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Surface-Based Neuroimaging Pattern of Multiple System Atrophy.

Zhan Wang1, Jiajie Mo2, Jianguo Zhang2

  • 1Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (Z.W., T.F.); China National Clinical Research Center for Neurological Disease, NCRC-ND, Beijing, China (Z.W., T.F.).

Academic Radiology
|July 26, 2023
PubMed
Summary

Neuroimaging reveals distinct patterns in multiple system atrophy (MSA), with reduced putaminal volume in MSA-P and pontine volume in MSA-C. These findings aid in personalized diagnosis of MSA.

Keywords:
Diagnostic effectMachine learningMultiple system atrophyNeuroimaging patternSurface-based analysis

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Area of Science:

  • Neurology
  • Radiology
  • Neuroimaging

Background:

  • Multiple system atrophy (MSA) presents diagnostic challenges due to overlapping symptoms with other neurological disorders.
  • Neuroimaging is crucial for understanding MSA pathophysiology and improving diagnostic accuracy.

Purpose of the Study:

  • To identify optimal neuroimaging features for diagnosing multiple system atrophy (MSA).
  • To explore neuroimaging patterns, map subcortical alterations, and develop a diagnostic model for MSA.

Main Methods:

  • Retrospective analysis of MRI and susceptibility-weighted imaging in patients with MSA (parkinsonian [MSA-P] and cerebellar [MSA-C] types), Parkinson's disease, and controls.
  • Quantification of neuroimaging features and mapping of subcortical morphological alterations.

Main Results:

  • Significantly decreased normalized putaminal volume in MSA-P and pontine volume in MSA-C compared to controls.
  • A threshold-based clinical prediction model achieved a Youden index of 0.871-0.928 for MSA.
  • Neuroimaging patterns in MSA were localized to the lateral putamen, with a prediction model achieving 83.9%-100% classification accuracy.

Conclusions:

  • Quantitative neuroimaging features and surface-based morphologic anomalies serve as biomarkers for MSA.
  • These findings offer new approaches for personalized clinical diagnosis of multiple system atrophy.