Markov Chains for the Study of Brain Connectivity via MEG Signals in Parkinson’s Disease

Understanding brain connectivity is essential for advancing neuroscience, particularly in the study of neurodegenerative disorders such as Parkinson’s disease (PD). This work introduces a novel application of Markov Chains to magnetoencephalography (MEG) data analysis, focusing on the expected two-hop hitting time. This metric quantifies the occurrence for the information to return to a certain region via passing through a second region, offering deeper insights into functional connectivity disruptions in PD. By constructing a transition probability matrix from MEG-derived connectivity data, we analyze the dynamics of neural signal propagation across different brain regions. Statistical analyses comparing PD patients and healthy controls reveal significant alterations in the beta band, highlighting the potential of this approach for characterizing PD-related connectivity impairments. Our results demonstrate that the expected two-hop hitting time provides a complementary measure to existing connectivity analysis. This study paves the way for further investigations into the role of Markovian models in understanding pathological brain network alterations.