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Aberrant intrinsic networks in schizophrenia and bipolar disorder in an auditory oddball task
Sui Jing(隋婧); Hao He; Qingbao Yu; Tuelay Adali; Godfrey D Pearlson; Vince D Calhoun
2012
会议名称2012 The 18th annual confernece of Organization for Human Brain Mapping Annual Meeting(OHBM 2012)
会议日期2012/6/10-16
会议地点Beijing, China
摘要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.
关键词Schizophrenia Bipolar Disorder
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/20788
专题脑网络组研究中心
作者单位Institute of Automation Chinese Academy of Sciences
推荐引用方式
GB/T 7714
Sui Jing,Hao He,Qingbao Yu,et al. Aberrant intrinsic networks in schizophrenia and bipolar disorder in an auditory oddball task[C],2012.
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