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Multimodal Classification of Schizophrenia Patients with MEG and fMRI Data Using Static and Dynamic Connectivity Measures

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,...

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Autores principales: 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 D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5070283/
https://www.ncbi.nlm.nih.gov/pubmed/27807403
http://dx.doi.org/10.3389/fnins.2016.00466
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author 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 D.
author_facet 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 D.
author_sort Cetin, Mustafa S.
collection PubMed
description 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.
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spelling pubmed-50702832016-11-02 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 D. Front Neurosci Neuroscience 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. Frontiers Media S.A. 2016-10-19 /pmc/articles/PMC5070283/ /pubmed/27807403 http://dx.doi.org/10.3389/fnins.2016.00466 Text en Copyright © 2016 Cetin, Houck, Rashid, Agacoglu, Stephen, Sui, Canive, Mayer, Aine, Bustillo and Calhoun. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
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 D.
Multimodal Classification of Schizophrenia Patients with MEG and fMRI Data Using Static and Dynamic Connectivity Measures
title Multimodal Classification of Schizophrenia Patients with MEG and fMRI Data Using Static and Dynamic Connectivity Measures
title_full Multimodal Classification of Schizophrenia Patients with MEG and fMRI Data Using Static and Dynamic Connectivity Measures
title_fullStr Multimodal Classification of Schizophrenia Patients with MEG and fMRI Data Using Static and Dynamic Connectivity Measures
title_full_unstemmed Multimodal Classification of Schizophrenia Patients with MEG and fMRI Data Using Static and Dynamic Connectivity Measures
title_short Multimodal Classification of Schizophrenia Patients with MEG and fMRI Data Using Static and Dynamic Connectivity Measures
title_sort multimodal classification of schizophrenia patients with meg and fmri data using static and dynamic connectivity measures
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5070283/
https://www.ncbi.nlm.nih.gov/pubmed/27807403
http://dx.doi.org/10.3389/fnins.2016.00466
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