Supervised multimodal fusion and its application in searching joint neuromarkers of working memory deficits in schizophrenia

Shile Qi, Vince D. Calhoun, Theo G.M. Van Erp, Eswar Damaraju, Juan Bustillo, Yuhui Du, Jessica A. Turner, Daniel H. Mathalon, Judith M. Ford, James Voyvodic, Bryon A. Mueller, Aysenil Belger, Sarah Mc Ewen, Steven G. Potkin, Adrian Preda, F. Birn, Tianzi Jiang, Jing Sui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4021-4026
Number of pages6
ISBN (Electronic)9781457702204
DOIs
StatePublished - Oct 13 2016
Externally publishedYes
Event38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States
Duration: Aug 16 2016Aug 20 2016

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2016-October
ISSN (Print)1557-170X

Other

Other38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
CountryUnited States
CityOrlando
Period8/16/168/20/16

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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  • Cite this

    Qi, S., Calhoun, V. D., Van Erp, T. G. M., Damaraju, E., Bustillo, J., Du, Y., Turner, J. A., Mathalon, D. H., Ford, J. M., Voyvodic, J., Mueller, B. A., Belger, A., Ewen, S. M., Potkin, S. G., Preda, A., Birn, F., Jiang, T., & Sui, J. (2016). Supervised multimodal fusion and its application in searching joint neuromarkers of working memory deficits in schizophrenia. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 (pp. 4021-4026). [7591609] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2016-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2016.7591609