A framework to identify structured behavioral patterns within rodent spatial trajectories

Abstract Animal behavior is highly structured. Yet, structured behavioral patterns—or “statistical ethograms”—are not immediately apparent from the full spatiotemporal data that behavioral scientists usually collect. Here, we introduce a framework to quantitatively characterize rodent behavior durin...

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Autores principales:	Francesco Donnarumma, Roberto Prevete, Domenico Maisto, Simone Fuscone, Emily M. Irvine, Matthijs A. A. van der Meer, Caleb Kemere, Giovanni Pezzulo
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9c9220afc71d49d291463aa7569b5061
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spelling	oai:doaj.org-article:9c9220afc71d49d291463aa7569b50612021-12-02T14:12:08ZA framework to identify structured behavioral patterns within rodent spatial trajectories10.1038/s41598-020-79744-72045-2322https://doaj.org/article/9c9220afc71d49d291463aa7569b50612021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79744-7https://doaj.org/toc/2045-2322Abstract Animal behavior is highly structured. Yet, structured behavioral patterns—or “statistical ethograms”—are not immediately apparent from the full spatiotemporal data that behavioral scientists usually collect. Here, we introduce a framework to quantitatively characterize rodent behavior during spatial (e.g., maze) navigation, in terms of movement building blocks or motor primitives. The hypothesis that we pursue is that rodent behavior is characterized by a small number of motor primitives, which are combined over time to produce open-ended movements. We assume motor primitives to be organized in terms of two sparsity principles: each movement is controlled using a limited subset of motor primitives (sparse superposition) and each primitive is active only for time-limited, time-contiguous portions of movements (sparse activity). We formalize this hypothesis using a sparse dictionary learning method, which we use to extract motor primitives from rodent position and velocity data collected during spatial navigation, and successively to reconstruct past trajectories and predict novel ones. Three main results validate our approach. First, rodent behavioral trajectories are robustly reconstructed from incomplete data, performing better than approaches based on standard dimensionality reduction methods, such as principal component analysis, or single sparsity. Second, the motor primitives extracted during one experimental session generalize and afford the accurate reconstruction of rodent behavior across successive experimental sessions in the same or in modified mazes. Third, in our approach the number of motor primitives associated with each maze correlates with independent measures of maze complexity, hence showing that our formalism is sensitive to essential aspects of task structure. The framework introduced here can be used by behavioral scientists and neuroscientists as an aid for behavioral and neural data analysis. Indeed, the extracted motor primitives enable the quantitative characterization of the complexity and similarity between different mazes and behavioral patterns across multiple trials (i.e., habit formation). We provide example uses of this computational framework, showing how it can be used to identify behavioural effects of maze complexity, analyze stereotyped behavior, classify behavioral choices and predict place and grid cell displacement in novel environments.Francesco DonnarummaRoberto PreveteDomenico MaistoSimone FusconeEmily M. IrvineMatthijs A. A. van der MeerCaleb KemereGiovanni PezzuloNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-20 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Francesco Donnarumma Roberto Prevete Domenico Maisto Simone Fuscone Emily M. Irvine Matthijs A. A. van der Meer Caleb Kemere Giovanni Pezzulo A framework to identify structured behavioral patterns within rodent spatial trajectories
description	Abstract Animal behavior is highly structured. Yet, structured behavioral patterns—or “statistical ethograms”—are not immediately apparent from the full spatiotemporal data that behavioral scientists usually collect. Here, we introduce a framework to quantitatively characterize rodent behavior during spatial (e.g., maze) navigation, in terms of movement building blocks or motor primitives. The hypothesis that we pursue is that rodent behavior is characterized by a small number of motor primitives, which are combined over time to produce open-ended movements. We assume motor primitives to be organized in terms of two sparsity principles: each movement is controlled using a limited subset of motor primitives (sparse superposition) and each primitive is active only for time-limited, time-contiguous portions of movements (sparse activity). We formalize this hypothesis using a sparse dictionary learning method, which we use to extract motor primitives from rodent position and velocity data collected during spatial navigation, and successively to reconstruct past trajectories and predict novel ones. Three main results validate our approach. First, rodent behavioral trajectories are robustly reconstructed from incomplete data, performing better than approaches based on standard dimensionality reduction methods, such as principal component analysis, or single sparsity. Second, the motor primitives extracted during one experimental session generalize and afford the accurate reconstruction of rodent behavior across successive experimental sessions in the same or in modified mazes. Third, in our approach the number of motor primitives associated with each maze correlates with independent measures of maze complexity, hence showing that our formalism is sensitive to essential aspects of task structure. The framework introduced here can be used by behavioral scientists and neuroscientists as an aid for behavioral and neural data analysis. Indeed, the extracted motor primitives enable the quantitative characterization of the complexity and similarity between different mazes and behavioral patterns across multiple trials (i.e., habit formation). We provide example uses of this computational framework, showing how it can be used to identify behavioural effects of maze complexity, analyze stereotyped behavior, classify behavioral choices and predict place and grid cell displacement in novel environments.
format	article
author	Francesco Donnarumma Roberto Prevete Domenico Maisto Simone Fuscone Emily M. Irvine Matthijs A. A. van der Meer Caleb Kemere Giovanni Pezzulo
author_facet	Francesco Donnarumma Roberto Prevete Domenico Maisto Simone Fuscone Emily M. Irvine Matthijs A. A. van der Meer Caleb Kemere Giovanni Pezzulo
author_sort	Francesco Donnarumma
title	A framework to identify structured behavioral patterns within rodent spatial trajectories
title_short	A framework to identify structured behavioral patterns within rodent spatial trajectories
title_full	A framework to identify structured behavioral patterns within rodent spatial trajectories
title_fullStr	A framework to identify structured behavioral patterns within rodent spatial trajectories
title_full_unstemmed	A framework to identify structured behavioral patterns within rodent spatial trajectories
title_sort	framework to identify structured behavioral patterns within rodent spatial trajectories
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9c9220afc71d49d291463aa7569b5061
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A framework to identify structured behavioral patterns within rodent spatial trajectories

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