Discriminating Bipolar Disorder from Major Depression Using Whole-Brain Functional Connectivity: A Feature Selection Analysis with Svm-Foba Algorithm
文献类型：会议论文
| 作者 | Nanfeng Jie ; Elizabeth A Osuch; Maohu Xiao; Xiaoying Ma; Michael Wammes; Tianzi Jiang; Sui Jing(隋婧); Vince D Calhoun
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| 出版日期 | 2015 |
| 会议日期 | 2015/9/17-20 |
| 会议地点 | Boston, MA, USA |
| 关键词 | Feature Selection Svm-foba Bipolar Disorder Major Depression Disorder Functional Connectivity |
| 英文摘要 | Multimodal fusion is an effective approach to better understand brain disease. To date, most current fusion approaches are unsupervised; there is need for a multivariate method that can adopt prior information to guide multimodal fusion. Here we proposed a novel supervised fusion model, called "MCCAR+jICA", which enables both identification of multimodal co-alterations and linking the covarying brain regions with a specific reference signal, e.g., cognitive scores. The proposed method has been validated on both simulated and real human brain data. Features from 3 modalities (fMRI, sMRI, dMRI) obtained from 147 schizophrenia patients and 147 age-matched healthy controls were included as fusion input, who participated in the Function Biomedical Informatics Research Network (FBIRN) Phase III study. Our aim was to investigate the group co-alterations seen in three types of MRI data that are also correlated with working memory performance. One joint IC was found both significantly group-discriminating (p=7.4E-06, 0.001, 7.0E-09) and highly correlated with working memory scores(r=0.296, 0.241, 0.301) and PANSS negative scores (r=-0.229, -0.276, -0.240) for fMRI, dMRI and sMRI, respectively. Given the simulation and FBIRN results, MCCAR+jICA is shown to be an effective multivariate approach to extract accurate and stable multimodal components associated with a particular measure of interest, and promises a wide application in identifying potential neuromarkers for mental disorders. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/20792]  |
| 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 | Nanfeng Jie,Elizabeth A Osuch,Maohu Xiao,et al. Discriminating Bipolar Disorder from Major Depression Using Whole-Brain Functional Connectivity: A Feature Selection Analysis with Svm-Foba Algorithm[C]. 见:. Boston, MA, USA. 2015/9/17-20. |
入库方式： OAI收割
来源：自动化研究所
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