1
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Downey JE, Schone HR, Foldes ST, Greenspon C, Liu F, Verbaarschot C, Biro D, Satzer D, Moon CH, Coffman BA, Youssofzadeh V, Fields D, Hobbs TG, Okorokova E, Tyler-Kabara EC, Warnke PC, Gonzalez-Martinez J, Hatsopoulos NG, Bensmaia SJ, Boninger ML, Gaunt RA, Collinger JL. A roadmap for implanting microelectrode arrays to evoke tactile sensations through intracortical microstimulation. medRxiv 2024:2024.04.26.24306239. [PMID: 38712177 PMCID: PMC11071570 DOI: 10.1101/2024.04.26.24306239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Intracortical microstimulation (ICMS) is a method for restoring sensation to people with paralysis as part of a bidirectional brain-computer interface to restore upper limb function. Evoking tactile sensations of the hand through ICMS requires precise targeting of implanted electrodes. Here we describe the presurgical imaging procedures used to generate functional maps of the hand area of the somatosensory cortex and subsequent planning that guided the implantation of intracortical microelectrode arrays. In five participants with cervical spinal cord injury, across two study locations, this procedure successfully enabled ICMS-evoked sensations localized to at least the first four digits of the hand. The imaging and planning procedures developed through this clinical trial provide a roadmap for other brain-computer interface studies to ensure successful placement of stimulation electrodes.
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2
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Shelchkova ND, Downey JE, Greenspon CM, Okorokova EV, Sobinov AR, Verbaarschot C, He Q, Sponheim C, Tortolani AF, Moore DD, Kaufman MT, Lee RC, Satzer D, Gonzalez-Martinez J, Warnke PC, Miller LE, Boninger ML, Gaunt RA, Collinger JL, Hatsopoulos NG, Bensmaia SJ. Microstimulation of human somatosensory cortex evokes task-dependent, spatially patterned responses in motor cortex. Nat Commun 2023; 14:7270. [PMID: 37949923 PMCID: PMC10638421 DOI: 10.1038/s41467-023-43140-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 11/01/2023] [Indexed: 11/12/2023] Open
Abstract
The primary motor (M1) and somatosensory (S1) cortices play critical roles in motor control but the signaling between these structures is poorly understood. To fill this gap, we recorded - in three participants in an ongoing human clinical trial (NCT01894802) for people with paralyzed hands - the responses evoked in the hand and arm representations of M1 during intracortical microstimulation (ICMS) in the hand representation of S1. We found that ICMS of S1 activated some M1 neurons at short, fixed latencies consistent with monosynaptic activation. Additionally, most of the ICMS-evoked responses in M1 were more variable in time, suggesting indirect effects of stimulation. The spatial pattern of M1 activation varied systematically: S1 electrodes that elicited percepts in a finger preferentially activated M1 neurons excited during that finger's movement. Moreover, the indirect effects of S1 ICMS on M1 were context dependent, such that the magnitude and even sign relative to baseline varied across tasks. We tested the implications of these effects for brain-control of a virtual hand, in which ICMS conveyed tactile feedback. While ICMS-evoked activation of M1 disrupted decoder performance, this disruption was minimized using biomimetic stimulation, which emphasizes contact transients at the onset and offset of grasp, and reduces sustained stimulation.
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Affiliation(s)
- Natalya D Shelchkova
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
| | - John E Downey
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA.
| | - Charles M Greenspon
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA
| | | | - Anton R Sobinov
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA
| | - Ceci Verbaarschot
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Qinpu He
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
| | - Caleb Sponheim
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
| | - Ariana F Tortolani
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
| | - Dalton D Moore
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
| | - Matthew T Kaufman
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA
- Neuroscience Institute, University of Chicago, Chicago, IL, USA
| | - Ray C Lee
- Schwab Rehabilitation Hospital, Chicago, IL, USA
| | - David Satzer
- Department of Neurological Surgery, University of Chicago, Chicago, IL, USA
| | | | - Peter C Warnke
- Neuroscience Institute, University of Chicago, Chicago, IL, USA
- Department of Neurological Surgery, University of Chicago, Chicago, IL, USA
| | - Lee E Miller
- Department of Physiology, Northwestern University, Chicago, IL, USA
| | - Michael L Boninger
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Robert A Gaunt
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Jennifer L Collinger
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Nicholas G Hatsopoulos
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA
- Neuroscience Institute, University of Chicago, Chicago, IL, USA
| | - Sliman J Bensmaia
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA
- Neuroscience Institute, University of Chicago, Chicago, IL, USA
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3
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Greenspon CM, Shelchkova ND, Valle G, Hobbs TG, Berger-Wolf EI, Hutchison BC, Dogruoz E, Verbarschott C, Callier T, Sobinov AR, Okorokova EV, Jordan PM, Prasad D, He Q, Liu F, Kirsch RF, Miller JP, Lee RC, Satzer D, Gonzalez-Martinez J, Warnke PC, Miller LE, Boninger ML, Ajiboye AB, Graczyk EL, Downey JE, Collinger JL, Hatsopoulos NG, Gaunt RA, Bensmaia SJ. Tessellation of artificial touch via microstimulation of human somatosensory cortex. bioRxiv 2023:2023.06.23.545425. [PMID: 37425877 PMCID: PMC10327055 DOI: 10.1101/2023.06.23.545425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
When we interact with objects, we rely on signals from the hand that convey information about the object and our interaction with it. A basic feature of these interactions, the locations of contacts between the hand and object, is often only available via the sense of touch. Information about locations of contact between a brain-controlled bionic hand and an object can be signaled via intracortical microstimulation (ICMS) of somatosensory cortex (S1), which evokes touch sensations that are localized to a specific patch of skin. To provide intuitive location information, tactile sensors on the robotic hand drive ICMS through electrodes that evoke sensations at skin locations matching sensor locations. This approach requires that ICMS-evoked sensations be focal, stable, and distributed over the hand. To systematically investigate the localization of ICMS-evoked sensations, we analyzed the projected fields (PFs) of ICMS-evoked sensations - their location and spatial extent - from reports obtained over multiple years from three participants implanted with microelectrode arrays in S1. First, we found that PFs vary widely in their size across electrodes, are highly stable within electrode, are distributed over large swaths of each participant's hand, and increase in size as the amplitude or frequency of ICMS increases. Second, while PF locations match the locations of the receptive fields (RFs) of the neurons near the stimulating electrode, PFs tend to be subsumed by the corresponding RFs. Third, multi-channel stimulation gives rise to a PF that reflects the conjunction of the PFs of the component channels. By stimulating through electrodes with largely overlapping PFs, then, we can evoke a sensation that is experienced primarily at the intersection of the component PFs. To assess the functional consequence of this phenomenon, we implemented multichannel ICMS-based feedback in a bionic hand and demonstrated that the resulting sensations are more localizable than are those evoked via single-channel ICMS.
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Affiliation(s)
- Charles M Greenspon
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | | | - Giacomo Valle
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | - Taylor G Hobbs
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA
| | - Ev I Berger-Wolf
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | - Brianna C Hutchison
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH
| | - Efe Dogruoz
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | - Ceci Verbarschott
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA
| | - Thierri Callier
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | - Anton R Sobinov
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | | | - Patrick M Jordan
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | - Dillan Prasad
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | - Qinpu He
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL
| | - Fang Liu
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA
| | - Robert F Kirsch
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH
- School of Medicine, Case Western Reserve University, Cleveland, OH
- Louis Stokes Cleveland VA Medical Center, Cleveland, OH
| | - Jonathan P Miller
- School of Medicine, Case Western Reserve University, Cleveland, OH
- The Neurological Institute, University Hospitals Cleveland Medical Center, Cleveland, OH
- Louis Stokes Cleveland VA Medical Center, Cleveland, OH
| | - Ray C Lee
- Schwab Rehabilitation Hospital, Chicago, IL
| | - David Satzer
- Department of Neurological Surgery, University of Chicago, Chicago, IL
| | | | - Peter C Warnke
- Department of Neurological Surgery, University of Chicago, Chicago, IL
| | - Lee E Miller
- Department of Neuroscience, Northwestern University, Chicago, IL
- Department of Biomedical Engineering, Northwestern University, Evanston, IL
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL
- Shirley Ryan Ability Lab, Chicago, IL
| | - Michael L Boninger
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA
| | - Abidemi B Ajiboye
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH
- School of Medicine, Case Western Reserve University, Cleveland, OH
- Louis Stokes Cleveland VA Medical Center, Cleveland, OH
| | - Emily L Graczyk
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH
- School of Medicine, Case Western Reserve University, Cleveland, OH
- Louis Stokes Cleveland VA Medical Center, Cleveland, OH
| | - John E Downey
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | - Jennifer L Collinger
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA
| | - Nicholas G Hatsopoulos
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL
- Neuroscience Institute, University of Chicago, Chicago, IL
| | - Robert A Gaunt
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA
| | - Sliman J Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL
- Neuroscience Institute, University of Chicago, Chicago, IL
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4
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Greenspon CM, Valle G, Hobbs TG, Verbaarschot C, Callier T, Okorokova EV, Shelchkova ND, Sobinov AR, Jordan PM, Weiss JM, Fitzgerald EE, Prasad D, van Driesche A, Lee RC, Satzer D, Gonzalez-Martinez J, Warnke PC, Miller LE, Boninger ML, Collinger JL, Gaunt RA, Downey JE, Hatsopoulos NG, Bensmaia SJ. Biomimetic multi-channel microstimulation of somatosensory cortex conveys high resolution force feedback for bionic hands. bioRxiv 2023:2023.02.18.528972. [PMID: 36824713 PMCID: PMC9949113 DOI: 10.1101/2023.02.18.528972] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2023]
Abstract
Manual interactions with objects are supported by tactile signals from the hand. This tactile feedback can be restored in brain-controlled bionic hands via intracortical microstimulation (ICMS) of somatosensory cortex (S1). In ICMS-based tactile feedback, contact force can be signaled by modulating the stimulation intensity based on the output of force sensors on the bionic hand, which in turn modulates the perceived magnitude of the sensation. In the present study, we gauged the dynamic range and precision of ICMS-based force feedback in three human participants implanted with arrays of microelectrodes in S1. To this end, we measured the increases in sensation magnitude resulting from increases in ICMS amplitude and participant's ability to distinguish between different intensity levels. We then assessed whether we could improve the fidelity of this feedback by implementing "biomimetic" ICMS-trains, designed to evoke patterns of neuronal activity that more closely mimic those in natural touch, and by delivering ICMS through multiple channels at once. We found that multi-channel biomimetic ICMS gives rise to stronger and more distinguishable sensations than does its single-channel counterpart. Finally, we implemented biomimetic multi-channel feedback in a bionic hand and had the participant perform a compliance discrimination task. We found that biomimetic multi-channel tactile feedback yielded improved discrimination over its single-channel linear counterpart. We conclude that multi-channel biomimetic ICMS conveys finely graded force feedback that more closely approximates the sensitivity conferred by natural touch.
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Affiliation(s)
- Charles M. Greenspon
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | - Giacomo Valle
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | - Taylor G. Hobbs
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA
| | - Ceci Verbaarschot
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA
| | - Thierri Callier
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL
| | | | | | - Anton R. Sobinov
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | - Patrick M. Jordan
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | - Jeffrey M. Weiss
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA
| | - Emily E. Fitzgerald
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | - Dillan Prasad
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | - Ashley van Driesche
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | - Ray C. Lee
- Schwab Rehabilitation Hospital, Chicago, IL
| | - David Satzer
- Department of Neurological Surgery, University of Chicago, Chicago, IL
| | | | - Peter C. Warnke
- Department of Neurological Surgery, University of Chicago, Chicago, IL
| | - Lee E. Miller
- Department of Neuroscience, Northwestern University, Chicago, IL
- Department of Biomedical Engineering, Northwestern University, Evanston, IL
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL
- Shirley Ryan Ability Lab, Chicago, IL
| | - Michael L. Boninger
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA
| | - Jennifer L. Collinger
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA
| | - Robert A. Gaunt
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA
| | - John E. Downey
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | - Nicholas G. Hatsopoulos
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL
- Neuroscience Institute, University of Chicago, Chicago, IL
| | - Sliman J. Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL
- Neuroscience Institute, University of Chicago, Chicago, IL
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5
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Guthrie MD, Herrera AJ, Downey JE, Brane LJ, Boninger ML, Collinger JL. The impact of distractions on intracortical brain–computer interface control of a robotic arm. Brain-Computer Interfaces 2021. [DOI: 10.1080/2326263x.2021.1980292] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Michael D. Guthrie
- Rehab Neural Engineering Labs, Department of Bioengineering, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Angelica J Herrera
- Rehab Neural Engineering Labs, Department of Bioengineering, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
| | - John E. Downey
- Rehab Neural Engineering Labs, Department of Bioengineering, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA
| | - Lucas J. Brane
- Rehab Neural Engineering Labs, Department of Bioengineering, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Michael L. Boninger
- Rehab Neural Engineering Labs, Department of Bioengineering, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Veterans Affairs, Human Engineering Research Laboratories, Va Center of Excellence, Pittsburgh, Pa, USA
| | - Jennifer L. Collinger
- Rehab Neural Engineering Labs, Department of Bioengineering, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Veterans Affairs, Human Engineering Research Laboratories, Va Center of Excellence, Pittsburgh, Pa, USA
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6
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Hughes CL, Flesher SN, Weiss JM, Downey JE, Boninger M, Collinger JL, Gaunt RA. Neural stimulation and recording performance in human sensorimotor cortex over 1500 days. J Neural Eng 2021; 18. [PMID: 34320481 DOI: 10.1088/1741-2552/ac18ad] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 07/28/2021] [Indexed: 01/12/2023]
Abstract
Objective.Intracortical microstimulation (ICMS) in somatosensory cortex can restore sensation to people with spinal cord injury. However, the recording quality from implanted microelectrodes can degrade over time and limitations in stimulation longevity have been considered a potential barrier to the clinical use of ICMS. Our objective was to evaluate recording stability of intracortical electrodes implanted in the motor and somatosensory cortex of one person. The electrodes in motor cortex had platinum tips and were not stimulated, while the electrodes in somatosensory cortex had sputtered iridium oxide film (SIROF) tips and were stimulated. Additionally, we measured how well ICMS was able to evoke sensations over time.Approach. We implanted microelectrode arrays with SIROF tips in the somatosensory cortex (SIROF-sensory) of a human participant with a cervical spinal cord injury. We regularly stimulated these electrodes to evoke tactile sensations on the hand. Here, we quantify the stability of these electrodes in comparison to non-stimulated platinum electrodes implanted in the motor cortex (platinum-motor) over 1500 days with recorded signal quality and electrode impedances. Additionally, we quantify the stability of ICMS-evoked sensations using detection thresholds.Main results. We found that recording quality, as assessed by the number of electrodes with high-amplitude waveforms (>100µV peak-to-peak), peak-to-peak voltage, noise, and signal-to-noise ratio, decreased over time on SIROF-sensory and platinum-motor electrodes. However, SIROF-sensory electrodes were more likely to continue to record high-amplitude signals than platinum-motor electrodes. Interestingly, the detection thresholds for stimulus-evoked sensations decreased over time from a median of 31.5μA at day 100-10.4μA at day 1500, with the largest changes occurring between day 100 and 500.Significance. These results demonstrate that ICMS in human somatosensory cortex can be provided over long periods of time without deleterious effects on recording or stimulation capabilities. In fact, the sensitivity to stimulation improved over time.
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Affiliation(s)
- Christopher L Hughes
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Sharlene N Flesher
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Jeffrey M Weiss
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - John E Downey
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, United States of America
| | - Michael Boninger
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Jennifer L Collinger
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States of America.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Robert A Gaunt
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States of America.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, United States of America
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7
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Flesher SN, Downey JE, Weiss JM, Hughes CL, Herrera AJ, Tyler-Kabara EC, Boninger ML, Collinger JL, Gaunt RA. A brain-computer interface that evokes tactile sensations improves robotic arm control. Science 2021; 372:831-836. [PMID: 34016775 DOI: 10.1126/science.abd0380] [Citation(s) in RCA: 138] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 03/25/2021] [Indexed: 12/14/2022]
Abstract
Prosthetic arms controlled by a brain-computer interface can enable people with tetraplegia to perform functional movements. However, vision provides limited feedback because information about grasping objects is best relayed through tactile feedback. We supplemented vision with tactile percepts evoked using a bidirectional brain-computer interface that records neural activity from the motor cortex and generates tactile sensations through intracortical microstimulation of the somatosensory cortex. This enabled a person with tetraplegia to substantially improve performance with a robotic limb; trial times on a clinical upper-limb assessment were reduced by half, from a median time of 20.9 to 10.2 seconds. Faster times were primarily due to less time spent attempting to grasp objects, revealing that mimicking known biological control principles results in task performance that is closer to able-bodied human abilities.
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Affiliation(s)
- Sharlene N Flesher
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - John E Downey
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Organismal Biology, University of Chicago, Chicago, IL, USA
| | - Jeffrey M Weiss
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
| | - Christopher L Hughes
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Angelica J Herrera
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | | | - Michael L Boninger
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA.,McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA.,VA Center of Excellence, Department of Veterans Affairs, Pittsburgh, PA, USA
| | - Jennifer L Collinger
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA. .,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA.,VA Center of Excellence, Department of Veterans Affairs, Pittsburgh, PA, USA
| | - Robert A Gaunt
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA. .,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
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8
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Downey JE, Quick KM, Schwed N, Weiss JM, Wittenberg GF, Boninger ML, Collinger JL. The Motor Cortex Has Independent Representations for Ipsilateral and Contralateral Arm Movements But Correlated Representations for Grasping. Cereb Cortex 2020; 30:5400-5409. [PMID: 32494819 DOI: 10.1093/cercor/bhaa120] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Revised: 03/25/2020] [Accepted: 04/15/2020] [Indexed: 11/14/2022] Open
Abstract
Motor commands for the arm and hand generally arise from the contralateral motor cortex, where most of the relevant corticospinal tract originates. However, the ipsilateral motor cortex shows activity related to arm movement despite the lack of direct connections. The extent to which the activity related to ipsilateral movement is independent from that related to contralateral movement is unclear based on conflicting conclusions in prior work. Here we investigate bilateral arm and hand movement tasks completed by two human subjects with intracortical microelectrode arrays implanted in the left hand and arm area of the motor cortex. Neural activity was recorded while they attempted to perform arm and hand movements in a virtual environment. This enabled us to quantify the strength and independence of motor cortical activity related to continuous movements of each arm. We also investigated the subjects' ability to control both arms through a brain-computer interface. Through a number of experiments, we found that ipsilateral arm movement was represented independently of, but more weakly than, contralateral arm movement. However, the representation of grasping was correlated between the two hands. This difference between hand and arm representation was unexpected and poses new questions about the different ways the motor cortex controls the hands and arms.
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Affiliation(s)
- John E Downey
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL 60637, United States.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, United States.,Center for the Neural Basis of Cognition, Pittsburgh, PA 1523, United States.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213, United States
| | - Kristin M Quick
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213, United States
| | - Nathaniel Schwed
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, United States
| | - Jeffrey M Weiss
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213, United States
| | - George F Wittenberg
- VA Pittsburgh Healthcare System, Pittsburgh, PA 15206, United States.,Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15213, United States
| | - Michael L Boninger
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, United States.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213, United States.,VA Pittsburgh Healthcare System, Pittsburgh, PA 15206, United States
| | - Jennifer L Collinger
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, United States.,Center for the Neural Basis of Cognition, Pittsburgh, PA 1523, United States.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213, United States.,VA Pittsburgh Healthcare System, Pittsburgh, PA 15206, United States
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9
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Downey JE, Weiss JM, Flesher SN, Thumser ZC, Marasco PD, Boninger ML, Gaunt RA, Collinger JL. Implicit Grasp Force Representation in Human Motor Cortical Recordings. Front Neurosci 2018; 12:801. [PMID: 30429772 PMCID: PMC6220062 DOI: 10.3389/fnins.2018.00801] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 10/15/2018] [Indexed: 01/19/2023] Open
Abstract
In order for brain-computer interface (BCI) systems to maximize functionality, users will need to be able to accurately modulate grasp force to avoid dropping heavy objects while also being able to handle fragile items. We present a case-study consisting of two experiments designed to identify whether intracortical recordings from the motor cortex of a person with tetraplegia could predict intended grasp force. In the first task, we were able classify neural responses to attempted grasps of four objects, each of which required similar grasp kinematics but different implicit grasp force targets, with 69% accuracy. In the second task, the subject attempted to move a virtual robotic arm in space to grasp a simple virtual object. For each trial, the subject was asked to grasp the virtual object with the force appropriate for one of the four objects from the first experiment, with the goal of measuring an implicit representation of grasp force. While the subject knew the grasp force during all phases of the trial, accurate classification was only achieved during active grasping, not while the hand moved to, transported, or released the object. In both tasks, misclassifications were most often to the object with an adjacent force requirement. In addition to the implications for understanding the representation of grasp force in motor cortex, these results are a first step toward creating intelligent algorithms to help BCI users grasp and manipulate a variety of objects that will be encountered in daily life. Clinical Trial Identifier: NCT01894802 https://clinicaltrials.gov/ct2/show/NCT01894802.
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Affiliation(s)
- John E Downey
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States.,Center for the Neural Basis of Cognition, Pittsburgh, PA, United States.,Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, United States.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, United States
| | - Jeffrey M Weiss
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States.,Center for the Neural Basis of Cognition, Pittsburgh, PA, United States.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, United States
| | - Sharlene N Flesher
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States.,Center for the Neural Basis of Cognition, Pittsburgh, PA, United States.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, United States.,Department of Neurosurgery, Stanford University, Stanford, CA, United States
| | - Zachary C Thumser
- Laboratory for Bionic Integration, Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States.,Research Service, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, United States
| | - Paul D Marasco
- Laboratory for Bionic Integration, Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States.,Advanced Platform Technology Center of Excellence, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, United States
| | - Michael L Boninger
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, United States.,VA Pittsburgh Healthcare System, Pittsburgh, PA, United States
| | - Robert A Gaunt
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States.,Center for the Neural Basis of Cognition, Pittsburgh, PA, United States.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, United States
| | - Jennifer L Collinger
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States.,Center for the Neural Basis of Cognition, Pittsburgh, PA, United States.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, United States.,VA Pittsburgh Healthcare System, Pittsburgh, PA, United States
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10
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Downey JE, Schwed N, Chase SM, Schwartz AB, Collinger JL. Intracortical recording stability in human brain–computer interface users. J Neural Eng 2018; 15:046016. [PMID: 29553484 DOI: 10.1088/1741-2552/aab7a0] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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11
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Downey JE, Brane L, Gaunt RA, Tyler-Kabara EC, Boninger ML, Collinger JL. Motor cortical activity changes during neuroprosthetic-controlled object interaction. Sci Rep 2017; 7:16947. [PMID: 29209023 PMCID: PMC5717217 DOI: 10.1038/s41598-017-17222-3] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Accepted: 11/22/2017] [Indexed: 12/21/2022] Open
Abstract
Brain-computer interface (BCI) controlled prosthetic arms are being developed to restore function to people with upper-limb paralysis. This work provides an opportunity to analyze human cortical activity during complex tasks. Previously we observed that BCI control became more difficult during interactions with objects, although we did not quantify the neural origins of this phenomena. Here, we investigated how motor cortical activity changed in the presence of an object independently of the kinematics that were being generated using intracortical recordings from two people with tetraplegia. After identifying a population-wide increase in neural firing rates that corresponded with the hand being near an object, we developed an online scaling feature in the BCI system that operated without knowledge of the task. Online scaling increased the ability of two subjects to control the robotic arm when reaching to grasp and transport objects. This work suggests that neural representations of the environment, in this case the presence of an object, are strongly and consistently represented in motor cortex but can be accounted for to improve BCI performance.
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Affiliation(s)
- John E Downey
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, USA
| | - Lucas Brane
- School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Robert A Gaunt
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, USA.,School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Elizabeth C Tyler-Kabara
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Michael L Boninger
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania, USA
| | - Jennifer L Collinger
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. .,Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, USA. .,School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. .,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. .,VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania, USA.
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12
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Muelling K, Venkatraman A, Valois JS, Downey JE, Weiss J, Javdani S, Hebert M, Schwartz AB, Collinger JL, Bagnell JA. Autonomy infused teleoperation with application to brain computer interface controlled manipulation. Auton Robots 2017. [DOI: 10.1007/s10514-017-9622-4] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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13
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Flesher SN, Collinger JL, Foldes ST, Weiss JM, Downey JE, Tyler-Kabara EC, Bensmaia SJ, Schwartz AB, Boninger ML, Gaunt RA. Intracortical microstimulation of human somatosensory cortex. Sci Transl Med 2016; 8:361ra141. [PMID: 27738096 DOI: 10.1126/scitranslmed.aaf8083] [Citation(s) in RCA: 374] [Impact Index Per Article: 46.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Accepted: 10/05/2016] [Indexed: 12/21/2022]
Abstract
Intracortical microstimulation of the somatosensory cortex offers the potential for creating a sensory neuroprosthesis to restore tactile sensation. Whereas animal studies have suggested that both cutaneous and proprioceptive percepts can be evoked using this approach, the perceptual quality of the stimuli cannot be measured in these experiments. We show that microstimulation within the hand area of the somatosensory cortex of a person with long-term spinal cord injury evokes tactile sensations perceived as originating from locations on the hand and that cortical stimulation sites are organized according to expected somatotopic principles. Many of these percepts exhibit naturalistic characteristics (including feelings of pressure), can be evoked at low stimulation amplitudes, and remain stable for months. Further, modulating the stimulus amplitude grades the perceptual intensity of the stimuli, suggesting that intracortical microstimulation could be used to convey information about the contact location and pressure necessary to perform dexterous hand movements associated with object manipulation.
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Affiliation(s)
- Sharlene N Flesher
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Jennifer L Collinger
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213, USA.,Department of Veterans Affairs Medical Center, Pittsburgh, PA 15206, USA
| | - Stephen T Foldes
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213, USA.,Department of Veterans Affairs Medical Center, Pittsburgh, PA 15206, USA
| | - Jeffrey M Weiss
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - John E Downey
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA
| | - Elizabeth C Tyler-Kabara
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213, USA.,Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA.,McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA
| | - Sliman J Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL 60637, USA
| | - Andrew B Schwartz
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA.,McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA.,Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA 15213, USA.,Systems Neuroscience Institute, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Michael L Boninger
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213, USA.,Department of Veterans Affairs Medical Center, Pittsburgh, PA 15206, USA.,McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA
| | - Robert A Gaunt
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA. .,Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213, USA
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14
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Downey JE, Weiss JM, Muelling K, Venkatraman A, Valois JS, Hebert M, Bagnell JA, Schwartz AB, Collinger JL. Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping. J Neuroeng Rehabil 2016; 13:28. [PMID: 26987662 PMCID: PMC4797113 DOI: 10.1186/s12984-016-0134-9] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2015] [Accepted: 03/04/2016] [Indexed: 11/26/2022] Open
Abstract
Background Recent studies have shown that brain-machine interfaces (BMIs) offer great potential for restoring upper limb function. However, grasping objects is a complicated task and the signals extracted from the brain may not always be capable of driving these movements reliably. Vision-guided robotic assistance is one possible way to improve BMI performance. We describe a method of shared control where the user controls a prosthetic arm using a BMI and receives assistance with positioning the hand when it approaches an object. Methods Two human subjects with tetraplegia used a robotic arm to complete object transport tasks with and without shared control. The shared control system was designed to provide a balance between BMI-derived intention and computer assistance. An autonomous robotic grasping system identified and tracked objects and defined stable grasp positions for these objects. The system identified when the user intended to interact with an object based on the BMI-controlled movements of the robotic arm. Using shared control, BMI controlled movements and autonomous grasping commands were blended to ensure secure grasps. Results Both subjects were more successful on object transfer tasks when using shared control compared to BMI control alone. Movements made using shared control were more accurate, more efficient, and less difficult. One participant attempted a task with multiple objects and successfully lifted one of two closely spaced objects in 92 % of trials, demonstrating the potential for users to accurately execute their intention while using shared control. Conclusions Integration of BMI control with vision-guided robotic assistance led to improved performance on object transfer tasks. Providing assistance while maintaining generalizability will make BMI systems more attractive to potential users. Trial registration NCT01364480 and NCT01894802. Electronic supplementary material The online version of this article (doi:10.1186/s12984-016-0134-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- John E Downey
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Jeffrey M Weiss
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Arun Venkatraman
- Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | | | - Martial Hebert
- Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - J Andrew Bagnell
- Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Andrew B Schwartz
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.,Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jennifer L Collinger
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA. .,Center for the Neural Basis of Cognition, Pittsburgh, PA, USA. .,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA. .,VA Pittsburgh Healthcare System, Pittsburgh, PA, USA. .,University of Pittsburgh, 3520 5th Avenue, Suite 300, Pittsburgh, PA, 15213, USA.
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15
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Wodlinger B, Downey JE, Tyler-Kabara EC, Schwartz AB, Boninger ML, Collinger JL. Ten-dimensional anthropomorphic arm control in a human brain-machine interface: difficulties, solutions, and limitations. J Neural Eng 2014; 12:016011. [PMID: 25514320 DOI: 10.1088/1741-2560/12/1/016011] [Citation(s) in RCA: 254] [Impact Index Per Article: 25.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE In a previous study we demonstrated continuous translation, orientation and one-dimensional grasping control of a prosthetic limb (seven degrees of freedom) by a human subject with tetraplegia using a brain-machine interface (BMI). The current study, in the same subject, immediately followed the previous work and expanded the scope of the control signal by also extracting hand-shape commands from the two 96-channel intracortical electrode arrays implanted in the subject's left motor cortex. APPROACH Four new control signals, dictating prosthetic hand shape, replaced the one-dimensional grasping in the previous study, allowing the subject to control the prosthetic limb with ten degrees of freedom (three-dimensional (3D) translation, 3D orientation, four-dimensional hand shaping) simultaneously. MAIN RESULTS Robust neural tuning to hand shaping was found, leading to ten-dimensional (10D) performance well above chance levels in all tests. Neural unit preferred directions were broadly distributed through the 10D space, with the majority of units significantly tuned to all ten dimensions, instead of being restricted to isolated domains (e.g. translation, orientation or hand shape). The addition of hand shaping emphasized object-interaction behavior. A fundamental component of BMIs is the calibration used to associate neural activity to intended movement. We found that the presence of an object during calibration enhanced successful shaping of the prosthetic hand as it closed around the object during grasping. SIGNIFICANCE Our results show that individual motor cortical neurons encode many parameters of movement, that object interaction is an important factor when extracting these signals, and that high-dimensional operation of prosthetic devices can be achieved with simple decoding algorithms. ClinicalTrials.gov Identifier: NCT01364480.
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Affiliation(s)
- B Wodlinger
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA. Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
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16
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Collinger JL, Wodlinger B, Downey JE, Wang W, Tyler-Kabara EC, Weber DJ, McMorland AJC, Velliste M, Boninger ML, Schwartz AB. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 2013; 381:557-64. [PMID: 23253623 PMCID: PMC3641862 DOI: 10.1016/s0140-6736(12)61816-9] [Citation(s) in RCA: 940] [Impact Index Per Article: 85.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
BACKGROUND Paralysis or amputation of an arm results in the loss of the ability to orient the hand and grasp, manipulate, and carry objects, functions that are essential for activities of daily living. Brain-machine interfaces could provide a solution to restoring many of these lost functions. We therefore tested whether an individual with tetraplegia could rapidly achieve neurological control of a high-performance prosthetic limb using this type of an interface. METHODS We implanted two 96-channel intracortical microelectrodes in the motor cortex of a 52-year-old individual with tetraplegia. Brain-machine-interface training was done for 13 weeks with the goal of controlling an anthropomorphic prosthetic limb with seven degrees of freedom (three-dimensional translation, three-dimensional orientation, one-dimensional grasping). The participant's ability to control the prosthetic limb was assessed with clinical measures of upper limb function. This study is registered with ClinicalTrials.gov, NCT01364480. FINDINGS The participant was able to move the prosthetic limb freely in the three-dimensional workspace on the second day of training. After 13 weeks, robust seven-dimensional movements were performed routinely. Mean success rate on target-based reaching tasks was 91·6% (SD 4·4) versus median chance level 6·2% (95% CI 2·0-15·3). Improvements were seen in completion time (decreased from a mean of 148 s [SD 60] to 112 s [6]) and path efficiency (increased from 0·30 [0·04] to 0·38 [0·02]). The participant was also able to use the prosthetic limb to do skilful and coordinated reach and grasp movements that resulted in clinically significant gains in tests of upper limb function. No adverse events were reported. INTERPRETATION With continued development of neuroprosthetic limbs, individuals with long-term paralysis could recover the natural and intuitive command signals for hand placement, orientation, and reaching, allowing them to perform activities of daily living. FUNDING Defense Advanced Research Projects Agency, National Institutes of Health, Department of Veterans Affairs, and UPMC Rehabilitation Institute.
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17
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Li N, Downey JE, Bar-Shir A, Gilad AA, Walczak P, Kim H, Joel SE, Pekar JJ, Thakor NV, Pelled G. Optogenetic-guided cortical plasticity after nerve injury. Proc Natl Acad Sci U S A 2011; 108:8838-43. [PMID: 21555573 PMCID: PMC3102379 DOI: 10.1073/pnas.1100815108] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Peripheral nerve injury causes sensory dysfunctions that are thought to be attributable to changes in neuronal activity occurring in somatosensory cortices both contralateral and ipsilateral to the injury. Recent studies suggest that distorted functional response observed in deprived primary somatosensory cortex (S1) may be the result of an increase in inhibitory interneuron activity and is mediated by the transcallosal pathway. The goal of this study was to develop a strategy to manipulate and control the transcallosal activity to facilitate appropriate plasticity by guiding the cortical reorganization in a rat model of sensory deprivation. Since transcallosal fibers originate mainly from excitatory pyramidal neurons somata situated in laminae III and V, the excitatory neurons in rat S1 were engineered to express halorhodopsin, a light-sensitive chloride pump that triggers neuronal hyperpolarization. Results from electrophysiology, optical imaging, and functional MRI measurements are concordant with that within the deprived S1, activity in response to intact forepaw electrical stimulation was significantly increased by concurrent illumination of halorhodopsin over the healthy S1. Optogenetic manipulations effectively decreased the adverse inhibition of deprived cortex and revealed the major contribution of the transcallosal projections, showing interhemispheric neuroplasticity and thus, setting a foundation to develop improved rehabilitation strategies to restore cortical functions.
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Affiliation(s)
- Nan Li
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205
- Department of Biomedical Engineering and
| | - John E. Downey
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205
| | - Amnon Bar-Shir
- Cellular Imaging Section, Vascular Biology Program, Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205; and
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287
| | - Assaf A. Gilad
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205
- Cellular Imaging Section, Vascular Biology Program, Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205; and
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287
| | - Piotr Walczak
- Cellular Imaging Section, Vascular Biology Program, Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205; and
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287
| | - Heechul Kim
- Cellular Imaging Section, Vascular Biology Program, Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205; and
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287
| | - Suresh E. Joel
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287
| | - James J. Pekar
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287
| | | | - Galit Pelled
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287
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Rowe JL, Downey JE, Faust M, Horn MJ. Psychological and demographic predictors of successful weight loss following silastic ring vertical stapled gastroplasty. Psychol Rep 2000; 86:1028-36. [PMID: 10876361 DOI: 10.2466/pr0.2000.86.3.1028] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
To identify psychological factors involved in obesity 45 individuals (40 women and 5 men), ranging in age from 21 to 54 years (M age = 41 yr.), who were candidates for silastic ring vertical stapled gastroplasty were assessed on the Millon Behavioral Health Inventory and the Millon Multiaxial Clinical Inventory-III. In addition, a number of demographic variables such as education, marital status, and age of onset of obesity were considered. Analysis indicated that significant predictors of weight loss at a 6-mo. postoperative assessment include age of onset of obesity and scores on the Schizoid scale of the Millon-III. These findings may be of assistance in identifying personality variables associated with changes in weight if replicated in a larger sample.
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Affiliation(s)
- J L Rowe
- University of South Alabama, USA
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Downey JE, Wahrman J. Monitoring and evaluating JCAH surveyors and the survey process. QRB Qual Rev Bull 1984; 10:46-7. [PMID: 6424078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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20
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Abstract
Indirect evidence has suggested that lipid peroxidation is associated with iron overload in vivo. As a measure of lipid peroxidation, pentane expired in the breath of rats loaded with an accumulated dose of either 100 mg or 186-200 mg of iron injected intraperitoneally as iron dextran was measured over a 7 to 8 week period, and the effect on pentane production of feeding antioxidant-supplemented diets was determined. By the seventh week of feeding the diets, rats fed 0.3% L-ascorbic acid produced 17% less (P = 0.03) pentane than did rats fed the basal antioxidant-deficient diet, whereas rats fed 0.004% dl-alpha-tocopherol acetate produced 92% less (P less than 0.001). After being fed the basal diet for 7 weeks, iron-loaded rats produced 76 +/- 9 pmol pentane/100 g body wt/min. When synthetic antioxidants were added to the diet at a concentration of 0.25%, the order of effectiveness in decreasing pentane production after 1 week was: N,N'-diphenyl-p-phenylenediamine greater than ethoxyquin greater than butylated hydroxyanisole greater than butylated hydroxytoluene greater than propyl gallate approximately equal to no antioxidant. After removal of either ethoxyquin or N,N'-diphenyl-p-phenylenediamine from the diets for 1 week, pentane production increased to a high level. The total amount of lipid soluble fluorophores in individual spleens of rats fed N,N'-diphenyl-p-phenylenediamine, ethoxyquin, dl-alpha- tocopherol acetate, ascorbic acid and no antioxidant were correlated significantly with the corresponding total integrated amount of pentane produced by the individual rats over the 7 to 8 week period. This study has provided some of the most direct evidence to date that lipid peroxidation is associated with iron overload in vivo.
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21
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Downey JE, Weedman RD, Graveline K. Analysis of alcoholism facilities' compliance with JCAH standards. QRB Qual Rev Bull 1982; 8:27-8. [PMID: 6815605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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22
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Litov RE, Gee DL, Downey JE, Tappel AL. The role of peroxidation during chronic and acute exposure to ethanol as determined by pentane expiration in the rat. Lipids 1981; 16:52-63. [PMID: 7194411 DOI: 10.1007/bf02534921] [Citation(s) in RCA: 40] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Weanling rats were fed one of 3 diets containing 0, 11 or 200 international units (IU) dl-alpha-tocopherol acetate/kg diet for 4 weeks. Following this period, the drinking water was replaced with an 18% solution of ethanol (v/v). An isocaloric D-glucose solution was substituted for the drinking water of a control group of rats fed the vitamin-E-deficient diet for 4 weeks. The 4 treatment groups were maintained on the diet and drinking regimen for 20 weeks. Basal levels of expired pentane were determined at weeks 0, 1, 3, 5, 7 and 9. Chronic ethanol consumption did not influence basal pentane production during the 9-week treatment. Basal levels of expired pentane were affected by dietary vitamin E. Rats supplemented with vitamin E had basal pentane levels less than one-half of the level of rats fed a vitamin-E-deficient diet (p less than 0.001). After 14 weeks of treatment, the 2 groups of rats fed a vitamin-E-deficient diet were administrated p.o. an acute dose of 6 g of ethanol/kg body wt. Pentane expired above basal levels during the following 4-hr period correlated with the amount of hepatic triglycerides determined at the conclusion of the experiment. The etiology of ethanol toxicity is a complex and multifactorial system made up of many biological variables that influence lipid peroxidation. The appropriate choices of experimental designs and methods are important in examining the role of lipid peroxidation.
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Downey JE, Irving DH, Tappel AL. Effects of dietary antioxidants on in vivo lipid peroxidation in the rat as measured by pentane production. Lipids 1978; 13:403-7. [PMID: 672481 DOI: 10.1007/bf02533709] [Citation(s) in RCA: 38] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Litov RE, Irving DH, Downey JE, Tappel AL. Lipid peroxidation: a mechanism involved in acute ethanol toxicity as demonstrated by in vivo pentane production in the rat. Lipids 1978; 13:305-7. [PMID: 661517 DOI: 10.1007/bf02533677] [Citation(s) in RCA: 74] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The effect of a single dose of ethanol on lipid peroxidation in three groups of rats fed different amounts of vitamin E was determined by the measurement of pentane in the breath. All rats had increased pentane production above basal levels by 15 min following oral administration of 6 g ethanol/kg body wt. The increase in total pentane production during a 13-hr test period after intragastric administration of ethanol was greater in the rats fed the vitamin E-deficient diet than in the rats fed vitamin E-supplemented diets (alpha = 2P = 0.02). The results support the hypothesis that acute ethanol toxicity involves lipid peroxidation and further demonstrate the usefulness in toxicological studies of monitoring pentane as an index of lipid peroxidation in vivo.
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