A Giovannucci1, E A Pnevmatikakis2, B Deverett3, T Pereira4, J Fondriest3, M J Brady3, S S-H Wang3, W Abbas5, P Parés5, D Masip5. 1. Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA; Princeton Neuroscience Institute and Department of Molecular Biology, Princeton University, Princeton, NJ, USA. Electronic address: agiovannucci@flatironinstitute.org. 2. Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA. 3. Princeton Neuroscience Institute and Department of Molecular Biology, Princeton University, Princeton, NJ, USA; Robert Wood Johnson Medical School, New Brunswick, NJ, USA. 4. Princeton Neuroscience Institute and Department of Molecular Biology, Princeton University, Princeton, NJ, USA. 5. Department of Computer Science, Universitat Oberta de Catalunya, Barcelona, Spain.
Abstract
BACKGROUND: 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.
BACKGROUND: 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.
Authors: Andrea Giovannucci; Aleksandra Badura; Ben Deverett; Farzaneh Najafi; Talmo D Pereira; Zhenyu Gao; Ilker Ozden; Alexander D Kloth; Eftychios Pnevmatikakis; Liam Paninski; Chris I De Zeeuw; Javier F Medina; Samuel S-H Wang Journal: Nat Neurosci Date: 2017-03-20 Impact factor: 24.884
Authors: Alexander D Kloth; Aleksandra Badura; Amy Li; Adriana Cherskov; Sara G Connolly; Andrea Giovannucci; M Ali Bangash; Giorgio Grasselli; Olga Peñagarikano; Claire Piochon; Peter T Tsai; Daniel H Geschwind; Christian Hansel; Mustafa Sahin; Toru Takumi; Paul F Worley; Samuel S-H Wang Journal: Elife Date: 2015-07-09 Impact factor: 8.140
Authors: Tapan P Patel; David M Gullotti; Pepe Hernandez; W Timothy O'Brien; Bruce P Capehart; Barclay Morrison; Cameron Bass; James E Eberwine; Ted Abel; David F Meaney Journal: Front Behav Neurosci Date: 2014-10-08 Impact factor: 3.558
Authors: Nathan G Clack; Daniel H O'Connor; Daniel Huber; Leopoldo Petreanu; Andrew Hires; Simon Peron; Karel Svoboda; Eugene W Myers Journal: PLoS Comput Biol Date: 2012-07-05 Impact factor: 4.475
Authors: Laurens Witter; Cathrin B Canto; Tycho M Hoogland; Jornt R de Gruijl; Chris I De Zeeuw Journal: Front Neural Circuits Date: 2013-08-21 Impact factor: 3.492
Authors: Fabio Bertan; Lena Wischhof; Liudmila Sosulina; Manuel Mittag; Dennis Dalügge; Alessandra Fornarelli; Fabrizio Gardoni; Elena Marcello; Monica Di Luca; Martin Fuhrmann; Stefan Remy; Daniele Bano; Pierluigi Nicotera Journal: Cell Death Differ Date: 2020-07-08 Impact factor: 15.828