Identifying Progression-Specific Alzheimer’s Subtypes Using Multimodal Transformer

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease, yet its current treatments are limited to stopping disease progression. Moreover, the effectiveness of these treatments remains uncertain due to the heterogeneity of the disease. Therefore, it is essential to identify disease subtypes at a very early stage. Current data-driven approaches can classify subtypes during later stages of AD or related disorders, but face challenges when predicting in the asymptomatic or prodromal stage. Furthermore, most existing models lack explainability in their classification and rely solely on a single modality for assessment, limiting the scope of their analysis. Thus, we propose a multimodal framework that utilizes early-stage indicators, including imaging, genetics, and clinical assessments, to classify AD patients into subtypes at an early stage. In our framework, we introduce a tri-modal co-attention mechanism (Tri-COAT) to explicitly capture cross-modal feature associations. Our proposed model outperforms baseline models and sheds light on essential associations across multimodal features supported by the known biological mechanisms.