We would like to thank Noah Pettit from the Harvey lab for the discussion and suggestions while developing the protocol in this manuscript. This work was supported by a BBRF Young Investigator Award and NIMH 1R21MH122965 (G.F.T.), in addition to NINDS R56NS128177 (R.H., C.L.) and NIMH R01MH068542 (R.H.).

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</ol>

Clay Lacefield is the founder and maintainer of OpenMaze.org, which provides designs for the OMwSmall PCB used in this protocol free for download.

This open-source VR system for mice will only function if the serial connections are made properly between the rotary and behavior ESP32 microcontrollers and the single-board computer (step 2), which can be confirmed using the IDE serial monitor (step 2.4.5). For successful behavioral results from this protocol (step 4), the mice must be habituated to the apparatus and be comfortable running on the wheel for liquid rewards (steps 4.3-4.5). This requires sufficient (but not excessive) water restriction, as mice given ad libitum water in the homecage will not run and lick for rewards (i.e., to indicate their perceived location), and dehydrated mice may be lethargic and not run on the wheel. It is also worth noting that there are alternative methods for motivating mouse behavior without water restriction24; however, we did not test these methods here. For the training procedure, animals that do not run initially may be given ad hoc (i.e., non-spatial) water rewards by the experimenter via an attached optional button press, or the wheel may be moved gently to encourage locomotion. To develop random foraging behavior, mice that run but do not lick should be run with non-operant rewards (behavior ESP32 code: isOperant = 0;, step 4.5.1) until they run and lick for rewards, and they can then be run with alternating laps of non-operant and operant reward zones (altOpt=1; step 4.5.2) until they start to lick on operant laps before moving to fully operant random reward zones (step 4.5.3).
While we have provided complete instructions and example results for a basic set of experiments aimed at eliciting one form of spatial learning (conditioned licking at a hidden reward location in the virtual linear track environment), the same basic hardware and software setup can also be modified for the delivery of more complex visuospatial environments using the pi3d Python package for Raspberry Pi. For example, this system can incorporate more complex mazes such as corridors with variable lengths, multiple patterns and 3D objects, and naturalistic 3D VR landscapes. Furthermore, the behavioral software for the delivery of water rewards and other non-visual stimuli can be modified for other training paradigms by altering key variables (presented at the beginning of the behavior ESP32 code) or by inserting new types of spatial events into the same code. We are happy to advise users regarding methods for implementing other types of behavioral experiments with this VR setup or in troubleshooting.
Immersive VR environments have proven a versatile tool for studying the underlying neural mechanisms of spatial navigation6,7,8, reward-learning behaviors9, and visual perception25 both in clinical and animal studies. The main advantage of this approach is that the experimenter has tight control over contextual elements such as visual cues and specific spatial stimuli (e.g., rewards and olfactory, auditory, or tactile stimuli), which is not practical in real-world environments with freely moving animals. It should be noted, however, that differences may exist in the manner in which VR environments are encoded by brain areas such as the hippocampus when compared with the use of real-world environments26,27. With this caveat, the use of VR environments allows experimenters to perform a large number of behavioral trials with carefully controlled stimuli, allowing the dissociation of the contributions of distinct sensory elements to spatial navigation.
The complexity of building custom VR setups often requires an extensive background in engineering and computer programming, which may increase the time of setup and limit the number of apparatuses that can be constructed to train mice for experimentation. VR setups are also available from commercial vendors; however, these solutions can be expensive and limited if the user wants to implement new features or expand the training/recording capacity to more than one setup. The estimated price range of the open-source VR setup presented here is &#60;$1,000 (USD); however, a simplified version for training (e.g., lacking goniometers for head angle adjustment) can be produced for &#60;$500 (USD), thus allowing the construction of multiple setups for training mice on a larger scale. The modular arrangement of components also allows the integration of VR with other systems for behavioral control, such as the treadmill system with spatial olfactory and tactile stimuli we have used previously20, and, thus, VR and other stimulus modalities are not mutually exclusive.
This open-source VR system with its associated hardware (running wheel, projection screen, and head-fixation apparatus), electronics setup (single-board computer and ESP32 microcontrollers), and software (VR GUI and behavior code) provides an inexpensive, compact, and easy-to-use setup for delivering parameterized immersive VR environments to mice during head-restrained spatial navigation. This behavior may then be synchronized with neural imaging or electrophysiological recording to examine neural activity during spatial learning (step 2.3.7). The spectrum of experimental techniques compatible with VR is wide, ranging from spatial learning behavior alone to combination with fiber photometry, miniscope imaging, single-photon and multi-photon imaging, and electrophysiological techniques (e.g., Neuropixels or intracellular recording). While head restraint is necessary for some of these recording techniques, the extremely precise nature of stimulus presentation and the stereotyped nature of the behavior may also be useful for other techniques not requiring head fixation, such as miniscope imaging and fiber photometry. It should be noted, however, that our capacitive sensor-based solution for detecting licks may introduce significant noise on electrophysiological traces. To avoid such artifacts, optical or other (e.g., mechanical), sensor-based solutions should be implemented for lick detection.
Future improvements to the VR system will be uploaded to the project GitHub page (https://github.com/GergelyTuri/HallPassVR), so users should check this page regularly for updates. For example, we are in the process of replacing the hardwired serial connections between the microcontrollers and the single-board computer with Bluetooth functionality, which is native to the ESP32 microcontrollers already used in this design. In addition, we are planning to upgrade the HallPassVR GUI to allow the specification of different paths in each behavioral session to contain different positions for key landmark visual stimuli on different laps. This will allow greater flexibility for dissociating the impact of specific visual and contextual features on the neural encoding of space during spatial learning.

Spatial navigation is an ethologically important behavior by which animals encode the features of new locations into a cognitive map, which is used for finding areas of possible reward and avoiding areas of potential danger. Inextricably linked with memory, the cognitive processes underlying spatial navigation share a neural substrate in the hippocampus1 and cortex, where neural circuits in these areas integrate incoming information and form cognitive maps of environments and events for later recall2. While the discovery of place cells in the hippocampus3,4 and grid cells in the entorhinal cortex5 has shed light on how the cognitive map within the hippocampus is formed, many questions remain about how specific neural subtypes, microcircuits, and individual subregions of the hippocampus (the dentate gyrus, and cornu ammonis areas, CA3-1) interact and participate in spatial memory formation and recall.
In vivo two-photon imaging has been a useful tool in uncovering cellular and population dynamics in sensory neurophysiology6,7; however, the typical necessity for head restraint limits the utility of this method for examining mammalian spatial behavior. The advent of virtual reality (VR)8 has addressed this shortcoming by presenting immersive and realistic visuospatial environments while head-restrained mice run on a ball or treadmill to study spatial and contextual encoding in the hippocampus8,9,10 and cortex11. Furthermore, the use of VR environments with behaving mice has allowed neuroscience researchers to dissect the components of spatial behavior by precisely controlling the elements of the VR environment12 (e.g., visual flow, contextual modulation) in ways not possible in real-world experiments of spatial learning, such as the Morris water maze, Barnes maze, or hole board tasks.
Visual VR environments are typically rendered on the graphical processing unit (GPU) of a computer, which handles the load of rapidly computing the thousands of polygons necessary to model a moving 3D environment on a screen in real time. The large processing requirements generally require the use of a separate PC with a GPU that renders the visual environment to a monitor, multiple screens13, or a projector14 as the movement is recorded from a treadmill, wheel, or foam ball under the animal. The resulting apparatus for controlling, rendering, and projecting the VR environment is, therefore, relatively expensive, bulky, and cumbersome. Furthermore, many such environments in the literature have been implemented using proprietary software that is both costly and can only be run on a dedicated PC.
For these reasons, we have designed an open-source VR system to study spatial learning behaviors in head-restrained mice using a Raspberry Pi single-board computer. This Linux computer is both small and inexpensive yet contains a GPU chip for 3D rendering, allowing the integration of VR environments with the display or behavioral apparatus in varied individual setups. Furthermore, we have developed a graphical software package written in Python, &#34;HallPassVR&#34;, which utilizes the single-board computer to render a simple visuospatial environment, a virtual linear track or hallway, by recombining custom visual features selected using a graphical user interface (GUI). This is combined with microcontroller subsystems (e.g., ESP32 or Arduino) to measure locomotion and coordinate behavior, such as by the delivery of other modalities of sensory stimuli or rewards to facilitate reinforcement learning. This system provides an inexpensive, flexible, and easy-to-use alternative method for delivering visuospatial VR environments to head-restrained mice during two-photon imaging (or other techniques requiring head fixation) for studying the neural circuits underlying spatial learning behavior.

<table><tbody><tr><td>1/4 &quot; diam aluminum rod</td><td>McMaster-Carr</td><td>9062K26</td><td>3&quot; in length for wheel axle</td></tr><tr><td>1/4&quot;-20 cap screws, 3/4&quot; long (x2)</td><td>Amazon.com</td><td>B09ZNMR41V</td><td>for affixing head post holders to optical posts</td></tr><tr><td>2&quot;x7&quot; T-slotted aluminum bar (x2)</td><td>8020.net</td><td>1020</td><td>wheel/animal mounting frame</td></tr><tr><td>6&quot; diam, 3&quot; wide acrylic cylinder (1/8&quot; thick)</td><td>Canal Plastics</td><td>33210090702</td><td>Running wheel (custom width cut at canalplastics.com)</td></tr><tr><td>8-32 x 1/2&quot; socket head screws</td><td>McMaster-Carr</td><td>92196A194</td><td>fastening head post holder to optical post&amp;nbsp;</td></tr><tr><td>Adjustable arm (14&quot;)</td><td>Amazon.com</td><td>B087BZGKSL</td><td>to hold/adjust lick spout</td></tr><tr><td>Analysis code (MATLAB)</td><td></td><td>custom written</td><td>file at github.com/GergelyTuri/HallPassVR/software/Analysis code</td></tr><tr><td>Axle mounting flange, 1/4&quot; ID</td><td>Pololu</td><td>1993</td><td>for mounting wheel to axle</td></tr><tr><td>Ball bearing (5/8&quot; OD, 1/4&quot; ID, x2)</td><td>McMaster-Carr</td><td>57155K324</td><td>for mounting wheel axle to frame</td></tr><tr><td>Behavior ESP32 code</td><td></td><td>custom written</td><td>file at github.com/GergelyTuri/HallPassVR/software/Arduino code/Behavior board</td></tr><tr><td>Black opaque matte acrylic sheets (1/4&quot; thick)</td><td>Canal Plastics</td><td>32918353422</td><td>laser cut file at github.com/GergelyTuri/HallPassVR/hardware/VR screen assembly</td></tr><tr><td>Clear acrylic sheet (1/4&quot; thick)</td><td>Canal Plastics</td><td>32920770574</td><td>laser cut file at github.com/GergelyTuri/HallPassVR/hardware/VR wheel assembly</td></tr><tr><td>ESP32 devKitC v4 (x2)</td><td>Amazon.com</td><td>B086YS4Z3F</td><td>microcontroller for behavior and rotary encoder</td></tr><tr><td>ESP32 shield</td><td>OpenMaze.org</td><td>OMwSmall</td><td>description at www.openmaze.org (https://claylacefield.wixsite.com/openmazehome/copy-of-om2shield). ZIP gerber files at: https://github.com/claylacefield/OpenMaze/tree/master/OM_PCBs</td></tr><tr><td>Fasteners and brackets&amp;nbsp;</td><td>8020.net</td><td>4138, 3382,3280</td><td>for wheel frame mounts</td></tr><tr><td>goniometers</td><td>Edmund Optics</td><td>66-526, 66-527</td><td>optional for behavior. Fine tuning head for imaging</td></tr><tr><td>HallPassVR python code</td><td></td><td>custom written</td><td>file at github.com/GergelyTuri/HallPassVR/software/HallPassVR</td></tr><tr><td>Head post holder</td><td></td><td>custom design</td><td>3D design file at github.com/GergelyTuri/HallPassVR/hardware/VR head mount/Headpost Clamp</td></tr><tr><td>LED projector</td><td>Texas Instruments</td><td>DLPDLCR230NPEVM</td><td>or other small LED projector</td></tr><tr><td>Lick spout</td><td>VWR</td><td>20068-638</td><td>(or ~16 G metal hypodermic tubing)</td></tr><tr><td>M 2.5 x 6 set screws</td><td>McMaster-Carr</td><td>92015A097</td><td>securing head post&amp;nbsp;</td></tr><tr><td>Matte white diffusion paper</td><td>Amazon.com</td><td></td><td>screen material</td></tr><tr><td>Metal headposts</td><td></td><td>custom design</td><td>3D design file at github.com/GergelyTuri/HallPassVR/hardware/VR head mount/head post designs</td></tr><tr><td>Miscellenous tubing and tubing adapters (1/16&quot; ID)</td><td></td><td></td><td>for constructing the water line</td></tr><tr><td>Optical breadboard</td><td>Thorlabs</td><td>as per user&#039;s requirements</td><td></td></tr><tr><td>Optical posts, 1/2&quot; diam (2x)</td><td>Thorlabs</td><td>TR4</td><td>for head fixation setup</td></tr><tr><td>Processing code</td><td></td><td>custom written</td><td>file at github.com/GergelyTuri/HallPassVR/software/Processing code</td></tr><tr><td>Raspberry Pi 4B</td><td>raspberry.com, adafruit.com</td><td></td><td>Single-board computer for rendering of HallPassVR envir.</td></tr><tr><td>Right angle clamp</td><td>Thorlabs</td><td>RA90</td><td>for head fixation setup</td></tr><tr><td>Rotary encoder (quadrature, 256 step)</td><td>DigiKey</td><td>ENS1J-B28-L00256L</td><td>to measure wheel rotation</td></tr><tr><td>Rotary encoder ESP32 code</td><td></td><td>custom written</td><td>file at github.com/GergelyTuri/HallPassVR/software/Arduino code/Rotary encoder</td></tr><tr><td>SCIGRIP 10315 acrylic cement</td><td>Amazon.com</td><td></td><td></td></tr><tr><td>Shaft coupler</td><td>McMaster-Carr</td><td>9861T426</td><td>to couple rotary encoder shaft with axle</td></tr><tr><td>Silver mirror acrylic sheets</td><td>Canal Plastics</td><td>32913817934</td><td>laser cut file at github.com/GergelyTuri/HallPassVR/hardware/VR screen assembly</td></tr><tr><td>Solenoid valve</td><td>Parker</td><td>003-0137-900</td><td>to administer water rewards</td></tr></tbody></table>

an open-source virtual reality system for the measurement of spatial learning in head-restrained mice

Head-restrained behavioral experiments in mice allow neuroscientists to observe neural circuit activity with high-resolution electrophysiological and optical imaging tools while delivering precise sensory stimuli to a behaving animal. Recently, human and rodent studies using virtual reality (VR) environments have shown VR to be an important tool for uncovering the neural mechanisms underlying spatial learning in the hippocampus and cortex, due to the extremely precise control over parameters such as spatial and contextual cues. Setting up virtual environments for rodent spatial behaviors can, however, be costly and require an extensive background in engineering and computer programming. Here, we present a simple yet powerful system based upon inexpensive, modular, open-source hardware and software that enables researchers to study spatial learning in head-restrained mice using a VR environment. This system uses coupled microcontrollers to measure locomotion and deliver behavioral stimuli while head-restrained mice run on a wheel in concert with a virtual linear track environment rendered by a graphical software package running on a single-board computer. The emphasis on distributed processing allows researchers to design flexible, modular systems to elicit and measure complex spatial behaviors in mice in order to determine the connection between neural circuit activity and spatial learning in the mammalian brain.

This open-source virtual reality behavioral setup allowed us to quantify licking behavior as a read-out of spatial learning as head-restrained mice navigated a virtual linear track environment. Seven C57BL/6 mice of both sexes at 4 months of age were placed on a restricted water schedule and first trained to lick continuously at low levels while running on the wheel for random spatial rewards (&#34;random foraging&#34;) without VR. Although their performance was initially affected when moved to the VR projection screen setup with a 2 m random hallway pattern, it returned to previous levels within several VR sessions (Figure 4A). The mice that developed the random foraging strategy with VR (six of the seven mice, 86%; one mouse failed to run consistently and was excluded) were then required to lick at an uncued operant reward zone at 0.5 m following a single visual location cue in the middle of an otherwise featureless 2 m virtual track in order to receive water rewards (&#34;hidden reward task&#34;). According to the current pilot data with this system, four of the seven (57%) mice were able to learn the hidden reward task with a single visual cue in two to four sessions, as shown by licking near the reward zone with increasing selectivity (Table 1, Figure 4B,C), which is similar to our previous results with a non-VR treadmill17. This fact is important in the study of spatial learning, as it allows for the monitoring and/or manipulation of neural activity during critical periods of learning without extensive training. Furthermore, the mice exhibited both substantial within-session as well as between-session learning (Figure 4C), providing an opportunity to observe both the short-term and long-term neural circuit adaptations that accompany spatial learning. We did not test the learning rate of an equivalent non-VR task, but many classical real-world hippocampus-dependent spatial tasks, such as the Morris water maze, require even more extensive training and present dramatically fewer behavioral trials and, thus, are less suitable for the monitoring of learning behavior along with neural activity changes.
While a majority of mice in this pilot group (57%) were able to learn the hidden reward task in a small number of sessions, additional mice may exhibit spatial learning over longer timescales, and individualized training should increase this fraction of mice. Indeed, variations in learning rates may be useful for dissociating the specific relationships between neural activity in brain areas such as the hippocampus and behavioral learning. However, we observed that a small percentage of mice did not learn to run on the wheel or lick for either non-operant or operant rewards (one of the seven mice, 14%) and, thus, could not be used for subsequent experiments. Additional handling and habituation and a reduction in the general state of stress of the animal through further reinforcement, such as by using desirable food treats, may be useful for helping these animals adopt active running and licking during head-restrained behavior on the wheel.
By manipulating the presence and position of the cue and reward zones on intermittent laps on the virtual track, an experimenter may further discern the dependence of spatially selective licking on specific channels of information in VR to determine, for example, how mice rely on local or distant cues or self-motion information to establish their location in an environment. The licking selectivity of mice that have learned the hidden reward location should be affected by the shift or omission of the visual cue along the track if they actively utilize this spatial cue as a landmark, as we have shown in a recent work using spatial olfactory cues20. However, even with the simple example we have presented here, the highly selective licking achieved by the mice (Figure 4C, right) indicates that they encode the VR visual environment to inform their decisions about where they are and, therefore, when to lick, as the reward zone is only evident in relation to visual cues in the VR environment. This VR system also allows the presentation of other modalities of spatial and contextual cues in addition to the visual VR environment, such as olfactory, tactile, and auditory cues, which can be used to test the selectivity of neural activity and behavior for complex combinations of distinct sensory cues. Additionally, although we did not test for the dependence of task performance on hippocampal activity, a recent study using a similar task but with tactile cues showed a perturbation of spatial learning with hippocampal inactivation23, which should be confirmed for the VR hidden reward task performed in this study.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/64863/64863fig01.jpg" /> 
Figure 1: Head-restrained VR hardware setup: Projection screen, running wheel, and head-fixation apparatus. (A) A 3D design schematic of the running wheel and projection screen. (B) Completed VR behavioral setup. The VR environment is rendered on (1) a single-board computer and projected onto a parabolic (2) rear-projection screen (based on Chris Harvey&#39;s lab&#39;s design15,16). (3) Wheel assembly. (4) Head post holder. (5) Water reservoir for reward delivery. (C) Top view of the projection screen and behavioral setup. (1) LED projector. (2) Mirror for rear-projecting the VR corridor onto the curved screen. (3) Running wheel. (D) Rear view of the wheel assembly. Wheel rotations are translated by the (1) rotary encoder and transmitted to the single-board computer via an (2) ESP32 microcontroller. (3) A dual-axis goniometer is used to fine-tune the head position for optical imaging. (E) Setup at the level of mouse insertion, showing the (1) head-fixation apparatus and (2) lick port placement over the (3) running wheel surface. (F) Photograph of the (1) lick port attached to the (2) flex arm for precise placement of the reward spout near the mouth of the mouse. Rewards are given via a (3) solenoid valve controlled by the behavior ESP32 (via the OpenMaze OMwSmall PCB). Also visible is the rotary encoder coupled to the (4) wheel axle and (5) the goniometer for head angle adjustment. <a href="https://www.jove.com/files/ftp_upload/64863/64863fig01large.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/64863/64863fig02.jpg" /> 
Figure 2: VR electronics setup schematic. This schematic depicts the most relevant connections between the electronic components in the open-source virtual reality system for mice. (A) Mice are head-restrained on a custom 3D-printed head-fixation apparatus above an acrylic running wheel. (B) The rotation of the wheel axle when the mouse is running is detected by a high-resolution rotary encoder connected to a microcontroller (Rotary decoder ESP32). (C) Movement information is conveyed via a serial connection to a single-board computer running the HallPassVR GUI software and 3D environment, which updates the position in the VR virtual linear track environment based on the mouse&#39;s locomotion. (D) The rendered VR environment is sent to the projector/screen via the HDMI #2 video output of the single-board computer (VR video HDMI). (E) Movement information from the rotary encoder ESP32 is also sent to another microcontroller (Behavior ESP32 with the OpenMaze OMwSmall PCB), which uses the mouse&#39;s position to control spatial, non-VR behavioral events (such as reward zones or spatial olfactory, tactile, or auditory stimuli) in concert with the VR environment and measures the mouse&#39;s licking of the reward spout via capacitive touch sensing. <a href="https://www.jove.com/files/ftp_upload/64863/64863fig02large.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/64863/64863fig03.jpg" /> 
Figure 3: Graphical software GUI and behavior. (A) HallPassVR GUI: Four images are selected to tile over each spatial pattern covering one-third of the track length (or the previously saved combination pattern is loaded) for three patterns in each path equal to the track length. Ceiling and floor images are selected, and then Start is pressed to initialize the VR environment on the single-board computer&#39;s HDMI output (projection screen). (B) Example virtual corridor created with the GUI parameters shown in A and used for a hidden reward experiment to test spatial learning. (C) Photograph of a head-restrained mouse running on the wheel in the virtual environment shown in B. (D) The top panel shows the online plot of animal behavior in a VR environment from the included Processing sketch to record and plot the behavioral data. Licks, laps, and rewards are plotted per 30 s time bins for the 30 min session during hidden reward spatial learning. The bottom panel shows the current mouse position (black) and the location of any reward zones (gray) during behavior. <a href="https://www.jove.com/files/ftp_upload/64863/64863fig03large.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/64863/64863fig04.jpg" /> 
Figure 4: Spatial learning using the graphical software environment. Representative spatial licking data from one animal (A) during random foraging with random cues along the virtual linear track and (B-C) 2 days of training with a static hidden reward zone at 1.5 m with a single visual cue in the middle of the track. (A) Day 0 random foraging for four reward zones per lap, selected randomly from eight positions spaced evenly along the 2 m virtual linear track. (Left) The average number of licks per spatial bin (5 cm) over the 30 min session (top: VR hallway with random visual stimulus panels). (Right) Number of licks in each 5 cm spatial bin per lap during this session, represented by a heatmap. (B) Day 1, the first day of training with a single reward zone at 1.5 m (red box on the track diagram, top) using a virtual track containing a single high-contrast stimulus at position 0.8-1.2 m. (Left) Average spatial lick counts over the session, showing increasing licks when the animal approaches the reward zone. (Right) Spatial licks per lap, showing increased selectivity of licking in the pre-reward region. (C) Day 2, from the same hidden reward task and virtual hallway as Day 1 and from the same mouse. (Left) Total licks per spatial bin, showing a decrease in licks outside of the pre-reward zone. (Right) Spatial licks per lap on Day 2, showing increased licking prior to the reward zone and decreased licking elsewhere, indicating the development of spatially specific anticipatory licking. This shows that this animal has learned the (uncued) hidden reward location and developed a strategy to minimize effort (licking) in regions where they do not expect a reward to be present. <a href="https://www.jove.com/files/ftp_upload/64863/64863fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Behavioral outcome</td>
			<td>Number of mice</td>
			<td>Percentage of mice</td>
		</tr>
		<tr>
			<td>Mouse did not run/lick</td>
			<td>1</td>
			<td>14%</td>
		</tr>
		<tr>
			<td>Random foraging only</td>
			<td>2</td>
			<td>29%</td>
		</tr>
		<tr>
			<td>Learned hidden reward</td>
			<td>4</td>
			<td>57%</td>
		</tr>
		<tr>
			<td>Total (N)</td>
			<td>7</td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 1: VR spatial learning behavioral pilot results. Seven C57BL/6 mice of both sexes at 4 months of age were progressively trained to perform a spatial hidden reward task in VR. Of these mice, one mouse did not run/lick after initial training (one of the seven mice, 14%), while six of the remaining mice learned to run on the wheel and lick for random spatial rewards in the random foraging step of training (six of the seven mice, 86%). Four of the six mice that were able to perform the random foraging behavior subsequently learned to lick selectively in anticipation of the non-cued reward in the hidden reward task (four of the seven mice, 57% of mice in total, four of the six mice, 66% of random foraging mice), while two did not (two of the seven mice, 29%).

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An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice

Here, we present a simplified open-source hardware and software setup for investigating mouse spatial learning using virtual reality (VR). This system displays a virtual linear track to a head-restrained mouse running on a wheel by utilizing a network of microcontrollers and a single-board computer running an easy-to-use Python graphical software package.

Head-restrained behavioral experiments in mice allow neuroscientists to observe neural circuit activity with high-resolution electrophysiological and ...

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Nanyang Technological University

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JoVE Journal

Neuroscience

2.0K Views.  New York State Psychiatric Institute. Dit open source virtual reality-systeem is een belangrijk hulpmiddel voor het bestuderen van ruimtelijk leren in de hersenen, omdat het onderzoekers in staat stelt om een consistente set ruimtelijke stimuli te presenteren aan een hoofd-ingetogen muis met behulp van een eenvoudige modulaire elektronische opstelling. Het voordeel van dit systeem is dat het goedkoop, eenvoudig in te stellen, compact en modulair is, waardoor meerdere gedragsopstellingen kunnen worden gebouwd voor training en inte...

Video: An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice

Two-photon Calcium Imaging in Mice Navigating a Virtual Reality Environment

In recent years, two-photon imaging has become an invaluable tool in neuroscience, as it allows for chronic measurement of the activity of genetically identified cells during behavior1-6. Here we describe methods to perform two-photon imaging in mouse cortex while the animal navigates a virtual reality environment. We focus on the aspects of the experimental procedures that are key to imaging in a behaving animal in a brightly lit virtual environment. The key problems that arise in this experimental setup that we here address are: minimizing brain motion related artifacts, minimizing light leak from the virtual reality projection system, and minimizing laser induced tissue damage. We also provide sample software to control the virtual reality environment and to do pupil tracking. With these procedures and resources it should be possible to convert a conventional two-photon microscope for use in behaving mice.

Here we describe the experimental procedures involved in two-photon imaging of mouse cortex during behavior in a virtual reality environment.

In recent years, two-photon imaging has become an invaluable tool in neuroscience, as it allows for chronic measurement of the activity of genetically ...

Behavior

Video-oculography in Mice

Eye movements are very important in order to track an object or to stabilize an image on the retina during movement. Animals without a fovea, such as the mouse, have a limited capacity to lock their eyes onto a target. In contrast to these target directed eye movements, compensatory ocular eye movements are easily elicited in afoveate animals1,2,3,4. Compensatory ocular movements are generated by processing vestibular and optokinetic information into a command signal that will drive the eye muscles. The processing of the vestibular and optokinetic information can be investigated separately and together, allowing the specification of a deficit in the oculomotor system. The oculomotor system can be tested by evoking an optokinetic reflex (OKR), vestibulo-ocular reflex (VOR) or a visually-enhanced vestibulo-ocular reflex (VVOR). The OKR is a reflex movement that compensates for "full-field" image movements on the retina, whereas the VOR is a reflex eye movement that compensates head movements. The VVOR is a reflex eye movement that uses both vestibular as well as optokinetic information to make the appropriate compensation. The cerebellum monitors and is able to adjust these compensatory eye movements. Therefore, oculography is a very powerful tool to investigate brain-behavior relationship under normal as well as under pathological conditions (f.e. of vestibular, ocular and/or cerebellar origin).
Testing the oculomotor system, as a behavioral paradigm, is interesting for several reasons. First, the oculomotor system is a well understood neural system5. Second, the oculomotor system is relative simple6; the amount of possible eye movement is limited by its ball-in-socket architecture ("single joint") and the three pairs of extra-ocular muscles7. Third, the behavioral output and sensory input can easily be measured, which makes this a highly accessible system for quantitative analysis8. Many behavioral tests lack this high level of quantitative power. And finally, both performance as well as plasticity of the oculomotor system can be tested, allowing research on learning and memory processes9.
Genetically modified mice are nowadays widely available and they form an important source for the exploration of brain functions at various levels10. In addition, they can be used as models to mimic human diseases. Applying oculography on normal, pharmacologically-treated or genetically modified mice is a powerful research tool to explore the underlying physiology of motor behaviors under normal and pathological conditions. Here, we describe how to measure video-oculography in mice8.

Video-oculography is a very quantitative method to investigate ocular motor performance as well as motor learning. Here, we describe how to measure video-oculography in mice. Applying this technique on normal, pharmacologically-treated or genetically modified mice is a powerful research tool to explore the underlying physiology of motor behaviors.

Eye movements are very important in order to track an object or to stabilize an image on the retina during movement. Animals without a fovea, such as the ...

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Investigating the interactions among multiple participants is a challenge for researchers from various disciplines, including the decision sciences and spatial cognition. With a local area network and dedicated software platform, experimenters can efficiently monitor the behavior of the participants that are simultaneously immersed in a desktop virtual environment and digitalize the collected data. These capabilities allow for experimental designs in spatial cognition and navigation research that would be difficult (if not impossible) to conduct in the real world. Possible experimental variations include stress during an evacuation, cooperative and competitive search tasks, and other contextual factors that may influence emergent crowd behavior. However, such a laboratory requires maintenance and strict protocols for data collection in a controlled setting. While the external validity of laboratory studies with human participants is sometimes questioned, a number of recent papers suggest that the correspondence between real and virtual environments may be sufficient for studying social behavior in terms of trajectories, hesitations, and spatial decisions. In this article, we describe a method for conducting experiments on decision-making and navigation with up to 36 participants in a networked desktop virtual reality setup (i.e., the Decision Science Laboratory or DeSciL). This experiment protocol can be adapted and applied by other researchers in order to set up a networked desktop virtual reality laboratory.

This paper describes a method for conducting multi-user experiments on decision-making and navigation using a networked computer laboratory.

Investigating the interactions among multiple participants is a challenge for researchers from various disciplines, including the decision sciences and ...

Utilizing a Reconfigurable Maze System to Enhance the Reproducibility of Spatial Navigation Tests in Rodents

Several maze shapes are used to test spatial navigation performance and behavioral phenotypes. Traditionally, each experiment requires a unique maze shape, thus requiring several separate mazes in different configurations. The maze geometry cannot be reconfigured in a single environment to accommodate scalability and reproducibility. The reconfigurable maze is a unique approach to address the limitations, allowing quick and flexible configurations of maze pathways in a repeatable manner. It consists of interlocking pathways and includes feeders, treadmills, movable walls, and shut-off sensors. The current protocol describes how the reconfigurable maze can replicate existing mazes, including the T-shaped, plus-shaped, W-shaped, and figure-eight mazes. Initially, the T-shaped maze was constructed inside a single experimental room, followed by modifications. The rapid and scalable protocol outlined herein demonstrates the flexibility of the reconfigurable maze, achieved through the addition of components and behavioral training phases in a stepwise manner. The reconfigurable maze systematically and precisely assesses the performance of multiple aspects of spatial navigation behavior.

The present protocol describes a reconfigurable maze, a unique system for testing spatial navigation and behavioral phenotypes in rodents. The adaptability of this maze system enables the execution of various experiments in a single physical environment. The ease of structural rearrangement generates reliable and reproducible experimental results.

Several maze shapes are used to test spatial navigation performance and behavioral phenotypes. Traditionally, each experiment requires a unique maze ...

Game-Based Exploration: Unlocking Human Spatial Navigation Abilities

Assessing Human Spatial Navigation in a Virtual Space and its Sensitivity to Exercise

Spatial navigation (SN) is the ability to locomote through the environment, which requires an understanding of where one is located in time and space. This capacity is known to rely on the sequential firing of place cells within the hippocampus. SN is an important behavior to investigate as this process deteriorates with age, especially in neurodegenerative disorders. However, the investigation of SN is limited by the lack of sophisticated behavioral techniques to assess this hippocampal-dependent task. Therefore, the goal of this protocol was to develop a novel, real-world approach to studying SN in humans. Specifically, an active virtual SN task was developed using a cross-platform game engine. During the encoding phase, participants navigated their way through a virtual city to locate landmarks. During the remembering phase, participants remembered where these reward locations were and delivered items to these locations. Time to find each location was captured and episodic memory was assessed by a free recall phase, including aspects of place, order, item, and association. Movement behavior (x, y, and z coordinates) was assessed through an asset available in the game engine. Importantly, results from this task demonstrate that it accurately captures both spatial learning and memory abilities as well as episodic memory. Further, findings indicate that this task is sensitive to exercise, which improves hippocampal functioning. Overall, the findings suggest a novel way to track human hippocampal functioning over the course of time, with this behavior being sensitive to physical activity training paradigms.

Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function

Here, we present a novel, brief, and active spatial navigation task that assesses both spatial navigation and episodic memory ability. Importantly, spatial navigation and episodic memory were associated with one another, and this task demonstrated sensitivity to exercise.

Spatial navigation (SN) is the ability to locomote through the environment, which requires an understanding of where one is located in time and space. ...

Understanding Spatial Learning in Mice Using the Active Place Avoidance Test

The Active Place Avoidance (APA) Test, an Effective, Versatile and Repeatable Spatial Learning Task for Mice

Hippocampus-dependent spatial learning in rodents has been tested using a variety of methods. These include the Morris water maze (MWM), Y-maze, and novel object location (NOL) tasks. More recently, the active place avoidance (APA) task has been developed as an alternative to these more traditional approaches. In the APA task, mice must use spatial cues placed around a rotating arena to avoid a stationary shock zone. Due to the multiple parameters that can be adjusted, the APA task has been demonstrated to be a very versatile approach. It lends itself to being used longitudinally and repeatedly for the same cohort of mice. Here, we provide a detailed protocol to successfully conduct the APA task. We also highlight alternative APA approaches that can be used to examine different components of spatial learning. We describe the data collection and analysis processes. Critical steps during the APA task are discussed to increase the likelihood of successfully conducting the test. The APA task has several advantages over more traditional spatial navigation tests. It is appropriate to use with aged mice or those with disease phenotypes such as Alzheimer's disease. The complexity of the task can be easily altered, allowing a wide range of mouse strains to be tested. Further, the APA task is suitable for testing animals that have undergone surgery or experimental interventions that may have affected motor or neural function, such as stroke or traumatic brain injury.

Author Spotlight: Investigating the Impact of Aging on Hippocampal-Dependent Spatial Learning

Here, we present a protocol for the active place avoidance test, a hippocampus-dependent spatial learning paradigm designed for rodents. Altering key parameters allows for re-testing of animals before and after treatments or over time.

Hippocampus-dependent spatial learning in rodents has been tested using a variety of methods. These include the Morris water maze (MWM), Y-maze, and novel ...

Behavioral Training Procedures for Head-fixed Virtual Reality in Mice

Virtual reality (VR) combined with head-fixation is increasingly being utilized in behavioral neuroscience studies as it allows complex behavioral assays to be performed in head-fixed mice. This enables precise behavioral recordings while incorporating various neurophysiological techniques that require head-fixation to minimize movement-related signal noise during neural recordings. However, despite the growing use of VR, there is little published data on the detailed methodology of how to implement it. In this study, a training protocol is developed whereby male and female C57B16/J mice are trained to run down a virtual linear corridor, the length of which is increased from 1-3 m over multiple training sessions. Building upon this foundation, this study investigated the feasibility of mice performing complex behaviors within VR using a Y-maze paradigm. The task required navigating to the arm with black walls from the choice point in the Y-maze. After reaching a criterion of two consecutive days equal to or greater than 70% correct, the mice progressed to increasingly difficult sensory discrimination. The findings provide important details on the methodologies useful for the successful training of mice in VR and demonstrate that mice exhibit learning capabilities in navigating the Y-maze. The methodology presented not only offers insights into training duration in VR-based assays but also underscores the potential for probing intricate behaviors in mice, opening avenues for more comprehensive neuroscience investigations.

The article describes the experimental procedures for the commonly used linear track virtual reality (VR) paradigm in mice as well as determining the feasibility of running complex VR tasks by testing a Y-shaped signal discrimination task.

Virtual reality (VR) combined with head-fixation is increasingly being utilized in behavioral neuroscience studies as it allows complex behavioral assays ...

Long-term Sensory Conflict in Freely Behaving Mice

Long-term sensory conflict protocols are a valuable means of studying motor learning. The presented protocol produces a persistent sensory conflict for experiments aimed at studying long-term learning in mice. By permanently wearing a device fixed on their heads, mice are continuously exposed to a sensory mismatch between visual and vestibular inputs while freely moving in home cages. Therefore, this protocol readily enables the study of the visual system and multisensory interactions over an extended timeframe that would not be accessible otherwise. In addition to lowering the experimental costs of long-term sensory learning in naturally behaving mice, this approach accommodates the combination of in vivo and in vitro experiments. In the reported example, video-oculography is performed to quantify the vestibulo-ocular reflex (VOR) and optokinetic reflex (OKR) before and after learning. Mice exposed to this long-term sensory conflict between visual and vestibular inputs presented a strong VOR gain decrease but exhibited few OKR changes. Detailed steps of device assembly, animal care, and reflex measurements are hereby reported.

The presented protocol produces a persistent sensory conflict for experiments aimed at studying long-term learning. By permanently wearing a fixed device on their heads, mice are continuously exposed to a sensory mismatch between visual and vestibular inputs while freely moving in home cages.

Long-term sensory conflict protocols are a valuable means of studying motor learning. The presented protocol produces a persistent sensory conflict for ...

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Climbing fiber inputs to Purkinje cells provide instructive signals critical for cerebellum-dependent associative learning. Studying these signals in head-fixed mice facilitates the use of imaging, electrophysiological, and optogenetic methods. Here, a low-cost behavioral platform (~$1000) was developed that allows tracking of associative learning in head-fixed mice that locomote freely on a running wheel. The platform incorporates two common associative learning paradigms: eyeblink conditioning and delayed tactile startle conditioning. Behavior is tracked using a camera and the wheel movement by a detector. We describe the components and setup and provide a detailed protocol for training and data analysis. This platform allows the incorporation of optogenetic stimulation and fluorescence imaging. The design allows a single host computer to control multiple platforms for training multiple animals simultaneously.

We have developed a single platform to track animal behavior during two climbing fiber-dependent associative learning tasks. The low-cost design allows integration with optogenetic or imaging experiments directed towards climbing fiber-associated cerebellar activity.

Climbing fiber inputs to Purkinje cells provide instructive signals critical for cerebellum-dependent associative learning. Studying these signals in ...

Virtual Reality Tools for Assessing Unilateral Spatial Neglect: A Novel Opportunity for Data Collection

Unilateral spatial neglect (USN) is a syndrome characterized by inattention to or inaction in one side of space and affects between 23-46% of acute stroke survivors. The diagnosis and characterization of these symptoms in individual patients can be challenging and often requires skilled clinical staff. Virtual reality (VR) presents an opportunity to develop novel assessment tools for patients with USN.
We aimed to design and build a VR tool to detect and characterize subtle USN symptoms, and to test the tool on subjects treated with inhibitory repetitive transcranial magnetic stimulation (TMS) of cortical regions associated with USN.
We created three experimental conditions by applying TMS to two distinct regions of cortex associated with visuospatial processing- the superior temporal gyrus (STG) and the supramarginal gyrus (SMG) - and applied sham TMS as a control. We then placed subjects in a virtual reality environment in which they were asked to identify the flowers with lateral asymmetries of flowers distributed across bushes in both hemispaces, with dynamic difficulty adjustment based on each subject's performance. 
We found significant differences in average head yaw between subjects stimulated at the STG and those stimulated at the SMG and marginally significant effects in the average visual axis.
VR technology is becoming more accessible, affordable, and robust, presenting an exciting opportunity to create useful and novel game-like tools. In conjunction with TMS, these tools could be used to study specific, isolated, artificial neurological deficits in healthy subjects, informing the creation of VR-based diagnostic tools for patients with deficits due to acquired brain injury. This study is the first to our knowledge in which artificially generated USN symptoms have been evaluated with a VR task.

The goal was to design, build, and pilot a novel virtual reality task to detect and characterize unilateral spatial neglect, a syndrome affecting 23-46% of acute stroke survivors, expanding the role of virtual reality in the study and management of neurologic disease.

Unilateral spatial neglect (USN) is a syndrome characterized by inattention to or inaction in one side of space and affects between 23-46% of acute stroke ...

An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice

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