Analyzing Information Exchange in Parkinson's Disease via Eigenvector Centrality: A Source-Level...

Area of Science:

• Neuroscience
• Biomarkers
• Medical Imaging

Background:

• Parkinson's disease (PD) is a progressive neurodegenerative disorder with complex motor and non-motor symptoms.
• Understanding brain connectivity alterations in PD is vital for improved diagnosis and management.
• Current diagnostic and management strategies require enhancement through objective biomarkers.

Purpose of the Study:

• To investigate brain connectivity differences in Parkinson's disease (PD) patients compared to healthy controls (HCs).
• To explore the utility of Magnetoencephalography (MEG) and eigenvector centrality (EC) measures in characterizing PD-related brain alterations.
• To assess the correlation between brain connectivity measures and clinical impairment in PD.

Main Methods:

• Utilized Magnetoencephalography (MEG) to record brain activity in PD patients and HCs.
• Applied eigenvector centrality (EC) analysis across different frequency bands (alpha, beta) to quantify brain connectivity.
• Correlated EC measures with clinical impairment scores, specifically the Unified Parkinson's Disease Rating Scale Part III (UPDRS-III).

Main Results:

• Significant differences in EC were observed between PD patients and HCs in the alpha (8-12 Hz) and beta (13-30 Hz) frequency bands.
• PD patients exhibited higher EC values in the frontal lobe within the alpha frequency band compared to HCs.
• EC measures showed statistically significant correlations with UPDRS-III scores, indicating a link to clinical impairment.

Conclusions:

• MEG-derived EC measures can effectively identify alterations in brain connectivity associated with Parkinson's disease.
• These connectivity alterations, particularly in the alpha and beta bands, may serve as potential biomarkers for PD severity.
• The findings support the use of advanced neuroimaging techniques for objective assessment and management of PD.