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Replay of Learned Neural Firing Sequences during Rest in Human Motor Cortex. Cell Rep 2021; 31:107581. [PMID: 32375031 PMCID: PMC7337233 DOI: 10.1016/j.celrep.2020.107581] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 03/13/2020] [Accepted: 04/07/2020] [Indexed: 11/24/2022] Open
Abstract
The offline “replay” of neural firing patterns underlying waking experience, previously observed in non-human animals, is thought to be a mechanism for memory consolidation. Here, we test for replay in the human brain by recording spiking activity from the motor cortex of two participants who had intracortical microelectrode arrays placed chronically as part of a brain-computer interface pilot clinical trial. Participants took a nap before and after playing a neurally controlled sequence-copying game that consists of many repetitions of one “repeated” sequence sparsely interleaved with varying “control” sequences. Both participants performed repeated sequences more accurately than control sequences, consistent with learning. We compare the firing rate patterns that caused the cursor movements when performing each sequence to firing rate patterns throughout both rest periods. Correlations with repeated sequences increase more from pre- to post-task rest than do correlations with control sequences, providing direct evidence of learning-related replay in the human brain. Eichenlaub et al. show that in the motor cortex of brain-computer interface trial participants, the firing rate patterns corresponding to a previously learned motor sequence are replayed during rest. These findings provide direct evidence of memory replay in the human brain.
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2
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Hosman T, Hynes JB, Saab J, Wilcoxen KG, Buchbinder BR, Schmansky N, Cash SS, Eskandar EN, Simeral JD, Franco B, Kelemen J, Vargas-Irwin CE, Hochberg LR. Auditory cues reveal intended movement information in middle frontal gyrus neuronal ensemble activity of a person with tetraplegia. Sci Rep 2021; 11:98. [PMID: 33431994 PMCID: PMC7801741 DOI: 10.1038/s41598-020-77616-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 11/12/2020] [Indexed: 01/29/2023] Open
Abstract
Intracortical brain-computer interfaces (iBCIs) allow people with paralysis to directly control assistive devices using neural activity associated with the intent to move. Realizing the full potential of iBCIs critically depends on continued progress in understanding how different cortical areas contribute to movement control. Here we present the first comparison between neuronal ensemble recordings from the left middle frontal gyrus (MFG) and precentral gyrus (PCG) of a person with tetraplegia using an iBCI. As expected, PCG was more engaged in selecting and generating intended movements than in earlier perceptual stages of action planning. By contrast, MFG displayed movement-related information during the sensorimotor processing steps preceding the appearance of the action plan in PCG, but only when the actions were instructed using auditory cues. These results describe a previously unreported function for neurons in the human left MFG in auditory processing contributing to motor control.
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Affiliation(s)
- Tommy Hosman
- School of Engineering, Brown University, Providence, RI, USA
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA
- Center for Neurorestoration and Neurotechnology, Rehabilitation Research and Development Service, Department of Veterans Affairs Medical Center, Providence, RI, USA
| | - Jacqueline B Hynes
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA
- Department of Neuroscience, Brown University, Providence, RI, USA
| | - Jad Saab
- School of Engineering, Brown University, Providence, RI, USA
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA
- Center for Neurorestoration and Neurotechnology, Rehabilitation Research and Development Service, Department of Veterans Affairs Medical Center, Providence, RI, USA
| | - Kaitlin G Wilcoxen
- Neuroscience Graduate Program, Brown University, Providence, RI, USA
- Center for Neurorestoration and Neurotechnology, Rehabilitation Research and Development Service, Department of Veterans Affairs Medical Center, Providence, RI, USA
| | | | - Nicholas Schmansky
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Sydney S Cash
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Emad N Eskandar
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurosurgery, Albert Einstein College of Medicine, Montefiore Medical Center, New York, NY, USA
| | - John D Simeral
- School of Engineering, Brown University, Providence, RI, USA
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA
- Center for Neurorestoration and Neurotechnology, Rehabilitation Research and Development Service, Department of Veterans Affairs Medical Center, Providence, RI, USA
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Brian Franco
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- NeuroPace, Inc., Mountain View, CA, USA
| | - Jessica Kelemen
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Carlos E Vargas-Irwin
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA.
- Department of Neuroscience, Brown University, Providence, RI, USA.
- Center for Neurorestoration and Neurotechnology, Rehabilitation Research and Development Service, Department of Veterans Affairs Medical Center, Providence, RI, USA.
| | - Leigh R Hochberg
- School of Engineering, Brown University, Providence, RI, USA.
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA.
- Center for Neurorestoration and Neurotechnology, Rehabilitation Research and Development Service, Department of Veterans Affairs Medical Center, Providence, RI, USA.
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
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Diomedi S, Vaccari FE, Filippini M, Fattori P, Galletti C. Mixed Selectivity in Macaque Medial Parietal Cortex during Eye-Hand Reaching. iScience 2020; 23:101616. [PMID: 33089104 PMCID: PMC7559278 DOI: 10.1016/j.isci.2020.101616] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 06/18/2020] [Accepted: 09/23/2020] [Indexed: 01/07/2023] Open
Abstract
The activity of neurons of the medial posterior parietal area V6A in macaque monkeys is modulated by many aspects of reach task. In the past, research was mostly focused on modulating the effect of single parameters upon the activity of V6A cells. Here, we used Generalized Linear Models (GLMs) to simultaneously test the contribution of several factors upon V6A cells during a fix-to-reach task. This approach resulted in the definition of a representative “functional fingerprint” for each neuron. We first studied how the features are distributed in the population. Our analysis highlighted the virtual absence of units strictly selective for only one factor and revealed that most cells are characterized by “mixed selectivity.” Then, exploiting our GLM framework, we investigated the dynamics of spatial parameters encoded within V6A. We found that the tuning is not static, but changed along the trial, indicating the sequential occurrence of visuospatial transformations helpful to guide arm movement. The parietal cortex integrates a variety of sensorimotor inputs to guide reaching GLM disentangled the effect of various reaching parameters upon cell activity V6A neurons were not functionally clustered, but characterized by mixed selectivity Spatial selectivity was dynamic and reached its peak during the movement phase
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Affiliation(s)
- Stefano Diomedi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Francesco E. Vaccari
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Matteo Filippini
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Corresponding author
| | - Patrizia Fattori
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Corresponding author
| | - Claudio Galletti
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
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Burkhart MC, Brandman DM, Franco B, Hochberg LR, Harrison MT. The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models. Neural Comput 2020; 32:969-1017. [PMID: 32187000 PMCID: PMC8259355 DOI: 10.1162/neco_a_01275] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
The Kalman filter provides a simple and efficient algorithm to compute the posterior distribution for state-space models where both the latent state and measurement models are linear and gaussian. Extensions to the Kalman filter, including the extended and unscented Kalman filters, incorporate linearizations for models where the observation model p ( observation | state ) is nonlinear. We argue that in many cases, a model for p ( state | observation ) proves both easier to learn and more accurate for latent state estimation. Approximating p ( state | observation ) as gaussian leads to a new filtering algorithm, the discriminative Kalman filter (DKF), which can perform well even when p ( observation | state ) is highly nonlinear and/or nongaussian. The approximation, motivated by the Bernstein-von Mises theorem, improves as the dimensionality of the observations increases. The DKF has computational complexity similar to the Kalman filter, allowing it in some cases to perform much faster than particle filters with similar precision, while better accounting for nonlinear and nongaussian observation models than Kalman-based extensions. When the observation model must be learned from training data prior to filtering, off-the-shelf nonlinear and nonparametric regression techniques can provide a gaussian model for p ( observation | state ) that cleanly integrates with the DKF. As part of the BrainGate2 clinical trial, we successfully implemented gaussian process regression with the DKF framework in a brain-computer interface to provide real-time, closed-loop cursor control to a person with a complete spinal cord injury. In this letter, we explore the theory underlying the DKF, exhibit some illustrative examples, and outline potential extensions.
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Affiliation(s)
- Michael C Burkhart
- Division of Applied Mathematics, Brown University, Providence, RI 02912, U.S.A.
| | - David M Brandman
- Department of Neuroscience, Brown University, Providence, RI 02912, U.S.A., and Department of Surgery (Neurosurgery), Dalhousie University, Halifax, NS, B3H 4R2, Canada
| | - Brian Franco
- Center for Neurotechnology and Neurorecovery, Neurology, Massachusetts General Hospital, Boston, MA 02114, U.S.A.
| | - Leigh R Hochberg
- Center for Neurotechnology and Neurorecovery, Neurology, Massachusetts General Hospital, Boston, MA 02114, U.S.A.; School of Engineering and Carney Institute for Brain Science, Brown University, Providence, RI 02912, U.S.A.; Neurology, Harvard Medical School, Boston, MA 02115, U.S.A.; and VA RR&D Center for Neurorestoration and Neurotechnology, Providence Veterans Affairs Medical Center, Providence, RI 02908, U.S.A.
| | - Matthew T Harrison
- Division of Applied Mathematics, Brown University, Providence, RI 02912, U.S.A.
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Bullard AJ, Hutchison BC, Lee J, Chestek CA, Patil PG. Estimating Risk for Future Intracranial, Fully Implanted, Modular Neuroprosthetic Systems: A Systematic Review of Hardware Complications in Clinical Deep Brain Stimulation and Experimental Human Intracortical Arrays. Neuromodulation 2019; 23:411-426. [DOI: 10.1111/ner.13069] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Revised: 08/05/2019] [Accepted: 09/10/2019] [Indexed: 01/08/2023]
Affiliation(s)
- Autumn J. Bullard
- Department of Biomedical Engineering University of Michigan Ann Arbor MI USA
| | | | - Jiseon Lee
- Department of Biomedical Engineering University of Michigan Ann Arbor MI USA
| | - Cynthia A. Chestek
- Department of Biomedical Engineering University of Michigan Ann Arbor MI USA
- Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA
| | - Parag G. Patil
- Department of Biomedical Engineering University of Michigan Ann Arbor MI USA
- Department of Neurosurgery University of Michigan Medical School Ann Arbor MI USA
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Brandman DM, Hosman T, Saab J, Burkhart MC, Shanahan BE, Ciancibello JG, Sarma AA, Milstein DJ, Vargas-Irwin CE, Franco B, Kelemen J, Blabe C, Murphy BA, Young DR, Willett FR, Pandarinath C, Stavisky SD, Kirsch RF, Walter BL, Bolu Ajiboye A, Cash SS, Eskandar EN, Miller JP, Sweet JA, Shenoy KV, Henderson JM, Jarosiewicz B, Harrison MT, Simeral JD, Hochberg LR. Rapid calibration of an intracortical brain-computer interface for people with tetraplegia. J Neural Eng 2019; 15:026007. [PMID: 29363625 DOI: 10.1088/1741-2552/aa9ee7] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
OBJECTIVE Brain-computer interfaces (BCIs) can enable individuals with tetraplegia to communicate and control external devices. Though much progress has been made in improving the speed and robustness of neural control provided by intracortical BCIs, little research has been devoted to minimizing the amount of time spent on decoder calibration. APPROACH We investigated the amount of time users needed to calibrate decoders and achieve performance saturation using two markedly different decoding algorithms: the steady-state Kalman filter, and a novel technique using Gaussian process regression (GP-DKF). MAIN RESULTS Three people with tetraplegia gained rapid closed-loop neural cursor control and peak, plateaued decoder performance within 3 min of initializing calibration. We also show that a BCI-naïve user (T5) was able to rapidly attain closed-loop neural cursor control with the GP-DKF using self-selected movement imagery on his first-ever day of closed-loop BCI use, acquiring a target 37 s after initiating calibration. SIGNIFICANCE These results demonstrate the potential for an intracortical BCI to be used immediately after deployment by people with paralysis, without the need for user learning or extensive system calibration.
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Affiliation(s)
- David M Brandman
- Neuroscience Graduate Program, Brown University, Providence, RI, United States of America. Department of Neuroscience, Brown University, Providence, RI, United States of America. Brown Institute for Brain Science, Brown University, Providence, RI, United States of America. Department of Surgery (Neurosurgery), Dalhousie University, Halifax, NS, Canada
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Brandman DM, Burkhart MC, Kelemen J, Franco B, Harrison MT, Hochberg LR. Robust Closed-Loop Control of a Cursor in a Person with Tetraplegia using Gaussian Process Regression. Neural Comput 2018; 30:2986-3008. [PMID: 30216140 DOI: 10.1162/neco_a_01129] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Intracortical brain computer interfaces can enable individuals with paralysis to control external devices through voluntarily modulated brain activity. Decoding quality has been previously shown to degrade with signal nonstationarities-specifically, the changes in the statistics of the data between training and testing data sets. This includes changes to the neural tuning profiles and baseline shifts in firing rates of recorded neurons, as well as nonphysiological noise. While progress has been made toward providing long-term user control via decoder recalibration, relatively little work has been dedicated to making the decoding algorithm more resilient to signal nonstationarities. Here, we describe how principled kernel selection with gaussian process regression can be used within a Bayesian filtering framework to mitigate the effects of commonly encountered nonstationarities. Given a supervised training set of (neural features, intention to move in a direction)-pairs, we use gaussian process regression to predict the intention given the neural data. We apply kernel embedding for each neural feature with the standard radial basis function. The multiple kernels are then summed together across each neural dimension, which allows the kernel to effectively ignore large differences that occur only in a single feature. The summed kernel is used for real-time predictions of the posterior mean and variance under a gaussian process framework. The predictions are then filtered using the discriminative Kalman filter to produce an estimate of the neural intention given the history of neural data. We refer to the multiple kernel approach combined with the discriminative Kalman filter as the MK-DKF. We found that the MK-DKF decoder was more resilient to nonstationarities frequently encountered in-real world settings yet provided similar performance to the currently used Kalman decoder. These results demonstrate a method by which neural decoding can be made more resistant to nonstationarities.
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Affiliation(s)
- David M Brandman
- Neuroscience Graduate Program, Department of Neuroscience, Carney Institute for Brain Science, and School of Engineering, Brown University, Providence, RI 02912, U.S.A.; and Department of Surgery (Neurosurgery), Dalhousie University, Halifax, NS B3H 347 Canada
| | - Michael C Burkhart
- Division of Applied Mathematics, Brown University, Providence, RI 02912, U.S.A.
| | - Jessica Kelemen
- Center for Neurotechnology and Neurorecovery, Neurology, Massachusetts General Hospital, Boston, MA 02114, U.S.A.
| | - Brian Franco
- Center for Neurotechnology and Neurorecovery, Neurology, Massachusetts General Hospital, Boston, MA 02114, U.S.A.
| | - Matthew T Harrison
- Division of Applied Mathematics, Brown University, Providence, RI 02912, U.S.A.
| | - Leigh R Hochberg
- Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs Medical Center, Providence, RI 02908; Carney Institute for Brain Science and School of Engineering, Brown University, Providence, RI 02912; Center for Neurotechnology and Neurorecovery, Neurology, Massachusetts General Hospital, Boston, MA 02114; and Neurology, Harvard Medical School, Boston, MA 02115, U.S.A.
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Jarosiewicz B, Sarma AA, Saab J, Franco B, Cash SS, Eskandar EN, Hochberg LR. Retrospectively supervised click decoder calibration for self-calibrating point-and-click brain-computer interfaces. JOURNAL OF PHYSIOLOGY, PARIS 2016; 110:382-391. [PMID: 28286237 PMCID: PMC5591042 DOI: 10.1016/j.jphysparis.2017.03.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2016] [Revised: 12/20/2016] [Accepted: 03/01/2017] [Indexed: 11/25/2022]
Abstract
Brain-computer interfaces (BCIs) aim to restore independence to people with severe motor disabilities by allowing control of acursor on a computer screen or other effectors with neural activity. However, physiological and/or recording-related nonstationarities in neural signals can limit long-term decoding stability, and it would be tedious for users to pause use of the BCI whenever neural control degrades to perform decoder recalibration routines. We recently demonstrated that a kinematic decoder (i.e. a decoder that controls cursor movement) can be recalibrated using data acquired during practical point-and-click control of the BCI by retrospectively inferring users' intended movement directions based on their subsequent selections. Here, we extend these methods to allow the click decoder to also be recalibrated using data acquired during practical BCI use. We retrospectively labeled neural data patterns as corresponding to "click" during all time bins in which the click log-likelihood (decoded using linear discriminant analysis, or LDA) had been above the click threshold that was used during real-time neural control. We labeled as "non-click" those periods that the kinematic decoder's retrospective target inference (RTI) heuristics determined to be consistent with intended cursor movement. Once these neural activity patterns were labeled, the click decoder was calibrated using standard supervised classifier training methods. Combined with real-time bias correction and baseline firing rate tracking, this set of "retrospectively labeled" decoder calibration methods enabled a BrainGate participant with amyotrophic lateral sclerosis (T9) to type freely across 11 research sessions spanning 29days, maintaining high-performance neural control over cursor movement and click without needing to interrupt virtual keyboard use for explicit calibration tasks. By eliminating the need for tedious calibration tasks with prescribed targets and pre-specified click times, this approach advances the potential clinical utility of intracortical BCIs for individuals with severe motor disability.
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Affiliation(s)
- Beata Jarosiewicz
- Neuroscience, Brown University, Providence, RI 02912, United States; Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Veterans Affairs Medical Center, Providence, RI 02908, United States; Brown Institute for Brain Science, Brown University, Providence, RI 02912, United States.
| | - Anish A Sarma
- Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Veterans Affairs Medical Center, Providence, RI 02908, United States; Brown Institute for Brain Science, Brown University, Providence, RI 02912, United States; School of Engineering, Brown University, Providence, RI 02912, United States
| | - Jad Saab
- School of Engineering, Brown University, Providence, RI 02912, United States; Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Veterans Affairs Medical Center, Providence, RI 02908, United States; Brown Institute for Brain Science, Brown University, Providence, RI 02912, United States
| | - Brian Franco
- Neurology, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Sydney S Cash
- Neurology, Massachusetts General Hospital, Boston, MA 02114, United States; Neurology, Harvard Medical School, Boston, MA 02115, United States
| | - Emad N Eskandar
- Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, United States; Neuroscience, Harvard Medical School, Boston, MA 02115, United States
| | - Leigh R Hochberg
- Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Veterans Affairs Medical Center, Providence, RI 02908, United States; Brown Institute for Brain Science, Brown University, Providence, RI 02912, United States; School of Engineering, Brown University, Providence, RI 02912, United States; Neurology, Massachusetts General Hospital, Boston, MA 02114, United States; Neurology, Harvard Medical School, Boston, MA 02115, United States
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Best MD, Takahashi K, Suminski AJ, Ethier C, Miller LE, Hatsopoulos NG. Comparing offline decoding performance in physiologically defined neuronal classes. J Neural Eng 2016; 13:026004. [PMID: 26824791 PMCID: PMC4855848 DOI: 10.1088/1741-2560/13/2/026004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Objective Recently, several studies have documented the presence of a bimodal distribution of spike waveform widths in primary motor cortex. Although narrow and wide spiking neurons, corresponding to the two modes of the distribution, exhibit different response properties, it remains unknown if these differences give rise to differential decoding performance between these two classes of cells. Approach We used a Gaussian mixture model to classify neurons into narrow and wide physiological classes. Using similar-size, random samples of neurons from these two physiological classes, we trained offline decoding models to predict a variety of movement features. We compared offline decoding performance between these two physiologically defined populations of cells. Main results We found that narrow spiking neural ensembles decode motor parameters better than wide spiking neural ensembles including kinematics, kinetics, and muscle activity. Significance These findings suggest that the utility of neural ensembles in brain machine interfaces may be predicted from their spike waveform widths.
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Affiliation(s)
- Matthew D Best
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL 60637, USA
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Mateo S, Di Rienzo F, Bergeron V, Guillot A, Collet C, Rode G. Motor imagery reinforces brain compensation of reach-to-grasp movement after cervical spinal cord injury. Front Behav Neurosci 2015; 9:234. [PMID: 26441568 PMCID: PMC4566051 DOI: 10.3389/fnbeh.2015.00234] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2015] [Accepted: 08/19/2015] [Indexed: 01/19/2023] Open
Abstract
Individuals with cervical spinal cord injury (SCI) that causes tetraplegia are challenged with dramatic sensorimotor deficits. However, certain rehabilitation techniques may significantly enhance their autonomy by restoring reach-to-grasp movements. Among others, evidence of motor imagery (MI) benefits for neurological rehabilitation of upper limb movements is growing. This literature review addresses MI effectiveness during reach-to-grasp rehabilitation after tetraplegia. Among articles from MEDLINE published between 1966 and 2015, we selected ten studies including 34 participants with C4 to C7 tetraplegia and 22 healthy controls published during the last 15 years. We found that MI of possible non-paralyzed movements improved reach-to-grasp performance by: (i) increasing both tenodesis grasp capabilities and muscle strength; (ii) decreasing movement time (MT), and trajectory variability; and (iii) reducing the abnormally increased brain activity. MI can also strengthen motor commands by potentiating recruitment and synchronization of motoneurons, which leads to improved recovery. These improvements reflect brain adaptations induced by MI. Furthermore, MI can be used to control brain-computer interfaces (BCI) that successfully restore grasp capabilities. These results highlight the growing interest for MI and its potential to recover functional grasping in individuals with tetraplegia, and motivate the need for further studies to substantiate it.
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Affiliation(s)
- Sébastien Mateo
- ImpAct Team, Lyon Neuroscience Research Center, Université Lyon 1, Université de Lyon, INSERM U1028, CNRS UMR5292 Lyon, France ; Hospices Civils de Lyon, Hôpital Henry Gabrielle, Plateforme Mouvement et Handicap Lyon, France ; Centre de Recherche et d'Innovation sur le Sport, EA 647, Performance Motrice, Mentale et du Matériel, Université Lyon 1, Université de Lyon Villeurbanne, France ; Ecole Normale Supérieure de Lyon, CNRS UMR5672 Lyon, France
| | - Franck Di Rienzo
- Centre de Recherche et d'Innovation sur le Sport, EA 647, Performance Motrice, Mentale et du Matériel, Université Lyon 1, Université de Lyon Villeurbanne, France
| | - Vance Bergeron
- Ecole Normale Supérieure de Lyon, CNRS UMR5672 Lyon, France
| | - Aymeric Guillot
- Centre de Recherche et d'Innovation sur le Sport, EA 647, Performance Motrice, Mentale et du Matériel, Université Lyon 1, Université de Lyon Villeurbanne, France ; Institut Universitaire de France Paris, France
| | - Christian Collet
- Centre de Recherche et d'Innovation sur le Sport, EA 647, Performance Motrice, Mentale et du Matériel, Université Lyon 1, Université de Lyon Villeurbanne, France
| | - Gilles Rode
- ImpAct Team, Lyon Neuroscience Research Center, Université Lyon 1, Université de Lyon, INSERM U1028, CNRS UMR5292 Lyon, France ; Hospices Civils de Lyon, Hôpital Henry Gabrielle, Plateforme Mouvement et Handicap Lyon, France
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