jia XZ, Ren J. The effect of seed location on functional connectivity: evidence from an image-based meta-analysis. Front Neurosci 2023; 17:1120741. [PMID: 37325032 PMCID: PMC10264592 DOI: 10.3389/fnins.2023.1120741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 05/15/2023] [Indexed: 06/17/2023] Open
Abstract
Introduction Default mode network (DMN) is the most involved network in the study of brain development and brain diseases. Resting-state functional connectivity (rsFC) is the most used method to study DMN, but different studies are inconsistent in the selection of seed. To evaluate the effect of different seed selection on rsFC, we conducted an image-based meta-analysis (IBMA). Methods We identified 59 coordinates of seed regions of interest (ROIs) within the default mode network (DMN) from 11 studies (retrieved from Web of Science and Pubmed) to calculate the functional connectivity; then, the uncorrected t maps were obtained from the statistical analyses. The IBMA was performed with the t maps. Results We demonstrate that the overlap of meta-analytic maps across different seeds' ROIs within DMN is relatively low, which cautions us to be cautious with seeds' selection. Discussion Future studies using the seed-based functional connectivity method should take the reproducibility of different seeds into account. The choice of seed may significantly affect the connectivity results.
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Affiliation(s)
- Meng-Ting Li
- School of Psychology, Zhejiang Normal University, Jinhua, China
| | - Jia-Wei Sun
- Department of Clinical Neuroscience, Division of Neuro, Karolinska Institutet, Stockholm, Sweden
| | - Lin-Lin Zhan
- School of Western Studies, Heilongjiang University, Harbin, China
| | | | - Ya-Ting Lv
- Center for Cognition and Brain Disorders, The Affiliated Hospital, Hangzhou Normal University, Hangzhou, China
| | - Xi-Ze Jia
- School of Psychology, Zhejiang Normal University, Jinhua, China
| | - Jun Ren
- School of Psychology, Zhejiang Normal University, Jinhua, China
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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. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND 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] [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] [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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