Optimized EEG and fMRI Biomarker Fusion Using Federated Learning for Parkinson's Disease Diagnosis

Diagnosing Parkinson's disease (PD) is particularly challenging due to the intricate and variable nature of its biomarkers, which span motor and non-motor symptoms, differ across individuals, and evolve over time. While machine learning has been used to automate this process, most studies focus on limited biomarkers due to dataset constraints. This study introduces a Federated Learning (FL) framework that integrates electroencephalography (EEG) and resting-state functional magnetic resonance imaging (rs-fMRI) data for improved PD classification. Unlike traditional fusion-based studies integrating multiple biomarkers from the same subject group, the Federated Learning framework processes EEG and fMRI data separately from distinct subject groups. Client nodes treat these as independent datasets and utilize convolutional neural networks (CNNs), Graph CNNs, and ResNet-18 models for analysis. A central server then aggregates insights, simulating a diagnostic center to evaluate the relevance of additional biomarkers for enhanced PD detection utilizing support vector machines (SVM) and federated dynamic model aggregation (Fed-Dyn). Additionally, gender-specific evaluations suggest that male-exclusive models outperform female models in biomarker representation. The study underscores the necessity of demographic-aware frameworks and optimized fusion techniques for early-stage PD detection.Clinical Relevance- This study significantly enhances early PD diagnosis by integrating EEG and fMRI biomarkers through Federated Learning (FL), offering a more comprehensive view of neurodegenerative changes while preserving patient privacy. By addressing gender-specific biomarker differences and tailoring models to diverse patient profiles and disease stages, it supports precision medicine and equitable healthcare. The advanced fusion techniques improve diagnostic accuracy in terms of ROC-AUC score, aiding clinical decision-making and enabling scalable telemedicine solutions. Beyond PD, the framework holds potential for broader neurodegenerative research and sets benchmarks for biomarker-based diagnostics, paving the way for impactful advancements in precision neurology.