Identifying subtypes of bipolar disorder based on clinical and neurobiological characteristics

Abstract The ability to classify patients with bipolar disorder (BD) is restricted by their heterogeneity, which limits the understanding of their neuropathology. Therefore, we aimed to investigate clinically discernible and neurobiologically distinguishable BD subtypes. T1-weighted and resting-stat...

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Autores principales:	Yen-Ling Chen, Pei-Chi Tu, Tzu-Hsuan Huang, Ya-Mei Bai, Tung-Ping Su, Mu-Hong Chen, Yu-Te Wu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e2c4a639f19b4830817886a5bd7915e4
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spelling	oai:doaj.org-article:e2c4a639f19b4830817886a5bd7915e42021-12-02T19:02:30ZIdentifying subtypes of bipolar disorder based on clinical and neurobiological characteristics10.1038/s41598-021-96645-52045-2322https://doaj.org/article/e2c4a639f19b4830817886a5bd7915e42021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96645-5https://doaj.org/toc/2045-2322Abstract The ability to classify patients with bipolar disorder (BD) is restricted by their heterogeneity, which limits the understanding of their neuropathology. Therefore, we aimed to investigate clinically discernible and neurobiologically distinguishable BD subtypes. T1-weighted and resting-state functional magnetic resonance images of 112 patients with BD were obtained, and patients were segregated according to diagnostic subtype (i.e., types I and II) and clinical patterns, including the number of episodes and hospitalizations and history of suicide and psychosis. For each clinical pattern, fewer and more occurrences subgroups and types I and II were classified through nested cross-validation for robust performance, with minimum redundancy and maximum relevance, in feature selection. To assess the proportion of variance in cognitive performance explained by the neurobiological markers, multiple linear regression between verbal memory and the selected features was conducted. Satisfactory performance (mean accuracy, 73.60%) in classifying patients with a high or low number of episodes was attained through functional connectivity, mostly from default-mode and motor networks. Moreover, these neurobiological markers explained 62% of the variance in verbal memory. The number of episodes is a potentially critical aspect of the neuropathology of BD. Neurobiological markers can help identify BD neuroprogression.Yen-Ling ChenPei-Chi TuTzu-Hsuan HuangYa-Mei BaiTung-Ping SuMu-Hong ChenYu-Te WuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yen-Ling Chen Pei-Chi Tu Tzu-Hsuan Huang Ya-Mei Bai Tung-Ping Su Mu-Hong Chen Yu-Te Wu Identifying subtypes of bipolar disorder based on clinical and neurobiological characteristics
description	Abstract The ability to classify patients with bipolar disorder (BD) is restricted by their heterogeneity, which limits the understanding of their neuropathology. Therefore, we aimed to investigate clinically discernible and neurobiologically distinguishable BD subtypes. T1-weighted and resting-state functional magnetic resonance images of 112 patients with BD were obtained, and patients were segregated according to diagnostic subtype (i.e., types I and II) and clinical patterns, including the number of episodes and hospitalizations and history of suicide and psychosis. For each clinical pattern, fewer and more occurrences subgroups and types I and II were classified through nested cross-validation for robust performance, with minimum redundancy and maximum relevance, in feature selection. To assess the proportion of variance in cognitive performance explained by the neurobiological markers, multiple linear regression between verbal memory and the selected features was conducted. Satisfactory performance (mean accuracy, 73.60%) in classifying patients with a high or low number of episodes was attained through functional connectivity, mostly from default-mode and motor networks. Moreover, these neurobiological markers explained 62% of the variance in verbal memory. The number of episodes is a potentially critical aspect of the neuropathology of BD. Neurobiological markers can help identify BD neuroprogression.
format	article
author	Yen-Ling Chen Pei-Chi Tu Tzu-Hsuan Huang Ya-Mei Bai Tung-Ping Su Mu-Hong Chen Yu-Te Wu
author_facet	Yen-Ling Chen Pei-Chi Tu Tzu-Hsuan Huang Ya-Mei Bai Tung-Ping Su Mu-Hong Chen Yu-Te Wu
author_sort	Yen-Ling Chen
title	Identifying subtypes of bipolar disorder based on clinical and neurobiological characteristics
title_short	Identifying subtypes of bipolar disorder based on clinical and neurobiological characteristics
title_full	Identifying subtypes of bipolar disorder based on clinical and neurobiological characteristics
title_fullStr	Identifying subtypes of bipolar disorder based on clinical and neurobiological characteristics
title_full_unstemmed	Identifying subtypes of bipolar disorder based on clinical and neurobiological characteristics
title_sort	identifying subtypes of bipolar disorder based on clinical and neurobiological characteristics
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e2c4a639f19b4830817886a5bd7915e4
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Identifying subtypes of bipolar disorder based on clinical and neurobiological characteristics

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