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Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks

Here, we describe a system capable of tracking specific mouse paw movements at high frame rates (70.17 Hz) with a high level of accuracy (mean = 0.95, SD < 0.01). Short-latency markerless tracking of specific body parts opens up the possibility of manipulating motor feedback. We present a softwar...

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Autores principales: Forys, Brandon J., Xiao, Dongsheng, Gupta, Pankaj, Murphy, Timothy H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society for Neuroscience 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7307631/
https://www.ncbi.nlm.nih.gov/pubmed/32409507
http://dx.doi.org/10.1523/ENEURO.0096-20.2020
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author Forys, Brandon J.
Xiao, Dongsheng
Gupta, Pankaj
Murphy, Timothy H.
author_facet Forys, Brandon J.
Xiao, Dongsheng
Gupta, Pankaj
Murphy, Timothy H.
author_sort Forys, Brandon J.
collection PubMed
description Here, we describe a system capable of tracking specific mouse paw movements at high frame rates (70.17 Hz) with a high level of accuracy (mean = 0.95, SD < 0.01). Short-latency markerless tracking of specific body parts opens up the possibility of manipulating motor feedback. We present a software and hardware scheme built on DeepLabCut—a robust movement-tracking deep neural network framework—which enables real-time estimation of paw and digit movements of mice. Using this approach, we demonstrate movement-generated feedback by triggering a USB-GPIO (general-purpose input/output)-controlled LED when the movement of one paw, but not the other, selectively exceeds a preset threshold. The mean time delay between paw movement initiation and LED flash was 44.41 ms (SD = 36.39 ms), a latency sufficient for applying behaviorally triggered feedback. We adapt DeepLabCut for real-time tracking as an open-source package we term DeepCut2RealTime. The ability of the package to rapidly assess animal behavior was demonstrated by reinforcing specific movements within water-restricted, head-fixed mice. This system could inform future work on a behaviorally triggered “closed loop” brain–machine interface that could reinforce behaviors or deliver feedback to brain regions based on prespecified body movements.
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spelling pubmed-73076312020-06-23 Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks Forys, Brandon J. Xiao, Dongsheng Gupta, Pankaj Murphy, Timothy H. eNeuro Research Article: Methods/New Tools Here, we describe a system capable of tracking specific mouse paw movements at high frame rates (70.17 Hz) with a high level of accuracy (mean = 0.95, SD < 0.01). Short-latency markerless tracking of specific body parts opens up the possibility of manipulating motor feedback. We present a software and hardware scheme built on DeepLabCut—a robust movement-tracking deep neural network framework—which enables real-time estimation of paw and digit movements of mice. Using this approach, we demonstrate movement-generated feedback by triggering a USB-GPIO (general-purpose input/output)-controlled LED when the movement of one paw, but not the other, selectively exceeds a preset threshold. The mean time delay between paw movement initiation and LED flash was 44.41 ms (SD = 36.39 ms), a latency sufficient for applying behaviorally triggered feedback. We adapt DeepLabCut for real-time tracking as an open-source package we term DeepCut2RealTime. The ability of the package to rapidly assess animal behavior was demonstrated by reinforcing specific movements within water-restricted, head-fixed mice. This system could inform future work on a behaviorally triggered “closed loop” brain–machine interface that could reinforce behaviors or deliver feedback to brain regions based on prespecified body movements. Society for Neuroscience 2020-06-05 /pmc/articles/PMC7307631/ /pubmed/32409507 http://dx.doi.org/10.1523/ENEURO.0096-20.2020 Text en Copyright © 2020 Forys et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
spellingShingle Research Article: Methods/New Tools
Forys, Brandon J.
Xiao, Dongsheng
Gupta, Pankaj
Murphy, Timothy H.
Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks
title Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks
title_full Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks
title_fullStr Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks
title_full_unstemmed Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks
title_short Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks
title_sort real-time selective markerless tracking of forepaws of head fixed mice using deep neural networks
topic Research Article: Methods/New Tools
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7307631/
https://www.ncbi.nlm.nih.gov/pubmed/32409507
http://dx.doi.org/10.1523/ENEURO.0096-20.2020
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