Disrupted topological organization of resting-state functional brain networks in Parkinson’s disease patients with glucocerebrosidase gene mutations

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Abstract
Purpose The mutations of the glucocerebrosidase (GBA) gene are the greatest genetic risk factor for Parkinson’s disease (PD). The mechanism underlying the association between GBA mutations and PD has not been fully elucidated. Methods Using resting-state functional magnetic resonance imaging and graph theory analysis to investigate the disrupted topological organization in PD patients with GBA mutation (GBA-PD). Eleven GBA-PD patients, 11 noncarriers with PD, and 18 healthy controls (HCs) with a similar age and sex distribution were recruited. Individual whole-brain functional connectome was constructed, and the global and nodal topological disruptions were calculated among groups. Partial correlation analyses between the clinical features of patients with PD and topological alterations were performed. Results The GBA-PD group showed prominently decreased characteristic path length (Lp) and increased global efficiency (Eg) compared to HCs at the global level; a significantly increased nodal betweenness centrality in the medial prefrontal cortex (mPFC) and precuneus within the default mode network, and precentral gyrus within the sensorimotor network, while a significantly decreased betweenness centrality in nodes within the cingulo-opercular network compared to the noncarrier group at the regional level. The altered nodal betweenness centrality of mPFC was positively correlated with fatigue severity scale scores in all patients with PD. Conclusion The preliminary pilot study found that GBA-PD patients had a higher functional integration at the global level. The nodal result of the mPFC is congruent with the potential fatigue pathology in PD and is suggestive of a profound effect of GBA mutations on the clinical fatigue in patients with PD.
Funding Information
  • National Key Research and Development Program of China (2021YFC2501203)
  • Sichuan Science and Technology Program (2022ZDZX0023)

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