Time stability and coherence analysis of multiunit, single-unit and local field potential neuronal signals in chronically implanted brain electrodes.
Open accessAbstract
Introducing neural sensing and decoding to open-loop neurostimulation technologies has the potential to significantly improve the diagnosis and treatment of a wide variety of diseases treated through bioelectronic medicine. Chronically implanted multi-electrode arrays (MEA) can be used for such neural sensing and are critical for obtaining data of high spatial and temporal resolution to provide accurate decoding. Signals recorded from these arrays include local field potentials (LFP), and multiunit (MU) and single-unit (SU) activity. LFP offer signal stability over time, but at the expense of decreased spatial resolution. SU activity, on the other hand, offers better spatial resolution, but is considered less stable in chronic applications. MU activity, which represents an aggregate spiking activity of a population of neurons on the order of several hundred microns away from the recording tip, is considered a signal that can offer a compromise between the two signals. Here we used a wavelet decomposition method to extract and characterize the LFP, MU and SU signals obtained from a 96-channel MEA implanted in the motor cortex of a nonhuman primate over a 7.5-month period. We observed that not only are the MU signals more stable over time compared with SU activity, but that they are also significantly less correlated among electrodes compared with LFP over the spatial scale of the implanted array. Histological analysis of tissue sections also revealed a 51% reduction in the number of neuronal cell bodies within a radius around the electrode tips of the implanted tissue compared with control tissue. Our results indicate that MU activity offers long-term signal stability with less correlated signals, potentially providing an effective signal for neural sensing in bioelectronic medicine.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The number of reliable spikes captured by intracortical electrodes decreases with time.
“Some studies show that the number of reliable spikes captured by intracortical electrodes decreases with time (Sharma et al., 2015)”
From Toward a personalized closed-loop stimulation of the visual cortex: Advances and challenges (2022) · cites this paper as Sharma et al., 2015
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Daubechies, I. C., & Heil, C. (1992) · Computers in Physics · doi:10.1063/1.4823127 · Cited by 15,729 · see citing works
Response 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 · see citing works
Large-scale recording of neuronal ensembles
Buzsáki, G. (2004) · Nature Neuroscience · doi:10.1038/nn1233 · Cited by 1,875 · see citing works
Biran, R., Martin, D. C., & Tresco, P. A. (2005) · Experimental Neurology · doi:10.1016/j.expneurol.2005.04.020 · Cited by 893 · see citing works
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 · see citing works
Schwartz, A. B. (2004) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.27.070203.144233 · Cited by 560 · see citing works
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 · see citing works
Suner, S., Fellows, M. R., Vargas-Irwin, C., Nakata, G. K., & Donoghue, J. P. (2005) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2005.857687 · Cited by 415 · see citing works
Williams, J. C., Rennaker, R. L., & Kipke, D. R. (1999) · Brain Research Protocols · doi:10.1016/s1385-299x(99)00034-3 · Cited by 396 · see citing works
Cortical Local Field Potential Encodes Movement Intentions in the Posterior Parietal Cortex
Scherberger, H., Jarvis, M. R., & Andersen, R. A. (2005) · Neuron · doi:10.1016/j.neuron.2005.03.004 · Cited by 389 · see citing works
Kipke, D. R., Vetter, R. J., Williams, J. C., & Hetke, J. F. (2003) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2003.814443 · Cited by 364 · see citing works
Wavelet Analysis of Neuroelectric Waveforms: A Conceptual Tutorial
Samar, V. J., Bopardikar, A. S., Rao, R., & Swartz, K. P. (1999) · Brain and Language · doi:10.1006/brln.1998.2024 · Cited by 355 · see citing works
Selecting the signals for a brain–machine interface
Andersen, R. A., Musallam, S., & Pesaran, B. (2004) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2004.10.005 · Cited by 329 · see citing works
Predicting Movement from Multiunit Activity
Stark, E., & Abeles, M. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.1321-07.2007 · Cited by 299 · see citing works
Liu, X., McCreery, D. B., Carter, R. R., Bullara, L. A., Yuen, T. G. H., & Agnew, W. F. (1999) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.788468 · Cited by 272 · see citing works
Potter, K. A., Buck, A. C., Self, W. K., & Capadona, J. R. (2012) · Journal of Neural Engineering · doi:10.1088/1741-2560/9/4/046020 · Cited by 239 · see citing works
Diffusion barriers evoked in the rat cortex by reactive astrogliosis
Roitbak, T., & Sykov�, E. (1999) · Glia · doi:10.1002/(sici)1098-1136(199910)28:1 · Cited by 208 · see citing works
Bansal, A. K., Truccolo, W., Vargas-Irwin, C. E., & Donoghue, J. P. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00781.2011 · Cited by 200 · see citing works
Quantifying long-term microelectrode array functionality using chronic in vivo impedance testing
Prasad, A., & Sanchez, J. C. (2012) · Journal of Neural Engineering · doi:10.1088/1741-2560/9/2/026028 · Cited by 157 · see citing works
Long-term chronic recording from cortical neurons
Schmidt, E. M., Bak, M. J., & McIntosh, J. S. (1976) · Experimental Neurology · doi:10.1016/0014-4886(76)90220-x · Cited by 129 · see citing works
Farina, D., do Nascimento, O. F., Lucas, M.-F., & Doncarli, C. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.01.011 · Cited by 108 · see citing works
Freire, M. A. M., Morya, E., Faber, J., dos Santos, J. R., Guimarães, J. S., Lemos, N. A. M., Sameshima, K., Pereira, A., Ribeiro, S. T. G., & Nicolelis, M. A. L. (2011) · PLoS ONE · doi:10.1371/journal.pone.0027554 · Cited by 97 · see citing works
Stimulation of primary motor cortex for intractable deafferentation pain
Saitoh, Y., Hirayama, A., Kishima, H., Oshino, S., Hirata, M., Kato, A., & Yoshimine, T. (2007) · Springer eBooks · doi:10.1007/978-3-211-35205-2_11 · Cited by 87 · see citing works
Role of the primary motor cortex in the maintenance and treatment of pain in fibromyalgia
Saavedra, L. C., de Mendonça, M. E., & Fregni, F. (2014) · Medical Hypotheses · doi:10.1016/j.mehy.2014.06.007 · Cited by 82 · see citing works
Wavelet Methods for Spike Detection in Mouse Renal Sympathetic Nerve Activity
Brychta, R. J., Tuntrakool, S., Appalsamy, M., Keller, N. R., Robertson, D. J., Shiavi, R., & Diedrich, A. (2006) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.883830 · Cited by 81 · see citing works
Single-trial evoked potential estimation using wavelets
Wang, Z., Maier, A., Leopold, D. A., et al. (2006) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2006.08.011 · Cited by 74 · see citing works
Stimulation of primary motor cortex for intractable deafferentation pain
Saitoh, Y., & Yoshimine, T. (2007) · Springer eBooks · doi:10.1007/978-3-211-33081-4_6 · Cited by 72 · see citing works
The effect of chronic intracortical microstimulation on the electrode–tissue interface
Chen, K. H., Dammann, J. F., Boback, J. L., Tenore, F. V., Otto, K. J., Gaunt, R. A., & Bensmaia, S. J. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/2/026004 · Cited by 59 · see citing works
Quantifying Time-Varying Multiunit Neural Activity Using Entropy-Based Measures
Choi, Y.-S., Koenig, M. A., Jia, X., & Thakor, N. V. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2049266 · Cited by 39 · see citing works
Wavelet Transform for Real-Time Detection of Action Potentials in Neural Signals
Quotb, A., Bornat, Y., & Renaud, S. (2011) · Frontiers in Neuroengineering · doi:10.3389/fneng.2011.00007 · Cited by 35 · see citing works
Romcy-Pereira, R. N., de Araújo, D. B., Leite, J. P., et al. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.08.027 · Cited by 22 · see citing works
Long-term, stable behavior of local field potentials during brain machine interface use
Scheid, M. R., Flint, R. D., Wright, Z. A., et al. (2013) · doi:10.1109/embc.2013.6609498 · Cited by 14 · see citing works
Neural Decoding and Applications in Bioelectronic Medicine
Bouton, C. E. (2015) · Bioelectronic Medicine · doi:10.15424/bioelectronmed.2014.00012 · Cited by 14 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- DeYoe EA. (1983) An investigation in the awake macaque of the threshold for the detection of electri- cal currents applied to striate cortex: psychophysical properties and laminar differences [dissertation]. Rochester (NY): University of Rochester.
Cited by
Sorted by citations
Restoring cortical control of functional movement in a human with quadriplegia
Bouton, C. E., Shaikhouni, A., Annetta, N. V., Bockbrader, M. A., Friedenberg, D. A., Nielson, D. M., Sharma, G., Sederberg, P. B., Glenn, B. C., Mysiw, W. J., et al. (2016) · Nature · doi:10.1038/nature17435 · Cited by 924 · see citing works
Self-citationRobust and accurate decoding of hand kinematics from entire spiking activity using deep learning
Ahmadi, N., Constandinou, T. G., & Bouganis, C.-S. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abde8a · Cited by 69 · see citing works
Toward a personalized closed-loop stimulation of the visual cortex: Advances and challenges
Grani, F., Soto-Sánchez, C., Fimia, A., & Fernández, E. (2022) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2022.1034270 · Cited by 16 · see citing works
- Authors
- Sharma, G., Annetta, N., Friedenberg, D., Blanco, T., Vasconcelos, D., Shaikhouni, A., Rezai, A. R., & Bouton, C.
- Affiliations
- Battelle (US)
- The Ohio State University (US)
- Corresponding author
- Sharma, Gaurav
- Bouton, Chad
- Venue
- Bioelectronic Medicine
- Published
- 2015-06
- Volume
- 2
- Issue
- 1
- Pages
- 63-71
- ISSN
- 2332-8886 (ISSN-L)
- OpenAlex
- W2898282183
- Cited by
- 41
- References
- 33
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 9 · 2022: 3 · 2021: 5 · 2020: 2 · 2019: 3 · 2018: 7 · 2017: 2 · 2016: 5
- FWCI
- 2.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- creativecommons.org/licenses/by/4.0 (tdm)
- Locations
- Bioelectronic Medicine (publishedVersion, cc-by-nc-nd)
- Funders
- Battelle
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Health Sciences › Medicine › Neurology › Neurological disorders and treatments
- Keywords
- multi-unit activity, single-unit activity, local field potentials, signal stability, temporal stability, coherence analysis, wavelet decomposition, motor cortex, non-human primates, neural decoding
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review