Cerebellar gray matter alterations predict deep brain stimulation outcomes in Meige syndrome.

ABSTRACT

BACKGROUND: The physiopathologic mechanism of Meige syndrome (MS) has not been clarified, and neuroimaging studies centering on cerebellar changes in MS are scarce. Moreover, even though deep brain stimulation (DBS) of the subthalamic nucleus (STN) has been recognized as an effective surgical treatment for MS, there has been no reliable biomarker to predict its efficacy. OBJECTIVE: To characterize the volumetric alterations of gray matter (GM) in the cerebellum in MS and to identify GM measurements related to a good STN-DBS outcome. METHODS: We used voxel-based morphometry and lobule-based morphometry to compare the regional and lobular GM differences in the cerebellum between 47 MS patients and 52 normal human controls (HCs), as well as between 31 DBS responders and 10 DBS non-responders. Both volumetric analyses were achieved using the Spatially Unbiased Infratentorial Toolbox (SUIT). Further, we performed partial correlation analyses to probe the relationship between the cerebellar GM changes and clinical scores. Finally, we plotted the receiver operating characteristic (ROC) curve to select biomarkers for MS diagnosis and DBS outcomes prediction. RESULTS: Compared to HCs, MS patients had GM atrophy in lobule Crus I, lobule VI, lobule VIIb, lobule VIIIa, and lobule VIIIb. Compared to DBS responders, DBS non-responders had lower GM volume in the left lobule VIIIb. Moreover, partial correlation analyses revealed a positive relationship between the GM volume of the significant regions/lobules and the symptom improvement rate after DBS surgery. ROC analyses demonstrated that the GM volume of the significant cluster in the left lobule VIIIb could not only distinguish MS patients from HCs but also predict the outcomes of STN-DBS surgery with high accuracy. CONCLUSION: MS patients display bilateral GM shrinkage in the cerebellum relative to HCs. Regional GM volume of the left lobule VIIIb can be a reliable biomarker for MS diagnosis and DBS outcomes prediction.