Explainable Graph Neural Network Based on Metabolic Brain Imaging for Differential Diagnosis of Parkinsonism

Ronghua Ling ¹ Xingxing Cen ² Shaoyou Wu ³ Min Wang ^3,4 Ying Zhang ³ Juanjuan Jiang ⁵ Jiaying Lu ⁶ Yingqian Liu ⁷ Chuantao Zuo ⁶ Jiehui Jiang ³ Yinghui Yang ^8* Zhuangzhi Yan ^3*

• ¹ School of Communication and Information Engineering, Shanghai University, Shanghai, Shanghai Municipality, China
• ² Donghua University, Shanghai, Shanghai Municipality, China
• ³ School of Life Sciences, Shanghai University, Shanghai, China
• ⁴ Shanghai University, Shanghai, China
• ⁵ College of Medical imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
• ⁶ National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, Shanghai Municipality, China
• ⁷ Shandong University of Aeronautics, Binzhou, China, Binzhou, China
• ⁸ School of Health Management, Shanghai Jianqiao University, Shanghai, Shanghai, China

Background: Accurate differentiation of Parkinsonian syndromes remains challenging due to overlapping clinical manifestations and subtle neuroimaging variations. This study introduces an explainable graph neural network (GNN) framework integrating a Regional Radiomics Similarity Network (R2SN) and Transformer-based attention mechanisms to address this diagnostic dilemma.Methods: Our study prospectively enrolled 1,495 participants, including 220 healthy controls and 1,275 patients diagnosed with idiopathic Parkinson's disease (IPD), multiple system atrophy (MSA), or progressive supranuclear palsy (PSP), all undergoing standardized 18 F-fluorodeoxyglucose positron emission tomography imaging. Metabolic networks were constructed by encoding edge weights derived from radiomic feature similarity matrices, enabling simultaneous quantification of microscopic metabolic heterogeneity and macroscale network reorganization.The proposed framework achieved superior classification performance with F1-scores of 92.5% (MSA), 96.3% (IPD), and 86.7% (PSP), significantly outperforming comparators by 5.5-8.3%. Multiscale interpretability analysis revealed: 1) Regional hypometabolism in pathognomonic nodes (putamen in IPD, midbrain tegmentum in PSP); 2) Disease-specific connectivity disruptions (midbrainprefrontal disconnection in PSP, cerebellar-pontine decoupling in MSA). The substructure attention mechanism reduced computational complexity by 41% while enhancing diagnostic specificity (PSP precision +5.2%).The proposed R2SN-based explainable GNN framework for Parkinsonian syndrome differentiation establishes a new paradigm for precision subtyping of neurodegenerative disorders, with methodological extensibility to other network-driven neurological conditions.