4.7 Article

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

Journal

MEDICAL IMAGE ANALYSIS
Volume 76, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.media.2021.102297

Keywords

Brain imaging genetics; Sparse canonical correlation analysis; Multi-task learning; Outcome prediction

Funding

  1. National Institutes of Health [R01 EB022574, R01 LM013463, U01 AG068057, RF1 AG063481, R01 AG058854]
  2. National Science Foundation [IIS 1837964]
  3. National Research Foundation of Korea [NRF-2020R1A6A3A03038525]
  4. NIH Institutes and Centers [1U54MH091657]
  5. McDonnell Center for Systems Neuroscience at Washington University

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In this study, a novel multi-task learning based structured sparse canonical correlation analysis (MTS2CCA) was proposed to deliver interpretable results and improve integration in imaging genetics studies. The proposed model outperformed state-of-the-art competing methods on both simulation and real imaging genetic data in terms of feature selection accuracy and performance, revealing promising features related to sleep on real imaging genetic data.
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 anal-ysis 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 inter-esting 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. (c) 2021 Elsevier B.V. All rights reserved.

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