Multimodal based classification of schizophrenia patients

Mustafa S. Cetin, Jon M. Houck, Victor M. Vergara, Robyn L. Miller, Vince Calhoun

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

Abstract

Schizophrenia is currently diagnosed by physicians through clinical assessment and their evaluation of patient's self-reported experiences over the longitudinal course of the illness. There is great interest in identifying biologically based markers at the onset of illness, rather than relying on the evolution of symptoms across time. Functional network connectivity shows promise in providing individual subject predictive power. The majority of previous studies considered the analysis of functional connectivity during resting-state using only fMRI. However, exclusive reliance on fMRI to generate such networks, may limit inference on dysfunctional connectivity, which is hypothesized to underlie patient symptoms. In this work, we propose a framework for classification of schizophrenia patients and healthy control subjects based on using both fMRI and band limited envelope correlation metrics in MEG to interrogate functional network components in the resting state. Our results show that the combination of these two methods provide valuable information that captures fundamental characteristics of brain network connectivity in schizophrenia. Such information is useful for prediction of schizophrenia patients. Classification accuracy performance was improved significantly (up to ≈ 7%) relative to only the fMRI method and (up to ≈ 21%) relative to only the MEG method.

Original languageEnglish (US)
Title of host publication2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2629-2632
Number of pages4
ISBN (Electronic)9781424492718
DOIs
StatePublished - Nov 4 2015
Event37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy
Duration: Aug 25 2015Aug 29 2015

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2015-November
ISSN (Print)1557-170X

Other

Other37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
CountryItaly
CityMilan
Period8/25/158/29/15

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ASJC Scopus subject areas

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

Cite this

Cetin, M. S., Houck, J. M., Vergara, V. M., Miller, R. L., & Calhoun, V. (2015). Multimodal based classification of schizophrenia patients. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 (pp. 2629-2632). [7318931] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2015-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2015.7318931