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Failure mode analysis of silicon-based intracortical microelectrode arrays in non-human primates
Open accessAbstract
OBJECTIVE: Brain-computer interfaces (BCIs) using chronically implanted intracortical microelectrode arrays (MEAs) have the potential to restore lost function to people with disabilities if they work reliably for years. Current sensors fail to provide reliably useful signals over extended periods of time for reasons that are not clear. This study reports a comprehensive retrospective analysis from a large set of implants of a single type of intracortical MEA in a single species, with a common set of measures in order to evaluate failure modes. APPROACH: Since 1996, 78 silicon MEAs were implanted in 27 monkeys (Macaca mulatta). We used two approaches to find reasons for sensor failure. First, we classified the time course leading up to complete recording failure as acute (abrupt) or chronic (progressive). Second, we evaluated the quality of electrode recordings over time based on signal features and electrode impedance. Failure modes were divided into four categories: biological, material, mechanical, and unknown. MAIN RESULTS: Recording duration ranged from 0 to 2104 days (5.75 years), with a mean of 387 days and a median of 182 days (n = 78). Sixty-two arrays failed completely with a mean time to failure of 332 days (median = 133 days) while nine array experiments were electively terminated for experimental reasons (mean = 486 days). Seven remained active at the close of this study (mean = 753 days). Most failures (56%) occurred within a year of implantation, with acute mechanical failures the most common class (48%), largely because of connector issues (83%). Among grossly observable biological failures (24%), a progressive meningeal reaction that separated the array from the parenchyma was most prevalent (14.5%). In the absence of acute interruptions, electrode recordings showed a slow progressive decline in spike amplitude, noise amplitude, and number of viable channels that predicts complete signal loss by about eight years. Impedance measurements showed systematic early increases, which did not appear to affect recording quality, followed by a slow decline over years. The combination of slowly falling impedance and signal quality in these arrays indicates that insulating material failure is the most significant factor. SIGNIFICANCE: This is the first long-term failure mode analysis of an emerging BCI technology in a large series of non-human primates. The classification system introduced here may be used to standardize how neuroprosthetic failure modes are evaluated. The results demonstrate the potential for these arrays to record for many years, but achieving reliable sensors will require replacing connectors with implantable wireless systems, controlling the meningeal reaction, and improving insulation materials. These results will focus future research in order to create clinical neuroprosthetic sensors, as well as valuable research tools, that are able to safely provide reliable neural signals for over a decade.
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“Previous demonstrations of intracortical insertion of MEAs showed injury and neuronal loss at the implantation location99, as well as signal instabilities contributing to signal loss and performance decline over time100,101.”
From The speech neuroprosthesis (2024) · cites this paper as [100]
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“In spite of the formidable results of using UEA/USEA technologies described above, most penetrating microelectrode arrays currently have maximum in vivo lifetimes of several months or a few years (Suner et al 2005, Prasad et al 2012, Barrese et al 2013).”
From Clinical applications of penetrating neural interfaces and Utah Electrode Array technologies (2016) · cites this paper as Barrese et al 2013
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Cites
Neuronal ensemble control of prosthetic devices by a human with tetraplegia
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. (2006) · Nature · doi:10.1038/nature04970 · Cited by 3,295
Self-citationReach and grasp by people with tetraplegia using a neurally controlled robotic arm
Hochberg, L. R., Bacher, D., Jarosiewicz, B., Masse, N. Y., Simeral, J. D., Vogel, J., Haddadin, S., Liu, J., Cash, S. S., van der Smagt, P., et al. (2012) · Nature · doi:10.1038/nature11076 · Cited by 2,719
Self-citationResponse of brain tissue to chronically implanted neural electrodes
Polikov, V. S., Tresco, P. A., & Reichert, W. M. (2005) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2005.08.015 · Cited by 1,896
Biran, R., Martin, D. C., & Tresco, P. A. (2005) · Experimental Neurology · doi:10.1016/j.expneurol.2005.04.020 · Cited by 893
Brain responses to micro-machined silicon devices
Szarowski, D. H., Andersen, M. D., Retterer, S. T., Spence, A. J., Isaacson, M. S., Craighead, H. G., Turner, J. N., & Shain, W. G. (2003) · Brain Research · doi:10.1016/s0006-8993(03)03023-3 · Cited by 811
Chronic recording capability of the Utah Intracortical Electrode Array in cat sensory cortex
Rousche, P. J., & Normann, R. A. (1998) · Journal of Neuroscience Methods · doi:10.1016/s0165-0270(98)00031-4 · Cited by 646
Cerebral Astrocyte Response to Micromachined Silicon Implants
Turner, J. N., Shain, W. G., Szarowski, D. H., Andersen, M., Martins, S., Isaacson, M. S., & Craighead, H. G. (1999) · Experimental Neurology · doi:10.1006/exnr.1998.6983 · Cited by 603
Single-neuron dynamics in human focal epilepsy
Truccolo, W., Donoghue, J., Hochberg, L. R., Eskandar, E. N., Madsen, J. R., Anderson, W. S., Brown, E. N., Halgren, E., & Cash, S. S. (2011) · Nature Neuroscience · doi:10.1038/nn.2782 · Cited by 528
Self-citationFactors influencing the biocompatibility of insertable silicon microshafts in cerebral cortex
Edell, D. J., Toi, V. V., McNeil, V. M., & Clark, L. D. (1992) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.141202 · Cited by 481
McConnell, G. C., Rees, H. D., Levey, A. I., Gutekunst, C.-A., Gross, R. E., & Bellamkonda, R. V. (2009) · Journal of Neural Engineering · doi:10.1088/1741-2560/6/5/056003 · Cited by 480
Research works printed but not linked: 2
- McConnell G C, 2009, J. Neural Eng., 6
- Prasad A, 2012, J. Neural Eng., 9
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Hejazi, M., Tong, W., Ibbotson, M. R., Prawer, S., & Garrett, D. J. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.658703 · Cited by 77
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Thielen, B., & Meng, E. F. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abf6f2 · Cited by 78
Du, Z. J., Luo, X., Weaver, C. L., & Cui, X. T. (2015) · Journal of Materials Chemistry C · doi:10.1039/c5tc00145e · Cited by 70
Welle, E. J., Patel, P. R., Woods, J. E., Petrossians, A., della Valle, E., Vega-Medina, A., Richie, J. M., Cai, D., Weiland, J. D., & Chestek, C. A. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab8343 · Cited by 68
Sponheim, C., Papadourakis, V., Collinger, J. L., Downey, J., Weiss, J., Pentousi, L., Elliott, K., & Hatsopoulos, N. G. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac3eaf · Cited by 66
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Stiller, A. M., Black, B. J., Kung, C., Ashok, A., Cogan, S. F., Varner, V. D., & Pancrazio, J. J. (2018) · Micromachines · doi:10.3390/mi9090443 · Cited by 64
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Jastrzebska‐Perfect, P., Chowdhury, S., Spyropoulos, G. D., Zhao, Z., Cea, C., Gelinas, J. N., & Khodagholy, D. (2020) · Advanced Functional Materials · doi:10.1002/adfm.201909165 · Cited by 68
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Samejima, S., Khorasani, A., Ranganathan, V. N., Nakahara, J., Tolley, N. M., Boissenin, A., Shalchyan, V., Daliri, M. R., Smith, J. R., & Moritz, C. T. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3090269 · Cited by 69
Influence of resveratrol release on the tissue response to mechanically adaptive cortical implants
Nguyen, J. K., Jorfi, M., Buchanan, K. L., Park, D. J., Foster, E. J., Tyler, D. J., Rowan, S. J., Weder, C., & Capadona, J. R. (2015) · Acta Biomaterialia · doi:10.1016/j.actbio.2015.11.001 · Cited by 63
Cassar, I. R., Yu, C., Sambangi, J., Lee, C. D., Whalen, J. J., Petrossians, A., & Grill, W. M. (2019) · Biomaterials · doi:10.1016/j.biomaterials.2019.03.017 · Cited by 63
Shoffstall, A. J., Paiz, J. E., Miller, D. M., Rial, G. M., Willis, M. T., Menendez, D. M., Hostler, S. R., & Capadona, J. R. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa9f32 · Cited by 60
Black, B., Kanneganti, A., Joshi‐Imre, A., Rihani, R. T., Chakraborty, B., Abbott, J., Pancrazio, J. J., & Cogan, S. F. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00181.2018 · Cited by 62
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Massey, T. L., Santacruz, S. R., Hou, J. F., Pister, K. S. J., Carmena, J. M., & Maharbiz, M. M. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/aae8d9 · Cited by 58
Real-time estimation and biofeedback of single-neuron firing rates using local field potentials
Hall, T. M., Nazarpour, K., & Jackson, A. (2014) · Nature Communications · doi:10.1038/ncomms6462 · Cited by 61
A silicon carbide array for electrocorticography and peripheral nerve recording
Diaz-Botia, C. A., Luna, L. E., Neely, R., Chamanzar, M., Carraro, C., Carmena, J. M., Sabes, P. N., Maboudian, R., & Maharbiz, M. M. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa7698 · Cited by 62
Long-term intracortical microelectrode array performance in a human: a 5 year retrospective analysis
Colachis, S. C., Dunlap, C. F., Annetta, N. V., Tamrakar, S., Bockbrader, M. A., & Friedenberg, D. A. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac1add · Cited by 67
Čvančara, P., Boretius, T., López-Álvarez, V. M., Maciejasz, P., Andreu, D., Raspopovic, S., Petrini, F., Micera, S., Granata, G., Fernandez, E., et al. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab9a9a · Cited by 58
Design and demonstration of an intracortical probe technology with tunable modulus
Simon, D., Charkhkar, H., John, C. S., Rajendran, S. H., Kang, T. H., Reit, R., Arreaga‐Salas, D., McHail, D. G., Knaack, G. L., Sloan, A. M., et al. (2016) · Journal of Biomedical Materials Research Part A · doi:10.1002/jbm.a.35896 · Cited by 59
Yeon, P., Mirbozorgi, S. A., Lim, J., & Ghovanloo, M. (2017) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2017.2775638 · Cited by 59
Cutrone, A., & Micera, S. (2019) · Advanced Healthcare Materials · doi:10.1002/adhm.201801345 · Cited by 58
Closed-loop interaction with the cerebral cortex: a review of wireless implant technology
Kohler, F., Gkogkidis, C. A., Bentler, C., Wang, X., Gierthmuehlen, M., Fischer, J., Stolle, C., Reindl, L. M., Rickert, J., Stieglitz, T., et al. (2017) · Brain-Computer Interfaces · doi:10.1080/2326263x.2017.1338011 · Cited by 57
Bioinspired Underwater Propulsors
Buren, T. W. V., Floryan, D., & Smits, A. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139058995.006 · Cited by 57
A Review: Research Progress of Neural Probes for Brain Research and Brain–Computer Interface
Luo, J., Xue, N., & Chen, J. (2022) · Biosensors · doi:10.3390/bios12121167 · Cited by 63
Highly Customizable 3D Microelectrode Arrays for In Vitro and In Vivo Neuronal Tissue Recordings
Shihada, J. A., Jung, M., Decke, S., Koschinski, L., Musall, S., Montes, V. R., & Offenhäusser, A. (2024) · Advanced Science · doi:10.1002/advs.202305944 · Cited by 57
Stiller, A. M., Usoro, J. O., Frewin, C. L., Danda, V. R., Ecker, M., Joshi‐Imre, A., Musselman, K. C., Voit, W. E., Modi, R., Pancrazio, J. J., et al. (2018) · Micromachines · doi:10.3390/mi9100500 · Cited by 56
Potter–Baker, K. A., Nguyen, J. K., Kovach, K. M., Gitomer, M. M., Srail, T. W., Stewart, W. G., Skousen, J. L., & Capadona, J. R. (2014) · Journal of Materials Chemistry B · doi:10.1039/c4tb00125g · Cited by 55
Malaga, K. A., Schroeder, K. E., Patel, P. R., Irwin, Z. T., Thompson, D. E., Bentley, J. N., Lempka, S. F., Chestek, C. A., & Patil, P. G. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/1/016010 · Cited by 55
O’Shea, D. J., Trautmann, E. M., Chandrasekaran, C., Stavisky, S. D., Kao, J. C., Sahani, M., Ryu, S. I., Deisseroth, K. A., & Shenoy, K. V. (2016) · Experimental Neurology · doi:10.1016/j.expneurol.2016.08.003 · Cited by 54
Chip-Scale Coils for Millimeter-Sized Bio-Implants
Feng, P., Yeon, P., Cheng, Y., Ghovanloo, M., & Constandinou, T. G. (2018) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2018.2853670 · Cited by 53
Continuous sensorimotor rhythm based brain computer interface learning in a large population
Stieger, J., Engel, S. A., & He, B. (2021) · Scientific Data · doi:10.1038/s41597-021-00883-1 · Cited by 65
Targeting CD14 on blood derived cells improves intracortical microelectrode performance
Bedell, H. W., Hermann, J. K., Ravikumar, M., Lin, S., Rein, A., Li, X., Molinich, E., Smith, P. D., Selkirk, S. M., Miller, R. H., et al. (2018) · Biomaterials · doi:10.1016/j.biomaterials.2018.02.014 · Cited by 51
Chen, K., Wellman, S. M., Yaxiaer, Y., Eles, J. R., & Kozai, T. D. Y. (2020) · Biomaterials · doi:10.1016/j.biomaterials.2020.120526 · Cited by 46
Lee, H. K., Yang, Y. J., Koirala, G. R., Oh, S., & Kim, T. (2024) · Biomaterials · doi:10.1016/j.biomaterials.2024.122632 · Cited by 53
A guide towards long-term functional electrodes interfacing neuronal tissue
Renz, A. F., Reichmuth, A. M., Stauffer, F., Thompson‐Steckel, G., & Vörös, J. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aae0c2 · Cited by 54
Potter–Baker, K. A., Stewart, W. G., Tomaszewski, W. H., Wong, C., Meador, W. D., Ziats, N. P., & Capadona, J. R. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/4/046002 · Cited by 49
Engineering strategies towards overcoming bleeding and glial scar formation around neural probes
Otte, E., Vlachos, A., & Asplund, M. (2022) · Cell and Tissue Research · doi:10.1007/s00441-021-03567-9 · Cited by 54
Lee, S., Thunemann, M., Lee, K., Cleary, D. R., Tonsfeldt, K. J., Oh, H., Azzazy, F., Tchoe, Y., Bourhis, A. M., Hossain, L., et al. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202112045 · Cited by 50
Wireless Battery-free and Fully Implantable Organ Interfaces
Bhatia, A., Hanna, J., Stuart, T., Kasper, K. A., Clausen, D. M., & Gutruf, P. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00425 · Cited by 50
In vivo Characterization of Amorphous Silicon Carbide As a Biomaterial for Chronic Neural Interfaces
Knaack, G. L., McHail, D. G., Borda, G., Koo, B., Peixoto, N., Cogan, S. F., Dumas, T. C., & Pancrazio, J. J. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00301 · Cited by 53
Acquisition of Neural Action Potentials Using Rapid Multiplexing Directly at the Electrodes
Sharma, M., Gardner, A. T., Strathman, H. J., Warren, D. J., Silver, J., & Walker, R. M. (2018) · Micromachines · doi:10.3390/mi9100477 · Cited by 52
Autotaxin and lysophospholipids in rheumatoid arthritis.
Bourgoin, S. G., & Zhao, C. (2010) · PubMed · Cited by 49
Patel, P. R., Welle, E. J., Letner, J. G., Shen, H., Bullard, A. J., Caldwell, C. M., Vega-Medina, A., Richie, J. M., Thayer, H. E., Patil, P. G., et al. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/acab86 · Cited by 49
Adversarial Domain Adaptation for Stable Brain-Machine Interfaces
Farshchian, A., Gallego, J. A., Cohen, J. P., Bengio, Y., Miller, L. E., & Solla, S. A. (2018) · arXiv · doi:10.48550/arxiv.1810.00045 · Cited by 48
In vivo spatiotemporal dynamics of astrocyte reactivity following neural electrode implantation
Savya, S. P., Li, F., Lam, S., Wellman, S. M., Stieger, K. C., Chen, K., Eles, J. R., & Kozai, T. D. Y. (2022) · Biomaterials · doi:10.1016/j.biomaterials.2022.121784 · Cited by 46
Long-term stability of sensitivity to intracortical microstimulation of somatosensory cortex
Callier, T., Schluter, E. W., Tabot, G. A., Miller, L. E., Tenore, F. V., & Bensmaı̈a, S. J. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/5/056010 · Cited by 48
Micro- and Nanotechnologies for Optical Neural Interfaces
Pisanello, F., Sileo, L., & Vittorio, M. D. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00070 · Cited by 49
Implantable optoelectronic probes for in vivo optogenetics
Iseri, E., & Kuzum, D. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa60b3 · Cited by 50
Szymanski, L. J., Kellis, S., Liu, C. Y., Jones, K. T., Andersen, R. A., Commins, D., Lee, B., McCreery, D. B., & Miller, C. A. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac127e · Cited by 48
Sommakia, S., Lee, H. C., Gaire, J., & Otto, K. J. (2014) · Current Opinion in Solid State and Materials Science · doi:10.1016/j.cossms.2014.07.005 · Cited by 48
Bedell, H. W., Schaub, N. J., Capadona, J. R., & Ereifej, E. S. (2019) · Acta Biomaterialia · doi:10.1016/j.actbio.2019.11.017 · Cited by 45
Wellman, S. M., Guzman, K., Stieger, K. C., Brink, L. E., Sridhar, S., Dubaniewicz, M. T., Li, L., Cambi, F., & Kozai, T. D. Y. (2020) · Biomaterials · doi:10.1016/j.biomaterials.2020.119842 · Cited by 44
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Physiological properties of brain-machine interface input signals
Slutzky, M. W., & Flint, R. D. (2017) · Journal of Neurophysiology · doi:10.1152/jn.00070.2017 · Cited by 47
Gels, jets, mosquitoes, and magnets: a review of implantation strategies for soft neural probes
Apollo, N. V., Murphy, B. B., Prezelski, K., Driscoll, N., Richardson, A. G., Lucas, T. H., & Vitale, F. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abacd7 · Cited by 47
Non-causal spike filtering improves decoding of movement intention for intracortical BCIs
Masse, N. Y., Jarosiewicz, B., Simeral, J. D., Bacher, D., Stavisky, S. D., Cash, S. S., Oakley, E. M., Berhanu, E., Eskandar, E. N., Friehs, G. M., et al. (2014) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2014.08.004 · Cited by 43
Self-citationA comparison of neuroinflammation to implanted microelectrodes in rat and mouse models
Potter–Baker, K. A., Ravikumar, M., Burke, A., Meador, W. D., Householder, K. T., Buck, A. C., Sunil, S., Stewart, W. G., Anna, J. P., Tomaszewski, W. H., et al. (2014) · Biomaterials · doi:10.1016/j.biomaterials.2014.03.076 · Cited by 45
Spike Train SIMilarity Space (SSIMS): A Framework for Single Neuron and Ensemble Data Analysis
Vargas-Irwin, C. E., Brandman, D. M., Zimmermann, J. B., Donoghue, J. P., & Black, M. J. (2014) · Neural Computation · doi:10.1162/neco_a_00684 · Cited by 44
Self-citationOakes, R. S., Polei, M. D., Skousen, J. L., & Tresco, P. A. (2017) · Biomaterials · doi:10.1016/j.biomaterials.2017.10.001 · Cited by 46
An ovine model of cerebral catheter venography for implantation of an endovascular neural interface
Oxley, T. J., Opie, N. L., Rind, G. S., Liyanage, K., John, S. E., Ronayne, S. M., McDonald, A. J., Dornom, A., Lovell, T. J. H., Mitchell, P. J., et al. (2017) · Journal of neurosurgery · doi:10.3171/2016.11.jns161754 · Cited by 43
Vargas-Irwin, C. E., Franquemont, L., Black, M. J., & Donoghue, J. P. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.1574-15.2015 · Cited by 44
Self-citationWellman, S. M., Cambi, F., & Kozai, T. D. Y. (2018) · Biomaterials · doi:10.1016/j.biomaterials.2018.08.046 · Cited by 43
Ceramic packaging in neural implants
Shen, K., & Maharbiz, M. M. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abd683 · Cited by 47
Yang, Q., Wu, B., Eles, J. R., Vazquez, A. L., Kozai, T. D. Y., & Cui, X. T. (2020) · Advanced Biosystems · doi:10.1002/adbi.201900287 · Cited by 43
Flexible high-density microelectrode arrays for closed-loop brain–machine interfaces: a review
Liu, X., Gong, Y., Jiang, Z., Stevens, T., & Li, W. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1348434 · Cited by 44
Leveraging neural dynamics to extend functional lifetime of brain-machine interfaces
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Monolayer Graphene Coating of Intracortical Probes for Long‐Lasting Neural Activity Monitoring
Bourrier, A., Shkorbatova, P. Y., Bonizzato, M., Rey, E., Barraud, Q., Courtine, G., Othmen, R., Reita, V., Bouchiat, V., & Delacour, C. (2019) · Advanced Healthcare Materials · doi:10.1002/adhm.201801331 · Cited by 43
Fernández, E., Alfaro, A., & González-López, P. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00681 · Cited by 35
Yeon, P., Mirbozorgi, S. A., Ash, B., Eckhardt, H., & Ghovanloo, M. (2016) · Micromachines · doi:10.3390/mi7090154 · Cited by 40
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Bouton, C. E. (2017) · Journal of Internal Medicine · doi:10.1111/joim.12610 · Cited by 40
Neurophysiology and neural engineering: a review
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Lee, H. C., Gaire, J., Currlin, S., McDermott, M. D., Park, K., & Otto, K. J. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00513 · Cited by 40
Heelan, C., Lee, J.-H., O’Shea, R. T., Lynch, L., Brandman, D. M., Truccolo, W., & Nurmikko, A. V. (2019) · Communications Biology · doi:10.1038/s42003-019-0707-9 · Cited by 40
Thompson, C. H., Riggins, T. E., Patel, P. R., Chestek, C. A., Li, W., & Purcell, E. K. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab7030 · Cited by 38
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González‐González, M. A., Conde, S. V., Latorre, R., Thébault, S. C., Pratelli, M., Spitzer, N. C., Verkhratsky, A., Tremblay, M., Akçora, C. G., Hernandez-Reynoso, A. G., et al. (2024) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2024.1321872 · Cited by 47
Engineering and commercialization of human-device interfaces, from bone to brain
Tate, M. L. K., Detamore, M. S., Capadona, J. R., Woolley, A. J., & Knothe, U. R. (2016) · Biomaterials · doi:10.1016/j.biomaterials.2016.03.038 · Cited by 40
Eles, J. R., Vazquez, A. L., Kozai, T. D. Y., & Cui, X. T. (2018) · Biomaterials · doi:10.1016/j.biomaterials.2018.12.031 · Cited by 41
Stieglitz, T. (2019) · Neuroethics · doi:10.1007/s12152-019-09406-7 · Cited by 42
Neural Prosthetics: A Review of Empirical vs. Systems Engineering Strategies
Loeb, G. E. (2018) · Applied Bionics and Biomechanics · doi:10.1155/2018/1435030 · Cited by 38
Long-term stability of the chronic epidural wireless recorder WIMAGINE in tetraplegic patients
Larzabal, C., Bonnet, S., Costecalde, T., Auboiroux, V., Charvet, G., Chabardes, S., Aksenova, T., & Sauter-Starace, F. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac2003 · Cited by 37
Vasudevan, S., Patel, K., & Welle, C. G. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2552/14/1/016008 · Cited by 38
Hermann, J. K., Ravikumar, M., Shoffstall, A. J., Ereifej, E. S., Kovach, K. M., Chang, J. B., Soffer, A., Wong, C., Ali, A., Smith, P., et al. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aaa03e · Cited by 38
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Feng, Z., Sun, Y., Qian, L., Qi, Y., Wang, Y., Guan, C., & Sun, Y. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3115799 · Cited by 37
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Fang, J., Huang, S., Liu, F., He, G., Li, X., Huang, X., Chen, H., & Xie, X. (2022) · Nano-Micro Letters · doi:10.1007/s40820-022-00818-4 · Cited by 37
Brain-controlled muscle stimulation for the restoration of motor function
Éthier, C., & Miller, L. E. (2014) · Neurobiology of Disease · doi:10.1016/j.nbd.2014.10.014 · Cited by 40
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Wang, L., Jiang, K., & Shen, G. (2021) · Applied Physics Letters · doi:10.1063/5.0057020 · Cited by 36
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Yeon, P., Bakir, M. S., & Ghovanloo, M. (2018) · Custom Integrated Circuits Conference · doi:10.1109/cicc.2018.8357048 · Cited by 36
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Beygi, M., Bentley, J. T., Frewin, C. L., Kuliasha, C. A., Takshi, A. I., Bernardin, E. K., Via, F. L., & Saddow, S. E. (2019) · Micromachines · doi:10.3390/mi10070430 · Cited by 41
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King, B. J., Read, G. J. M., & Salmon, P. M. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2111041 · Cited by 36
Caldwell, R., Mandal, H. S., Sharma, R., Solzbacher, F., Tathireddy, P., & Rieth, L. W. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa69d3 · Cited by 36
Rosenfeld, J. V., Wong, Y. T., Yan, E., Szlawski, J., Mohan, A., Clark, J. C., Rosa, M., & Lowery, A. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab9e1c · Cited by 35
Chronic monitoring of lower urinary tract activity via a sacral dorsal root ganglia interface
Khurram, A., Ross, S. E., Sperry, Z. J., Ouyang, Z., Stephan, C., Jiman, A. A., & Bruns, T. M. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa6801 · Cited by 34
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Straka, M., Shafer, B., Vasudevan, S., Welle, C. G., & Rieth, L. W. (2018) · Micromachines · doi:10.3390/mi9110587 · Cited by 35
Potter–Baker, K. A., & Capadona, J. R. (2015) · ACS Macro Letters · doi:10.1021/mz500743a · Cited by 35
From softening polymers to multimaterial based bioelectronic devices
Ecker, M., Joshi-Imre, A., Modi, R., Frewin, C. L., Garcia-Sandoval, A., Maeng, J., Gutierrez-Heredia, G., Pancrazio, J. J., & Voit, W. E. (2019) · Multifunctional Materials · doi:10.1088/2399-7532/aaed58 · Cited by 34
Chronic stability of a neuroprosthesis comprising multiple adjacent Utah arrays in monkeys
Chen, X., Wang, F., Kooijmans, R. N., Klink, P. C., Boehler, C., Asplund, M., & Roelfsema, P. R. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/ace07e · Cited by 45
Gao, W., Yan, Z., Zhou, H., Xie, Y., Wang, H., Yang, J., Yu, J., Ni, C., Liu, P., Xie, M., et al. (2025) · Journal of Nanobiotechnology · doi:10.1186/s12951-025-03573-x · Cited by 38
Microfabricated intracortical extracellular matrix-microelectrodes for improving neural interfaces
Shen, W., Das, S., Vitale, F., Richardson, A. G., Ananthakrishnan, A., Struzyna, L. A., Brown, D. P., Song, N., Ramkumar, M., Lucas, T. H., et al. (2018) · Microsystems & Nanoengineering · doi:10.1038/s41378-018-0030-5 · Cited by 37
Metadata
- Authors
- Barrese, J. C., Rao, N., Paroo, K., Triebwasser, C., Vargas-Irwin, C., Franquemont, L., & Donoghue, J. P.
- Affiliations
- Brown University (US)
- Corresponding author
- Barrese, James C
- Rao, Naveen
- Paroo, Kaivon
- Triebwasser, Corey
- Vargas-Irwin, Carlos
- Franquemont, Lachlan
- Donoghue, John P
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2013-11-12
- Volume
- 10
- Issue
- 6
- Pages
- 066014
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- PMC
- PMC4868924
- OpenAlex
- W1999169980
- Cited by
- 600
- References
- 42
- Citations by year
- 2026: 39 · 2025: 40 · 2024: 43 · 2023: 49 · 2022: 38 · 2021: 63 · 2020: 68 · 2019: 53 · 2018: 67 · 2017: 47 · 2016: 34 · 2015: 28 · 2014: 28 · 2013: 1
- FWCI
- 14.88
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS025074)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Analog and Mixed-Signal Circuit Design
- Keywords
- intracortical microelectrode arrays, failure mode analysis, brain-computer interface, chronic implantation, non-human primates, rhesus monkeys, silicon microelectrodes, electrode impedance, meningeal reaction, connector failure, neuroprosthetics, chronic neural recording, recording duration
- MeSH terms
- Animals, Cerebral Cortex, Electrodes, Implanted, Female, Macaca mulatta, Male, Microelectrodes, Silicon, Equipment Failure Analysis
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