Anatomically informed Bayesian spatial priors for fMRI analysis

TL;DR

This paper introduces two anatomically informed Bayesian spatial models for fMRI analysis that incorporate local smoothing based on anatomical structures, leading to more accurate posterior probability maps aligned with brain anatomy.

Contribution

The paper presents novel Bayesian spatial priors that integrate anatomical information via tensor fields, improving fMRI data analysis accuracy.

Findings

Posterior maps follow anatomical structures more closely.

Models outperform traditional isotropic smoothing methods.

Enhanced boundary preservation in fMRI analysis.

Abstract

Existing Bayesian spatial priors for functional magnetic resonance imaging (fMRI) data correspond to stationary isotropic smoothing filters that may oversmooth at anatomical boundaries. We propose two anatomically informed Bayesian spatial models for fMRI data with local smoothing in each voxel based on a tensor field estimated from a T1-weighted anatomical image. We show that our anatomically informed Bayesian spatial models results in posterior probability maps that follow the anatomical structure.