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Brain Network Patterns in Patients With Multiple System Atrophy: Spatial Independent Component Analysis Using FDG-PET

Haotian Wang¹, Bo Wang¹, Yi Liao²

¹Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.

|February 11, 2026

Summary

This study identified five key brain networks involved in Multiple System Atrophy (MSA) using FDG-PET imaging. These findings help understand the complex mechanisms underlying MSA heterogeneity.

Area of Science:

Neuroimaging
Neuroscience
Medical Imaging

Background:

Multiple System Atrophy (MSA) is a progressive neurodegenerative disorder with significant clinical heterogeneity.
This heterogeneity poses challenges in diagnosis and treatment development.
Understanding the underlying large-scale brain network mechanisms is crucial for advancing MSA care.

Purpose of the Study:

To deconstruct the heterogeneity of Multiple System Atrophy (MSA) using spatial independent component analysis (ICA) of 18F-fluorodeoxyglucose (FDG) PET.
To elucidate the large-scale brain network mechanisms contributing to MSA's diverse clinical presentations.
To investigate the relationships between identified brain networks, clinical symptoms, and neurochemical markers.

Main Methods:

Cross-sectional study involving 95 patients with MSA and 102 healthy controls (HCs).
FDG-PET imaging was performed on all participants; clinical assessments and dopamine transporter (DAT) PET were conducted in MSA patients.
Spatial ICA was applied to identify metabolic covariance networks, followed by moderation analysis and structural equation modeling (SEM).

Main Results:

Five MSA-related independent components (ICs) were identified: cerebellar, salience, compensatory, default mode network (DMN), and basal ganglia networks.
The cerebellar network correlated with cognitive impairment, cerebellar symptoms, and posterior putamen DAT.
The compensatory network was linked to parkinsonian symptoms, while the basal ganglia network was associated with motor symptoms and DAT.
DMN moderated the relationship between the cerebellar network and cognitive function.

Conclusions:

Metabolic abnormalities in MSA can be effectively decomposed into five distinct large-scale brain networks.
This network-based approach provides a comprehensive understanding of MSA's heterogeneous mechanisms.
The findings offer insights into potential therapeutic targets and diagnostic strategies for MSA.

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