TY - GEN
T1 - A dirty multi-task learning method for multi-modal brain imaging genetics
AU - for the Alzheimer’s Disease Neuroimaging Initiative
AU - Du, Lei
AU - Liu, Fang
AU - Liu, Kefei
AU - Yao, Xiaohui
AU - Risacher, Shannon L.
AU - Han, Junwei
AU - Guo, Lei
AU - Saykin, Andrew J.
AU - Shen, Li
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Brain imaging genetics
KW - Multi-modal brain imaging
KW - Multi-task sparse canonical correlation analysis
UR - http://www.scopus.com/inward/record.url?scp=85075656438&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32251-9_49
DO - 10.1007/978-3-030-32251-9_49
M3 - 会议稿件
AN - SCOPUS:85075656438
SN - 9783030322502
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 447
EP - 455
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
A2 - Shen, Dinggang
A2 - Yap, Pew-Thian
A2 - Liu, Tianming
A2 - Peters, Terry M.
A2 - Khan, Ali
A2 - Staib, Lawrence H.
A2 - Essert, Caroline
A2 - Zhou, Sean
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 17 October 2019
ER -