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Emotion regulation in bipolar disorder type-I: multivariate analysis of fMRI data

BACKGROUND: Bipolar disorder type-I (BD-I) patients are known to show emotion regulation abnormalities. In a previous fMRI study using an explicit emotion regulation paradigm, we compared responses from 19 BD-I patients and 17 matched healthy controls (HC). A standard general linear model-based univ...

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Autores principales: Kondo, Fumika, Whitehead, Jocelyne C., Corbalán, Fernando, Beaulieu, Serge, Armony, Jorge L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10039967/
https://www.ncbi.nlm.nih.gov/pubmed/36964848
http://dx.doi.org/10.1186/s40345-023-00292-w
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author Kondo, Fumika
Whitehead, Jocelyne C.
Corbalán, Fernando
Beaulieu, Serge
Armony, Jorge L.
author_facet Kondo, Fumika
Whitehead, Jocelyne C.
Corbalán, Fernando
Beaulieu, Serge
Armony, Jorge L.
author_sort Kondo, Fumika
collection PubMed
description BACKGROUND: Bipolar disorder type-I (BD-I) patients are known to show emotion regulation abnormalities. In a previous fMRI study using an explicit emotion regulation paradigm, we compared responses from 19 BD-I patients and 17 matched healthy controls (HC). A standard general linear model-based univariate analysis revealed that BD patients showed increased activations in inferior frontal gyrus when instructed to decrease their emotional response as elicited by neutral images. We implemented multivariate pattern recognition analyses on the same data to examine if we could classify conditions within-group as well as HC versus BD. METHODS: We reanalyzed explicit emotion regulation data using a multivariate pattern recognition approach, as implemented in PRONTO software. The original experimental paradigm consisted of a full 2 × 2 factorial design, with valence (Negative/Neutral) and instruction (Look/Decrease) as within subject factors. RESULTS: The multivariate models were able to accurately classify different task conditions when HC and BD were analyzed separately (63.24%–75.00%, p = 0.001–0.012). In addition, the models were able to correctly classify HC versus BD with significant accuracy in conditions where subjects were instructed to downregulate their felt emotion (59.60%–60.84%, p = 0.014–0.018). The results for HC versus BD classification demonstrated contributions from the salience network, several occipital and frontal regions, inferior parietal lobes, as well as other cortical regions, to achieve above-chance classifications. CONCLUSIONS: Our multivariate analysis successfully reproduced some of the main results obtained in the previous univariate analysis, confirming that these findings are not dependent on the analysis approach. In particular, both types of analyses suggest that there is a significant difference of neural patterns between conditions within each subject group. The multivariate approach also revealed that reappraisal conditions provide the most informative activity for differentiating HC versus BD, irrespective of emotional valence (negative or neutral). The current results illustrate the importance of investigating the cognitive control of emotion in BD. We also propose a set of candidate regions for further study of emotional control in BD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40345-023-00292-w.
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spelling pubmed-100399672023-03-27 Emotion regulation in bipolar disorder type-I: multivariate analysis of fMRI data Kondo, Fumika Whitehead, Jocelyne C. Corbalán, Fernando Beaulieu, Serge Armony, Jorge L. Int J Bipolar Disord Research BACKGROUND: Bipolar disorder type-I (BD-I) patients are known to show emotion regulation abnormalities. In a previous fMRI study using an explicit emotion regulation paradigm, we compared responses from 19 BD-I patients and 17 matched healthy controls (HC). A standard general linear model-based univariate analysis revealed that BD patients showed increased activations in inferior frontal gyrus when instructed to decrease their emotional response as elicited by neutral images. We implemented multivariate pattern recognition analyses on the same data to examine if we could classify conditions within-group as well as HC versus BD. METHODS: We reanalyzed explicit emotion regulation data using a multivariate pattern recognition approach, as implemented in PRONTO software. The original experimental paradigm consisted of a full 2 × 2 factorial design, with valence (Negative/Neutral) and instruction (Look/Decrease) as within subject factors. RESULTS: The multivariate models were able to accurately classify different task conditions when HC and BD were analyzed separately (63.24%–75.00%, p = 0.001–0.012). In addition, the models were able to correctly classify HC versus BD with significant accuracy in conditions where subjects were instructed to downregulate their felt emotion (59.60%–60.84%, p = 0.014–0.018). The results for HC versus BD classification demonstrated contributions from the salience network, several occipital and frontal regions, inferior parietal lobes, as well as other cortical regions, to achieve above-chance classifications. CONCLUSIONS: Our multivariate analysis successfully reproduced some of the main results obtained in the previous univariate analysis, confirming that these findings are not dependent on the analysis approach. In particular, both types of analyses suggest that there is a significant difference of neural patterns between conditions within each subject group. The multivariate approach also revealed that reappraisal conditions provide the most informative activity for differentiating HC versus BD, irrespective of emotional valence (negative or neutral). The current results illustrate the importance of investigating the cognitive control of emotion in BD. We also propose a set of candidate regions for further study of emotional control in BD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40345-023-00292-w. Springer Berlin Heidelberg 2023-03-25 /pmc/articles/PMC10039967/ /pubmed/36964848 http://dx.doi.org/10.1186/s40345-023-00292-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Research
Kondo, Fumika
Whitehead, Jocelyne C.
Corbalán, Fernando
Beaulieu, Serge
Armony, Jorge L.
Emotion regulation in bipolar disorder type-I: multivariate analysis of fMRI data
title Emotion regulation in bipolar disorder type-I: multivariate analysis of fMRI data
title_full Emotion regulation in bipolar disorder type-I: multivariate analysis of fMRI data
title_fullStr Emotion regulation in bipolar disorder type-I: multivariate analysis of fMRI data
title_full_unstemmed Emotion regulation in bipolar disorder type-I: multivariate analysis of fMRI data
title_short Emotion regulation in bipolar disorder type-I: multivariate analysis of fMRI data
title_sort emotion regulation in bipolar disorder type-i: multivariate analysis of fmri data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10039967/
https://www.ncbi.nlm.nih.gov/pubmed/36964848
http://dx.doi.org/10.1186/s40345-023-00292-w
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