RACF: A Multimodal Deep Learning Framework for Parkinson’s Disease Diagnosis Using SNP and MRI Data

The clinical diagnosis of Parkinson’s disease (PD) primarily relies on clinician-administered observational assessment tools, such as the Unified Parkinson’s Disease Rating Scale (UPDRS). However, these approaches are significantly influenced by subjectivity and exhibit insufficient sensitivity for early-stage symptom detection. The introduction of deep learning techniques has opened new avenues for the early diagnosis of PD. In contrast to traditional methods, deep learning models are capable of processing large-scale, high-dimensional, and complex datasets to automatically learn latent feature relationships, making them particularly suitable for scenarios involving multimodal data fusion. The multimodal diagnosis of PD is confronted with two enduring challenges: (1) the dependence on pre-existing knowledge of established genetic risk loci, and (2) the low efficiency and limited interpretability in handling interactions among cross-modal features. To address these challenges, this study introduces an innovative multimodal deep learning framework with two primary contributions: (1) a Genome-Wide Association Study (GWAS)-Transformer architecture that autonomously selects single nucleotide polymorphism (SNP) features through GWAS and utilizes a multi-head attention mechanism to model potential associations between non-risk loci, thereby eliminating the reliance on known susceptibility genes; (2) a Residual Attention Contrastive Fusion (RACF) module that tackles the heterogeneity of cross-modal features by dynamically allocating attention weights and applying contrastive loss constraints. Evaluation results on the Parkinson’s Progression Markers Initiative (PPMI) dataset demonstrate that our model achieves a classification accuracy of 91.2% and an AUC of 0.94, and predicts nine potential novel risk loci. This work presents a novel paradigm for the discovery of new risk loci based on deep learning and offers valuable insights from a multi-omics perspective for advancing PD research.