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.

All methods involving animal use described here have been approved by the Institutional Animal Care and Use Committee (IACUC) of the University of Michigan.
1. Setting up the reaching chamber
NOTE: See Ellens et al.14 for details and diagrams of the apparatus. Part numbers refer to Figure 1.
<ol>
	<li>Bond clear polycarbonate panels with acrylic cement to build the reaching chamber (15 cm wide ...

<ol>
	<li>Chen, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+automated+behavioral+apparatus+to+combine+parameterized+reaching+and+grasping+movements+in+3D+space.">An automated behavioral apparatus to combine parameterized reaching and grasping movements in 3D space.</a> Journal of Neuroscience Methods. , (2018).</li><li>Sacrey, L. A. R. A., Alaverdashvili, M., Whishaw, I. Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Similar+hand+shaping+in+reaching-for-food+(skilled+reaching)+in+rats+and+humans+provides+evidence+of+homology+in+release,+collection,+and+manipulation+movements.">Similar hand shaping in reaching-for-food (skilled reaching) in rats and humans provides evidence of homology in release, collection, and manipulation movements.</a> Behavioural Brain Research. 204, 153-161 (2009).</li><li>Whishaw, I. Q., Kolb, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Decortication+abolishes+place+but+not+cue+learning+in+rats.">Decortication abolishes place but not cue learning in rats.</a> Behavioural Brain Research. 11, 123-134 (1984).</li><li>Klein, A., Dunnett, S. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+Skilled+Forelimb+Movement+in+Rats:+The+Single+Pellet+Reaching+Test+and+Staircase+Test.">Analysis of Skilled Forelimb Movement in Rats: The Single Pellet Reaching Test and Staircase Test.</a> Current Protocols in Neuroscience. 58, 8.28.1-8.28.15 (2012).</li><li>Whishaw, I. Q., Pellis, S. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+structure+of+skilled+forelimb+reaching+in+the+rat:+a+proximally+driven+movement+with+a+single+distal+rotatory+component.">The structure of skilled forelimb reaching in the rat: a proximally driven movement with a single distal rotatory component.</a> Behavioural Brain Research. 41, 49-59 (1990).</li><li>Zeiler, S. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Medial+premotor+cortex+shows+a+reduction+in+inhibitory+markers+and+mediates+recovery+in+a+mouse+model+of+focal+stroke.">Medial premotor cortex shows a reduction in inhibitory markers and mediates recovery in a mouse model of focal stroke.</a> Stroke. 44, 483-489 (2013).</li><li>Fenrich, K. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+single+pellet+grasping+using+automated+ad+libitum+full-time+training+robot.">Improved single pellet grasping using automated ad libitum full-time training robot.</a> Behavioural Brain Research. 281, 137-148 (2015).</li><li>Azim, E., Jiang, J., Alstermark, B., Jessell, T. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Skilled+reaching+relies+on+a+V2a+propriospinal+internal+copy+circuit.">Skilled reaching relies on a V2a propriospinal internal copy circuit.</a> Nature. , (2014).</li><li>Guo, J. Z. Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cortex+commands+the+performance+of+skilled+movement.">Cortex commands the performance of skilled movement.</a> Elife. 4, e10774 (2015).</li><li>Nica, I., Deprez, M., Nuttin, B., Aerts, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+Assessment+of+Endpoint+and+Kinematic+Features+of+Skilled+Reaching+in+Rats.">Automated Assessment of Endpoint and Kinematic Features of Skilled Reaching in Rats.</a> Frontiers in Behavioral Neuroscience. 11, 255 (2017).</li><li>Wong, C. C., Ramanathan, D. S., Gulati, T., Won, S. J., Ganguly, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+automated+behavioral+box+to+assess+forelimb+function+in+rats.">An automated behavioral box to assess forelimb function in rats.</a> Journal of Neuroscience Methods. 246, 30-37 (2015).</li><li>Torres-Esp&#237;n, A., Forero, J., Schmidt, E. K. A., Fouad, K., Fenrich, K. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+motorized+pellet+dispenser+to+deliver+high+intensity+training+of+the+single+pellet+reaching+and+grasping+task+in+rats.">A motorized pellet dispenser to deliver high intensity training of the single pellet reaching and grasping task in rats.</a> Behavioural Brain Research. 336, 67-76 (2018).</li><li>Mathis, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DeepLabCut:+markerless+pose+estimation+of+user-defined+body+parts+with+deep+learning.">DeepLabCut: markerless pose estimation of user-defined body parts with deep learning.</a> Nature Neuroscience. 21, 1281-1289 (2018).</li><li>Ellens, D. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+automated+rat+single+pellet+reaching+system+with+high-speed+video+capture.">An automated rat single pellet reaching system with high-speed video capture.</a> Journal of Neuroscience Methods. 271, 119-127 (2016).</li></ol>

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-tim...

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&#34; 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&#34; 8mm f1.4 w/ locking Iris &#38; 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&#34; 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&#34; funnel</td>
		</tr>
		<tr>
			<td>guide tube</td>
			<td>ePlastics</td>
			<td>ACREXT.500X.250</td>
			<td>1/2&#34; OD x 1/4&#34; 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&#34; DIA. Cast Acrylic Rod (2&#39; length)</td>
		</tr>
		<tr>
			<td>plastic T connector</td>
			<td>United States Plastic Corp</td>
			<td>#62065</td>
			<td>3/8&#34; x 3/8&#34; x 3/8&#34; 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&#34; x 12&#34; x 36&#34; Natural Acetal Sheet</td>
		</tr>
		<tr>
			<td>Light diffuser films</td>
			<td>inventables</td>
			<td>23114-01</td>
			<td>.007x8.5x11&#34;, matte two sides</td>
		</tr>
		<tr>
			<td>cabinet and custom frame materials</td>
			<td>various (Home Depot, etc.)</td>
			<td></td>
			<td>3/4&#34; 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&#34; 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&#34; x 48&#34;)</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&#34;)</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 happen...

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 ...

This method builds upon established rat skilled reaching protocols to automate training and testing providing an efficient means to acquire large data sets relatively quickly. It minimizes experimenter effort while allowing three-dimensional reconstruction of forelimb kinematics. The kinematics can be used to evaluate the effects of precisely timed interventions or correlated with physiologic recordings.With some adjustments, this technique can also be applied in mice. Begin by turning on the lights and place the rat in a skilled reaching chamber. Use forceps to hold a pellet through the reaching slot at the front of the box.Allow the rat to eat three pellets from the forceps. Pull the pellet back the next time the rat tries to eat the pellet from the forceps and repeat until the rat reaches for the pellet with one paw the most out of 11 reaches indicating paw preference. Next, align the pellet delivery rod with the side of the reaching slot contralateral to the rat's preferred paw.Place a pellet on the delivery rod. Then use forceps to bait the rat with the pellet but direct the rat towards the delivery rod so that its paw hits the pellet on the rod. After the rat has attempted 10 reaches to the delivery rod without being baited, advance the rat to the next phase.Next, position the pellet delivery rod based on the rat's paw preference and set it to position two. Set the height position of the pellet delivery rod using the actuator remote. Once the delivery rod is at the correct height, hold down the desired number until the light blinks red to set.Then place the rat in the chamber and bait the rat to the back with a pellet. When the rat moves far enough to the back of the chamber that it would break the infrared beam if the automated version was running, move the pellet delivery rod to position three. Finally, wait for the rat to reach for the pellet and then move the pellet delivery rod back to position two.Place a new pellet on the delivery rod if knocked off. Repeat these steps gradually baiting the rat less and less until the rat begins to one, move to the back to request a pellet without being baited, and two, immediately move to the front after requesting a pellet in the back. After the rat has done this 10 times, begin training on the automated task.To set up the automated system, turn on the lights in the chamber and refill the pellet reservoir. Position the pellet delivery rod according to the rat's paw preference. Check that the actuator positions are set correctly.Next, turn on the computer and open the skilled reaching program. Enter the rat ID number under subject and select the paw preference from the hand dropdown menu. Specify the save path for the videos.Then set the session time and the number of max videos. Set the pellet lift duration as well and enable the early reach penalty. Next, to take calibration images, place the helping hand inside the reaching chamber and poke the alligator clip through the reaching slot.Hold the cube in front of the reaching slot with the alligator clip. Then position the cube so that the red side appears in the top mirror, the green side in the left mirror, and the blue side in the right mirror. In the behavioral program, ensure the ROI threshold is set to a very large value.Click the run button and once the camera initialized button turns green, press start to begin video acquisition. Click Cal Mode then take an image by clicking Take Cal Image. Move the cube slightly and take another image.Repeat again for a total of three images. Finally, stop the program by clicking stop and then click the stop sign button. Remove the helping hand and cube from the box.Be careful not to bump anything in the behavioral chamber after calibration images have been taken that day. First, place the rat in the skilled reaching chamber. Click on the white arrow to run the program.Then set the position of the ROI for paw detection by adjusting x offset, y offset, ROI width, and ROI height. Position the ROI in the side mirror that shows the dorsum of the paw directly in front of the reaching slot and click start to begin the program. While the rat is not reaching, adjust the low ROI threshold value until the live ROI trigger value is oscillating between zero and one.Then set the ROI threshold to be significantly greater than the live ROI trigger value during nose pokes and lower than the live ROI trigger value when the rat reaches. Adjust until videos are consistently triggered when the rat reaches but not when it pokes its nose through the slot. Monitor the first few trials to ensure that everything is working correctly.When a rat reaches before requesting a pellet, observe the early reaches number increase. When a rat reaches after requesting a pellet, observe the videos number increase while a copy of the video is saved as a bin file. Finally, after the session time or max number of videos is reached, press the stop sign button.Results indicate that after 20 days, rats acquire the skilled reaching task. Here, the reach trajectories from a single session are shown. This represents only initial paw advancement for ease of presentation.Further, if the paw detection trigger is not accurately set, there is significant variability in the frame at which the paw breaches the reaching slot which may lead to variability in when interventions are triggered during reaching movements. Other than careful handling of the animals, no precautions will be taken in this protocol. It is most important to set the ROI threshold correctly so that videos or other interventions are triggered at the appropriate moments.This method can be paired with several other techniques such as optogenetics, calcium imaging, and genetic models which can address questions ranging from basic motor control to movement disorder pathophysiology. This technique permits correlation of detailed reach and grasp kinematics with physiologic recordings or interventions allowing researchers to answer not just if but how movements change with neural circuitry changes.

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13.6K Views.  University of Michigan. This method builds upon established rat skilled reaching protocols to automate training and testing providing an efficient means to acquire large data sets relatively quickly. It minimizes experimenter effort while allowing three-dimensional reconstruction of forelimb kinematics. The kinematics can be used to evaluate the effects of precisely timed interventions or correlated with physiologic recordings.With some adjustments, this technique can also be applied in mice. Begin by turning...