Bayesian inference for group-level cortical surface image-on-scalar regression with Gaussian process priors.

ABSTRACT: In regression-based analyses of group-level neuroimage data, researchers typically fit a series of marginal general linear models to image outcomes at each spatially referenced pixel. Spatial regularization of effects of interest is usually induced indirectly by applying spatial smoothing to the data during preprocessing. While this procedure often works well, the resulting inference can be poorly calibrated. Spatial modeling of effects of interest leads to more powerful analyses; however, the number of locations in a typical neuroimage can preclude standard computing methods in this setting. Here, we contribute a Bayesian spatial regression model for group-level neuroimaging analyses. We induce regularization of spatially varying regression coefficient functions through Gaussian process p

SUBMITTER: Whiteman AS 

PROVIDER: S-EPMC11518852 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature