Characterization of Cerebellar Atrophy and Resting State Functional Connectivity Patterns in Sporadic Adult-Onset Ataxia of Unknown Etiology (SAOA).

Sporadic adult-onset ataxia of unknown etiology (SAOA) is a non-genetic neurodegenerative disorder of the cerebellum of unknown cause which manifests with progressive ataxia without severe autonomic failure. Although SAOA is associated with cerebellar degeneration, little is known about the specific cerebellar atrophy pattern in SAOA. Thirty-seven SAOA patients and 49 healthy controls (HCs) were included at two centers. We investigated the structural and functional characteristics of SAOA brains using voxel-based morphometry (VBM) and resting-state functional imaging (rs-fMRI). In order to examine the functional consequence of structural cerebellar alterations, the amplitude of low-frequency fluctuation (ALFF) and degree centrality (DC) were analyzed, and then assessed their relation with disease severity, disease duration, and age of onset within these regions. Group differences were investigated using two-sample t tests, controlling for age, gender, site, and the total intracranial volume. The VBM analysis revealed a significant, mostly bilateral reduction of local gray matter (GM) volume in lobules I-V, V, VI, IX, X, and vermis VIII a/b in SAOA patients, compared with HCs. The GM volume loss in these regions was significantly associated with disease severity, disease duration, and age of onset. The disease-related atrophy regions did not show any functional alternations compared with HCs but were functionally characterized by high ALFF and poor DC compared with intact cerebellar regions. Our data revealed volume reduction in SAOA in cerebellar regions that are known to be involved in motor and somatosensory processing, corresponding with the clinical phenotype of SAOA. Our data suggest that the atrophy occurs in those cerebellar regions which are characterized by high ALFF and poor DC. Further studies have to show if these findings are specific for SAOA, and if they can be used to predict disease progression.