TY  - JOUR
AU  - Kong, Ru
AU  - Li, Jingwei
AU  - Orban, Csaba
AU  - Sabuncu, Mert R
AU  - Liu, Hesheng
AU  - Schaefer, Alexander
AU  - Sun, Nanbo
AU  - Zuo, Xi-Nian
AU  - Holmes, Avram J
AU  - Eickhoff, Simon
AU  - Yeo, B T Thomas
TI  - Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion
JO  - Cerebral cortex
VL  - 29
IS  - 6
SN  - 1460-2199
CY  - Oxford
PB  - Oxford Univ. Press
M1  - FZJ-2018-03421
SP  - 2533–2551
PY  - 2019
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:29878084
UR  - <Go to ISI:>//WOS:000482132400017
DO  - DOI:10.1093/cercor/bhy123
UR  - https://juser.fz-juelich.de/record/848149
ER  -