Probabilistic Boolean Network Analysis of Brain Connectivity in Parkinson's Disease

Abstract

Recent research has suggested that disrupted interactions between certain brain regions may contribute to the symptoms of neurological diseases such as Parkinson's 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 networks (PBNs) for modeling brain connectivity due to its solid stochastic properties, computational simplicity, robustness to uncertainty, and ability to deal with small-size data, typical for fMRI data sets. Applying the proposed PBN framework to real fMRI data recorded from PD and control subjects, the PBN method detected statistically significant differing interactions between task-related regions of interest (ROIs) across groups. Comparing the PBN results in PD subjects before and after they had taken L-dopa medication, the principal treatment for PD, suggests that a key mechanism of action of this medication is relative normalization of disrupted brain connectivity.