Metabolic network remodeling and AI-driven precision diagnostics in geriatric Parkinson's disease: Advances in multimodal imaging.

Research on the pathological mechanisms of Parkinson's disease (PD) reveals a significant association between nigrostriatal dopaminergic neuron degeneration and abnormal metabolic networks. However, the substantial increase in clinical heterogeneity complicates early diagnosis and subtyping. Significant progress has been made in recent years through multimodal neuroimaging studies. Current research utilizing ¹⁸F-FDG PET/CT combined with multimodal neuroimaging techniques has systematically revealed characteristic PD metabolic patterns: ​​hypermetabolism in the putamen is significantly associated with motor symptoms, while hypometabolism in the frontal/parietal lobes is closely linked to cognitive decline​​. In contrast, multiple system atrophy (MSA) manifests as ​​hypometabolism in the cerebellar-pontine region​​, and progressive supranuclear palsy (PSP) exhibits ​​functional dissociation within the midbrain-frontal network​​. Recent advancements demonstrate that artificial intelligence (AI)-driven multimodal radiomics analysis, by integrating metabolic and structural features, ​​significantly enhances PD subtyping classification efficacy and differential diagnostic accuracy​​. Furthermore, multiple studies have confirmed that ​​metabolic abnormalities precede morphological changes​​, suggesting their potential as early biomarkers. Collectively, current evidence indicates that ​​distinctive metabolic network patterns​​-such as the contrast in cerebellar metabolism between PD and MSA-coupled with ​​AI-driven deep mining of multimodal data, provide a critical foundation for the precise subtyping of neurodegenerative diseases and personalized therapeutic interventions​​.Future research should focus on establishing ​​multicenter data-sharing frameworks and standardized metabolic databases​​. These initiatives are essential to ​​further optimize the generalization capability of AI models and accelerate the clinical translation of metabolic biomarkers​​.