Quantification of early learning and movement sub-structure predictive of motor performance

Abstract Time-to-fall off an accelerating rotating rod (rotarod) is widely utilized to evaluate rodent motor performance. We reasoned that this simple outcome could be refined with additional measures explicit in the task (however inconspicuously) to examine what we call movement sub-structure. Our...

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Autores principales:	Vikram Jakkamsetti, William Scudder, Gauri Kathote, Qian Ma, Gustavo Angulo, Aksharkumar Dobariya, Roger N. Rosenberg, Bruce Beutler, Juan M. Pascual
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6e16eac057f74d9383075a1f4cc92614
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spelling	oai:doaj.org-article:6e16eac057f74d9383075a1f4cc926142021-12-02T15:33:12ZQuantification of early learning and movement sub-structure predictive of motor performance10.1038/s41598-021-93944-92045-2322https://doaj.org/article/6e16eac057f74d9383075a1f4cc926142021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93944-9https://doaj.org/toc/2045-2322Abstract Time-to-fall off an accelerating rotating rod (rotarod) is widely utilized to evaluate rodent motor performance. We reasoned that this simple outcome could be refined with additional measures explicit in the task (however inconspicuously) to examine what we call movement sub-structure. Our goal was to characterize normal variation or motor impairment more robustly than by using time-to-fall. We also hypothesized that measures (or features) early in the sub-structure could anticipate the learning expected of a mouse undergoing serial trials. Using normal untreated and baclofen-treated movement-impaired mice, we defined these features and automated their analysis using paw video-tracking in three consecutive trials, including paw location, speed, acceleration, variance and approximate entropy. Spectral arc length yielded speed and acceleration uniformity. We found that, in normal mice, paw movement smoothness inversely correlated with rotarod time-to-fall for the three trials. Greater approximate entropy in vertical movements, and opposite changes in horizontal movements, correlated with greater first-trial time-to-fall. First-trial horizontal approximate entropy in the first few seconds predicted subsequent time-to-fall. This allowed for the separation, after only one rotarod trial, of different-weight, untreated mouse groups, and for the detection of mice otherwise unimpaired after baclofen, which displayed a time-to-fall similar to control. A machine-learning support vector machine classifier corroborated these findings. In conclusion, time-to-fall off a rotarod correlated well with several measures, including some obtained during the first few seconds of a trial, and some responsive to learning over the first two trials, allowing for predictions or preemptive experimental manipulations before learning completion.Vikram JakkamsettiWilliam ScudderGauri KathoteQian MaGustavo AnguloAksharkumar DobariyaRoger N. RosenbergBruce BeutlerJuan M. PascualNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Vikram Jakkamsetti William Scudder Gauri Kathote Qian Ma Gustavo Angulo Aksharkumar Dobariya Roger N. Rosenberg Bruce Beutler Juan M. Pascual Quantification of early learning and movement sub-structure predictive of motor performance
description	Abstract Time-to-fall off an accelerating rotating rod (rotarod) is widely utilized to evaluate rodent motor performance. We reasoned that this simple outcome could be refined with additional measures explicit in the task (however inconspicuously) to examine what we call movement sub-structure. Our goal was to characterize normal variation or motor impairment more robustly than by using time-to-fall. We also hypothesized that measures (or features) early in the sub-structure could anticipate the learning expected of a mouse undergoing serial trials. Using normal untreated and baclofen-treated movement-impaired mice, we defined these features and automated their analysis using paw video-tracking in three consecutive trials, including paw location, speed, acceleration, variance and approximate entropy. Spectral arc length yielded speed and acceleration uniformity. We found that, in normal mice, paw movement smoothness inversely correlated with rotarod time-to-fall for the three trials. Greater approximate entropy in vertical movements, and opposite changes in horizontal movements, correlated with greater first-trial time-to-fall. First-trial horizontal approximate entropy in the first few seconds predicted subsequent time-to-fall. This allowed for the separation, after only one rotarod trial, of different-weight, untreated mouse groups, and for the detection of mice otherwise unimpaired after baclofen, which displayed a time-to-fall similar to control. A machine-learning support vector machine classifier corroborated these findings. In conclusion, time-to-fall off a rotarod correlated well with several measures, including some obtained during the first few seconds of a trial, and some responsive to learning over the first two trials, allowing for predictions or preemptive experimental manipulations before learning completion.
format	article
author	Vikram Jakkamsetti William Scudder Gauri Kathote Qian Ma Gustavo Angulo Aksharkumar Dobariya Roger N. Rosenberg Bruce Beutler Juan M. Pascual
author_facet	Vikram Jakkamsetti William Scudder Gauri Kathote Qian Ma Gustavo Angulo Aksharkumar Dobariya Roger N. Rosenberg Bruce Beutler Juan M. Pascual
author_sort	Vikram Jakkamsetti
title	Quantification of early learning and movement sub-structure predictive of motor performance
title_short	Quantification of early learning and movement sub-structure predictive of motor performance
title_full	Quantification of early learning and movement sub-structure predictive of motor performance
title_fullStr	Quantification of early learning and movement sub-structure predictive of motor performance
title_full_unstemmed	Quantification of early learning and movement sub-structure predictive of motor performance
title_sort	quantification of early learning and movement sub-structure predictive of motor performance
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6e16eac057f74d9383075a1f4cc92614
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Quantification of early learning and movement sub-structure predictive of motor performance

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