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
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Evarts, E.V. Pyramidal tract activity associated with a conditioned hand movement in the monkey. J. Neurophysiol. 29, 1011–1027 (1966).
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).
Supèr, H. & Roelfsema, P.R. Chronic multiunit recordings in behaving animals: advantages and limitations. Prog. Brain Res. 147, 263–282 (2005).
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).
Nicolelis, M.A.L. Actions from thoughts. Nature 409, 403–407 (2001).
Nicolelis, M.A.L. et al. Chronic, multisite, multielectrode recordings in macaque monkeys. Proc. Natl. Acad. Sci. USA 100, 11041–11046 (2003).
Wessberg, J. et al. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408, 361–365 (2000).
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).
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).
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).
Marblestone, A.H. et al. Physical principles for scalable neural recording. Front. Comput. Neurosci. 7, 137 (2013).
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).
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).
Rizk, M. et al. A fully implantable 96-channe 96-channel neural data acquisition system. J. Neural Eng. 6, 026002 (2009).
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).
Lebedev, M.A. & Nicolelis, M.A.L. Brain-machine interfaces: past, present and future. Trends Neurosci. 29, 536–546 (2006).
Lebedev, M.A. et al. Future developments in brain-machine interface research. Clinics (Sao Paulo) 66 (suppl. 1), 25–32 (2011).
Lebedev, M.A. & Nicolelis, M.A.L. Toward a whole-body neuroprosthetic. Prog. Brain Res. 194, 47–60 (2011).
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).
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).
Zacksenhouse, M. & Nemets, S. in Methods for Neural Ensemble Recordings 2nd edn. (ed. Nicolelis, M.A.L.) Ch. 4 (CRC Press, 2008).
O'Doherty, J.E. et al. Active tactile exploration enabled by a brain-machine-brain interface. Nature 479, 228–231 (2011).
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).
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).
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).
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).
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).
Carmena, J.M. et al. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol. 1, E42 (2003).
Li, Z. et al. Unscented Kalman filter for brain-machine interfaces. PLoS ONE 4, e6243 (2009).
Nicolelis, M.A.L. Brain-machine interfaces to restore motor function and probe neural circuits. Nat. Rev. Neurosci. 4, 417–422 (2003).
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).
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).
Freire, M.A.M. et al. Comprehensive analysis of tissue preservation and recording quality from chronic multielectrode implants. PLoS ONE 6, e27554 (2011).
Ochsner, K.N. & Lieberman, M.D. The emergence of social cognitive neuroscience. Am. Psychol. 56, 717–734 (2001).
Mattout, J. Brain-computer interfaces: a neuroscience paradigm of social interaction? A matter of perspective. Front. Hum. Neurosci. 6, 114 (2012).
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).
Ifft, P.J., Lebedev, M.A. & Nicolelis, M.A.L. Cortical correlates of Fitts' law. Front. Integr. Neurosci. 5, 85 (2011).
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).
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).
Lewicki, M.S. A review of methods for spike sorting: the detection and classification of neural action potentials. Network 9, R53–R78 (1998).
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
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
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
About this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/nmeth.2936
This article is cited by
-
Dynamical flexible inference of nonlinear latent factors and structures in neural population activity
Nature Biomedical Engineering (2023)
-
Translational opportunities and challenges of invasive electrodes for neural interfaces
Nature Biomedical Engineering (2023)
-
Towards hippocampal navigation for brain–computer interfaces
Scientific Reports (2023)
-
Engineering strategies towards overcoming bleeding and glial scar formation around neural probes
Cell and Tissue Research (2022)
-
Semi-Implantable Bioelectronics
Nano-Micro Letters (2022)