ArticleImaging neuroscience (Cambridge, Mass.)2025
A hierarchical Bayesian brain parcellation framework for fusion of functional imaging datasets.
Article in Imaging neuroscience (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A bias-accounting meta-analytic approach refines and expands the cerebellar behavioral topography.Neuroscience and biobehavioral reviews · 2025Pooled it
- Individualized parcellation reveals functional boundaries in human prefrontal cortex.bioRxiv : the preprint server for biology · 2026Article
- Precision Functional Parcellation of the Human Cortex via Rest-Task fMRI Fusion.bioRxiv : the preprint server for biology · 2026Article
- A bias-accounting meta-analytic approach refines and expands the cerebellar behavioral topography.bioRxiv : the preprint server for biology · 2025Article
- A hierarchical atlas of the human cerebellum for functional precision mapping.Nature communications · 2024Article
- Structural covariation between cerebellum and neocortex intrinsic structural covariation links cerebellum subregions to the cerebral cortex.Journal of neurophysiology · 2024Article
- A hierarchical atlas of the human cerebellum for functional precision mapping.bioRxiv : the preprint server for biology · 2024Article
- Challenges in Identifying Individualized Brain Biomarkers of Late Life Depression.Advances in geriatric medicine and research · 2023Article
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6 authors.
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Abstract
Different task-based and resting-state imaging datasets provide complementary information about the organization of the human brain. Brain parcellations based on single datasets will, therefore, be biased toward the particular type of information present in each dataset. To overcome this limitation, we propose here a hierarchical Bayesian framework that can learn a probabilistic brain parcellation across numerous task-based and resting-state datasets, exploiting their combined strengths. The framework is partitioned into a spatial arrangement model that defines the probability of each voxel belonging to a specific parcel (the probabilistic group atlas), and a set of dataset-specific emission models that define the probability of the observed data given the parcel of the voxel. Using the human cerebellum as an example, we show that the framework optimally combines information from different datasets to achieve a new population-based atlas that outperforms atlases based on single datasets. Furthermore, we demonstrate that using only 10 min of individual data, the framework is able to generate individual brain parcellations that outperform group atlases.
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