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Murphy EF, Means A, Li C, Baez H, Gomez-Ramirez M. Strength of activation and temporal dynamics of bioluminescent-optogenetics in response to systemic injections of the luciferin. Neuroimage 2024; 301:120882. [PMID: 39362505 DOI: 10.1016/j.neuroimage.2024.120882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 09/23/2024] [Accepted: 10/01/2024] [Indexed: 10/05/2024] Open
Abstract
BioLuminescent OptoGenetics ("BL-OG") is a chemogenetic method that can evoke optogenetic reactions in the brain non-invasively. In BL-OG, an enzyme that catalyzes a light producing reaction (i.e., a luciferase) is tethered to an optogenetic element that is activated in response to bioluminescent light. Bioluminescence is generated by injecting a chemical substrate (luciferin, e.g., h-Coelenterazine; h-CTZ) that is catalyzed by the luciferase. By directly injecting the luciferin into the brain, we show that bioluminescent light is proportional to spiking activity, and this relationship scales as a function of luciferin dosage. Here, we build on these previous observations by characterizing the temporal dynamics and dose response curves of bioluminescence generated by luminopsins (LMOs), a proxy of BL-OG effects, to intravenous (IV) injections of the luciferin. We imaged bioluminescence through a thinned skull of mice running on a wheel, while delivering h-CTZ via the tail vein with different dosage concentrations and injection rates. The data reveal a systematic relationship between strength of bioluminescence and h-CTZ dosage, with higher concentration generating stronger bioluminescence. We also found that bioluminescent activity occurs rapidly (< 60 s after IV injection) regardless of concentration dosage. However, as expected, the onset time of bioluminescence is delayed as the injection rate decreases. Notably, the strength and time decay of bioluminescence is invariant to the injection rate of h-CTZ. Taken together, these data show that BL-OG effects are highly consistent across injection parameters of h-CTZ, highlighting the reliability of BL-OG as a minimally invasive neuromodulation method.
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Affiliation(s)
- Emily F Murphy
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14642, USA
| | - Aniya Means
- The Ernest J. Del Monte Institute for Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642, USA
| | - Chen Li
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14642, USA
| | - Hector Baez
- Center for Visual Science, University of Rochester, Rochester NY 14642, USA
| | - Manuel Gomez-Ramirez
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14642, USA; The Ernest J. Del Monte Institute for Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642, USA; Center for Visual Science, University of Rochester, Rochester NY 14642, USA.
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Greenspon CM, Shelchkova ND, Hobbs TG, Bensmaia SJ, Gaunt RA. Intracortical microstimulation of human somatosensory cortex induces natural perceptual biases. Brain Stimul 2024; 17:1178-1185. [PMID: 39413869 DOI: 10.1016/j.brs.2024.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 09/09/2024] [Accepted: 10/10/2024] [Indexed: 10/18/2024] Open
Abstract
Time-order error, a psychophysical phenomenon in which the duration in between successive stimuli alters perception, has been studied for decades by neuroscientists and psychologists. To date, however, the locus of these effects is unknown. We use intracortical microstimulation of somatosensory cortex in three humans with spinal cord injury as a tool to bypass initial stages of processing and restrict the possible locations that signals could be modified. Using a 2-interval forced choice amplitude discrimination paradigm, we first assessed the extent to which order effects are observed. Comparing trials where the standard stimulus was in the first or second interval, we found that systematic biases are exhibited, typically causing the intensity of the second stimulus to be overestimated The degree of this overestimation for individual electrodes was dependent on the perceptual sensitivity to changes in stimulus amplitude. To investigate the role of memory on this phenomenon, we implemented a 2-interval magnitude estimation task in which participants were instructed to ignore the first stimulus and again found that the perceptual intensity of the second stimulus tended to be enhanced by the first in a manner that depended on the amplitude and duration of the first stimulus. Finally, we repeated both paradigms while varying the inter-stimulus interval to examine the timescale over which these effects occur and found that longer inter-stimulus intervals reduced the effect size. These results show that direct activation of primary somatosensory cortex is sufficient to induce time-order errors.
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Affiliation(s)
- Charles M Greenspon
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA.
| | - Natalya D Shelchkova
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
| | - Taylor G Hobbs
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Sliman J Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA; Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA; Neuroscience Institute, University of Chicago, Chicago, IL, USA
| | - Robert A Gaunt
- 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
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3
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Flores FJ, Dalla Betta I, Tauber J, Schreier DR, Stephen EP, Wilson MA, Brown EN. Electrographic seizures during low-current thalamic deep brain stimulation in mice. Brain Stimul 2024; 17:975-979. [PMID: 39134207 DOI: 10.1016/j.brs.2024.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Revised: 07/31/2024] [Accepted: 08/09/2024] [Indexed: 08/19/2024] Open
Abstract
BACKGROUND Deep brain stimulation of the central thalamus (CT-DBS) has potential for modulating states of consciousness, but it can also trigger electrographic seizures, including poly-spike-wave trains (PSWT). OBJECTIVES To report the probability of inducing PSWTs during CT-DBS in awake, freely-moving mice. METHODS Mice were implanted with electrodes to deliver unilateral and bilateral CT-DBS at different frequencies while recording electroencephalogram (EEG). We titrated stimulation current by gradually increasing it at each frequency until a PSWT appeared. Subsequent stimulations to test arousal modulation were performed at the current one step below the current that caused a PSWT during titration. RESULTS In 2.21% of the test stimulations (10 out of 12 mice), CT-DBS caused PSWTs at currents lower than the titrated current, including currents as low as 20 μA. CONCLUSION Our study found a small but significant probability of inducing PSWTs even after titration and at relatively low currents. EEG should be closely monitored for electrographic seizures when performing CT-DBS in both research and clinical settings.
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Affiliation(s)
- Francisco J Flores
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, 55 Fruit St, Boston, 02114, MA, USA; Center for Brains, Minds, and Machines, Massachusetts Institute of Technology, 43 Vassar St, Cambridge, 02139, MA, USA; Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 43 Vassar St, Cambridge, 02139, MA, USA.
| | - Isabella Dalla Betta
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, 55 Fruit St, Boston, 02114, MA, USA; Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 43 Vassar St, Cambridge, 02139, MA, USA.
| | - John Tauber
- Department of Mathematics and Statistics, Boston University, 665 Commonwealth Ave, Boston, 02215, MA, USA.
| | - David R Schreier
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, 55 Fruit St, Boston, 02114, MA, USA; Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 43 Vassar St, Cambridge, 02139, MA, USA; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, 101 Merrimac St, Boston, 02114, MA, USA; Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Freiburgstrasse 16, Bern, 3010, Switzerland.
| | - Emily P Stephen
- Department of Mathematics and Statistics, Boston University, 665 Commonwealth Ave, Boston, 02215, MA, USA.
| | - Matthew A Wilson
- Center for Brains, Minds, and Machines, Massachusetts Institute of Technology, 43 Vassar St, Cambridge, 02139, MA, USA; Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 43 Vassar St, Cambridge, 02139, MA, USA.
| | - Emery N Brown
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, 55 Fruit St, Boston, 02114, MA, USA; Center for Brains, Minds, and Machines, Massachusetts Institute of Technology, 43 Vassar St, Cambridge, 02139, MA, USA; Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 43 Vassar St, Cambridge, 02139, MA, USA; Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology, 45 Carleton St, Cambridge, 02142, MA, USA.
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4
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Hudetz AG. Microstimulation reveals anesthetic state-dependent effective connectivity of neurons in cerebral cortex. Front Neurosci 2024; 18:1387098. [PMID: 39035779 PMCID: PMC11258030 DOI: 10.3389/fnins.2024.1387098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 06/07/2024] [Indexed: 07/23/2024] Open
Abstract
Introduction Complex neuronal interactions underlie cortical information processing that can be compromised in altered states of consciousness. Here intracortical microstimulation was applied to investigate anesthetic state-dependent effective connectivity of neurons in rat visual cortex in vivo. Methods Extracellular activity was recorded at 32 sites in layers 5/6 while stimulating with charge-balanced discrete pulses at each electrode in random order. The same stimulation pattern was applied at three levels of anesthesia with desflurane and in wakefulness. Spikes were sorted and classified by their waveform features as putative excitatory and inhibitory neurons. Network motifs were identified in graphs of effective connectivity constructed from monosynaptic cross-correlograms. Results Microstimulation caused early (<10 ms) increase followed by prolonged (11-100 ms) decrease in spiking of all neurons throughout the electrode array. The early response of excitatory but not inhibitory neurons decayed rapidly with distance from the stimulation site over 1 mm. Effective connectivity of neurons with significant stimulus response was dense in wakefulness and sparse under anesthesia. The number of network motifs, especially those of higher order, increased rapidly as the anesthesia was withdrawn indicating a substantial increase in network connectivity as the animals woke up. Conclusion The results illuminate the impact of anesthesia on functional integrity of local cortical circuits affecting the state of consciousness.
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Affiliation(s)
- Anthony G Hudetz
- Department of Anesthesiology, Center for Consciousness Science, University of Michigan, Ann Arbor, MI, United States
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Greenspon CM, Shelchkova ND, Hobbs TG, Bensmaia SJ, Gaunt RA. Intracortical microstimulation of human somatosensory cortex is sufficient to induce perceptual biases. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.19.24305901. [PMID: 38712172 PMCID: PMC11071569 DOI: 10.1101/2024.04.19.24305901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Time-order error, a psychophysical phenomenon in which the duration in between successive stimuli alters perception, has been studied for decades by neuroscientists and psychologists. To date, however, the locus of these effects is unknown. We use intracortical microstimulation of somatosensory cortex in humans as a tool to bypass initial stages of processing and restrict the possible locations that signals could be modified. We find that, using both amplitude discrimination and magnitude estimation paradigms, intracortical microstimulation reliably evoked time-order errors across all participants. Points of subjective equality were symmetrically shifted during amplitude discrimination experiments and the intensity of a successive stimulus was perceived as being more intense when compared to single stimulus trials in magnitude estimation experiments. The error was reduced for both paradigms at longer inter-stimulus intervals. These results show that direct activation of primary somatosensory cortex is sufficient to induce time-order errors.
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Affiliation(s)
- Charles M. Greenspon
- 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
| | - 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
| | - Robert A. Gaunt
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA
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6
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Suematsu N, Vazquez AL, Kozai TDY. Activation and depression of neural and hemodynamic responses induced by the intracortical microstimulation and visual stimulation in the mouse visual cortex. J Neural Eng 2024; 21:026033. [PMID: 38537268 PMCID: PMC11002944 DOI: 10.1088/1741-2552/ad3853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 02/28/2024] [Accepted: 03/27/2024] [Indexed: 04/09/2024]
Abstract
Objective. Intracortical microstimulation (ICMS) can be an effective method for restoring sensory perception in contemporary brain-machine interfaces. However, the mechanisms underlying better control of neuronal responses remain poorly understood, as well as the relationship between neuronal activity and other concomitant phenomena occurring around the stimulation site.Approach. Different microstimulation frequencies were investigatedin vivoon Thy1-GCaMP6s mice using widefield and two-photon imaging to evaluate the evoked excitatory neural responses across multiple spatial scales as well as the induced hemodynamic responses. Specifically, we quantified stimulation-induced neuronal activation and depression in the mouse visual cortex and measured hemodynamic oxyhemoglobin and deoxyhemoglobin signals using mesoscopic-scale widefield imaging.Main results. Our calcium imaging findings revealed a preference for lower-frequency stimulation in driving stronger neuronal activation. A depressive response following the neural activation preferred a slightly higher frequency stimulation compared to the activation. Hemodynamic signals exhibited a comparable spatial spread to neural calcium signals. Oxyhemoglobin concentration around the stimulation site remained elevated during the post-activation (depression) period. Somatic and neuropil calcium responses measured by two-photon microscopy showed similar dependence on stimulation parameters, although the magnitudes measured in soma was greater than in neuropil. Furthermore, higher-frequency stimulation induced a more pronounced activation in soma compared to neuropil, while depression was predominantly induced in soma irrespective of stimulation frequencies.Significance. These results suggest that the mechanism underlying depression differs from activation, requiring ample oxygen supply, and affecting neurons. Our findings provide a novel understanding of evoked excitatory neuronal activity induced by ICMS and offer insights into neuro-devices that utilize both activation and depression phenomena to achieve desired neural responses.
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Affiliation(s)
- Naofumi Suematsu
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Alberto L Vazquez
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States of America
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, United States of America
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, United States of America
- Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Takashi D Y Kozai
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States of America
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, United States of America
- Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, United States of America
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, United States of America
- NeuroTech Center, University of Pittsburgh Brain Institute, Pittsburgh, PA, United States of America
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7
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Dadarlat MC, Sun YJ, Stryker MP. Activity-dependent recruitment of inhibition and excitation in the awake mammalian cortex during electrical stimulation. Neuron 2024; 112:821-834.e4. [PMID: 38134920 PMCID: PMC10949925 DOI: 10.1016/j.neuron.2023.11.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 08/04/2023] [Accepted: 11/22/2023] [Indexed: 12/24/2023]
Abstract
Electrical stimulation is an effective tool for mapping and altering brain connectivity, with applications ranging from treating pharmacology-resistant neurological disorders to providing sensory feedback for neural prostheses. Paramount to the success of these applications is the ability to manipulate electrical currents to precisely control evoked neural activity patterns. However, little is known about stimulation-evoked responses in inhibitory neurons nor how stimulation-evoked activity patterns depend on ongoing neural activity. In this study, we used 2-photon imaging and cell-type specific labeling to measure single-cell responses of excitatory and inhibitory neurons to electrical stimuli in the visual cortex of awake mice. Our data revealed strong interactions between electrical stimulation and pre-stimulus activity of single neurons in awake animals and distinct recruitment and response patterns for excitatory and inhibitory neurons. This work demonstrates the importance of cell-type-specific labeling of neurons in future studies.
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Affiliation(s)
- Maria C Dadarlat
- Department of Physiology, University of California, San Francisco, San Francisco, CA 94158, USA; Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47906, USA.
| | - Yujiao Jennifer Sun
- Department of Physiology, University of California, San Francisco, San Francisco, CA 94158, USA; Institute of Ophthalmology, University College London, London EC1V 9EL, UK
| | - Michael P Stryker
- Department of Physiology, University of California, San Francisco, San Francisco, CA 94158, USA
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8
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Kumaravelu K, Grill WM. Neural mechanisms of the temporal response of cortical neurons to intracortical microstimulation. Brain Stimul 2024; 17:365-381. [PMID: 38492885 PMCID: PMC11090107 DOI: 10.1016/j.brs.2024.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 02/20/2024] [Accepted: 03/12/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND Intracortical microstimulation (ICMS) is used to map neuronal circuitry in the brain and restore lost sensory function, including vision, hearing, and somatosensation. The temporal response of cortical neurons to single pulse ICMS is remarkably stereotyped and comprises short latency excitation followed by prolonged inhibition and, in some cases, rebound excitation. However, the neural origin of the different response components to ICMS are poorly understood, and the interactions between the three response components during trains of ICMS pulses remains unclear. OBJECTIVE We used computational modeling to determine the mechanisms contributing to the temporal response to ICMS in model cortical neurons. METHODS We implemented a biophysically based computational model of a cortical column comprising neurons with realistic morphology and synapses and quantified the temporal response of cortical neurons to different ICMS protocols. We characterized the temporal responses to single pulse ICMS across stimulation intensities and inhibitory (GABA-B/GABA-A) synaptic strengths. To probe interactions between response components, we quantified the response to paired pulse ICMS at different inter-pulse intervals and the response to short trains at different stimulation frequencies. Finally, we evaluated the performance of biomimetic ICMS trains in evoking sustained neural responses. RESULTS Single pulse ICMS evoked short latency excitation followed by a period of inhibition, but model neurons did not exhibit post-inhibitory excitation. The strength of short latency excitation increased and the duration of inhibition increased with increased stimulation amplitude. Prolonged inhibition resulted from both after-hyperpolarization currents and GABA-B synaptic transmission. During the paired pulse protocol, the strength of short latency excitation evoked by a test pulse decreased marginally compared to those evoked by a single pulse for interpulse intervals (IPI) < 100 m s. Further, the duration of inhibition evoked by the test pulse was prolonged compared to single pulse for IPIs <50 m s and was not predicted by linear superposition of individual inhibitory responses. For IPIs>50 m s, the duration of inhibition evoked by the test pulse was comparable to those evoked by a single pulse. Short ICMS trains evoked repetitive excitatory responses against a background of inhibition. However, the strength of the repetitive excitatory response declined during ICMS at higher frequencies. Further, the duration of inhibition at the cessation of ICMS at higher frequencies was prolonged compared to the duration following a single pulse. Biomimetic pulse trains evoked comparable neural response between the onset and offset phases despite the presence of stimulation induced inhibition. CONCLUSIONS The cortical column model replicated the short latency excitation and long-lasting inhibitory components of the stereotyped neural response documented in experimental studies of ICMS. Both cellular and synaptic mechanisms influenced the response components generated by ICMS. The non-linear interactions between response components resulted in dynamic ICMS-evoked neural activity and may play an important role in mediating the ICMS-induced precepts.
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Affiliation(s)
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Durham, NC, USA; Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA; Department of Neurobiology, Duke University, Durham, NC, USA; Department of Neurosurgery, Duke University, Durham, NC, USA.
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9
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Donati E, Valle G. Neuromorphic hardware for somatosensory neuroprostheses. Nat Commun 2024; 15:556. [PMID: 38228580 PMCID: PMC10791662 DOI: 10.1038/s41467-024-44723-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 01/03/2024] [Indexed: 01/18/2024] Open
Abstract
In individuals with sensory-motor impairments, missing limb functions can be restored using neuroprosthetic devices that directly interface with the nervous system. However, restoring the natural tactile experience through electrical neural stimulation requires complex encoding strategies. Indeed, they are presently limited in effectively conveying or restoring tactile sensations by bandwidth constraints. Neuromorphic technology, which mimics the natural behavior of neurons and synapses, holds promise for replicating the encoding of natural touch, potentially informing neurostimulation design. In this perspective, we propose that incorporating neuromorphic technologies into neuroprostheses could be an effective approach for developing more natural human-machine interfaces, potentially leading to advancements in device performance, acceptability, and embeddability. We also highlight ongoing challenges and the required actions to facilitate the future integration of these advanced technologies.
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Affiliation(s)
- Elisa Donati
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
| | - Giacomo Valle
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA.
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10
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Suematsu N, Vazquez AL, Kozai TD. Activation and depression of neural and hemodynamic responses induced by the intracortical microstimulation and visual stimulation in the mouse visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.01.573814. [PMID: 38260671 PMCID: PMC10802282 DOI: 10.1101/2024.01.01.573814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Objective . Intracortical microstimulation can be an effective method for restoring sensory perception in contemporary brain-machine interfaces. However, the mechanisms underlying better control of neuronal responses remain poorly understood, as well as the relationship between neuronal activity and other concomitant phenomena occurring around the stimulation site. Approach . Different microstimulation frequencies were investigated in vivo on Thy1-GCaMP6s mice using widefield and two-photon imaging to evaluate the evoked excitatory neural responses across multiple spatial scales as well as the induced hemodynamic responses. Specifically, we quantified stimulation-induced neuronal activation and depression in the mouse visual cortex and measured hemodynamic oxyhemoglobin and deoxyhemoglobin signals using mesoscopic-scale widefield imaging. Main results . Our calcium imaging findings revealed a preference for lower-frequency stimulation in driving stronger neuronal activation. A depressive response following the neural activation preferred a slightly higher frequency stimulation compared to the activation. Hemodynamic signals exhibited a comparable spatial spread to neural calcium signals. Oxyhemoglobin concentration around the stimulation site remained elevated during the post-activation (depression) period. Somatic and neuropil calcium responses measured by two-photon microscopy showed similar dependence on stimulation parameters, although the magnitudes measured in soma was greater than in neuropil. Furthermore, higher-frequency stimulation induced a more pronounced activation in soma compared to neuropil, while depression was predominantly induced in soma irrespective of stimulation frequencies. Significance . These results suggest that the mechanism underlying depression differs from activation, requiring ample oxygen supply, and affecting neurons. Our findings provide a novel understanding of evoked excitatory neuronal activity induced by intracortical microstimulation and offer insights into neuro-devices that utilize both activation and depression phenomena to achieve desired neural responses.
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11
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Margalit SN, Slovin H. Spatio-temporal activation patterns of neuronal population evoked by optostimulation and the comparison to electrical microstimulation. Sci Rep 2023; 13:12689. [PMID: 37542091 PMCID: PMC10403613 DOI: 10.1038/s41598-023-39808-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 07/31/2023] [Indexed: 08/06/2023] Open
Abstract
Optostimulation and electrical microstimulation are well-established techniques that enable to artificially stimulate the brain. While the activation patterns evoked by microstimulation in cortical network are well characterized, much less is known for optostimulation. Specifically, the activation maps of neuronal population at the membrane potential level and direct measurements of these maps were barely reported. In addition, only a few studies compared the activation patterns evoked by microstimulation and optostimulation. In this study we addressed these issues by applying optostimulation in the barrel cortex of anesthetized rats after a short (ShortExp) or a long (LongExp) opsin expression time and compared it to microstimulation. We measured the membrane potential of neuronal populations at high spatial (meso-scale) and temporal resolution using voltage-sensitive dye imaging. Longer optostimulation pulses evoked higher neural responses spreading over larger region relative to short pulses. Interestingly, similar optostimulation pulses evoked stronger and more prolonged population response in the LongExp vs. the ShortExp condition. Finally, the spatial activation patterns evoked in the LongExp condition showed an intermediate state, with higher resemblance to the microstimulation at the stimulation site. Therefore, short microstimulation and optostimulation can induce wide spread activation, however the effects of optostimulation depend on the opsin expression time.
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Affiliation(s)
| | - Hamutal Slovin
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel.
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12
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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 : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.23.545425. [PMID: 37425877 PMCID: PMC10327055 DOI: 10.1101/2023.06.23.545425] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [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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13
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Lycke R, Kim R, Zolotavin P, Montes J, Sun Y, Koszeghy A, Altun E, Noble B, Yin R, He F, Totah N, Xie C, Luan L. Low-threshold, high-resolution, chronically stable intracortical microstimulation by ultraflexible electrodes. Cell Rep 2023; 42:112554. [PMID: 37235473 PMCID: PMC10592461 DOI: 10.1016/j.celrep.2023.112554] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 04/08/2023] [Accepted: 05/05/2023] [Indexed: 05/28/2023] Open
Abstract
Intracortical microstimulation (ICMS) enables applications ranging from neuroprosthetics to causal circuit manipulations. However, the resolution, efficacy, and chronic stability of neuromodulation are often compromised by adverse tissue responses to the indwelling electrodes. Here we engineer ultraflexible stim-nanoelectronic threads (StimNETs) and demonstrate low activation threshold, high resolution, and chronically stable ICMS in awake, behaving mouse models. In vivo two-photon imaging reveals that StimNETs remain seamlessly integrated with the nervous tissue throughout chronic stimulation periods and elicit stable, focal neuronal activation at low currents of 2 μA. Importantly, StimNETs evoke longitudinally stable behavioral responses for over 8 months at a markedly low charge injection of 0.25 nC/phase. Quantified histological analyses show that chronic ICMS by StimNETs induces no neuronal degeneration or glial scarring. These results suggest that tissue-integrated electrodes provide a path for robust, long-lasting, spatially selective neuromodulation at low currents, which lessens risk of tissue damage or exacerbation of off-target side effects.
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Affiliation(s)
- Roy Lycke
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Rice Neuroengineering Initiative, Rice University, Houston, TX 77005, USA
| | - Robin Kim
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Rice Neuroengineering Initiative, Rice University, Houston, TX 77005, USA
| | - Pavlo Zolotavin
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Rice Neuroengineering Initiative, Rice University, Houston, TX 77005, USA
| | - Jon Montes
- Rice Neuroengineering Initiative, Rice University, Houston, TX 77005, USA; Department of Bioengineering, Rice University, Houston, TX 77005, USA
| | - Yingchu Sun
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Rice Neuroengineering Initiative, Rice University, Houston, TX 77005, USA
| | - Aron Koszeghy
- Helsinki Institute of Life Science (HiLIFE), University of Helsinki, 00790 Helsinki, Finland
| | - Esra Altun
- Rice Neuroengineering Initiative, Rice University, Houston, TX 77005, USA; Material Science and NanoEngineering, Rice University, Houston, TX 77005, USA
| | - Brian Noble
- Rice Neuroengineering Initiative, Rice University, Houston, TX 77005, USA; Applied Physics Program, Rice University, Houston, TX 77005, USA
| | - Rongkang Yin
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Rice Neuroengineering Initiative, Rice University, Houston, TX 77005, USA
| | - Fei He
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Rice Neuroengineering Initiative, Rice University, Houston, TX 77005, USA
| | - Nelson Totah
- Helsinki Institute of Life Science (HiLIFE), University of Helsinki, 00790 Helsinki, Finland; Faculty of Pharmacy, University of Helsinki, 00790 Helsinki, Finland
| | - Chong Xie
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Rice Neuroengineering Initiative, Rice University, Houston, TX 77005, USA; Department of Bioengineering, Rice University, Houston, TX 77005, USA.
| | - Lan Luan
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; Rice Neuroengineering Initiative, Rice University, Houston, TX 77005, USA; Department of Bioengineering, Rice University, Houston, TX 77005, USA.
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14
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Abstract
Brain-machine interfaces (BMIs) aim to treat sensorimotor neurological disorders by creating artificial motor and/or sensory pathways. Introducing artificial pathways creates new relationships between sensory input and motor output, which the brain must learn to gain dexterous control. This review highlights the role of learning in BMIs to restore movement and sensation, and discusses how BMI design may influence neural plasticity and performance. The close integration of plasticity in sensory and motor function influences the design of both artificial pathways and will be an essential consideration for bidirectional devices that restore both sensory and motor function.
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Affiliation(s)
- Maria C Dadarlat
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
| | - Ryan A Canfield
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | - Amy L Orsborn
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
- Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, USA
- Washington National Primate Research Center, Seattle, Washington, USA
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15
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Hughes C, Kozai T. Dynamic amplitude modulation of microstimulation evokes biomimetic onset and offset transients and reduces depression of evoked calcium responses in sensory cortices. Brain Stimul 2023; 16:939-965. [PMID: 37244370 PMCID: PMC10330928 DOI: 10.1016/j.brs.2023.05.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 05/15/2023] [Accepted: 05/17/2023] [Indexed: 05/29/2023] Open
Abstract
BACKGROUND Intracortical microstimulation (ICMS) is an emerging approach to restore sensation to people with neurological injury or disease. Biomimetic microstimulation, or stimulus trains that mimic neural activity in the brain through encoding of onset and offset transients, could improve the utility of ICMS for brain-computer interface (BCI) applications, but how biomimetic microstimulation affects neural activation is not understood. Current "biomimetic" ICMS trains aim to reproduce the strong onset and offset transients evoked in the brain by sensory input through dynamic modulation of stimulus parameters. Stimulus induced depression of neural activity (decreases in evoked intensity over time) is also a potential barrier to clinical implementation of sensory feedback, and dynamic microstimulation may reduce this effect. OBJECTIVE We evaluated how bio-inspired ICMS trains with dynamic modulation of amplitude and/or frequency change the calcium response, spatial distribution, and depression of neurons in the somatosensory and visual cortices. METHODS Calcium responses of neurons were measured in Layer 2/3 of visual and somatosensory cortices of anesthetized GCaMP6s mice in response to ICMS trains with fixed amplitude and frequency (Fixed) and three dynamic ICMS trains that increased the stimulation intensity during the onset and offset of stimulation by modulating the amplitude (DynAmp), frequency (DynFreq), or amplitude and frequency (DynBoth). ICMS was provided for either 1-s with 4-s breaks (Short) or for 30-s with 15-s breaks (Long). RESULTS DynAmp and DynBoth trains evoked distinct onset and offset transients in recruited neural populations, while DynFreq trains evoked population activity similar to Fixed trains. Individual neurons had heterogeneous responses primarily based on how quickly they depressed to ICMS, where neurons farther from the electrode depressed faster and a small subpopulation (1-5%) were modulated by DynFreq trains. Neurons that depressed to Short trains were also more likely to depress to Long trains, but Long trains induced more depression overall due to the increased stimulation length. Increasing the amplitude during the hold phase resulted in an increase in recruitment and intensity which resulted in more depression and reduced offset responses. Dynamic amplitude modulation reduced stimulation induced depression by 14.6 ± 0.3% for Short and 36.1 ± 0.6% for Long trains. Ideal observers were 0.031 ± 0.009 s faster for onset detection and 1.33 ± 0.21 s faster for offset detection with dynamic amplitude encoding. CONCLUSIONS Dynamic amplitude modulation evokes distinct onset and offset transients, reduces depression of neural calcium activity, and decreases total charge injection for sensory feedback in BCIs by lowering recruitment of neurons during long maintained periods of ICMS. In contrast, dynamic frequency modulation evokes distinct onset and offset transients in a small subpopulation of neurons but also reduces depression in recruited neurons by reducing the rate of activation.
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Affiliation(s)
- Christopher Hughes
- Department of Bioengineering, University of Pittsburgh, USA; Center for the Neural Basis of Cognition, USA
| | - Takashi Kozai
- Department of Bioengineering, University of Pittsburgh, USA; Center for the Neural Basis of Cognition, USA; Department of Neuroscience, University of Pittsburgh, USA; McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA; NeuroTech Center, University of Pittsburgh Brain Institute, Pittsburgh, PA, USA.
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16
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Bundy DT, Barbay S, Hudson HM, Frost SB, Nudo RJ, Guggenmos DJ. Stimulation-Evoked Effective Connectivity (SEEC): An in-vivo approach for defining mesoscale corticocortical connectivity. J Neurosci Methods 2023; 384:109767. [PMID: 36493978 DOI: 10.1016/j.jneumeth.2022.109767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 11/07/2022] [Accepted: 12/05/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Cortical electrical stimulation is a versatile technique for examining the structure and function of cortical regions and for implementing novel therapies. While electrical stimulation has been used to examine the local spread of neural activity, it may also enable longitudinal examination of mesoscale interregional connectivity. NEW METHOD Here, we sought to use intracortical microstimulation (ICMS) in conjunction with recordings of multi-unit action potentials to assess the mesoscale effective connectivity within sensorimotor cortex. Neural recordings were made from multielectrode arrays placed into sensory, motor, and premotor regions during surgical experiments in three squirrel monkeys. During each recording, single-pulse ICMS was repeatably delivered to a single region. Mesoscale effective connectivity was calculated from ICMS-evoked changes in multi-unit firing. RESULTS Multi-unit action potentials were able to be detected on the order of 1 ms after each ICMS pulse. Across sensorimotor regions, short-latency (< 2.5 ms) ICMS-evoked neural activity strongly correlated with known anatomical connections. Additionally, ICMS-evoked responses remained stable across the experimental period, despite small changes in electrode locations and anesthetic state. COMPARISON WITH EXISTING METHODS Previous imaging studies investigating cross-regional responses to stimulation are limited to utilizing indirect hemodynamic responses and thus lack the temporal specificity of ICMS-evoked responses. CONCLUSIONS These results show that monitoring ICMS-evoked neural activity, in a technique we refer to as Stimulation-Evoked Effective Connectivity (SEEC), is a viable way to longitudinally assess effective connectivity, enabling studies comparing the time course of connectivity changes with the time course of changes in behavioral function.
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Affiliation(s)
- David T Bundy
- Departiment of Physical Medicine and Rehabilitation, University of Kansas Medical Center, Kansas City, KS, USA
| | - Scott Barbay
- Departiment of Physical Medicine and Rehabilitation, University of Kansas Medical Center, Kansas City, KS, USA
| | - Heather M Hudson
- Departiment of Physical Medicine and Rehabilitation, University of Kansas Medical Center, Kansas City, KS, USA
| | - Shawn B Frost
- Departiment of Physical Medicine and Rehabilitation, University of Kansas Medical Center, Kansas City, KS, USA
| | - Randolph J Nudo
- Departiment of Physical Medicine and Rehabilitation, University of Kansas Medical Center, Kansas City, KS, USA; Landon Center on Aging, University of Kansas Medical Center, Kansas City, KS, USA.
| | - David J Guggenmos
- Departiment of Physical Medicine and Rehabilitation, University of Kansas Medical Center, Kansas City, KS, USA
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17
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A simple model considering spiking probability during extracellular axon stimulation. PLoS One 2022; 17:e0264735. [PMID: 35446861 PMCID: PMC9022861 DOI: 10.1371/journal.pone.0264735] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 02/14/2022] [Indexed: 11/20/2022] Open
Abstract
The spiking probability of an electrically stimulated axon as a function of stimulus amplitude increases in a sigmoidal dependency from 0 to 1. However, most computer simulation studies for neuroprosthetic applications calculate thresholds for neural targets with a deterministic model and by reducing the sigmoid curve to a step function, they miss an important information about the control signal, namely how the spiking efficiency increases with stimulus intensity. Here, this spiking efficiency is taken into account in a compartment model of the Hodgkin Huxley type where a noise current is added in every compartment with an active membrane. A key parameter of the model is a common factor knoise which defines the ion current fluctuations across the cell membrane for every compartment by its maximum sodium ion conductance. In the standard model Gaussian signals are changed every 2.5 μs as a compromise of accuracy and computational costs. Additionally, a formula for other noise transmission times is presented and numerically tested. Spiking probability as a function of stimulus intensity can be approximated by the cumulative distribution function of the normal distribution with RS = σ/μ. Relative spread RS, introduced by Verveen, is a measure for the spread (normalized by the threshold intensity μ), that decreases inversely with axon diameter. Dynamic range, a related measure used in neuroprosthetic studies, defines the intensity range between 10% and 90% spiking probability. We show that (i) the dynamic range normalized by threshold is 2.56 times RS, (ii) RS increases with electrode—axon distance and (iii) we present knoise values for myelinated and unmyelinated axon models in agreement with recoded RS data. The presented method is applicable for other membrane models and can be extended to whole neurons that are described by multi-compartment models.
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18
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Sombeck JT, Heye J, Kumaravelu K, Goetz SM, Peterchev AV, Grill WM, Bensmaia S, Miller LE. Characterizing the short-latency evoked response to intracortical microstimulation across a multi-electrode array. J Neural Eng 2022; 19:10.1088/1741-2552/ac63e8. [PMID: 35378515 PMCID: PMC9142773 DOI: 10.1088/1741-2552/ac63e8] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 04/04/2022] [Indexed: 11/12/2022]
Abstract
Objective.Persons with tetraplegia can use brain-machine interfaces to make visually guided reaches with robotic arms. Without somatosensory feedback, these movements will likely be slow and imprecise, like those of persons who retain movement but have lost proprioception. Intracortical microstimulation (ICMS) has promise for providing artificial somatosensory feedback. ICMS that mimics naturally occurring neural activity, may allow afferent interfaces that are more informative and easier to learn than stimulation evoking unnaturalistic activity. To develop such biomimetic stimulation patterns, it is important to characterize the responses of neurons to ICMS.Approach.Using a Utah multi-electrode array, we recorded activity evoked by both single pulses and trains of ICMS at a wide range of amplitudes and frequencies in two rhesus macaques. As the electrical artifact caused by ICMS typically prevents recording for many milliseconds, we deployed a custom rapid-recovery amplifier with nonlinear gain to limit signal saturation on the stimulated electrode. Across all electrodes after stimulation, we removed the remaining slow return to baseline with acausal high-pass filtering of time-reversed recordings.Main results.After single pulses of stimulation, we recorded what was likely transsynaptically-evoked activity even on the stimulated electrode as early as ∼0.7 ms. This was immediately followed by suppressed neural activity lasting 10-150 ms. After trains, this long-lasting inhibition was replaced by increased firing rates for ∼100 ms. During long trains, the evoked response on the stimulated electrode decayed rapidly while the response was maintained on non-stimulated channels.Significance.The detailed description of the spatial and temporal response to ICMS can be used to better interpret results from experiments that probe circuit connectivity or function of cortical areas. These results can also contribute to the design of stimulation patterns to improve afferent interfaces for artificial sensory feedback.
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Affiliation(s)
- Joseph T Sombeck
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States of America
| | - Juliet Heye
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
| | - Karthik Kumaravelu
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
| | - Stefan M Goetz
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States of America
- Department of Neurosurgery, Duke University, Durham, NC, United States of America
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, United States of America
- Duke Institute for Brain Sciences, Duke University, Durham, NC, United States of America
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom
| | - Angel V Peterchev
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States of America
- Department of Neurosurgery, Duke University, Durham, NC, United States of America
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, United States of America
- Duke Institute for Brain Sciences, Duke University, Durham, NC, United States of America
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States of America
- Department of Neurobiology, Duke University, Durham, NC, United States of America
- Department of Neurosurgery, Duke University, Durham, NC, United States of America
- Duke Institute for Brain Sciences, Duke University, Durham, NC, United States of America
| | - Sliman Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, United States of America
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, United States of America
- Neuroscience Institute, University of Chicago, Chicago, IL, United States of America
| | - Lee E Miller
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States of America
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States of America
- Shirley Ryan AbilityLab, Chicago, IL, United States of America
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19
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Kunigk NG, Urdaneta ME, Malone IG, Delgado F, Otto KJ. Reducing Behavioral Detection Thresholds per Electrode via Synchronous, Spatially-Dependent Intracortical Microstimulation. Front Neurosci 2022; 16:876142. [PMID: 35784835 PMCID: PMC9247280 DOI: 10.3389/fnins.2022.876142] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 05/31/2022] [Indexed: 12/04/2022] Open
Abstract
Intracortical microstimulation (ICMS) has shown promise in restoring quality of life to patients suffering from paralysis, specifically when used in the primary somatosensory cortex (S1). However, these benefits can be hampered by long-term degradation of electrode performance due to the brain's foreign body response. Advances in microfabrication techniques have allowed for the development of neuroprostheses with subcellular electrodes, which are characterized by greater versatility and a less detrimental immune response during chronic use. These probes are hypothesized to enable more selective, higher-resolution stimulation of cortical tissue with long-term implants. However, microstimulation using physiologically relevant charges with these smaller-scale devices can damage electrode sites and reduce the efficacy of the overall device. Studies have shown promise in bypassing this limitation by spreading the stimulation charge between multiple channels in an implanted electrode array, but to our knowledge the usefulness of this strategy in laminar arrays with electrode sites spanning each layer of the cortex remains unexplored. To investigate the efficacy of simultaneous multi-channel ICMS in electrode arrays with stimulation sites spanning cortical depth, we implanted laminar electrode arrays in the primary somatosensory cortex of rats trained in a behavioral avoidance paradigm. By measuring detection thresholds, we were able to quantify improvements in ICMS performance using a simultaneous multi-channel stimulation paradigm. The charge required per site to elicit detection thresholds was halved when stimulating from two adjacent electrode sites, although the overall charge used by the implant was increased. This reduction in threshold charge was more pronounced when stimulating with more than two channels and lessened with greater distance between stimulating channels. Our findings suggest that these improvements are based on the synchronicity and polarity of each stimulus, leading us to conclude that these improvements in stimulation efficiency per electrode are due to charge summation as opposed to a summation of neural responses to stimulation. Additionally, the per-site charge reductions are seen regardless of the cortical depth of each utilized channel. This evocation of physiological detection thresholds with lower stimulation currents per electrode site has implications for the feasibility of stimulation regimes in future advanced neuroprosthetic devices, which could benefit from reducing the charge output per site.
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Affiliation(s)
- Nicolas G. Kunigk
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Morgan E. Urdaneta
- Department of Neuroscience, University of Florida, Gainesville, FL, United States
| | - Ian G. Malone
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States
| | - Francisco Delgado
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Kevin J. Otto
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
- Department of Neuroscience, University of Florida, Gainesville, FL, United States
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States
- Department of Materials Science and Engineering, University of Florida, Gainesville, FL, United States
- Department of Neurology, University of Florida, Gainesville, FL, United States
- McKnight Brain Institute, University of Florida, Gainesville, FL, United States
- *Correspondence: Kevin J. Otto,
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