| Supervised multimodal fusion and its application in searching joint neuromarkers of working memory deficits in schizophrenia | |
Shile Qi1 ; Vince D. Calhoun2; Theo G. M. van Erp2; Eswar Damaraju2; Juan Bustillo2; Yuhui Du2; Jessica A. Turner2; Daniel H. Mathalon3; Judith M. Ford3; James Voyvodic4; Bryon A. Mueller4; Aysenil Belger5; Sarah Mc Ewen6; Steven G. Potkin7; Adrian Preda7; Tianzi Jiang（蒋田仔）1; Sui Jing(隋婧)1
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| 2016 | |
| 会议名称 | Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the |
| 会议日期 | 2016-10-18 |
| 会议地点 | Orlando, FL, USA |
| 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. |
| 关键词 | . |
| 文献类型 | 会议论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/20294 |
| 专题 | 脑图谱与类脑智能实验室_脑网络组研究 |
| 通讯作者 | Sui Jing(隋婧) |
| 作者单位 | 1.中科院自动化研究所 2.the Mind Research Network 3.San Francisco VA Medical Center 4.Department of Radiology, Brain Imaging and Analysis Center, Duke University 5.Department of Psychiatry, University of Minnesota, Minneapolis 6.Department of Psychiatry, University of North Carolina School of Medicine, 7.Department of Psychiatry and Human Behavior, University of California at Irvine |
| 第一作者单位 | 中国科学院自动化研究所 |
| 通讯作者单位 | 中国科学院自动化研究所 |
| 推荐引用方式 GB/T 7714 | Shile Qi,Vince D. Calhoun,Theo G. M. van Erp,et al. Supervised multimodal fusion and its application in searching joint neuromarkers of working memory deficits in schizophrenia[C],2016. |
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| Supervised Multimoda（1029KB） | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 | ||
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