Pierantonio Parmiani1, Cristina Lucchetti2, Claudio Bonifazzi3, Gianfranco Franchi4. 1. Department of Biomedical and Specialty Surgical Sciences, Section of Human Physiology, University of Ferrara, 44121, Ferrara, Italy; Center for Translational Neurophysiology, Istituto Italiano di Tecnologia, 44121, Ferrara, Italy. Electronic address: pierantonio.parmiani@unife.it. 2. Department of Biomedical, Metabolic and Neural Sciences, Section of Physiology and Neuroscience, University of Modena and Reggio Emilia, 43125, Modena, Italy. 3. Department of Biomedical and Specialty Surgical Sciences, Section of Human Physiology, University of Ferrara, 44121, Ferrara, Italy. 4. Department of Biomedical and Specialty Surgical Sciences, Section of Human Physiology, University of Ferrara, 44121, Ferrara, Italy. Electronic address: fhg@unife.it.
Abstract
BACKGROUND: In the rat, the single-pellet reaching task includes orienting, reaching, grasping and retracting movements. It has previously been described by notation techniques, high-speed video and cineradiographic recordings. Recently, high-definition cameras have been used to track paw and digit movements with DeepLabCut, a machine-learning algorithm for markerless estimation of paw position. NEW METHOD: Our new approach consists of positioning three high-speed infrared digital cameras to track the full motion of markers on the rat's body. This provided a previously unavailable 3D recording of skilled reaching kinematics in the rat moving freely in the reaching box, which were analysed by Qualisys Track Manager software and MATLAB. RESULTS: This method enabled description of kinematic parameters unobtainable without motion tracking and provided insight into the spatiotemporal metrics of movements used to perform skilled reaching. It revealed that orientation features three steps and reaching has two bimodal start-point distributions, one along the horizontal axis and one along the vertical axis. At the end of reaching, the wrist/paw occupies the same position as the nose at the end of orienting. In grasping, averaging trajectories confirmed the marker lowering and target approaching. COMPARISON WITH EXISTING METHODS: Our method required significantly reduced time to label data and obviates the need for off-line manual marking of videos. It provides an efficient means of capturing volumes containing the entire range of marker movements. CONCLUSIONS: This study validated a new and efficient approach for quantifying rat movement kinematics, useful for comparing preclinical and clinical conditions.
BACKGROUND: In the rat, the single-pellet reaching task includes orienting, reaching, grasping and retracting movements. It has previously been described by notation techniques, high-speed video and cineradiographic recordings. Recently, high-definition cameras have been used to track paw and digit movements with DeepLabCut, a machine-learning algorithm for markerless estimation of paw position. NEW METHOD: Our new approach consists of positioning three high-speed infrared digital cameras to track the full motion of markers on the rat's body. This provided a previously unavailable 3D recording of skilled reaching kinematics in the rat moving freely in the reaching box, which were analysed by Qualisys Track Manager software and MATLAB. RESULTS: This method enabled description of kinematic parameters unobtainable without motion tracking and provided insight into the spatiotemporal metrics of movements used to perform skilled reaching. It revealed that orientation features three steps and reaching has two bimodal start-point distributions, one along the horizontal axis and one along the vertical axis. At the end of reaching, the wrist/paw occupies the same position as the nose at the end of orienting. In grasping, averaging trajectories confirmed the marker lowering and target approaching. COMPARISON WITH EXISTING METHODS: Our method required significantly reduced time to label data and obviates the need for off-line manual marking of videos. It provides an efficient means of capturing volumes containing the entire range of marker movements. CONCLUSIONS: This study validated a new and efficient approach for quantifying rat movement kinematics, useful for comparing preclinical and clinical conditions.
Authors: J D Laurence-Chasen; Armita R Manafzadeh; Nicholas G Hatsopoulos; Callum F Ross; Fritzie I Arce-McShane Journal: J Exp Biol Date: 2020-09-04 Impact factor: 3.312
Authors: Fabrizio Grieco; Briana J Bernstein; Barbara Biemans; Lior Bikovski; C Joseph Burnett; Jesse D Cushman; Elsbeth A van Dam; Sydney A Fry; Bar Richmond-Hacham; Judith R Homberg; Martien J H Kas; Helmut W Kessels; Bastijn Koopmans; Michael J Krashes; Vaishnav Krishnan; Sreemathi Logan; Maarten Loos; Katharine E McCann; Qendresa Parduzi; Chaim G Pick; Thomas D Prevot; Gernot Riedel; Lianne Robinson; Mina Sadighi; August B Smit; William Sonntag; Reinko F Roelofs; Ruud A J Tegelenbosch; Lucas P J J Noldus Journal: Front Behav Neurosci Date: 2021-09-24 Impact factor: 3.617