Bayesian Semiparametric Hidden Markov Random Partition Fields for Factor Collapse on Graphs: A Study of Cortical Mapping of Fingertips
Abstract
Understanding how the brain's cortical regions respond to stimuli like fingertip tapping is a significant challenge in neuroscience, especially with high-resolution functional magnetic resonance imaging data. To address this, we propose a new statistical method for evaluating how a factor's influence locally varies across a complex graph, such as the brain's cortex. Our approach is designed to handle the complexities of large graph sizes and computational demands. Our method uses a novel Bayesian hidden Markov random field to partition the factor's influence into collapsed states with similar effects on the outcome at each node. This unique model promotes sparsity in the partitions and penalizes large variations across adjacent nodes. The result is a highly detailed local influence map that captures subtle changes in the patterns and magnitudes of a factor's influence across the graph. To ensure efficient analysis of large datasets, we developed a specialized Markov chain Monte Carlo algorithm. Our simulation experiments demonstrate significant improvements over existing techniques in both accuracy and scalability. Ultimately, our method provides a powerful new framework for exploring the intricate relationship between stimuli and brain activity, offering a clearer picture of the cortical mapping of fingertips.
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BibTeX
@misc{moya2026bayesian,
title = {{Bayesian Semiparametric Hidden Markov Random Partition Fields for Factor Collapse on Graphs: A Study of Cortical Mapping of Fingertips}},
author = {Blake Moya and Kevin Sitek and Arkaprava Roy and Bharath Chandrasekaran and Abhra Sarkar},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.13319},
eprint = {2609.13319},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.13319},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Bayesian Semiparametric Hidden Markov Random Partition Fields for Factor Collapse on Graphs: A Study of Cortical Mapping of Fingertips
AU - Blake Moya
AU - Kevin Sitek
AU - Arkaprava Roy
AU - Bharath Chandrasekaran
AU - Abhra Sarkar
PY - 2026
DA - 2026/09/10
PB - arXiv
DO - 10.48550/arxiv.2609.13319
UR - https://arxiv.org/abs/2609.13319
AB - Understanding how the brain's cortical regions respond to stimuli like fingertip tapping is a significant challenge in neuroscience, especially with high-resolution functional magnetic resonance imaging data. To address this, we propose a new statistical method for evaluating how a factor's influence locally varies across a complex graph, such as the brain's cortex. Our approach is designed to handle the complexities of large graph sizes and computational demands. Our method uses a novel Bayesian hidden Markov random field to partition the factor's influence into collapsed states with similar effects on the outcome at each node. This unique model promotes sparsity in the partitions and penalizes large variations across adjacent nodes. The result is a highly detailed local influence map that captures subtle changes in the patterns and magnitudes of a factor's influence across the graph. To ensure efficient analysis of large datasets, we developed a specialized Markov chain Monte Carlo algorithm. Our simulation experiments demonstrate significant improvements over existing techniques in both accuracy and scalability. Ultimately, our method provides a powerful new framework for exploring the intricate relationship between stimuli and brain activity, offering a clearer picture of the cortical mapping of fingertips.
ER -