Purpose Parkinson disease (PD) is known as the second most common and growing neurodegenerative disorder in the world. It is widely known that the appearance of motor and non-motor symptoms causes disorders in the brain network of such patients. Methods This study evaluates the brain connectivity of PD patients' (n = 15) in comparison with healthy individuals (n = 15) using functional magnetic resonance imaging (fMRI). First, independent component analysis (ICA) was implemented on the preprocessed data to extract resting-state networks (RSNs) as functional connectivity (FC) for evaluating intra-network connectivity values. Granger causality analysis (GCA) and transfer entropy (TE) are extracted as effective connectivity (EC) methods for assessing the network and regional time courses for assessing inter and intra-network connectivity measures. Finally, F-test was used to find the discriminative regions between the groups. Results Thirty ICA maps were identified as independent components, and seven were selected as RSNs. In terms of spatial maps, we found that patients had altered connectivity between Auditory and dorsal Default Mode Network. Several networks and regions were also discriminative between the groups in inter and intra network connectivity analysis, respectively. Conclusions The results show that EC methods such as GCA and TE are promising in extracting local information of PD. The reason can be considered in terms of being directional and causal in this type of connectivity, which is in relation to the concept of neuronal substrates. Also, TE might be more accurate than GCA, since TE is nonlinear which is consistent with the nature of the data. To the best of our knowledge, there was not any research that employed EC and ICA on Parkinson's resting-state fMRI data, and analyzed it using ICA time courses as well as regional time series.

Purpose Parkinson's disease (PD) is widely known as a neurodegenerative disorder of the nervous system for which there is no cure. Accordingly, researchers can utilize neuroimaging techniques like functional magnetic resonance imaging (fMRI) to investigate neural activities in the brain non-invasively. Most previous research works construct brain graphs based on linear correlations for functional connectivity (FC) analysis. In this study, we compared linear and nonlinear functional connectivity methods. Methods The objective of our study is to implement 5 functional connectivity methods on 14 resting-state fMRI networks (RSNs) based on the FIND RSN template that is divided into 90 regions. Kernel Mutual information (KMI), a unique nonlinear connectivity approach based on Mutual information (MI), is also employed. Consequently, the validity of the methods was assessed using local graph measures and statistical analysis. Results The results show that nonlinear methods outperformed linear ones using the outcome of graph theory. In the non-linear functional connectivity methods, all seven graph measures showed a significant difference between two groups: healthy control (HC) and Parkinson's disease (PD), but only one graph measure showed a significant difference in the linear functional connectivity methods. Furthermore, while K-Corenness centrality has been utilized in previous studies to diagnose and assess various neurodegenerative illnesses, it is employed for the first time in our study to diagnose Parkinson's patients using fMRI data. Conclusions According to the findings of this study, nonlinear functional connectivity should be investigated in Parkinson's disease and other neurodegenerative diseases.