1
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Wang Y, Eichert N, Paquola C, Rodriguez-Cruces R, DeKraker J, Royer J, Cabalo DG, Auer H, Ngo A, Leppert IR, Tardif CL, Rudko DA, Leech R, Amunts K, Valk SL, Smallwood J, Evans AC, Bernhardt BC. Multimodal gradients unify local and global cortical organization. Nat Commun 2025; 16:3911. [PMID: 40280959 PMCID: PMC12032020 DOI: 10.1038/s41467-025-59177-4] [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: 09/02/2024] [Accepted: 04/11/2025] [Indexed: 04/29/2025] Open
Abstract
Functional specialization of brain areas and subregions, as well as their integration into large-scale networks, are key principles in neuroscience. Consolidating both local and global perspectives on cortical organization, however, remains challenging. Here, we present an approach to integrate inter- and intra-areal similarities of microstructure, structural connectivity, and functional interactions. Using high-field in-vivo 7 tesla (7 T) Magnetic Resonance Imaging (MRI) data and a probabilistic post-mortem atlas of cortical cytoarchitecture, we derive multimodal gradients that capture cortex-wide organization. Inter-areal similarities follow a canonical sensory-fugal gradient, linking cortical integration with functional diversity across tasks. However, intra-areal heterogeneity does not follow this pattern, with greater variability in association cortices. Findings are replicated in an independent 7 T dataset and a 100-subject 3 tesla (3 T) cohort. These results highlight a robust coupling between local arealization and global cortical motifs, advancing our understanding of how specialization and integration shape human brain function.
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Affiliation(s)
- Yezhou Wang
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
| | - Nicole Eichert
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Casey Paquola
- Institute of Neuroscience and Medicine (INM-7), Forschungszentrum Jülich, Jülich, Germany
| | - Raul Rodriguez-Cruces
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Jordan DeKraker
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Jessica Royer
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Donna Gift Cabalo
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Hans Auer
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Alexander Ngo
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Ilana R Leppert
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Christine L Tardif
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - David A Rudko
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Robert Leech
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
- C. and O. Vogt Institute of Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University of Düsseldorf, Düsseldorf, Germany
| | - Sofie L Valk
- Institute of Neuroscience and Medicine (INM-7), Forschungszentrum Jülich, Jülich, Germany
- Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | | | - Alan C Evans
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
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2
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Sun L, Zhao T, Liang X, Xia M, Li Q, Liao X, Gong G, Wang Q, Pang C, Yu Q, Bi Y, Chen P, Chen R, Chen Y, Chen T, Cheng J, Cheng Y, Cui Z, Dai Z, Deng Y, Ding Y, Dong Q, Duan D, Gao JH, Gong Q, Han Y, Han Z, Huang CC, Huang R, Huo R, Li L, Lin CP, Lin Q, Liu B, Liu C, Liu N, Liu Y, Liu Y, Lu J, Ma L, Men W, Qin S, Qiu J, Qiu S, Si T, Tan S, Tang Y, Tao S, Wang D, Wang F, Wang J, Wang P, Wang X, Wang Y, Wei D, Wu Y, Xie P, Xu X, Xu Y, Xu Z, Yang L, Yuan H, Zeng Z, Zhang H, Zhang X, Zhao G, Zheng Y, Zhong S, He Y. Human lifespan changes in the brain's functional connectome. Nat Neurosci 2025; 28:891-901. [PMID: 40181189 DOI: 10.1038/s41593-025-01907-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 02/04/2025] [Indexed: 04/05/2025]
Abstract
Functional connectivity of the human brain changes through life. Here, we assemble task-free functional and structural magnetic resonance imaging data from 33,250 individuals at 32 weeks of postmenstrual age to 80 years from 132 global sites. We report critical inflection points in the nonlinear growth curves of the global mean and variance of the connectome, peaking in the late fourth and late third decades of life, respectively. After constructing a fine-grained, lifespan-wide suite of system-level brain atlases, we show distinct maturation timelines for functional segregation within different systems. Lifespan growth of regional connectivity is organized along a spatiotemporal cortical axis, transitioning from primary sensorimotor regions to higher-order association regions. These findings elucidate the lifespan evolution of the functional connectome and can serve as a normative reference for quantifying individual variation in development, aging and neuropsychiatric disorders.
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Affiliation(s)
- Lianglong Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xinyuan Liang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qiongling Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Qian Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Chenxuan Pang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qian Yu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yanchao Bi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Pindong Chen
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Rui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Taolin Chen
- Department of Radiology, Huaxi MR Research Center (HMRRC), Institute of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Xiamen Key Laboratory of Psychoradiology and Neuromodulation, Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing, China
| | - Zhengjia Dai
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yao Deng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yuyin Ding
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Dingna Duan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
- IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Qiyong Gong
- Department of Radiology, Huaxi MR Research Center (HMRRC), Institute of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Xiamen Key Laboratory of Psychoradiology and Neuromodulation, Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Zaizhu Han
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Chu-Chung Huang
- Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Ruiwang Huang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Ran Huo
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Lingjiang Li
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, China
| | - Ching-Po Lin
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, China
- Department of Education and Research, Taipei City Hospital, Taipei, China
| | - Qixiang Lin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Bangshan Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Hunan Medical Center for Mental Health, Changsha, China
| | - Chao Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Ningyu Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Ying Liu
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Yong Liu
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Jing Lu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Leilei Ma
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Tianmei Si
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Ji'nan, China
| | - Fei Wang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Jiali Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, China
| | - Xiaoqin Wang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Yankun Wu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Peng Xie
- Chongqing Key Laboratory of Neurobiology, Chongqing, China
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiufeng Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuehua Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Zhilei Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Liyuan Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Zilong Zeng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Haibo Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Gai Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yanting Zheng
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Suyu Zhong
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
- Chinese Institute for Brain Research, Beijing, China.
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3
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Lyu W, Thung KH, Huynh KM, Wang L, Lin W, Ahmad S, Yap PT. The Growing Little Brain: Cerebellar Functional Development from Cradle to School. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2024.10.12.617938. [PMID: 39416101 PMCID: PMC11482888 DOI: 10.1101/2024.10.12.617938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/19/2024]
Abstract
Despite the cerebellum's crucial role in brain functions, its early development, particularly in relation to the cerebrum, remains poorly understood. Here, we examine cerebellocortical connectivity using over 1,000 high-quality resting-state functional MRI scans of children from birth to 60 months. By mapping cerebellar topography with fine temporal detail for the first time, we show the hierarchical organization of cerebellocortical functional connectivity from infancy. We observe dynamic shifts in cerebellar network gradients, which become more focal with age while generally maintaining stable anchor points similar to adults, highlighting the cerebellum's evolving yet stable role in functional integration during early development. Our findings provide the first evidence of cerebellar connections to higher-order networks at birth, which generally strengthen with age, emphasizing the cerebellum's early role in cognitive processing beyond sensory and motor functions. Our study provides insights into early cerebellocortical interactions, reveals functional asymmetry and sex-specific patterns in cerebellar development, and lays the groundwork for future research on cerebellum-related disorders in children.
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Affiliation(s)
- Wenjiao Lyu
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Kim-Han Thung
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Khoi Minh Huynh
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Li Wang
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Sahar Ahmad
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Pew-Thian Yap
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
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4
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Yang H, Wu G, Li Y, Xu X, Cong J, Xu H, Ma Y, Li Y, Chen R, Pines A, Xu T, Sydnor VJ, Satterthwaite TD, Cui Z. Connectional axis of individual functional variability: Patterns, structural correlates, and relevance for development and cognition. Proc Natl Acad Sci U S A 2025; 122:e2420228122. [PMID: 40100626 PMCID: PMC11962465 DOI: 10.1073/pnas.2420228122] [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: 10/03/2024] [Accepted: 02/12/2025] [Indexed: 03/20/2025] Open
Abstract
The human cerebral cortex exhibits intricate interareal functional synchronization at the macroscale, with substantial individual variability in these functional connections. However, the spatial organization of functional connectivity (FC) variability across the human connectome edges and its significance in cognitive development remain unclear. Here, we identified a connectional axis in the edge-level FC variability. The variability declined continuously along this axis from within-network to between-network connections and from the edges linking association networks to those linking the sensorimotor and association networks. This connectional axis of functional variability is associated with spatial pattern of structural connectivity variability. Moreover, the connectional variability axis evolves in youth with an flatter axis slope. We also observed that the slope of the connectional variability axis was positively related to the performance in the higher-order cognition. Together, our results reveal a connectional axis in functional variability that is linked with structural connectome variability, refines during development, and is relevant to cognition.
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Affiliation(s)
- Hang Yang
- Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing102206, China
- Chinese Institute for Brain Research, Beijing102206, China
| | - Guowei Wu
- Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing102206, China
- Chinese Institute for Brain Research, Beijing102206, China
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing100101, China
| | - Yaoxin Li
- Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing102206, China
- Chinese Institute for Brain Research, Beijing102206, China
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI48109
| | - Xiaoyu Xu
- Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing102206, China
- Chinese Institute for Brain Research, Beijing102206, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing100875, China
| | - Jing Cong
- Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing102206, China
- Chinese Institute for Brain Research, Beijing102206, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing100875, China
| | - Haoshu Xu
- Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing102206, China
- Chinese Institute for Brain Research, Beijing102206, China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing100871, China
| | - Yiyao Ma
- Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing102206, China
- Chinese Institute for Brain Research, Beijing102206, China
| | - Yang Li
- Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing102206, China
- Chinese Institute for Brain Research, Beijing102206, China
| | - Runsen Chen
- Vanke School of Public Health, Tsinghua University, Beijing100084, China
| | - Adam Pines
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA94305
| | - Ting Xu
- Center for the Integrative Developmental Neuroscience, Child Mind Institute, New York, NY10022
| | - Valerie J. Sydnor
- Department of Psychiatry, University of Pittsburgh Medical Center, Pittsburgh, PA15213
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Theodore D. Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Zaixu Cui
- Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing102206, China
- Chinese Institute for Brain Research, Beijing102206, China
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5
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Luo AC, Meisler SL, Sydnor VJ, Alexander-Bloch A, Bagautdinova J, Barch DM, Bassett DS, Davatzikos C, Franco AR, Goldsmith J, Gur RE, Gur RC, Hu F, Jaskir M, Kiar G, Keller AS, Larsen B, Mackey AP, Milham MP, Roalf DR, Shafiei G, Shinohara RT, Somerville LH, Weinstein SM, Yeatman JD, Cieslak M, Rokem A, Satterthwaite TD. Two Axes of White Matter Development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.03.19.644049. [PMID: 40166142 PMCID: PMC11957034 DOI: 10.1101/2025.03.19.644049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/02/2025]
Abstract
Despite decades of neuroimaging research, how white matter develops along the length of major tracts in humans remains unknown. Here, we identify fundamental patterns of white matter maturation by examining developmental variation along major, long-range cortico-cortical tracts in youth ages 5-23 years using diffusion MRI from three large-scale, cross-sectional datasets (total N = 2,710). Across datasets, we delineate two replicable axes of human white matter development. First, we find a deep-to-superficial axis, in which superficial tract regions near the cortical surface exhibit greater age-related change than deep tract regions. Second, we demonstrate that the development of superficial tract regions aligns with the cortical hierarchy defined by the sensorimotor-association axis, with tract ends adjacent to sensorimotor cortices maturing earlier than those adjacent to association cortices. These results reveal developmental variation along tracts that conventional tract-average analyses have previously obscured, challenging the implicit assumption that white matter tracts mature uniformly along their length. Such developmental variation along tracts may have functional implications, including mitigating ephaptic coupling in densely packed deep tract regions and tuning neural synchrony through hierarchical development in superficial tract regions - ultimately refining neural transmission in youth.
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Affiliation(s)
- Audrey C. Luo
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Steven L. Meisler
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Valerie J. Sydnor
- Department of Psychiatry, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Aaron Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Joëlle Bagautdinova
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Deanna M. Barch
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Dani S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, USA
- The Santa Fe Institute, Santa Fe, NM, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Alexandre R. Franco
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Strategic Data Initiatives, Child Mind Institute, New York, NY, USA
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
| | - Jeff Goldsmith
- Department of Biostatistics, Columbia University Mailman School of Public Health, New York, New York
| | - Raquel E. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ruben C. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Fengling Hu
- Penn Statistics in Imaging and Visualization Center, Perelman School of Medicine, University of Pennsylvania, , Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Marc Jaskir
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Gregory Kiar
- Center for Data Analytics, Innovation, and Rigor, Child Mind Institute, New York, NY, USA
| | - Arielle S. Keller
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
- Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, CT, USA
| | - Bart Larsen
- Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Allyson P. Mackey
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael P. Milham
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Center for Data Analytics, Innovation, and Rigor, Child Mind Institute, New York, NY, USA
| | - David R. Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Golia Shafiei
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Russell T. Shinohara
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Statistics in Imaging and Visualization Center, Perelman School of Medicine, University of Pennsylvania, , Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Leah H. Somerville
- Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Sarah M. Weinstein
- Department of Epidemiology and Biostatistics, Temple University College of Public Health, Philadelphia, PA, USA
| | - Jason D. Yeatman
- Graduate School of Education, Stanford University, Stanford, CA, USA
- Department of Psychology, Stanford University, Stanford, CA, USA
- Division of Developmental-Behavioral Pediatrics, Department of Pediatrics, Stanford University School of Medicine, Stanford,California, USA
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Ariel Rokem
- Department of Psychology and eScience Institute, University of Washington, Seattle, Washington, United States of America
| | - Theodore D. Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
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6
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Calabro FJ, LeCroy D, Foran W, Sydnor VJ, Parr AC, Constantinidis C, Luna B. Developmental decorrelation of local cortical activity through adolescence supports high-dimensional encoding and working memory. Dev Cogn Neurosci 2025; 73:101541. [PMID: 40086409 PMCID: PMC11951985 DOI: 10.1016/j.dcn.2025.101541] [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: 09/03/2024] [Revised: 01/24/2025] [Accepted: 02/12/2025] [Indexed: 03/16/2025] Open
Abstract
Adolescence is a key period for the maturation of cognitive control during which cortical circuitry is refined through processes such as synaptic pruning, but how these refinements modulate local functional dynamics to support cognition remains only partially characterized. Here, we used data from a longitudinal, adolescent cohort (N = 134 individuals ages 10-31 years, N = 202 total sessions) that completed MRI scans at ultra-high field (7 Tesla). We used resting state fMRI data to compute surface-based regional homogeneity (ReHo)-a measure of time-dependent correlations in fMRI activity between a vertex and its immediate neighbors-as an index of local functional connectivity across the cortex. We found widespread decreases in ReHo, suggesting increasing heterogeneity and specialization of functional circuits through adolescence. Decreases in ReHo included a spatial component which overlapped with sensorimotor and cingulo-opercular networks, in which ReHo decreases were associated with developmental stabilization of working memory performance. We show that decreases in ReHo are associated with higher intrinsic coding dimensionality, demonstrating how functional specialization of these circuits may confer computational benefits by facilitating increased capacity for encoding information. These results suggest a remodeling of cortical activity in adolescence through which local functional circuits become increasingly specialized, higher-dimensional, and more capable of supporting adult-like cognitive functioning.
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Affiliation(s)
- Finnegan J Calabro
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Dylan LeCroy
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Will Foran
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States
| | - Valerie J Sydnor
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States
| | - Ashley C Parr
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States
| | - Christos Constantinidis
- Program in Neuroscience, Vanderbilt University, Nashville, TN, United States; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States; Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Beatriz Luna
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States; Department of Psychology, University of Pittsburgh, Pittsburgh, PA, United States
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7
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Shafiei G, Esper NB, Hoffmann MS, Ai L, Chen AA, Cluce J, Covitz S, Giavasis S, Lane C, Mehta K, Moore TM, Salo T, Tapera TM, Calkins ME, Colcombe S, Davatzikos C, Gur RE, Gur RC, Pan PM, Jackowski AP, Rokem A, Rohde LA, Shinohara RT, Tottenham N, Zuo XN, Cieslak M, Franco AR, Kiar G, Salum GA, Milham MP, Satterthwaite TD. Reproducible Brain Charts: An open data resource for mapping brain development and its associations with mental health. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.02.24.639850. [PMID: 40060681 PMCID: PMC11888297 DOI: 10.1101/2025.02.24.639850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/18/2025]
Abstract
Major mental disorders are increasingly understood as disorders of brain development. Large and heterogeneous samples are required to define generalizable links between brain development and psychopathology. To this end, we introduce the Reproducible Brain Charts (RBC), an open data resource that integrates data from 5 large studies of brain development in youth from three continents (N=6,346; 45% Female). Confirmatory bifactor models were used to create harmonized psychiatric phenotypes that capture major dimensions of psychopathology. Following rigorous quality assurance, neuroimaging data were carefully curated and processed using consistent pipelines in a reproducible manner with DataLad, the Configurable Pipeline for the Analysis of Connectomes (C-PAC), and FreeSurfer. Initial analyses of RBC data emphasize the benefit of careful quality assurance and data harmonization in delineating developmental effects and associations with psychopathology. Critically, all RBC data - including harmonized psychiatric phenotypes, unprocessed images, and fully processed imaging derivatives - are openly shared without a data use agreement via the International Neuroimaging Data-sharing Initiative. Together, RBC facilitates large-scale, reproducible, and generalizable research in developmental and psychiatric neuroscience.
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Affiliation(s)
- G Shafiei
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA, USA
| | - N B Esper
- Child Mind Institute, New York, NY, USA
| | - M S Hoffmann
- Department of Neuropsychiatry, Universidade Federal de Santa Maria (UFSM), Santa Maria, Brazil
- Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- National Institute of Developmental Psychiatry & National Center for Innovation and Research in Mental Health, Brazil
- Care Policy and Evaluation Centre, London School of Economics and Political Science, London, UK
| | - L Ai
- Child Mind Institute, New York, NY, USA
| | - A A Chen
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - J Cluce
- Child Mind Institute, New York, NY, USA
| | - S Covitz
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | | | - C Lane
- Child Mind Institute, New York, NY, USA
| | - K Mehta
- Department of Neuroscience, Columbia University, New York, NY, USA
| | - T M Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA, USA
| | - T Salo
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA, USA
| | - T M Tapera
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - M E Calkins
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA, USA
| | - S Colcombe
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
| | - C Davatzikos
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - R E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA, USA
| | - R C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA, USA
| | - P M Pan
- Department of Psychiatry, Federal University of São Paulo (UNIFESP), São Paulo, Brazil
| | - A P Jackowski
- Department of Psychiatry, Federal University of São Paulo (UNIFESP), São Paulo, Brazil
| | - A Rokem
- Department of Psychology, University of Washington, Seattle, WA, USA
- eScience Institute, University of Washington, Seattle, WA
| | - L A Rohde
- Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - R T Shinohara
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - N Tottenham
- Department of Psychology, Columbia University, New York, NY, USA
| | - X N Zuo
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - M Cieslak
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA, USA
| | - A R Franco
- Child Mind Institute, New York, NY, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
| | - G Kiar
- Child Mind Institute, New York, NY, USA
| | - G A Salum
- Child Mind Institute, New York, NY, USA
- Graduate Program in Psychiatry and Behavioral Sciences, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- National Institute of Developmental Psychiatry & National Center for Innovation and Research in Mental Health, Brazil
- ADHD Outpatient Program & Developmental Psychiatry Program, Hospital de Clinicas de Porto Alegre, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Medical Council UNIFAJ & UNIMAX, Brazil
| | - M P Milham
- Child Mind Institute, New York, NY, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA
| | - T D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI) of Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
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8
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Taskov T, Dushanova J. Role of Sex in Shaping Brain Network Organization During Reading in Developmental Dyslexia. CHILDREN (BASEL, SWITZERLAND) 2025; 12:207. [PMID: 40003309 PMCID: PMC11854611 DOI: 10.3390/children12020207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2024] [Revised: 01/28/2025] [Accepted: 02/06/2025] [Indexed: 02/27/2025]
Abstract
Background/Methods: The influence of sex on brain organization was investigated in functional reading networks in 8-year-old children, in those typically developing and those with developmental dyslexia (DD), utilizing the minimum spanning tree model. Results: The word reading task revealed subtle sex differences in brain connectivity and highlighted even small individual variations in functional connectivity characteristics, particularly among boys with DD. In girls, significantly stronger connections and core hubs were identified within and between motor, parietal, and visual networks in posterior brain regions in both hemispheres, particularly in the θ (dyslexics) and δ (normolexics) frequency bands. In contrast, boys showed a more diffuse connectivity pattern, predominantly in the left hemisphere, encompassing anterior heteromodal and sensorimotor networks. Girls exhibited greater network complexity (bigger leaf fraction, kappa, and tree hierarchy), particularly in the θ and δ frequency bands, while boys with DD showed increased network efficiency, except for in the γ2 band (smaller diameter and bigger leaf fraction). Therefore, gender-specific differences in brain network organization may affect reading development and dyslexia. While sex may influence brain network development, its impact on the sensorimotor and frontoparietal networks of 8-year-old children is relatively limited. Significant sex differences were observed in only a small subset of children, primarily in higher (β2-γ2) frequency bands. Conclusions: Interindividual variations were evident only in boys with DD, impacting both sensorimotor and association networks. Different rates of cortical network maturation between sexes with DD during childhood may contribute to variations associated with disruptions in brain network development, even within fundamental networks like the sensorimotor network.
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Affiliation(s)
| | - Juliana Dushanova
- Institute of Neurobiology, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 23, 1113 Sofia, Bulgaria;
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9
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Li H, Cui Z, Cieslak M, Salo T, Moore TM, Gur RE, Gur RC, Shinohara RT, Oathes DJ, Davatzikos C, Satterthwaite TD, Fan Y. Spatial Heterogeneity and Subtypes of Functional Connectivity Development in Youth. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.01.24.634828. [PMID: 39896611 PMCID: PMC11785253 DOI: 10.1101/2025.01.24.634828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/04/2025]
Abstract
The brain functional connectome development is fundamental to neurocognitive growth in youth. While brain age prediction has been widely used to assess connectome development at the individual level, traditional approaches providing a global index overlook the spatial variability and inter-individual heterogeneity of functional connectivity (FC) development across the cortex. In this study, we introduced a regional brain development index to assess spatially fine-grained FC development. We examined the spatial variability of FC development and stratified individuals into subtypes with distinct patterns of spatial heterogeneity in region-wise FC development across the cortex through clustering. An evaluation conducted on a sample of youths aged 8-23 years using fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) revealed three distinct FC development subtypes. Individuals with advanced FC development aligned with the hierarchical brain organization along the sensorimotor-association (S-A) axis demonstrated superior cognitive performance compared to those with other patterns. These patterns were replicated in the Human Connectome Project Development (HCP-D) cohort, confirming their robustness. Further analysis revealed associations between FC development and gene expression, with enriched genes linked to neural differentiation, synaptogenesis, and myelination. These findings suggest that spatial heterogeneity in FC development reflects underlying cortical microstructure and hierarchical cortical organization, underscoring its critical role in understanding neurocognitive maturation and individual variability during youth.
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Affiliation(s)
- Hongming Li
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Matthew Cieslak
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Taylor Salo
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tyler M. Moore
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Lifespan Informatics and Neuroimaging Center, 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
| | - Russell T. Shinohara
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Desmond J. Oathes
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Brain Imaging and Stimulation, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Brain Science, Translation, Innovation, and Modulation Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuromodulation in Depression and Stress, University of Pennsylvania, Philadelphia, PA 19104, USA
- Departments of Neuroscience, Neurosurgery and Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D. Satterthwaite
- Center for Biomedical Image Computing 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
- Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
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10
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Baker AE, Galván A, Fuligni AJ. The connecting brain in context: How adolescent plasticity supports learning and development. Dev Cogn Neurosci 2025; 71:101486. [PMID: 39631105 PMCID: PMC11653146 DOI: 10.1016/j.dcn.2024.101486] [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: 05/31/2024] [Revised: 11/01/2024] [Accepted: 11/25/2024] [Indexed: 12/07/2024] Open
Abstract
Puberty initiates significant neurobiological changes that amplify adolescents' responsiveness to their environment, facilitating neural adaptation through processes like synaptic pruning, myelination, and neuronal reorganization. This heightened neuroplasticity, combined with their burgeoning social curiosity and appetite for risk, propels adolescents to explore diverse new environments and forge social bonds. Such exploration can accelerate experiential learning and the formation of social networks as adolescents prepare for adult independence. This review examines the complex interplay between adolescent neuroplasticity, environmental influences, and learning processes, synthesizing findings from recent studies that illustrate how factors such as social interactions, school environments, and neighborhood contexts influence both the transient activation and enduring organization of the developing brain. We advocate for incorporating social interaction into adolescent-tailored interventions, leveraging their social plasticity to optimize learning and development during this critical phase. Going forward, we discuss the importance of longitudinal studies that employ multimodal approaches to characterize the dynamic interactions between development and environment, highlighting recent advancements in quantifying environmental impacts in studies of developmental neuroscience. Ultimately, this paper provides an updated synopsis of adolescent neuroplasticity and the environment, underscoring the potential for environmental enrichment programs to support healthy brain development and resilience at this critical development stage.
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11
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Hansen JY, Cauzzo S, Singh K, García-Gomar MG, Shine JM, Bianciardi M, Misic B. Integrating brainstem and cortical functional architectures. Nat Neurosci 2024; 27:2500-2511. [PMID: 39414973 PMCID: PMC11614745 DOI: 10.1038/s41593-024-01787-0] [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: 11/06/2023] [Accepted: 09/13/2024] [Indexed: 10/18/2024]
Abstract
The brainstem is a fundamental component of the central nervous system, yet it is typically excluded from in vivo human brain mapping efforts, precluding a complete understanding of how the brainstem influences cortical function. In this study, we used high-resolution 7-Tesla functional magnetic resonance imaging to derive a functional connectome encompassing cortex and 58 brainstem nuclei spanning the midbrain, pons and medulla. We identified a compact set of integrative hubs in the brainstem with widespread connectivity with cerebral cortex. Patterns of connectivity between brainstem and cerebral cortex manifest as neurophysiological oscillatory rhythms, patterns of cognitive functional specialization and the unimodal-transmodal functional hierarchy. This persistent alignment between cortical functional topographies and brainstem nuclei is shaped by the spatial arrangement of multiple neurotransmitter receptors and transporters. We replicated all findings using 3-Tesla data from the same participants. Collectively, this work demonstrates that multiple organizational features of cortical activity can be traced back to the brainstem.
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Affiliation(s)
- Justine Y Hansen
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Simone Cauzzo
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Parkinson's Disease and Movement Disorders Unit, Center for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
| | - Kavita Singh
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - María Guadalupe García-Gomar
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Escuela Nacional de Estudios Superiores, Unidad Juriquilla, Universidad Nacional Autónoma de México, Querétaro, Mexico
| | - James M Shine
- Brain and Mind Centre, The University of Sydney, Sydney, Australia
| | - Marta Bianciardi
- Brainstem Imaging Laboratory, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Sleep Medicine, Harvard University, Boston, MA, USA
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada.
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12
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Linke AC, Chen B, Olson L, Cordova M, Wilkinson M, Wang T, Herrera M, Salmina M, Rios A, Mahmalji J, Do T, Vu J, Budman M, Walker A, Fishman I. Altered Development of the Hurst Exponent in the Medial Prefrontal Cortex in Preschoolers With Autism. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00271-4. [PMID: 39293740 DOI: 10.1016/j.bpsc.2024.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 08/23/2024] [Accepted: 09/10/2024] [Indexed: 09/20/2024]
Abstract
BACKGROUND Atypical balance of excitation (E) and inhibition (I) in the brain is thought to contribute to the emergence and symptomatology of autism spectrum disorder (ASD). E/I ratio can be estimated from resting-state functional magnetic resonance imaging (fMRI) using the Hurst exponent, H. A recent study reported decreased ventromedial prefrontal cortex (vmPFC) H in male adults with ASD. Part of the default mode network (DMN), the vmPFC plays an important role in emotion regulation, decision making, and social cognition. It frequently shows altered function and connectivity in individuals with autism. METHODS The current study presents the first fMRI evidence of altered early development of vmPFC H and its link to DMN functional connectivity and emotional control in toddlers and preschoolers with ASD. A total of 83 children (45 with ASD), ages 1.5-5 years, underwent natural sleep fMRI as part of a longitudinal study. RESULTS In a cross-sectional analysis, vmPFC H decreased with age in children with ASD, reflecting increasing E/I ratio, but not in typically developing children. This effect remained significant when controlling for gestational age at birth, socioeconomic status, or ethnicity. The same pattern was also observed in a subset of children with longitudinal fMRI data acquired 2 years apart on average. Lower vmPFC H was also associated with reduced functional connectivity within the DMN as well as with higher emotional control deficits (although only significant transdiagnostically). CONCLUSIONS These results suggest an early onset of E/I imbalances in the vmPFC in ASD, with likely consequences for the maturation of the DMN.
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Affiliation(s)
- Annika C Linke
- Department of Psychology, San Diego State University, San Diego, California; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California; Center for Autism and Developmental Disorders, San Diego State University, San Diego, California.
| | - Bosi Chen
- San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California; Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, New York
| | - Lindsay Olson
- San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California; Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, California
| | - Michaela Cordova
- San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California
| | - Molly Wilkinson
- San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California
| | - Tiffany Wang
- Department of Psychology, University of California San Diego, La Jolla, California
| | - Meagan Herrera
- Department of Psychology, San Diego State University, San Diego, California
| | - Madison Salmina
- Department of Psychology, San Diego State University, San Diego, California
| | - Adriana Rios
- Department of Psychology, San Diego State University, San Diego, California
| | - Judy Mahmalji
- Department of Psychology, San Diego State University, San Diego, California
| | - Tess Do
- Department of Psychology, San Diego State University, San Diego, California
| | - Jessica Vu
- Department of Psychology, San Diego State University, San Diego, California
| | - Michelle Budman
- Department of Psychology, San Diego State University, San Diego, California
| | - Alexis Walker
- Department of Psychology, San Diego State University, San Diego, California
| | - Inna Fishman
- Department of Psychology, San Diego State University, San Diego, California; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California; Center for Autism and Developmental Disorders, San Diego State University, San Diego, California
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13
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Lee K, Ji JL, Fonteneau C, Berkovitch L, Rahmati M, Pan L, Repovš G, Krystal JH, Murray JD, Anticevic A. Human brain state dynamics are highly reproducible and associated with neural and behavioral features. PLoS Biol 2024; 22:e3002808. [PMID: 39316635 PMCID: PMC11421804 DOI: 10.1371/journal.pbio.3002808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 08/15/2024] [Indexed: 09/26/2024] Open
Abstract
Neural activity and behavior vary within an individual (states) and between individuals (traits). However, the mapping of state-trait neural variation to behavior is not well understood. To address this gap, we quantify moment-to-moment changes in brain-wide co-activation patterns derived from resting-state functional magnetic resonance imaging. In healthy young adults, we identify reproducible spatiotemporal features of co-activation patterns at the single-subject level. We demonstrate that a joint analysis of state-trait neural variations and feature reduction reveal general motifs of individual differences, encompassing state-specific and general neural features that exhibit day-to-day variability. The principal neural variations co-vary with the principal variations of behavioral phenotypes, highlighting cognitive function, emotion regulation, alcohol and substance use. Person-specific probability of occupying a particular co-activation pattern is reproducible and associated with neural and behavioral features. This combined analysis of state-trait variations holds promise for developing reproducible neuroimaging markers of individual life functional outcome.
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Affiliation(s)
- Kangjoo Lee
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Clara Fonteneau
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Lucie Berkovitch
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Saclay CEA Centre, Neurospin, Gif-Sur-Yvette Cedex, France
- Department of Psychiatry, GHU Paris Psychiatrie et Neurosciences, Service Hospitalo-Universitaire, Paris, France
- Université Paris Cité, Paris, France
| | - Masih Rahmati
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Lining Pan
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Grega Repovš
- Department of Psychology, University of Ljubljana, Ljubljana, Slovenia
| | - John H. Krystal
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - John D. Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Department of Physics, Yale University, New Haven, Connecticut, United States of America
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire, United States of America
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Department of Psychology, Yale University, New Haven, Connecticut, United States of America
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14
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Lei T, Liao X, Liang X, Sun L, Xia M, Xia Y, Zhao T, Chen X, Men W, Wang Y, Ma L, Liu N, Lu J, Zhao G, Ding Y, Deng Y, Wang J, Chen R, Zhang H, Tan S, Gao JH, Qin S, Tao S, Dong Q, He Y. Functional network modules overlap and are linked to interindividual connectome differences during human brain development. PLoS Biol 2024; 22:e3002653. [PMID: 39292711 PMCID: PMC11441662 DOI: 10.1371/journal.pbio.3002653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 09/30/2024] [Accepted: 08/29/2024] [Indexed: 09/20/2024] Open
Abstract
The modular structure of functional connectomes in the human brain undergoes substantial reorganization during development. However, previous studies have implicitly assumed that each region participates in one single module, ignoring the potential spatial overlap between modules. How the overlapping functional modules develop and whether this development is related to gray and white matter features remain unknown. Using longitudinal multimodal structural, functional, and diffusion MRI data from 305 children (aged 6 to 14 years), we investigated the maturation of overlapping modules of functional networks and further revealed their structural associations. An edge-centric network model was used to identify the overlapping modules, and the nodal overlap in module affiliations was quantified using the entropy measure. We showed a regionally heterogeneous spatial topography of the overlapping extent of brain nodes in module affiliations in children, with higher entropy (i.e., more module involvement) in the ventral attention, somatomotor, and subcortical regions and lower entropy (i.e., less module involvement) in the visual and default-mode regions. The overlapping modules developed in a linear, spatially dissociable manner, with decreased entropy (i.e., decreased module involvement) in the dorsomedial prefrontal cortex, ventral prefrontal cortex, and putamen and increased entropy (i.e., increased module involvement) in the parietal lobules and lateral prefrontal cortex. The overlapping modular patterns captured individual brain maturity as characterized by chronological age and were predicted by integrating gray matter morphology and white matter microstructural properties. Our findings highlight the maturation of overlapping functional modules and their structural substrates, thereby advancing our understanding of the principles of connectome development.
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Affiliation(s)
- Tianyuan Lei
- Department of Psychiatry, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Xinyuan Liang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Lianglong Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yunman Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xiaodan Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Leilei Ma
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Ningyu Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Jing Lu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Gai Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yuyin Ding
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yao Deng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Jiali Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Rui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Haibo Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Shuping Tan
- Psychiatry Research Center, Beijing Huilongguan Hospital, Peking University Huilongguan Clinical College, Beijing, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
- IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
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15
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Xu X, Yang H, Cong J, Sydnor V, Cui Z. Structural connectivity matures along a sensorimotor-association connectional axis in youth. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.17.599267. [PMID: 38948845 PMCID: PMC11212872 DOI: 10.1101/2024.06.17.599267] [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
Childhood and adolescence are associated with protracted developmental remodeling of cortico-cortical structural connectivity. However, how heterochronous development in white matter structural connectivity spatially and temporally unfolds across the macroscale human connectome remains unknown. Leveraging non-invasive diffusion MRI data from both cross-sectional (N = 590) and longitudinal (baseline: N = 3,949; two-year follow-up: N = 3,155) developmental datasets, we found that structural connectivity development diverges along a pre-defined sensorimotor-association (S-A) connectional axis from ages 8.1 to 21.9 years. Specifically, we observed a continuum of developmental profiles that spans from an early childhood increase in connectivity strength in sensorimotor-sensorimotor connections to a late adolescent increase in association-association connectional strength. The S-A connectional axis also captured spatial variations in associations between structural connectivity and both higher-order cognition and general psychopathology. Together, our findings reveal a hierarchical axis in the development of structural connectivity across the human connectome.
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Affiliation(s)
- Xiaoyu Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University; Beijing, 100875, China
- Chinese Institute for Brain Research, Beijing; Beijing, 102206, China
| | - Hang Yang
- Chinese Institute for Brain Research, Beijing; Beijing, 102206, China
| | - Jing Cong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University; Beijing, 100875, China
- Chinese Institute for Brain Research, Beijing; Beijing, 102206, China
| | - Valerie Sydnor
- Department of Psychiatry, University of Pittsburgh Medical Center; Pittsburgh, PA, USA
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing; Beijing, 102206, China
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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: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [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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