A Dirty Multi-task Learning Method for Multi-modal Brain Imaging Genetics
A Dirty Multi-task Learning Method for Multi-modal Brain Imaging Genetics
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DOI：
10.1007/978-3-030-32251-9_49
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
Lei Du;Fang Liu;Kefei Liu;Xiaohui Yao;S. Risacher;Junwei Han;Lei Guo;A. Saykin;Li Shen
中科院分区：
文献类型：
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作者：
Lei Du;Fang Liu;Kefei Liu;Xiaohui Yao;S. Risacher;Junwei Han;Lei Guo;A. Saykin;Li Shen
脑成像遗传学是脑科学的一个重要研究课题，它将遗传变异与大脑结构或功能结合起来，揭示大脑疾病的遗传基础。不同技术采集的成像数据，对同一个大脑进行不同的测量，可能会携带互补但不同的信息。不幸的是，我们不知道在多种成像方式中表型差异的共享程度，这可能追溯到复杂的遗传机制。在这项研究中，我们提出了一种新的脏多任务SCCA来分析涉及多模式脑成像定量性状（QTs）的成像遗传学问题。该方法不仅可以识别跨多种模式的共享snp和qt，还可以识别特定于模式的snp和qt，显示出发现复杂的多snp -多qt关联的灵活能力。与多视图SCCA和多任务SCCA相比，该方法在合成和真实神经影像遗传数据上都具有更好的典型相关系数和典型权重。这表明脏多任务SCCA可能是多模态脑成像遗传学的一种有意义和强大的替代方法。
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.