NeuroMoE++: Patient-Adaptive Multi-Level Multimodal Fusion With Mixture-of-Experts for Neurological Disorder Classification

Early diagnosis of neurological disorders (NDs) benefits from multiple complementary signals, including anatomical MRI, diffusion imaging, functional activation, and clinical or serum biomarkers. These modalities capture distinct aspects of neurodegeneration and have motivated recent multimodal learning approaches. However, most existing methods combine modalities only at the feature or decision level through late fusion, meaning that modalities are processed independently and never influence one another during feature extraction. As a result, subtle cross-modality patterns that are highly informative in prodromal stages remain underutilized. To address this limitation, we propose NeuroMoE++, a hierarchical end-to-end framework that enables explicit interaction across modalities while preserving their individual structure. After obtaining unimodal and cross-modality predictions, a subject-driven adaptive integration produces patient-specific decisions. This combination allows the model to capture complementary structure, leverage fused evidence when beneficial, and remain interpretable from a clinical standpoint. Experiments on real clinical datasets demonstrate that NeuroMoE++ substantially outperforms unimodal and late-fusion baselines, achieving 84.94% accuracy. These results highlight the value of explicit cross-modality reasoning for practical neurological diagnosis.