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.

  • Review Article
  • Published:

Novel electrode technologies for neural recordings

Nature Reviews Neuroscience volume 20, pages 330–345 (2019)Cite this article

Subjects

An Author Correction to this article was published on 16 April 2019

This article has been updated

Abstract

Neural recording electrode technologies have contributed considerably to neuroscience by enabling the extracellular detection of low-frequency local field potential oscillations and high-frequency action potentials of single units. Nevertheless, several long-standing limitations exist, including low multiplexity, deleterious chronic immune responses and long-term recording instability. Driven by initiatives encouraging the generation of novel neurotechnologies and the maturation of technologies to fabricate high-density electronics, novel electrode technologies are emerging. Here, we provide an overview of recently developed neural recording electrode technologies with high spatial integration, long-term stability and multiple functionalities. We describe how these emergent neurotechnologies can approach the ultimate goal of illuminating chronic brain activity with minimal disruption of the neural environment, thereby providing unprecedented opportunities for neuroscience research in the future.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Neural recording electrode technologies.
Fig. 2: Basic principles and physical constraints of electrode technologies.
Fig. 3: Emerging neural recording electrode technologies.

Similar content being viewed by others

Change history

  • 16 April 2019

    In part b of Figure 2 in this article, the left bounds of the boxes representing the spatiotemporal resolution of ‘EEG/MEG’ and ‘ECoG’ were incorrect. Specifically, the limits of highest temporal resolution for EEG/MEG and ECoG were shown as ~200 ms and ~10 ms and are now corrected to ~2 ms and < 1 ms, respectively. In addition, the lower bounds of the boxes representing ‘fMRI/PET’ and ‘EEG/MEG’ incorrectly showed the highest spatial resolution limits of these technologies as ~1 mm and have been corrected to <1 mm and <10 mm, respectively. The upper bound of the ‘Implantable electrical probes’ box also incorrectly showed the spatial span as ~0.1 mm and has been corrected to between 0.1 and 1 mm due to different spans in different dimensions. The figure has been updated in the online version of the article.

References

  1. Galvani, L. De Viribus Electricitatis in Motu Musculari Commentarius [Italian] (Bologna Accademia delle Scienze, 1791).

  2. von Helmholtz, H. Messungen über Fortpflanzungsgeschwindigkeit der Reizung in den Nerven [German]. Archiv Anat. Physiol. Wissenschaftliche Med. 19, 199–216 (1852).

    Google Scholar 

  3. Erlanger, J. & Gasser, H. S. Electrical Signs Of Nervous Activity (Humphrey Milford, Oxford Univ. Press, 1937).

  4. Hodgkin, A. L. & Huxley, A. F. Action potentials recorded from inside a nerve fibre. Nature 144, 710–711 (1939).

    Article  Google Scholar 

  5. Hubel, D. H. Tungsten microelectrode for recording from single units. Science 125, 549–550 (1957).

    Article  CAS  PubMed  Google Scholar 

  6. Hubel, D. H. & Wiesel, T. N. Receptive fields, binocular interaction and functional architecture in cats visual cortex. J. Physiol. 160, 106–154 (1962).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. McNaughton, B. L., O'Keefe, J. & Barnes, C. A. The stereotrode — a new technique for simultaneous isolation of several single units in the central nervous-system from multiple unit records. J. Neurosci. Methods 8, 391–397 (1983).

    Article  CAS  PubMed  Google Scholar 

  8. Wise, K. D., Angell, J. B. & Starr, A. An integrated-circuit approach to extracellular microelectrodes. IEEE Trans. Biomed. Eng. 17, 238–247 (1970).

    Article  CAS  PubMed  Google Scholar 

  9. Campbell, P. K., Jones, K. E. & Normann, R. A. A 100 electrode intracortical array: structural variability. Biomed. Sci. Instrum. 26, 161–165 (1990).

    CAS  PubMed  Google Scholar 

  10. Buzsaki, G., Anastassiou, C. A. & Koch, C. The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes. Nat. Rev. Neurosci. 13, 407–420 (2012). This review article discusses the neurophysiological basis of different types of electrical signals in the brain and how these signals are measured with different electrode technologies.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Buzsaki, G. Theta oscillations in the hippocampus. Neuron 33, 325–340 (2002).

    Article  CAS  PubMed  Google Scholar 

  12. Yuste, R. From the neuron doctrine to neural networks. Nat. Rev. Neurosci. 16, 487–497 (2015). This historical overview summarizes the evolution of the fundamental neuroscience viewpoint from the neuron doctrine to neural network models with a focus on how this evolution has been fuelled by the emergence of multineuronal recording methods.

    Article  CAS  PubMed  Google Scholar 

  13. Harris, K. D., Quiroga, R. Q., Freeman, J. & Smith, S. L. Improving data quality in neuronal population recordings. Nat. Neurosci. 19, 1165–1174 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Carter, M. & Shieh, J. C. Guide to Research Techniques in Neuroscience (Academic Press, 2015).

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

    Article  CAS  PubMed  Google Scholar 

  16. Rossant, C. et al. Spike sorting for large, dense electrode arrays. Nat. Neurosci. 19, 634–641 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Logothetis, N. K., Pauls, J., Augath, M., Trinath, T. & Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157 (2001).

    Article  CAS  PubMed  Google Scholar 

  18. Keller, C. J., Chen, C., Lado, F. A. & Khodakhah, K. The limited utility of multiunit data in differentiating neuronal population activity. PLOS ONE 11, e0153154 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Ponce, C. R., Lomber, S. G. & Livingstone, M. S. Posterior inferotemporal cortex cells use multiple input pathways for shape encoding. J. Neurosci. 37, 5019–5034 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  21. Harris, K. D., Henze, D. A., Csicsvari, J., Hirase, H. & Buzsaki, G. Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. J. Neurophysiol. 84, 401–414 (2000).

    Article  CAS  PubMed  Google Scholar 

  22. Hubel, D. H. & Wiesel, T. N. Receptive fields of single neurones in the cat's striate cortex. J. Physiol. 148, 574–591 (1959).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. O’Keefe, J. & Dostrovsky, J. The hippocampus as a spatial map: preliminary evidence from unit activity in the freely-moving rat. Brain Res. 34, 171–175 (1971).

    Article  PubMed  Google Scholar 

  24. Desimone, R., Albright, T. D., Gross, C. G. & Bruce, C. Stimulus-selective properties of inferior temporal neurons in the macaque. J. Neurosci. 4, 2051–2062 (1984).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Schultz, W. Responses of midbrain dopamine neurons to behavioral trigger stimuli in the monkey. J. Neurophysiol. 56, 1439–1461 (1986).

    Article  CAS  PubMed  Google Scholar 

  26. Newsome, W. T., Britten, K. H. & Movshon, J. A. Neuronal correlates of a perceptual decision. Nature 341, 52–54 (1989).

    Article  CAS  PubMed  Google Scholar 

  27. Hafting, T., Fyhn, M., Molden, S., Moser, M. B. & Moser, E. I. Microstructure of a spatial map in the entorhinal cortex. Nature 436, 801–806 (2005).

    Article  CAS  PubMed  Google Scholar 

  28. Quiroga, R. Q., Reddy, L., Kreiman, G., Koch, C. & Fried, I. Invariant visual representation by single neurons in the human brain. Nature 435, 1102–1107 (2005).

    Article  CAS  PubMed  Google Scholar 

  29. Hochberg, L. R. et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164–171 (2006).

    Article  CAS  PubMed  Google Scholar 

  30. Lin, M. Z. & Schnitzer, M. J. Genetically encoded indicators of neuronal activity. Nat. Neurosci. 19, 1142–1153 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Poldrack, R. A. & Farah, M. J. Progress and challenges in probing the human brain. Nature 526, 371–379 (2015).

    Article  CAS  PubMed  Google Scholar 

  32. Yang, W. J. & Yuste, R. In vivo imaging of neural activity. Nat. Methods 14, 349–359 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ji, N., Freeman, J. & Smith, S. L. Technologies for imaging neural activity in large volumes. Nat. Neurosci. 19, 1154–1164 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Deisseroth, K. Optogenetics: 10 years of microbial opsins in neuroscience. Nat. Neurosci. 18, 1213–1225 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Jorgenson, L. A. et al. The BRAIN Initiative: developing technology to catalyse neuroscience discovery. Philos. Trans. R. Soc. Lond. B Biol. Sci. 370, 20140164 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Reardon, S. Worldwide brain-mapping project sparks excitement — and concern. Nature 537, 597 (2016).

    Article  CAS  PubMed  Google Scholar 

  37. Grinvald, A. & Hildesheim, R. VSDI: a new era in functional imaging of cortical dynamics. Nat. Rev. Neurosci. 5, 874–885 (2004).

    Article  CAS  PubMed  Google Scholar 

  38. Khan, H. N., Hounshell, D. A. & Fuchs, E. R. Science and research policy at the end of Moore’s law. Nat. Electron. 1, 14–21 (2018).

    Article  Google Scholar 

  39. Stevenson, I. H. & Kording, K. P. How advances in neural recording affect data analysis. Nat. Neurosci. 14, 139–142 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Chen, R., Canales, A. & Anikeeva, P. Neural recording and modulation technologies. Nat. Rev. Mater. 2, 16093 (2017). This comprehensive review article focuses on recent materials-driven progress in neural probes.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Salatino, J. W., Ludwig, K. A., Kozai, T. D. Y. & Purcell, E. K. Glial responses to implanted electrodes in the brain. Nat. Biomed. Eng. 1, 862–877 (2017). This timely review article highlights the latest evidence on the role of glial cells in neural circuits. On the basis of this evidence, it also provides insights on the design of implanted brain probes from the perspective of the mechanical properties of the probe materials and the glial response in the neural tissue.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Eroglu, C. & Barres, B. A. Regulation of synaptic connectivity by glia. Nature 468, 223–231 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Clarke, L. E. & Barres, B. A. Emerging roles of astrocytes in neural circuit development. Nat. Rev. Neurosci. 14, 311–321 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Neves, H. P. et al. The NeuroProbes project: a concept for electronic depth control. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2008, 1857 (2008).

    Google Scholar 

  45. Torfs, T. et al. Two-dimensional multi-channel neural probes with electronic depth control. IEEE Trans. Biomed. Circuits Syst. 5, 403–412 (2011).

    Article  CAS  PubMed  Google Scholar 

  46. Seidl, K. et al. CMOS-based high-density silicon microprobe arrays for electronic depth control in intracortical neural recording-characterization and application. J. Microelectromech. Syst. 21, 1426–1435 (2012).

    Article  Google Scholar 

  47. Cheng, M. Y. et al. 3D probe array integrated with a front-end 100-channel neural recording ASIC. J. Micromech. Microeng. 24, 125010 (2014).

    Article  CAS  Google Scholar 

  48. Fiath, R. et al. Large-scale recording of thalamocortical circuits: in vivo electrophysiology with the two-dimensional electronic depth control silicon probe. J. Neurophysiol. 116, 2312–2330 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Jun, J. J. et al. Fully integrated silicon probes for high-density recording of neural activity. Nature 551, 232–236 (2017). This work demonstrates highly multiplexed integration of 960 recording electrodes into the same Michigan-type MEA.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Raducanu, B. C. et al. Time multiplexed active neural probe with 1356 parallel recording sites. Sensors (Basel) 17, 2388 (2017).

    Google Scholar 

  51. Rios, G., Lubenov, E. V., Chi, D., Roukes, M. L. & Siapas, A. G. Nanofabricated neural probes for dense 3D recordings of brain activity. Nano Lett. 16, 6857–6862 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Stringer, C. et al. Spontaneous behaviors drive multidimensional, brain-wide population activity. Preprint at bioRxiv https://doi.org/10.1101/306019 (2018).

    Article  Google Scholar 

  53. Angotzi, G. N. et al. A synchronous neural recording platform for multiple high-resolution CMOS probes and passive electrode arrays. IEEE Trans. Biomed. Circuits Syst. 12, 532–542 (2018).

    Article  PubMed  Google Scholar 

  54. Steinmetz, N. A., Koch, C., Harris, K. D. & Carandini, M. Challenges and opportunities for large-scale electrophysiology with Neuropixels probes. Curr. Opin. Neurobiol. 50, 92–100 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Chung, J. E. et al. High-density, long-lasting, and multi-region electrophysiological recordings using polymer electrode arrays. Neuron 101, 21–31 (2019).

    Article  CAS  PubMed  Google Scholar 

  56. Feiner, R. & Dvir, T. Tissue-electronics interfaces: from implantable devices to engineered tissues. Nat. Rev. Mater. 3, 17076 (2018). This review discusses the design principles for implantable electronic devices to interface with biological tissue and applications in electrophysiology and tissue engineering.

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Khodagholy, D. et al. NeuroGrid: recording action potentials from the surface of the brain. Nat. Neurosci. 18, 310–315 (2015). This article provides the first demonstration of single-unit electrophysiology from the cortical surface with an ECoG array.

    Article  CAS  PubMed  Google Scholar 

  59. Gao, P. et al. A theory of multineuronal dimensionality, dynamics and measurement. Preprint at bioRxiv https://doi.org/10.1101/214262 (2017).

    Article  Google Scholar 

  60. Dickey, A. S., Suminski, A., Amit, Y. & Hatsopoulos, N. G. Single-unit stability using chronically implanted multielectrode arrays. J. Neurophysiol. 102, 1331–1339 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Vetter, R. J., Williams, J. C., Hetke, J. F., Nunamaker, E. A. & Kipke, D. R. Chronic neural recording using silicon-substrate microelectrode arrays implanted in cerebral cortex. IEEE Trans. Biomed. Eng. 51, 896–904 (2004).

    Article  PubMed  Google Scholar 

  62. Khodagholy, D., Gelinas, J. N. & Buzsaki, G. Learning-enhanced coupling between ripple oscillations in association cortices and hippocampus. Science 358, 369–372 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Avena-Koenigsberger, A., Misic, B. & Sporns, O. Communication dynamics in complex brain networks. Nat. Rev. Neurosci. 19, 17–33 (2018).

    Article  CAS  Google Scholar 

  64. Frankland, P. W. & Bontempi, B. The organization of recent and remote memories. Nat. Rev. Neurosci. 6, 119–130 (2005).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  66. Welkenhuysen, M. et al. An integrated multi-electrode-optrode array for in vitro optogenetics. Sci. Rep. 6, 20353 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Zhang, A. Q. & Lieber, C. M. Nano-bioelectronics. Chem. Rev. 116, 215–257 (2016).

    Article  CAS  PubMed  Google Scholar 

  68. Kruskal, P. B., Jiang, Z., Gao, T. & Lieber, C. M. Beyond the patch clamp: nanotechnologies for intracellular recording. Neuron 86, 21–24 (2015).

    Article  CAS  PubMed  Google Scholar 

  69. Patolsky, F. et al. Detection, stimulation, and inhibition of neuronal signals with high-density nanowire transistor arrays. Science 313, 1100–1104 (2006).

    Article  CAS  PubMed  Google Scholar 

  70. Tian, B. Z. et al. Three-dimensional, flexible nanoscale field-effect transistors as localized bioprobes. Science 329, 830–834 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Parameswaran, R. & Tian, B. Z. Rational design of semiconductor nanostructures for functional subcellular interfaces. Acc. Chem. Res. 51, 1014–1022 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  73. Benfenati, V. et al. A transparent organic transistor structure for bidirectional stimulation and recording of primary neurons. Nat. Mater. 12, 672–680 (2013).

    Article  CAS  PubMed  Google Scholar 

  74. Cohen-Karni, T. et al. Synthetically encoded ultrashort-channel nanowire transistors for fast, pointlike cellular signal detection. Nano Lett. 12, 2639–2644 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Hong, G. S., Antaris, A. L. & Dai, H. J. Near-infrared fluorophores for biomedical imaging. Nat. Biomed. Eng. 1, 0010 (2017).

    Article  Google Scholar 

  76. Hoover, E. E. & Squier, J. A. Advances in multiphoton microscopy technology. Nat. Photonics 7, 93–101 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Hochbaum, D. R. et al. All-optical electrophysiology in mammalian neurons using engineered microbial rhodopsins. Nat. Methods 11, 825–833 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Herry, C. & Johansen, J. P. Encoding of fear learning and memory in distributed neuronal circuits. Nat. Neurosci. 17, 1644–1654 (2014).

    Article  CAS  PubMed  Google Scholar 

  79. Jackson, A. & Fetz, E. E. Compact movable microwire array for long-term chronic unit recording in cerebral cortex of primates. J. Neurophysiol. 98, 3109–3118 (2007).

    Article  PubMed  Google Scholar 

  80. Chestek, C. A. et al. Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex. J. Neural Eng. 8, 045005 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Liu, J. et al. Syringe-injectable electronics. Nat. Nanotechnol. 10, 629–636 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Xie, C. et al. Three-dimensional macroporous nanoelectronic networks as minimally invasive brain probes. Nat. Mater. 14, 1286–1292 (2015).

    Article  CAS  PubMed  Google Scholar 

  83. Fu, T.-M. et al. Stable long-term chronic brain mapping at the single-neuron level. Nat. Methods 13, 875–882 (2016). This work demonstrates chronically stable tracking of the same individual neurons over 8 months from the mouse brain with mesh electronics and presents a comprehensive set of rigorous metrics for single-unit-based analyses.

    Article  CAS  PubMed  Google Scholar 

  84. Zhou, T. et al. Syringe-injectable mesh electronics integrate seamlessly with minimal chronic immune response in the brain. Proc. Natl Acad. Sci. USA 114, 5894–5899 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Hong, G., Yang, X., Zhou, T. & Lieber, C. M. Mesh electronics: a new paradigm for tissue-like brain probes. Curr. Opin. Neurobiol. 50, 33–41 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Hong, G., Viveros, R. D., Zwang, T. J., Yang, X. & Lieber, C. M. Tissue-like neural probes for understanding and modulating the brain. Biochemistry 57, 3995–4004 (2018).

    Article  CAS  PubMed  Google Scholar 

  87. Fu, T.-M., Hong, G., Viveros, R. D., Zhou, T. & Lieber, C. M. Highly scalable multichannel mesh electronics for stable chronic brain electrophysiology. Proc. Natl Acad. Sci. USA 114, E10046–E10055 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Park, S. et al. One-step optogenetics with multifunctional flexible polymer fibers. Nat. Neurosci. 20, 612–619 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Luan, L. et al. Ultraflexible nanoelectronic probes form reliable, glial scar-free neural integration. Sci. Adv. 3, e1601966 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Guitchounts, G., Markowitz, J. E., Liberti, W. A. & Gardner, T. J. A carbon-fiber electrode array for long-term neural recording. J. Neural Eng. 10, 046016 (2013).

    Article  PubMed  Google Scholar 

  91. Saxena, T. & Bellamkonda, R. V. Implantable electronics a sensor web for neurons. Nat. Mater. 14, 1190–1191 (2015).

    Article  CAS  PubMed  Google Scholar 

  92. Hong, G. S. et al. Syringe injectable electronics: precise targeted delivery with quantitative input/output connectivity. Nano Lett. 15, 6979–6984 (2015).

    Article  CAS  PubMed  Google Scholar 

  93. Schuhmann, T. G., Yao, J., Hong, G. S., Fu, T. M. & Lieber, C. M. Syringe-injectable electronics with a plug-and-play input/output interface. Nano Lett. 17, 5836–5842 (2017).

    Article  CAS  PubMed  Google Scholar 

  94. Shadlen, M. N. & Newsome, W. T. The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J. Neurosci. 18, 3870–3896 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  96. Dzirasa, K., Fuentes, R., Kumar, S., Potes, J. M. & Nicolelis, M. A. L. Chronic in vivo multi-circuit neurophysiological recordings in mice. J. Neurosci. Methods 195, 36–46 (2011).

    Article  Google Scholar 

  97. Kozai, T. D. Y. et al. Ultrasmall implantable composite microelectrodes with bioactive surfaces for chronic neural interfaces. Nat. Mater. 11, 1065–1073 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Canales, A. et al. Multifunctional fibers for simultaneous optical, electrical and chemical interrogation of neural circuits in vivo. Nat. Biotechnol. 33, 277–284 (2015). This work demonstrates incorporation of optical fibres, recording electrodes and microfluidic channels into the same multifunctional neural probe.

    Article  CAS  PubMed  Google Scholar 

  99. Wei, X. et al. Nanofabricated ultraflexible electrode arrays for high-density intracortical recording. Adv. Sci. 5, 1700625 (2018).

    Article  CAS  Google Scholar 

  100. Morrison, J. H. & Baxter, M. G. The ageing cortical synapse: hallmarks and implications for cognitive decline. Nat. Rev. Neurosci. 13, 240–250 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Burke, S. N. & Barnes, C. A. Neural plasticity in the ageing brain. Nat. Rev. Neurosci. 7, 30–40 (2006).

    Article  CAS  PubMed  Google Scholar 

  102. Wang, M. et al. Neuronal basis of age-related working memory decline. Nature 476, 210–213 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Hong, G. et al. A method for single-neuron chronic recording from the retina in awake mice. Science 360, 1447–1451 (2018). This study uses mesh electronics to chronically track the activity of the same RGCs in mouse retina after non-surgical, intravitreal injection.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Service, R. F. Bioelectronics herald the rise of the cyborg. Science 358, 1233–1234 (2017).

    Article  CAS  PubMed  Google Scholar 

  105. Lien, A. D. & Scanziani, M. Cortical direction selectivity emerges at convergence of thalamic synapses. Nature 558, 80–86 (2018).

    Article  CAS  PubMed  Google Scholar 

  106. Zhao, Z. et al. Nanoelectronic coating enabled versatile multifunctional neural probes. Nano Lett. 17, 4588–4595 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Kandel, E. R. Principles of Neural Science (McGraw-Hill, 2013).

  108. Grosenick, L., Marshel, J. H. & Deisseroth, K. Closed-loop and activity-guided optogenetic control. Neuron 86, 106–139 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Aston-Jones, G. & Deisseroth, K. Recent advances in optogenetics and pharmacogenetics. Brain Res. 1511, 1–5 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Bernal-Casas, D., Lee, H. J., Weitz, A. J. & Lee, J. H. Studying brain circuit function with dynamic causal modeling for optogenetic fMRI. Neuron 93, 522–532 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Canales, A., Park, S., Kilias, A. & Anikeeva, P. Multifunctional fibers as tools for neuroscience and neuroengineering. Acc. Chem. Res. 51, 829–838 (2018).

    Article  CAS  PubMed  Google Scholar 

  112. Miocinovic, S., Somayajula, S., Chitnis, S. & Vitek, J. L. History, applications, and mechanisms of deep brain stimulation. JAMA Neurol. 70, 163–171 (2013).

    Article  PubMed  Google Scholar 

  113. Borchers, S., Himmelbach, M., Logothetis, N. & Karnath, H. O. Direct electrical stimulation of human cortex — the gold standard for mapping brain functions? Nat. Rev. Neurosci. 13, 63–70 (2012).

    Article  CAS  Google Scholar 

  114. Ashkan, K., Rogers, P., Bergman, H. & Ughratdar, I. Insights into the mechanisms of deep brain stimulation. Nat. Rev. Neurol. 13, 548–554 (2017).

    Article  PubMed  Google Scholar 

  115. Ramirez-Zamora, A. et al. Evolving applications, technological challenges and future opportunities in neuromodulation: Proceedings of the Fifth Annual Deep Brain Stimulation Think Tank. Front. Neurosci. 11, 734 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  116. Cicchetti, F. & Barker, R. A. The glial response to intracerebrally delivered therapies for neurodegenerative disorders: is this a critical issue? Front. Pharmacol. 5, 139 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Neely, R. M., Piech, D. K., Santacruz, S. R., Maharbiz, M. M. & Carmena, J. M. Recent advances in neural dust: towards a neural interface platform. Curr. Opin. Neurobiol. 50, 64–71 (2018).

    Article  CAS  PubMed  Google Scholar 

  118. Seo, D. et al. Wireless recording in the peripheral nervous system with ultrasonic neural dust. Neuron 91, 529–539 (2016).

    Article  CAS  PubMed  Google Scholar 

  119. Johnson, B. C. et al. in 2018 IEEE Custom Integrated Circuits Conf. (CICC) 1–4 (IEEE, 2018).

  120. Packer, A. M., Roska, B. & Hausser, M. Targeting neurons and photons for optogenetics. Nat. Neurosci. 16, 805–815 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Stark, E., Koos, T. & Buzsaki, G. Diode probes for spatiotemporal optical control of multiple neurons in freely moving animals. J. Neurophysiol. 108, 349–363 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  122. Wu, F. et al. Monolithically integrated µLEDs on silicon neural probes for high-resolution optogenetic studies in behaving animals. Neuron 88, 1136–1148 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Anikeeva, P. et al. Optetrode: a multichannel readout for optogenetic control in freely moving mice. Nat. Neurosci. 15, 163–170 (2012).

    Article  CAS  Google Scholar 

  124. Royer, S. et al. Multi-array silicon probes with integrated optical fibers: light-assisted perturbation and recording of local neural circuits in the behaving animal. Eur. J. Neurosci. 31, 2279–2291 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  125. Wang, J. et al. Integrated device for combined optical neuromodulation and electrical recording for chronic in vivo applications. J. Neural Eng. 9, 016001 (2012).

    Article  PubMed  Google Scholar 

  126. LeChasseur, Y. et al. A microprobe for parallel optical and electrical recordings from single neurons in vivo. Nat. Methods 8, 319–U363 (2011).

    Article  CAS  PubMed  Google Scholar 

  127. Lu, C. et al. Flexible and stretchable nanowire-coated fibers for optoelectronic probing of spinal cord circuits. Sci. Adv. 3, e1600955 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  128. Kampasi, K. et al. Fiberless multicolor neural optoelectrode for in vivo circuit analysis. Sci. Rep. 6, 30961 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Son, Y. et al. In vivo optical modulation of neural signals using monolithically integrated two-dimensional neural probe arrays. Sci. Rep. 5, 15466 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Lee, J., Ozden, I., Song, Y. K. & Nurmikko, A. V. Transparent intracortical microprobe array for simultaneous spatiotemporal optical stimulation and multichannel electrical recording. Nat. Methods 12, 1157–1162 (2015).

    Article  CAS  PubMed  Google Scholar 

  131. Lee, H. J. et al. A multichannel neural probe with embedded microfluidic channels for simultaneous in vivo neural recording and drug delivery. Lab Chip 15, 1590–1597 (2015).

    Article  CAS  PubMed  Google Scholar 

  132. Shin, H. et al. Neural probes with multi-drug delivery capability. Lab Chip 15, 3730–3737 (2015).

    Article  CAS  PubMed  Google Scholar 

  133. Zrenner, C., Belardinelli, P., Muller-Dahlhaus, F. & Ziemann, U. Closed-loop neuroscience and non-invasive brain stimulation: a tale of two loops. Front. Cell. Neurosci. 10, 92 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  134. Cui, Y., Wei, Q. Q., Park, H. K. & Lieber, C. M. Nanowire nanosensors for highly sensitive and selective detection of biological and chemical species. Science 293, 1289–1292 (2001).

    Article  CAS  PubMed  Google Scholar 

  135. Kim, T. I. et al. Injectable, cellular-scale optoelectronics with applications for wireless optogenetics. Science 340, 211–216 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Gossler, C. et al. GaN-based micro-LED arrays on flexible substrates for optical cochlear implants. J. Phys. D Appl. Phys. 47, 205401 (2014).

    Article  CAS  Google Scholar 

  137. Klein, E., Gossler, C., Paul, O. & Ruther, P. High-density µLED-based optical cochlear implant with improved thermomechanical behavior. Front. Neurosci. 12, 659 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  138. Park, S. I. et al. Soft, stretchable, fully implantable miniaturized optoelectronic systems for wireless optogenetics. Nat. Biotechnol. 33, 1280–1286 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Montgomery, K. L. et al. Wirelessly powered, fully internal optogenetics for brain, spinal and peripheral circuits in mice. Nat. Methods 12, 969–974 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. Geva-Sagiv, M., Las, L., Yovel, Y. & Ulanovsky, N. Spatial cognition in bats and rats: from sensory acquisition to multiscale maps and navigation. Nat. Rev. Neurosci. 16, 94–108 (2015).

    Article  CAS  PubMed  Google Scholar 

  141. Moser, E. I., Moser, M. B. & McNaughton, B. L. Spatial representation in the hippocampal formation: a history. Nat. Neurosci. 20, 1448–1464 (2017).

    Article  CAS  PubMed  Google Scholar 

  142. Hardcastle, K., Ganguli, S. & Giocomo, L. M. Cell types for our sense of location: where we are and where we are going. Nat. Neurosci. 20, 1474–1482 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Dai, X. C., Hong, G. S., Gao, T. & Lieber, C. M. Mesh nanoelectronics: seamless integration of electronics with tissues. Acc. Chem. Res. 51, 309–318 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Tian, B. Z. et al. Macroporous nanowire nanoelectronic scaffolds for synthetic tissues. Nat. Mater. 11, 986–994 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Scholvin, J. et al. Close-packed silicon microelectrodes for scalable spatially oversampled neural recording. IEEE Trans. Biomed. Eng. 63, 120–130 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  146. Qing, Q. et al. Nanowire transistor arrays for mapping neural circuits in acute brain slices. Proc. Natl Acad. Sci. USA 107, 1882–1887 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  147. Lacour, S. P., Courtine, G. & Guck, J. Materials and technologies for soft implantable neuroprostheses. Nat. Rev. Mater. 1, 16063 (2016). This review highlights the importance of minimizing the physical and mechanical mismatch between neural tissues and implantable electrodes from a perspective of neuroprosthetics.

    Article  CAS  Google Scholar 

  148. Lee, H., Bellamkonda, R. V., Sun, W. & Levenston, M. E. Biomechanical analysis of silicon microelectrode-induced strain in the brain. J. Neural Eng. 2, 81–89 (2005).

    Article  PubMed  Google Scholar 

  149. Schwarz, D. A. et al. Chronic, wireless recordings of large-scale brain activity in freely moving rhesus monkeys. Nat. Methods 11, 670–676 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Rousche, P. J. et al. Flexible polyimide-based intracortical electrode arrays with bioactive capability. IEEE Trans. Biomed. Eng. 48, 361–371 (2001).

    Article  CAS  PubMed  Google Scholar 

  151. Minev, I. R. et al. Electronic dura mater for long-term multimodal neural interfaces. Science 347, 159–163 (2015).

    Article  CAS  PubMed  Google Scholar 

  152. Tyler, W. J. The mechanobiology of brain function. Nat. Rev. Neurosci. 13, 867–878 (2012).

    Article  CAS  PubMed  Google Scholar 

  153. 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).

    Article  PubMed  Google Scholar 

  154. Henze, D. A. et al. Intracellular features predicted by extracellular recordings in the hippocampus in vivo. J. Neurophysiol. 84, 390–400 (2000).

    Article  CAS  PubMed  Google Scholar 

  155. Schmitzer-Torbert, N., Jackson, J., Henze, D., Harris, K. & Redish, A. D. Quantitative measures of cluster quality for use in extracellular recordings. Neuroscience 131, 1–11 (2005).

    Article  CAS  PubMed  Google Scholar 

  156. Schmitzer-Torbert, N. & Redish, A. D. Neuronal activity in the rodent dorsal striatum in sequential navigation: separation of spatial and reward responses on the multiple T task. J. Neurophysiol. 91, 2259–2272 (2004).

    Article  PubMed  Google Scholar 

  157. Gray, C. M., Maldonado, P. E., Wilson, M. & McNaughton, B. Tetrodes markedly improve the reliability and yield of multiple single-unit isolation from multi-unit recordings in cat striate cortex. J. Neurosci. Methods 63, 43–54 (1995).

    Article  CAS  PubMed  Google Scholar 

  158. Jog, M. S. et al. Tetrode technology: advances in implantable hardware, neuroimaging, and data analysis techniques. J. Neurosci. Methods 117, 141–152 (2002).

    Article  CAS  PubMed  Google Scholar 

  159. Insanally, M. et al. A low-cost, multiplexed µECoG system for high-density recordings in freely moving rodents. J. Neural Eng. 13, 026030 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank S. R. Patel for helpful discussions. C.M.L. acknowledges support of this work by the US Air Force Office of Scientific Research (FA9550-14-1-0136) and a US National Institutes of Health Director’s Pioneer Award (1DP1EB025835-01). G.H. acknowledges support of this work by the Pathway to Independence Award (Parent K99/R00) from the US National Institute on Aging of the National Institutes of Health (4R00AG056636-03).

Reviewer information

Nature Reviews Neuroscience thanks J. Robinson and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Authors and Affiliations

  1. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA

    Guosong Hong & Charles M. Lieber

  2. Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA

    Guosong Hong

  3. Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA

    Guosong Hong

  4. Center for Brain Science, Harvard University, Cambridge, MA, USA

    Charles M. Lieber

  5. John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    Charles M. Lieber

Authors
  1. Guosong Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Charles M. Lieber
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Both authors researched data for the article, made substantial contributions to discussion of the content, wrote the article and reviewed or edited the manuscript before submission.

Corresponding author

Correspondence to Charles M. Lieber.

Ethics declarations

Competing interests

C.M.L. and G.H. are co-inventors on patents and patent applications relating to the article that have been filed by the authors’ institution (Harvard University), described below. The authors are not involved in efforts related to commercialization of this intellectual property (IP), including start-up companies or working with another company or group that might license the IP. ‘Scaffolds comprising nanoelectronic components, tissues, and other applications’, inventors C.M.L., J. Liu, B. Tian, T. Dvir, R. S. Langer and D. S. Kohane; US9,457,128 (issued); describes nanoscale transistors for cell recording. ‘Systems and methods for injectable devices’, inventors C.M.L., J. Liu, Z. Cheng, G.H., T.-M. Fu and T. Zhou; 61/975,601 (pending), PCT/US2015/024252 (pending) and 15/301,792 (pending); describes injectable mesh electronics. ‘Syringe injectable electronics: precise targeted delivery with quantitative input/output connectivity’, inventors C.M.L., G.H., T.-M. Fu and J. Huang; 62/201,006 (expired); describes injection method of mesh electronics. ‘Techniques and systems for injection and/or connection of electrical devices’, inventors C.M.L., G.H., T.-M. Fu and J. Huang; 62/209,255 (pending), PCT/US2016/045587 (issued) and 15/749,617 (pending); describes injection method of mesh electronics. ‘Interfaces for syringe-injectable electronics’, inventors C.M.L., T. G. Schuhmann, J. Yao, G.H. and T.-M. Fu; 62/505,562 (pending); describes the electrical connection method of mesh electronics.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Glossary

Tetrodes

Microelectrode arrays each consisting of four closely bundled and independently addressable microwire electrodes. Action potentials from the same neuron can be detected with varying amplitudes across all four electrodes.

Michigan-type microelectrode arrays

Microelectrode arrays with recording sites distributed along the length of each silicon shank.

Utah-type microelectrode arrays

Microelectrode arrays of silicon shanks (usually 10 × 10) with recording sites located only at the tip of each silicon shank.

Local field potentials

(LFPs). Extracellular electric field oscillations in the 0–100 Hz frequency band that mainly arise from collective transmembrane ionic currents and act as a medium for network-level communication.

Fourier transform

A mathematical method that decomposes a waveform (for example, the neural recording trace), which is usually a function of time, into the frequencies that make it up.

Spectrogram

A visual representation of frequencies of time-varying neural signals in a spectrum. A spectrogram is generated after performing Fourier transform of the neural recording trace.

Transistors

Semiconductor devices that can be used to switch or amplify electronic signals and thus act as basic building blocks of integrated circuits in modern electronic devices.

Impedance

Effective resistance of an electrical circuit to current when an oscillating voltage is applied. It has the same Ohm units as resistance but applies to AC circuits.

Channels

Separate paths in neural probes in which electrical, optical and chemical signals can flow to measure or modulate neural activity in an independently addressable manner.

Multiplexity

Number of independent channels in a given neural probe. The multiplexity of a neural probe represents the information throughput at the brain–probe interface.

Complementary metal–oxide–semiconductor

(CMOS). A technology with design style that uses complementary pairs of p-type and n-type metal–oxide–semiconductor field-effect transistors for low-energy-cost logical functions in integrated circuits.

Young’s modulus

An intrinsic material property that measures the ability of a material to resist its change (increase or decrease) in length under tensile or compressive forces.

Ripple

Fast and synchronous oscillatory pattern of the local field potential in the CA1 pyramidal layer of the hippocampus, usually in the frequency range of 100–200 Hz.

Photovoltaic effect

A process that generates voltage or electric current in a material upon exposure to light. Not to be confused with photoelectric effect, in which electrons are ejected from the material.

Field-effect transistors

(FETs). Three-terminal semiconductor devices in which the current flow (output) between the source and drain electrodes is modulated by the voltage (input) at the gate electrode.

Bending stiffness

The measure of the ability of a structure to resist bending deformation to applied force. It depends on the Young’s modulus of the material and the geometrical features of the structure.

Spike train cross-correlations

Pairwise comparisons of spike trains that reveal the latency in firing activity and potential monosynaptic excitatory or inhibitory interactions between two neurons.

Principal component analysis

(PCA). A statistical procedure to reduce the dimensionality and extract features of the extracellular spike waveform, thus assigning spikes to different neurons on the basis of waveform difference.

Thermal drawing process

A process commonly used in optical fibre production whereby macroscopic materials are heated and stretched to wires and sheets with greatly reduced thickness.

Closed-loop feedback

Electrical, optical and biochemical input to the nervous system that is adjusted by and used to control the measured output of neural activity.

Photonic integrated circuits

Optical counterparts of electronic integrated circuits, in which multiple photonic devices with distinct functions are integrated in the same circuitry.

Deep reactive ion etching

(DRIE). A type of reactive ion etching process used to create sharp-edged holes and trenches in the substrate usually with high anisotropy and high aspect ratio.

Ictal

The acute episode during which an epileptic seizure occurs. The ictal period features abnormal bursts of electrical impulses in the brain and loss of consciousness.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hong, G., Lieber, C.M. Novel electrode technologies for neural recordings. Nat Rev Neurosci 20, 330–345 (2019). https://doi.org/10.1038/s41583-019-0140-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41583-019-0140-6

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research