期刊
CEREBRAL CORTEX
卷 29, 期 6, 页码 2533-2551出版社
OXFORD UNIV PRESS INC
DOI: 10.1093/cercor/bhy123
关键词
behavior prediction; brain parcellation; individual differences; network topography; resting-state functional connectivity
资金
- Singapore Ministry of Education Tier 2 [MOE2014-T2-2-016]
- National University of Singapore (NUS) Strategic Research [DPRT/944/09/14]
- NUS School of Medicine Aspiration Fund [R185000271720]
- Singapore National Medical Research Council [CBRG/0088/2015]
- NUS Young Investigator Award
- Singapore National Research Foundation (NRF) Fellowship (Class of 2017)
- National Institute of Mental Health [1R01NS091604, P50MH106435, 573 K01MH099232, R01MH074457]
- Beijing Municipal Science & Technology Commission [Z161100002616009]
- National Basic Research (973) Program [2015CB351702]
- Natural Science Foundation of China [81471740, 81220108014]
- National R&D Infrastructure and Facility Development Program of China [DKA2017-12-02-21]
- Beijing Municipal Science and Tech Commission [Z161100002616023, Z171100000117012]
- Deutsche Forschungsgemeinschaft (DFG) [EI 816/11-1]
- Helmholtz Portfolio Theme Supercomputing and Modeling for the Human Brain
- European Union's Horizon 2020 Research and Innovation Programme [7202070]
- Athinoula A. Martinos Center for Biomedical Imaging at the Massachusetts General Hospital [1S10RR023401, 1S10RR019307, 1S10RR023043]
- Center for Brain Science Neuroinformatics Research Group
- Center for Human Genetic Research
- 16 NIH Institutes and Centers [1U54MH091657]
- McDonnell Center for Systems Neuroscience at Washington University
- Athinoula A. Martinos Center for Biomedical Imaging
Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to delineate individual-specific brain networks. A major question is whether individual-specific network topography (i.e., location and spatial arrangement) is behaviorally relevant. Here, we propose a multi-session hierarchical Bayesian model (MS-HBM) for estimating individual-specific cortical networks and investigate whether individual-specific network topography can predict human behavior. The multiple layers of the MS-HBM explicitly differentiate intra-subject (within-subject) from inter-subject (between-subject) network variability. By ignoring intra-subject variability, previous network mappings might confuse intra-subject variability for inter-subject differences. Compared with other approaches, MS-HBM parcellations generalized better to new rs-fMRI and task-fMRI data from the same subjects. More specifically, MS-HBM parcellations estimated from a single rs-fMRI session (10 min) showed comparable generalizability as parcellations estimated by 2 state-of-the-art methods using 5 sessions (50 min). We also showed that behavioral phenotypes across cognition, personality, and emotion could be predicted by individual-specific network topography with modest accuracy, comparable to previous reports predicting phenotypes based on connectivity strength. Network topography estimated by MS-HBM was more effective for behavioral prediction than network size, as well as network topography estimated by other parcellation approaches. Thus, similar to connectivity strength, individual-specific network topography might also serve as a fingerprint of human behavior.
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