Classifying patients with depressive and anxiety disorders according to symptom network structures: A Gaussian graphical mixture model-based clustering.

Patients with mental disorders often suffer from comorbidity. Transdiagnostic understandings of mental disorders are expected to provide more accurate and detailed descriptions of psychopathology and be helpful in developing efficient treatments. Although conventional clustering techniques, such as...

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Autores principales:	Jun Kashihara, Yoshitake Takebayashi, Yoshihiko Kunisato, Masaya Ito
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a14d285ae48044f7a2f43208b511475f
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spelling	oai:doaj.org-article:a14d285ae48044f7a2f43208b511475f2021-12-02T20:08:42ZClassifying patients with depressive and anxiety disorders according to symptom network structures: A Gaussian graphical mixture model-based clustering.1932-620310.1371/journal.pone.0256902https://doaj.org/article/a14d285ae48044f7a2f43208b511475f2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0256902https://doaj.org/toc/1932-6203Patients with mental disorders often suffer from comorbidity. Transdiagnostic understandings of mental disorders are expected to provide more accurate and detailed descriptions of psychopathology and be helpful in developing efficient treatments. Although conventional clustering techniques, such as latent profile analysis, are useful for the taxonomy of psychopathology, they provide little implications for targeting specific symptoms in each cluster. To overcome these limitations, we introduced Gaussian graphical mixture model (GGMM)-based clustering, a method developed in mathematical statistics to integrate clustering and network statistical approaches. To illustrate the technical details and clinical utility of the analysis, we applied GGMM-based clustering to a Japanese sample of 1,521 patients (Mage = 42.42 years), who had diagnostic labels of major depressive disorder (MDD; n = 406), panic disorder (PD; n = 198), social anxiety disorder (SAD; n = 116), obsessive-compulsive disorder (OCD; n = 66), comorbid MDD and any anxiety disorder (n = 636), or comorbid anxiety disorders (n = 99). As a result, we identified the following four transdiagnostic clusters characterized by i) strong OCD and PD symptoms, and moderate MDD and SAD symptoms; ii) moderate MDD, PD, and SAD symptoms, and weak OCD symptoms; iii) weak symptoms of all four disorders; and iv) strong symptoms of all four disorders. Simultaneously, a covariance symptom network within each cluster was visualized. The discussion highlighted that the GGMM-based clusters help us generate clinical hypotheses for transdiagnostic clusters by enabling further investigations of each symptom network, such as the calculation of centrality indexes.Jun KashiharaYoshitake TakebayashiYoshihiko KunisatoMasaya ItoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0256902 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jun Kashihara Yoshitake Takebayashi Yoshihiko Kunisato Masaya Ito Classifying patients with depressive and anxiety disorders according to symptom network structures: A Gaussian graphical mixture model-based clustering.
description	Patients with mental disorders often suffer from comorbidity. Transdiagnostic understandings of mental disorders are expected to provide more accurate and detailed descriptions of psychopathology and be helpful in developing efficient treatments. Although conventional clustering techniques, such as latent profile analysis, are useful for the taxonomy of psychopathology, they provide little implications for targeting specific symptoms in each cluster. To overcome these limitations, we introduced Gaussian graphical mixture model (GGMM)-based clustering, a method developed in mathematical statistics to integrate clustering and network statistical approaches. To illustrate the technical details and clinical utility of the analysis, we applied GGMM-based clustering to a Japanese sample of 1,521 patients (Mage = 42.42 years), who had diagnostic labels of major depressive disorder (MDD; n = 406), panic disorder (PD; n = 198), social anxiety disorder (SAD; n = 116), obsessive-compulsive disorder (OCD; n = 66), comorbid MDD and any anxiety disorder (n = 636), or comorbid anxiety disorders (n = 99). As a result, we identified the following four transdiagnostic clusters characterized by i) strong OCD and PD symptoms, and moderate MDD and SAD symptoms; ii) moderate MDD, PD, and SAD symptoms, and weak OCD symptoms; iii) weak symptoms of all four disorders; and iv) strong symptoms of all four disorders. Simultaneously, a covariance symptom network within each cluster was visualized. The discussion highlighted that the GGMM-based clusters help us generate clinical hypotheses for transdiagnostic clusters by enabling further investigations of each symptom network, such as the calculation of centrality indexes.
format	article
author	Jun Kashihara Yoshitake Takebayashi Yoshihiko Kunisato Masaya Ito
author_facet	Jun Kashihara Yoshitake Takebayashi Yoshihiko Kunisato Masaya Ito
author_sort	Jun Kashihara
title	Classifying patients with depressive and anxiety disorders according to symptom network structures: A Gaussian graphical mixture model-based clustering.
title_short	Classifying patients with depressive and anxiety disorders according to symptom network structures: A Gaussian graphical mixture model-based clustering.
title_full	Classifying patients with depressive and anxiety disorders according to symptom network structures: A Gaussian graphical mixture model-based clustering.
title_fullStr	Classifying patients with depressive and anxiety disorders according to symptom network structures: A Gaussian graphical mixture model-based clustering.
title_full_unstemmed	Classifying patients with depressive and anxiety disorders according to symptom network structures: A Gaussian graphical mixture model-based clustering.
title_sort	classifying patients with depressive and anxiety disorders according to symptom network structures: a gaussian graphical mixture model-based clustering.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/a14d285ae48044f7a2f43208b511475f
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Classifying patients with depressive and anxiety disorders according to symptom network structures: A Gaussian graphical mixture model-based clustering.

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