Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion

Kong, Ru; Li, Jingwei; Sun, Nanbo; Yeo, B T Thomas; Sabuncu, Mert R; Orban, Csaba; Holmes, Avram J; Zuo, Xi-Nian; Schaefer, Alexander; Eickhoff, Simon; Liu, Hesheng

doi:10.1093/cercor/bhy123

Journal Article

FZJ-2018-03421

Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion

Kong, R. ; Li, J. ; Orban, C. ; Sabuncu, M. R. ; Liu, H. ; Schaefer, A. ; Sun, N. ; Zuo, X.-N. ; Holmes, A. J. ; Eickhoff, S.FZJ* ; Yeo, B. T. T. (Corresponding author)

2019
Oxford Univ. Press Oxford

Cerebral cortex 29(6), 2533–2551 (2019) [10.1093/cercor/bhy123]

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Please use a persistent id in citations: doi:10.1093/cercor/bhy123

Abstract: 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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