ArticleNeurology2026
Brain Network Patterns in Patients With Multiple System Atrophy: Spatial Independent Component Analysis Using FDG-PET Data.
Article in Neurology, 2026. 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.
- Breaking barriers and fighting inequality in Parkinson's disease care: Personal reflections of movement disorders clinicians.Journal of Parkinson's disease · 2026Article
- Regional dopaminergic dysfunction patterns discriminate Parkinson's disease from multiple system atrophy parkinsonian subtype.Clinical parkinsonism & related disorders · 2026Article
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Authors and funding
15 authors.
Funding
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Abstract
BACKGROUND AND
objectivesMultiple system atrophy (MSA) is a progressive neurodegenerative disorder presenting significant clinical heterogeneity, posing challenges in diagnosis and treatment. This study uses spatial independent component analysis (ICA) of
methodsThis cross-sectional study included patients with MSA and healthy controls (HCs) from the Second Affiliated Hospital, Zhejiang University School of Medicine. MSA diagnosis was based on the 2022 Movement Disorder Society MSA criteria. All participants underwent FDG-PET imaging. Clinical assessments and dopamine transporter (DAT) PET were performed in patients with MSA. Spatial ICA was applied to identify metabolic covariance networks. Moderation analysis and structural equation modeling (SEM) were used to explore underlying pathway mechanisms.
resultsNinety-five patients with MSA and 102 HCs were included (mean age: 62.25 vs 61.84 years; 51% vs 43% female). Five MSA-related ICs were identified: cerebellar network, salience network, compensatory network, default mode network (DMN), and basal ganglia network (all DISCUSSION: This study demonstrated that the metabolic abnormalities in MSA can be decomposed into 5 large-scale brain networks, providing a comprehensive understanding of the disease's heterogeneous mechanisms.
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