SIMBA: Scalable Image Modeling using a Bayesian Approach, A Consistent Framework for Including Spatial Dependencies in fMRI Studies.

ABSTRACT: Bayesian spatial modeling provides a flexible framework for whole-brain fMRI analysis by explicitly incorporating spatial dependencies, overcoming the limitations of traditional massive univariate approaches that lead to information waste. In this work, we introduce SIMBA, a Scalable Image Modeling using a Bayesian Approach, for group-level fMRI analysis, which places Gaussian process (GP) priors on spatially varying functions to capture smooth and interpretable spatial association patterns across the brain volume. To address the significant computational challenges of GP inference in high-dimensional neuroimaging data, we employ a low-rank kernel approximation that enables projection into a reduced-dimensional subspace. This allows for efficient posterior computation without sacrificing s

SUBMITTER: Zhong Y 

PROVIDER: S-EPMC12754714 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature