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Cerebellar and Brainstem White Matter Geometric Alterations in Multiple System Atrophy: A DFA-Based Biomarker for Disease Staging

  • Hua Zhu
  • , Shuxiang Zhu
  • , Meixin Zhao
  • , Ziyan Zhu
  • , Yuxuan Shao
  • , Xinxi Lu
  • , Tao Liu
  • , Haogang Zhu
  • , Ni Shu
  • , Hua Lin*
  • , Jian Cheng*
  • *Corresponding author for this work
  • Beihang University
  • Capital Medical University
  • Peking University
  • Beijing Normal University

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article numbere70623
JournalCNS Neuroscience and Therapeutics
Volume31
Issue number12
DOIs
StatePublished - Dec 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • biomarker
  • brainstem
  • director field analysis
  • geometrics
  • multiple system atrophy
  • white matter

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