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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.

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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.

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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.