Effect Assessment of Parkinson Disease on Default Mode Network of the Brain with ICA and SCA Methods in Resting State FMRI Data

Parkinson’s disease (PD) is a progressive neurological disorder characterized by tremor, rigidity, and slowness of movements. Determining changes of spontaneous activity and connectivity of the brain is a critical step towards treatment of PD patients. Resting State functional Magnetic Resonance Imaging (RS-fMRI) is a non-invasive method that we use in this work to investigate changes of default mode network of the brain in PD. To this end, we apply two methods, Seed Correlation Analysis (SCA) and probabilistic independent Component Analysis (PICA). The results of advanced statistical group analysis on SCA values show that there is negative significant correlation between motor cortex and cerebellum in healthy, while this connection in PD is positive and not significant. This result implies the disturbance of equilibrium function of the brain in resting. Moreover, in both groups, there is significant positive correlation between areas located in basal ganglia. The results show that in healthy, there is not significant correlation between motor areas and basal ganglia, while in PD there are significant negative correlations between motor cortex and cerebellum with areas located in basal ganglia. The comparison of five ICs extracted by PICA showed lower DMN activation in basal ganglia. Finally, The result of our study show that the functional correlations between ROIs are more affected in PD than pattern maps of activity by PICA.