BayesDLMfMRI: Bayesian Matrix-Variate Dynamic Linear Models for Task-based fRMI Modeling in R

TL;DR

BayesDLMfMRI is an R package that applies Bayesian matrix-variate dynamic linear models to analyze task-based fMRI data, enabling efficient individual and group-level brain activation detection with parallel computation capabilities.

Contribution

This work introduces a novel R package that implements Bayesian matrix-variate dynamic linear models for fMRI analysis at both individual and group levels.

Findings

Provides functions for inference on brain activation at individual and group levels.

Supports parallel computation for whole-brain and voxel-specific analysis.

Facilitates Bayesian analysis of task-based fMRI data.

Abstract

This article introduces an R package to perform statistical analysis for task-based fMRI data at both individual and group levels. The analysis to detect brain activation at the individual level is based on modeling the fMRI signal using Matrix-Variate Dynamic Linear Models (MDLM). Therefore, the analysis for the group stage is based on posterior distributions of the state parameter obtained from the modeling at the individual level. In this way, this package offers several R functions with different algorithms to perform inference on the state parameter to assess brain activation for both individual and group stages. Those functions allow for parallel computation when the analysis is performed for the entire brain as well as analysis at specific voxels when it is required.