CNS neuroscience & therapeutics · 2025
Cerebellar and Brainstem White Matter Geometric Alterations in Multiple System Atrophy: A DFA-Based Biomarker for Disease Staging.
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Zhu Hua, Zhu Shuxiang, Zhao Meixin, Zhu Ziyan, Shao Yuxuan, Lu Xinxi, Liu Tao, Zhu Haogang, Shu Ni, Lin Hua, Cheng Jian
Аннотация
AIMS: To characterize white matter geometric pathology in cerebellar subtype of multiple system atrophy (MSA-C) using director field analysis (DFA) and identify stage-specific biomarkers.
METHODS: We 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.
RESULTS: MSA-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).
CONCLUSION: DFA 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.
METHODS: We 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.
RESULTS: MSA-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).
CONCLUSION: DFA 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.