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NeuroGrid: recording action potentials from the surface of the brain

Abstract

Recording from neural networks at the resolution of action potentials is critical for understanding how information is processed in the brain. Here, we address this challenge by developing an organic material–based, ultraconformable, biocompatible and scalable neural interface array (the ‘NeuroGrid’) that can record both local field potentials(LFPs) and action potentials from superficial cortical neurons without penetrating the brain surface. Spikes with features of interneurons and pyramidal cells were simultaneously acquired by multiple neighboring electrodes of the NeuroGrid, allowing for the isolation of putative single neurons in rats. Spiking activity demonstrated consistent phase modulation by ongoing brain oscillations and was stable in recordings exceeding 1 week's duration. We also recorded LFP-modulated spiking activity intraoperatively in patients undergoing epilepsy surgery. The NeuroGrid constitutes an effective method for large-scale, stable recording of neuronal spikes in concert with local population synaptic activity, enhancing comprehension of neural processes across spatiotemporal scales and potentially facilitating diagnosis and therapy for brain disorders.

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Figure 1: NeuroGrid structure and spike recordings in freely moving rats.
Figure 2: Neuron clustering and spike-waveform characterization.
Figure 3: Phase modulation of NeuroGrid spikes by brain oscillations.
Figure 4: Intraoperative NeuroGrid recording of LFP and spikes in epilepsy patients.

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References

  1. Buzsáki, G. Large-scale recording of neuronal ensembles. Nat. Neurosci. 7, 446–451 (2004).

    PubMed  Google Scholar 

  2. Alivisatos, A.P. et al. The brain activity map. Science 339, 1284–1285 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Carandini, M. From circuits to behavior: a bridge too far? Nat. Neurosci. 15, 507–509 (2012).

    CAS  PubMed  Google Scholar 

  4. Adrian, E.D. & Moruzzi, G. Impulses in the pyramidal tract. J. Physiol. (Lond.) 97, 153–199 (1939).

    CAS  Google Scholar 

  5. Wilson, M.A. & McNaughton, B.L. Dynamics of the hippocampal ensemble code for space. Science 261, 1055–1058 (1993).

    CAS  PubMed  Google Scholar 

  6. Wise, K.D. & Najafi, K. Microfabrication techniques for integrated sensors and microsystems. Science 254, 1335–1342 (1991).

    CAS  PubMed  Google Scholar 

  7. Buzsáki, G. & Draguhn, A. Neuronal oscillations in cortical networks. Science 304, 1926–1929 (2004).

    PubMed  Google Scholar 

  8. Campbell, P.K., Jones, K.E., Huber, R.J., Horch, K.W. & Normann, R.a. A silicon-based, three-dimensional neural interface: manufacturing processes for an intracortical electrode array. IEEE Trans. Biomed. Eng. 38, 758–768 (1991).

    CAS  PubMed  Google Scholar 

  9. Polikov, V.S., Tresco, P.A. & Reichert, W.M. Response of brain tissue to chronically implanted neural electrodes. J. Neurosci. Methods 148, 1–18 (2005).

    PubMed  Google Scholar 

  10. Gold, C., Henze, D.A., Koch, C. & Buzsáki, G. On the origin of the extracellular action potential waveform: a modeling study. J. Neurophysiol. 95, 3113–3128 (2006).

    CAS  PubMed  Google Scholar 

  11. Buzsáki, G. Somadendritic backpropagation of action potentials in cortical pyramidal cells of the awake rat. J. Neurophysiol. 79, 1587–1591 (1998).

    PubMed  Google Scholar 

  12. Harris, K.D. et al. Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. J. Neurophysiol. 84, 401–414 (2000).

    CAS  PubMed  Google Scholar 

  13. Engel, A.K., Fries, P. & Singer, W. Dynamic predictions: oscillations and synchrony in top-down processing. Nat. Rev. Neurosci. 2, 704–716 (2001).

    CAS  PubMed  Google Scholar 

  14. Viventi, J. et al. Flexible, foldable, actively multiplexed, high-density electrode array for mapping brain activity in vivo. Nat. Neurosci. 14, 1599–1605 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Stavrinidou, E. et al. Direct measurement of ion mobility in a conducting polymer. Adv. Mater. 25, 4488–4493 (2013).

    CAS  PubMed  Google Scholar 

  16. Owens, R.M. & Malliaras, G.G. Organic electronics at the interface with biology. MRS Bull. 35, 449–456 (2010).

    CAS  Google Scholar 

  17. Khodagholy, D. et al. Highly conformable conducting polymer electrodes for in vivo recordings. Adv. Mater. 23, 1–5 10.1002/adma.201102378 (2011).

    Article  CAS  Google Scholar 

  18. Khodagholy, D. et al. In vivo recordings of brain activity using organic transistors. Nat. Commun. 4, 1575 (2013).

    PubMed  Google Scholar 

  19. Einevoll, G.T. et al. Laminar population analysis: estimating firing rates and evoked synaptic activity from multielectrode recordings in rat barrel cortex. J. Neurophysiol. 97, 2174–2190 (2007).

    PubMed  Google Scholar 

  20. Ranck, J.B. Studies on single neurons and septum in dorsal hippocampal in unrestrained rats. Exp. Neurol. 41, 461–531 (1973).

    PubMed  Google Scholar 

  21. Stark, E. et al. Inhibition-induced theta resonance in cortical circuits. Neuron 80, 1263–1276 (2013).

    CAS  PubMed  Google Scholar 

  22. Sirota, A. et al. Entrainment of neocortical neurons and gamma oscillations by the hippocampal theta rhythm. Neuron 60, 683–697 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Robbins, A.A., Fox, S.E., Holmes, G.L., Scott, R.C. & Barry, J.M. Short duration waveforms recorded extracellularly from freely moving rats are representative of axonal activity. Front. Neural Circuits 7, 181 (2013).

    PubMed  PubMed Central  Google Scholar 

  24. Barthó, P. et al. Characterization of neocortical principal cells and interneurons by network interactions and extracellular features. J. Neurophysiol. 92, 600–608 (2004).

    PubMed  Google Scholar 

  25. Steriade, M., McCormick, D.A. & Sejnowski, T.J. Thalamocortical oscillations in the sleeping and aroused brain. Science 262, 679–685 (1993).

    CAS  PubMed  Google Scholar 

  26. Nir, Y. et al. Regional slow waves and spindles in human sleep. Neuron 70, 153–169 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Contreras, D. et al. Intracellular and computational characterization of the intracortical inhibitory control of synchronized thalamic inputs in vivo. J. Neurophysiol. 78, 335–350 (1997).

    CAS  PubMed  Google Scholar 

  28. Csicsvari, J., Hirase, H., Czurkó, A., Mamiya, A. & Buzsáki, G. Fast network oscillations in the hippocampal CA1 region of the behaving rat. J. Neurosci. 19, RC20 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Mesgarani, N., Cheung, C., Johnson, K. & Chang, E.F. Phonetic feature encoding in human superior temporal gyrus. Science 343, 1006–1010 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Crone, N.E., Sinai, A. & Korzeniewska, A. High-frequency gamma oscillations and human brain mapping with electrocorticography. Prog. Brain Res. 159, 275–295 (2006).

    PubMed  Google Scholar 

  31. Waziri, A., Schevon, C. & Cappell, J. Initial surgical experience with a dense cortical microarray in epileptic patients undergoing craniotomy for subdural electrode implantation. Neurosurgery 64, 540–545 (2009).

    PubMed  Google Scholar 

  32. Rubehn, B., Bosman, C., Oostenveld, R., Fries, P. & Stieglitz, T. A MEMS-based flexible multichannel ECoG-electrode array. J. Neural Eng. 6, 036003 (2009).

    PubMed  Google Scholar 

  33. Kim, D.-H. et al. Dissolvable films of silk fibroin for ultrathin conformal bio-integrated electronics. Nat. Mater. 9, 511–517 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Besson, P., Andermann, F., Dubeau, F. & Bernasconi, A. Small focal cortical dysplasia lesions are located at the bottom of a deep sulcus. Brain 131, 3246–3255 (2008).

    PubMed  Google Scholar 

  35. Berggren, M. & Richter-Dahlfors, A. Organic bioelectronics. Adv. Mater. 19, 3201–3213 (2007).

    CAS  Google Scholar 

  36. Rivnay, J., Owens, R.M. & Malliaras, G.G. The rise of organic bioelectronics. Chem. Mater. 26, 679–685 (2014).

    CAS  Google Scholar 

  37. Abidian, M.R. & Martin, D.C. Experimental and theoretical characterization of implantable neural microelectrodes modified with conducting polymer nanotubes. Biomaterials 29, 1273–1283 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Peterka, D.S., Takahashi, H. & Yuste, R. Imaging voltage in neurons. Neuron 69, 9–21 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Chichilnisky, E.J. & Baylor, D.a. Receptive-field microstructure of blue-yellow ganglion cells in primate retina. Nat. Neurosci. 2, 889–893 (1999).

    CAS  PubMed  Google Scholar 

  40. Meister, M., Pine, J. & Baylor, D.a. Multi-neuronal signals from the retina: acquisition and analysis. J. Neurosci. Methods 51, 95–106 (1994).

    CAS  PubMed  Google Scholar 

  41. Marre, O. et al. Mapping a complete neural population in the retina. J. Neurosci. 32, 14859–14873 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Lee, S., Kruglikov, I., Huang, Z.J., Fishell, G. & Rudy, B. A disinhibitory circuit mediates motor integration in the somatosensory cortex. Nat. Neurosci. 16, 1662–1670 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Thesen, T. et al. Sequential then interactive processing of letters and words in the left fusiform gyrus. Nat. Commun. 3, 1284 (2012).

    PubMed  Google Scholar 

  44. Bragin, A., Engel, J., Wilson, C.L., Fried, I. & Mathern, G.W. Hippocampal and entorhinal cortex high-frequency oscillations (100–500 Hz) in human epileptic brain and in kainic acid–treated rats with chronic seizures. Epilepsia 40, 127–137 (1999).

    CAS  PubMed  Google Scholar 

  45. Jacobs, J. & Kahana, M.J. Neural representations of individual stimuli in humans revealed by gamma-band electrocorticographic activity. J. Neurosci. 29, 10203–10214 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Ray, S. & Maunsell, J.H.R. Different origins of gamma rhythm and high-gamma activity in macaque visual cortex. PLoS Biol. 9, e1000610 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Hatsopoulos, N.G. & Donoghue, J. The science of neural interface systems. Annu. Rev. Neurosci. 32, 249–266 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by US National Institutes of Health Grants (NS074015, MH54671, MH102840), the National Science Foundation, the Mathers Foundation and the James S. McDonnell Foundation. The device fabrication was performed at Microelectronic Centre of Provence and the Cornell NanoScale Facility (CNF), a member of the National Nanotechnology Infrastructure Network, which is supported by the National Science Foundation (Grant ECCS-0335765). D.K. is supported through the Simons Foundation (junior fellow). J.G. is supported by the Pediatric Scientist Development Program through a grant from the March of Dimes Foundation. We thank M. Sessolo (University of Valencia), J. Rivnay and M. Ferro (Ecole des Mines), and A. Peyrache and G. Girardeau (NYU Langone Medical Center) for fruitful discussion. We thank M. Skvarla, R. Ilic and M. Metzler from the CNF for their technical support during device fabrication. We thank H. McKellar and A. Boomhaur for managing the institutional review board (IRB) protocol of intraoperative epilepsy patient recordings.

Author information

Authors and Affiliations

Authors

Contributions

D.K., G.G.M. and G.B. conceived the project. D.K. designed, fabricated and characterized the devices. D.K. and J.N.G. did the rodent in vivo experiments. D.K. and J.N.G. analyzed neural data. D.K., J.N.G. and T.T. did the intraoperative patient recordings. W.D. was the attending neurosurgeon and supervised the intra-operative recordings. T.T. and O.D. supervised the epilepsy patient recordings and IRB approval process. D.K., J.N.G. and G.B. wrote the paper with input from the other authors.

Corresponding author

Correspondence to György Buzsáki.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Detailed NeuroGrid structure and electrical characteristics.

a) Optical micrograph of a 256-channel NeuroGrid (scale = 1 mm). Optical micrograph of PEDOT:PSS-based recording sites (inset; scale = 10 μm).

b) Optical micrograph of 64-channel NeuroGrid conforming to a 100 μm diameter cylinder (scale = 200 μm).

c) Current measurement by a single NeuroGrid electrode site in response to 0.5 V stimulation at 50 mHz is stable for over 9 hours. At shorter time scales (upper plots) waveforms are similar between widely separated time points (red boxes).

d) Comparison of electrode impedance over a broad range of frequencies between the NeuroGrid (filled circles) and conventional Au-based electrodes (open circles). Impedance of NeuroGrid electrodes is consistent between different arrays (inset; blue circles) and more than an order of magnitude less than conventional implantable silicon probes (inset; red circles).

Supplementary Figure 2 Frequency characterization of the NeuroGrid and implantable probes.

a) Comparison of signal power over physiologically relevant frequencies for NeuroGrid (blue lines) and silicon probe (red lines) during REM sleep (upper traces) and post-mortem (lower traces).

b) SNR of surface recording by the NeuroGrid and depth recording by a silicon probe.

Supplementary Figure 3 Extracellular action potential waveforms and recording stability in cortex.

a) Placement of a 64-channel NeuroGrid on rat somatosensory cortex (expanded version of Fig. 1b).

b) Spike-triggered averages of multiple individual units recorded at each recording site overlaid on NeuroGrid geometry. Recording sites that were located over major blood vessels, as demonstrated in corresponding anatomical photograph (scale = 300 μm) of NeuroGrid placement, did not resolve any spikes (scale = 3 ms by 50 μV).

c) Spatial extent and morphology of a sample subset of multiple individual trigger-averaged extracellular action potentials from (b) are consistent over 10 days of recording (scale = 3 ms by 50 μV).

Supplementary Figure 4 Extracellular action potential waveforms and recording stability in hippocampus.

a) Simultaneous implantation of a 64-channel NeuroGrid and a 4-shank silicon probe in rat hippocampus (scale = 300 μm).

b) Spatial extent and morphology of a sample subset of multiple individual trigger-averaged extracellular action potentials on different NeuroGrid recording sites are consistent over 10 days of recording (scale = 3 ms, 100 μV).

Supplementary Figure 5 Simultaneous recording of ripples and units by the NeuroGrid (green) and a silicon probe (blue) in the hippocampus.

a) Raster plot of spike firing during ripples as recorded by the NeuroGrid on the hippocampal surface and a silicon probe inserted into CA1, immediately next to the NeuroGrid.

b) Raw LFP showing a ripple recorded on multiple NeuroGrid electrodes and simultaneously captured by multiple sites of a linear silicon probe in CA1 (scale = 100 ms by 500 μV). Recording sites of the silicon probe are separated by 20 µm in the vertical direction. The tip of the probe is in the pyramidal layer.

c) Band-pass filtered traces at ripple frequency (100 – 250 Hz) of the NeuroGrid and silicon probe recordings above (scale = 100 ms, 200 μV).

d) High-pass filtered (fc = 500 Hz) time traces of the NeuroGrid and silicon probe LFP recordings above (scale = 100 ms, 100 μV).

e) Autocorrelograms (in color) of a putative single unit’s spiking activity as recorded simultaneously by the NeuroGrid and a silicon probe. Cross-correlation (black) of spiking activity demonstrates co-occurrence of recorded spikes (bin size = 1 ms). Note the similar form of the autocorrelograms, though fewer spikes are recorded with the NeuroGrid.

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Khodagholy, D., Gelinas, J., Thesen, T. et al. NeuroGrid: recording action potentials from the surface of the brain. Nat Neurosci 18, 310–315 (2015). https://doi.org/10.1038/nn.3905

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