Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Chronic, wireless recordings of large-scale brain activity in freely moving rhesus monkeys

Abstract

Advances in techniques for recording large-scale brain activity contribute to both the elucidation of neurophysiological principles and the development of brain-machine interfaces (BMIs). Here we describe a neurophysiological paradigm for performing tethered and wireless large-scale recordings based on movable volumetric three-dimensional (3D) multielectrode implants. This approach allowed us to isolate up to 1,800 neurons (units) per animal and simultaneously record the extracellular activity of close to 500 cortical neurons, distributed across multiple cortical areas, in freely behaving rhesus monkeys. The method is expandable, in principle, to thousands of simultaneously recorded channels. It also allows increased recording longevity (5 consecutive years) and recording of a broad range of behaviors, such as social interactions, and BMI paradigms in freely moving primates. We propose that wireless large-scale recordings could have a profound impact on basic primate neurophysiology research while providing a framework for the development and testing of clinically relevant neuroprostheses.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Recording cubes and primate headcap.
Figure 2: Large-scale activity recordings.
Figure 3: Recording longevity.
Figure 4: Wireless brain-machine interface (monkey C data).
Figure 5: Recordings of freely behaving monkeys.

Similar content being viewed by others

References

  1. Evarts, E.V. Pyramidal tract activity associated with a conditioned hand movement in the monkey. J. Neurophysiol. 29, 1011–1027 (1966).

    Article  CAS  Google Scholar 

  2. Nicolelis, M.A.L., Lin, R.C.S., Woodward, D.J. & Chapin, J.K. Induction of immediate spatiotemporal changes in thalamic networks by peripheral block of ascending cutaneous information. Nature 361, 533–536 (1993).

    Article  CAS  Google Scholar 

  3. Supèr, H. & Roelfsema, P.R. Chronic multiunit recordings in behaving animals: advantages and limitations. Prog. Brain Res. 147, 263–282 (2005).

    Article  Google Scholar 

  4. Nicolelis, M.A.L., Ghazanfar, A.A., Faggin, B.M., Votaw, S. & Oliveira, L.M.O. Reconstructing the engram: simultaneous, multisite, many single neuron recordings. Neuron 18, 529–537 (1997).

    Article  CAS  Google Scholar 

  5. Nicolelis, M.A.L. Actions from thoughts. Nature 409, 403–407 (2001).

    Article  CAS  Google Scholar 

  6. Nicolelis, M.A.L. et al. Chronic, multisite, multielectrode recordings in macaque monkeys. Proc. Natl. Acad. Sci. USA 100, 11041–11046 (2003).

    Article  CAS  Google Scholar 

  7. Wessberg, J. et al. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408, 361–365 (2000).

    Article  CAS  Google Scholar 

  8. Chapin, J.K., Moxon, K.A., Markowitz, R.S. & Nicolelis, M.A.L. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat. Neurosci. 2, 664–670 (1999).

    Article  CAS  Google Scholar 

  9. Nicolelis, M.A.L. & Lebedev, M.A. Principles of neural ensemble physiology underlying the operation of brain-machine interfaces. Nat. Rev. Neurosci. 10, 530–540 (2009).

    Article  CAS  Google Scholar 

  10. Azevedo, F.A.C. et al. Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J. Comp. Neurol. 513, 532–541 (2009).

    Article  Google Scholar 

  11. Marblestone, A.H. et al. Physical principles for scalable neural recording. Front. Comput. Neurosci. 7, 137 (2013).

    Article  Google Scholar 

  12. Chestek, C.A. et al. HermesC: low-power wireless neural recording system for freely moving primates. IEEE Trans. Neural Syst. Rehabil. Eng. 17, 330–338 (2009).

    Article  Google Scholar 

  13. Bonfanti, A. et al. A multi-channel low-power system-on-chip for single-unit recording and narrowband wireless transmission of neural signal. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2010, 1555–1560 (2010).

    CAS  PubMed  Google Scholar 

  14. Rizk, M. et al. A fully implantable 96-channe 96-channel neural data acquisition system. J. Neural Eng. 6, 026002 (2009).

    Article  Google Scholar 

  15. Borton, D.A., Yin, M., Aceros, J. & Nurmikko, A. An implantable wireless neural interface for recording cortical circuit dynamics in moving primates. J. Neural Eng. 10, 026010 (2013).

    Article  Google Scholar 

  16. Lebedev, M.A. & Nicolelis, M.A.L. Brain-machine interfaces: past, present and future. Trends Neurosci. 29, 536–546 (2006).

    Article  CAS  Google Scholar 

  17. Lebedev, M.A. et al. Future developments in brain-machine interface research. Clinics (Sao Paulo) 66 (suppl. 1), 25–32 (2011).

    Article  Google Scholar 

  18. Lebedev, M.A. & Nicolelis, M.A.L. Toward a whole-body neuroprosthetic. Prog. Brain Res. 194, 47–60 (2011).

    Article  Google Scholar 

  19. Lebedev, M.A. et al. Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface. J. Neurosci. 25, 4681–4693 (2005).

    Article  CAS  Google Scholar 

  20. Lebedev, M.A., O'Doherty, J.E. & Nicolelis, M.A.L. Decoding of temporal intervals from cortical ensemble activity. J. Neurophysiol. 99, 166–186 (2008).

    Article  Google Scholar 

  21. Zacksenhouse, M. & Nemets, S. in Methods for Neural Ensemble Recordings 2nd edn. (ed. Nicolelis, M.A.L.) Ch. 4 (CRC Press, 2008).

  22. O'Doherty, J.E. et al. Active tactile exploration enabled by a brain-machine-brain interface. Nature 479, 228–231 (2011).

    Article  CAS  Google Scholar 

  23. Shokur, S. et al. Expanding the primate body schema in sensorimotor cortex by virtual touches of an avatar. Proc. Natl. Acad. Sci. USA 110, 15121–15126 (2013).

    Article  CAS  Google Scholar 

  24. Ifft, P.J., Shokur, S., Li, Z., Lebedev, M.A. & Nicolelis, M.A.L. A brain-machine interface enables bimanual arm movements in monkeys. Sci. Transl. Med. 5, 210ra154 (2013).

    Article  Google Scholar 

  25. Lu, C.W., Patil, P.G. & Chestek, C.A. Current challenges to the clinical translation of brain machine interface technology. Int. Rev. Neurobiol. 107, 137–160 (2012).

    Article  Google Scholar 

  26. Patil, P.G., Carmena, J.M., Nicolelis, M.A.L. & Turner, D.A. Ensemble recordings of human subcortical neurons as a source of motor control signals for a brain-machine interface. Neurosurgery 55, 27–35, discussion 35–38 (2004).

    Article  Google Scholar 

  27. Hanson, T.L., Fuller, A.M., Lebedev, M.A., Turner, D.A. & Nicolelis, M.A.L. Subcortical neuronal ensembles: an analysis of motor task association, tremor, oscillations, and synchrony in human patients. J. Neurosci. 32, 8620–8632 (2012).

    Article  CAS  Google Scholar 

  28. Carmena, J.M. et al. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol. 1, E42 (2003).

    Article  Google Scholar 

  29. Li, Z. et al. Unscented Kalman filter for brain-machine interfaces. PLoS ONE 4, e6243 (2009).

    Article  Google Scholar 

  30. Nicolelis, M.A.L. Brain-machine interfaces to restore motor function and probe neural circuits. Nat. Rev. Neurosci. 4, 417–422 (2003).

    Article  CAS  Google Scholar 

  31. Nicolelis, M.A.L., Lehew, G.C. & Krupa, D.J. Miniaturized high-density multichannel electrode array for long-term neuronal recordings. US patent 6,993,392 (2006).

  32. Maynard, E.M., Nordhausen, C.T. & Normann, R.A. The Utah Intracortical Electrode Array: a recording structure for potential brain-computer interfaces. Electroencephalogr. Clin. Neurophysiol. 102, 228–239 (1997).

    Article  CAS  Google Scholar 

  33. Freire, M.A.M. et al. Comprehensive analysis of tissue preservation and recording quality from chronic multielectrode implants. PLoS ONE 6, e27554 (2011).

    Article  CAS  Google Scholar 

  34. Ochsner, K.N. & Lieberman, M.D. The emergence of social cognitive neuroscience. Am. Psychol. 56, 717–734 (2001).

    Article  CAS  Google Scholar 

  35. Mattout, J. Brain-computer interfaces: a neuroscience paradigm of social interaction? A matter of perspective. Front. Hum. Neurosci. 6, 114 (2012).

    Article  Google Scholar 

  36. O'Doherty, J.E., Lebedev, M.A., Li, Z. & Nicolelis, M.A.L. Virtual active touch using randomly patterned intracortical microstimulation. IEEE Trans. Neural Syst. Rehabil. Eng. 20, 85–93 (2012).

    Article  Google Scholar 

  37. Ifft, P.J., Lebedev, M.A. & Nicolelis, M.A.L. Cortical correlates of Fitts' law. Front. Integr. Neurosci. 5, 85 (2011).

    Article  Google Scholar 

  38. Fitzsimmons, N.A., Lebedev, M.A., Peikon, I.D. & Nicolelis, M.A.L. Extracting kinematic parameters for monkey bipedal walking from cortical neuronal ensemble activity. Front. Integr. Neurosci. 3, 1–19 (2009).

    Article  Google Scholar 

  39. O'Doherty, J.E., Lebedev, M.A., Hanson, T.L., Fitzsimmons, N.A. & Nicolelis, M.A.L. A brain-machine interface instructed by direct intracortical microstimulation. Front. Integr. Neurosci. 3, 20 (2009).

    Article  Google Scholar 

  40. Lewicki, M.S. A review of methods for spike sorting: the detection and classification of neural action potentials. Network 9, R53–R78 (1998).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Awards from the US National Institute of Mental Health (NIMH) DP1MH099903 and the US National Institute of Neurological Disorders and Stroke (NINDS) R01NS073952 to M.A.L.N. supported this research. We thank L. Oliveira and T. Phillips for their gracious support during all implantation surgeries and experimental logics. Additionally, many thanks go to S. Halkiotis for her continued help in the manuscript revision and submission process. We also thank T. Vinholo for performing the extensive histology that is shown in this work and H. Powell for analyzing video data from the free roaming experiment.

Author information

Authors and Affiliations

Authors

Contributions

D.A.S., M.A.L. and M.A.L.N. designed experiments and wrote the paper. D.A.S., V.S., M.A.L. and M.A.L.N. analyzed data. D.A.S., M.A.L., S.R., A.R., P.J.I., A.T., T.L.H. and K.Z.Z. performed the experiments. D.A.S., J.M. and G.L. designed and constructed animal headcaps and manufactured wireless units. G.L. designed and constructed the microwire recording cubes. D.A.S. and T.L.H. wrote the wireless software. T.L.H. designed and constructed the wireless system. T.L.H. and Z.L. wrote the BMI software and contributed analysis code. D.F.D. designed and performed the surgical procedures.

Corresponding author

Correspondence to Miguel A L Nicolelis.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–13 and Supplementary Tables 1–4 (PDF 2673 kb)

Wireless BMI

30 second clip of monkey C performing cursor control with only brain activity, task overlay showing the cursor on the bottom right corner (MP4 3042 kb)

BMI wheelchair control

Video showing macaque controlling robotic cart end effector with its cortical activity. (MP4 4466 kb)

Freely moving wireless recordings

Sample video showing freely moving macaque with an overlay of firing activity divided into left and right hemisphere. Sample frames from video show behavior with firing activity overlay. Each square represents the firing activity of a single neuron, averaged over 3 30s bins (video framerate). (MP4 2210 kb)

Neuron waveform chronology

Animation of mean waveforms for a single connector (left hemisphere M1) in monkey M. Transparent colored area represented standard deviation of waveform. Date of recording is shown on top (note some images can correspond to the same date of recording). (MP4 5537 kb)

Neuron waveform chronology

Animation of mean waveforms for a single connector (left hemisphere M1) in monkey N. Transparent colored area represented standard deviation of waveform. Date of recording is shown on top (note some images can correspond to the same date of recording). (MP4 3679 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Schwarz, D., Lebedev, M., Hanson, T. et al. Chronic, wireless recordings of large-scale brain activity in freely moving rhesus monkeys. Nat Methods 11, 670–676 (2014). https://doi.org/10.1038/nmeth.2936

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.2936

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing