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Functional network modules overlap and are linked to interindividual connectome differences during human brain development.
Lei, Tianyuan; Liao, Xuhong; Liang, Xinyuan; Sun, Lianglong; Xia, Mingrui; Xia, Yunman; Zhao, Tengda; Chen, Xiaodan; Men, Weiwei; Wang, Yanpei; Ma, Leilei; Liu, Ningyu; Lu, Jing; Zhao, Gai; Ding, Yuyin; Deng, Yao; Wang, Jiali; Chen, Rui; Zhang, Haibo; Tan, Shuping; Gao, Jia-Hong; Qin, Shaozheng; Tao, Sha; Dong, Qi; He, Yong.
Affiliation
  • Lei T; Department of Psychiatry, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
  • Liao X; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
  • Liang X; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
  • Sun L; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
  • Xia M; School of Systems Science, Beijing Normal University, Beijing, China.
  • Xia Y; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
  • Zhao T; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
  • Chen X; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
  • Men W; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
  • Wang Y; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
  • Ma L; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
  • Liu N; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
  • Lu J; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
  • Zhao G; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
  • Ding Y; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
  • Deng Y; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
  • Wang J; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
  • Chen R; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
  • Zhang H; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
  • Tan S; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
  • Gao JH; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
  • Qin S; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
  • Tao S; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
  • Dong Q; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
  • He Y; Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China.
PLoS Biol ; 22(9): e3002653, 2024 Sep.
Article in En | MEDLINE | ID: mdl-39292711
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.

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: PLoS Biol Journal subject: BIOLOGIA Year: 2024 Document type: Article Affiliation country: China Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: PLoS Biol Journal subject: BIOLOGIA Year: 2024 Document type: Article Affiliation country: China Country of publication: United States