Probabilistic Boolean Network for inferring brain connectivity using FMRI data

Recent research has suggested disrupted interactions between brain regions may contribute to some of the symptoms of Parkinson disease (PD). It is therefore important to develop models for inferring brain functional connectivity from non-invasive imaging data, such as functional magnetic resonance imaging (fMRI). In this paper, we propose applying probabilistic Boolean network (PBN) for modeling brain connectivity due to its solid stochastic properties, computational simplicity, robustness to uncertainty, and capability to deal with small-size data, typical for fMRI data sets. Applying the proposed PBN framework to real fMRI data recorded from PD subjects, we noticed that the PBN method detected statistically significant brain connectivity between region-of-interest (ROIs) in PD and normal subjects. In addition, the PBN results suggest a mechanism of the effectiveness of L-dopa, the principal treatment for PD.