ArticleCNS neuroscience & therapeutics2025
Cerebellar and Brainstem White Matter Geometric Alterations in Multiple System Atrophy: A DFA-Based Biomarker for Disease Staging.
Article in CNS neuroscience & therapeutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Anti-Sez6L2 antibody-associated autoimmune cerebellar ataxia: a rare case with implications of rituximab therapy.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Article
- Cerebellar and Brainstem White Matter Geometric Alterations in Multiple System Atrophy: A DFA-Based Biomarker for Disease Staging.CNS neuroscience & therapeutics · 2025Article
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Abstract
aimsTo characterize white matter geometric pathology in cerebellar subtype of multiple system atrophy (MSA-C) using director field analysis (DFA) and identify stage-specific biomarkers.
methodsWe analyzed single-shell diffusion MRI (b = 1000) in 31 MSA-C patients (15 early-, 16 late-stage) and 33 controls. DFA quantified axonal geometry (splay/bend/twist), complemented by fixel-based analysis (FBA) and brainstem volumetry. Group comparisons used threshold free cluster enhancement (TFCE) (p < 0.05 FWE-corrected). DFA-altered regions were correlated with clinical scores. AutoGluon evaluated classification performance using different feature sets.
resultsMSA-C exhibited distinct geometric degeneration patterns: cerebellar pathways showed reduced splay, bend, and twist (reflecting Wallerian degeneration), whereas brainstem tracts demonstrated dissociated geometry (increased splay/bend but decreased twist). Brainstem twist reduction strongly differentiated early- and late-stage MSA-C (AUC = 0.95). Clinically, middle cerebellar peduncle bend correlated with motor progression (UMSARS-II: r = 0.48), while cerebellar splay reduction predicted ataxia severity (SARA: r = -0.43).
conclusionDFA captures circuit-specific white matter pathology in MSA-C, with brainstem twist emerging as a novel biomarker associated with disease stage. The integration of geometric metrics with automated machine learning provides a robust framework for early diagnosis and disease staging, highlighting distinct neurodegenerative mechanisms in cerebellar versus brainstem pathways.
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