%0 Journal Article
%A Kong, Ru
%A Yang, Qing
%A Gordon, Evan
%A Xue, Aihuiping
%A Yan, Xiaoxuan
%A Orban, Csaba
%A Zuo, Xi-Nian
%A Spreng, Nathan
%A Ge, Tian
%A Holmes, Avram
%A Eickhoff, Simon
%A Yeo, B T Thomas
%T Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior
%J Cerebral cortex
%V 31
%N 10
%@ 1047-3211
%C Oxford
%I Oxford Univ. Press
%M FZJ-2021-05979
%P 4477 - 4500
%D 2021
%X 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.
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:33942058
%U <Go to ISI:>//WOS:000695807700007
%R 10.1093/cercor/bhab101
%U https://juser.fz-juelich.de/record/904409