The Home-Cage Automated Skilled Reaching Apparatus (HASRA): Individualized Training of Group-Housed Mice in a Single Pellet Reaching Task

Methods for evaluating gait associated dynamic balance and coordination in rodents

Balance is the dynamic and unconscious control of the body's centre of mass to maintain postural equilibrium. Regulated by the vestibular system, head movement and acceleration are processed by the brain to adjust joints. Several conditions result in a loss of balance, including Alzheimer's Disease, Parkinson's Disease, Menière's Disease and cervical spondylosis, all of which are caused by damage to certain parts of the vestibular pathways. Studies about the impairment of the vestibular system are challenging to carry out in human trials due to smaller study sizes limiting applications of the results and a lacking understanding of the human balance control mechanism. In contrast, more controlled research can be performed in animal studies which have fewer confounding factors than human models and allow specific conditions that affect balance to be replicated. Balance control can be studied using rodent balance-related behavioural tests after spinal or brain lesions, such as the Basso, Beattie and Bresnahan (BBB) Locomotor Scale, Foot Fault Scoring System, Ledged Beam Test, Beam Walking Test, and Ladder Beam Test, which are discussed in this review article along with their advantages and disadvantages. These tests can be performed in preclinical rodent models of femoral nerve injury, stroke, spinal cord injury and neurodegenerative diseases.

ReachingBot: an automated and scalable benchtop device for highly parallel Single Pellet Reach-and-Grasp training and assessment in mice

BACKGROUND

The single pellet reaching and grasp (SPRG) task is a behavioural assay widely used to study motor learning, control and recovery after nervous system injury in animals. The manual training and assessment of the SPRG is labour intensive and time consuming and has led to the development of multiple devices which automate the SPRG task.

NEW METHOD

Here, using robotics, computer vision, and machine learning analysis of videos, we describe a device that can be left unattended, presents pellets to mice, and, using two supervised learning algorithms, classifies the outcome of each trial with an accuracy of greater than 94% without the use of graphical processing units (GPUs). Our devices can also be operated using our cross-platform Graphical User Interface (GUI).

RESULTS

We show that these devices train and assess mice in parallel. 21 out of 30 mice retrieved >40% of pellets successfully following the training period. Following ischaemic stroke; some mice showed large persistent deficits whilst others showed only transient deficits. This highlights the heterogeneity in reaching outcomes following stroke.

COMPARISON WITH EXISTING METHOD(S)

Current state-of-the-art desktop methods either still require supervision, manual classification of trial outcome, or expensive locally-installed hardware such as graphical processing units (GPUs).

CONCLUSIONS

ReachingBots successfully automated SPRG training and assessment and revealed the heterogeneity in reaching outcomes following stroke. We conjecture that reach-and-grasp is represented in motor cortex bilaterally but with greater asymmetry in some mice than in others.

AutoRG: An automatized reach-to-grasp platform technology for assessing forelimb motor function, neural circuit activation, and cognition in rodents

BACKGROUND

Rodent reach-to-grasp function assessment is a translationally powerful model for evaluating neurological function impairments and recovery responses. Existing assessment platforms are experimenter-dependent, costly, or low-throughput with limited output measures. Further, a direct histologic comparison of neural activation has never been conducted between any novel, automated platform and the well-established single pellet skilled reach task (SRT).

NEW METHOD

To address these technological and knowledge gaps, we designed an open-source, low-cost Automatized Reach-to-Grasp (AutoRG) pull platform that reduces experimenter interventions and variability. We assessed reach-to-grasp function in rats across seven progressively difficult stages using AutoRG. We mapped AutoRG and SRT-activated motor circuitries in the rat brain using volumetric imaging of the immediate early gene-encoded Arc (activity-regulated cytoskeleton-associated) protein.

RESULTS

Rats demonstrated robust forelimb reaching and pulling behavior after training in AutoRG. Reliable force versus time responses were recorded for individual reach events in real time, which were used to derive several secondary functional measures of performance. Moreover, we provide the first demonstration that for a training period of 30 min, AutoRG and SRT both engage similar neural responses in the caudal forelimb area (CFA), rostral forelimb area (RFA), and sensorimotor area (S1).

CONCLUSION

AutoRG is the first low-cost, open-source pull system designed for the scale-up of volitional forelimb motor function testing and characterization of rodent reaching behavior. The similarities in neuronal activation patterns observed in the rat motor cortex after SRT and AutoRG assessments validate the AutoRG as a rigorously characterized, scalable alternative to the conventional SRT and expensive commercial systems.

Challenges of a small world analysis for the continuous monitoring of behavior in mice

Documenting a mouse's "real world" behavior in the "small world" of a laboratory cage with continuous video recordings offers insights into phenotypical expression of mouse genotypes, development and aging, and neurological disease. Nevertheless, there are challenges in the design of a small world, the behavior selected for analysis, and the form of the analysis used. Here we offer insights into small world analyses by describing how acute behavioral procedures can guide continuous behavioral methodology. We show how algorithms can identify behavioral acts including walking and rearing, circadian patterns of action including sleep duration and waking activity, and the organization of patterns of movement into home base activity and excursions, and how they are altered with aging. We additionally describe how specific tests can be incorporated within a mouse's living arrangement. We emphasize how machine learning can condense and organize continuous activity that extends over extended periods of time.

When Spinal Neuromodulation Meets Sensorimotor Rehabilitation: Lessons Learned From Animal Models to Regain Manual Dexterity After a Spinal Cord Injury

Electrical neuromodulation has strongly hit the foundations of spinal cord injury and repair. Clinical and experimental studies have demonstrated the ability to neuromodulate and engage spinal cord circuits to recover volitional motor functions lost after the injury. Although the science and technology behind electrical neuromodulation has attracted much of the attention, it cannot be obviated that electrical stimulation must be applied concomitantly to sensorimotor rehabilitation, and one would be very difficult to understand without the other, as both need to be finely tuned to efficiently execute movements. The present review explores the difficulties faced by experimental and clinical neuroscientists when attempting to neuromodulate and rehabilitate manual dexterity in spinal cord injured subjects. From a translational point of view, we will describe the major rehabilitation interventions employed in animal research to promote recovery of forelimb motor function. On the other hand, we will outline some of the state-of-the-art findings when applying electrical neuromodulation to the spinal cord in animal models and human patients, highlighting how evidences from lumbar stimulation are paving the path to cervical neuromodulation.

The Emergence of Stereotyped Kinematic Synergies when Mice Reach to Grasp Following Stroke

Reaching tasks are commonly used in preclinical and clinical studies to assess the acquisition of fine motor skills and recovery of function following stroke. These tasks are often used to assess functional deficits in the absence of quantifying the quality of movement which requires kinematic analysis. To meet this need, this study uses a kinematic analysis in mice performing the Montoya staircase task at 5 and 14 days following a cortical photothrombosis-induced stroke. Following stroke, the mice had reaching impairments associated with sustained deficits including longer, unsmooth, and less individuated paw trajectories. Two weeks after stroke we also detected the emergence of abnormal elbow and shoulder angles, flexion/extensions, and stereotyped kinematic synergies. These data suggest that proximal and distal segments acting in concert is paramount during post-stroke reaching and encourage further analysis of synergies within the translational pipeline of preclinical to clinical studies.

Impaired Refinement of Kinematic Variability in Huntington Disease Mice on an Automated Home Cage Forelimb Motor Task

The effective development of novel therapies in mouse models of neurologic disorders relies on behavioral assessments that provide accurate read-outs of neuronal dysfunction and/or degeneration. We designed an automated behavioral testing system (PiPaw), which integrates an operant lever-pulling task directly into the mouse home cage. This task is accessible to group-housed mice 24 h per day, enabling high-throughput longitudinal analysis of forelimb motor learning. Moreover, this design eliminates the need for exposure to novel environments and minimizes experimenter interaction, significantly reducing two of the largest stressors associated with animal behavior. Male mice improved their performance of this task over 1 week of testing by reducing intertrial variability of reward-related kinematic parameters (pull amplitude or peak velocity). In addition, mice displayed short-term improvements in reward rate, and a concomitant decrease in movement variability, over the course of brief bouts of task engagement. We used this system to assess motor learning in mouse models of the inherited neurodegenerative disorder, Huntington disease (HD). Despite having no baseline differences in task performance, male Q175-FDN HD mice were unable to modulate the variability of their movements to increase reward on either short or long timescales. Task training was associated with a decrease in the amplitude of spontaneous excitatory activity recorded from striatal medium spiny neurons in the hemisphere contralateral to the trained forelimb in WT mice; however, no such changes were observed in Q175-FDN mice. This behavioral screening platform should prove useful for preclinical drug trials toward improved treatments in HD and other neurologic disorders.SIGNIFICANCE STATEMENT In order to develop effective therapies for neurologic disorders, such as Huntington disease (HD), it is important to be able to accurately and reliably assess the behavior of mouse models of these conditions. Moreover, these behavioral assessments should provide an accurate readout of underlying neuronal dysfunction and/or degeneration. In this paper, we used an automated behavioral testing system to assess motor learning in mice within their home cage. Using this system, we were able to study motor abnormalities in HD mice with an unprecedented level of detail, and identified a specific behavioral deficit associated with an underlying impairment in striatal neuronal plasticity. These results validate the usefulness of this system for assessing behavior in mouse models of HD and other neurologic disorders.

Closed-loop automated reaching apparatus (CLARA) for interrogating complex motor behaviors

Objective.Closed-loop neuromodulation technology is a rapidly expanding category of therapeutics for a broad range of indications. Development of these innovative neurological devices requires high-throughput systems for closed-loop stimulation of model organisms, while monitoring physiological signals and complex, naturalistic behaviors. To address this need, we developed CLARA, a closed-loop automated reaching apparatus.Approach.Using breakthroughs in computer vision, CLARA integrates fully-automated, markerless kinematic tracking of multiple features to classify animal behavior and precisely deliver neural stimulation based on behavioral outcomes. CLARA is compatible with advanced neurophysiological tools, enabling the testing of neurostimulation devices and identification of novel neurological biomarkers.Results.The CLARA system tracks unconstrained skilled reach behavior in 3D at 150 Hz without physical markers. The system fully automates trial initiation and pellet delivery and is capable of accurately delivering stimulation in response to trial outcome with short latency. Kinematic data from the CLARA system provided novel insights into the dynamics of reach consistency over the course of learning, suggesting that learning selectively improves reach failures but does not alter the kinematics of successful reaches. Additionally, using the closed-loop capabilities of CLARA, we demonstrate that vagus nerve stimulation (VNS) improves skilled reach performance and increases reach trajectory consistency in healthy animals.Significance.The CLARA system is the first mouse behavior apparatus that uses markerless pose tracking to provide real-time closed-loop stimulation in response to the outcome of an unconstrained motor task. Additionally, we demonstrate that the CLARA system was essential for our investigating the role of closed-loop VNS stimulation on motor performance in healthy animals. This approach has high translational relevance for developing neurostimulation technology based on complex human behavior.

Localizing Microemboli within the Rodent Brain through Block-Face Imaging and Atlas Registration

Brain microinfarcts are prevalent in humans, however because of the inherent difficulty of identifying and localizing individual microinfarcts, brain-wide quantification is impractical. In mice, microinfarcts have been created by surgically introducing microemboli into the brain, but a major limitation of this model is the absence of automated methods to identify and localize individual occlusions. We present a novel and semi-automated workflow to identify the anatomic location of fluorescent emboli (microspheres) within the mouse brain through histologic processing and atlas registration. By incorporating vibratome block-face imaging with the QuickNII brain registration tool, we show that the anatomic location of microspheres can be accurately registered to brain structures within the Allen mouse brain (AMB) atlas (e.g., somatomotor areas, hippocampal region, visual areas, etc.). Compared with registering images of slide mounted sections to the AMB atlas, microsphere location was more accurately determined when block-face images were used. As a proof of principle, using this workflow we compared the distribution of microspheres within the brains of mice that were either perfused or immersion fixed. No significant effect of perfusion on total microsphere number or location was detected. In general, microspheres were distributed brain-wide, with the largest density found in the thalamus. In sum, our block-face imaging workflow enables efficient characterization of the widespread distribution of fluorescent microemboli, facilitating future investigation into the impact of microinfarct load and location on brain health.

Automation of training and testing motor and related tasks in pre-clinical behavioural and rehabilitative neuroscience

Testing and training animals in motor and related tasks is a cornerstone of pre-clinical behavioural and rehabilitative neuroscience. Yet manually testing and training animals in these tasks is time consuming and analyses are often subjective. Consequently, there have been many recent advances in automating both the administration and analyses of animal behavioural training and testing. This review is an in-depth appraisal of the history of, and recent developments in, the automation of animal behavioural assays used in neuroscience. We describe the use of common locomotor and non-locomotor tasks used for motor training and testing before and after nervous system injury. This includes a discussion of how these tasks help us to understand the underlying mechanisms of neurological repair and the utility of some tasks for the delivery of rehabilitative training to enhance recovery. We propose two general approaches to automation: automating the physical administration of behavioural tasks (i.e., devices used to facilitate task training, rehabilitative training, and motor testing) and leveraging the use of machine learning in behaviour analysis to generate large volumes of unbiased and comprehensive data. The advantages and disadvantages of automating various motor tasks as well as the limitations of machine learning analyses are examined. In closing, we provide a critical appraisal of the current state of automation in animal behavioural neuroscience and a prospective on some of the advances in machine learning we believe will dramatically enhance the usefulness of these approaches for behavioural neuroscientists.