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Progress in Chemistry 2021, Vol. 33 Issue (4): 568-580 DOI: 10.7536/PC200811 Previous Articles   Next Articles

• Review •

Application of Graphene in Neural Activity Recording

Suye Lv1,2,3, Liang Zou1,2,3, Shouliang Guan1,2,3, Hongbian Li1,2()   

  1. 1 CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology,Beijing 100190, China
    2 CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, National Center for Nanoscience and Technology,Beijing 100190, China
    3 University of Chinese Academy of Sciences,Beijing 100049, China
  • Received: Revised: Online: Published:
  • Contact: Hongbian Li
  • Supported by:
    the National Natural Science Foundation of China(51972073)
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As a powerful tool for monitoring brain activities, neural electrodes have been playing a crucial role in the understanding of brain functions and the treatment of neurological disorders. In particular, the construction of a stable electrode-neural interface is critical for the stable chronic neural recording. However, conventional neural electrodes are mainly constructed with rigid materials, whose Young’s moduli are several orders of magnitude higher than that of the brain tissue. This large mechanical mismatch causes the micromotion of the neural electrodes, which elicits inflammatory response of the brain tissue and thus limits the stable chronic neural recording. With the one-atom thickness, graphene has been considered as a promising active material for neural electrodes with stable electrode-neural interfaces for its high conductivity, excellent flexibility and good biocompatibility. In this review, we provide an overview of graphene for neural activity recording, from their modulation on the growth of neurons to applications in bothin vitro and in vivo neural activity recording. At last, challenges and prospects of graphene for neural activity recording are proposed.

Contents

1 Introduction

2 Graphene microelectrodes for cell culture and in vitro neural activity recording

2.1 Graphene for cell growth and modulation

2.2 Graphene microelectrodes for in vitro neural activity recording

3 Graphene microelectrodes for in vivo neural activity recording

3.1 Graphene-based electrocorticography(ECoG) electrodes for neural activity recording

3.2 Graphene-based intracortical electrodes for neural activity recording

4 Conclusion and prospects

Key words: graphene, neural electrodes, electrophysiology, interface, stable chronic recording
Fig.1 Graphene for cell culture and modulation. (a) The differentiation of hNSCs on glass(left) and graphene(right)[45]. Copyright 2011, Wiley-VCH.(b) Schematic illustration of patterned fluorinated graphene(FG) substrates;(c) Aligned growth of stem cell on patterned FG[47]. Copyright 2012, Wiley -VCH.(d) SEM image of a three-dimensional graphene foam(3D GF);(e) The results of hNSCs differentiated on 3D GF and 2D graphene film[54]. Copyright 2013, Nature Publishing Group.(f) Schematic illustration of graphene/polymer micro-roll for cell encapsulation;(g) Schematic illustration of the formation of a neuronal network connected with neuron-laden micro-rolls[58]. Copyright 2019, The Royal Society of Chemistry.(h) Schematic illustration of a graphene nerve conduit;(i) Staining image for regenerated nerves[59], Copyright 2018, Nature Publishing Group.(j) Representative traces of the spontaneous network activity of neurons grown on different substrates[61]. Copyright 2018, Nature Publishing Group.
Fig.2 Graphene microelectrodes for in vitro neural activity recording.(a) Structure of a 60-channel graphene MEA;(b) Schematic illustration of the setup for action potential recording;(c) Spontaneous active potentials and noises recorded from graphene microelectrodes[71]. Copyright 2015, Nature Publishing Group.(d) A schematic illustration of the graphene-VACNT neural electrode;(e) FE-SEM images of the interface between rat cortical neurons and the graphene-VACNT electrode;(f) Comparison of the spike amplitude recorded with TiN electrode and VACNT electrode[73]. Copyright 2017, The Royal Society of Chemistry.(g) Schematic illustration of an inverted microscope setup using transparent graphene microelectrodes;(h) Immuno-fluorescence micrographs of the neurons cultured on the G-FETs;(i) The extracellular potentials obtained from the G-FETs;(j) The waveforms of extracellular spikes calibrated from(i)[75]. Copyright 2017, Frontiers Media S.A
Fig.3 Graphene ECoG electrodes for neural activity recording.(a) Schematic of the layered structure of the transparent graphene μECoG microelectrode array.(b) Fluorescence image of the cortical vasculature under transparent graphene site.(c) Optical evoked potentials recorded by the transparent graphene μECoG microelectrode array [78]. Copyright 2014, Nature Publishing Group.(d) Optical image of the graphene microelectrode array;(e) and(f) Under the direct illumination of the graphene electrode with laser, no artifact was observed[82]. Copyright 2018, Nature Publishing Group.(g) Optical image of a graphene SGFET array;(h) Spontaneous oscillatory activity recorded by graphene SGFET and platinum black electrodes;(i) Signal-to-noise ratio versus frequency for graphene SGFETs(light redlines) and platinum black electrodes[88]. Copyright 2017, Wiley-VCH
Fig.4 Graphene-based implantable electrodes.(a) Schematic illustration of the G-Cu implanted neural electrodes;(b) Comparison of the normalized viability of PC12 cells cultured with G-Cu and bare Cu microwire samples;(c) Representative acute recording of high-frequency electrophysiological signal using a G-Cu microelectrode[95]. Copyright 2016, American Chemical Society.(d) Schematic illustration of graphene fiber probe;(e) Cross-section SEM image of graphene fiber probe;(f) Neural activities recorded from the graphene fiber probe[97]. Copyright 2019, Wiley-VCH.(g) Microscope image of a dual-modality graphene SGFET electrode array;(h) Schematic and optical images of dual-modality recording setup;(i) Representative signals of surface and depth recording and time lag between surface and depth spikes[98]. Copyright 2018, Elsevier
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