# RACF: A Multimodal Deep Learning Framework for Parkinson's Disease Diagnosis Using SNP and MRI Data
## 4 Discussion  
### Advantages and Innovations of the RACF Framework for Multimodal Fusion  
The proposed RACF network demonstrates significant performance advantages in the multimodal diagnosis of PD. First, in the extraction of SNP features, the GWAS-Transformer  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**SNP** & **Chromosome** & **Gene** & **Function** \\ \hline rs242557 & 17 & MAPT & Encoding tau protein \\ rs17649553 & 17 & MAPT & Affecting language memory ability \\ rs11724635 & 4 & BST1 & Intracellular calcium ion regulation \\ rs12185268 & 17 & MAPT & Encoding tau protein \\ rs6595513 & 5 & LINC01170 & Intron variant \\ rs2736990 & 4 & SNCA & Accumulation of alpha-synuclein \\ rs11248051 & 4 & GAK & Regulation of cell division and microtubule stability \\ rs4698412 & 4 & BST1 & Regulation of NAD+ metabolism and immune system activity \\ rs947211 & 1 & PARK16 & Regulation of RAB7L1 gene expression \\ rs1013496 & 5 & LINC01170 & Intron variant \\ rs11711441 & 3 & MCCC1/LAMP3 & Amino acid metabolism / cellular immunity \\ rs4771268 & 13 & MBNL2 & Neural development and neuronal function \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline without Contrastive Learning & 0.849 & 0.829 & 0.843 & 0.818 \\ without Residual Connection & 0.717 & 0.763 & 0.735 & 0.723 \\ RACF \(\rightarrow\) Simple feature concatenation & 0.697 & 0.751 & 0.667 & 0.641 \\ Ours (Full Model) & 0.912 & 0.859 & 0.895 & 0.943 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation Study Results.  
architecture unbiasedly selects significantly associated SNP loci through GWAS, overcoming the traditional reliance on known risk loci. Combined with the MultiHead-Attention mechanism, the model dynamically captures nonlinear interactions between SNP loci, such as the potential regulatory relationship between rs356220 (SNCA gene) and rs947211 (PARK16 gene). Secondly, in the extraction of MRI features, the modified 3D DenseNet121 network captures multiscale brain structure features through dense connections and transition layers, while the data augmentation strategy enhances the robustness of the model. The preprocessing pipeline ensures the input of standardized brain images. For the cross-modal fusion strategy, RACF performs feature alignment through dynamic attention mechanisms (Formula (4)) and contrastive learning (Formula (5)), utilizing a 4-head attention mechanism to compute interaction weights. Finally, residual connections are incorporated to alleviate the vanishing gradient problem. The contrastive learning module maximizes inter-modal consistency using cosine similarity, effectively mitigating the heterogeneity of feature spaces. Experimental results show that RACF can extract rich information from both genetic and imaging data, effectively fuse them, and successfully differentiate PD patients from normal controls in the PPMI dataset.  
Compared with unimodal models and existing methods, the proposed multimodal feature fusion method performs better. In the interpretability analysis, the top 20 SNP loci selected by SHAP values include 11 known loci (e.g., rs11230569, rs356220) and 9 novel loci (e.g., rs78251200, rs4538475). The Grad-CAM heatmaps clearly highlight the basal ganglia and substantia nigra regions, particularly key areas such as the caudate nucleus and globus pallidus within the basal ganglia (Figure 5). These regions show a high degree of correspondence with the distribution of Lewy bodies in Braak staging and the areas of metabolic abnormalities observed in PET scans, further validating the pathological relevance and the accuracy of the decision-making process of the model [25].