Multi-task learning based structured sparse canonical correlation analysis for brain imaging genetics

Mansu Kim, Eun Jeong Min, Kefei Liu, Jingwen Yan, Andrew J. Saykin, Jason H. Moore, Qi Long, Li Shen

Abstract: Highlights • A new canonical correlation analysis model, which incorporates the biologically meaningful structures with the multi-task learning framework, is proposed to discover sparse, discriminative, and interpretable shared representation across multi-modal imaging genetics data. • An efficient iteratively reweighted algorithm via alternating optimization is proposed to solve the proposed problem with proven convergence. • Extensive experiments on both simulation and real data demonstrate the promise and clinical benefits of the proposed model over multiple competing algorithms. Abstract The advances in technologies for acquiring brain imaging and high-throughput genetic data allow the researcher to access a large amount of multi-modal data. Although the sparse canonical correlation analysis is a powerful bi-multivariate association analysis technique for feature selection, we are still facing major challenges in integrating multi-modal imaging genetic data and yielding biologically meaningful interpretation of imaging genetic findings. In this study, we propose a novel multi-task learning based structured sparse canonical correlation analysis (MTS2CCA) to deliver interpretable results and improve integration in imaging genetics studies. We perform comparative studies with state-of-the-art competing methods on both simulation and real imaging genetic data. On the simulation data, our proposed model has achieved the best performance in terms of canonical correlation coefficients, estimation accuracy, and feature selection accuracy. On the real imaging genetic data, our proposed model has revealed promising features of single-nucleotide polymorphisms and brain regions related to sleep. The identified features can be used to improve clinical score prediction using promising imaging genetic biomarkers. An interesting future direction is to apply our model to additional neurological or psychiatric cohorts such as patients with Alzheimer’s or Parkinson’s disease to demonstrate the generalizability of our method.