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Complexity and variability analyses of motor activity distinguish mood states in bipolar disorder

Changes in motor activity are core symptoms of mood episodes in bipolar disorder. The manic state is characterized by increased variance, augmented complexity and irregular circadian rhythmicity when compared to healthy controls. No previous studies have compared mania to euthymia intra-individually...

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Autores principales: Jakobsen, Petter, Stautland, Andrea, Riegler, Michael Alexander, Côté-Allard, Ulysse, Sepasdar, Zahra, Nordgreen, Tine, Torresen, Jim, Fasmer, Ole Bernt, Oedegaard, Ketil Joachim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
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Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8782466/
https://www.ncbi.nlm.nih.gov/pubmed/35061801
http://dx.doi.org/10.1371/journal.pone.0262232
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author Jakobsen, Petter
Stautland, Andrea
Riegler, Michael Alexander
Côté-Allard, Ulysse
Sepasdar, Zahra
Nordgreen, Tine
Torresen, Jim
Fasmer, Ole Bernt
Oedegaard, Ketil Joachim
author_facet Jakobsen, Petter
Stautland, Andrea
Riegler, Michael Alexander
Côté-Allard, Ulysse
Sepasdar, Zahra
Nordgreen, Tine
Torresen, Jim
Fasmer, Ole Bernt
Oedegaard, Ketil Joachim
author_sort Jakobsen, Petter
collection PubMed
description Changes in motor activity are core symptoms of mood episodes in bipolar disorder. The manic state is characterized by increased variance, augmented complexity and irregular circadian rhythmicity when compared to healthy controls. No previous studies have compared mania to euthymia intra-individually in motor activity. The aim of this study was to characterize differences in motor activity when comparing manic patients to their euthymic selves. Motor activity was collected from 16 bipolar inpatients in mania and remission. 24-h recordings and 2-h time series in the morning and evening were analyzed for mean activity, variability and complexity. Lastly, the recordings were analyzed with the similarity graph algorithm and graph theory concepts such as edges, bridges, connected components and cliques. The similarity graph measures fluctuations in activity reasonably comparable to both variability and complexity measures. However, direct comparisons are difficult as most graph measures reveal variability in constricted time windows. Compared to sample entropy, the similarity graph is less sensitive to outliers. The little-understood estimate Bridges is possibly revealing underlying dynamics in the time series. When compared to euthymia, over the duration of approximately one circadian cycle, the manic state presented reduced variability, displayed by decreased standard deviation (p = 0.013) and augmented complexity shown by increased sample entropy (p = 0.025). During mania there were also fewer edges (p = 0.039) and more bridges (p = 0.026). Similar significant changes in variability and complexity were observed in the 2-h morning and evening sequences, mainly in the estimates of the similarity graph algorithm. Finally, augmented complexity was present in morning samples during mania, displayed by increased sample entropy (p = 0.015). In conclusion, the motor activity of mania is characterized by altered complexity and variability when compared within-subject to euthymia.
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spelling pubmed-87824662022-01-22 Complexity and variability analyses of motor activity distinguish mood states in bipolar disorder Jakobsen, Petter Stautland, Andrea Riegler, Michael Alexander Côté-Allard, Ulysse Sepasdar, Zahra Nordgreen, Tine Torresen, Jim Fasmer, Ole Bernt Oedegaard, Ketil Joachim PLoS One Research Article Changes in motor activity are core symptoms of mood episodes in bipolar disorder. The manic state is characterized by increased variance, augmented complexity and irregular circadian rhythmicity when compared to healthy controls. No previous studies have compared mania to euthymia intra-individually in motor activity. The aim of this study was to characterize differences in motor activity when comparing manic patients to their euthymic selves. Motor activity was collected from 16 bipolar inpatients in mania and remission. 24-h recordings and 2-h time series in the morning and evening were analyzed for mean activity, variability and complexity. Lastly, the recordings were analyzed with the similarity graph algorithm and graph theory concepts such as edges, bridges, connected components and cliques. The similarity graph measures fluctuations in activity reasonably comparable to both variability and complexity measures. However, direct comparisons are difficult as most graph measures reveal variability in constricted time windows. Compared to sample entropy, the similarity graph is less sensitive to outliers. The little-understood estimate Bridges is possibly revealing underlying dynamics in the time series. When compared to euthymia, over the duration of approximately one circadian cycle, the manic state presented reduced variability, displayed by decreased standard deviation (p = 0.013) and augmented complexity shown by increased sample entropy (p = 0.025). During mania there were also fewer edges (p = 0.039) and more bridges (p = 0.026). Similar significant changes in variability and complexity were observed in the 2-h morning and evening sequences, mainly in the estimates of the similarity graph algorithm. Finally, augmented complexity was present in morning samples during mania, displayed by increased sample entropy (p = 0.015). In conclusion, the motor activity of mania is characterized by altered complexity and variability when compared within-subject to euthymia. Public Library of Science 2022-01-21 /pmc/articles/PMC8782466/ /pubmed/35061801 http://dx.doi.org/10.1371/journal.pone.0262232 Text en © 2022 Jakobsen et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Jakobsen, Petter
Stautland, Andrea
Riegler, Michael Alexander
Côté-Allard, Ulysse
Sepasdar, Zahra
Nordgreen, Tine
Torresen, Jim
Fasmer, Ole Bernt
Oedegaard, Ketil Joachim
Complexity and variability analyses of motor activity distinguish mood states in bipolar disorder
title Complexity and variability analyses of motor activity distinguish mood states in bipolar disorder
title_full Complexity and variability analyses of motor activity distinguish mood states in bipolar disorder
title_fullStr Complexity and variability analyses of motor activity distinguish mood states in bipolar disorder
title_full_unstemmed Complexity and variability analyses of motor activity distinguish mood states in bipolar disorder
title_short Complexity and variability analyses of motor activity distinguish mood states in bipolar disorder
title_sort complexity and variability analyses of motor activity distinguish mood states in bipolar disorder
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8782466/
https://www.ncbi.nlm.nih.gov/pubmed/35061801
http://dx.doi.org/10.1371/journal.pone.0262232
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