Disorganization of Equilibrium Directional Interactions in the Brain Motor Network of Parkinson’s Disease: New Insight of Resting State Analysis using Granger Causality and Graphical approach

Mahdieh Ghasemi, Ali Mahloojifar

DOI:

Abstract


Parkinson’s disease (PD) is a progressive neurological disorder characterized by tremor, rigidity, and slowness of movements. Specific changes associated with various pathological attacks in Parkinson’s Disease can be indicated in causality interactions of the brain Network from resting state fMRI data. In this paper, we aimed to reveal the network architecture of the directed influence brain network using multivariate Granger causality analysis and graph theory in patients with PD compared with control group. Functional magnetic resonance imaging (rs-fMRI) at rest from 10 PD patients and 10 controls were analyzed. Topological properties of the networks showed that, flow of information in PD is smaller than healthy. We found that there is a balanced local network in healthy include positive pair-wise cross connections between Caudate and Cerebellum and, reciprocal connections between motor cortex and caudate in left and right hemispheres.  The results showed that this local network is disrupted in PD due to disturbance of the interactions in the motor networks. These findings suggested alteration of the functional management of the brain in the resting state, that affect the information organization from and to the other brain regions related to both primary dysfunctions and higher-level cognition impairments in PD. Furthermore, we showed that ROIs with high degree values could detect as betweenness centrality nodes. Our results demonstrate that properties of small-world connectivity could also recognize and quantify the characteristics of directed influence brain networks in Parkinson’s disease.

Keywords


Functional Magnetic Resonance Imaging (fMRI); Resting State; Multivariate Granger Causality analysis(MGCA); Parkinson’s Disease (PD); graph theory

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