Microelectrodes in the Electrophysiological Neural Probes: History
View Latest Version
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Jiahui Luo
,
Ning Xue
,
Jiamin Chen

Electrophysiological neural probes already have mature tools at different scales; patch clamps, which can record electrical activities at a single-cell scale, are the best tool for studying ion channel activity. With the development of microelectromechanical systems (MEMS) technology, high-density Si-based microelectrode arrays (MEAs) have successfully realized the recording of high-throughput and high time resolution of brain electrical signals.

  • micromechanical technology
  • neural probes
  • electrodes flexibility

1. Introduction

The neural network of the human brain is composed of an estimated 86 billion neurons [1], which convey information across complex temporal patterns within the neuronal network. Deciphering the fundamental mechanisms and processes of the human mind plays an important role in recognizing human thoughts, emotions, and ultimately explains human behavior. On the other hand, the brain–computer interface (BCI), which allows direct communication between the brain’s electrical activity and an external device [2], most commonly a computer, does not rely on conventional brain information output pathways (peripheral nerve and muscle tissue) and has been widely concerned in medical, industrial, and household settings [3][4]. In order to realize the bidirectional interaction, it is essential for accurate acquisition and feedback of neural signals.
Activities in support of the deciphering of nervous system codes and the bidirectional interaction of BCIs rely on advanced methodologies and engineering systems at different scales [5]. Macroscopically, non-invasive methods such as electroencephalogram (EEG) [6][7][8][9], functional near-infrared spectroscopy (fNIRS) [10][11], and magnetoencephalography (MEG) [12][13][14][15] have been widely utilized due to their high time resolution. However, to analyze the neural circuits behind cognition and behavior, it is also necessary to understand how neurons connect and communicate at the cellular and molecular levels; therefore, the ideal sensing tool must span from the single neuron to its complex network of connections [16]. Neural probes are defined as devices inserted or implanted into the brain or other nervous tissues [17] that meet the above requirements.
Electrophysiological neural probes already have mature tools at different scales; patch clamps, which can record electrical activities at a single-cell scale, are the best tool for studying ion channel activity. With the development of microelectromechanical systems (MEMS) technology, high-density Si-based microelectrode arrays (MEAs) have successfully realized the recording of high-throughput and high time resolution of brain electrical signals. However, as the size of the electrode recording site decreases, it leads to low capacitance and high impedance at the electrode/tissue interface, which seriously affects the recording resolution [18][19]. At the same time, the Young's modulus mismatch between traditional rigid electrode materials and soft biological tissues exacerbates the rejection of the probe invasive site, resulting in a decrease in electrode performance. Therefore, recent studies are more interested in the flexibility of neural microelectrodes, as it is expected to increase the charge storage capacity and reduce the interfacial impedance, thereby improving the signal-to-noise ratio (SNR) of electrophysiological signal detection.
In addition, many state-of-the-art neural probes for brain research have been reported over the past few decades. Among these achievements, the genetic modification of nerve cells with ion channels that are sensitive to light brought the promising new method of “optogenetics” into the neurosciences [20] with millisecond temporal resolution and single-cell spatial resolution [21]. The optoprobes embodied the theory of optogenetics through engineering design, providing a powerful tool for neuroscience research. In addition, magnetophysiology, concentrating on the magnetic field signals generated by ionic neuronal currents according to Biot–Savart law, acts as a complementary technique to electrical measurements with the advantages of non-contact, non-distortion, and no reference. The neural probe for magnetophysiology technology is to integrate micron-size magnetoresistance (MR) sensors based on spin electronics on a needle-shaped micromachined probe, named “magnetrodes” [22] in this review for a magnetic equivalent of electrodes. Intrusive magnetic recording of magnetrodes allows the distance from the field source to sensors to be shortened, so the amplitude of the signal (expected to be no larger than a few nT) is larger than that of MEG (hundreds of fT).

2. Microelectrodes

2.1. Rigid Microelectrodes

As one of the most mature tools for neural probes, implantable microelectrodes can accurately record electrical signals at the neuron level. For this reason, they have been widely applied to the basis of neurobiology, and have greatly promoted the development of BCI. According to the different electrode materials, microelectrodes can be divided into glass micropipette electrodes [23][24][25], metal microwire electrodes [26][27][28], and semiconductor substrate electrodes [29][30][31]. Glass micropipette electrodes, also known as patch clamps, are used to record the electrical activity of ion channels on biological membranes [32]. Compared with micropipette electrodes made of high-temperature drawn capillary glass tubes, microwire electrodes have lower high-frequency impedance, higher signal-to-noise ratio, and better mechanical properties, which can detect the fluctuation of voltage value without damaging cell activity. Microwire electrodes are the earliest implantable microelectrodes used for long-term recording of brain activity [33]. However, when the number of channels increases, the distance between the microwire electrodes cannot be precisely controlled during arrangement so that the consistency of electrode performance cannot be guaranteed, and electrode arrays assembly is also not easy to achieve [34].
With the development of photolithography and silicon etching technology, metal microwire microelectrodes are gradually replaced by silicon microelectrodes with good mechanical properties and biocompatibility. The Utah electrode [35][36] and Michigan electrode [37] are the two most representative types of silicon-based microelectrode arrays. For the Utah electrode array (UEA), the electrode recording point is exposed at the tip of each microneedle by mechanical cutting combined with chemical etching, and then metal is deposited [36]. The rest of the needle is insulated with polyimide to obtain a microelectrode array with precise size and spacing [38]. UEA-based BCI systems have been approved by the United States Food and Drug Administration (FDA) for some clinical trials. In 2006, Hochberg et al. [39] used implantable BCI for the first time to enable quadriplegic patients to drive computer screen cursors and activate simple robotic devices just by thinking. Over the years, the number of clinical studies and the leaps they have made in the clinical field have increased significantly. In March of this year, Chaudhary et al. [40] implanted BCI into the amyotrophic lateral sclerosis (ALS) patient who lost all muscle-based communication pathways and he selected a letter to form words and phrases to communicate his needs and experiences via auditory neurofeedback training, which also proved that brain-based volitional communication is possible even in a completely locked-in state. The UEA is not only used for recording, but also for stimulation purposes. For example, inducing tactile feedback in the hand region of the somatosensory cortex may help improve the accuracy of BCI devices [41], as well as the dexterity of prosthetics [42].

The Michigan electrode array is a needle electrode similar to UEA. The difference is that it has multiple plane recording sites on the needle [43][44][45], which can achieve high-density stereo recording. In 2017, Barz et al. [46] chronically implanted assembled 3D Michigan electrode arrays into non-human primates trained to perform a reach and grasp motor task. This result supports the design of application-specific neural interfaces in neuroscience research.

2.2. Strategies for Microelectrode Flexibility

Based on MEMS technology, microelectrode arrays made of rigid silicon can effectively obtain high-density activity information of brain neurons. Nevertheless, the rigidity of silicon makes it unable to match the physical properties of biological tissues. At the same time, because of its non-deformable characteristics, it will cause damage to cells when the tissue moves, so it is not suitable for long-term implantation in the human body. Therefore, it is desirable to optimize the performance of implantable electrodes through electrode flexibility.

The flexibility of neural electrodes includes choosing flexible polymers as substrates and replacing traditional metal electrodes with various new electrode materials. Firstly, an electrode with flexible probes is a typical method for electrode flexibility, providing an interface more suitable for neural tissue by exhibiting more suitability. The selected flexible substrate materials should have good biocompatibility, flexibility, and compatibility with the microfabrication processes, such as polydimethylsiloxane (PDMS) [47][48][49], polyimide (PI) [50][51], Parylene [52][53], and SU-8 [54]. Additionally, the geometry of the probe can also affect the rejection of the local tissue near the electrode.Wu et al. designed a fishbone-shaped polyimide neural electrode that effectively reduced the tissue reaction by increasing the distance between the electrode and the probe [55].

On the other hand, electrode performance can be further improved by various types of organic electroactive electrode materials with high charge injection capability and excellent electrochemical performance, which includes carbon-based nanomaterials, i.e., carbon nanotubes (CNTs) [56][57][58], graphene [59][60][61], and conductive polymers (CPs) [62][63][64]. Further, nanocomposites of the above materials have also become popular choices. For example, combining conducting polymer Poly (3,4-ethylenedioxythiophene) (PEDOT) with carbon-based nanomaterials with high mechanical hardness can prevent PEDOT films from deforming and cracking after long-term operation [65]. Recently, Vajrala et al. [66] fabricated novel nanocomposites of highly porous and robust PEDOT-CNF by a simple and reproducible electrodeposition method (Figure 1d), and the experimental results showed that it has superior performance to pure PEDOT materials.

2.3. Methods of Flexible Microelectrode Insertion

Some studies have confirmed that flexible electrodes can indeed reduce the impact on the surrounding brain tissue [67]. However, an important problem is that flexible neural probes may be too soft to penetrate the meninges and reach the target site, thus requiring the use of additional stiffening structures. The method of stiff backbone layers [68] and insertion shuttles [69][70][71] can improve the rigidity of flexible probes, but the former will limit the flexibility of the device, and the latter will temporarily increase the footprint of the implant, causing additional damage to the nerve tissue during the implantation process. A more acceptable approach is to temporarily reinforce the probe with a bioresorbable coating, which restores the flexibility of the probe after the coating dissolves. Commonly used bioabsorbable coatings include poly (ethylene glycol) (PEG) [72], poly (lactic-co-glycolic acid) (PLGA) [73], silk fibroin [74], sucrose [75], maltose [76], dextran [77], and their bilayer structures [78][79]. More than just an insertion aid, these polymers act as biofriendly coatings to mitigate rejection. In actual use, the appropriate bio-coating should be selected according to the application scenario, combined with the stiffness, degradation rate, and bioresorbability of the polymer [80].

This entry is adapted from the peer-reviewed paper 10.3390/bios12121167

References

  1. Azevedo, F.A.C.; Carvalho, L.R.B.; Grinberg, L.T.; Farfel, J.M.; Ferretti, R.E.L.; Leite, R.E.P.; Jacob, W.; Lent, R.; Herculano-Houzel, S. Equal Numbers of Neuronal and Nonneuronal Cells Make the Human Brain an Isometrically Scaled-Up Primate Brain. J. Comp. Neurol. 2009, 513, 532–541.
  2. Fetz, E.E. Restoring motor function with bidirectional neural interfaces. Prog. Brain Res. 2015, 218, 241–252.
  3. Bell, C.J.; Shenoy, P.; Chalodhorn, R.; Rao, R.P.N. Control of a humanoid robot by a noninvasive brain-computer interface in humans. J. Neural Eng. 2008, 5, 214–220.
  4. Kansaku, K.; Hata, N.; Takano, K. My thoughts through a robot’s eyes: An augmented reality-brain-machine interface. Neurosci. Res. 2010, 66, 219–222.
  5. Vázquez-Guardado, A.; Yang, Y.; Bandodkar, A.J.; Rogers, J.A. Recent advances in neurotechnologies with broad potential for neuroscience research. Nat. Neurosci. 2020, 23, 1522–1536.
  6. Millán Jdel, R.; Renkens, F.; Mouriño, J.; Gerstner, W. Noninvasive brain-actuated control of a mobile robot by human EEG. IEEE Trans. Biomed. Eng. 2004, 51, 1026–1033.
  7. Rebsamen, B.; Guan, C.; Zhang, H.; Wang, C.; Teo, C.; Ang, M.H., Jr.; Burdet, E. A brain controlled wheelchair to navigate in familiar environments. IEEE Trans. Neural Syst. Rehabil. Eng. 2010, 18, 590–598.
  8. Müller, K.R.; Tangermann, M.; Dornhege, G.; Krauledat, M.; Curio, G.; Blankertz, B. Machine learning for real-time single-trial EEG-analysis: From brain-computer interfacing to mental state monitoring. J. Neurosci. Methods 2008, 167, 82–90.
  9. Pfurtscheller, G.; da Silva, F.H.L. Event-related EEG/MEG synchronization and desynchronization: Basic principles. Clin. Neurophysiol. 1999, 110, 1842–1857.
  10. Fazli, S.; Mehnert, J.; Steinbrink, J.; Curio, G.; Villringer, A.; Müller, K.R.; Blankertz, B. Enhanced performance by a hybrid NIRS-EEG brain computer interface. Neuroimage 2012, 59, 519–529.
  11. Coyle, S.M.; Ward, T.E.; Markham, C.M. Brain-computer interface using a simplified functional near-infrared spectroscopy system. J. Neural Eng. 2007, 4, 219–226.
  12. Kauhanen, L.; Nykopp, T.; Lehtonen, J.; Jylänki, P.; Heikkonen, J.; Rantanen, P.; Alaranta, H.; Sams, M. EEG and MEG brain-computer interface for tetraplegic patients. IEEE Trans. Neural Syst. Rehabil. Eng. 2006, 14, 190–193.
  13. Yeom, H.G.; Kim, J.S.; Chung, C.K. Estimation of the velocity and trajectory of three-dimensional reaching movements from non-invasive magnetoencephalography signals. J. Neural Eng. 2013, 10, 026006.
  14. Gross, J. Magnetoencephalography in Cognitive Neuroscience: A Primer. Neuron 2019, 104, 189–204.
  15. Hamalainen, M.; Hari, R.; Ilmoniemi, R.J.; Knuutila, J.; Lounasmaa, O.V. Magnetoencephalography—Theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev. Mod. Phys. 1993, 65, 413–497.
  16. Seymour, J.P.; Wu, F.; Wise, K.D.; Yoon, E. State-of-the-art MEMS and microsystem tools for brain research. Microsyst. Nanoeng. 2017, 3, 16066.
  17. Choi, J.-R.; Kim, S.-M.; Ryu, R.-H.; Kim, S.-P.; Sohn, J.-W. Implantable Neural Probes for Brain-Machine Interfaces? Current Developments and Future Prospects. Exp. Neurobiol. 2018, 27, 453–471.
  18. Cogan, S.F. Neural stimulation and recording electrodes. Annu. Rev. Biomed. Eng. 2008, 10, 275–309.
  19. Viswam, V.; Obien, M.E.J.; Franke, F.; Frey, U.; Hierlemann, A. Optimal Electrode Size for Multi-Scale Extracellular-Potential Recording From Neuronal Assemblies. Front. Neurosci. 2019, 13, 385.
  20. Alt, M.T.; Fiedler, E.; Rudmann, L.; Ordonez, J.S.; Ruther, P.; Stieglitz, T. Let There Be Light—Optoprobes for Neural Implants. Proc. IEEE 2017, 105, 101–138.
  21. Boyden, E.S.; Zhang, F.; Bamberg, E.; Nagel, G.; Deisseroth, K. Millisecond-timescale, genetically targeted optical control of neural activity. Nat. Neurosci. 2005, 8, 1263–1268.
  22. Caruso, L.; Wunderle, T.; Lewis, C.M.; Valadeiro, J.; Trauchessec, V.; Trejo Rosillo, J.; Amaral, J.P.; Ni, J.; Jendritza, P.; Fermon, C.; et al. In Vivo Magnetic Recording of Neuronal Activity. Neuron 2017, 95, 1283–1291.e1284.
  23. Chowdhury, T. Fabrication of extremely fine glass micropipette electrodes. J. Phys. E Sci. Instrum. 1969, 2, 1087.
  24. Simons, D.J.; Land, P.W. A reliable technique for marking the location of extracellular recording sites using glass micropipettes. Neurosci. Lett. 1987, 81, 100–104.
  25. Pine, J. Recording action potentials from cultured neurons with extracellular microcircuit electrodes. J. Neurosci. Methods 1980, 2, 19–31.
  26. Palmer, C. A microwire technique for recording single neurons in unrestrained animals. Brain Res. Bull. 1978, 3, 285–289.
  27. Lehew, G.; Nicolelis, M.A.L. State-of-the-Art Microwire Array Design for Chronic Neural Recordings in Behaving Animals, 2nd ed.; CRC Press/Taylor & Francis: Boca Raton, FL, USA, 2008.
  28. Verloop, A.J.; Holsheimer, J. A simple method for the construction of electrode arrays. J. Neurosci. Methods 1984, 11, 173–178.
  29. Fekete, Z. Recent advances in silicon-based neural microelectrodes and microsystems: A review. Sens. Actuators B Chem. 2015, 215, 300–315.
  30. Barrese, J.C.; Rao, N.; Paroo, K.; Triebwasser, C.; Vargas-Irwin, C.; Franquemont, L.; Donoghue, J.P. Failure mode analysis of silicon-based intracortical microelectrode arrays in non-human primates. J. Neural Eng. 2013, 10, 066014.
  31. Ghane-Motlagh, B.; Sawan, M. Design and implementation challenges of microelectrode arrays: A review. Mater. Sci. Appl. 2013, 4, 483.
  32. Bretag, A.H. The glass micropipette electrode: A history of its inventors and users to 1950. J. Gen. Physiol. 2017, 149, 417–430.
  33. Strumwasser, F. Long-Term Recording from Single Neurons in Brain of Unrestrained Mammals. Science 1958, 127, 469–470.
  34. Nicolelis, M.A.L.; Dimitrov, D.; Carmena, J.M.; Crist, R.; Lehew, G.; Kralik, J.D.; Wise, S.P. Chronic, multisite, multielectrode recordings in macaque monkeys. Proc. Natl. Acad. Sci. USA 2003, 100, 11041–11046.
  35. Jones, K.E.; Campbell, P.K.; Normann, R.A. A glass/silicon composite intracortical electrode array. Ann. Biomed. Eng. 1992, 20, 423–437.
  36. 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. 1991, 38, 758–768.
  37. Scholvin, J.; Kinney, J.P.; Bernstein, J.G.; Moore-Kochlacs, C.; Kopell, N.; Fonstad, C.G.; Boyden, E.S. Close-Packed Silicon Microelectrodes for Scalable Spatially Oversampled Neural Recording. IEEE Trans. Biomed. Eng. 2016, 63, 120–130.
  38. Bhandari, R.; Negi, S.; Solzbacher, F. Wafer-scale fabrication of penetrating neural microelectrode arrays. Biomed. Microdevices 2010, 12, 797–807.
  39. Hochberg, L.R.; Serruya, M.D.; Friehs, G.M.; Mukand, J.A.; Saleh, M.; Caplan, A.H.; Branner, A.; Chen, D.; Penn, R.D.; Donoghue, J.P. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 2006, 442, 164–171.
  40. Chaudhary, U.; Vlachos, I.; Zimmermann, J.B.; Espinosa, A.; Tonin, A.; Jaramillo-Gonzalez, A.; Khalili-Ardali, M.; Topka, H.; Lehmberg, J.; Friehs, G.M.; et al. Spelling interface using intracortical signals in a completely locked-in patient enabled via auditory neurofeedback training. Nat. Commun. 2022, 13, 1236.
  41. Kim, S.; Callier, T.; Tabot, G.A.; Gaunt, R.A.; Tenore, F.V.; Bensmaia, S.J. Behavioral assessment of sensitivity to intracortical microstimulation of primate somatosensory cortex. Proc Natl Acad Sci USA 2015, 112, 15202–15207.
  42. Flesher, S.N.; Downey, J.E.; Weiss, J.M.; Hughes, C.L.; Herrera, A.J.; Tyler-Kabara, E.C.; Boninger, M.L.; Collinger, J.L.; Gaunt, R.A. A brain-computer interface that evokes tactile sensations improves robotic arm control. Science 2021, 372, 831–836.
  43. Branner, A.; Normann, R.A. A multielectrode array for intrafascicular recording and stimulation in sciatic nerve of cats. Brain Res. Bull. 2000, 51, 293–306.
  44. Bai, Q.; Wise, K.D.; Anderson, D.J. A high-yield microassembly structure for three-dimensional microelectrode arrays. IEEE Trans. Biomed. Eng. 2000, 47, 281–289.
  45. Wise, K.D.; Angell, J.B.; Starr, A. An integrated-circuit approach to extracellular microelectrodes. IEEE Trans. Biomed. Eng. 1970, 17, 238–247.
  46. Barz, F.; Livi, A.; Lanzilotto, M.; Maranesi, M.; Bonini, L.; Paul, O.; Ruther, P. Versatile, modular 3D microelectrode arrays for neuronal ensemble recordings: From design to fabrication, assembly, and functional validation in non-human primates. J. Neural Eng. 2017, 14, 036010.
  47. Wang, X.; Gu, Y.; Xiong, Z.; Cui, Z.; Zhang, T. Silk-Molded Flexible, Ultrasensitive, and Highly Stable Electronic Skin for Monitoring Human Physiological Signals. Adv. Mater. 2014, 26, 1336–1342.
  48. Baek, J.Y.; An, J.H.; Choi, J.M.; Park, K.S.; Lee, S.H. Flexible polymeric dry electrodes for the long-term monitoring of ECG. Sens. Actuators A Phys. 2008, 143, 423–429.
  49. Tang, J.; Guo, H.; Zhao, M.; Yang, J.; Tsoukalas, D.; Binzhen, Z.; Liu, J.; Xue, C.; Zhang, W. Highly Stretchable Electrodes on Wrinkled Polydimethylsiloxane Substrates. Sci. Rep. 2015, 5, 16527.
  50. Rousche, P.J.; Pellinen, D.S.; Pivin, D.P., Jr.; Williams, J.C.; Vetter, R.J.; Kipke, D.R. Flexible polyimide-based intracortical electrode arrays with bioactive capability. IEEE Trans. Biomed. Eng. 2001, 48, 361–371.
  51. Moon, J.H.; Baek, D.H.; Choi, Y.Y.; Lee, K.H.; Kim, H.C.; Lee, S.H. Wearable polyimide-PDMS electrodes for intrabody communication. J. Micromech. Microeng. 2010, 20, 025032.
  52. Park, D.-W.; Schendel, A.A.; Mikael, S.; Brodnick, S.K.; Richner, T.J.; Ness, J.P.; Hayat, M.R.; Atry, F.; Frye, S.T.; Pashaie, R.; et al. Graphene-based carbon-layered electrode array technology for neural imaging and optogenetic applications. Nat. Commun. 2014, 5, 5258.
  53. Li, W.; Rodger, D.C.; Pinto, A.; Meng, E.; Weiland, J.D.; Humayun, M.S.; Tai, Y.-C. Parylene-based integrated wireless single-channel neurostimulator. Sens. Actuators A Phys. 2011, 166, 193–200.
  54. Chen, C.-H.; Lin, C.-T.; Hsu, W.-L.; Chang, Y.-C.; Yeh, S.-R.; Li, L.-J.; Yao, D.-J. A flexible hydrophilic-modified graphene microprobe for neural and cardiac recording. Nanomed. Nanotechnol. Biol. Med. 2013, 9, 600–604.
  55. Wu, F.; Im, M.; Yoon, E. A flexible fish-bone-shaped neural probe strengthened by biodegradable silk coating for enhanced biocompatibility. In Proceedings of the 2011 16th International Solid-State Sensors, Actuators and Microsystems Conference, Beijing, China, 5–9 June 2011; pp. 966–969.
  56. Ansaldo, A.; Castagnola, E.; Maggiolini, E.; Fadiga, L.; Ricci, D. Superior electrochemical performance of carbon nanotubes directly grown on sharp microelectrodes. ACS Nano 2011, 5, 2206–2214.
  57. Sridharan, A.; Muthuswamy, J. Soft, Conductive, Brain-Like, Coatings at Tips of Microelectrodes Improve Electrical Stability under Chronic, In Vivo Conditions. Micromachines 2021, 12, 761.
  58. Shoval, A.; Adams, C.; David-Pur, M.; Shein, M.; Hanein, Y.; Sernagor, E. Carbon nanotube electrodes for effective interfacing with retinal tissue. Front. Neuroeng. 2009, 2, 4.
  59. Hess, L.H.; Jansen, M.; Maybeck, V.; Hauf, M.V.; Seifert, M.; Stutzmann, M.; Sharp, I.D.; Offenhäusser, A.; Garrido, J.A. Graphene transistor arrays for recording action potentials from electrogenic cells. Adv. Mater. 2011, 23, 5045–5049.
  60. Zhan, B.; Li, C.; Yang, J.; Jenkins, G.; Huang, W.; Dong, X. Graphene field-effect transistor and its application for electronic sensing. Small 2014, 10, 4042–4065.
  61. Du, Z.-Z.; Li, W.; Ai, W.; Tai, Q.; Xie, L.-H.; Cao, Y.; Liu, J.-Q.; Yi, M.-D.; Ling, H.-F.; Li, Z.-H.; et al. Chemoselective reduction of graphene oxide and its application in nonvolatile organic transistor memory devices. RSC Adv. 2013, 3, 25788–25791.
  62. Green, R.; Abidian, M.R. Conducting Polymers for Neural Prosthetic and Neural Interface Applications. Adv. Mater. 2015, 27, 7620–7637.
  63. Maziz, A.; Özgür, E.; Bergaud, C.; Uzun, L. Progress in conducting polymers for biointerfacing and biorecognition applications. Sens. Actuators Rep. 2021, 3, 100035.
  64. Kim, D.H.; Wiler, J.A.; Anderson, D.J.; Kipke, D.R.; Martin, D.C. Conducting polymers on hydrogel-coated neural electrode provide sensitive neural recordings in auditory cortex. Acta Biomater. 2010, 6, 57–62.
  65. Luo, X.; Weaver, C.L.; Zhou, D.D.; Greenberg, R.; Cui, X.T. Highly stable carbon nanotube doped poly(3,4-ethylenedioxythiophene) for chronic neural stimulation. Biomaterials 2011, 32, 5551–5557.
  66. Vajrala, V.S.; Saunier, V.; Nowak, L.G.; Flahaut, E.; Bergaud, C.; Maziz, A. Nanofibrous PEDOT-Carbon Composite on Flexible Probes for Soft Neural Interfacing. Front. Bioeng. Biotechnol. 2021, 9, 780197.
  67. Sohal, H.S.; Clowry, G.J.; Jackson, A.; O’Neill, A.; Baker, S.N. Mechanical Flexibility Reduces the Foreign Body Response to Long-Term Implanted Microelectrodes in Rabbit Cortex. PLoS ONE 2016, 11, e0165606.
  68. Kee-Keun, L.; Jiping, H.; Amarjit, S.; Stephen, M.; Gholamreza, E.; Bruce, K.; Gregory, R. Polyimide-based intracortical neural implant with improved structural stiffness. J. Micromech. Microeng. 2004, 14, 32.
  69. Felix, S.H.; Shah, K.G.; Tolosa, V.M.; Sheth, H.J.; Tooker, A.C.; Delima, T.L.; Jadhav, S.P.; Frank, L.M.; Pannu, S.S. Insertion of flexible neural probes using rigid stiffeners attached with biodissolvable adhesive. J. Vis. Exp. 2013, 79, e50609.
  70. Kim, B.J.; Kuo, J.T.; Hara, S.A.; Lee, C.D.; Yu, L.; Gutierrez, C.A.; Hoang, T.Q.; Pikov, V.; Meng, E. 3D Parylene sheath neural probe for chronic recordings. J. Neural Eng. 2013, 10, 045002.
  71. Kozai, T.D.; Kipke, D.R. Insertion shuttle with carboxyl terminated self-assembled monolayer coatings for implanting flexible polymer neural probes in the brain. J. Neurosci. Methods 2009, 184, 199–205.
  72. Takeuchi, S.; Ziegler, D.; Yoshida, Y.; Mabuchi, K.; Suzuki, T. Parylene flexible neural probes integrated with microfluidic channels. Lab Chip 2005, 5, 519–523.
  73. Foley, C.P.; Nishimura, N.; Neeves, K.B.; Schaffer, C.B.; Olbricht, W.L. Flexible microfluidic devices supported by biodegradable insertion scaffolds for convection-enhanced neural drug delivery. Biomed. Microdev. 2009, 11, 915–924.
  74. Tien, L.W.; Wu, F.; Tang-Schomer, M.D.; Yoon, E.; Omenetto, F.G.; Kaplan, D.L. Silk as a Multifunctional Biomaterial Substrate for Reduced Glial Scarring around Brain-Penetrating Electrodes. Adv. Funct. Mater. 2013, 23, 3185–3193.
  75. Jeon, M.; Cho, J.; Kim, Y.K.; Jung, D.; Yoon, E.-S.; Shin, S.; Cho, I.-J. Partially flexible MEMS neural probe composed of polyimide and sucrose gel for reducing brain damage during and after implantation. J. Micromech. Microeng. 2014, 24, 025010.
  76. Xiang, Z.; Yen, S.-C.; Xue, N.; Sun, T.; Tsang, W.M.; Zhang, S.; Liao, L.-D.; Thakor, N.V.; Lee, C. Ultra-thin flexible polyimide neural probe embedded in a dissolvable maltose-coated microneedle. J. Micromech. Microeng. 2014, 24, 065015.
  77. Kil, D.; Bovet Carmona, M.; Ceyssens, F.; Deprez, M.; Brancato, L.; Nuttin, B.; Balschun, D.; Puers, R. Dextran as a Resorbable Coating Material for Flexible Neural Probes. Micromachines 2019, 10, 61.
  78. Pas, J.; Rutz, A.L.; Quilichini, P.P.; Slézia, A.; Ghestem, A.; Kaszas, A.; Donahue, M.J.; Curto, V.F.; O’Connor, R.P.; Bernard, C.; et al. A bilayered PVA/PLGA-bioresorbable shuttle to improve the implantation of flexible neural probes. J. Neural Eng. 2018, 15, 065001.
  79. Lecomte, A.; Castagnola, V.; Descamps, E.; Dahan, L.; Blatché, M.C.; Dinis, T.M.; Leclerc, E.; Egles, C.; Bergaud, C. Silk and PEG as means to stiffen a parylene probe for insertion in the brain: Toward a double time-scale tool for local drug delivery. J. Micromech. Microeng. 2015, 25, 125003.
  80. Lecomte, A.; Descamps, E.; Bergaud, C. A review on mechanical considerations for chronically-implanted neural probes. J. Neural Eng. 2018, 15, 031001.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
This entry is offline, you can click here to edit this entry!
ScholarVision Creations
Feedback