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.

脊髓小脑共济失调 3 型 (SCA3) 患者的临床特征是进行性小脑共济失调合并小脑退化。以前的神经影像学研究表明，使用单变量方法与小脑萎缩相关的共济失调严重程度。然而，小脑萎缩模式是否可用于在个体水平上定量预测 SCA3 患者的共济失调严重程度仍然很大程度上尚未探索。在这项研究中，包括一组 66 名 SCA3 患者和 58 名健康对照。为 SCA3 患者收集疾病持续时间和共济失调评估，包括共济失调评估和评定量表 (SARA) 和国际合作共济失调评定量表 (ICARS)。获得高分辨率 T1 加权 MRI，使用空间无偏幕下模板工具箱为所有参与者提取小脑灰质 (GM)。我们使用多元机器学习技术研究了 SCA3 中小脑灰质 (GM) 损失模式与共济失调评估之间的关联。我们发现 RVR 的应用允许对两个 SARA 分数进行定量预测（留一主题交叉验证：相关性 = 0.56，p值 = 0.001；均方误差 (MSE) = 20.51，p值 = 0.001；十倍交叉验证：相关性 = 0.52，p值 = 0.001；MSE = 21.00，p值 = 0.001）和 ICARS 评分（留一受试者交叉验证：相关性 = 0.59，p值 = 0.001；MSE = 139.69，p值 = 0.001；十倍交叉验证验证：相关性 = 0.57，p值 = 0.001；MSE = 145.371，p值 = 0.001）具有统计显着的准确性。这些结果提供了概念验证，即 SCA3 患者的共济失调严重程度可以通过使用多体素模式分析的小脑 GM 的改变模式来预测。