Anatomically informed Bayesian spatial priors for fMRI analysis
Methodology
2019-10-21 v1 Image and Video Processing
Neurons and Cognition
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.
Cite
@article{arxiv.1910.08415,
title = {Anatomically informed Bayesian spatial priors for fMRI analysis},
author = {David Abramian and Per Sidén and Hans Knutsson and Mattias Villani and Anders Eklund},
journal= {arXiv preprint arXiv:1910.08415},
year = {2019}
}