MIRA-Net: A Cross-Cohort Representation Learning Framework for Parkinson's Disease Classification Using Acoustic and Beta-Band MEG Biomarkers

Accurate diagnosis of Parkinson's disease (PD) remains challenging due to substantial inter-subject variability and the absence of widely accessible, objective multimodal biomarkers. Although speech and magnetoencephalography (MEG) biomarkers have individually demonstrated strong discriminative potential, their joint utilization is constrained by the absence of subject-level paired datasets - a fundamental gap that has prevented cross-modal validation at the individual level. We argue that this makes cross-cohort representation learning not merely a pragmatic workaround, but the most realistic and clinically transferable framework for multimodal PD assessment. In real-world deployment, acoustic screening and neuroimaging biomarkers are acquired through separate clinical pathways and must be integrated across heterogeneous patient populations. To address this, we propose MIRA-Net (Modality-Invariant Residual Adversarial Network). This cross-cohort representation learning framework integrates acoustic speech features from four established UCI datasets (n = 193) with beta-band MEG biomarkers from the NatMEG-PD dataset (n = 127) for PD classification. MIRA-Net employs RF-SHAP feature selection, gradient-reversal-based domain adaptation, and supervised contrastive alignment to learn participant-independent, modality-invariant embeddings. The framework is evaluated under Rest, Go, and Passive task conditions against Early Fusion, Vanilla DANN, and Supervised Contrastive Learning baselines. MIRA-Net achieves a peak accuracy of 86.23% (Go condition, Stacking classifier) with AUC values exceeding 0.88 under repeated cross-validation, alongside a sensitivity of 89.4% and specificity of 83.1%. Friedman tests confirm statistically significant performance differences among fusion strategies (p < 0.003 across all conditions). These results demonstrate that cross-cohort representation learning can extract robust disease-discriminative signatures without synchronized multimodal recordings, offering a practical pathway toward AI-assisted PD assessment in resource-constrained clinical settings.