Associating Multi-Modal Brain Imaging Phenotypes and Genetic Risk Factors via a Dirty Multi-Task Learning Method

Abstract

Brain imaging genetics becomes more and more important in brain science, which integrates genetic variations and brain structures or functions to study the genetic basis of brain disorders. The multi-modal imaging data collected by different technologies, measuring the same brain distinctly, might carry complementary information. Unfortunately, we do not know the extent to which the phenotypic variance is shared among multiple imaging modalities, which further might trace back to the complex genetic mechanism. In this paper, we propose a novel dirty multi-task sparse canonical correlation analysis (SCCA) to study imaging genetic problems with multi-modal brain imaging quantitative traits (QTs) involved. The proposed method takes advantages of the multi-task learning and parameter decomposition. It can not only identify the shared imaging QTs and genetic loci across multiple modalities, but also identify the modality-specific imaging QTs and genetic loci, exhibiting a flexible capability of identifying complex multi-SNP-multi-QT associations. Using the state-of-the-art multi-view SCCA and multi-task SCCA, the proposed method shows better or comparable canonical correlation coefficients and canonical weights on both synthetic and real neuroimaging genetic data. In addition, the identified modality-consistent biomarkers, as well as the modality-specific biomarkers, provide meaningful and interesting information, demonstrating the dirty multi-task SCCA could be a powerful alternative method in multi-modal brain imaging genetics.

Fig. 1.

Illustration of the group-sparsity, individual-sparsity and element-sparsity for canonical weight U. The group-sparsity indicates that SNPs in the same group are informative for all SCCA tasks simultaneously. The individual-sparsity across all tasks indicates that a SNP (imaging QT) is informative for all SCCA tasks. The element-sparsity indicates that a SNP (imaging QT) is only informative for a specific SCCA task.

Fig. 2.

Comparison of canonical weights in terms of each task for synthetic data sets. For each data set, the canonical weights U is shown on the left, and V is shown on the right. The top row shows the ground truth of U and V, and the remaining rows correspond to the SCCA methods: (1) mSCCA; (2) MTSCCA; (3) the proposed method. Our method has two weights for X and each Yc owing to the parameter decomposition. Within each panel, there are four rows corresponding to four SCCA tasks (denoted as T1~T4) between X and each Yc.

Fig. 3.

Comparison of canonical weights of SNPs in terms of each task. Each row corresponds to an SCCA method: (1) mSCCA; (2) MTSCCA and (3) the proposed method. Our method has two weights for SNPs and imaging QTs owing to the parameter decomposition. Within each panel, there are three rows corresponding to three SCCA tasks.

Fig. 4.

Comparison of canonical weights of imaging QTs in terms of each task. Each row corresponds to an SCCA method: (1) mSCCA; (2) MTSCCA and (3) the proposed method. Our method has two weights for SNPs and imaging QTs owing to the parameter decomposition. Within each panel, there are three rows corresponding to three SCCA tasks.

Fig. 5.

The measurement distributions of imaging QTs (mean value the first ROI of each SCCA task) among different diagnostic groups and different imaging modalities. (a) The left medial orbitofrontal gyrus. (b) The left posterior cingulate gyrus. (c) The left hippocampus lobe.

Fig. 6.

Pairwise comparisons for modality-specific QT-SNP-diagnosis interactions within HCs, SMCs, EMCIs, LMCIs and ADs, respectively. Two-way ANOVA was applied to access the effects of genotype and baseline diagnosis on imaging QTs. Age, gender, years of education, handedness were included as covariates. (a) The beta-amyloid deposition in the left medial orbitofrontal gyrus, rs73052335 and diagnostic groups. (b) The glucose metabolism in the left posterior cingulate gyrus, rs10119 and diagnostic groups. (c) The atrophy in the left hippocampus lobe, rs12721046 and diagnostic groups.