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Metabolic Patterns in Multiple System Atrophy: Brain Network Insights from Spatial ICA of FDG-PET

J. Li, H. Wang, B. Wang, Z. Cen, W. Luo (Hangzhou, China)

Meeting: 2025 International Congress

Keywords: Multiple system atrophy(MSA): Pathophysiology, Positron emission tomography(PET)

Category: MSA, PSP, CBS: Neuroimaging

Objective: This study employs spatial independent component analysis (ICA) on 18F-fluorodeoxyglucose (FDG)-PET imaging to decompose metabolic heterogeneity in Multiple system atrophy (MSA), identifying key brain network mechanisms.

Background: MSA is a progressive neurodegenerative disorder presenting significant clinical heterogeneous, posing challenges in diagnosis and treatment.

Method: MSA patients (n=95) and healthy controls (n=102) underwent FDG-PET imaging. Spatial ICA identified metabolic covariance networks. Clinical assessments, including motor (MDS-UPDRS III), cognitive (MMSE, MoCA), cerebellar symptom scales, and dopamine transporter (DAT)-PET imaging, were conducted. Clinical correlations were examined.

Results: Spatial ICA identified five independent components (ICs) significantly associated with MSA: cerebellar network (IC1, p=3.01×10⁻³⁰), basal ganglia network (IC15, p=9.47×10⁻⁶), salience network (IC6, p=3.15×10⁻⁹), default mode network (DMN; IC10, p=0.02), and compensatory network (IC7, p=2.67×10⁻⁷, [figure 1]). IC1 correlated positively with cognitive performance (MMSE: β=13.87, p=9.04×10⁻⁴; MoCA: β=14.63, p=1.13×10⁻⁴; [figure 2]C,D), cerebellar symptoms (limb ataxia: p=3.18×10⁻⁴), and negatively with posterior putamen DAT binding (β=-72.79, p=0.0031; [figure 2]E). IC15 negatively correlated with parkinsonian motor severity (MDS-UPDRS III: β=-28.13, p=8.37×10⁻⁴; [figure 2]G) and positively with posterior putamen DAT (β=48.24, p=0.0038; [figure 2]H). IC7, displaying increased metabolism, positively correlated with motor severity (MDS-UPDRS III: β=3.66, p=1.08×10⁻³; [figure 2]F). Additionally, IC10 (DMN) and IC6 weights negatively correlated with disease duration (IC10: β=-20.63, p=0.027; IC6: β=-17.53, p=0.013).

Conclusion: Spatial ICA of FDG-PET revealed distinct metabolic covariance networks in MSA, each reflecting specific clinical and pathological dimensions. These findings offer valuable biomarkers to improve MSA diagnosis, evaluate disease severity, and guide therapeutic interventions.

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To cite this abstract in AMA style:

J. Li, H. Wang, B. Wang, Z. Cen, W. Luo. Metabolic Patterns in Multiple System Atrophy: Brain Network Insights from Spatial ICA of FDG-PET [abstract]. Mov Disord. 2025; 40 (suppl 1). https://www.mdsabstracts.org/abstract/metabolic-patterns-in-multiple-system-atrophy-brain-network-insights-from-spatial-ica-of-fdg-pet/. Accessed November 20, 2025.
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