Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort

Abstract

Motivation: Identifying the genetic basis of the brain structure, function and disorder by using the imaging quantitative traits (QTs) as endophenotypes is an important task in brain science. Brain QTs often change over time while the disorder progresses and thus understanding how the genetic factors play roles on the progressive brain QT changes is of great importance and meaning. Most existing imaging genetics methods only analyze the baseline neuroimaging data, and thus those longitudinal imaging data across multiple time points containing important disease progression information are omitted.

Results: We propose a novel temporal imaging genetic model which performs the multi-task sparse canonical correlation analysis (T-MTSCCA). Our model uses longitudinal neuroimaging data to uncover that how single nucleotide polymorphisms (SNPs) play roles on affecting brain QTs over the time. Incorporating the relationship of the longitudinal imaging data and that within SNPs, T-MTSCCA could identify a trajectory of progressive imaging genetic patterns over the time. We propose an efficient algorithm to solve the problem and show its convergence. We evaluate T-MTSCCA on 408 subjects from the Alzheimer's Disease Neuroimaging Initiative database with longitudinal magnetic resonance imaging data and genetic data available. The experimental results show that T-MTSCCA performs either better than or equally to the state-of-the-art methods. In particular, T-MTSCCA could identify higher canonical correlation coefficients and capture clearer canonical weight patterns. This suggests that T-MTSCCA identifies time-consistent and time-dependent SNPs and imaging QTs, which further help understand the genetic basis of the brain QT changes over the time during the disease progression.

Availability and implementation: The software and simulation data are publicly available at https://github.com/dulei323/TMTSCCA.

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

Comparison of the mean canonical correlation coefficients (CCCs) obtained from 10-fold cross-validation trials. The CCCs at all time points are illustrated, where CCC_T1 is calculated between the SNP data and the imaging data at T1, and so forth.

Fig. 2.

Heat maps of canonical weights on synthetic data. Rows 1–4: Ground truth, mSCCA, TGSCCA and T-MTSCCA, respectively. In each row, U is on the left panel and V is on the right. Within each panel, there are four canonical weights associating with four time points. For our method, there are four canonical weights corresponding to four time points, i.e. T1, T2, T3, T4, for both U and V. For mSCCA and TGSCCA, the weight matrix U is stacked by the same canonical weight vector u.

Fig. 3.

Comparison of the mean canonical correlation coefficients (CCCs) obtained from 10-fold testing trials on ADNI.

Fig. 4.

Comparison of canonical weights in terms of SNPs averaged from 10-fold cross-validation trials. Each row corresponds to a SCCA method: (1) mSCCA; (2) TGSCCA and (3) T-MTSCCA. For our method, there are four rows corresponding to four time points, i.e. BL, M6, M12 and M24. For mSCCA and TGSCCA, the four rows are stacked by the same u.

Fig. 5.

Comparison of canonical weights in terms of each imaging QTs averaged from 10-fold cross-validation trials. Each row corresponds to a SCCA method: (1) mSCCA; (2) TGSCCA and (3) T-MTSCCA. Within each panel, there are four rows corresponding to four time points of imaging QTs, i.e. BL, M6, M12 and M24.