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The futility of long-term predictions in bipolar disorder: mood fluctuations are the result of deterministic chaotic processes
BACKGROUND: Understanding the underlying architecture of mood regulation in bipolar disorder (BD) is important, as we are starting to conceptualize BD as a more complex disorder than one of recurring manic or depressive episodes. Nonlinear techniques are employed to understand and model the behavior...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8486895/ https://www.ncbi.nlm.nih.gov/pubmed/34596784 http://dx.doi.org/10.1186/s40345-021-00235-3 |
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author | Ortiz, Abigail Bradler, Kamil Mowete, Maxine MacLean, Stephane Garnham, Julie Slaney, Claire Mulsant, Benoit H. Alda, Martin |
author_facet | Ortiz, Abigail Bradler, Kamil Mowete, Maxine MacLean, Stephane Garnham, Julie Slaney, Claire Mulsant, Benoit H. Alda, Martin |
author_sort | Ortiz, Abigail |
collection | PubMed |
description | BACKGROUND: Understanding the underlying architecture of mood regulation in bipolar disorder (BD) is important, as we are starting to conceptualize BD as a more complex disorder than one of recurring manic or depressive episodes. Nonlinear techniques are employed to understand and model the behavior of complex systems. Our aim was to assess the underlying nonlinear properties that account for mood and energy fluctuations in patients with BD; and to compare whether these processes were different in healthy controls (HC) and unaffected first-degree relatives (FDR). We used three different nonlinear techniques: Lyapunov exponent, detrended fluctuation analysis and fractal dimension to assess the underlying behavior of mood and energy fluctuations in all groups; and subsequently to assess whether these arise from different processes in each of these groups. RESULTS: There was a positive, short-term autocorrelation for both mood and energy series in all three groups. In the mood series, the largest Lyapunov exponent was found in HC (1.84), compared to BD (1.63) and FDR (1.71) groups [F (2, 87) = 8.42, p < 0.005]. A post-hoc Tukey test showed that Lyapunov exponent in HC was significantly higher than both the BD (p = 0.003) and FDR groups (p = 0.03). Similarly, in the energy series, the largest Lyapunov exponent was found in HC (1.85), compared to BD (1.76) and FDR (1.67) [F (2, 87) = 11.02; p < 0.005]. There were no significant differences between groups for the detrended fluctuation analysis or fractal dimension. CONCLUSIONS: The underlying nature of mood variability is in keeping with that of a chaotic system, which means that fluctuations are generated by deterministic nonlinear process(es) in HC, BD, and FDR. The value of this complex modeling lies in analyzing the nature of the processes involved in mood regulation. It also suggests that the window for episode prediction in BD will be inevitably short. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40345-021-00235-3. |
format | Online Article Text |
id | pubmed-8486895 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-84868952021-10-04 The futility of long-term predictions in bipolar disorder: mood fluctuations are the result of deterministic chaotic processes Ortiz, Abigail Bradler, Kamil Mowete, Maxine MacLean, Stephane Garnham, Julie Slaney, Claire Mulsant, Benoit H. Alda, Martin Int J Bipolar Disord Research BACKGROUND: Understanding the underlying architecture of mood regulation in bipolar disorder (BD) is important, as we are starting to conceptualize BD as a more complex disorder than one of recurring manic or depressive episodes. Nonlinear techniques are employed to understand and model the behavior of complex systems. Our aim was to assess the underlying nonlinear properties that account for mood and energy fluctuations in patients with BD; and to compare whether these processes were different in healthy controls (HC) and unaffected first-degree relatives (FDR). We used three different nonlinear techniques: Lyapunov exponent, detrended fluctuation analysis and fractal dimension to assess the underlying behavior of mood and energy fluctuations in all groups; and subsequently to assess whether these arise from different processes in each of these groups. RESULTS: There was a positive, short-term autocorrelation for both mood and energy series in all three groups. In the mood series, the largest Lyapunov exponent was found in HC (1.84), compared to BD (1.63) and FDR (1.71) groups [F (2, 87) = 8.42, p < 0.005]. A post-hoc Tukey test showed that Lyapunov exponent in HC was significantly higher than both the BD (p = 0.003) and FDR groups (p = 0.03). Similarly, in the energy series, the largest Lyapunov exponent was found in HC (1.85), compared to BD (1.76) and FDR (1.67) [F (2, 87) = 11.02; p < 0.005]. There were no significant differences between groups for the detrended fluctuation analysis or fractal dimension. CONCLUSIONS: The underlying nature of mood variability is in keeping with that of a chaotic system, which means that fluctuations are generated by deterministic nonlinear process(es) in HC, BD, and FDR. The value of this complex modeling lies in analyzing the nature of the processes involved in mood regulation. It also suggests that the window for episode prediction in BD will be inevitably short. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40345-021-00235-3. Springer Berlin Heidelberg 2021-10-01 /pmc/articles/PMC8486895/ /pubmed/34596784 http://dx.doi.org/10.1186/s40345-021-00235-3 Text en © The Author(s) 2021 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 Ortiz, Abigail Bradler, Kamil Mowete, Maxine MacLean, Stephane Garnham, Julie Slaney, Claire Mulsant, Benoit H. Alda, Martin The futility of long-term predictions in bipolar disorder: mood fluctuations are the result of deterministic chaotic processes |
title | The futility of long-term predictions in bipolar disorder: mood fluctuations are the result of deterministic chaotic processes |
title_full | The futility of long-term predictions in bipolar disorder: mood fluctuations are the result of deterministic chaotic processes |
title_fullStr | The futility of long-term predictions in bipolar disorder: mood fluctuations are the result of deterministic chaotic processes |
title_full_unstemmed | The futility of long-term predictions in bipolar disorder: mood fluctuations are the result of deterministic chaotic processes |
title_short | The futility of long-term predictions in bipolar disorder: mood fluctuations are the result of deterministic chaotic processes |
title_sort | futility of long-term predictions in bipolar disorder: mood fluctuations are the result of deterministic chaotic processes |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8486895/ https://www.ncbi.nlm.nih.gov/pubmed/34596784 http://dx.doi.org/10.1186/s40345-021-00235-3 |
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