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000904409 1001_ $$aKong, Ru$$b0
000904409 245__ $$aIndividual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior
000904409 260__ $$aOxford$$bOxford Univ. Press$$c2021
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000904409 520__ $$aResting-state functional magnetic resonance imaging (rs-fMRI) allows estimation of individual-specific cortical parcellations. We have previously developed a multi-session hierarchical Bayesian model (MS-HBM) for estimating high-quality individual-specific network-level parcellations. Here, we extend the model to estimate individual-specific areal-level parcellations. While network-level parcellations comprise spatially distributed networks spanning the cortex, the consensus is that areal-level parcels should be spatially localized, that is, should not span multiple lobes. There is disagreement about whether areal-level parcels should be strictly contiguous or comprise multiple noncontiguous components; therefore, we considered three areal-level MS-HBM variants spanning these range of possibilities. Individual-specific MS-HBM parcellations estimated using 10 min of data generalized better than other approaches using 150 min of data to out-of-sample rs-fMRI and task-fMRI from the same individuals. Resting-state functional connectivity derived from MS-HBM parcellations also achieved the best behavioral prediction performance. Among the three MS-HBM variants, the strictly contiguous MS-HBM exhibited the best resting-state homogeneity and most uniform within-parcel task activation. In terms of behavioral prediction, the gradient-infused MS-HBM was numerically the best, but differences among MS-HBM variants were not statistically significant. Overall, these results suggest that areal-level MS-HBMs can capture behaviorally meaningful individual-specific parcellation features beyond group-level parcellations.
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000904409 7001_ $$0P:(DE-HGF)0$$aYang, Qing$$b1
000904409 7001_ $$aGordon, Evan$$b2
000904409 7001_ $$aXue, Aihuiping$$b3
000904409 7001_ $$aYan, Xiaoxuan$$b4
000904409 7001_ $$aOrban, Csaba$$b5
000904409 7001_ $$00000-0001-9110-585X$$aZuo, Xi-Nian$$b6
000904409 7001_ $$aSpreng, Nathan$$b7
000904409 7001_ $$aGe, Tian$$b8
000904409 7001_ $$aHolmes, Avram$$b9
000904409 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b10$$ufzj
000904409 7001_ $$0P:(DE-HGF)0$$aYeo, B T Thomas$$b11$$eCorresponding author
000904409 773__ $$0PERI:(DE-600)1483485-6$$a10.1093/cercor/bhab101$$gVol. 31, no. 10, p. 4477 - 4500$$n10$$p4477 - 4500$$tCerebral cortex$$v31$$x1047-3211$$y2021
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