Disentangling Disease-sensitive Multimodal Neuroimaging Phenotypes and Related Genetic Factors: A Multimodal Study of ADNI Cohort

Understanding neurological manifestations and their genetic architectures are important for exploring the etiology and pathology of brain disorders. Multimodal neuroimaging data carry complementary information and are known to exhibit shared and specific characteristics from different perspectives. Hence, exploring modality-shared and modality-specific imaging features as well as their genetic underpinnings is a challenging but beneficial task. Unfortunately, this issue has been largely unexploited. In this paper, to fill this gap, we propose a fresh and straightforward insight, referred as Multimodality-Disentangled Phenotype-Genotype Correlation approach (MDPGC). Specifically, we design a unified framework for exploring the multimodality-disentangled characteristics of image-based phenotypes, and further detect genetic variants associated with the disorder using modality-shared and modality-specific biomarkers as intermediate phenotypes. Extensive experimental results on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset reveal that our method attains superior correlation coefficients compared to state-of-the-art methods, and at the same time provided excellent interpretability. In addition, the subsequent analysis demonstrates that MDPGC successfully identifies different types of characteristics of imaging phenotypes and reveals relevant genetic variations. These findings not only contribute to AD diagnosis but also help better understand the pathological and pathogenic mechanisms of brain disorders.