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Identifying subtypes of bipolar disorder based on clinical and neurobiological characteristics

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 functio...

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Autores principales: Chen, Yen-Ling, Tu, Pei-Chi, Huang, Tzu-Hsuan, Bai, Ya-Mei, Su, Tung-Ping, Chen, Mu-Hong, Wu, Yu-Te
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8385023/
https://www.ncbi.nlm.nih.gov/pubmed/34429498
http://dx.doi.org/10.1038/s41598-021-96645-5
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author Chen, Yen-Ling
Tu, Pei-Chi
Huang, Tzu-Hsuan
Bai, Ya-Mei
Su, Tung-Ping
Chen, Mu-Hong
Wu, Yu-Te
author_facet Chen, Yen-Ling
Tu, Pei-Chi
Huang, Tzu-Hsuan
Bai, Ya-Mei
Su, Tung-Ping
Chen, Mu-Hong
Wu, Yu-Te
author_sort Chen, Yen-Ling
collection PubMed
description 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.
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spelling pubmed-83850232021-09-01 Identifying subtypes of bipolar disorder based on clinical and neurobiological characteristics Chen, Yen-Ling Tu, Pei-Chi Huang, Tzu-Hsuan Bai, Ya-Mei Su, Tung-Ping Chen, Mu-Hong Wu, Yu-Te Sci Rep Article 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. Nature Publishing Group UK 2021-08-24 /pmc/articles/PMC8385023/ /pubmed/34429498 http://dx.doi.org/10.1038/s41598-021-96645-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Chen, Yen-Ling
Tu, Pei-Chi
Huang, Tzu-Hsuan
Bai, Ya-Mei
Su, Tung-Ping
Chen, Mu-Hong
Wu, Yu-Te
Identifying subtypes of bipolar disorder based on clinical and neurobiological characteristics
title 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_short Identifying subtypes of bipolar disorder based on clinical and neurobiological characteristics
title_sort identifying subtypes of bipolar disorder based on clinical and neurobiological characteristics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8385023/
https://www.ncbi.nlm.nih.gov/pubmed/34429498
http://dx.doi.org/10.1038/s41598-021-96645-5
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