A Method for Assessing Working Memory in Rats Using Controlled Virtual Environment
The aim of the study is to develop an experimental method to effectively assess the working memory in rats. The method uses a state-of-the-art controlled virtual environment with a virtual maze. The setup includes a treadmill for rodents, a fixation system, a dome for displaying virtual environment, and a control unit.
Materials and Methods
Biological part of the investigation. In our study, young healthy Wistar rats aged 6–7 months were used. The initial stage involved habituating the experimental animals to the experimenter over a period of two weeks. The habituation process was conducted in several successive steps. First, the rats were acclimated to wearing a jacket, which is part of the apparatus that holds the animal in the experimental setup. Next, they were familiarized with the fixation system. Following this, the rats were introduced to the treadmill (a sphere), and finally, they were acclimated to the entire setup. Subsequently, the rats were gradually habituated to the virtual maze and the associated reward system through positive reinforcement. This approach helped minimize stress and facilitated their adaptation to the new conditions. The second stage involved exploring the virtual space and learning the features of the virtual maze, including walls, turns, and the end goal. During the learning phase, the animals received positive reinforcement in the form of sugared water from the automatic water dispenser for correctly performed tasks. To navigate the T-maze, the rats used visual cues such as wall color and figures on the wall. At this stage, the rats learned to use virtual space to achieve their goals. Once the rats showed evident progress in learning the virtual environment, we implemented a protocol to assess their working memory. This assessment was based on the time it took for the rats to find the maze arm that provided positive reinforcement.
Engineering part of the investigation. The animal is positioned on a foam plastic sphere with a 30 cm radius, using a custom device that allows its head and paws to remain mobile. Bearing fix the sphere in place, enabling the rat to rotate freely around its vertical axis. The rat’s forward and backward movements cause the sphere to rotate, simulating a treadmill. The sphere’s movements are detected by two infrared sensors (adapted from optical LED mice with USB interfaces) and transmitted to a computer, which generates an image of the virtual environment — a maze with landmarks on its walls. The virtual environment, created using the Unity Real-Time 3D Development Platform, is projected onto a custom-designed dome display containing the sphere and the lab rat. The setup provided the rat with a 360° field of view.
Conclusion. In our study, we present a setup that includes a projector, a dome display, a sphere (treadmill), a virtual T-maze, motion capture sensors, systems for securing animals to the sphere, and positive reinforcement delivery systems. We have developed an optimal protocol for immersing laboratory animals into a virtual environment and evaluating their cognitive functions, particularly working memory. The application of virtual reality in biological experiments enables more precise control over study conditions and allows for the creation of highly accurate and realistic behavioral protocols to assess cognitive functions in animals. This approach enhances our understanding of the mechanisms underlying working memory and their relationship with behavioral processes in rats and other animals.
- Lai C., Tanaka S., Harris T.D., Lee A.K. Volitional activation of remote place representations with a hippocampal brain-machine interface. Science 2023; 382(6670): 566–573, https://doi.org/10.1126/science.adh5206.
- Safaryan K., Mehta M.R. Enhanced hippocampal theta rhythmicity and emergence of eta oscillation in virtual reality. Nat Neurosci 2021; 24(8): 1065–1070, https://doi.org/10.1038/s41593-021-00871-z.
- Thurley K., Ayaz A. Virtual reality systems for rodents. Curr Zool 2017; 63(1): 109–119, https://doi.org/10.1093/cz/zow070.
- Aronov D., Tank D.W. Engagement of neural circuits underlying 2D spatial navigation in a rodent virtual reality system. Neuron 2014; 84(2): 442–456, https://doi.org/10.1016/j.neuron.2014.08.042.
- Hölscher C., Schnee A., Dahmen H., Setia L., Mallot H.A. Rats are able to navigate in virtual environments. J Exp Biol 2005; 208(Pt 3): 561–569, https://doi.org/10.1242/jeb.01371.
- Harvey C.D., Collman F., Dombeck D.A., Tank D.W. Intracellular dynamics of hippocampal place cells during virtual navigation. Nature 2009; 461(7266): 941–946, https://doi.org/10.1038/nature08499.
- Low I.I.C., Williams A.H., Campbell M.G., Linderman S.W., Giocomo L.M. Dynamic and reversible remapping of network representations in an unchanging environment. Neuron 2021; 109(18): 2967–2980.e11, https://doi.org/10.1016/j.neuron.2021.07.005.
- The visual system in vertebrates. Crescitelli F. (editor). Springer Science & Business Media; 2013.
- Chen G., King J.A., Lu Y., Cacucci F., Burgess N. Spatial cell firing during virtual navigation of open arenas by head-restrained mice. eLife 2018, 7: e34789, https://doi.org/10.7554/eLife.34789.026.
- Gorina Ya.V., Lopatina O.L., Komleva Yu.K., Iptyshev A.M., Pol’nikov A.M., Salmina A.B. Radial arm maze as a tool for assess the spatial learning and memory in mice. Sibirskoe meditsinskoe obozrenie 2016; 5(101): 46–52, https://doi.org/10.20333/25000136-2016-5-46-52.
- Ruse S.A., Harvey P.D., Davis V.G., Atkins A.S., Fox K.H., Keefe R.S. Virtual reality functional capacity assessment in schizophrenia: preliminary data regarding feasibility and correlations with cognitive and functional capacity performance. Schizophr Res Cogn 2014; 1(1): e21–e26, https://doi.org/10.1016/j.scog.2014.01.004.
- Driscoll L.N., Pettit N.L., Minderer M., Chettih S.N., Harvey C.D. Dynamic reorganization of neuronal activity patterns in parietal cortex. Cell 2017; 170(5): 986–999.e16, https://doi.org/10.1016/j.cell.2017.07.021.
- Pinke D., Issa J.B., Dara G.A., Dobos G., Dombeck D.A. Full field-of-view virtual reality goggles for mice. Neuron 2023; 111(24): 3941–3952.e6, https://doi.org/10.1016/j.neuron.2023.11.019.
- Naik H., Bastien R., Navab N., Couzin I.D. Animals in virtual environments. IEEE Trans Vis Comput Graph 2020; 26(5): 2073–2083, https://doi.org/10.1109/TVCG.2020.2973063.
- Brunec I.K., Robin J., Olsen R.K., Moscovitch M., Barense M.D. Integration and differentiation of hippocampal memory traces. Neurosci Biobehav Rev 2020; 118: 196–208, https://doi.org/10.1016/j.neubiorev.2020.07.024.
- Baddeley A., Eysenck M., Anderson M. Memory. Psychology Pres; 2009.
- Sligte I.G., Vandenbroucke A.R., Scholte H.S., Lamme V.A. Detailed sensory memory, sloppy working memory. Front Psychol 2010; 1: 175, https://doi.org/10.3389/fpsyg.2010.00175.
- Bird C.M., Burgess N. The hippocampus and memory: insights from spatial processing. Nat Rev Neurosci 2008; 9(3): 182–194, https://doi.org/10.1038/nrn2335.
- Knierim J.J. The hippocampus. Curr Biol 2015; 25(23): R1116–R1121, https://doi.org/10.1016/j.cub.2015.10.049.
- Pfeiffer B.E. The content of hippocampal “replay”. Hippocampus 2020; 30(1): 6–18, https://doi.org/10.1002/hipo.22824.