| Multimodal Fusion With Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia. | |
Shile Qi1 ; Vince D. Calhoun2; Theo G. M. van Erp2; Juan Bustillo2; Eswar Damaraju2; Jessica A. Turner2; Yuhui Du2; Jiayu Chen2; Qingbao Yu2; Daniel H. Mathalon3; Judith M. Ford3; James Voyvodic4; Bryon A. Mueller4; Aysenil Belger5; Sarah Mc Ewen6; Steven G. Potkin7; Steven G. Potkin7; Tianzi Jiang1; Sui Jing(隋婧)1
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| Source Publication | IEEE Trans Med Imaging.
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| 2018 | |
| Volume | 37(1)Issue:201801Pages:93-105 |
| Abstract | By exploiting cross-information among multiple imaging data, multimodal fusion has often been used to better understand brain diseases. However, most current fusion approaches are blind, without adopting any prior information. There is increasing interest to uncover the neurocognitive mapping of specific clinical measurements on enriched brain imaging data; hence, a supervised, goaldirected model that employs prior information as a reference to guide multimodal data fusion is much needed and becomes a natural option. Here, we proposed a fusion with reference model called “multi-site canonical correlation analysis with reference + joint-independent component analysis” (MCCAR+jICA), which can precisely identify co-varying multimodal imaging patterns closely related to the reference, such as cognitive scores. In a three-way fusion simulation, the proposed method was compared with its alternatives on multiple facets; MCCAR+jICA outperforms others with higher estimation precision and high accuracy on identifying a target component with the right correspondence. In human imaging data, working memory performance was utilized as a reference to investigate the co-varying working memory-associated brain patterns among three modalities and how they are impaired in schizophrenia. Two independent cohorts (294 and 83 subjects respectively) were used. Similar brain maps were identified between the two cohorts along with substantial overlaps in the central executive network in fMRI, salience network in sMRI, and major white matter tracts in dMRI. These regions have been linked with working memory deficits in schizophrenia in multiple reports and MCCAR+jICA further verified them in a repeatable, joint manner, demonstrating the ability of the proposed method to identify potential neuromarkers for mental disorders. |
| Keyword | Multimodal Fusion With Reference Mccar Supervised Learning Schizophrenia Working Memory Ica Mccb Cminds |
| WOS Keyword | Multimodal fusion with reference, MCCAR, supervised learning, schizophrenia, working memory, ICA, MCCB, CMINDS. |
| Indexed By | SSCi |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/20285 |
| Collection | 脑网络组研究中心 |
| Corresponding Author | Sui Jing(隋婧) |
| Affiliation | 1.中国科学院自动化研究所 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 |
| First Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Recommended Citation GB/T 7714 | Shile Qi,Vince D. Calhoun,Theo G. M. van Erp,et al. Multimodal Fusion With Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia.[J]. IEEE Trans Med Imaging.,2018,37(1)(201801):93-105. |
| APA | Shile Qi.,Vince D. Calhoun.,Theo G. M. van Erp.,Juan Bustillo.,Eswar Damaraju.,...&Sui Jing.(2018).Multimodal Fusion With Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia..IEEE Trans Med Imaging.,37(1)(201801),93-105. |
| MLA | Shile Qi,et al."Multimodal Fusion With Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia.".IEEE Trans Med Imaging. 37(1).201801(2018):93-105. |
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| Multimodal Fusion wi（1484KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Download | |
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