Surface-Based Neuroimaging Pattern of Multiple System Atrophy

Zhan Wang; Jiajie Mo; Jianguo Zhang; Tao Feng; Kai Zhang

doi:10.1016/j.acra.2023.04.014

Surface-Based Neuroimaging Pattern of Multiple System Atrophy

Acad Radiol. 2023 Dec;30(12):2999-3009. doi: 10.1016/j.acra.2023.04.014. Epub 2023 Jul 24.

Authors

Zhan Wang¹, Jiajie Mo², Jianguo Zhang², Tao Feng¹, Kai Zhang³

Affiliations

¹ Center 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.).
² Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (J.M., J.Z., K.Z.); Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China (J.M., J.Z., K.Z.); Beijing Key Laboratory of Neurostimulation, Beijing, China (J.M., J.Z., K.Z.).
³ Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (J.M., J.Z., K.Z.); Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China (J.M., J.Z., K.Z.); Beijing Key Laboratory of Neurostimulation, Beijing, China (J.M., J.Z., K.Z.). Electronic address: zhangkai62035@sina.com.

PMID: 37495425
DOI: 10.1016/j.acra.2023.04.014

Abstract

Rationale and objectives: Overlapping parkinsonism, cerebellar ataxia, and pyramidal signs render challenges in the clinical diagnosis of multiple system atrophy (MSA). The neuroimaging pattern is valuable to understand its pathophysiology and improve its diagnostic effect.

Materials and methods: We retrospectively obtained magnetic resonance imaging and susceptibility-weighted imaging in patients with MSA (including parkinsonian type [MSA-P] and cerebellar type [MSA-C]), Parkinson's disease, and normal controls. We quantified neuroimaging features to identify the optimal threshold for diagnosis. Furthermore, we explore neuroimaging patterns of MSA by mapping the subcortical morphological alterations and constructing a diagnostic model.

Results: Compared to controls, normalized putaminal volume significantly decreased in patients with MSA-P (P < .001) and normalized pontine volume significantly decreased in patients with MSA-C (P < .001). The Youden index of the threshold-based clinical prediction model was 0.871-0.928 in patients with MSA. The neuroimaging pattern in patients with MSA was jointly located in the lateral putamen, and the neuroimaging pattern prediction model achieved a classification accuracy of 83.9%-100%.

Conclusion: The quantitative neuroimaging features and surface-based morphologic anomalies represent the markers of MSA and open new avenues for personalized clinical diagnosis.

Keywords: Diagnostic effect; Machine learning; Multiple system atrophy; Neuroimaging pattern; Surface-based analysis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Diagnosis, Differential
Humans
Magnetic Resonance Imaging / methods
Models, Statistical
Multiple System Atrophy* / diagnostic imaging
Neuroimaging
Prognosis
Retrospective Studies