Effects of non-linear correlation measures on brain functional connectivity in Parkinson's disease

Parkinson's disease (PD) is one of the most prevalent and growing disorders. The most reason for this disease is the abnormalities in brain functional organization of PD patients. Functional magnetic resonance imaging in the resting state (rs-fMRI) is a useful technique to assess brain dysfunctions in patients. The objective of our research is to generate the closest model of complex brain network by different approaches. Hence we constructed the brain graphs employing one linear and three non-linear correlation metrics in order to investigate complicated relations among signals. The local and global metrics of the produced correlation matrices were extracted utilizing graph theory. Evaluating centralization, a global metric, exhibited a decrease in PD patients compared with healthy controls. In addition, we investigated significant changes of nodal degree in patients. The achieved results on graph measures implied alterations of brain functional connectivity. To conclude, we disclosed new findings in brain functional networks of PD patients by non-linear correlation measures.