Inspired by pathogenic mechanisms: A novel gradual multi-modal fusion framework for mild cognitive impairment diagnosis

Mild cognitive impairment (MCI) is a precursor to Alzheimer's disease (AD), and its progression involves complex pathogenic mechanisms. Specifically, disturbed by gene variants, the regulation of gene expression ultimately changes brain structure, resulting in the progression of brain diseases. However, the existing works rarely take these mechanisms into account when designing their diagnosis methods. Therefore, we propose a novel gradual multi-modal fusion framework to fuse representative data from each stage of disease progression in hybrid feature space, including single nucleotide polymorphism (SNP), gene expression (GE), and magnetic resonance imaging (MRI). Specifically, to integrate genetic sequence and expression data, we design a SNP-GE fusion module, which performs multi-modal fusion to obtain genetic embedding by considering the relation between SNP and GE. Compared with SNP-GE fusion, representation of genetic embedding and MRI have more obvious heterogeneity, especially correlation with disease. Therefore, we propose to align the manifold of genetic and imaging representations, which can explore the high-order relationship between imaging and genetic data in the presence of modal heterogeneity. Our proposed framework was validated using the Alzheimer's Disease Neuroimaging Initiative dataset, and achieved diagnosis accuracy of 76.88%, 72.84%, 87.72%, and 95.00% for distinguishing MCI from control normal, lately MCI from early MCI, MCI from AD, and AD from control normal, respectively. Additionally, our proposed framework helps to identify some multi-modal biomarkers related to MCI progression. In summary, our proposed framework is effective not only for MCI diagnosis but also for guiding the further development of genetic and imaging-based brain studies. Our code is published at https://github.com/tianxu8822/workflow_MCI/tree/main/.