Random Walker Based Estimation and Spatial Analysis of Probabilistic fMRI Activation Maps

Conventional univariate fMRI analysis typically examines each voxel in isolation despite the fact that voxel interactions may be another indication of brain activation. Here, we propose using a graph-theoretical algorithm called “Random Walker” (RW), to estimate probabilistic activation maps that encompass both activation effects and functional connectivity. The RW algorithm has the distinct advantage of providing a unique, globallyoptimal closed-form solution for computing the posterior probabilities. To explore the implications of incorporating functional connectivity, we applied our previously proposed invariant spatial features to the RW-based probabilistic activation maps, which detected activation changes in multiple brain regions that conform well to prior neuroscience knowledge. In contrast, similar analysis on traditional activation statistics maps, which ignores functional connectivity, resulted in reduced detection, thus demonstrating benefits for integrating additional functional attributes into the activation detection procedures.