TY - GEN
T1 - Three-way FMRI-DTI-methylation data fusion based on mCCA+jICA and its application to schizophrenia
AU - Sui, Jing
AU - He, Hao
AU - Liu, Jingyu
AU - Yu, Qingbao
AU - Adali, Tulay
AU - Pearlson, Godfrey D.
AU - Calhoun, Vince D.
PY - 2012
Y1 - 2012
N2 - Multi-modal fusion is an effective approach in biomedical imaging which combines multiple data types in a joint analysis and overcomes the problem that each modality provides a limited view of the brain. In this paper, we propose an exploratory fusion model, we term mCCA+jICA, by combining two multivariate approaches: multi-set canonical correlation analysis (mCCA) and joint independent component analysis (jICA). This model can freely combine multiple, disparate data sets and explore their joint information in an accurate and effective manner, so that high decomposition accuracy and valid modal links can be achieved simultaneously. We compared mCCA+jICA with its alternatives in simulation and applied it to real fMRI-DTI-methylation data fusion, to identify brain abnormalities in schizophrenia. The results replicate previous reports and add to our understanding of the neural correlates of schizophrenia, and suggest more generally a promising approach to identify potential brain illness biomarkers.
AB - Multi-modal fusion is an effective approach in biomedical imaging which combines multiple data types in a joint analysis and overcomes the problem that each modality provides a limited view of the brain. In this paper, we propose an exploratory fusion model, we term mCCA+jICA, by combining two multivariate approaches: multi-set canonical correlation analysis (mCCA) and joint independent component analysis (jICA). This model can freely combine multiple, disparate data sets and explore their joint information in an accurate and effective manner, so that high decomposition accuracy and valid modal links can be achieved simultaneously. We compared mCCA+jICA with its alternatives in simulation and applied it to real fMRI-DTI-methylation data fusion, to identify brain abnormalities in schizophrenia. The results replicate previous reports and add to our understanding of the neural correlates of schizophrenia, and suggest more generally a promising approach to identify potential brain illness biomarkers.
UR - http://www.scopus.com/inward/record.url?scp=84883038771&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84883038771&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2012.6346519
DO - 10.1109/EMBC.2012.6346519
M3 - Conference contribution
C2 - 23366480
AN - SCOPUS:84883038771
SN - 9781424441198
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2692
EP - 2695
BT - 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012
T2 - 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
Y2 - 28 August 2012 through 1 September 2012
ER -