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This longitudinal study tracked brain functional connectivity changes in 16 individuals with spinocerebellar ataxia type 7 (SCA7) over 24 months using resting-state functional MRI. Researchers found progressive reorganization of brain networks, particularly affecting visual and cerebellar systems, with connectivity abnormalities correlating with cognitive decline and disease severity. Machine learning models using these connectivity patterns achieved 96.4% accuracy in distinguishing SCA7 patients from healthy controls.
Why it matters
These findings suggest that resting-state functional connectivity could serve as an objective biomarker for tracking SCA7 progression and potentially evaluating treatment responses in this rare neurodegenerative disease. The strong correlations between brain connectivity changes and clinical measures may enable earlier detection and monitoring of disease advancement.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Spinocerebellar ataxia type 7 (SCA7) is a rare neurodegenerative disorder characterized by progressive cerebellar ataxia and visual impairment. We investigated longitudinal changes in resting-state functional connectivity and their clinical associations. Resting-state functional MRI was acquired from 16 individuals with SCA7 and 16 age- and sex-matched healthy controls across three visits over 24 months. Network-to-network functional connectivity was quantified, and machine-learning models were trained using functional connectivity features. SCA7 showed lower MoCA (p = 0.045) and MMSE (p = 0.025) scores and progressive worsening of ataxia (SARA, p < 0.001). Significant Group x Visit interactions were observed for Visual-Default Mode (p = 0.012) and Somatomotor-Cerebellar Dorsal Attention connectivity (p = 0.038). Functional connectivity abnormalities involved cortical, cortico-cerebellar, and cerebellar networks that became more widespread at the final follow-up assessment, with the visual network emerging as the most consistently affected system across analyses. Functional connectivity abnormalities were associated with cognitive performance (MMSE: r = -0.62, p = 0.01) and disease severity (SARA: r = 0.589, p = 0.016). Functional connectivity features accurately classified SCA7 and healthy controls (accuracy = 96.4%, F1 = 0.969). These findings support resting-state functional connectivity as a candidate biomarker warranting further validation in larger, independent cohorts.