Disruption of the Brain Resting State Networks in Parkinsonism

In the recent years, neuroimaging research on functional Magnetic Resonance Imaging (fMRI) is used in many pathological and mental conditions. The analysis of alterations in the resting state networks (RSN) is an important method for the scrupulous understanding of the function and connectivity changes of the disease in order to provide new diagnostic and therapeutic approaches. In this paper, we studied the resting-state functional MRI (Rs-fMRI) data in Parkinson’s disease (PD) to explore the complex disruption in the RSNs and the functional interactions between them. A total Rs-fMRI data of 10 Parkinsonism and 10 healthy people in the 3T-MRI system were considered. Probabilistic independent component analysis (PICA) was used to extract network components. RSNs were identified using spatial correlation with a rest reference template network. Dual regression and randomize technique calculated individual differences between the groups. Group component maps resulted in some main clusters of RSN that significantly overlapped with the reference network, such as the visual cortex, salience network, and supplementary motor area. Individual differences between RSN maps identified temporal, salience and cingulate networks as the main clusters. Most of the previous studies investigated the functional connectivity alterations in PD by seed-based analysis. Here, we employed the data-driven approach based on group PICA to extract and evaluate RSN changes in all related neural networks. Our finding indicates that changes of the functional architecture of the RSNs are associated with PD.