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

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

SUBMITTER: Kong R 

PROVIDER: S-EPMC6519695 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature