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In this work, we propose a modeling procedure for fMRI data analysis using aBayesian Matrix-Variate Dynamic Linear Model (MVDLM). With this type of model,less complex than the more traditional temporal-spatial models, we are able totake into account the temporal and -- at least locally -- the spatialstructures that are usually present in this type of data. Despite employing avoxel-wise approach, every voxel in the brain is jointly modeled with itsnearest neighbors, which are defined through a euclidian metric. MVDLM's havebeen widely used in applications where the interest lies in to performpredictions and/or analysis of covariance structures among time series. In thiscontext, our interest is rather to assess the dynamic effects which are relatedto voxel activation. In order to do so, we develop three algorithms to simulateonline-trajectories related to the state parameter and with those curves orsimulated trajectories we compute a Monte Carlo evidence for voxel activation.Through two practical examples and two different types of assessments, we showthat our method can be viewed for the practitioners as a reliable tool for fMRIdata analysis. Despite all the examples and analysis are illustrated just for asingle subject analysis, we also describe how more general group analysis canbe implemented.

Assessing Dynamic Effects on a Bayesian Matrix-Variate Dynamic Linear Model: an Application to fMRI Data Analysis