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An optical brain-to-brain interface supports rapid information transmission for precise locomotion control

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Abstract

Brain-to-brain interfaces (BtBIs) hold exciting potentials for direct communication between individual brains. However, technical challenges often limit their performance in rapid information transfer. Here, we demonstrate an optical brain-to-brain interface that transmits information regarding locomotor speed from one mouse to another and allows precise, real-time control of locomotion across animals with high information transfer rate. We found that the activity of the genetically identified neuromedin B (NMB) neurons within the nucleus incertus (NI) precisely predicts and critically controls locomotor speed. By optically recording Ca2+ signals from the NI of a “Master” mouse and converting them to patterned optogenetic stimulations of the NI of an “Avatar” mouse, the BtBI directed the Avatar mice to closely mimic the locomotion of their Masters with information transfer rate about two orders of magnitude higher than previous BtBIs. These results thus provide proof-of-concept that optical BtBIs can rapidly transmit neural information and control dynamic behaviors across individuals.

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References

  • Baek, H.J., Chang, M.H., Heo, J., and Park, K.S. (2019). Enhancing the usability of brain-computer interface systems. Comput Intel Neurosci 2019, 1–12.

    Article  CAS  Google Scholar 

  • Billinger, M., Daly, I., Kaiser, V., Jin, J., Allison, B.Z., Müller-Putz, G.R., Brunner, C., Allison, B.Z., Dunne, S., Leeb, R., et al. (2013). Is It Significant? Guidelines for Reporting BCI Performance. Towards Practical Brain-Computer Interfaces: Bridging the Gap from Research to Real-World Applications. (Berlin: Springer), pp. 333–354.

    Google Scholar 

  • Boyden, E.S., Zhang, F., Bamberg, E., Nagel, G., and Deisseroth, K. (2005). Millisecond-timescale, genetically targeted optical control of neural activity. Nat Neurosci 8, 1263–1268.

    Article  CAS  PubMed  Google Scholar 

  • Chaudhary, U., Birbaumer, N., and Ramos-Murguialday, A. (2016). Brain computer interfaces for communication and rehabilitation. Nat Rev Neurol 12, 513–525.

    Article  PubMed  Google Scholar 

  • Chen, X., Zhao, B., Wang, Y., and Gao, X. (2019). Combination of highfrequency SSVEP-based BCI and computer vision for controlling a robotic arm. J Neural Eng 16, 026012.

    Article  PubMed  Google Scholar 

  • De Massari, D., Ruf, C.A., Furdea, A., Matuz, T., van der Heiden, L., Halder, S., Silvoni, S., and Birbaumer, N. (2013). Brain communication in the locked-in state. Brain 136, 1989–2000.

    Article  PubMed  Google Scholar 

  • Deadwyler, S.A., Berger, T.W., Sweatt, A.J., Song, D., Chan, R.H.M., Opris, I., Gerhardt, G.A., Marmarelis, V.Z., and Hampson, R.E. (2013). Donor/recipient enhancement of memory in rat hippocampus. Front Syst Neurosci 7, 120.

    Article  PubMed  PubMed Central  Google Scholar 

  • Fawcett, T. (2006). An introduction to ROC analysis. Patt Recogn Lett 27, 861–874.

    Article  Google Scholar 

  • Grau, C., Ginhoux, R., Riera, A., Nguyen, T.L., Chauvat, H., Berg, M., Amengual, J.L., Pascual-Leone, A., and Ruffini, G. (2014). Conscious brain-to-brain communication in humans using non-invasive technologies. PLoS ONE 8, e105225.

    Article  CAS  Google Scholar 

  • Guo, Q., Zhou, J., Feng, Q., Lin, R., Gong, H., Luo, Q., Zeng, S., Luo, M., and Fu, L. (2015). Multi-channel fiber photometry for population neuronal activity recording. Biomed Opt Express 6, 3919–3931.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Han, X., Lin, K., Gao, S., and Gao, X. (2019). A novel system of SSVEPbased human-robot coordination. J Neural Eng 16, 016006.

    Article  PubMed  Google Scholar 

  • Hangya, B., Borhegyi, Z., Szilagyi, N., Freund, T.F., and Varga, V. (2009). GABAergic neurons of the medial septum lead the hippocampal network during theta activity. J Neurosci 29, 8094–8102.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hildt, E. (2015). What will this do to me and my brain? Ethical issues in brain-to-brain interfacing. Front Syst Neurosci 9, 17.

    Article  PubMed  PubMed Central  Google Scholar 

  • Hildt, E. (2019). Multi-person brain-to-brain interfaces: ethical issues. Front Neurosci 13, 1177.

    Article  PubMed  PubMed Central  Google Scholar 

  • Hong, K.S., and Khan, M.J. (2017). Hybrid brain-computer interface techniques for improved classification accuracy and increased number of commands: a review. Front Neurorobot 11, 35.

    Article  PubMed  PubMed Central  Google Scholar 

  • Jiang, L., Stocco, A., Losey, D.M., Abernethy, J.A., Prat, C.S., and Rao, R. P.N. (2019). BrainNet: a multi-person brain-to-brain interface for direct collaboration between brains. Sci Rep 9, 6115.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Jin, J., Allison, B.Z., Sellers, E.W., Brunner, C., Horki, P., Wang, X., and Neuper, C. (2011). Optimized stimulus presentation patterns for an event-related potential EEG-based brain-computer interface. Med Biol Eng Comput 49, 181–191.

    Article  PubMed  Google Scholar 

  • Khalaf, A., Sejdic, E., and Akcakaya, M. (2019). Common spatial pattern and wavelet decomposition for motor imagery EEG- fTCD braincomputer interface. J NeuroSci Methods 320, 98–106.

    Article  PubMed  Google Scholar 

  • Kyriazis, M. (2015). Systems neuroscience in focus: from the human brain to the global brain? Front Syst Neurosci 9, 7.

    Article  PubMed  PubMed Central  Google Scholar 

  • LaFleur, K., Cassady, K., Doud, A., Shades, K., Rogin, E., and He, B. (2013). Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface. J Neural Eng 10, 046003.

    Article  PubMed  Google Scholar 

  • Lee, W., Kim, S., Kim, B., Lee, C., Chung, Y.A., Kim, L., and Yoo, S.S. (2017). Non-invasive transmission of sensorimotor information in humans using an EEG/focused ultrasound brain-to-brain interface. PLoS ONE 12, e0178476.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Li, G., and Zhang, D. (2016). Brain-computer interface controlled cyborg: establishing a functional information transfer pathway from human brain to cockroach brain. PLoS ONE 11, e0150667.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Li, Y., Zhong, W., Wang, D., Feng, Q., Liu, Z., Zhou, J., Jia, C., Hu, F., Zeng, J., Guo, Q., et al. (2016). Serotonin neurons in the dorsal raphe nucleus encode reward signals. Nat Commun 7, 10503.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lu, L., Ren, Y., Yu, T., Liu, Z., Wang, S., Tan, L., Zeng, J., Feng, Q., Lin, R., Liu, Y., et al. (2020). Control of locomotor speed, arousal, and hippocampal theta rhythms by the nucleus incertus. Nat Commun 11, 262.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Maksimenko, V.A., Hramov, A.E., Frolov, N.S., Lüttjohann, A., Nedaivozov, V.O., Grubov, V.V., Runnova, A.E., Makarov, V.V., Kurths, J., and Pisarchik, A.N. (2018). Increasing human performance by sharing cognitive load using brain-to-brain interface. Front Neurosci 12, 949.

    Article  PubMed  PubMed Central  Google Scholar 

  • Mashat, M.E.M., Li, G., and Zhang, D. (2017). Human-to-human closedloop control based on brain-to-brain interface and muscle-to-muscle interface. Sci Rep 7, 11001.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Meng, J., Streitz, T., Gulachek, N., Suma, D., and He, B. (2018). Threedimensional brain-computer interface control through simultaneous overt spatial attentional and motor imagery tasks. IEEE Trans Biomed Eng 65, 2417–2427.

    Article  PubMed  PubMed Central  Google Scholar 

  • Pais-Vieira, M., Chiuffa, G., Lebedev, M., Yadav, A., and Nicolelis, M.A.L. (2015). Building an organic computing device with multiple interconnected brains. Sci Rep 5, 11869.

    Article  PubMed  PubMed Central  Google Scholar 

  • Pais-Vieira, M., Lebedev, M., Kunicki, C., Wang, J., and Nicolelis, M.A.L. (2013). A brain-to-brain interface for real-time sharing of sensorimotor information. Sci Rep 3, 1319.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ramakrishnan, A., Ifft, P.J., Pais-Vieira, M., Byun, Y.W., Zhuang, K.Z., Lebedev, M.A., and Nicolelis, M.A.L. (2015). Computing arm movements with a monkey brainet. Sci Rep 5, 10767.

    Article  PubMed  PubMed Central  Google Scholar 

  • Rao, R.P.N., Stocco, A., Bryan, M., Sarma, D., Youngquist, T.M., Wu, J., and Prat, C.S. (2014). A direct brain-to-brain interface in humans. PLoS ONE 9, e111332.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Roseberry, T.K., Lee, A.M., Lalive, A.L., Wilbrecht, L., Bonci, A., and Kreitzer, A.C. (2016). Cell-type-specific control of brainstem locomotor circuits by basal ganglia. Cell 164, 526–537.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Schlogl, A., Keinrath, C., Scherer, R., and Pfurtscheller. (2003). Information transfer of an EEG-based brain computer interface. Proceedings of the 1st International IEEE EMBS, 641–644.

    Google Scholar 

  • Shannon, C.E. (1948). A mathematical theory of communication. Bell Syst Tech J 27, 623–656.

    Article  Google Scholar 

  • Stocco, A., Prat, C.S., Losey, D.M., Cronin, J.A., Wu, J., Abernethy, J.A., and Rao, R.P.N. (2015). Playing 20 questions with the mind: collaborative problem solving by humans using a brain-to-brain interface. PLoS ONE 10, e0137303.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Tehovnik, E.J., and Chen, L.L. (2015). Brain control and information transfer. Exp Brain Res 233, 3335–3347.

    Article  PubMed  Google Scholar 

  • Tehovnik, E.J., and Teixeira-e-Silva, Z. (2014). Brain-to-brain interface for real-time sharing of sensorimotor information: A commentary. OA Neurosci 2, 1–3.

    Google Scholar 

  • Tehovnik, E.J., Woods, L.C., and Slocum, W.M. (2013). Transfer of information by BMI. Neuroscience 255, 134–146.

    Article  CAS  PubMed  Google Scholar 

  • Trimper, J.B., Wolpe, P.R., and Rommelfanger, K.S. (2014). When “I” becomes “We”: ethical implications of emerging brain-to-brain interfacing technologies. Front Neuroeng 7, 1–4.

    Article  Google Scholar 

  • Yoo, S.S., Kim, H., Filandrianos, E., Taghados, S.J., and Park, S. (2013). Non-invasive brain-to-brain interface (BBI): establishing functional links between two brains. PLoS ONE 8, e60410.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yuan, P., Gao, X., Allison, B., Wang, Y., Bin, G., and Gao, S. (2013). A study of the existing problems of estimating the information transfer rate in online brain-computer interfaces. J Neural Eng 10, 026014.

    Article  PubMed  Google Scholar 

  • Zhang, J., Wang, B., Zhang, C., Xiao, Y., and Wang, M.Y. (2019a). An EEG/EMG/EOG-based multimodal human-machine interface to realtime control of a soft robot hand. Front Neurorobot 13, 7.

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhang, S., Yuan, S., Huang, L., Zheng, X., Wu, Z., Xu, K., and Pan, G. (2019b). Human mind control of rat cyborg's continuous locomotion with wireless brain-to-brain interface. Sci Rep 9, 1321.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Acknowledgements

We thank J. Snyder for comments and language polish. M.L. is supported by Ministry of Science and Technology of China (2015BAI08B02), the National Natural Science Foundation of China (91432114 and 91632302), and the Beijing Municipal Government.

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Correspondence to Minmin Luo.

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Lu, L., Wang, R. & Luo, M. An optical brain-to-brain interface supports rapid information transmission for precise locomotion control. Sci. China Life Sci. 63, 875–885 (2020). https://doi.org/10.1007/s11427-020-1675-x

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