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http://hdl.handle.net/10609/93181
Title: Automated gesture tracking in head-fixed mice
Author: Giovannucci, Andrea
Pnevmatikakis, Eftychios A.
Deverett, Ben
Pereira, Talmo D.
Fondriest, James
Brady, M.J.
Wang, Samuel S.H
Abbas, Waseem
Parés Casellas, Pere
Masip Rodó, David
Others: Universitat Oberta de Catalunya. Estudis d'Informàtica, Multimèdia i Telecomunicació
Princeton University
Simons Foundation
Keywords: head-fixed
behavior
tracking
Issue Date: 13-Jul-2017
Publisher: Journal of Neuroscience Methods
Citation: Giovannucci, A., Pnevmatikakis, E.A., Deverett, B., Pereira, T.D., Fondriest, J., Brady, M.J., Wang, S.S.-H., Abbas, W., Parés Casellas, P. & Masip Rodo, D. (2018). Automated gesture tracking in head-fixed mice. Journal of Neuroscience Methods, 300(), 194-195. doi: 10.1016/j.jneumeth.2017.07.014
Project identifier: info:eu-repo/grantAgreement/CBIR12FEL031
info:eu-repo/grantAgreement/R01-1NS045193
info:eu-repo/grantAgreement/TIN2015-66951-C2-2-R
info:eu-repo/grantAgreement/4DGE-1148900
Also see: https://doi.org/10.1016/j.jneumeth.2017.07.014
Abstract: The preparation consisting of a head-fixed mouse on a spherical or cylindrical treadmill offers unique advantages in a variety of experimental contexts. Head fixation provides the mechanical stability necessary for optical and electrophysiological recordings and stimulation. Additionally, it can be combined with virtual environments such as T-mazes, enabling these types of recording during diverse behaviors. New method, in this paper we present a low-cost, easy-to-build acquisition system, along with scalable computational methods to quantitatively measure behavior (locomotion and paws, whiskers, and tail motion patterns) in head-fixed mice locomoting on cylindrical or spherical treadmills. Existing methods, several custom supervised and unsupervised methods have been developed for measuring behavior in mice. However, to date there is no low-cost, turn-key, general-purpose, and scalable system for acquiring and quantifying behavior in mice. Results, we benchmark our algorithms against ground truth data generated either by manual labeling or by simpler methods of feature extraction. We demonstrate that our algorithms achieve good performance, both in supervised and unsupervised settings. Conclusions, we present a low-cost suite of tools for behavioral quantification, which serve as valuable complements to recording and stimulation technologies being developed for the head-fixed mouse preparation.
Language: English
URI: http://hdl.handle.net/10609/93181
ISSN: 0165-0270MIAR
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