1
|
Lamorie-Foote K, Kramer DR, Sundaram S, Cavaleri J, Gilbert ZD, Tang AM, Bashford L, Liu CY, Kellis S, Lee B. Primary somatosensory cortex organization for engineering artificial somatosensation. Neurosci Res 2024; 204:1-13. [PMID: 38278220 DOI: 10.1016/j.neures.2024.01.005] [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: 08/30/2023] [Revised: 01/12/2024] [Accepted: 01/17/2024] [Indexed: 01/28/2024]
Abstract
Somatosensory deficits from stroke, spinal cord injury, or other neurologic damage can lead to a significant degree of functional impairment. The primary (SI) and secondary (SII) somatosensory cortices encode information in a medial to lateral organization. SI is generally organized topographically, with more discrete cortical representations of specific body regions. SII regions corresponding to anatomical areas are less discrete and may represent a more functional rather than topographic organization. Human somatosensory research continues to map cortical areas of sensory processing with efforts primarily focused on hand and upper extremity information in SI. However, research into SII and other body regions is lacking. In this review, we synthesize the current state of knowledge regarding the cortical organization of human somatosensation and discuss potential applications for brain computer interface. In addition to accurate individualized mapping of cortical somatosensation, further research is required to uncover the neurophysiological mechanisms of how somatosensory information is encoded in the cortex.
Collapse
Affiliation(s)
- Krista Lamorie-Foote
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Daniel R Kramer
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States; Department of Neurological Surgery, University of Colorado School of Medicine, Denver, CO, United States
| | - Shivani Sundaram
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States.
| | - Jonathon Cavaleri
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Zachary D Gilbert
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Austin M Tang
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States; Department of Neurological Surgery, University of Texas at Houston, Houston, TX, United States
| | - Luke Bashford
- Department of Biology and Biological Engineering, T&C Chen Institute for Neuroscience, California Institute of Technology, Pasadena, CA, United States; Department of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Charles Y Liu
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States; USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, United States
| | - Spencer Kellis
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States; USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, United States
| | - Brian Lee
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States; USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, United States
| |
Collapse
|
2
|
Slack JC, Zeiser SL, Yadav AP. The role of stimulus periodicity on spinal cord stimulation-induced artificial sensations in rodents. J Neural Eng 2024; 21:026003. [PMID: 38382104 PMCID: PMC10912903 DOI: 10.1088/1741-2552/ad2b89] [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: 09/24/2023] [Revised: 01/26/2024] [Accepted: 02/21/2024] [Indexed: 02/23/2024]
Abstract
Objective.Sensory feedback is critical for effectively controlling brain-machine interfaces and neuroprosthetic devices. Spinal cord stimulation (SCS) is proposed as a technique to induce artificial sensory perceptions in rodents, monkeys, and humans. However, to realize the full potential of SCS as a sensory neuroprosthetic technology, a better understanding of the effect of SCS pulse train parameter changes on sensory detection and discrimination thresholds is necessary.Approach.Here we investigated whether stimulation periodicity impacts rats' ability to detect and discriminate SCS-induced perceptions at different frequencies.Main results.By varying the coefficient of variation (CV) of interstimulus pulse interval, we showed that at lower frequencies, rats could detect highly aperiodic SCS pulse trains at lower amplitudes (i.e. decreased detection thresholds). Furthermore, rats learned to discriminate stimuli with subtle differences in periodicity, and the just-noticeable differences from a highly aperiodic stimulus were smaller than those from a periodic stimulus.Significance.These results demonstrate that the temporal structure of an SCS pulse train is an integral parameter for modulating sensory feedback in neuroprosthetic applications.
Collapse
Affiliation(s)
- Jacob C Slack
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, United States of America
| | - Sidnee L Zeiser
- Department of Biomedical Engineering, Purdue University Indianapolis, Indianapolis, IN, United States of America
| | - Amol P Yadav
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, United States of America
- Department of Neurosurgery, UNC School of Medicine, Chapel Hill, NC, United States of America
- Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| |
Collapse
|
3
|
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: 0] [Impact Index Per Article: 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.
Collapse
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.
| |
Collapse
|
4
|
Várkuti B, Halász L, Hagh Gooie S, Miklós G, Smits Serena R, van Elswijk G, McIntyre CC, Lempka SF, Lozano AM, Erōss L. Conversion of a medical implant into a versatile computer-brain interface. Brain Stimul 2024; 17:39-48. [PMID: 38145752 DOI: 10.1016/j.brs.2023.12.011] [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: 10/13/2023] [Revised: 12/19/2023] [Accepted: 12/20/2023] [Indexed: 12/27/2023] Open
Abstract
BACKGROUND Information transmission into the human nervous system is the basis for a variety of prosthetic applications. Spinal cord stimulation (SCS) systems are widely available, have a well documented safety record, can be implanted minimally invasively, and are known to stimulate afferent pathways. Nonetheless, SCS devices are not yet used for computer-brain-interfacing applications. OBJECTIVE Here we aimed to establish computer-to-brain communication via medical SCS implants in a group of 20 individuals who had been operated for the treatment of chronic neuropathic pain. METHODS In the initial phase, we conducted interface calibration with the aim of determining personalized stimulation settings that yielded distinct and reproducible sensations. These settings were subsequently utilized to generate inputs for a range of behavioral tasks. We evaluated the required calibration time, task training duration, and the subsequent performance in each task. RESULTS We could establish a stable spinal computer-brain interface in 18 of the 20 participants. Each of the 18 then performed one or more of the following tasks: A rhythm-discrimination task (n = 13), a Morse-decoding task (n = 3), and/or two different balance/body-posture tasks (n = 18; n = 5). The median calibration time was 79 min. The median training time for learning to use the interface in a subsequent task was 1:40 min. In each task, every participant demonstrated successful performance, surpassing chance levels. CONCLUSION The results constitute the first proof-of-concept of a general purpose computer-brain interface paradigm that could be deployed on present-day medical SCS platforms.
Collapse
Affiliation(s)
| | - László Halász
- Albert-Szentgyörgyi Medical School, Doctoral School of Clinical Medicine, Clinical and Experimental Research for Reconstructive and Organ-Sparing Surgery, University of Szeged, Szeged, Hungary
| | | | - Gabriella Miklós
- CereGate GmbH, München, Germany; National Institute of Mental Health, Neurology, and Neurosurgery, Budapest, Hungary; János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, Hungary
| | - Ricardo Smits Serena
- CereGate GmbH, München, Germany; Department of Orthopaedics and Sports Orthopaedics, Klinikum Rechts der Isar, Technical University of Munich, München, Germany
| | | | - Cameron C McIntyre
- Department of Biomedical Engineering and Department of Neurosurgery, Duke University, Durham, NC, USA
| | - Scott F Lempka
- Department of Biomedical Engineering, Department of Anesthesiology and the Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Andres M Lozano
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Loránd Erōss
- National Institute of Mental Health, Neurology, and Neurosurgery, Budapest, Hungary
| |
Collapse
|
5
|
Bensmaia SJ, Tyler DJ, Micera S. Restoration of sensory information via bionic hands. Nat Biomed Eng 2023; 7:443-455. [PMID: 33230305 PMCID: PMC10233657 DOI: 10.1038/s41551-020-00630-8] [Citation(s) in RCA: 85] [Impact Index Per Article: 85.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 09/13/2020] [Indexed: 12/19/2022]
Abstract
Individuals who have lost the use of their hands because of amputation or spinal cord injury can use prosthetic hands to restore their independence. A dexterous prosthesis requires the acquisition of control signals that drive the movements of the robotic hand, and the transmission of sensory signals to convey information to the user about the consequences of these movements. In this Review, we describe non-invasive and invasive technologies for conveying artificial sensory feedback through bionic hands, and evaluate the technologies' long-term prospects.
Collapse
Affiliation(s)
- 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.
- Grossman Institute for Neuroscience, Quantitative Biology, and Human Behavior, University of Chicago, Chicago, IL, USA.
| | - Dustin J Tyler
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, OH, USA
| | - Silvestro Micera
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.
- Translational Neural Engineering Laboratory, Center for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Federale de Lausanne, Lausanne, Switzerland.
| |
Collapse
|
6
|
Kumar G, Ma CHE. Toward a cerebello-thalamo-cortical computational model of spinocerebellar ataxia. Neural Netw 2023; 162:541-556. [PMID: 37023628 DOI: 10.1016/j.neunet.2023.01.045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 12/07/2022] [Accepted: 01/28/2023] [Indexed: 02/05/2023]
Abstract
Computational neural network modelling is an emerging approach for optimization of drug treatment of neurological disorders and fine-tuning of rehabilitation strategies. In the current study, we constructed a cerebello-thalamo-cortical computational neural network model to simulate a mouse model of cerebellar ataxia (pcd5J mice) by manipulating cerebellar bursts through reduction of GABAergic inhibitory input. Cerebellar output neurons were projected to the thalamus and bidirectionally connected with the cortical network. Our results showed that reduction of inhibitory input in the cerebellum orchestrated the cortical local field potential (LFP) dynamics to generate specific motor outputs of oscillations of the theta, alpha, and beta bands in the computational model as well as in mouse motor cortical neurons. The therapeutic potential of deep brain stimulation (DBS) was tested in the computational model by increasing the sensory input to restore cortical output. Ataxia mice showed normalization of the motor cortex LFP after cerebellum DBS. We provide a novel approach to computational modelling to investigate the effect of DBS by mimicking cerebellar ataxia involving degeneration of Purkinje cells. Simulated neural activity coincides with findings from neural recordings of ataxia mice. Our computational model could thus represent cerebellar pathologies and provide insight into how to improve disease symptoms by restoring neuronal electrophysiological properties using DBS.
Collapse
Affiliation(s)
- Gajendra Kumar
- Department of Neuroscience, City University of Hong Kong, Tat Chee Avenue, Hong Kong Special Administrative Region.
| | - Chi Him Eddie Ma
- Department of Neuroscience, City University of Hong Kong, Tat Chee Avenue, Hong Kong Special Administrative Region.
| |
Collapse
|
7
|
Belkacem AN, Jamil N, Khalid S, Alnajjar F. On closed-loop brain stimulation systems for improving the quality of life of patients with neurological disorders. Front Hum Neurosci 2023; 17:1085173. [PMID: 37033911 PMCID: PMC10076878 DOI: 10.3389/fnhum.2023.1085173] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 03/06/2023] [Indexed: 04/11/2023] Open
Abstract
Emerging brain technologies have significantly transformed human life in recent decades. For instance, the closed-loop brain-computer interface (BCI) is an advanced software-hardware system that interprets electrical signals from neurons, allowing communication with and control of the environment. The system then transmits these signals as controlled commands and provides feedback to the brain to execute specific tasks. This paper analyzes and presents the latest research on closed-loop BCI that utilizes electric/magnetic stimulation, optogenetic, and sonogenetic techniques. These techniques have demonstrated great potential in improving the quality of life for patients suffering from neurodegenerative or psychiatric diseases. We provide a comprehensive and systematic review of research on the modalities of closed-loop BCI in recent decades. To achieve this, the authors used a set of defined criteria to shortlist studies from well-known research databases into categories of brain stimulation techniques. These categories include deep brain stimulation, transcranial magnetic stimulation, transcranial direct-current stimulation, transcranial alternating-current stimulation, and optogenetics. These techniques have been useful in treating a wide range of disorders, such as Alzheimer's and Parkinson's disease, dementia, and depression. In total, 76 studies were shortlisted and analyzed to illustrate how closed-loop BCI can considerably improve, enhance, and restore specific brain functions. The analysis revealed that literature in the area has not adequately covered closed-loop BCI in the context of cognitive neural prosthetics and implanted neural devices. However, the authors demonstrate that the applications of closed-loop BCI are highly beneficial, and the technology is continually evolving to improve the lives of individuals with various ailments, including those with sensory-motor issues or cognitive deficiencies. By utilizing emerging techniques of stimulation, closed-loop BCI can safely improve patients' cognitive and affective skills, resulting in better healthcare outcomes.
Collapse
Affiliation(s)
- Abdelkader Nasreddine Belkacem
- Department of Computer and Network Engineering, College of Information Technology, UAE University, Al-Ain, United Arab Emirates
- *Correspondence: Abdelkader Nasreddine Belkacem
| | - Nuraini Jamil
- Department of Computer Science and Software Engineering, College of Information Technology, UAE University, Al-Ain, United Arab Emirates
| | - Sumayya Khalid
- Department of Computer Science and Software Engineering, College of Information Technology, UAE University, Al-Ain, United Arab Emirates
| | - Fady Alnajjar
- Department of Computer Science and Software Engineering, College of Information Technology, UAE University, Al-Ain, United Arab Emirates
- Center for Brain Science, RIKEN, Saitama, Japan
- Fady Alnajjar
| |
Collapse
|
8
|
Saalmann YB, Mofakham S, Mikell CB, Djuric PM. Microscale multicircuit brain stimulation: Achieving real-time brain state control for novel applications. CURRENT RESEARCH IN NEUROBIOLOGY 2022; 4:100071. [PMID: 36619175 PMCID: PMC9816916 DOI: 10.1016/j.crneur.2022.100071] [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: 04/30/2022] [Revised: 11/30/2022] [Accepted: 12/19/2022] [Indexed: 12/30/2022] Open
Abstract
Neurological and psychiatric disorders typically result from dysfunction across multiple neural circuits. Most of these disorders lack a satisfactory neuromodulation treatment. However, deep brain stimulation (DBS) has been successful in a limited number of disorders; DBS typically targets one or two brain areas with single contacts on relatively large electrodes, allowing for only coarse modulation of circuit function. Because of the dysfunction in distributed neural circuits - each requiring fine, tailored modulation - that characterizes most neuropsychiatric disorders, this approach holds limited promise. To develop the next generation of neuromodulation therapies, we will have to achieve fine-grained, closed-loop control over multiple neural circuits. Recent work has demonstrated spatial and frequency selectivity using microstimulation with many small, closely-spaced contacts, mimicking endogenous neural dynamics. Using custom electrode design and stimulation parameters, it should be possible to achieve bidirectional control over behavioral outcomes, such as increasing or decreasing arousal during central thalamic stimulation. Here, we discuss one possible approach, which we term microscale multicircuit brain stimulation (MMBS). We discuss how machine learning leverages behavioral and neural data to find optimal stimulation parameters across multiple contacts, to drive the brain towards desired states associated with behavioral goals. We expound a mathematical framework for MMBS, where behavioral and neural responses adjust the model in real-time, allowing us to adjust stimulation in real-time. These technologies will be critical to the development of the next generation of neurostimulation therapies, which will allow us to treat problems like disorders of consciousness and cognition.
Collapse
Affiliation(s)
- Yuri B. Saalmann
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA
- Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Sima Mofakham
- Department of Neurological Surgery, Stony Brook University Hospital, Stony Brook, NY, USA
- Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Charles B. Mikell
- Department of Neurological Surgery, Stony Brook University Hospital, Stony Brook, NY, USA
| | - Petar M. Djuric
- Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA
| |
Collapse
|
9
|
Zamora M, Toth R, Morgante F, Ottaway J, Gillbe T, Martin S, Lamb G, Noone T, Benjaber M, Nairac Z, Sehgal D, Constandinou TG, Herron J, Aziz TZ, Gillbe I, Green AL, Pereira EAC, Denison T. DyNeuMo Mk-1: Design and pilot validation of an investigational motion-adaptive neurostimulator with integrated chronotherapy. Exp Neurol 2022; 351:113977. [PMID: 35016994 PMCID: PMC7612891 DOI: 10.1016/j.expneurol.2022.113977] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 12/20/2021] [Accepted: 01/06/2022] [Indexed: 11/19/2022]
Abstract
There is growing interest in using adaptive neuromodulation to provide a more personalized therapy experience that might improve patient outcomes. Current implant technology, however, can be limited in its adaptive algorithm capability. To enable exploration of adaptive algorithms with chronic implants, we designed and validated the 'Picostim DyNeuMo Mk-1' (DyNeuMo Mk-1 for short), a fully-implantable, adaptive research stimulator that titrates stimulation based on circadian rhythms (e.g. sleep, wake) and the patient's movement state (e.g. posture, activity, shock, free-fall). The design leverages off-the-shelf consumer technology that provides inertial sensing with low-power, high reliability, and relatively modest cost. The DyNeuMo Mk-1 system was designed, manufactured and verified using ISO 13485 design controls, including ISO 14971 risk management techniques to ensure patient safety, while enabling novel algorithms. The system was validated for an intended use case in movement disorders under an emergency-device authorization from the Medicines and Healthcare Products Regulatory Agency (MHRA). The algorithm configurability and expanded stimulation parameter space allows for a number of applications to be explored in both central and peripheral applications. Intended applications include adaptive stimulation for movement disorders, synchronizing stimulation with circadian patterns, and reacting to transient inertial events such as posture changes, general activity, and walking. With appropriate design controls in place, first-in-human research trials are now being prepared to explore the utility of automated motion-adaptive algorithms.
Collapse
Affiliation(s)
- Mayela Zamora
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford, United Kingdom; MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
| | - Robert Toth
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Francesca Morgante
- Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St George's, University of London, London, United Kingdom; Department of Neurosurgery, Atkinson Morley Regional Neurosciences Centre, St George's Hospital, London, United Kingdom
| | | | - Tom Gillbe
- Bioinduction Ltd., Bristol, United Kingdom
| | - Sean Martin
- Department of Neurosurgery, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Guy Lamb
- Bioinduction Ltd., Bristol, United Kingdom
| | - Tara Noone
- Bioinduction Ltd., Bristol, United Kingdom
| | - Moaad Benjaber
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford, United Kingdom; MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Zachary Nairac
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford, United Kingdom
| | - Devang Sehgal
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford, United Kingdom
| | - Timothy G Constandinou
- Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom; Care Research and Technology Centre, UK Dementia Research Institute, London, United Kingdom
| | - Jeffrey Herron
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - Tipu Z Aziz
- Department of Neurosurgery, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | | | - Alexander L Green
- Department of Neurosurgery, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Erlick A C Pereira
- Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St George's, University of London, London, United Kingdom; Department of Neurosurgery, Atkinson Morley Regional Neurosciences Centre, St George's Hospital, London, United Kingdom
| | - Timothy Denison
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford, United Kingdom; MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
| |
Collapse
|
10
|
Functional Characterization of Human Pluripotent Stem Cell-Derived Models of the Brain with Microelectrode Arrays. Cells 2021; 11:cells11010106. [PMID: 35011667 PMCID: PMC8750870 DOI: 10.3390/cells11010106] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 12/22/2021] [Accepted: 12/24/2021] [Indexed: 12/26/2022] Open
Abstract
Human pluripotent stem cell (hPSC)-derived neuron cultures have emerged as models of electrical activity in the human brain. Microelectrode arrays (MEAs) measure changes in the extracellular electric potential of cell cultures or tissues and enable the recording of neuronal network activity. MEAs have been applied to both human subjects and hPSC-derived brain models. Here, we review the literature on the functional characterization of hPSC-derived two- and three-dimensional brain models with MEAs and examine their network function in physiological and pathological contexts. We also summarize MEA results from the human brain and compare them to the literature on MEA recordings of hPSC-derived brain models. MEA recordings have shown network activity in two-dimensional hPSC-derived brain models that is comparable to the human brain and revealed pathology-associated changes in disease models. Three-dimensional hPSC-derived models such as brain organoids possess a more relevant microenvironment, tissue architecture and potential for modeling the network activity with more complexity than two-dimensional models. hPSC-derived brain models recapitulate many aspects of network function in the human brain and provide valid disease models, but certain advancements in differentiation methods, bioengineering and available MEA technology are needed for these approaches to reach their full potential.
Collapse
|
11
|
Shi L, Fan S, Yuan T, Fang H, Zheng J, Xiao Z, Diao Y, Zhu G, Zhang Q, Liu H, Zhang H, Meng F, Zhang J, Yang A. Microstimulation Is a Promising Approach in Achieving Better Lead Placement in Subthalamic Nucleus Deep Brain Stimulation Surgery. Front Neurol 2021; 12:683532. [PMID: 34630273 PMCID: PMC8493285 DOI: 10.3389/fneur.2021.683532] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 08/16/2021] [Indexed: 11/24/2022] Open
Abstract
Background: The successful application of subthalamic nucleus (STN) deep brain stimulation (DBS) surgery relies mostly on optimal lead placement, whereas the major challenge is how to precisely localize STN. Microstimulation, which can induce differentiating inhibitory responses between STN and substantia nigra pars reticulata (SNr) near the ventral border of STN, has indicated a great potential of breaking through this barrier. Objective: This study aims to investigate the feasibility of localizing the boundary between STN and SNr (SSB) using microstimulation and promote better lead placement. Methods: We recorded neurophysiological data from 41 patients undergoing STN-DBS surgery with microstimulation in our hospital. Trajectories with typical STN signal were included. Microstimulation was applied near the bottom of STN to determine SSB, which was validated by the imaging reconstruction of DBS leads. Results: In most trajectories with microstimulation (84.4%), neuronal firing in STN could not be inhibited by microstimulation, whereas in SNr long inhibition was observed following microstimulation. The success rate of localizing SSB was significantly higher in trajectories with microstimulation than those without. Moreover, results from imaging reconstruction and intraoperative neurological assessments demonstrated better lead location and higher therapeutic effectiveness in trajectories with microstimulation and accurately identified SSB. Conclusion: Microstimulation on microelectrode recording is an effective approach to localize the SSB. Our data provide clinical evidence that microstimulation can be routinely employed to achieve better lead placement.
Collapse
Affiliation(s)
- Lin Shi
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Beijing, China
| | - Shiying Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tianshuo Yuan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Huaying Fang
- Beijing Advanced Innovation Center for Imaging Theory and Technology, Capital Normal University, Beijing, China
- Academy for Multidisciplinary Studies, Capital Normal University, Beijing, China
| | - Jie Zheng
- Department of Ophthalmology, Children's Hospital, Harvard Medical School, Boston, MA, United States
| | - Zunyu Xiao
- Molecular Imaging Research Center, Harbin Medical University, Harbin, China
| | - Yu Diao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Guanyu Zhu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Quan Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Huanguang Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Hua Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Fangang Meng
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Beijing, China
| | - Anchao Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Beijing, China
| |
Collapse
|
12
|
Hughes CL, Flesher SN, Weiss JM, Boninger M, Collinger JL, Gaunt RA. Perception of microstimulation frequency in human somatosensory cortex. eLife 2021; 10:65128. [PMID: 34313221 PMCID: PMC8376245 DOI: 10.7554/elife.65128] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 07/22/2021] [Indexed: 12/11/2022] Open
Abstract
Microstimulation in the somatosensory cortex can evoke artificial tactile percepts and can be incorporated into bidirectional brain–computer interfaces (BCIs) to restore function after injury or disease. However, little is known about how stimulation parameters themselves affect perception. Here, we stimulated through microelectrode arrays implanted in the somatosensory cortex of two human participants with cervical spinal cord injury and varied the stimulus amplitude, frequency, and train duration. Increasing the amplitude and train duration increased the perceived intensity on all tested electrodes. Surprisingly, we found that increasing the frequency evoked more intense percepts on some electrodes but evoked less-intense percepts on other electrodes. These different frequency–intensity relationships were divided into three groups, which also evoked distinct percept qualities at different stimulus frequencies. Neighboring electrode sites were more likely to belong to the same group. These results support the idea that stimulation frequency directly controls tactile perception and that these different percepts may be related to the organization of somatosensory cortex, which will facilitate principled development of stimulation strategies for bidirectional BCIs.
Collapse
Affiliation(s)
- Christopher L Hughes
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, United States.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, United States.,Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, United States
| | - Sharlene N Flesher
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, United States.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, United States.,Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, United States.,Department of Neurosurgery, Stanford University, Stanford, United States.,Department of Electrical Engineering, Stanford University, Stanford, United States
| | - Jeffrey M Weiss
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, United States.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, United States
| | - Michael Boninger
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, United States.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, United States.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, United States.,Human Engineering Research Laboratories, VA Center of Excellence, Department of Veterans Affairs, Pittsburgh, United States
| | - Jennifer L Collinger
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, United States.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, United States.,Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, United States.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, United States.,Human Engineering Research Laboratories, VA Center of Excellence, Department of Veterans Affairs, Pittsburgh, United States
| | - Robert A Gaunt
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, United States.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, United States.,Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, United States.,Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, United States
| |
Collapse
|
13
|
Yadav AP, Li S, Krucoff MO, Lebedev MA, Abd-El-Barr MM, Nicolelis MAL. Generating artificial sensations with spinal cord stimulation in primates and rodents. Brain Stimul 2021; 14:825-836. [PMID: 34015518 DOI: 10.1016/j.brs.2021.04.024] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 04/01/2021] [Accepted: 04/30/2021] [Indexed: 11/17/2022] Open
Abstract
For patients who have lost sensory function due to a neurological injury such as spinal cord injury (SCI), stroke, or amputation, spinal cord stimulation (SCS) may provide a mechanism for restoring somatic sensations via an intuitive, non-visual pathway. Inspired by this vision, here we trained rhesus monkeys and rats to detect and discriminate patterns of epidural SCS. Thereafter, we constructed psychometric curves describing the relationship between different SCS parameters and the animal's ability to detect SCS and/or changes in its characteristics. We found that the stimulus detection threshold decreased with higher frequency, longer pulse-width, and increasing duration of SCS. Moreover, we found that monkeys were able to discriminate temporally- and spatially-varying patterns (i.e. variations in frequency and location) of SCS delivered through multiple electrodes. Additionally, sensory discrimination of SCS-induced sensations in rats obeyed Weber's law of just-noticeable differences. These findings suggest that by varying SCS intensity, temporal pattern, and location different sensory experiences can be evoked. As such, we posit that SCS can provide intuitive sensory feedback in neuroprosthetic devices.
Collapse
Affiliation(s)
- Amol P Yadav
- Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, IN, 46202, USA; Paul and Carole Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
| | - Shuangyan Li
- Department of Neurobiology, Duke University, Durham, NC, 27710, USA; State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Tianjin, 300130, PR China; Tianjin Key Laboratory Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin, 300130, PR China
| | - Max O Krucoff
- Department of Neurosurgery, Medical College of Wisconsin & Froedtert Health, Wauwatosa, WI, 53226, USA; Department of Biomedical Engineering, Marquette University & Medical College of Wisconsin, Milwaukee, WI, 53233, USA
| | - Mikhail A Lebedev
- Center for Neuroengineering, Duke University, Durham, NC, 27710, USA; Skolkovo Institute of Science and Technology, 30 Bolshoy Bulvar, Moscow, 143026, Russia
| | | | - Miguel A L Nicolelis
- Department of Neurosurgery, Duke University, Durham, NC, 27710, USA; Center for Neuroengineering, Duke University, Durham, NC, 27710, USA; Department of Neurobiology, Duke University, Durham, NC, 27710, USA; Department of Biomedical Engineering, Duke University, Durham, NC, 27710, USA; Department of Psychology and Neuroscience, Duke University, Durham, NC, 27710, USA; Department of Neurology, Duke University, Durham, NC, 27710, USA; Edmond and Lily Safra International Institute of Neuroscience, Natal, 59066060, Brazil
| |
Collapse
|
14
|
Elyahoodayan S, Jiang W, Lee CD, Shao X, Weiland G, Whalen JJ, Petrossians A, Song D. Stimulation and Recording of the Hippocampus Using the Same Pt-Ir Coated Microelectrodes. Front Neurosci 2021; 15:616063. [PMID: 33716647 PMCID: PMC7943859 DOI: 10.3389/fnins.2021.616063] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Accepted: 01/28/2021] [Indexed: 01/11/2023] Open
Abstract
Same-electrode stimulation and recording with high spatial resolution, signal quality, and power efficiency is highly desirable in neuroscience and neural engineering. High spatial resolution and signal-to-noise ratio is necessary for obtaining unitary activities and delivering focal stimulations. Power efficiency is critical for battery-operated implantable neural interfaces. This study demonstrates the capability of recording single units as well as evoked potentials in response to a wide range of electrochemically safe stimulation pulses through high-resolution microelectrodes coated with co-deposition of Pt-Ir. It also compares signal-to-noise ratio, single unit activity, and power efficiencies between Pt-Ir coated and uncoated microelectrodes. To enable stimulation and recording with the same microelectrodes, microelectrode arrays were treated with electrodeposited platinum-iridium coating (EPIC) and tested in the CA1 cell body layer of rat hippocampi. The electrodes' ability to (1) inject a large range of electrochemically reversable stimulation pulses to the tissue, and (2) record evoked potentials and single unit activities were quantitively assessed over an acute time period. Compared to uncoated electrodes, EPIC electrodes recorded signals with higher signal-to-noise ratios (coated: 9.77 ± 1.95 dB; uncoated: 1.95 ± 0.40 dB) and generated lower voltages (coated: 100 mV; uncoated: 650 mV) for a given stimulus (5 μA). The improved performance corresponded to lower energy consumptions and electrochemically safe stimulation above 5 μA (>0.38 mC/cm2), which enabled elicitation of field excitatory post synaptic potentials and population spikes. Spontaneous single unit activities were also modulated by varying stimulation intensities and monitored through the same electrodes. This work represents an example of stimulation and recording single unit activities from the same microelectrode, which provides a powerful tool for monitoring and manipulating neural circuits at the single neuron level.
Collapse
Affiliation(s)
- Sahar Elyahoodayan
- Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA, United States
| | - Wenxuan Jiang
- Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA, United States
| | | | - Xiecheng Shao
- Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA, United States
| | | | | | | | - Dong Song
- Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA, United States
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, United States
| |
Collapse
|
15
|
Kumaravelu K, Tomlinson T, Callier T, Sombeck J, Bensmaia SJ, Miller LE, Grill WM. A comprehensive model-based framework for optimal design of biomimetic patterns of electrical stimulation for prosthetic sensation. J Neural Eng 2020; 17:046045. [PMID: 32759488 PMCID: PMC8559728 DOI: 10.1088/1741-2552/abacd8] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
OBJECTIVE Touch and proprioception are essential to motor function as shown by the movement deficits that result from the loss of these senses, e.g. due to neuropathy of sensory nerves. To achieve a high-performance brain-controlled prosthetic arm/hand thus requires the restoration of somatosensation, perhaps through intracortical microstimulation (ICMS) of somatosensory cortex (S1). The challenge is to generate patterns of neuronal activation that evoke interpretable percepts. We present a framework to design optimal spatiotemporal patterns of ICMS (STIM) that evoke naturalistic patterns of neuronal activity and demonstrate performance superior to four previous approaches. APPROACH We recorded multiunit activity from S1 during a center-out reach task (from proprioceptive neurons in Brodmann's area 2) and during application of skin indentations (from cutaneous neurons in Brodmann's area 1). We implemented a computational model of a cortical hypercolumn and used a genetic algorithm to design STIM that evoked patterns of model neuron activity that mimicked their experimentally-measured counterparts. Finally, from the ICMS patterns, the evoked neuronal activity, and the stimulus parameters that gave rise to it, we trained a recurrent neural network (RNN) to learn the mapping function between the physical stimulus and the biomimetic stimulation pattern, i.e. the sensory encoder to be integrated into a neuroprosthetic device. MAIN RESULTS We identified ICMS patterns that evoked simulated responses that closely approximated the measured responses for neurons within 50 µm of the electrode tip. The RNN-based sensory encoder generalized well to untrained limb movements or skin indentations. STIM designed using the model-based optimization approach outperformed STIM designed using existing linear and nonlinear mappings. SIGNIFICANCE The proposed framework produces an encoder that converts limb state or patterns of pressure exerted onto the prosthetic hand into STIM that evoke naturalistic patterns of neuronal activation.
Collapse
Affiliation(s)
| | | | - Thierri Callier
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | - Joseph Sombeck
- Department of Biomedical Engineering, Northwestern University, Chicago, IL
| | - Sliman J. Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL
| | - Lee E. Miller
- Department of Biomedical Engineering, Northwestern University, Chicago, IL
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL
- Deptartment of Physiology, Northwestern University, Chicago, IL
| | - Warren M. Grill
- Department of Biomedical Engineering, Duke University, Durham, NC
- Department of Electrical and Computer Engineering, Duke University, Durham, NC
- Department of Neurobiology, Duke University, Durham, NC
- Department of Neurosurgery, Duke University, Durham, NC
| |
Collapse
|
16
|
Sohn WJ, Wang PT, Kellis S, Andersen RA, Liu CY, Heydari P, Nenadic Z, Do AH. A Prototype of a Fully-Implantable Charge-Balanced Artificial Sensory Stimulator for Bi-directional Brain-Computer-Interface (BD-BCI). ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:3083-3085. [PMID: 33018656 DOI: 10.1109/embc44109.2020.9176718] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Bi-directional brain-computer interfaces (BD-BCI) to restore movement and sensation must achieve concurrent operation of recording and decoding of motor commands from the brain and stimulating the brain with somatosensory feedback. Previously we developed and validated a benchtop prototype of a fully implantable BCI system for motor decoding. Here, a prototype artificial sensory stimulator was integrated into the benchtop system to develop a prototype of a fully-implantable BD-BCI. The artificial sensory stimulator incorporates an active charge balancing mechanism based on pulse-width modulation to ensure safe stimulation for chronically interfaced electrodes to prevent damage to brain tissue and electrodes. The feasibility of the BD-BCI system's active charge balancing was tested in phantom brain tissue. With the charge-balancing, the removal of the residual charges on an electrode was evident. This is a critical milestone toward fully-implantable BD-BCI systems.
Collapse
|
17
|
Yadav AP, Li S, Krucoff MO, Lebedev MA, Abd-el-barr MM, Nicolelis MA. Generating Artificial Sensations with Spinal Cord Stimulation in Primates and Rodents.. [DOI: 10.1101/2020.05.09.085647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
AbstractFor patients who have lost sensory function due to a neurological injury such as spinal cord injury (SCI), stroke, or amputation, spinal cord stimulation (SCS) may provide a mechanism for restoring somatic sensations via an intuitive, non-visual pathway. Inspired by this vision, here we trained rhesus monkeys and rats to detect and discriminate patterns of epidural SCS. Thereafter, we constructed psychometric curves describing the relationship between different SCS parameters and the animal’s ability to detect SCS and/or changes in its characteristics. We found that the stimulus detection threshold decreased with higher frequency, longer pulse-width, and increasing duration of SCS. Moreover, we found that monkeys were able to discriminate temporally- and spatially-varying patterns (i.e. variations in frequency and location) of SCS delivered through multiple electrodes. Additionally, sensory discrimination of SCS-induced sensations in rats obeyed Weber’s law of just noticeable differences. These findings suggest that by varying SCS intensity, temporal pattern, and location different sensory experiences can be evoked. As such, we posit that SCS can provide intuitive sensory feedback in neuroprosthetic devices.
Collapse
|
18
|
A Brain to Spine Interface for Transferring Artificial Sensory Information. Sci Rep 2020; 10:900. [PMID: 31964948 PMCID: PMC6972753 DOI: 10.1038/s41598-020-57617-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Accepted: 01/03/2020] [Indexed: 12/13/2022] Open
Abstract
Lack of sensory feedback is a major obstacle in the rapid absorption of prosthetic devices by the brain. While electrical stimulation of cortical and subcortical structures provides unique means to deliver sensory information to higher brain structures, these approaches require highly invasive surgery and are dependent on accurate targeting of brain structures. Here, we propose a semi-invasive method, Dorsal Column Stimulation (DCS) as a tool for transferring sensory information to the brain. Using this new approach, we show that rats can learn to discriminate artificial sensations generated by DCS and that DCS-induced learning results in corticostriatal plasticity. We also demonstrate a proof of concept brain-to-spine interface (BTSI), whereby tactile and artificial sensory information are decoded from the brain of an “encoder” rat, transformed into DCS pulses, and delivered to the spinal cord of a second “decoder” rat while the latter performs an analog-to-digital conversion during a sensory discrimination task. These results suggest that DCS can be used as an effective sensory channel to transmit prosthetic information to the brain or between brains, and could be developed as a novel platform for delivering tactile and proprioceptive feedback in clinical applications of brain-machine interfaces.
Collapse
|
19
|
Elyahoodayan S, Jiang W, Xu H, Song D. A Multi-Channel Asynchronous Neurostimulator With Artifact Suppression for Neural Code-Based Stimulations. Front Neurosci 2019; 13:1011. [PMID: 31611764 PMCID: PMC6776638 DOI: 10.3389/fnins.2019.01011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 09/05/2019] [Indexed: 11/18/2022] Open
Abstract
A novel neurostimulator for generating neural code-based, precise, asynchronous electrical stimulation pulses is designed, fabricated, and characterized. Through multiplexing, this system can deliver constant current biphasic pulses, with arbitrary temporal patterns, and pulse parameters to 32 electrodes using one pulse generator. The design also features a stimulus artifact suppression (SAS) technique that can be integrated with commercial amplifiers. Using an array of CMOS switches, electrodes are disconnected from recording amplifiers during stimulation, while the input of the recording system is shorted to ground through another CMOS switch to suppress ringing in the recording system. The timing of the switches used to block and suppress the stimulus artifact are crucial and are determined by the electrochemical properties of the electrode. This system allows stimulation and recording from the same electrodes to monitor local field potentials with short latencies from the region of stimulation for achieving feedback control of neural stimulation. In this way, timing between each pulse is controlled by inputs from an external source and stimulus magnitude is controlled by feed-back from neural response from the stimulated tissue. The system was implemented with low-power and compact packaged microchips to constitute an effective, cost-efficient, and miniaturized neurostimulator. The device has been first evaluated in phantom preparations and then tested in hippocampi of behaving rats. Benchtop results demonstrate the capability of the stimulator to generate arbitrary spatio-temporal pattern of stimulation pulses dictated by random number generators (RNGs) to control magnitude and timing between each individual biphasic pulse. In vivo results show that evoked potentials elicited by the neurostimulator can be recorded ∼2 ms after the termination of stimulus pulses from the same electrodes where stimulation pulses are delivered, whereas commercial amplifiers without such an artifact suppression typically result in tens to hundreds of milliseconds recovery period. This neurostimulator design is desirable in a variety of neural interface applications, particularly hippocampal memory prosthesis aiming to restore cognitive functions by reinstating neural code transmissions in the brain.
Collapse
Affiliation(s)
- Sahar Elyahoodayan
- Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA, United States
| | - Wenxuan Jiang
- Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA, United States
| | - Huijing Xu
- Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA, United States
| | - Dong Song
- Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA, United States.,Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, United States
| |
Collapse
|
20
|
Tekriwal A, Afshar NM, Santiago-Moreno J, Kuijper FM, Kern DS, Halpern CH, Felsen G, Thompson JA. Neural Circuit and Clinical Insights from Intraoperative Recordings During Deep Brain Stimulation Surgery. Brain Sci 2019; 9:brainsci9070173. [PMID: 31330813 PMCID: PMC6681002 DOI: 10.3390/brainsci9070173] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 07/17/2019] [Accepted: 07/18/2019] [Indexed: 12/15/2022] Open
Abstract
Observations using invasive neural recordings from patient populations undergoing neurosurgical interventions have led to critical breakthroughs in our understanding of human neural circuit function and malfunction. The opportunity to interact with patients during neurophysiological mapping allowed for early insights in functional localization to improve surgical outcomes, but has since expanded into exploring fundamental aspects of human cognition including reward processing, language, the storage and retrieval of memory, decision-making, as well as sensory and motor processing. The increasing use of chronic neuromodulation, via deep brain stimulation, for a spectrum of neurological and psychiatric conditions has in tandem led to increased opportunity for linking theories of cognitive processing and neural circuit function. Our purpose here is to motivate the neuroscience and neurosurgical community to capitalize on the opportunities that this next decade will bring. To this end, we will highlight recent studies that have successfully leveraged invasive recordings during deep brain stimulation surgery to advance our understanding of human cognition with an emphasis on reward processing, improving clinical outcomes, and informing advances in neuromodulatory interventions.
Collapse
Affiliation(s)
- Anand Tekriwal
- Department of Neurosurgery, University of Colorado School of Medicine, Aurora, CO 80203, USA
- Department of Physiology and Biophysics, University of Colorado School of Medicine, Aurora, CO 80203, USA
- Medical Scientist Training Program, University of Colorado School of Medicine, Aurora, CO 80203, USA
| | - Neema Moin Afshar
- Department of Physiology and Biophysics, University of Colorado School of Medicine, Aurora, CO 80203, USA
| | - Juan Santiago-Moreno
- Medical Scientist Training Program, University of Colorado School of Medicine, Aurora, CO 80203, USA
| | - Fiene Marie Kuijper
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Drew S Kern
- Department of Neurosurgery, University of Colorado School of Medicine, Aurora, CO 80203, USA
- Department of Neurology, University of Colorado School of Medicine, Aurora, CO 80203, USA
| | - Casey H Halpern
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Gidon Felsen
- Department of Physiology and Biophysics, University of Colorado School of Medicine, Aurora, CO 80203, USA
| | - John A Thompson
- Department of Neurosurgery, University of Colorado School of Medicine, Aurora, CO 80203, USA.
- Department of Neurology, University of Colorado School of Medicine, Aurora, CO 80203, USA.
| |
Collapse
|
21
|
Gunduz A, Opri E, Gilron R, Kremen V, Worrell G, Starr P, Leyde K, Denison T. Adding wisdom to 'smart' bioelectronic systems: a design framework for physiologic control including practical examples. ACTA ACUST UNITED AC 2019; 2:29-41. [PMID: 33868718 PMCID: PMC7610621 DOI: 10.2217/bem-2019-0008] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
This perspective provides an overview of how risk can be effectively considered in physiological control loops that strive for semi-to-fully automated operation. The perspective first introduces the motivation, user needs and framework for the design of a physiological closed-loop controller. Then, we discuss specific risk areas and use examples from historical medical devices to illustrate the key concepts. Finally, we provide a design overview of an adaptive bidirectional brain–machine interface, currently undergoing human clinical studies, to synthesize the design principles in an exemplar application.
Collapse
Affiliation(s)
- Aysegul Gunduz
- Department of Biomedical Engineering, University of Florida Gainesville, Gainesville, FL 32611, USA
| | - Enrico Opri
- Department of Biomedical Engineering, University of Florida Gainesville, Gainesville, FL 32611, USA
| | - Ro'ee Gilron
- School of Medicine, University of California San Francisco, San Francisco CA 94143, USA
| | - Vaclav Kremen
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Gregory Worrell
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Phil Starr
- School of Medicine, University of California San Francisco, San Francisco CA 94143, USA
| | - Kent Leyde
- Cadence Neuroscience Inc, Sammamish, WA 98074, USA
| | - Timothy Denison
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, UK
| |
Collapse
|
22
|
James ND, McMahon SB, Field-Fote EC, Bradbury EJ. Neuromodulation in the restoration of function after spinal cord injury. Lancet Neurol 2018; 17:905-917. [PMID: 30264729 DOI: 10.1016/s1474-4422(18)30287-4] [Citation(s) in RCA: 92] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Revised: 07/12/2018] [Accepted: 07/19/2018] [Indexed: 12/13/2022]
Abstract
Neuromodulation, the use of electrical interfaces to alter neuronal activity, has been successful as a treatment approach in several neurological disorders, including deep brain stimulation for Parkinson's disease and epidural spinal stimulation for chronic pain. Neuromodulation can also be beneficial for spinal cord injury, from assisting basic functions such as respiratory pacing and bladder control, through to restoring volitional movements and skilled hand function. Approaches range from electrical stimulation of peripheral muscles, either directly or via brain-controlled bypass devices, to stimulation of the spinal cord and brain. Limitations to widespread clinical application include durability of neuromodulation devices, affordability and accessibility of some approaches, and poor understanding of the underlying mechanisms. Efforts to overcome these challenges through advances in technology, together with pragmatic knowledge gained from clinical trials and basic research, could lead to personalised neuromodulatory interventions to meet the specific needs of individuals with spinal cord injury.
Collapse
Affiliation(s)
- Nicholas D James
- Regeneration Group, Wolfson Centre for Age-Related Diseases, Institute of Psychiatry, Psychology and Neuroscience, Guy's Campus, King's College London, London, UK; Center for Neuroprosthetics and Brain Mind Institute, School of Life Sciences, Swiss Federal Institute of Technology, Lausanne, Switzerland
| | - Stephen B McMahon
- Regeneration Group, Wolfson Centre for Age-Related Diseases, Institute of Psychiatry, Psychology and Neuroscience, Guy's Campus, King's College London, London, UK
| | - Edelle C Field-Fote
- Shepherd Center, Crawford Research Institute, Atlanta, GA, USA; Division of Physical Therapy, Emory University School of Medicine, Atlanta, GA, USA; Georgia Institute of Technology, School of Biological Sciences, Program in Applied Physiology, Atlanta, GA, USA
| | - Elizabeth J Bradbury
- Regeneration Group, Wolfson Centre for Age-Related Diseases, Institute of Psychiatry, Psychology and Neuroscience, Guy's Campus, King's College London, London, UK.
| |
Collapse
|