Aberrant intrinsic networks in schizophrenia and bipolar disorder in an auditory oddball task
文献类型：会议论文
| 作者 | Sui Jing(隋婧) ; Hao He; Qingbao Yu; Tuelay Adali; Godfrey D Pearlson; Vince D Calhoun
|
| 出版日期 | 2012 |
| 会议日期 | 2012/6/10-16 |
| 会议地点 | Beijing, China |
| 关键词 | Schizophrenia Bipolar Disorder |
| 英文摘要 | 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/20788]  |
| 专题 | 自动化研究所_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]. 见:. Beijing, China. 2012/6/10-16. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。