Abstract
Brain imaging genetics is an important research topic in brain science, which combines genetic variations and brain structures or functions to uncover the genetic basis of brain disorders. Imaging data collected by different technologies, measuring the same brain distinctly, might carry complementary but different information. Unfortunately, we do not know the extent to which phenotypic variance is shared among multiple imaging modalities, which might trace back to the complex genetic mechanism. In this study, we propose a novel dirty multi-task SCCA to analyze imaging genetics problems with multiple modalities of brain imaging quantitative traits (QTs) involved. The proposed method can not only identify the shared SNPs and QTs across multiple modalities, but also identify the modality-specific SNPs and QTs, showing a flexible capability of discovering the complex multi-SNP-multi-QT associations. Compared with the multi-view SCCA and multi-task SCCA, our method shows better canonical correlation coefficients and canonical weights on both synthetic and real neuroimaging genetic data. This demonstrates that the proposed dirty multi-task SCCA could be a meaningful and powerful alternative method in multi-modal brain imaging genetics.
This work was supported by NSFC [61602384]; NSFC of Shaanxi [2017JQ6001]; CPSF [2017M613202]; PSF of Shaanxi [2017BSHEDZZ81]; Fundamental Research Funds for Central Universities [3102018zy029] at Northwestern Polytechnical University. This work was also supported by the National Institutes of Health [R01 EB022574, R01 LM011360, U01 AG024904, P30 AG10133, R01 AG19771, R01 AG 042437, R01 AG046171, R01 AG040770 and NSF IIS 1837964] at University of Pennsylvania and Indiana University.
Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
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Du, L. et al. (2019). A Dirty Multi-task Learning Method for Multi-modal Brain Imaging Genetics. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_49
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DOI: https://doi.org/10.1007/978-3-030-32251-9_49
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