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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
2016
Conference NameEngineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the
Conference Date2016-10-18
Conference PlaceOrlando, FL, USA
Abstract.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.

Keyword.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20294
Collection脑网络组研究中心
Corresponding AuthorSui Jing(隋婧)
Affiliation1.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
Recommended Citation
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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