Classifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning

Abstract Given the subjective nature of conventional diagnostic methods for post-traumatic stress disorder (PTSD), an objectively measurable biomarker is highly desirable; especially to clinicians and researchers. Macroscopic neural circuits measured using magnetoencephalography (MEG) has previously...

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Autores principales:	Jing Zhang, J. Don Richardson, Benjamin T. Dunkley
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/06c493ccf0bf4887887a61e4696b5aa5
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spelling	oai:doaj.org-article:06c493ccf0bf4887887a61e4696b5aa52021-12-02T18:17:42ZClassifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning10.1038/s41598-020-62713-52045-2322https://doaj.org/article/06c493ccf0bf4887887a61e4696b5aa52020-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-62713-5https://doaj.org/toc/2045-2322Abstract Given the subjective nature of conventional diagnostic methods for post-traumatic stress disorder (PTSD), an objectively measurable biomarker is highly desirable; especially to clinicians and researchers. Macroscopic neural circuits measured using magnetoencephalography (MEG) has previously been shown to be indicative of the PTSD phenotype and severity. In the present study, we employed a machine learning-based classification framework using MEG neural synchrony to distinguish combat-related PTSD from trauma-exposed controls. Support vector machine (SVM) was used as the core classification algorithm. A recursive random forest feature selection step was directly incorporated in the nested SVM cross validation process (CV-SVM-rRF-FS) for identifying the most important features for PTSD classification. For the five frequency bands tested, the CV-SVM-rRF-FS analysis selected the minimum numbers of edges per frequency that could serve as a PTSD signature and be used as the basis for SVM modelling. Many of the selected edges have been reported previously to be core in PTSD pathophysiology, with frequency-specific patterns also observed. Furthermore, the independent partial least squares discriminant analysis suggested low bias in the machine learning process. The final SVM models built with selected features showed excellent PTSD classification performance (area-under-curve value up to 0.9). Testament to its robustness when distinguishing individuals from a heavily traumatised control group, these developments for a classification model for PTSD also provide a comprehensive machine learning-based computational framework for classifying other mental health challenges using MEG connectome profiles.Jing ZhangJ. Don RichardsonBenjamin T. DunkleyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jing Zhang J. Don Richardson Benjamin T. Dunkley Classifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning
description	Abstract Given the subjective nature of conventional diagnostic methods for post-traumatic stress disorder (PTSD), an objectively measurable biomarker is highly desirable; especially to clinicians and researchers. Macroscopic neural circuits measured using magnetoencephalography (MEG) has previously been shown to be indicative of the PTSD phenotype and severity. In the present study, we employed a machine learning-based classification framework using MEG neural synchrony to distinguish combat-related PTSD from trauma-exposed controls. Support vector machine (SVM) was used as the core classification algorithm. A recursive random forest feature selection step was directly incorporated in the nested SVM cross validation process (CV-SVM-rRF-FS) for identifying the most important features for PTSD classification. For the five frequency bands tested, the CV-SVM-rRF-FS analysis selected the minimum numbers of edges per frequency that could serve as a PTSD signature and be used as the basis for SVM modelling. Many of the selected edges have been reported previously to be core in PTSD pathophysiology, with frequency-specific patterns also observed. Furthermore, the independent partial least squares discriminant analysis suggested low bias in the machine learning process. The final SVM models built with selected features showed excellent PTSD classification performance (area-under-curve value up to 0.9). Testament to its robustness when distinguishing individuals from a heavily traumatised control group, these developments for a classification model for PTSD also provide a comprehensive machine learning-based computational framework for classifying other mental health challenges using MEG connectome profiles.
format	article
author	Jing Zhang J. Don Richardson Benjamin T. Dunkley
author_facet	Jing Zhang J. Don Richardson Benjamin T. Dunkley
author_sort	Jing Zhang
title	Classifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning
title_short	Classifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning
title_full	Classifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning
title_fullStr	Classifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning
title_full_unstemmed	Classifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning
title_sort	classifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/06c493ccf0bf4887887a61e4696b5aa5
work_keys_str_mv	AT jingzhang classifyingposttraumaticstressdisorderusingthemagnetoencephalographicconnectomeandmachinelearning AT jdonrichardson classifyingposttraumaticstressdisorderusingthemagnetoencephalographicconnectomeandmachinelearning AT benjamintdunkley classifyingposttraumaticstressdisorderusingthemagnetoencephalographicconnectomeandmachinelearning
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Classifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning

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