TY  - JOUR
AU  - Kong, Ru
AU  - Yang, Qing
AU  - Gordon, Evan
AU  - Xue, Aihuiping
AU  - Yan, Xiaoxuan
AU  - Orban, Csaba
AU  - Zuo, Xi-Nian
AU  - Spreng, Nathan
AU  - Ge, Tian
AU  - Holmes, Avram
AU  - Eickhoff, Simon
AU  - Yeo, B T Thomas
TI  - Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior
JO  - Cerebral cortex
VL  - 31
IS  - 10
SN  - 1047-3211
CY  - Oxford
PB  - Oxford Univ. Press
M1  - FZJ-2021-05979
SP  - 4477 - 4500
PY  - 2021
AB  - Resting-state functional magnetic resonance imaging (rs-fMRI) allows estimation of individual-specific cortical parcellations. We have previously developed a multi-session hierarchical Bayesian model (MS-HBM) for estimating high-quality individual-specific network-level parcellations. Here, we extend the model to estimate individual-specific areal-level parcellations. While network-level parcellations comprise spatially distributed networks spanning the cortex, the consensus is that areal-level parcels should be spatially localized, that is, should not span multiple lobes. There is disagreement about whether areal-level parcels should be strictly contiguous or comprise multiple noncontiguous components; therefore, we considered three areal-level MS-HBM variants spanning these range of possibilities. Individual-specific MS-HBM parcellations estimated using 10 min of data generalized better than other approaches using 150 min of data to out-of-sample rs-fMRI and task-fMRI from the same individuals. Resting-state functional connectivity derived from MS-HBM parcellations also achieved the best behavioral prediction performance. Among the three MS-HBM variants, the strictly contiguous MS-HBM exhibited the best resting-state homogeneity and most uniform within-parcel task activation. In terms of behavioral prediction, the gradient-infused MS-HBM was numerically the best, but differences among MS-HBM variants were not statistically significant. Overall, these results suggest that areal-level MS-HBMs can capture behaviorally meaningful individual-specific parcellation features beyond group-level parcellations.
LB  - PUB:(DE-HGF)16
C6  - pmid:33942058
UR  - <Go to ISI:>//WOS:000695807700007
DO  - DOI:10.1093/cercor/bhab101
UR  - https://juser.fz-juelich.de/record/904409
ER  -