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

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

SPECIES:
Human

DESCRIPTION:
This package includes the following documents:
1. MSHBM 17-network parcellations for 1029 HCP subjects (Kong et al. 2019)
2. Multi-resolution areal-level MSHBM parcellations (100,200,...,1000 ROIs) for 1029 HCP subjects (Kong et al. in press)
3. Multi-resolution individual-specific functional connectivity matrices (100,200,...,1000 ROIs) for 1029 HCP subjects (Kong et al. in press)

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Due to the limited header info size of parcellation *dlabel files, we failed to submit above data into BALSA. The BALSA team is looking into it. We have temporarily released the above data in Github:
https://github.com/ThomasYeoLab/Kong2022_ArealMSHBM

Relevant code for generating the areal-level MSHBM parcellations can be found here:
https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Kong2022_ArealMSHBM

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Refereces:
1. Kong, R., et al. Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion. Cereb Cortex 29, 2533–2551. 2019
2. Kong, R., et al. Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior. Cereb Cortex, in press.

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).

PUBLICATION:
Cerebral Cortex - DOI: 10.1093/cercor/bhab101

AUTHORS:

  • 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