Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior. (4th May 2021)

• Record Type: Journal Article
• Authors: Kong, Ru; Yang, Qing; Gordon, Evan; Xue, Aihuiping; Yan, Xiaoxuan; Orban, Csaba; Zuo, Xi-Nian; Spreng, Nathan; Ge, Tian; Holmes, Avram; Eickhoff, Simon; Yeo, B T Thomas
• Is Part Of: Cerebral cortex. Volume 31:Number 10(2021)
• Page Start: 4477
• Page End: 4500
• Publication Date: 2021-05-04
• Subjects: behavioral prediction -- brain parcellation -- difference -- individual -- resting-state functional connectivity; Cerebral cortex -- Periodicals; Brain -- Periodicals; 612.825
• DOI: 10.1093/cercor/bhab101
• Languages: English
• ISSNs: 1047-3211

Abstract

Abstract: 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. Multi-resolution trained models and parcellations are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Kong2022_ArealMSHBM ).