Please use this identifier to cite or link to this item: https://doi.org/https://doi.org/10.1093/cercor/bhab101
Title: Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior
Authors: KONG RU 
Qing Yang
Evan Gordon
Aihuiping Xue
Xiaoxuan Yan
CSABA ORBAN 
Xi-Nian Zuo
Nathan Spreng
Tian Ge
Avram Holmes
Simon Eickhoff
Yeo B.T.T. 
Issue Date: 31-Oct-2021
Citation: KONG RU, Qing Yang, Evan Gordon, Aihuiping Xue, Xiaoxuan Yan, CSABA ORBAN, Xi-Nian Zuo, Nathan Spreng, Tian Ge, Avram Holmes, Simon Eickhoff, Yeo B.T.T. (2021-10-31). Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior. Cerebral cortex 31 (10) : 4477–4500. ScholarBank@NUS Repository. https://doi.org/https://doi.org/10.1093/cercor/bhab101
Rights: Attribution 4.0 International
Abstract: Resting-state functional MRI (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, i.e., should not span multiple lobes. There is disagreement about whether areal-level parcels should be strictly contiguous or comprise multiple non-contiguous components, therefore we considered three areal-level MS-HBM variants spanning these range of possibilities. Individual-specific MS-HBM parcellations estimated using 10min of data generalized better than other approaches using 150min of data to out-of-sample rs-fMRI and task-fMRI from the same individuals. Resting-state functional connectivity (RSFC) derived from MS-HBM parcellations also achieved the best behavioral prediction performance. Among the three MS-HBM variants, the strictly contiguous MS-HBM (cMS-HBM) exhibited the best resting-state homogeneity and most uniform within-parcel task activation. In terms of behavioral prediction, the gradient-infused MS-HBM (gMS-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).
Source Title: Cerebral cortex
URI: https://scholarbank.nus.edu.sg/handle/10635/219197
ISSN: 1047-3211
1460-2199
DOI: https://doi.org/10.1093/cercor/bhab101
Rights: Attribution 4.0 International
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