1
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Hermosillo RJM, Moore LA, Feczko E, Miranda-Domínguez Ó, Pines A, Dworetsky A, Conan G, Mooney MA, Randolph A, Graham A, Adeyemo B, Earl E, Perrone A, Carrasco CM, Uriarte-Lopez J, Snider K, Doyle O, Cordova M, Koirala S, Grimsrud GJ, Byington N, Nelson SM, Gratton C, Petersen S, Feldstein Ewing SW, Nagel BJ, Dosenbach NUF, Satterthwaite TD, Fair DA. A precision functional atlas of personalized network topography and probabilities. Nat Neurosci 2024; 27:1000-1013. [PMID: 38532024 PMCID: PMC11089006 DOI: 10.1038/s41593-024-01596-5] [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: 02/14/2022] [Accepted: 02/08/2024] [Indexed: 03/28/2024]
Abstract
Although the general location of functional neural networks is similar across individuals, there is vast person-to-person topographic variability. To capture this, we implemented precision brain mapping functional magnetic resonance imaging methods to establish an open-source, method-flexible set of precision functional network atlases-the Masonic Institute for the Developing Brain (MIDB) Precision Brain Atlas. This atlas is an evolving resource comprising 53,273 individual-specific network maps, from more than 9,900 individuals, across ages and cohorts, including the Adolescent Brain Cognitive Development study, the Developmental Human Connectome Project and others. We also generated probabilistic network maps across multiple ages and integration zones (using a new overlapping mapping technique, Overlapping MultiNetwork Imaging). Using regions of high network invariance improved the reproducibility of executive function statistical maps in brain-wide associations compared to group average-based parcellations. Finally, we provide a potential use case for probabilistic maps for targeted neuromodulation. The atlas is expandable to alternative datasets with an online interface encouraging the scientific community to explore and contribute to understanding the human brain function more precisely.
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Affiliation(s)
- Robert J M Hermosillo
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA.
| | - Lucille A Moore
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Eric Feczko
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Óscar Miranda-Domínguez
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Adam Pines
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Ally Dworetsky
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Department of Psychology, Florida State University, Tallahassee, FL, USA
| | - Gregory Conan
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Michael A Mooney
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Center for Mental Health Innovation, Oregon Health and Science University, Portland, OR, USA
| | - Anita Randolph
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Alice Graham
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Eric Earl
- Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD, USA
| | - Anders Perrone
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Cristian Morales Carrasco
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | | | - Kathy Snider
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Olivia Doyle
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Michaela Cordova
- Joint Doctoral Program in Clinical Psychology, San Diego State University, San Diego, CA, USA
- Joint Doctoral Program in Clinical Psychology, University of California San Diego, San Diego, CA, USA
| | - Sanju Koirala
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Gracie J Grimsrud
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Nora Byington
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Steven M Nelson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Department of Psychology, Florida State University, Tallahassee, FL, USA
- Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | - Steven Petersen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Bonnie J Nagel
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
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2
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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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3
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Chu C, Li W, Shi W, Wang H, Wang J, Liu Y, Liu B, Elmenhorst D, Eickhoff SB, Fan L, Jiang T. Co-representation of Functional Brain Networks Is Shaped by Cortical Myeloarchitecture and Reveals Individual Behavioral Ability. J Neurosci 2024; 44:e0856232024. [PMID: 38290847 PMCID: PMC10977027 DOI: 10.1523/jneurosci.0856-23.2024] [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: 05/10/2023] [Revised: 01/16/2024] [Accepted: 01/20/2024] [Indexed: 02/01/2024] Open
Abstract
Large-scale functional networks are spatially distributed in the human brain. Despite recent progress in differentiating their functional roles, how the brain navigates the spatial coordination among them and the biological relevance of this coordination is still not fully understood. Capitalizing on canonical individualized networks derived from functional MRI data, we proposed a new concept, that is, co-representation of functional brain networks, to delineate the spatial coordination among them. To further quantify the co-representation pattern, we defined two indexes, that is, the co-representation specificity (CoRS) and intensity (CoRI), for separately measuring the extent of specific and average expression of functional networks at each brain location by using the data from both sexes. We found that the identified pattern of co-representation was anchored by cortical regions with three types of cytoarchitectural classes along a sensory-fugal axis, including, at the first end, primary (idiotypic) regions showing high CoRS, at the second end, heteromodal regions showing low CoRS and high CoRI, at the third end, paralimbic regions showing low CoRI. Importantly, we demonstrated the critical role of myeloarchitecture in sculpting the spatial distribution of co-representation by assessing the association with the myelin-related neuroanatomical and transcriptomic profiles. Furthermore, the significance of manifesting the co-representation was revealed in its prediction of individual behavioral ability. Our findings indicated that the spatial coordination among functional networks was built upon an anatomically configured blueprint to facilitate neural information processing, while advancing our understanding of the topographical organization of the brain by emphasizing the assembly of functional networks.
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Affiliation(s)
- Congying Chu
- Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, Jülich 52428, Germany
| | - Wen Li
- Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weiyang Shi
- Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Haiyan Wang
- Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - David Elmenhorst
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, Jülich 52428, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Forschungszentrum Jülich, Jülich 52428, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University, Düsseldorf 40204, Germany
| | - Lingzhong Fan
- Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100049, China
| | - Tianzi Jiang
- Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100049, China
- Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou 425000, Hunan Province, China
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4
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Rai S, Graff K, Tansey R, Bray S. How do tasks impact the reliability of fMRI functional connectivity? Hum Brain Mapp 2024; 45:e26535. [PMID: 38348730 PMCID: PMC10884875 DOI: 10.1002/hbm.26535] [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: 06/21/2023] [Revised: 10/13/2023] [Accepted: 11/01/2023] [Indexed: 02/24/2024] Open
Abstract
While there is growing interest in the use of functional magnetic resonance imaging-functional connectivity (fMRI-FC) for biomarker research, low measurement reliability of conventional acquisitions may limit applications. Factors known to impact FC reliability include scan length, head motion, signal properties, such as temporal signal-to-noise ratio (tSNR), and the acquisition state or task. As tasks impact signal in a region-wise fashion, they likely impact FC reliability differently across the brain, making task an important decision in study design. Here, we use the densely sampled Midnight Scan Club (MSC) dataset, comprising 5 h of rest and 6 h of task fMRI data in 10 healthy adults, to investigate regional effects of tasks on FC reliability. We further considered how BOLD signal properties contributing to tSNR, that is, temporal mean signal (tMean) and temporal standard deviation (tSD), vary across the brain, associate with FC reliability, and are modulated by tasks. We found that, relative to rest, tasks enhanced FC reliability and increased tSD for specific task-engaged regions. However, FC signal variability and reliability is broadly dampened during tasks outside task-engaged regions. From our analyses, we observed signal variability was the strongest driver of FC reliability. Overall, our findings suggest that the choice of task can have an important impact on reliability and should be considered in relation to maximizing reliability in networks of interest as part of study design.
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Affiliation(s)
- Shefali Rai
- Child and Adolescent Imaging Research ProgramUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Department of NeuroscienceUniversity of CalgaryCalgaryAlbertaCanada
| | - Kirk Graff
- Child and Adolescent Imaging Research ProgramUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Department of NeuroscienceUniversity of CalgaryCalgaryAlbertaCanada
| | - Ryann Tansey
- Child and Adolescent Imaging Research ProgramUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Department of NeuroscienceUniversity of CalgaryCalgaryAlbertaCanada
| | - Signe Bray
- Child and Adolescent Imaging Research ProgramUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Department of RadiologyUniversity of CalgaryCalgaryAlbertaCanada
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5
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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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6
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Moser J, Koirala S, Madison T, Labonte AK, Carrasco CM, Feczko E, Moore LA, Ahmed W, Myers MJ, Yacoub E, Trevo-Clemmens B, Larsen B, Laumann TO, Nelson SM, Vizioli L, Sylvester CM, Fair DA. Multi-echo Acquisition and Thermal Denoising Advances Infant Precision Functional Imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.27.564416. [PMID: 37961636 PMCID: PMC10634909 DOI: 10.1101/2023.10.27.564416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The characterization of individual functional brain organization with Precision Functional Mapping has provided important insights in recent years in adults. However, little is known about the ontogeny of inter-individual differences in brain functional organization during human development, but precise characterization of systems organization during periods of high plasticity might be most influential towards discoveries promoting lifelong health. Collecting and analyzing precision fMRI data during early development has unique challenges and emphasizes the importance of novel methods to improve data acquisition, processing, and analysis strategies in infant samples. Here, we investigate the applicability of two such methods from adult MRI research, multi-echo (ME) data acquisition and thermal noise removal with Noise reduction with distribution corrected principal component analysis (NORDIC), in precision fMRI data from three newborn infants. Compared to an adult example subject, T2* relaxation times calculated from ME data in infants were longer and more variable across the brain, pointing towards ME acquisition being a promising tool for optimizing developmental fMRI. The application of thermal denoising via NORDIC increased tSNR and the overall strength of functional connections as well as the split-half reliability of functional connectivity matrices in infant ME data. While our findings related to NORDIC denoising are coherent with the adult literature and ME data acquisition showed high promise, its application in developmental samples needs further investigation. The present work reveals gaps in our understanding of the best techniques for developmental brain imaging and highlights the need for further developmentally-specific methodological advances and optimizations, towards precision functional imaging in infants.
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Affiliation(s)
- Julia Moser
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Sanju Koirala
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Thomas Madison
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Alyssa K Labonte
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | | | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Lucille A Moore
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Weli Ahmed
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Michael J Myers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Brenden Trevo-Clemmens
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Bart Larsen
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Steven M Nelson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Luca Vizioli
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Chad M Sylvester
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
- Taylor Family Institute for Innovative Research, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
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7
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Lynch CJ, Elbau I, Ng T, Ayaz A, Zhu S, Manfredi N, Johnson M, Wolk D, Power JD, Gordon EM, Kay K, Aloysi A, Moia S, Caballero-Gaudes C, Victoria LW, Solomonov N, Goldwaser E, Zebley B, Grosenick L, Downar J, Vila-Rodriguez F, Daskalakis ZJ, Blumberger DM, Williams N, Gunning FM, Liston C. Expansion of a frontostriatal salience network in individuals with depression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.09.551651. [PMID: 37645792 PMCID: PMC10461904 DOI: 10.1101/2023.08.09.551651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Hundreds of neuroimaging studies spanning two decades have revealed differences in brain structure and functional connectivity in depression, but with modest effect sizes, complicating efforts to derive mechanistic pathophysiologic insights or develop biomarkers. 1 Furthermore, although depression is a fundamentally episodic condition, few neuroimaging studies have taken a longitudinal approach, which is critical for understanding cause and effect and delineating mechanisms that drive mood state transitions over time. The emerging field of precision functional mapping using densely-sampled longitudinal neuroimaging data has revealed unexpected, functionally meaningful individual differences in brain network topology in healthy individuals, 2-5 but these approaches have never been applied to individuals with depression. Here, using precision functional mapping techniques and 11 datasets comprising n=187 repeatedly sampled individuals and >21,000 minutes of fMRI data, we show that the frontostriatal salience network is expanded two-fold in most individuals with depression. This effect was replicable in multiple samples, including large-scale, group-average data (N=1,231 subjects), and caused primarily by network border shifts affecting specific functional systems, with three distinct modes of encroachment occurring in different individuals. Salience network expansion was unexpectedly stable over time, unaffected by changes in mood state, and detectable in children before the subsequent onset of depressive symptoms in adolescence. Longitudinal analyses of individuals scanned up to 62 times over 1.5 years identified connectivity changes in specific frontostriatal circuits that tracked fluctuations in specific symptom domains and predicted future anhedonia symptoms before they emerged. Together, these findings identify a stable trait-like brain network topology that may confer risk for depression and mood-state dependent connectivity changes in frontostriatal circuits that predict the emergence and remission of depressive symptoms over time.
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8
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Whitman ET, Knodt AR, Elliott ML, Abraham WC, Cheyne K, Hogan S, Ireland D, Keenan R, Leung JH, Melzer TR, Poulton R, Purdy SC, Ramrakha S, Thorne PR, Caspi A, Moffitt TE, Hariri AR. Functional topography of the neocortex predicts covariation in complex cognitive and basic motor abilities. Cereb Cortex 2023; 33:8218-8231. [PMID: 37015900 PMCID: PMC10321095 DOI: 10.1093/cercor/bhad109] [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: 01/09/2023] [Revised: 03/06/2023] [Accepted: 03/07/2023] [Indexed: 04/06/2023] Open
Abstract
Although higher-order cognitive and lower-order sensorimotor abilities are generally regarded as distinct and studied separately, there is evidence that they not only covary but also that this covariation increases across the lifespan. This pattern has been leveraged in clinical settings where a simple assessment of sensory or motor ability (e.g. hearing, gait speed) can forecast age-related cognitive decline and risk for dementia. However, the brain mechanisms underlying cognitive, sensory, and motor covariation are largely unknown. Here, we examined whether such covariation in midlife reflects variability in common versus distinct neocortical networks using individualized maps of functional topography derived from BOLD fMRI data collected in 769 45-year-old members of a population-representative cohort. Analyses revealed that variability in basic motor but not hearing ability reflected individual differences in the functional topography of neocortical networks typically supporting cognitive ability. These patterns suggest that covariation in motor and cognitive abilities in midlife reflects convergence of function in higher-order neocortical networks and that gait speed may not be simply a measure of physical function but rather an integrative index of nervous system health.
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Affiliation(s)
- Ethan T Whitman
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27710, USA
| | - Annchen R Knodt
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27710, USA
| | - Maxwell L Elliott
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | | | - Kirsten Cheyne
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin 9016, New Zealand
| | - Sean Hogan
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin 9016, New Zealand
| | - David Ireland
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin 9016, New Zealand
| | - Ross Keenan
- Brain Research New Zealand-Rangahau Roro Aotearoa, Centre of Research Excellence, Universities of Auckland and Otago, Auckland 1010, New Zealand
- Christchurch Radiology Group, Christchurch 8014, New Zealand
| | - Joan H Leung
- School of Psychology, University of Auckland, Auckland 1142, New Zealand
- Eisdell Moore Centre, University of Auckland, Auckland 1142, New Zealand
| | - Tracy R Melzer
- Brain Research New Zealand-Rangahau Roro Aotearoa, Centre of Research Excellence, Universities of Auckland and Otago, Auckland 1010, New Zealand
- Department of Medicine, University of Otago, Christchurch 9016, New Zealand
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin 9016, New Zealand
| | - Suzanne C Purdy
- Brain Research New Zealand-Rangahau Roro Aotearoa, Centre of Research Excellence, Universities of Auckland and Otago, Auckland 1010, New Zealand
- School of Psychology, University of Auckland, Auckland 1142, New Zealand
- Eisdell Moore Centre, University of Auckland, Auckland 1142, New Zealand
| | - Sandhya Ramrakha
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin 9016, New Zealand
| | - Peter R Thorne
- Brain Research New Zealand-Rangahau Roro Aotearoa, Centre of Research Excellence, Universities of Auckland and Otago, Auckland 1010, New Zealand
- Eisdell Moore Centre, University of Auckland, Auckland 1142, New Zealand
- School of Population Health, University of Auckland, Auckland 1142, New Zealand
| | - Avshalom Caspi
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27710, USA
- Center for Genomic and Computational Biology, Duke University, Durham, NC 27710, USA
- King’s College London, Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, & Neuroscience, London SE5 8AF, UK
- PROMENTA, Department of Psychology, University of Oslo, NO-0316 Oslo, Norway
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, USA
| | - Terrie E Moffitt
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27710, USA
- Center for Genomic and Computational Biology, Duke University, Durham, NC 27710, USA
- King’s College London, Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, & Neuroscience, London SE5 8AF, UK
- PROMENTA, Department of Psychology, University of Oslo, NO-0316 Oslo, Norway
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, USA
| | - Ahmad R Hariri
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27710, USA
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9
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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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10
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Pines A, Keller AS, Larsen B, Bertolero M, Ashourvan A, Bassett DS, Cieslak M, Covitz S, Fan Y, Feczko E, Houghton A, Rueter AR, Saggar M, Shafiei G, Tapera TM, Vogel J, Weinstein SM, Shinohara RT, Williams LM, Fair DA, Satterthwaite TD. Development of top-down cortical propagations in youth. Neuron 2023; 111:1316-1330.e5. [PMID: 36803653 PMCID: PMC10121821 DOI: 10.1016/j.neuron.2023.01.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 12/08/2022] [Accepted: 01/18/2023] [Indexed: 02/19/2023]
Abstract
Hierarchical processing requires activity propagating between higher- and lower-order cortical areas. However, functional neuroimaging studies have chiefly quantified fluctuations within regions over time rather than propagations occurring over space. Here, we leverage advances in neuroimaging and computer vision to track cortical activity propagations in a large sample of youth (n = 388). We delineate cortical propagations that systematically ascend and descend a cortical hierarchy in all individuals in our developmental cohort, as well as in an independent dataset of densely sampled adults. Further, we demonstrate that top-down, descending hierarchical propagations become more prevalent with greater demands for cognitive control as well as with development in youth. These findings emphasize that hierarchical processing is reflected in the directionality of propagating cortical activity and suggest top-down propagations as a potential mechanism of neurocognitive maturation in youth.
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Affiliation(s)
- Adam Pines
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA; The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Arielle S Keller
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Bart Larsen
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Maxwell Bertolero
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Arian Ashourvan
- Department of Psychology, The University of Kansas, Lawrence, KS 66045, USA
| | - Dani S Bassett
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA; Departments of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics & Astronomy, The University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, Santa Fe, NM 87051, USA
| | - Matthew Cieslak
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney Covitz
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Fan
- Department of Radiology, The University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Eric Feczko
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Amanda R Rueter
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Manish Saggar
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA
| | - Golia Shafiei
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Tinashe M Tapera
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Jacob Vogel
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah M Weinstein
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Theodore D Satterthwaite
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA.
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11
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Li H, Srinivasan D, Zhuo C, Cui Z, Gur RE, Gur RC, Oathes DJ, Davatzikos C, Satterthwaite TD, Fan Y. Computing personalized brain functional networks from fMRI using self-supervised deep learning. Med Image Anal 2023; 85:102756. [PMID: 36706636 PMCID: PMC10103143 DOI: 10.1016/j.media.2023.102756] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 07/20/2022] [Accepted: 01/18/2023] [Indexed: 01/22/2023]
Abstract
A novel self-supervised deep learning (DL) method is developed to compute personalized brain functional networks (FNs) for characterizing brain functional neuroanatomy based on functional MRI (fMRI). Specifically, a DL model of convolutional neural networks with an encoder-decoder architecture is developed to compute personalized FNs directly from fMRI data. The DL model is trained to optimize functional homogeneity of personalized FNs without utilizing any external supervision in an end-to-end fashion. We demonstrate that a DL model trained on fMRI scans from the Human Connectome Project can identify personalized FNs and generalizes well across four different datasets. We further demonstrate that the identified personalized FNs are informative for predicting individual differences in behavior, brain development, and schizophrenia status. Taken together, the self-supervised DL allows for rapid, generalizable computation of personalized FNs.
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Affiliation(s)
- Hongming Li
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dhivya Srinivasan
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Chuanjun Zhuo
- Key Laboratory of Brain Circuit Real Time Tracing (BCRTT-Lab), Beijing, 102206, China
| | - Zaixu Cui
- Tianjin University Affiliated Tianjin Fourth Center Hospital, Department of Psychiatry, Tianjin Medical University, Tianjin, China Chinese Institute for Brain Research, Beijing, 102206, China
| | - Raquel E. Gur
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, 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
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C. Gur
- Center for Biomedical Image Computation and Analytics, 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
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Desmond J. Oathes
- Center for Neuromodulation in Depression and Stress, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D. Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yong Fan
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
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12
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Zhou Z, Li H, Srinivasan D, Abdulkadir A, Nasrallah IM, Wen J, Doshi J, Erus G, Mamourian E, Bryan NR, Wolk DA, Beason-Held L, Resnick SM, Satterthwaite TD, Davatzikos C, Shou H, Fan Y. Multiscale functional connectivity patterns of the aging brain learned from harmonized rsfMRI data of the multi-cohort iSTAGING study. Neuroimage 2023; 269:119911. [PMID: 36731813 PMCID: PMC9992322 DOI: 10.1016/j.neuroimage.2023.119911] [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: 01/06/2023] [Accepted: 01/28/2023] [Indexed: 02/03/2023] Open
Abstract
To learn multiscale functional connectivity patterns of the aging brain, we built a brain age prediction model of functional connectivity measures at seven scales on a large fMRI dataset, consisting of resting-state fMRI scans of 4186 individuals with a wide age range (22 to 97 years, with an average of 63) from five cohorts. We computed multiscale functional connectivity measures of individual subjects using a personalized functional network computational method, harmonized the functional connectivity measures of subjects from multiple datasets in order to build a functional brain age model, and finally evaluated how functional brain age gap correlated with cognitive measures of individual subjects. Our study has revealed that functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction, the data harmonization significantly improved the brain age prediction performance, and the data harmonization in the functional connectivity measures' tangent space worked better than in their original space. Moreover, brain age gap scores of individual subjects derived from the brain age prediction model were significantly correlated with clinical and cognitive measures. Overall, these results demonstrated that multiscale functional connectivity patterns learned from a large-scale multi-site rsfMRI dataset were informative for characterizing the aging brain and the derived brain age gap was associated with cognitive and clinical measures.
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Affiliation(s)
- Zhen Zhou
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | - Hongming Li
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Dhivya Srinivasan
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ahmed Abdulkadir
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ilya M Nasrallah
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Junhao Wen
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Elizabeth Mamourian
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Nick R Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Diagnostic Medicine, University of Texas at Austin, Austin, TX, 78705, USA
| | - David A Wolk
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Neurology and Penn Memory Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Lori Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, 20892, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, 20892, USA
| | - Theodore D Satterthwaite
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn Statistic in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Psychiatry, Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Psychiatry, Brain Behavior Laboratory and Penn-CHOP Lifespan Brain Institute, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn Statistic in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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13
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Whitman ET, Knodt AR, Elliott ML, Abraham WC, Cheyne K, Hogan S, Ireland D, Keenan R, Lueng JH, Melzer TR, Poulton R, Purdy SC, Ramrakha S, Thorne PR, Caspi A, Moffitt TE, Hariri AR. Functional Topography of the Neocortex Predicts Covariation in Complex Cognitive and Basic Motor Abilities. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.09.523297. [PMID: 36711683 PMCID: PMC9881949 DOI: 10.1101/2023.01.09.523297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Although higher-order cognitive and lower-order sensorimotor abilities are generally regarded as distinct and studied separately, there is evidence that they not only covary but also that this covariation increases across the lifespan. This pattern has been leveraged in clinical settings where a simple assessment of sensory or motor ability (e.g., hearing, gait speed) can forecast age-related cognitive decline and risk for dementia. However, the brain mechanisms underlying cognitive, sensory, and motor covariation are largely unknown. Here, we examined whether such covariation in midlife reflects variability in common versus distinct neocortical networks using individualized maps of functional topography derived from BOLD fMRI data collected in 769 45-year old members of a population-representative cohort. Analyses revealed that variability in basic motor but not hearing ability reflected individual differences in the functional topography of neocortical networks typically supporting cognitive ability. These patterns suggest that covariation in motor and cognitive abilities in midlife reflects convergence of function in higher-order neocortical networks and that gait speed may not be simply a measure of physical function but rather an integrative index of nervous system health.
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Affiliation(s)
- Ethan T. Whitman
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Annchen R. Knodt
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Maxwell L. Elliott
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA, USA
| | | | - Kirsten Cheyne
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Sean Hogan
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin, New Zealand
| | - David Ireland
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Ross Keenan
- Brain Research New Zealand-Rangahau Roro Aotearoa, Centre of Research Excellence, Universities of Auckland and Otago, New Zealand
- Christchurch Radiology Group, Christchurch, New Zealand
| | - Joan H. Lueng
- School of Psychology, University of Auckland, New Zealand
- Eisdell Moore Centre, University of Auckland, New Zealand
| | - Tracy R. Melzer
- Brain Research New Zealand-Rangahau Roro Aotearoa, Centre of Research Excellence, Universities of Auckland and Otago, New Zealand
- Department of Medicine, University of Otago, Christchurch, New Zealand
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Suzanne C. Purdy
- Brain Research New Zealand-Rangahau Roro Aotearoa, Centre of Research Excellence, Universities of Auckland and Otago, New Zealand
- School of Psychology, University of Auckland, New Zealand
- Eisdell Moore Centre, University of Auckland, New Zealand
| | - Sandhya Ramrakha
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Peter R. Thorne
- Brain Research New Zealand-Rangahau Roro Aotearoa, Centre of Research Excellence, Universities of Auckland and Otago, New Zealand
- Eisdell Moore Centre, University of Auckland, New Zealand
- School of Population Health, University of Auckland, New Zealand
| | - Avshalom Caspi
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- King’s College London, Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, & Neuroscience, London, UK
- PROMENTA, Department of Psychology, University of Oslo, Norway
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Terrie E. Moffitt
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- King’s College London, Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, & Neuroscience, London, UK
- PROMENTA, Department of Psychology, University of Oslo, Norway
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Ahmad R. Hariri
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
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14
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The promise of a model-based psychiatry: building computational models of mental ill health. Lancet Digit Health 2022; 4:e816-e828. [PMID: 36229345 PMCID: PMC9627546 DOI: 10.1016/s2589-7500(22)00152-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 07/05/2022] [Accepted: 07/27/2022] [Indexed: 11/07/2022]
Abstract
Computational models have great potential to revolutionise psychiatry research and clinical practice. These models are now used across multiple subfields, including computational psychiatry and precision psychiatry. Their goals vary from understanding mechanisms underlying disorders to deriving reliable classification and personalised predictions. Rapid growth of new tools and data sources (eg, digital data, gamification, and social media) requires an understanding of the constraints and advantages of different modelling approaches in psychiatry. In this Series paper, we take a critical look at the range of computational models that are used in psychiatry and evaluate their advantages and disadvantages for different purposes and data sources. We describe mechanism-driven and mechanism-agnostic computational models and discuss how interpretability of models is crucial for clinical translation. Based on these evaluations, we provide recommendations on how to build computational models that are clinically useful.
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