Probability Connectivity-Based Multimodality Regression Analysis for Associating Disease-Specific Multimodal Brain Imaging Phenotypes with Genetic Risk Factors

Neuroimaging genetics is a powerful technique for discovering the relationships between genotype and imaging phenotype. However, many univariate or multivariate regression approaches have only focused on imaging quantitative traits (QTs) that are relevant to some genetic markers on distinct pathways and might not be disease specific. In addition, there are complex relations between subjects of distinct modalities and diagnosis labels, which contain useful information for the treatment of Alzheimer’s disease (AD). Here, a novel probability connectivity-based penalty is developed for incorporating the prior information to explore relations among different subjects with disease status. Specifically, the Pearson’s correlation coefficient (PCC) is used to construct a similarity matrix in a probability graph, first to express the connectivity weights between subjects, which can reflect the different correlations among subjects within the same class. Second, a diagnosis-aligned probability connectivity-based multimodal regression (DPCMR) method is employed to find the relations among modalities of distinct subjects. It also mines associations between genetic markers and imaging phenotypes. The AD risk single nucleotide polymorphism (SNP) APOE rs429358 and three modalities of neuroimaging data are used to verify the performance of all of the methods. The experimental results reveal that DPCMR has better performance and identifies some brain regions across multiple modalities related to diseases.