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Abstract Objectives Spinocerebellar ataxia type 3 is a disorder within the brain network. However, the relationship between the brain network and disease severity is still unclear. This study aims to investigate changes in the white matter (WM) structural motor network, both in preclinical and ataxic stages, and its relationship with disease severity. Methods For this study, 20 ataxic, 20 preclinical SCA3 patients, and 20 healthy controls were recruited and received MRI scans. Disease severity was quantified using the SARA and ICARS scores. The WM motor structural network was created using probabilistic fiber tracking and was analyzed using graph theory and network‐based statistics at global, nodal, and edge levels. In addition, the correlations between network topological measures and disease duration or clinical scores were analyzed. Results Preclinical patients showed increasing assortativity of the motor network, altered subnetwork including 12 edges of 11 nodes, and 5 brain regions presenting reduced nodal strength. In ataxic patients assortativity of the motor network also increased, but global efficiency, global strength, and transitivity decreased. Ataxic patients showed a wider altered subnetwork and a higher number of reduced nodal strengths. A negative correlation between the transitivity of the motor network and SARA and ICARS scores was observed in ataxic patients. Interpretation Changes to the WM motor network in SCA3 start before ataxia onset, and WM motor network involvement increases with disease progression. Global network topological measures of the WM motor network appear to be a promising image biomarker for disease severity. This study provides new insights into the pathophysiology of disease in SCA3/MJD.

Abstract Objective Spinocerebellar ataxia type 3 (SCA3) is one of the most common hereditary neurodegenerative diseases, with balance instability as main symptom. Balance quantification is crucial for evaluating the efficacy of therapeutic interventions. However, balance evaluation in SCA3 is often subject to bias. Here, we aimed to quantitatively evaluate postural instability and investigate the relationship between postural instability and clinical characteristics in SCA3 patients. Methods Sixty‐two SCA3 patients and 62 normal controls were recruited, and their postural balance was measured using a posturographic platform. Principal component analysis was performed as data reduction to identify postural instability factors. Multivariable linear regression was used to investigate potential risk factors for postural instability and to explore whether postural instability predicts the severity and progression of ataxia in SCA3 patients. Results We found SCA3 patients experience postural instability characterized by significant impairment in static and dynamic stability. The condition without visual feedback was the most sensitive measure in differentiating SCA3 from controls. Regression analyses revealed that ataxia severity predicted both static (P = 0.014) and dynamic stability (P = 0.001). Likewise, along with expanded CAG repeats (P