The authors would like to thank Karunesh Ganguly and his laboratory for advice on the skilled reaching task, and Alexander and Mackenzie Mathis for their help in adapting DeepLabCut. This work was supported by the National Institute of Neurological Disease and Stroke (grant number K08-NS072183) and the University of Michigan.

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The authors have nothing to disclose.

Rodent skilled reaching has become a standard tool to study motor system physiology and pathophysiology. We have described how to implement an automated rat skilled reaching task that allows: training and testing with minimal supervision, 3-D paw and digit trajectory reconstruction (during reaching, grasping, and paw retraction), real-time identification of the paw during reaching, and synchronization with external electronics. It is well-suited to correlate forelimb kinematics with physiology or to perform precisely-timed interventions during reaching movements.
Since we initially reported this design14, our training efficiency has improved so that almost 100% of rats acquire the task. We have identified several important factors that lead to consistently successful training. As with many tasks motivated by hunger, rats should be carefully monitored during caloric restriction to maintain 80&#8211;90% of their anticipated body weight. Handling the rats daily, even prior to training, is critically important to acclimate them to humans. Rats should be trained to reach before learning to return to the back of the chamber to request pellets&#8212;this greatly reduces training time and improves the likelihood that rats acquire the task. Finally, when transferred between seemingly identical chambers, rats often perform fewer reaches. This was especially true when chambers were used for the first time. We speculate that this is due to differences in scent between chambers. Whatever the reason, it is important to maintain as stable a training environment as possible, or acclimate the rats to all boxes in which testing might occur.
The apparatus described here is readily adaptable to specific needs. We described a rat version of the task, but have also implemented a mouse version (though it is difficult to identify individual digits with DeepLabCut in mice). Because individual video frames are marked with TTL pulses, videos can be synchronized with any recording system that accepts digital or analog inputs (e.g., electrophysiology amplifiers or photometry). Finally, head-fixed mice readily perform skilled reaching9, and a head-fixed version of this task could be implemented for 2-photon imaging or juxtacellular recordings. Importantly, we have only used this system with Long-Evans rats, whose nose and paw fur (black and white, respectively) differ enough in color that nose pokes are not mistaken for reaches (with appropriate ROI settings, Figure 8). This may be a problem for rats with similar coloration on their paws and noses (e.g., albino rats), but could be solved by coloring the paw with ink, nail polish, or tattoos.
The presented version of skilled reaching has several distinct features, which may be advantageous depending on the specific application. The relatively complicated hardware and need for real-time video processing make it poorly suited to home cage training6,7. On the other hand, home cage training makes it difficult to acquire high-speed high-resolution video from multiple angles, or tether the animals for physiologic recordings/interventions. The data acquisition cards and requirement for one computer per chamber makes each chamber relatively expensive, and the videos require significant digital storage space (~200 MB per 4 s video). We have implemented a simpler microcontroller-based version costing about $300 per chamber, though it lacks real-time feedback or the ability to synchronize with external devices. These boxes are essentially identical to those described here, but use a commercial camcorder and do not require a computer except to program the microcontroller (details of this set-up and associated software are available upon request). Real-time video processing on the FPGA frame-grabber is especially useful; we find that it more robustly identifies reaches in real time than infrared beams or proximity sensors (which may mistake the rat&#8217;s snout for the reaching paw). Furthermore, multiple triggers may be used to identify the paw at different reaching phases (e.g., approach to the slot, paw lift, extension through slot). This not only allows reproducible, precisely-timed neuronal perturbations, but can be used to trigger storage of short high-speed videos.
While our automated version of skilled reaching has several advantages for specific applications, it also has some limitations. As noted above, the high-speed, high-resolution camera is moderately expensive, but necessary to include mirror and direct views in a single image and capture the very quick reaching movement. Using one camera eliminates the need to synchronize and record multiple video streams simultaneously, or purchase multiple cameras and frame grabbers. The paw in the reflected view is effectively about twice as far from the camera (by ray-tracing) as in the direct view. This means that one of the views is always out of focus, though DLC still robustly identifies individual digits in both views (Figure 4, Figure 6). Finally, we used a color camera because, prior to the availability of DLC, we tried color coding the digits with tattoos. While it is possible that this learning-based program would be equally effective on black and white (or lower resolution) video, we can only verify the effectiveness of the hardware described here. Finally, our analysis code (other than DLC) is written primarily in a commercial software package (see Table of Materials) but should be straightforward to adapt to open source programming languages (e.g., Python) as needed.
There are several ways in which we are working to improve this system. Currently, the mirror view is partially occluded by the front panel. We have therefore been exploring ways to obtain multiple simultaneous views of the paw while minimizing obstructions. Another important development will be to automatically score the reaches (the system can track kinematics, but a human must still score successful versus failed reaches). Methods have been developed to determine whether pellets were knocked off the shelf/pedestal, but cannot determine whether the pellet was grasped or missed entirely11. By tracking the pellet with DLC, we are exploring algorithms to determine the number of reaches per trial, as well as whether the pellet was grasped, knocked off the pedestal, or missed entirely. Along those lines, we are also working to fully automate the workflow from data collection through video conversion, DLC processing, and automatic scoring. Ultimately, we envision a system in which multiple experiments can be run on one day, and by the next morning the full forelimb kinematics and reaching scores for each experiment have been determined.

Humans depend heavily on dexterous skill, defined as movements that require precisely coordinated multi-joint and digit movements. These skills are affected by a range of common central nervous system pathologies including structural lesions (e.g., stroke, tumor, demyelinating lesions), neurodegenerative disease (e.g., Parkinson's disease), and functional abnormalities of motor circuits (e.g., dystonia). Understanding how dexterous skills are learned and implemented by central motor circuits therefore has the potential to improve quality of life for a large population. Furthermore, such understanding is likely to improve motor performance in healthy people by optimizing training and rehabilitation strategies.
Dissecting the neural circuits underlying dexterous skill in humans is limited by technological and ethical considerations, necessitating the use of animal models. Nonhuman primates are commonly used to study dexterous limb movements given the similarity of their motor systems and behavioral repertoire to humans1. However, non-human primates are expensive with long generation times, limiting numbers of study subjects and genetic interventions. Furthermore, while the neuroscientific toolbox applicable to nonhuman primates is larger than for humans, many recent technological advances are either unavailable or significantly limited in primates.
Rodent skilled reaching is a complementary approach to studying dexterous motor control. Rats and mice can be trained to reach for, grasp, and retrieve a sugar pellet in a stereotyped sequence of movements homologous to human reaching patterns2. Due to their relatively short generation time and lower housing costs, as well as their ability to acquire skilled reaching over days to weeks, it is possible to study large numbers of subjects during both learning and skill consolidation phases. The use of rodents, especially mice, also facilitates the use of powerful modern neuroscientific tools (e.g., optogenetics, calcium imaging, genetic models of disease) to study dexterous skill.
Rodent skilled reaching has been used for decades to study normal motor control and how it is affected by specific pathologies like stroke and Parkinson's disease3. However, most versions of this task are labor and time-intensive, mitigating the benefits of studying rodents. Typical implementations involve placing rodents in a reaching chamber with a shelf in front of a narrow slot through which the rodent must reach. A researcher manually places sugar pellets on the shelf, waits for the animal to reach, and then places another one. Reaches are scored as successes or failures either in real time or by video review4. However, simply scoring reaches as successes or failures ignores rich kinematic data that can provide insight into how (as opposed to simply whether) reaching is impaired. This problem was addressed by implementing detailed review of reaching videos to identify and semi-quantitatively score reach submovements5. While this added some data regarding reach kinematics, it also significantly increased experimenter time and effort. Further, high levels of experimenter involvement can lead to inconsistencies in methodology and data analysis, even within the same lab.
More recently, several automated versions of skilled reaching have been developed. Some attach to the home cage6,7, eliminating the need to transfer animals. This both reduces stress on the animals and eliminates the need to acclimate them to a specialized reaching chamber. Other versions allow paw tracking so that kinematic changes under specific interventions can be studied8,9,10, or have mechanisms to automatically determine if pellets were knocked off the shelf11. Automated skilled reaching tasks are especially useful for high-intensity training , as may be required for rehabilitation after an injury12. Automated systems allow animals to perform large numbers of reaches over long periods of time without requiring intensive researcher involvement. Furthermore, systems which allow paw tracking and automated outcome scoring reduce researcher time spent performing data analysis.
We developed an automated rat skilled reaching system with several specialized features. First, by using a movable pedestal to bring the pellet into "reaching position" from below, we obtain a nearly unobstructed view of the forelimb. Second, a system of mirrors allows multiple simultaneous views of the reach with a single camera, allowing three-dimensional (3-D) reconstruction of reach trajectories using a high resolution, high-speed (300 fps) camera. With the recent development of robust machine learning algorithms for markerless motion tracking13, we now track not only the paw but individual knuckles to extract detailed reach and grasp kinematics. Third, a frame-grabber that performs simple video processing allows real-time identification of distinct reaching phases. This information is used to trigger video acquisition (continuous video acquisition is not practical due to file size), and can also be used to trigger interventions (e.g., optogenetics) at precise moments. Finally, individual video frames are triggered by transistor-transistor logic (TTL) pulses, allowing the video to be precisely synchronized with neural recordings (e.g., electrophysiology or photometry). Here, we describe how to build this system, train rats to perform the task, synchronize the apparatus with external systems, and reconstruct 3-D reach trajectories.

<table><tbody><tr><td>clear polycarbonate panels</td><td>TAP Plastics</td><td></td><td>cut to order (see box design)</td></tr><tr><td>infrared source/detector</td><td>Med Associates</td><td>ENV-253SD</td><td>30&quot; range</td></tr><tr><td>camera</td><td>Basler</td><td>acA2000-340kc</td><td>2046 x 1086 CMV2000 340 fps Color Camera Link</td></tr><tr><td>camera lens</td><td>Megapixel (computar)</td><td>M0814-MP2</td><td>2/3&quot; 8mm f1.4 w/ locking Iris &amp;amp; Focus</td></tr><tr><td>camera cables</td><td>Basler</td><td>#2000031083</td><td>Cable PoCL Camera Link SDR/MDR Full, 5 m - Data Cables</td></tr><tr><td>mirrors</td><td>Amazon</td><td></td><td></td></tr><tr><td>linear actuator</td><td>Concentrics</td><td>LACT6P</td><td>Linear Actuator 6&quot; Stroke (nominal), 110 Lb Force, 12 VDC, with Potentiometer</td></tr><tr><td>pellet reservoir/funnel</td><td>Amico (Amazon)</td><td>a12073000ux0890</td><td>6&quot; funnel</td></tr><tr><td>guide tube</td><td>ePlastics</td><td>ACREXT.500X.250</td><td>1/2&quot; OD x 1/4&quot; ID Clear. Extruded Plexiglass Acrylic Tube x 6ft long</td></tr><tr><td>pellet delivery rod</td><td>ePlastics</td><td>ACRCAR.250</td><td>0.250&quot; DIA. Cast Acrylic Rod (2&#039; length)</td></tr><tr><td>plastic T connector</td><td>United States Plastic Corp</td><td>#62065</td><td>3/8&quot; x 3/8&quot; x 3/8&quot; Hose ID Black HDPE Tee</td></tr><tr><td>LED lights</td><td>Lighting EVER</td><td>4100066-DW-F</td><td>12V Flexible Waterproof LED Light Strip, LED Tape, Daylight White, Super Bright 300 Units 5050 LEDS, 16.4Ft 5 M Spool</td></tr><tr><td>Light backing</td><td>ePlastics</td><td>ACTLNAT0.125X12X36</td><td>0.125&quot; x 12&quot; x 36&quot; Natural Acetal Sheet</td></tr><tr><td>Light diffuser films</td><td>inventables</td><td>23114-01</td><td>.007x8.5x11&quot;, matte two sides</td></tr><tr><td>cabinet and custom frame materials</td><td>various (Home Depot, etc.)</td><td></td><td>3/4&quot; fiber board (see protocol for dimensions of each structure)</td></tr><tr><td>acoustic foam</td><td>Acoustic First</td><td></td><td>FireFlex Wedge Acoustical Foam (2&quot; Thick)</td></tr><tr><td>ventilation fans</td><td>Cooler Master (Amazon)</td><td>B002R9RBO0</td><td>Rifle Bearing 80mm Silent Cooling Fan for Computer Cases and CPU Coolers</td></tr><tr><td>cabinet door hinges</td><td>Everbilt (Home Depot</td><td>#14609</td><td>continuous steel hinge (1.4&quot; x 48&quot;)</td></tr><tr><td>cabinet wheels</td><td>Everbilt (Home Depot</td><td>#49509</td><td>Soft rubber swivel plate caster with 90 lb. load rating and side brake</td></tr><tr><td>cabinet door handle</td><td>Everbilt (Home Depot</td><td>#15094</td><td>White light duty door pull (4.5&quot;)</td></tr><tr><td>computer</td><td>Hewlett Packard</td><td>Z620</td><td>HP Z620 Desktop Workstation</td></tr><tr><td>Camera Link Frame Grabber</td><td>National Instruments</td><td>#781585-01</td><td>PCIe-1473 Virtex-5 LX50 Camera Link - Full</td></tr><tr><td>Multifunction RIO Board</td><td>National Instruments</td><td>#781100-01</td><td>PCIe-17841R</td></tr><tr><td>Analog RIO Board Cable</td><td>National Instruments</td><td>SCH68M-68F-RMIO</td><td>Multifunction Cable</td></tr><tr><td>Digital RIO Board Cable</td><td>National Instruments</td><td>#191667-01</td><td>SHC68-68-RDIO Digital Cable for R Series</td></tr><tr><td>Analog Terminal Block</td><td>National Instruments</td><td>#782536-01</td><td>SCB-68A Noise Rejecting, Shielded I/O Connector Block</td></tr><tr><td>Digital Terminal Block</td><td>National Instruments</td><td>#782536-01</td><td>SCB-68A Noise Rejecting, Shielded I/O Connector Block</td></tr><tr><td>24 position relay rack</td><td>Measurement Computing Corp.</td><td>SSR-RACK24</td><td>Solid state relay backplane (Gordos/OPTO-22 type relays), 24-channel</td></tr><tr><td>DC switch</td><td>Measurement Computing Corp.</td><td>SSR-ODC-05</td><td>Solid state relay module, single, DC switch, 3 to 60 VDC @ 3.5 A</td></tr><tr><td>DC Sense</td><td>Measurement Computing Corp.</td><td>SSR-IDC-05</td><td>solid state relay module, single, DC sense, 3 to 32 VDC</td></tr><tr><td>DC Power Supply</td><td>BK Precision</td><td>1671A</td><td>Triple-Output 30V, 5A Digital Display DC Power Supply</td></tr><tr><td>sugar pellets</td><td>Bio Serv</td><td>F0023</td><td>Dustless Precision Pellets, 45 mg, Sucrose (Unflavored)</td></tr><tr><td>LabVIEW</td><td>National Instruments</td><td>LabVIEW 2014 SP1, 64 and 32-bit versions</td><td>64-bit LabVIEW is required to access enough memory to stream videos, but FPGA coding must be performed in 32-bit LabVIEW</td></tr><tr><td>MATLAB</td><td>Mathworks</td><td>Matlab R2019a</td><td>box calibration and trajectory reconstruction software is written in Matlab and requires the Computer Vision toolbox</td></tr></tbody></table>

automated rat single-pellet reaching with 3-dimensional reconstruction of paw and digit trajectories

Rodent skilled reaching is commonly used to study dexterous skills, but requires significant time and effort to implement the task and analyze the behavior. Several automated versions of skilled reaching have been developed recently. Here, we describe a version that automatically presents pellets to rats while recording high-definition video from multiple angles at high frame rates (300 fps). The paw and individual digits are tracked with DeepLabCut, a machine learning algorithm for markerless pose estimation. This system can also be synchronized with physiological recordings, or be used to trigger physiologic interventions (e.g., electrical or optical stimulation).

Rats acquire the skilled reaching task quickly once acclimated to the apparatus, with performance plateauing in terms of both numbers of reaches and accuracy over 1&#8211;2 weeks (Figure 5). Figure 6 shows sample video frames indicating structures identified by DeepLabCut, and Figure 7 shows superimposed individual reach trajectories from a single session. Finally, in Figure 8, we illustrate what happens if the paw detection trigger (steps 4.3.4&#8211;4.3.6) is not accurately set. There is significant variability in the frame at which the paw breaches the reaching slot. This is not a major problem in terms of analyzing reach kinematics. However, it could lead to variability in when interventions (e.g., optogenetics) are triggered during reaching movements.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59979/59979fig01.jpg" /> 
Figure 1: The skilled reaching chamber. 
Clockwise from top left are a side view, a view from the front and above, the frame in which the actuator is mounted (see step 1.8), and a view from the side and above. The skilled reaching chamber (1) has a door (2) cut into one side to allow rats to be placed into and taken out of the chamber. A slit is cut into the ceiling panel (12) to allow the animal to be tethered and holes are cut into the floor panel (13) to allow litter to fall through. Two infrared sensors (3) are aligned on either side of the back of the chamber. A mirror (4) is mounted above the reaching slot (14) at the front of the reaching chamber and two other mirrors (6) are mounted on either side of the reaching chamber. The skilled reaching chamber sits atop a support box (5). The high-definition camera (7) is mounted onto the support box in front of the reaching slot. Two pieces of black paper (18) are mounted on either side of the camera (7) to enhance contrast of the paw in the side mirrors (6). Below the support box is a frame (8) that supports the linear actuator (16) and pellet reservoir (9). A guide tube encasing the pellet delivery rod (10) is fit into the pellet reservoir and controlled by the linear actuator. Holes are cut into the actuator frame (17) and support box (15) above the pellet reservoir to allow the pellet delivery rod to move up and down freely. The box is illuminated with light panels (11) mounted to the cabinet walls and ceiling. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59979/59979fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/59979/59979fig02.jpg" /> 
Figure 2: Single trial structure. 
(A) A trial begins with the pellet delivery rod (controlled by a linear actuator) positioned at the &#8220;ready&#8221; position (position 2 &#8211; midway between floor and bottom of reaching slot). (B) The rat moves to the back of the chamber to break the infrared (IR) beam, which causes the pellet delivery rod to rise to position 3 (aligned with bottom of reaching slot). (C) The rat reaches through the reaching slot to grasp the pellet. Reaches are detected in real-time using an FPGA framegrabber that detects pixel intensity changes within a region of interest (ROI) in the side mirror view directly in front of the slot. When enough pixels match the user defined &#8220;paw intensity&#8221;, video acquisition is triggered. (D) Two seconds later the pellet is lowered to position 1, picking up a new pellet from the pellet reservoir before resetting to position 2. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59979/59979fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/59979/59979fig03.jpg" /> 
Figure 3: Sample calibration image. 
A helping hand is placed inside the skilled reaching chamber. An alligator clip pokes through the reaching slot to hold the calibration cube in place outside of the reaching chamber. The three checkerboard patterns are entirely visible in the direct view and the corresponding mirror views (green: left; red: top; and blue: right). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59979/59979fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/59979/59979fig04.jpg" /> 
Figure 4: Learning algorithm marker positions. 
Left column: direct view; right column: mirror view. Markers 1&#8211;4: MCP joints; 5&#8211;8: PIP joints; 9&#8211;12: digit tips; 13: dorsum of reaching paw; 14: nose; 15: dorsum of non-reaching paw. Marker 16 (pellet) is not visible. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59979/59979fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/59979/59979fig05.jpg" /> 
Figure 5: Rats rapidly acquire the automated skilled reaching task. 
Average first reach success rate (green, left axis) and average total trials (blue, right axis) over the first 20 training sessions in the automated skilled reaching task (n = 19). Each training session lasted 30 min. Error bars represent standard error of the mean. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59979/59979fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/59979/59979fig06.jpg" /> 
Figure 6: Sample video frames marked by the learning program. 
Left column: mirror view; right column: direct view. Cyan, red, yellow, and green dots mark digits 1&#8211;4, respectively. The white dot marks the nose, the black dot marks the pellet. Filled circles were identified by DeepLabCut. Open circles mark object positions estimated by where that object appeared in the opposite view. X&#8217;s are points re-projected onto the video frames from the estimates of their 3-D locations. This video was triggered at frame 300, as the paw passed through the slot. Top images are from the first frame when the reaching paw was detected. Bottom images are from the frame at which the second digit was maximally extended. These frames were identified by the image processing software. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59979/59979fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/59979/59979fig07.jpg" /> 
Figure 7: Sample 3-D trajectories from a single test session. 
Both axes show the same data, but rotated for ease of presentation. Black lines indicate mean trajectories. Cyan, red, yellow, and green are individual trajectories of the tips of digits 1&#8211;4, respectively. Blue lines indicate the trajectory of the paw dorsum. The large black dot indicates the sugar pellet located at (0,0,0). This represents only initial paw advancement for ease of presentation (including retractions and multiple reaches makes the figure almost uninterpretable). However, all kinematic data are available for analysis. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59979/59979fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/59979/59979fig08.jpg" /> 
Figure 8: Histograms of frame numbers in which specific reaching phases were identified for 2 different sessions. 
In one session (dark solid lines), the ROI trigger values were carefully set, and the paw was identified breaching the slot within the same few frames in each trial. In the other session (light dashed lines), the nose was often misidentified as the reaching paw, triggering video acquisition prematurely. Note that this would have little effect on off-line kinematic analyses unless the full reach was not captured. However, potential interventions triggered by the reaching paw would be poorly timed. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59979/59979fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories at JoVE.com

Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories

Rodent skilled reaching is commonly used to study dexterous skills, but requires significant time and effort to implement the task and analyze the behavior. We describe an automated version of skilled reaching with motion tracking and three-dimensional reconstruction of reach trajectories.

Rodent skilled reaching is commonly used to study dexterous skills, but requires significant time and effort to implement the task and analyze the ...

automated-rat-single-pellet-reaching-with-3-dimensional

Nanyang Technological University

Research

JoVE Journal

Behavior

13.9K Views.  University of Michigan. Rodent skilled reaching is commonly used to study dexterous skills, but requires significant time and effort to implement the task and analyze the behavior. We describe an automated version of skilled reaching with motion tracking and three-dimensional reconstruction of reach trajectories.

Video: Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories

Study Motor Skill Learning by Single-pellet Reaching Tasks in Mice

Reaching for and retrieving objects require precise and coordinated motor movements in the forelimb. When mice are repeatedly trained to grasp and retrieve food rewards positioned at a specific location, their motor performance (defined as accuracy and speed) improves progressively over time, and plateaus after persistent training. Once such reaching skill is mastered, its further maintenance does not require constant practice. Here we introduce a single-pellet reaching task to study the acquisition and maintenance of skilled forelimb movements in mice. In this video, we first describe the behaviors of mice that are commonly encountered in this learning and memory paradigm, and then discuss how to categorize these behaviors and quantify the observed results. Combined with mouse genetics, this paradigm can be utilized as a behavioral platform to explore the anatomical underpinnings, physiological properties, and molecular mechanisms of learning and memory.

Persistent practice improves the precision of coordinated movements.&#160;Here we introduce a single-pellet reaching task, which is designed to assess the learning and memory of forelimb skill in mice.&#160;

Reaching for and retrieving objects require precise and coordinated motor movements in the forelimb. When mice are repeatedly trained to grasp and ...

Acquisition of a High-precision Skilled Forelimb Reaching Task in Rats

Movements are the main measurable output of central nervous system function. Developing behavioral paradigms that allow detailed analysis of motor learning and execution is of critical importance in order to understand the principles and processes that underlie motor function. Here we present a paradigm to study movement acquisition within a daily session of training (within-session) representing the fast learning component and primary acquisition as well as skilled motor learning over several training sessions (between-session) representing the slow learning component and consolidation of the learned task. This behavioral paradigm increases the degree of difficulty and complexity of the motor skill task due to two features: First, the animal realigns its body prior to each pellet retrieval forcing renewed orientation and preventing movement execution from the same angle. Second, pellets are grasped from a vertical post that matches the diameter of the pellet and is placed in front of the cage. This requires a precise grasp for successful pellet retrieval and thus prevents simple pulling of the pellet towards the animal. In combination with novel genetics, imaging and electrophysiological technologies, this behavioral method will aid to understand the morphological, anatomical and molecular underpinnings of motor learning and memory.

A paradigm is presented to analyze the acquisition of a high-precision skilled forelimb reaching task in rats.

Movements are the main measurable output of central nervous system function. Developing behavioral paradigms that allow detailed analysis of motor ...

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Skilled reaching tasks are commonly used in studies of motor skill learning and motor function under healthy and pathological conditions, but can be time-intensive and ambiguous to quantify beyond simple success rates. Here, we describe the training procedure for reach-and-pull tasks with ETH Pattus, a robotic platform for automated forelimb reaching training that records pulling and hand rotation movements in rats. Kinematic quantification of the performed pulling attempts reveals the presence of distinct temporal profiles of movement parameters such as pulling velocity, spatial variability of the pulling trajectory, deviation from midline, as well as pulling success. We show how minor adjustments in the training paradigm result in alterations in these parameters, revealing their relation to task difficulty, general motor function or skilled task execution. Combined with electrophysiological, pharmacological and optogenetic techniques, this paradigm can be used to explore the mechanisms underlying motor learning and memory formation, as well as loss and recovery of function (e.g. after stroke).

A paradigm is presented for training and analysis of an automated skilled reaching task in rats. Analysis of pulling attempts reveals distinct subprocesses of motor learning.

Skilled reaching tasks are commonly used in studies of motor skill learning and motor function under healthy and pathological conditions, but can be ...

The Structure of Skilled Forelimb Reaching in the Rat: A Movement Rating Scale

Skilled reaching for food is an evolutionary ancient act and is displayed by many animal species, including those in the sister clades of rodents and primates. The video describes a test situation that allows filming of repeated acts of reaching for food by the rat that has been mildly food deprived. A rat is trained to reach through a slot in a holding box for food pellet that it grasps and then places in its mouth for eating. Reaching is accomplished in the main by proximally driven movements of the limb but distal limb movements are used for pronating the paw, grasping the food, and releasing the food into the mouth. Each reach is divided into at least 10 movements of the forelimb and the reaching act is facilitated by postural adjustments. Each of the movements is described and examples of the movements are given from a number of viewing perspectives. By rating each movement element on a 3-point scale, the reach can be quantified. A number of studies have demonstrated that the movement elements are altered by motor system damage, including damage to the motor cortex, basal ganglia, brainstem, and spinal cord. The movements are also altered in neurological conditions that can be modeled in the rat, including Parkinson's disease and Huntington's disease. Thus, the rating scale is useful for quantifying motor impairments and the effectiveness of neural restoration and rehabilitation. Because the reaching act for the rat is very similar to that displayed by humans and nonhuman primates, the scale can be used for comparative purposes. from a number of viewing perspectives. By rating each movement element on a 3-point scale, the reach can be quantified. A number of studies have demonstrated that the movement elements are altered by motor system damage, including damage to the motor cortex, basal ganglia, brainstem, and spinal cord. The movements are also altered in neurological conditions that can be modeled in the rat, including Parkinson's disease and Huntington's disease. Thus, the rating scale is useful for quantifying motor impairments and the effectiveness of neural restoration and rehabilitation.

Experiments on animals were performed in accordance with the guidelines and regulations set forth by the University of Lethbridge Animal Care Committee in accordance with the regulations of the Canadian Council on Animal Care.

The skilled reaching scale divides the movement by a forelimb in a reach for food act into composite elements each of which are evaluated with a three-point scale. The rating scale is described for a normal rat and can be applied toward evaluating neurological motor disorders.

Skilled reaching for food is an evolutionary ancient act and is displayed by many animal species, including those in the sister clades of rodents and ...

Biology

The Knob Supination Task: A Semi-automated Method for Assessing Forelimb Function in Rats

Tasks that accurately measure dexterity in animal models are critical to understand hand function. Current rat behavioral tasks that measure dexterity largely use video analysis of reaching or food manipulation. While these tasks are easy to implement and are robust across disease models, they are subjective and laborious for the experimenter. Automating traditional tasks or creating new automated tasks can make the tasks more efficient, objective, and quantitative. Since rats are less dexterous than primates, central nervous system (CNS) injury produces more subtle deficits in dexterity, however, supination is highly affected in rodents and crucial to hand function in primates. Therefore, we designed a semi-automated task that measures forelimb supination in rats. Rats are trained to reach and grasp a knob-shaped manipulandum and turn the manipulandum in supination to receive a reward. Rats can acquire the skill within 20 &plusmn; 5 days. While the early part of training is highly supervised, much of the training is done without direct supervision. The task reliably and reproducibly captures subtle deficits after injury and shows functional recovery that accurately reflects clinical recovery curves. Analysis of data is performed by specialized software through a graphical user interface that is designed to be intuitive. We also give solutions to common problems encountered during training, and show that minor corrections to behavior early in training produce reliable acquisition of supination. Thus, the knob supination task provides efficient and quantitative evaluation of a critical movement for dexterity in rats.

This manuscript describes a semi-automated task that quantifies supination in rats. Rats reach, grasp, and supinate a spherical manipulandum. The rat is rewarded with a pellet if the turn angle exceeds a criterion set by the user. This task increases throughput, sensitivity to injury, and objectivity compared to traditional tasks.

Tasks that accurately measure dexterity in animal models are critical to understand hand function. Current rat behavioral tasks that measure dexterity ...

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

We have recently demonstrated that implanting intracortical microelectrodes in the motor corteces of rats&#160;results in immediate and lasting motor deficits. Motor impairments were manually quantified through an open field grid test to measure the gross motor function and through a ladder test to measure the fine motor function. Here, we discuss a technique for the automated quantification of the video-recorded tests using our custom Capadona Behavioral Video Analysis System: Grid and Ladder Test, or BVAS. Leveraging simple and readily available coding software (see the Table of Materials), this program allows for the tracking of a single animal on both the open field grid and the ladder tests. In open field grid tracking, the code thresholds the video for intensity, tracks the position of the rat over the 3 min duration of the grid test, and analyzes the path. It then computes and returns measurements for the total distance traveled, the maximum velocity achieved, the number of left- and right-handed turns, and the total number of grid lines crossed by the rat. In ladder tracking, the code again thresholds the video for intensity, tracks the movement of the rat across the ladder, and returns calculated measurements including the time it took the rat to cross the ladder, the number of paw slips occurring below the plane of the ladder rungs, and the incidence of failures due to stagnation or reversals. We envision that the BVAS developed here can be employed for the analysis of motor function in a variety of applications, including many injury or disease models.

The current study aimed to automate the quantification of motor deficits in rats. The initial evaluation model assesses motor loss resulting from an intracortical microelectrode implantation in the motor cortex. We report on the development and use of a tracking algorithm using easily adaptable, simple, and readily available coding software.

We have recently demonstrated that implanting intracortical microelectrodes in the motor corteces of rats&#160;results in immediate and lasting motor ...

3D Kinematic Gait Analysis for Preclinical Studies in Rodents

The utility of three-dimensional (3D) kinematic motion analysis systems is limited in rodents. Part of the reason for this inadequacy is the use of complex algorithms and mathematical modeling that accompany 3D data collection and analysis procedures. This work provides a simple, user-friendly, step-by-step detailed methodology for 3D kinematic gait analysis during treadmill locomotion in healthy and neurotraumatic rats using a six-camera motion capture system. Also provided are details on 1) calibration of the system in an experimental set-up customized for quadrupedal locomotion, 2) data collection for treadmill locomotion in adult rats using markers positioned on all four limbs, 3) options available for video tracking and processing, and 4) basic 3D kinematic data generation and visualization and quantification of data using the built-in data collection software. Finally, it is suggested that the utility of this motion capture system be expanded to studying a variety of motor behaviors before and after neurotrauma.

Presented here is a protocol to collect and analyze three-dimensional kinematics of quadrupedal locomotion in rodents for preclinical studies.

The utility of three-dimensional (3D) kinematic motion analysis systems is limited in rodents. Part of the reason for this inadequacy is the use of ...

Evaluating Skilled Prehension in Mice Using an Auto-Trainer

We describe a method to introduce na&#239;ve mice to a novel prehension (reach-to-grasp) task. Mice are housed singly in cages with a frontal slot that permits the mouse to reach out of its cage and retrieve food pellets. Minimal food restriction is employed to encourage the mice to perform the food retrieval from the slot. As the mice begin to associate coming to the slot for food, the pellets are manually pulled away to stimulate extension and pronation of their paw to grasp and retrieve the pellet through the frontal slot. When the mice begin to reach for the pellets as they arrive at the slot, the behavioral assay can be performed by measuring the rate at which they successfully grasp and retrieve the desired pellet. They are then introduced to an auto-trainer that automates both the process of providing food pellets for the mouse to grasp, and the recording of successful and failed reaching and grasping attempts. This allows for the collection of reaching data for multiple mice with minimal effort, to be used in experimental analysis as appropriate.

Method to assess the impact of training on motor skills is a useful tool. Unfortunately, most behavioral assessments can be labor intensive and/or expensive.We describe here a robotic method of assessing prehension (reach-to-grasp) skill in mice.

We describe a method to introduce na&#239;ve mice to a novel prehension (reach-to-grasp) task. Mice are housed singly in cages with a frontal slot that ...

3D Kinematic Analysis for the Functional Evaluation in the Rat Model of Sciatic Nerve Crush Injury

Compared to the Sciatic Functional Index (SFI), kinematic analysis is a more reliable and sensitive method for performing functional evaluations of sciatic nerve injury rodent models. In this protocol, we describe a novel kinematic analysis method that uses a three-dimensional (3D) motion capture apparatus for functional evaluations using a rat sciatic nerve crush injury model. First, the rat is familiarized with treadmill walking. Markers are then attached to the designated bone landmarks and the rat is made to walk on the treadmill at the desired speed. Meanwhile, the posterior limb movements of the rat are recorded using four cameras. Depending on the software used, marker tracings are created using both automatic and manual modes and the desired data are produced after subtle adjustments. This method of kinematic analysis, which uses a 3D motion capture apparatus, offers numerous advantages, including superior precision and accuracy. Many more parameters can be investigated during the comprehensive functional evaluations. This method has several shortcomings that require consideration: The system is expensive, can be complicated to operate, and may produce data deviations due to skin shifting. Nevertheless, kinematic analysis using a 3D motion capture apparatus is useful for performing functional anterior and posterior limb evaluations. In the future, this method may become increasingly useful for generating accurate assessments of various traumas and diseases.

We introduce a kinematic analysis method that uses a three-dimensional motion capture apparatus containing four cameras and data processing software for performing functional evaluations during fundamental research involving rodent models.

Compared to the Sciatic Functional Index (SFI), kinematic analysis is a more reliable and sensitive method for performing functional evaluations of ...

Neuroscience

Applying the RatWalker System for Gait Analysis in a Genetic Rat Model of Parkinson's Disease

Parkinson's disease (PD) is a progressive neurodegenerative disorder caused by the loss of dopaminergic (DA) neurons in the substantia nigra pars compacta. Gait abnormalities, including decreased arm swing, slower walking speed, and shorter steps are common in PD patients and appear early in the course of disease. Thus, the quantification of motor patterns in animal models of PD will be important for phenotypic characterization during disease course and upon therapeutic treatment. Most cases of PD are idiopathic; however, the identification of hereditary forms of PD uncovered gene mutations and variants, such as loss-of-function mutations in Pink1 and Parkin, two proteins involved in mitochondrial quality control that could be harnessed to create animal models. While mice are resistant to neurodegeneration upon loss of Pink1 and Parkin (single and combined deletion), in rats, Pink1 but not Parkin deficiency leads to nigral DA neuron loss and motor impairment. Here, we report the utility of FTIR imaging to uncover gait changes in freely walking young (2 months of age) male rats with combined loss of Pink1 and Parkin prior to the development of gross visually apparent motor abnormality as these rats age (observed at 4-6 months), characterized by hindlimb dragging as previously reported in Pink1 knockout (KO) rats.

Here we describe the RatWalker system, built by redesigning the MouseWalker apparatus to accommodate for the increased size and weight of rats. This system uses frustrated total internal reflection (FTIR), high-speed video capture, and open-access analysis software to track and quantify gait parameters.