Whisking Asymmetry Signals Motor Preparation and the Behavioral State of Mice

Abstract

A central function of the brain is to plan, predict, and imagine the effect of movement in a dynamically changing environment. Here we show that in mice head-fixed in a plus-maze, floating on air, and trained to pick lanes based on visual stimuli, the asymmetric movement, and position of whiskers on the two sides of the face signals whether the animal is moving, turning, expecting reward, or licking. We show that (1) whisking asymmetry is coordinated with behavioral state, and that behavioral state can be decoded and predicted based on asymmetry, (2) even in the absence of tactile input, whisker positioning and asymmetry nevertheless relate to behavioral state, and (3) movement of the nose correlates with asymmetry, indicating that facial expression of the mouse is itself correlated with behavioral state. These results indicate that the movement of whiskers, a behavior that is not instructed or necessary in the task, can inform an observer about what a mouse is doing in the maze. Thus, the position of these mobile tactile sensors reflects a behavioral and movementpreparation state of the mouse.

Layer 6 ensembles can selectively regulate the behavioral impact and layer-specific representation of sensory deviants

“…Detecting a sudden change in platform position in the Gap Crossing with Deviation experiment likely results from a combination of multiple sensory parameters that change because of the changed position of the vibrissa contacts. Previous high-speed videography studies showed that these parameters include a change in amplitude of vibrissal contact deflections ( 33 same experimental conditions), as well as other parameters such as relative timing 33 , and likely deflection angles 36,37 , assymetry 38 , time course of the torque 39 , and vibrations 37,40 .…”

Real-Time Closed-Loop Feedback in Behavioral Time Scales Using DeepLabCut

“…In the last 20 years, multiple research groups have reported increasingly sophisticated, easy-to-use tools for automated annotation of video data of animal behavior ( Knutsen et al, 2005 ; Voigts et al, 2008 ; Perkon et al, 2011 ; Clack et al, 2012 ; Ohayon et al, 2013 ; Giovannucci et al, 2018 ; Dominiak et al, 2019 ; Vanzella et al, 2019 ; Betting et al, 2020 ; Petersen et al, 2020 ). One natural extension of this ability has been to apply these algorithms for on-line, closed-loop paradigms, where changes in behavior of the animal are detected as rapidly as possible, and the behavior is used to modify or manipulate the brain, the virtual environment or the context of behavior.…”

“…One challenge that real-time tracking with video data poses is the computational cost of tracking objects from video frames. Fortunately, the power of processors and algorithms has increased and this has enabled processing of individual frames in <10 ms ( Knutsen et al, 2005 ; Voigts et al, 2008 ; Perkon et al, 2011 ; Clack et al, 2012 ; Ohayon et al, 2013 ; Cao et al, 2018 ; Dominiak et al, 2019 ; Betting et al, 2020 ; Petersen et al, 2020 ). By comparison, little progress has been made to enhance acquisition latency of frames, i.e., the time required to transfer video frames between devices.…”

“…After subcutaneous lidocaine injection, skin and fascia were removed from the skull. A lightweight aluminum headpost was attached to the skull using a Rely-X (3 M) cement, followed in some cases by Jet acrylic black cement ( Dominiak et al, 2019 ; Sehara et al, 2019 ). Animals were monitored during recovery and were given antibiotics (enrofloxacin) and analgesics (buprenorphine and carprofen).…”

“…Traditionally, video data have been analyzed manually. More recently, various algorithms have been developed for automating movement detection ( Knutsen et al, 2005 ; Voigts et al, 2008 ; Perkon et al, 2011 ; Clack et al, 2012 ; Ohayon et al, 2013 ; Giovannucci et al, 2018 ; Dominiak et al, 2019 ; Vanzella et al, 2019 ; Betting et al, 2020 ; Petersen et al, 2020 ). With the development of DeepLabCut, a marker-less pose-estimation toolkit based on deep learning ( Mathis et al, 2018 ), computer vision approaches are being used for monitoring poses of animals and for tracking the movement of virtually any part of the body.…”