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Updated: Dec 27, 2025
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Global and regional connectivity analysis of resting-state function MRI brain images using graph theory in
Rutvi Prajapati1, Isaac Arnold Emerson1
1Bioinformatics Programming Laboratory, Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Objectives:
Parkinson's disease (PD) is the second most common neurodegenerative disorder which resists around 10 million people worldwide. It develops when nerve cells in a region of the brain that regulates movement become damaged; the symptoms usually begin gradually and become critical over time. In this study, we proposed to investigate the topological properties of functional brain networks within healthy controls (HCs) and PD patients. Also, we evaluated the gender difference among PD patients through graph theoretical approach.
Materials And Methods:
The rs-fMRI (resting-state functional magnetic resonance imaging) data of fifty-one PD patients and healthy controls was applied to generate the brain functional connectome. The functional whole-brain connectome was constructed by thresholding partial correlation matrices of 160 regions from Dosenbach brain atlas. From the graph theory approach, global and nodal metrics were analysed, and we observed considerable changes in PD patients in comparison with healthy controls.
Results:
Findings suggest that there is a significant difference in the topological characteristics of PD patients, and this was found to be evident in the default mode network (DMN) and occipital regions.
Conclusion:
This study provides essential insights from network changes to the clinically relevant information for the PD progression.
