Multimodal classification of schizophrenia patients with MEG and fMRI data using static and dynamic connectivity measures

Mustafa S. Cetin, Jon M. Houck, Barnaly Rashid, Oktay Agacoglu, Julia M. Stephen, Jing Sui, Jose Canive, Andy Mayer, Cheryl Aine, Juan R. Bustillo, Vince Daniel Calhoun

Research output: Contribution to journalArticle

Abstract

Mental disorders like schizophrenia are currently diagnosed by physicians/psychiatrists through clinical assessment and their evaluation of patient's self-reported experiences as the illness emerges. There is great interest in identifying biological markers of prognosis at the onset of illness, rather than relying on the evolution of symptoms across time. Functional network connectivity, which indicates a subject's overall level of "synchronicity" of activity between brain regions, demonstrates promise in providing individual subject predictive power. Many previous studies reported functional connectivity changes during resting-state using only functional magnetic resonance imaging (fMRI). Nevertheless, exclusive reliance on fMRI to generate such networks may limit the inference of the underlying dysfunctional connectivity, which is hypothesized to be a factor in patient symptoms, as fMRI measures connectivity via hemodynamics. Therefore, combination of connectivity assessments using fMRI and magnetoencephalography (MEG), which more directly measures neuronal activity, may provide improved classification of schizophrenia than either modality alone. Moreover, recent evidence indicates that metrics of dynamic connectivity may also be critical for understanding pathology in schizophrenia. In this work, we propose a new framework for extraction of important disease related features and classification of patients with schizophrenia based on using both fMRI and MEG to investigate functional network components in the resting state. Results of this study show that the integration of fMRI and MEG provides important information that captures fundamental characteristics of functional network connectivity in schizophrenia and is helpful for prediction of schizophrenia patient group membership. Combined fMRI/MEG methods, using static functional network connectivity analyses, improved classification accuracy relative to use of fMRI or MEG methods alone (by 15 and 12.45%, respectively), while combined fMRI/MEG methods using dynamic functional network connectivity analyses improved classification up to 5.12% relative to use of fMRI alone and up to 17.21% relative to use of MEG alone.

Original languageEnglish (US)
Article number466
JournalFrontiers in Neuroscience
Volume10
Issue numberOCT
DOIs
StatePublished - Oct 19 2016
Externally publishedYes

Fingerprint

Magnetoencephalography
Schizophrenia
Magnetic Resonance Imaging
Mental Disorders
Psychiatry
Biomarkers
Hemodynamics
Pathology
Physicians

Keywords

  • Classification
  • Connectivity
  • FMRI
  • MEG
  • Schizophrenia
  • Static and dynamic functional connectivity

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

Multimodal classification of schizophrenia patients with MEG and fMRI data using static and dynamic connectivity measures. / Cetin, Mustafa S.; Houck, Jon M.; Rashid, Barnaly; Agacoglu, Oktay; Stephen, Julia M.; Sui, Jing; Canive, Jose; Mayer, Andy; Aine, Cheryl; Bustillo, Juan R.; Calhoun, Vince Daniel.

In: Frontiers in Neuroscience, Vol. 10, No. OCT, 466, 19.10.2016.

Research output: Contribution to journalArticle

Cetin, MS, Houck, JM, Rashid, B, Agacoglu, O, Stephen, JM, Sui, J, Canive, J, Mayer, A, Aine, C, Bustillo, JR & Calhoun, VD 2016, 'Multimodal classification of schizophrenia patients with MEG and fMRI data using static and dynamic connectivity measures', Frontiers in Neuroscience, vol. 10, no. OCT, 466. https://doi.org/10.3389/fnins.2016.00466
Cetin, Mustafa S. ; Houck, Jon M. ; Rashid, Barnaly ; Agacoglu, Oktay ; Stephen, Julia M. ; Sui, Jing ; Canive, Jose ; Mayer, Andy ; Aine, Cheryl ; Bustillo, Juan R. ; Calhoun, Vince Daniel. / Multimodal classification of schizophrenia patients with MEG and fMRI data using static and dynamic connectivity measures. In: Frontiers in Neuroscience. 2016 ; Vol. 10, No. OCT.
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