Pattern of cerebellar grey matter loss associated with ataxia severity in spinocerebellar ataxias type 3: a...

Spinocerebellar ataxias type 3 (SCA3) patients are clinically characterized by progressive cerebellar ataxia combined with degeneration of the cerebellum. Previous neuroimaging studies have indicated ataxia severity associated with cerebellar atrophy using univariate methods. However, whether cerebellar atrophy patterns can be used to quantitatively predict ataxia severity in SCA3 patients at the individual level remains largely unexplored. In this study, a group of 66 SCA3 patients and 58 healthy controls were included. Disease duration and ataxia assessment, including the Scale for the Assessment and Rating of Ataxia (SARA) and the International Cooperative Ataxia Rating Scale (ICARS), were collected for SCA3 patients. The high-resolution T1-weighted MRI was obtained, and cerebellar grey matter (GM) was extracted using a spatially unbiased infratentorial template toolbox for all participants. We investigated the association between the pattern of cerebellar grey matter (GM) loss and ataxia assessment in SCA3 by using a multivariate machine learning technique. We found that the application of RVR allowed quantitative prediction of both SARA scores (leave-one-subject-out cross-validation: correlation = 0.56, p-value = 0.001; mean squared error (MSE) = 20.51, p-value = 0.001; ten-fold cross-validation: correlation = 0.52, p-value = 0.001; MSE = 21.00, p-value = 0.001) and ICARS score (leave-one-subject-out cross-validation: correlation = 0.59, p-value = 0.001; MSE = 139.69, p-value = 0.001; ten-fold cross-validation: correlation = 0.57, p-value = 0.001; MSE = 145.371, p-value = 0.001) with statistically significant accuracy. These results provide proof-of-concept that ataxia severity in SCA3 patients can be predicted by the alteration pattern of cerebellar GM using multi-voxel pattern analysis.