Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior

Ru Kong, Qing Yang, Evan Gordon, Aihuiping Xue, Xiaoxuan Yan, Csaba Orban, Xi Nian Zuo, Nathan Spreng, Tian Ge, Avram Holmes, Simon Eickhoff, B. T.Thomas Yeo

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

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).

Original languageEnglish
Pages (from-to)4477-4500
Number of pages24
JournalCerebral Cortex
Volume31
Issue number10
DOIs
StatePublished - Oct 1 2021

Keywords

  • behavioral prediction
  • brain parcellation
  • difference
  • individual
  • resting-state functional connectivity

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