Impaired brain network architecture in newly diagnosed Parkinson's disease based on graph theoretical analysis.

BACKGROUND: Resting state functional magnetic resonance imaging (rs-fMRI) has been applied to investigate topographic structure in Parkinson's disease (PD). Alteration of topographic architecture has been inconsistent in PD AIM: To investigate the network profile of PD using graph theoretical analysis.

METHOD: Twenty six newly diagnosed PD and 19 age- and gender- matched healthy controls (HC) were included in our analysis. Small-world profile and topographic profiles (nodal degree, global efficiency, local efficiency, cluster coefficient, shortest path length, betweenness centrality) were measured and compared between groups, with age and gender as covariates. We also performed correlation analysis between topographic features with motor severity measured by UPDRS III.

RESULTS: Small-world property was present in PD. Nodal degree, global efficiency, local efficiency and characteristic path length consistently revealed disruptive sensorimotor network, and visual network to a less degree in PD. By contrast, default mode network (DMN) and cerebellum in PD showed higher nodal degree, global efficiency and local efficiency, and lower characteristic path length. Global and local efficiency in the midbrain was higher in PD excluding substantia nigra. PD group also exhibited lower cluster coefficient in the subcortical motor network (thalamus and caudate nucleus). No significant correlation was found between topographic properties and motor severity.

CONCLUSION: PD exhibited disruptive sensorimotor and visual networks in early disease stage. DMN, a certain areas in the cerebellum and midbrain may compensate for disruptive sensorimotor and visual network in PD. Disruptive network architecture may be an early alteration of PD pathophysiology but may not serve as a valid biomarker yet.