Computer Methods for Automatic Locomotion and Gesture Tracking in Mice and Small Animals for Neuroscience Applications: A Survey

An Open-Source 3D-Printed Hindlimb Stabilization Apparatus for Reliable Measurement of Stimulation-Evoked Ankle Flexion in Rat

Currently there are numerous methods to evaluate peripheral nerve stimulation interfaces in rats, with stimulation-evoked ankle torque being one of the most prominent. Commercial rat ankle torque measurement systems and custom one-off solutions have been published in the literature. However, commercial systems are proprietary and costly and do not allow for customization. One-off lab-built systems have required specialized machining expertise, and building plans have previously not been made easily accessible. Here, detailed building plans are provided for a low-cost, open-source, and basic ankle torque measurement system from which additional customization can be made. A hindlimb stabilization apparatus was developed to secure and stabilize a rat's hindlimb, while allowing for simultaneous ankle-isometric torque and lower limb muscle electromyography (EMG). The design was composed mainly of adjustable 3D-printed components to accommodate anatomical differences between rat hindlimbs. Additionally, construction and calibration procedures of the rat hindlimb stabilization apparatus were demonstrated in this study. In vivo torque measurements were reliably acquired and corresponded to increasing stimulation amplitudes. Furthermore, implanted leads used for intramuscular EMG recordings complemented torque measurements and were used as an additional functional measurement in evaluating the performance of a peripheral nerve stimulation interface. In conclusion, an open-source and noninvasive platform, made primarily with 3D-printed components, was constructed for reliable data acquisition of evoked motor activity in rat models. The purpose of this apparatus is to provide researchers a versatile system with adjustable components that can be tailored to meet user-defined experimental requirements when evaluating motor function of the rat hindlimbs.

High-throughput gait acquisition system for freely moving mice

Normal and pathological locomotion can be discriminated by analyzing an animal's gait on a linear walkway. This step is labor intensive and introduces experimental bias due to the handling involved while placing and removing the animal between trials. We designed a system consisting of a runway embedded within a larger arena, which can be traversed ad libitum by unsupervised, freely moving mice, triggering the recording of short clips of locomotor activity. Multiple body parts were tracked using DeepLabCut and fed to an analysis pipeline (GaitGrapher) to extract gait metrics. We compared the results from unsupervised against the standard experimenter-supervised approach and found that gait parameters analyzed via the new approach were similar to a previously validated approach (Visual Gait Lab). These data show the utility of incorporating an unsupervised, automated, approach for collecting kinematic data for gait analysis.NEW & NOTEWORTHY The acquisition and analysis of walkway data is a time-consuming task. Here, we provide an unmonitored approach for collecting gait metrics that reduces the handling and stress of mice and saves time. A detailed pipeline is outlined that provides for the collection and analysis of data using an integrated suite of tools.

Gait analysis in chemotherapy-induced peripheral neurotoxicity rodent models

Gait analysis could be used in animal models as an indicator of sensory ataxia due to chemotherapy-induced peripheral neurotoxicity (CIPN). Over the years, gait analysis in in vivo studies has evolved from simple observations carried out by a trained operator to computerised systems with machine learning that allow the quantification of any variable of interest and the establishment of algorithms for behavioural classification. However, there is not a consensus on gait analysis use in CIPN animal models; therefore, we carried out a systematic review. Of 987 potentially relevant studies, 14 were included, in which different methods were analysed (observation, footprint and CatWalk™). We presented the state-of-the-art of possible approaches to analyse sensory ataxia in rodent models, addressing advantages and disadvantages of different methods available. Semi-automated methods may be of interest when preventive or therapeutic strategies are evaluated, also considering their methodological simplicity and automaticity; up to now, only CatWalk™ analysis has been tested. Future studies should expect that CIPN-affected animals tend to reduce hind paw support due to pain, allodynia or loss of sensation, and an increase in swing phase could or should be observed. Few available studies documented these impairments at the last time point, and only appeared later on respect to other earlier signs of CIPN (such as altered neurophysiological findings). For that reason, gait impairment could be interpreted as late repercussions of loss of sensory.

Shortwave infrared fluorescence imaging of peripheral organs in awake and freely moving mice

Extracting biological information from awake and unrestrained mice is imperative to in vivo basic and pre-clinical research. Accordingly, imaging methods which preclude invasiveness, anesthesia, and/or physical restraint enable more physiologically relevant biological data extraction by eliminating these extrinsic confounders. In this article, we discuss the recent development of shortwave infrared (SWIR) fluorescent imaging to visualize peripheral organs in freely-behaving mice, as well as propose potential applications of this imaging modality in the neurosciences.

Shortwave infrared (SWIR) fluorescence imaging of peripheral organs in awake and freely moving mice

Extracting biological information from awake and unrestrained mice is imperative to in vivo basic and pre-clinical research. Accordingly, imaging methods which preclude invasiveness, anesthesia, and/or physical restraint enable more physiologically relevant biological data extraction by eliminating these extrinsic confounders. In this article we discuss the recent development of shortwave infrared (SWIR) fluorescent imaging to visualize peripheral organs in freely-behaving mice, as well as propose potential applications of this imaging modality in the neurosciences.

Marker-Less Motion Capture of Insect Locomotion With Deep Neural Networks Pre-trained on Synthetic Videos

Motion capture of unrestrained moving animals is a major analytic tool in neuroethology and behavioral physiology. At present, several motion capture methodologies have been developed, all of which have particular limitations regarding experimental application. Whereas marker-based motion capture systems are very robust and easily adjusted to suit different setups, tracked species, or body parts, they cannot be applied in experimental situations where markers obstruct the natural behavior (e.g., when tracking delicate, elastic, and/or sensitive body structures). On the other hand, marker-less motion capture systems typically require setup- and animal-specific adjustments, for example by means of tailored image processing, decision heuristics, and/or machine learning of specific sample data. Among the latter, deep-learning approaches have become very popular because of their applicability to virtually any sample of video data. Nevertheless, concise evaluation of their training requirements has rarely been done, particularly with regard to the transfer of trained networks from one application to another. To address this issue, the present study uses insect locomotion as a showcase example for systematic evaluation of variation and augmentation of the training data. For that, we use artificially generated video sequences with known combinations of observed, real animal postures and randomized body position, orientation, and size. Moreover, we evaluate the generalization ability of networks that have been pre-trained on synthetic videos to video recordings of real walking insects, and estimate the benefit in terms of reduced requirement for manual annotation. We show that tracking performance is affected only little by scaling factors ranging from 0.5 to 1.5. As expected from convolutional networks, the translation of the animal has no effect. On the other hand, we show that sufficient variation of rotation in the training data is essential for performance, and make concise suggestions about how much variation is required. Our results on transfer from synthetic to real videos show that pre-training reduces the amount of necessary manual annotation by about 50%.

Locomotion in virtual environments predicts cardiovascular responsiveness to subsequent stressful challenges

Individuals differ in their physiological responsiveness to stressful challenges, and stress potentiates the development of many diseases. Heart rate variability (HRV), a measure of cardiac vagal break, is emerging as a strong index of physiological stress vulnerability. Thus, it is important to develop tools that identify predictive markers of individual differences in HRV responsiveness without exposing subjects to high stress. Here, using machine learning approaches, we show the strong predictive power of high-dimensional locomotor responses during novelty exploration to predict HRV responsiveness during stress exposure. Locomotor responses are collected in two ecologically valid virtual reality scenarios inspired by the animal literature and stress is elicited and measured in a third threatening virtual scenario. Our model's predictions generalize to other stressful challenges and outperforms other stress prediction instruments, such as anxiety questionnaires. Our study paves the way for the development of behavioral digital phenotyping tools for early detection of stress-vulnerable individuals.

Projection-specific deficits in synaptic transmission in adult Sapap3-knockout mice

Obsessive-compulsive disorder (OCD) is a circuit disorder involving corticostriatal projections, which play a role in motor control. The Sapap3-knockout (KO) mouse is a mouse model to study OCD and recapitulates OCD-like compulsion through excessive grooming behavior, with skin lesions appearing at advanced age. Deficits in corticostriatal control provide a link to the pathophysiology of OCD. However, there remain significant gaps in the characterization of the Sapap3-KO mouse, with respect to age, specificity of synaptic dysfunction, and locomotor phenotype. We therefore investigated the corticostriatal synaptic phenotype of Sapap3-KO mice using patch-clamp slice electrophysiology, in adult mice and with projection specificity. We also analyzed grooming across age and locomotor phenotype with a novel, unsupervised machine learning technique (MoSeq). Increased grooming in Sapap3-KO mice without skin lesions was age independent. Synaptic deficits persisted in adulthood and involved the projections from the motor cortices and cingulate cortex to the dorsolateral and dorsomedial striatum. Decreased synaptic strength was evident at the input from the primary motor cortex by reduction in AMPA receptor function. Hypolocomotion, i.e., slowness of movement, was consistently observed in Sapap3-KO mice. Our findings emphasize the utility of young adult Sapap3-KO mice to investigate corticostriatal synaptic dysfunction in motor control.

Fast, Flexible Closed-Loop Feedback: Tracking Movement in "Real-Millisecond-Time"

One of the principal functions of the brain is to control movement and rapidly adapt behavior to a changing external environment. Over the last decades our ability to monitor activity in the brain, manipulate it while also manipulating the environment the animal moves through, has been tackled with increasing sophistication. However, our ability to track the movement of the animal in real time has not kept pace. Here, we use a dynamic vision sensor (DVS) based event-driven neuromorphic camera system to implement real-time, low-latency tracking of a single whisker that mice can move at ∼25 Hz. The customized DVS system described here converts whisker motion into a series of events that can be used to estimate the position of the whisker and to trigger a position-based output interactively within 2 ms. This neuromorphic chip-based closed-loop system provides feedback rapidly and flexibly. With this system, it becomes possible to use the movement of whiskers or in principal, movement of any part of the body to reward, punish, in a rapidly reconfigurable way. These methods can be used to manipulate behavior, and the neural circuits that help animals adapt to changing values of a sequence of motor actions.