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<p>Parkinson’s disease (PD) is a neurodegenerative disorder linked to altered cerebral glucose metabolism detectable via <sup>18</sup>F-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.</p>

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Multimodal metabolic and functional network signatures for diagnosis of parkinson’s disease: a PET/MR study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月13日

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  • Xiaoyuan Li,
  • Yiyue Zhang,
  • Jie Wu,
  • Xiaochen Yao,
  • Zhenyu Zhao,
  • Rushuai Li,
  • Shuyue Ai,
  • Qing Gao,
  • Feng Wang

摘要

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.