Genotype-specific patterns of atrophy progression are more sensitive than clinical decline in SCA1, SCA3 and SCA6

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Spinocerebellar ataxias are dominantly inherited disorders that are associated with progressive brain degeneration, mainly affecting the cerebellum and brainstem. As part of the multicentre European integrated project on spinocerebellar ataxias study, 37 patients with spinocerebellar ataxia-1, 19 with spinocerebellar ataxia-3 and seven with spinocerebellar ataxia-6 were clinically examined and underwent magnetic resonance imaging at baseline and after a 2-year follow-up. All patients were compared with age-matched and gender-matched healthy control subjects. Magnetic resonance imaging analysis included three-dimensional volumetry and observer-independent longitudinal voxel-based morphometry. Volumetry revealed loss of brainstem, cerebellar and basal ganglia volume in all genotypes. Most sensitive to change was the pontine volume in spinocerebellar ataxia-1, striatal volume in spinocerebellar ataxia-3 and caudate volume in spinocerebellar ataxia-6. Sensitivity to change, as measured by standard response mean, of the respective MRI measures was greater than that of the most sensitive clinical measure, the Scale for the Assessment and Rating of Ataxia. Longitudinal voxel-based morphometry revealed greatest grey matter loss in the cerebellum and brainstem in spinocerebellar ataxia-1, in the putamen and pallidum in spinocerebellar ataxia-3 and in the cerebellum, thalamus, putamen and pallidum in spinocerebellar ataxia-6. There was a mild correlation between CAG repeat length and volume loss of the bilateral cerebellum and the pons in spinocerebellar ataxia-1. Quantitative volumetry and voxel-based morphometry imaging demonstrated genotype-specific patterns of atrophy progression in spinocerebellar ataxias-1, 3 and 6, and they showed a high sensitivity to detect change that was superior to clinical scales. These structural magnetic resonance imaging findings have the potential to serve as surrogate markers, which might help to delineate quantifiable endpoints and non-inva