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A Virtual Reality Platform for Context-Dependent Cognitive Research in Rodents

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Abstract

Animal survival necessitates adaptive behaviors in volatile environmental contexts. Virtual reality (VR) technology is instrumental to study the neural mechanisms underlying behaviors modulated by environmental context by simulating the real world with maximized control of contextual elements. Yet current VR tools for rodents have limited flexibility and performance (e.g., frame rate) for context-dependent cognitive research. Here, we describe a high-performance VR platform with which to study contextual behaviors immersed in editable virtual contexts. This platform was assembled from modular hardware and custom-written software with flexibility and upgradability. Using this platform, we trained mice to perform context-dependent cognitive tasks with rules ranging from discrimination to delayed-sample-to-match while recording from thousands of hippocampal place cells. By precise manipulations of context elements, we found that the context recognition was intact with partial context elements, but impaired by exchanges of context elements. Collectively, our work establishes a configurable VR platform with which to investigate context-dependent cognition with large-scale neural recording.

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References

  1. LeDoux JE. Emotion circuits in the brain. Annu Rev Neurosci 2000, 23: 155–184.

    Article  CAS  PubMed  Google Scholar 

  2. Maren S, Phan KL, Liberzon I. The contextual brain: Implications for fear conditioning, extinction and psychopathology. Nat Rev Neurosci 2013, 14: 417–428.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Fanselow MS, Poulos AM. The neuroscience of mammalian associative learning. Annu Rev Psychol 2005, 56: 207–234.

    Article  PubMed  Google Scholar 

  4. Maren S. Neurobiology of Pavlovian fear conditioning. Annu Rev Neurosci 2001, 24: 897–931.

    Article  CAS  PubMed  Google Scholar 

  5. Bohil CJ, Alicea B, Biocca FA. Virtual reality in neuroscience research and therapy. Nat Rev Neurosci 2011, 12: 752–762.

    Article  CAS  PubMed  Google Scholar 

  6. Laver KE, George S, Thomas S, Deutsch JE, Crotty M. Virtual reality for stroke rehabilitation. Cochrane Database Syst Rev 2011, 11: CD008349.

    Google Scholar 

  7. García-Betances RI, Arredondo Waldmeyer MT, Fico G, Cabrera-Umpiérrez MF. A succinct overview of virtual reality technology use in Alzheimer’s disease. Front Aging Neurosci 2015, 7: 80.

    PubMed  PubMed Central  Google Scholar 

  8. Dombeck DA, Harvey CD, Tian L, Looger LL, Tank DW. Functional imaging of hippocampal place cells at cellular resolution during virtual navigation. Nat Neurosci 2010, 13: 1433–1440.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Hölscher C, Schnee A, Dahmen H, Setia L, Mallot HA. Rats are able to navigate in virtual environments. J Exp Biol 2005, 208: 561–569.

    Article  PubMed  Google Scholar 

  10. Harvey CD, Collman F, Dombeck DA, Tank DW. Intracellular dynamics of hippocampal place cells during virtual navigation. Nature 2009, 461: 941–946.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Aronov D, Tank DW. Engagement of neural circuits underlying 2D spatial navigation in a rodent virtual reality system. Neuron 2014, 84: 442–456.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Liu L, Wang ZY, Liu Y, Xu C. An immersive virtual reality system for rodents in behavioral and neural research. Int J Autom Comput 2021, 18: 838–848.

    Article  Google Scholar 

  13. Giovannucci A, Friedrich J, Gunn P, Kalfon J, Brown BL, Koay SA. CaImAn an open source tool for scalable calcium imaging data analysis. Elife 2019, 8: e38173.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Zhou PC, Resendez SL, Rodriguez-Romaguera J, Jimenez JC, Neufeld SQ, Giovannucci A, et al. Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data. Elife 2018, 7: e28728.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Gonzalez WG, Zhang HW, Harutyunyan A, Lois C. Persistence of neuronal representations through time and damage in the Hippocampus. Science 2019, 365: 821–825.

    Article  CAS  PubMed  Google Scholar 

  16. Reis PM, Jung S, Aristoff JM, Stocker R. How cats lap: Water uptake by Felis catus. Science 2010, 330: 1231–1234.

    Article  CAS  PubMed  Google Scholar 

  17. Ruder L, Schina R, Kanodia H, Valencia-Garcia S, Pivetta C, Arber S. A functional map for diverse forelimb actions within brainstem circuitry. Nature 2021, 590: 445–450.

    Article  CAS  PubMed  Google Scholar 

  18. Wu Z, Litwin-Kumar A, Shamash P, Taylor A, Axel R, Shadlen MN. Context-dependent decision making in a premotor circuit. Neuron 2020, 106: 316-328.e6.

    Article  CAS  PubMed  Google Scholar 

  19. Tang YJ, Li L, Sun LQ, Yu JS, Hu Z, Lian KQ, et al. In vivo two-photon calcium imaging in dendrites of rabies virus-labeled V1 corticothalamic neurons. Neurosci Bull 2020, 36: 545–553.

    Article  CAS  PubMed  Google Scholar 

  20. Liu QQ, Yang X, Song R, Su JY, Luo MX, Zhong JL, et al. An infrared touch system for automatic behavior monitoring. Neurosci Bull 2021, 37: 815–830.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Huang K, Yang Q, Han YN, Zhang YL, Wang ZY, Wang LP, et al. An easily compatible eye-tracking system for freely-moving small animals. Neurosci Bull 2022, 38: 661–676.

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank all members of the Xu lab for helpful discussions and comments. We thank Drs. Chengyu Li and Haohong Li for technical support and sharing resources. This work was supported by the National Science and Technology Innovation 2030 Major Program (2022ZD0205000), the National Key R&D Program of China, the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB32010105, XDBS01010100), Shanghai Municipal Science and Technology Major Project (2018SHZDZX05), Lingang Lab (LG202104-01-08), the National Natural Science Foundation of China (31771180 and 91732106), and an International Collaborative Project of the Shanghai Science and Technology Committee (201978677).

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Author notes

  1. Xue-Tong Qu, Jin-Ni Wu and Yunqing Wen have contributed equally to this work.

Authors and Affiliations

  1. Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China

    Xue-Tong Qu, Jin-Ni Wu, Yunqing Wen, Long Chen, Li-Jie Zhan & Chun Xu

  2. University of the Chinese Academy of Sciences, Beijing, 100049, China

    Xue-Tong Qu, Jin-Ni Wu & Chun Xu

  3. Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, 201210, China

    Xue-Tong Qu, Jin-Ni Wu & Chun Xu

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Shi-Lei Lv, Li Liu & Yu Liu

  5. Department of Neurosurgery, Third Affiliated Hospital of Navy Military Medical University, Shanghai, 200438, China

    Tian-Yi Liu & Hua He

Authors
  1. Xue-Tong Qu
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  2. Jin-Ni Wu
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  3. Yunqing Wen
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  4. Long Chen
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  5. Shi-Lei Lv
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  6. Li Liu
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  7. Li-Jie Zhan
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  8. Tian-Yi Liu
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  9. Hua He
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  10. Yu Liu
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  11. Chun Xu
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Corresponding authors

Correspondence to Hua He, Yu Liu or Chun Xu.

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Qu, XT., Wu, JN., Wen, Y. et al. A Virtual Reality Platform for Context-Dependent Cognitive Research in Rodents. Neurosci. Bull. 39, 717–730 (2023). https://doi.org/10.1007/s12264-022-00964-0

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  • DOI: https://doi.org/10.1007/s12264-022-00964-0

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