Altered Whole-Brain and Network-Based Functional Connectivity in Parkinson's Disease

Area of Science:

• Neuroscience
• Medical Imaging
• Graph Theory

Background:

• Resting-state functional magnetic resonance imaging (fMRI) assesses neural connectivity changes.
• Eigenvector centrality mapping is a novel graph analysis approach for identifying brain network hubs.
• This method can detect localized differences in functional connectivity between patient groups.

Purpose of the Study:

• To examine whole-brain and network-level functional connectivity architecture changes in Parkinson's disease (PD).
• To investigate the utility of eigenvector centrality mapping in understanding PD-related network alterations.
• To correlate functional connectivity findings with clinical data in PD patients.

Main Methods:

• Whole-brain resting-state functional architecture was analyzed using eigenvector centrality mapping.
• Functional connectivity was assessed within eight known resting-state networks.
• Cross-sectional analysis compared PD patients (n=107) with controls (n=58), correlating findings with clinical measures.

Main Results:

• Eigenvector centrality mapping showed increased prominence in frontoparietal regions and decreased prominence in frontal/occipital areas in PD patients.
• Increased functional connectivity was observed within sensorimotor and visual networks in PD patients.
• Group differences in functional connectivity overlapped in posterior brain regions, including the posterior cingulate cortex and precuneus.

Conclusions:

• Altered functional brain connectivity architecture offers a new perspective on Parkinson's disease pathology.
• Involvement of specific highly connected brain regions (hubs) influences overall functional network architecture in PD.
• No significant correlations were found between clinico-functional imaging findings and clinical data.