MLLIFA: Multi-Level Learning and Interactive Fusion Algorithm Combined with Large Foundation Models for Alzheimer's Disease Diagnosis and Etiology Extraction

The joint analysis of macro-level imaging and micro-level genetic information facilitates a holistic understanding of the pathological processes underlying Alzheimer’s disease (AD). Although recent studies have made advances, most current approaches struggle to exploit the critical features embedded in imaging genetics data and fail to effectively reveal biological interactions. To remedy these shortcomings, this paper proposes a multi-level learning and interactive fusion framework based on large foundation models (LFMs), and accordingly develops an algorithm termed MLLIFA for the diagnosis of AD and the extraction of etiology. Specifically, MLLIFA first employs two LFMs to construct high-quality representations of brain region and gene features. Then, two sparse attention mechanisms are applied to extract key information from the constructed features. Finally, interaction learning is utilized to explore the latent relationships between features within biological contexts, guiding the effective fusion of multi-omics information. Experimental results on the ADNI dataset demonstrate that MLLIFA achieves an AD classification accuracy of 91.22%, outperforming state-of-the-art methods. Moreover, the proposed MLLIFA successfully identifies disease-related brain regions, risk genes, and brain-gene associations. These findings not only provide strong support for the precise diagnosis of AD but also offer new insights into the extraction of etiology. Based on macroscopic imaging data and microscopic genetic data, we combine LFMs and multiple attention mechanisms to construct a framework named MLLIFA. It can not only achieve effective diagnosis of AD, but also identify related etiological factors. • Large foundation models are used to generate high-quality features. • Sparse attention mechanisms are used for key feature selection. • An interactive attention mechanism is used to achieve feature fusion. • Experimental results demonstrate the superiority of our method in AD diagnosis.