ArticleScientific reports2025
Multimodal metabolic and functional network signatures for diagnosis of parkinson's disease: a PET/MR study.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A review of FDG-PET biomarker analysis methods in Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Brain metabolic and network profiles characterized by comorbid depressive symptoms and sleep disturbances in Parkinson's disease: anFrontiers in aging neuroscience · 2026Article
- Multimodal data-driven eye-movement subtypes and their cerebral glucose metabolic patterns in Parkinson's disease.Frontiers in aging neuroscience · 2026Article
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Authors and funding
9 authors.
Funding
Abstract
Parkinson’s disease (PD) is a neurodegenerative disorder linked to altered cerebral glucose metabolism detectable via 18F-FDG-PET. This study integrated PET/MR and Jensen-Shannon similarity estimation (JSSE) to investigate metabolic/functional network dynamics in PD. 25 PD patients and 16 age/sex-matched normal controls (NCs) underwent visual analysis, standardized uptake value ratio (SUVR) comparisons, and JSSE-based metabolic/functional network construction. Topological properties, regional connectivity, and metabolic-functional correlations were analyzed. A support vector machine (SVM) model incorporating these features differentiated PD from NC. PD patients exhibited elevated SUVR in temporal and thalamic regions but reduced SUVR in parietal/precentral areas. Metabolic networks in PD showed increased assortativity yet decreased normalized clustering coefficients and disrupted small-worldness. Ten metabolic connections were impaired, with the left caudate-temporal pathway showing the strongest reduction. PD demonstrated significantly higher metabolic-functional network correlations than NCs. Machine learning identified metabolic network connectivity as the optimal classifier (AUC = 0.91). Our findings reveal PD-associated metabolic reorganization and validate PET/MR-derived network features as potential biomarkers. The multimodal framework advances pathophysiological understanding through network-level metabolic-functional interplay analysis, offering novel insights into PD diagnostics and mechanistic research.
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