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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
Source PublicationIEEE Trans Med Imaging.
2018
Volume37(1)Issue:201801Pages:93-105
AbstractBy 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.

KeywordMultimodal Fusion With Reference Mccar Supervised Learning Schizophrenia Working Memory Ica Mccb Cminds
WOS KeywordMultimodal fusion with reference, MCCAR, supervised learning, schizophrenia, working memory, ICA, MCCB, CMINDS.
Indexed BySSCi
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20285
Collection脑网络组研究中心
Corresponding AuthorSui Jing(隋婧)
Affiliation1.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中国科学院自动化研究所
Corresponding Author Affilication中国科学院自动化研究所
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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