ArticleCerebral cortex (New York, N.Y. : 1991)2021
Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior.
Article in Cerebral cortex (New York, N.Y. : 1991), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 162 papers.
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Who cites it
162 citing papers in PubMed.
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- A language network in the individualized functional connectomes of 1199 human brains doing arbitrary tasks.Nature communications · 2026Article
- Sensorimotor circuit connectivity as a candidate biomarker for responsiveness to sertraline in obsessive-compulsive disorder.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- Early Adversity Selectively Reshapes the Somato-Cognitive Action Network in the Developing Brain.bioRxiv : the preprint server for biology · 2026Article
- Brain Graph Sparsification for fMRI-based Connectome Analysis: A Methodological Review.Neuroinformatics · 2026Review
- Functional brain organization is stable within individuals across years.bioRxiv : the preprint server for biology · 2026Article
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- Precision Functional Parcellation of the Human Cortex via Rest-Task fMRI Fusion.bioRxiv : the preprint server for biology · 2026Article
- The unity and diversity of functional brain connectivity underlying executive function tasks.Developmental cognitive neuroscience · 2026Article
- Convergent and divergent brain-cognition development in early adolescence.Nature communications · 2026Article
- Task and Resting-State Functional Magnetic Resonance Imaging Modeling of Brain-Behavior Relationships in Developmental Cohorts.Biological psychiatry · 2026Review
- Mapping Individualized Developmental Imbalance in Youth and Its Association with Psychopathology.bioRxiv : the preprint server for biology · 2026Article
102 more citing papers are in PubMed but not listed here.
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12 authors.
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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).
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