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Tracking changes in behavioural dynamics using prediction error

Automated analysis of video can now generate extensive time series of pose and motion in freely-moving organisms. This requires new quantitative tools to characterise behavioural dynamics. For the model roundworm Caenorhabditis elegans, body pose can be accurately quantified from video as coordinate...

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Autores principales: Lorimer, Tom, Goodridge, Rachel, Bock, Antonia K., Agarwal, Vitul, Saberski, Erik, Sugihara, George, Rifkin, Scott A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8115816/
https://www.ncbi.nlm.nih.gov/pubmed/33979384
http://dx.doi.org/10.1371/journal.pone.0251053
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author Lorimer, Tom
Goodridge, Rachel
Bock, Antonia K.
Agarwal, Vitul
Saberski, Erik
Sugihara, George
Rifkin, Scott A.
author_facet Lorimer, Tom
Goodridge, Rachel
Bock, Antonia K.
Agarwal, Vitul
Saberski, Erik
Sugihara, George
Rifkin, Scott A.
author_sort Lorimer, Tom
collection PubMed
description Automated analysis of video can now generate extensive time series of pose and motion in freely-moving organisms. This requires new quantitative tools to characterise behavioural dynamics. For the model roundworm Caenorhabditis elegans, body pose can be accurately quantified from video as coordinates in a single low-dimensional space. We focus on this well-established case as an illustrative example and propose a method to reveal subtle variations in behaviour at high time resolution. Our data-driven method, based on empirical dynamic modeling, quantifies behavioural change as prediction error with respect to a time-delay-embedded ‘attractor’ of behavioural dynamics. Because this attractor is constructed from a user-specified reference data set, the approach can be tailored to specific behaviours of interest at the individual or group level. We validate the approach by detecting small changes in the movement dynamics of C. elegans at the initiation and completion of delta turns. We then examine an escape response initiated by an aversive stimulus and find that the method can track return to baseline behaviour in individual worms and reveal variations in the escape response between worms. We suggest that this general approach—defining dynamic behaviours using reference attractors and quantifying dynamic changes using prediction error—may be of broad interest and relevance to behavioural researchers working with video-derived time series.
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spelling pubmed-81158162021-05-24 Tracking changes in behavioural dynamics using prediction error Lorimer, Tom Goodridge, Rachel Bock, Antonia K. Agarwal, Vitul Saberski, Erik Sugihara, George Rifkin, Scott A. PLoS One Research Article Automated analysis of video can now generate extensive time series of pose and motion in freely-moving organisms. This requires new quantitative tools to characterise behavioural dynamics. For the model roundworm Caenorhabditis elegans, body pose can be accurately quantified from video as coordinates in a single low-dimensional space. We focus on this well-established case as an illustrative example and propose a method to reveal subtle variations in behaviour at high time resolution. Our data-driven method, based on empirical dynamic modeling, quantifies behavioural change as prediction error with respect to a time-delay-embedded ‘attractor’ of behavioural dynamics. Because this attractor is constructed from a user-specified reference data set, the approach can be tailored to specific behaviours of interest at the individual or group level. We validate the approach by detecting small changes in the movement dynamics of C. elegans at the initiation and completion of delta turns. We then examine an escape response initiated by an aversive stimulus and find that the method can track return to baseline behaviour in individual worms and reveal variations in the escape response between worms. We suggest that this general approach—defining dynamic behaviours using reference attractors and quantifying dynamic changes using prediction error—may be of broad interest and relevance to behavioural researchers working with video-derived time series. Public Library of Science 2021-05-12 /pmc/articles/PMC8115816/ /pubmed/33979384 http://dx.doi.org/10.1371/journal.pone.0251053 Text en © 2021 Lorimer 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
Lorimer, Tom
Goodridge, Rachel
Bock, Antonia K.
Agarwal, Vitul
Saberski, Erik
Sugihara, George
Rifkin, Scott A.
Tracking changes in behavioural dynamics using prediction error
title Tracking changes in behavioural dynamics using prediction error
title_full Tracking changes in behavioural dynamics using prediction error
title_fullStr Tracking changes in behavioural dynamics using prediction error
title_full_unstemmed Tracking changes in behavioural dynamics using prediction error
title_short Tracking changes in behavioural dynamics using prediction error
title_sort tracking changes in behavioural dynamics using prediction error
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8115816/
https://www.ncbi.nlm.nih.gov/pubmed/33979384
http://dx.doi.org/10.1371/journal.pone.0251053
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