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Kremen V, Sladky V, Mivalt F, Gregg NM, Balzekas I, Marks V, Brinkmann BH, Lundstrom BN, Cui J, St Louis EK, Croarkin P, Alden EC, Fields J, Crockett K, Adolf J, Bilderbeek J, Hermes D, Messina S, Miller KJ, Van Gompel J, Denison T, Worrell GA. A platform for brain network sensing and stimulation with quantitative behavioral tracking: Application to limbic circuit epilepsy. medRxiv 2024:2024.02.09.24302358. [PMID: 38370724 PMCID: PMC10871449 DOI: 10.1101/2024.02.09.24302358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Temporal lobe epilepsy is a common neurological disease characterized by recurrent seizures. These seizures often originate from limbic networks and people also experience chronic comorbidities related to memory, mood, and sleep (MMS). Deep brain stimulation targeting the anterior nucleus of the thalamus (ANT-DBS) is a proven therapy, but the optimal stimulation parameters remain unclear. We developed a neurotechnology platform for tracking seizures and MMS to enable data streaming between an investigational brain sensing-stimulation implant, mobile devices, and a cloud environment. Artificial Intelligence algorithms provided accurate catalogs of seizures, interictal epileptiform spikes, and wake-sleep brain states. Remotely administered memory and mood assessments were used to densely sample cognitive and behavioral response during ANT-DBS. We evaluated the efficacy of low-frequency versus high-frequency ANT-DBS. They both reduced seizures, but low-frequency ANT-DBS showed greater reductions and better sleep and memory. These results highlight the potential of synchronized brain sensing and behavioral tracking for optimizing neuromodulation therapy.
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2
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Cui J, Mivalt F, Sladky V, Kim J, Richner TJ, Lundstrom BN, Van Gompel JJ, Wang HL, Miller KJ, Gregg N, Wu LJ, Denison T, Winter B, Brinkmann BH, Kremen V, Worrell GA. Acute to long-term characteristics of impedance recordings during neurostimulation in humans. J Neural Eng 2024; 21:10.1088/1741-2552/ad3416. [PMID: 38484397 PMCID: PMC11044203 DOI: 10.1088/1741-2552/ad3416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 03/14/2024] [Indexed: 03/26/2024]
Abstract
Objective.This study aims to characterize the time course of impedance, a crucial electrophysiological property of brain tissue, in the human thalamus (THL), amygdala-hippocampus, and posterior hippocampus over an extended period.Approach.Impedance was periodically sampled every 5-15 min over several months in five subjects with drug-resistant epilepsy using an investigational neuromodulation device. Initially, we employed descriptive piecewise and continuous mathematical models to characterize the impedance response for approximately three weeks post-electrode implantation. We then explored the temporal dynamics of impedance during periods when electrical stimulation was temporarily halted, observing a monotonic increase (rebound) in impedance before it stabilized at a higher value. Lastly, we assessed the stability of amplitude and phase over the 24 h impedance cycle throughout the multi-month recording.Main results.Immediately post-implantation, the impedance decreased, reaching a minimum value in all brain regions within approximately two days, and then increased monotonically over about 14 d to a stable value. The models accounted for the variance in short-term impedance changes. Notably, the minimum impedance of the THL in the most epileptogenic hemisphere was significantly lower than in other regions. During the gaps in electrical stimulation, the impedance rebound decreased over time and stabilized around 200 days post-implant, likely indicative of the foreign body response and fibrous tissue encapsulation around the electrodes. The amplitude and phase of the 24 h impedance oscillation remained stable throughout the multi-month recording, with circadian variation in impedance dominating the long-term measures.Significance.Our findings illustrate the complex temporal dynamics of impedance in implanted electrodes and the impact of electrical stimulation. We discuss these dynamics in the context of the known biological foreign body response of the brain to implanted electrodes. The data suggest that the temporal dynamics of impedance are dependent on the anatomical location and tissue epileptogenicity. These insights may offer additional guidance for the delivery of therapeutic stimulation at various time points post-implantation for neuromodulation therapy.
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Affiliation(s)
- Jie Cui
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
- Mayo College of Medicine and Science, Mayo Clinic, Rochester, Minnesota, USA
| | - Filip Mivalt
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
| | - Vladimir Sladky
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic
| | - Jiwon Kim
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | | | | | | | - Hai-long Wang
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Kai J. Miller
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Nicholas Gregg
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Long Jun Wu
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Timothy Denison
- Department of Engineering Science, University of Oxford; MRC Brain Network Dynamics Unit, University of Oxford, OX3 7DQ UK
| | - Bailey Winter
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Mayo College of Medicine and Science, Mayo Clinic, Rochester, Minnesota, USA
| | - Benjamin H. Brinkmann
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
| | - Vaclav Kremen
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University, Prague, Czech Republic
| | - Gregory A. Worrell
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
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Okun MS, Marjenin T, Ekanayake J, Gilbert F, Doherty SP, Pilkington J, French J, Kubu C, Lázaro-Muñoz G, Denison T, Giordano J. Definition of Implanted Neurological Device Abandonment: A Systematic Review and Consensus Statement. JAMA Netw Open 2024; 7:e248654. [PMID: 38687486 DOI: 10.1001/jamanetworkopen.2024.8654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/02/2024] Open
Abstract
Importance Establishing a formal definition for neurological device abandonment has the potential to reduce or to prevent the occurrence of this abandonment. Objective To perform a systematic review of the literature and develop an expert consensus definition for neurological device abandonment. Evidence Review After a Royal Society Summit on Neural Interfaces (September 13-14, 2023), a systematic English language review using PubMed was undertaken to investigate extant definitions of neurological device abandonment. Articles were reviewed for relevance to neurological device abandonment in the setting of deep brain, vagal nerve, and spinal cord stimulation. This review was followed by the convening of an expert consensus group of physicians, scientists, ethicists, and stakeholders. The group summarized findings, added subject matter experience, and applied relevant ethics concepts to propose a current operational definition of neurological device abandonment. Data collection, study, and consensus development were done between September 13, 2023, and February 1, 2024. Findings The PubMed search revealed 734 total articles, and after review, 7 articles were found to address neurological device abandonment. The expert consensus group addressed findings as germane to neurological device abandonment and added personal experience and additional relevant peer-reviewed articles, addressed stakeholders' respective responsibilities, and operationally defined abandonment in the context of implantable neurotechnological devices. The group further addressed whether clinical trial failure or shelving of devices would constitute or be associated with abandonment as defined. Referential to these domains and dimensions, the group proposed a standardized definition for abandonment of active implantable neurotechnological devices. Conclusions and Relevance This study's consensus statement suggests that the definition for neurological device abandonment should entail failure to provide fundamental aspects of patient consent; fulfill reasonable responsibility for medical, technical, or financial support prior to the end of the device's labeled lifetime; and address any or all immediate needs that may result in safety concerns or device ineffectiveness and that the definition of abandonment associated with the failure of a research trial should be contingent on specific circumstances.
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Affiliation(s)
- Michael S Okun
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, Gainesville, Florida
- Department of Neurosurgery, Norman Fixel Institute for Neurological Diseases, Gainesville, Florida
| | - Timothy Marjenin
- Musculoskeletal Clinical Regulatory Advisers, Washington, District of Columbia
| | - Jinendra Ekanayake
- Department of Neurosurgery, National Guard Hospital, Riyadh, Saudia Arabia
- Department of Electronic Engineering, Imperial College London, United Kingdom
- Quetz Ltd, Chelmsford, England
| | | | - Sean P Doherty
- Department of Medical Physics and Biomedical Engineering, University College London, London, England
- Amber Therapeutics Limited, London, England
| | | | | | - Cynthia Kubu
- Center for Neuro-Restoration, Cleveland Clinic, Cleveland, Ohio
| | - Gabriel Lázaro-Muñoz
- Center for Bioethics, Massachusetts General Hospital, Harvard Medical School, Boston
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston
| | - Timothy Denison
- Amber Therapeutics Limited, London, England
- Medical Research Council Brain Network Dynamics Unit, Departments of Engineering Sciences and Clinical Neurosciences, University of Oxford, Oxford, England
| | - James Giordano
- Department of Neurology, Georgetown University Medical Center, Washington, District of Columbia
- Department of Biochemistry, Georgetown University Medical Center, Washington, District of Columbia
- Neuroethics Studies Program, Georgetown University Medical Center, Washington, District of Columbia
- Defense Medical Ethics Center, Uniformed Services University of the Health Sciences, Bethesda, Maryland
- Department of Psychiatry, Uniformed Services University of the Health Sciences, Bethesda, Maryland
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Sermon JJ, Starr PA, Denison T, Duchet B. Pre-existing oscillatory activity as a condition for sub-harmonic entrainment of finely tuned gamma in Parkinson's disease. Brain Stimul 2024; 17:488-490. [PMID: 38685260 DOI: 10.1016/j.brs.2024.02.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 02/26/2024] [Indexed: 05/02/2024] Open
Affiliation(s)
- James J Sermon
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom; Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford, United Kingdom
| | - Philip A Starr
- Department of Neurological Surgery and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom; Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford, United Kingdom
| | - Benoit Duchet
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom.
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Sermon JJ, Benjaber M, Duchet B, Anso J, Olaru M, Starr PA, Denison T. 1:2 entrainment is not a device-induced artefact, except when it is. Brain Stimul 2024; 17:149-151. [PMID: 38331023 DOI: 10.1016/j.brs.2024.01.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/18/2024] [Accepted: 01/27/2024] [Indexed: 02/10/2024] Open
Affiliation(s)
- James J Sermon
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK; MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Moaad Benjaber
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK; MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Benoit Duchet
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Juan Anso
- Department of Neurological Surgery and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Maria Olaru
- Department of Neurological Surgery and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Philip A Starr
- Department of Neurological Surgery and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Timothy Denison
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK; MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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Anjum MF, Smyth C, Zuzuárregui R, Dijk DJ, Starr PA, Denison T, Little S. Multi-night cortico-basal recordings reveal mechanisms of NREM slow-wave suppression and spontaneous awakenings in Parkinson's disease. Nat Commun 2024; 15:1793. [PMID: 38413587 PMCID: PMC10899224 DOI: 10.1038/s41467-024-46002-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 02/12/2024] [Indexed: 02/29/2024] Open
Abstract
Sleep disturbance is a prevalent and disabling comorbidity in Parkinson's disease (PD). We performed multi-night (n = 57) at-home intracranial recordings from electrocorticography and subcortical electrodes using sensing-enabled Deep Brain Stimulation (DBS), paired with portable polysomnography in four PD participants and one with cervical dystonia (clinical trial: NCT03582891). Cortico-basal activity in delta increased and in beta decreased during NREM (N2 + N3) versus wakefulness in PD. DBS caused further elevation in cortical delta and decrease in alpha and low-beta compared to DBS OFF state. Our primary outcome demonstrated an inverse interaction between subcortical beta and cortical slow-wave during NREM. Our secondary outcome revealed subcortical beta increases prior to spontaneous awakenings in PD. We classified NREM vs. wakefulness with high accuracy in both traditional (30 s: 92.6 ± 1.7%) and rapid (5 s: 88.3 ± 2.1%) data epochs of intracranial signals. Our findings elucidate sleep neurophysiology and impacts of DBS on sleep in PD informing adaptive DBS for sleep dysfunction.
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Affiliation(s)
- Md Fahim Anjum
- Movement Disorders and Neuromodulation Centre, University California San Francisco, San Francisco, CA, USA.
| | - Clay Smyth
- Movement Disorders and Neuromodulation Centre, University California San Francisco, San Francisco, CA, USA
| | - Rafael Zuzuárregui
- Movement Disorders and Neuromodulation Centre, University California San Francisco, San Francisco, CA, USA
- Parkinson's Disease Research Education and Clinical Center, San Francisco Veteran's Affairs Medical Center, San Francisco, CA, USA
| | - Derk Jan Dijk
- Surrey Sleep Research Centre, University of Surrey, Guildford, UK
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London and The University of Surrey, Guildford, UK
| | - Philip A Starr
- Movement Disorders and Neuromodulation Centre, University California San Francisco, San Francisco, CA, USA
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
| | - Simon Little
- Movement Disorders and Neuromodulation Centre, University California San Francisco, San Francisco, CA, USA
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Cui J, Mivalt F, Sladky V, Kim J, Richner TJ, Lundstrom BN, Van Gompel JJ, Wang HL, Miller KJ, Gregg N, Wu LJ, Denison T, Winter B, Brinkmann BH, Kremen V, Worrell GA. Acute to long-term characteristics of impedance recordings during neurostimulation in humans. medRxiv 2024:2024.01.23.24301672. [PMID: 38343858 PMCID: PMC10854350 DOI: 10.1101/2024.01.23.24301672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
Objective This study aims to characterize the time course of impedance, a crucial electrophysiological property of brain tissue, in the human thalamus (THL), amygdala-hippocampus (AMG-HPC), and posterior hippocampus (post-HPC) over an extended period. Approach Impedance was periodically sampled every 5-15 minutes over several months in five subjects with drug-resistant epilepsy using an experimental neuromodulation device. Initially, we employed descriptive piecewise and continuous mathematical models to characterize the impedance response for approximately three weeks post-electrode implantation. We then explored the temporal dynamics of impedance during periods when electrical stimulation was temporarily halted, observing a monotonic increase (rebound) in impedance before it stabilized at a higher value. Lastly, we assessed the stability of amplitude and phase over the 24-hour impedance cycle throughout the multi-month recording. Main results Immediately post-implantation, the impedance decreased, reaching a minimum value in all brain regions within approximately two days, and then increased monotonically over about 14 days to a stable value. The models accounted for the variance in short-term impedance changes. Notably, the minimum impedance of the THL in the most epileptogenic hemisphere was significantly lower than in other regions. During the gaps in electrical stimulation, the impedance rebound decreased over time and stabilized around 200 days post-implant, likely indicative of the foreign body response and fibrous tissue encapsulation around the electrodes. The amplitude and phase of the 24-hour impedance oscillation remained stable throughout the multi-month recording, with circadian variation in impedance dominating the long-term measures. Significance Our findings illustrate the complex temporal dynamics of impedance in implanted electrodes and the impact of electrical stimulation. We discuss these dynamics in the context of the known biological foreign body response of the brain to implanted electrodes. The data suggest that the temporal dynamics of impedance are dependent on the anatomical location and tissue epileptogenicity. These insights may offer additional guidance for the delivery of therapeutic stimulation at various time points post-implantation for neuromodulation therapy.
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Affiliation(s)
- Jie Cui
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
- Mayo College of Medicine and Science, Mayo Clinic, Rochester, Minnesota, USA
| | - Filip Mivalt
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
| | - Vladimir Sladky
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic
| | - Jiwon Kim
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | | | | | | | - Hai-long Wang
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Kai J. Miller
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Nicholas Gregg
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Long Jun Wu
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Timothy Denison
- Department of Engineering Science, University of Oxford; MRC Brain Network Dynamics Unit, University of Oxford, OX3 7DQ UK
| | - Bailey Winter
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Mayo College of Medicine and Science, Mayo Clinic, Rochester, Minnesota, USA
| | - Benjamin H. Brinkmann
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
| | - Vaclav Kremen
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University, Prague, Czech Republic
| | - Gregory A. Worrell
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
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Fleming JE, Pont Sanchis I, Lemmens O, Denison-Smith A, West TO, Denison T, Cagnan H. From dawn till dusk: Time-adaptive bayesian optimization for neurostimulation. PLoS Comput Biol 2023; 19:e1011674. [PMID: 38091368 PMCID: PMC10718444 DOI: 10.1371/journal.pcbi.1011674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 11/09/2023] [Indexed: 12/18/2023] Open
Abstract
Stimulation optimization has garnered considerable interest in recent years in order to efficiently parametrize neuromodulation-based therapies. To date, efforts focused on automatically identifying settings from parameter spaces that do not change over time. A limitation of these approaches, however, is that they lack consideration for time dependent factors that may influence therapy outcomes. Disease progression and biological rhythmicity are two sources of variation that may influence optimal stimulation settings over time. To account for this, we present a novel time-varying Bayesian optimization (TV-BayesOpt) for tracking the optimum parameter set for neuromodulation therapy. We evaluate the performance of TV-BayesOpt for tracking gradual and periodic slow variations over time. The algorithm was investigated within the context of a computational model of phase-locked deep brain stimulation for treating oscillopathies representative of common movement disorders such as Parkinson's disease and Essential Tremor. When the optimal stimulation settings changed due to gradual and periodic sources, TV-BayesOpt outperformed standard time-invariant techniques and was able to identify the appropriate stimulation setting. Through incorporation of both a gradual "forgetting" and periodic covariance functions, the algorithm maintained robust performance when a priori knowledge differed from observed variations. This algorithm presents a broad framework that can be leveraged for the treatment of a range of neurological and psychiatric conditions and can be used to track variations in optimal stimulation settings such as amplitude, pulse-width, frequency and phase for invasive and non-invasive neuromodulation strategies.
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Affiliation(s)
- John E. Fleming
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford, United Kingdom
| | - Ines Pont Sanchis
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus Research Building, Oxford, United Kingdom
| | - Oscar Lemmens
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus Research Building, Oxford, United Kingdom
| | - Angus Denison-Smith
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus Research Building, Oxford, United Kingdom
| | - Timothy O. West
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford, United Kingdom
- Department of Bioengineering, Imperial College London, White City Campus, London, United Kingdom
| | - Timothy Denison
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford, United Kingdom
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus Research Building, Oxford, United Kingdom
| | - Hayriye Cagnan
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford, United Kingdom
- Department of Bioengineering, Imperial College London, White City Campus, London, United Kingdom
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Bou Assi E, Schindler K, de Bézenac C, Denison T, Desai S, Keller SS, Lemoine É, Rahimi A, Shoaran M, Rummel C. From basic sciences and engineering to epileptology: A translational approach. Epilepsia 2023; 64 Suppl 3:S72-S84. [PMID: 36861368 DOI: 10.1111/epi.17566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 02/23/2023] [Accepted: 02/28/2023] [Indexed: 03/03/2023]
Abstract
Collaborative efforts between basic scientists, engineers, and clinicians are enabling translational epileptology. In this article, we summarize the recent advances presented at the International Conference for Technology and Analysis of Seizures (ICTALS 2022): (1) novel developments of structural magnetic resonance imaging; (2) latest electroencephalography signal-processing applications; (3) big data for the development of clinical tools; (4) the emerging field of hyperdimensional computing; (5) the new generation of artificial intelligence (AI)-enabled neuroprostheses; and (6) the use of collaborative platforms to facilitate epilepsy research translation. We highlight the promise of AI reported in recent investigations and the need for multicenter data-sharing initiatives.
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Affiliation(s)
- Elie Bou Assi
- Department of Neuroscience, Université de Montréal, Montréal, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Canada
| | - Kaspar Schindler
- Department of Neurology, Inselspital, Sleep-Wake-Epilepsy-Center, Bern University Hospital, Bern University, Bern, Switzerland
| | - Christophe de Bézenac
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
- The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
- Department of Engineering Science, University of Oxford, Oxford, UK
| | | | - Simon S Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
- The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Émile Lemoine
- Centre de Recherche du CHUM (CRCHUM), Montréal, Canada
- Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, Canada
| | | | - Mahsa Shoaran
- Institute of Electrical and Micro Engineering, Neuro-X Institute, EPFL, Lausanne, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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He S, Baig F, Merla A, Torrecillos F, Perera A, Wiest C, Debarros J, Benjaber M, Hart MG, Ricciardi L, Morgante F, Hasegawa H, Samuel M, Edwards M, Denison T, Pogosyan A, Ashkan K, Pereira E, Tan H. Beta-triggered adaptive deep brain stimulation during reaching movement in Parkinson's disease. Brain 2023; 146:5015-5030. [PMID: 37433037 PMCID: PMC10690014 DOI: 10.1093/brain/awad233] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 05/30/2023] [Accepted: 06/28/2023] [Indexed: 07/13/2023] Open
Abstract
Subthalamic nucleus (STN) beta-triggered adaptive deep brain stimulation (ADBS) has been shown to provide clinical improvement comparable to conventional continuous DBS (CDBS) with less energy delivered to the brain and less stimulation induced side effects. However, several questions remain unanswered. First, there is a normal physiological reduction of STN beta band power just prior to and during voluntary movement. ADBS systems will therefore reduce or cease stimulation during movement in people with Parkinson's disease and could therefore compromise motor performance compared to CDBS. Second, beta power was smoothed and estimated over a time period of 400 ms in most previous ADBS studies, but a shorter smoothing period could have the advantage of being more sensitive to changes in beta power, which could enhance motor performance. In this study, we addressed these two questions by evaluating the effectiveness of STN beta-triggered ADBS using a standard 400 ms and a shorter 200 ms smoothing window during reaching movements. Results from 13 people with Parkinson's disease showed that reducing the smoothing window for quantifying beta did lead to shortened beta burst durations by increasing the number of beta bursts shorter than 200 ms and more frequent switching on/off of the stimulator but had no behavioural effects. Both ADBS and CDBS improved motor performance to an equivalent extent compared to no DBS. Secondary analysis revealed that there were independent effects of a decrease in beta power and an increase in gamma power in predicting faster movement speed, while a decrease in beta event related desynchronization (ERD) predicted quicker movement initiation. CDBS suppressed both beta and gamma more than ADBS, whereas beta ERD was reduced to a similar level during CDBS and ADBS compared with no DBS, which together explained the achieved similar performance improvement in reaching movements during CDBS and ADBS. In addition, ADBS significantly improved tremor compared with no DBS but was not as effective as CDBS. These results suggest that STN beta-triggered ADBS is effective in improving motor performance during reaching movements in people with Parkinson's disease, and that shortening of the smoothing window does not result in any additional behavioural benefit. When developing ADBS systems for Parkinson's disease, it might not be necessary to track very fast beta dynamics; combining beta, gamma, and information from motor decoding might be more beneficial with additional biomarkers needed for optimal treatment of tremor.
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Affiliation(s)
- Shenghong He
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Fahd Baig
- Neurosciences Research Centre, St George’s, University of London & St George’s University Hospitals NHS Foundation Trust, Institute of Molecular and Clinical Sciences, Cranmer Terrace, London SW17 0QT, UK
| | - Anca Merla
- Department of Neurosurgery, King’s College Hospital NHS Foundation Trust, London SE5 9RS, UK
| | - Flavie Torrecillos
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Andrea Perera
- Department of Neurosurgery, King’s College Hospital NHS Foundation Trust, London SE5 9RS, UK
| | - Christoph Wiest
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Jean Debarros
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Moaad Benjaber
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Michael G Hart
- Neurosciences Research Centre, St George’s, University of London & St George’s University Hospitals NHS Foundation Trust, Institute of Molecular and Clinical Sciences, Cranmer Terrace, London SW17 0QT, UK
| | - Lucia Ricciardi
- Neurosciences Research Centre, St George’s, University of London & St George’s University Hospitals NHS Foundation Trust, Institute of Molecular and Clinical Sciences, Cranmer Terrace, London SW17 0QT, UK
| | - Francesca Morgante
- Neurosciences Research Centre, St George’s, University of London & St George’s University Hospitals NHS Foundation Trust, Institute of Molecular and Clinical Sciences, Cranmer Terrace, London SW17 0QT, UK
| | - Harutomo Hasegawa
- Department of Neurosurgery, King’s College Hospital NHS Foundation Trust, London SE5 9RS, UK
| | - Michael Samuel
- Department of Neurology, King’s College Hospital NHS Foundation Trust, London, SE5 9RS, UK
| | - Mark Edwards
- Department of Clinical and Basic Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London WC2R 2LS, UK
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Alek Pogosyan
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Keyoumars Ashkan
- Department of Neurosurgery, King’s College Hospital NHS Foundation Trust, London SE5 9RS, UK
| | - Erlick Pereira
- Neurosciences Research Centre, St George’s, University of London & St George’s University Hospitals NHS Foundation Trust, Institute of Molecular and Clinical Sciences, Cranmer Terrace, London SW17 0QT, UK
| | - Huiling Tan
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
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Anjum MF, Smyth C, Dijk DJ, Starr P, Denison T, Little S. Multi-night cortico-basal recordings reveal mechanisms of NREM slow wave suppression and spontaneous awakenings at high-temporal resolution in Parkinson's disease. Res Sq 2023:rs.3.rs-3484527. [PMID: 37986864 PMCID: PMC10659541 DOI: 10.21203/rs.3.rs-3484527/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Background Sleep disturbance is a prevalent and highly disabling comorbidity in individuals with Parkinson's disease (PD) that leads to worsening of daytime symptoms, reduced quality of life and accelerated disease progression. Objectives We aimed to record naturalistic overnight cortico-basal neural activity in people with PD, in order to determine the neurophysiology of spontaneous awakenings and slow wave suppression in non-rapid eye movement (NREM) sleep, towards the development of novel sleep-targeted neurostimulation therapies. Methods Multi-night (n=58) intracranial recordings were performed at-home, from chronic electrocorticography and subcortical electrodes, with sensing-enabled Deep Brain Stimulation (DBS), paired with portable polysomnography. Four participants with PD and one participant with cervical dystonia were evaluated to determine the neural structures, signals and functional connectivity modulated during NREM sleep and prior to spontaneous awakenings. Intracranial recordings were performed both ON and OFF DBS to evaluate the impact of stimulation. Sleep staging was then classified with machine-learning models using intracranial cortico-basal signals on classical (30 s) and rapid (5 s) timescales. Results We demonstrate an increase in cortico-basal slow wave delta (1-4 Hz) activity and a decrease in beta (13-31 Hz) activity during NREM (N2 and N3) versus wakefulness in PD. Cortical-basal ganglia coherence was also found to be higher in the delta range and lower in the beta range during NREM. DBS stimulation resulted in a further elevation in cortical delta and a decrease in alpha (8-13 Hz) and low beta (13-15 Hz) power compared to the OFF stimulation state. Within NREM sleep, we observed a strong inverse interaction between subcortical beta and cortical slow wave activity and found that subcortical beta increases prior to spontaneous awakenings at high-temporal resolution (5s). Our machine-learning models trained on intracranial cortical or subcortical power features achieved high accuracy in both traditional (30s) and rapid (5s) time windows for NREM vs. wakefulness classification (30s: 92.6±1.7%; 5s: 88.3±2.1%). Conclusions Chronic, multi-night recordings in PD reveal increased cortico-basal slow wave, decreased beta activity, and changes in functional connectivity in NREM vs wakefulness, effects that are enhanced in the presence of DBS. Within NREM, subcortical beta and cortical delta are strongly inversely correlated and subcortical beta power increases prior to spontaneous awakenings. Our findings elucidate the network-level neurophysiology of sleep dysfunction in PD and the mechanistic impact of conventional DBS. Additionally, through accurate machine-learning classification of spontaneous awakenings, this study also provides a foundation for future personalized adaptive DBS therapies for sleep dysfunction in PD.
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Affiliation(s)
- Md Fahim Anjum
- Movement Disorders and Neuromodulation Centre, University California San Francisco, CA, USA
| | - Clay Smyth
- Movement Disorders and Neuromodulation Centre, University California San Francisco, CA, USA
| | - Derk-Jan Dijk
- Surrey Sleep Research Centre, University of Surrey, Guildford, United Kingdom
- UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London and the University of Surrey, Guildford, United Kingdom
| | - Philip Starr
- Movement Disorders and Neuromodulation Centre, University California San Francisco, CA, USA
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, University of Oxford, United Kingdom
| | - Simon Little
- Movement Disorders and Neuromodulation Centre, University California San Francisco, CA, USA
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12
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Milekovic T, Moraud EM, Macellari N, Moerman C, Raschellà F, Sun S, Perich MG, Varescon C, Demesmaeker R, Bruel A, Bole-Feysot LN, Schiavone G, Pirondini E, YunLong C, Hao L, Galvez A, Hernandez-Charpak SD, Dumont G, Ravier J, Le Goff-Mignardot CG, Mignardot JB, Carparelli G, Harte C, Hankov N, Aureli V, Watrin A, Lambert H, Borton D, Laurens J, Vollenweider I, Borgognon S, Bourre F, Goillandeau M, Ko WKD, Petit L, Li Q, Buschman R, Buse N, Yaroshinsky M, Ledoux JB, Becce F, Jimenez MC, Bally JF, Denison T, Guehl D, Ijspeert A, Capogrosso M, Squair JW, Asboth L, Starr PA, Wang DD, Lacour SP, Micera S, Qin C, Bloch J, Bezard E, Courtine G. A spinal cord neuroprosthesis for locomotor deficits due to Parkinson's disease. Nat Med 2023; 29:2854-2865. [PMID: 37932548 DOI: 10.1038/s41591-023-02584-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 09/08/2023] [Indexed: 11/08/2023]
Abstract
People with late-stage Parkinson's disease (PD) often suffer from debilitating locomotor deficits that are resistant to currently available therapies. To alleviate these deficits, we developed a neuroprosthesis operating in closed loop that targets the dorsal root entry zones innervating lumbosacral segments to reproduce the natural spatiotemporal activation of the lumbosacral spinal cord during walking. We first developed this neuroprosthesis in a non-human primate model that replicates locomotor deficits due to PD. This neuroprosthesis not only alleviated locomotor deficits but also restored skilled walking in this model. We then implanted the neuroprosthesis in a 62-year-old male with a 30-year history of PD who presented with severe gait impairments and frequent falls that were medically refractory to currently available therapies. We found that the neuroprosthesis interacted synergistically with deep brain stimulation of the subthalamic nucleus and dopaminergic replacement therapies to alleviate asymmetry and promote longer steps, improve balance and reduce freezing of gait. This neuroprosthesis opens new perspectives to reduce the severity of locomotor deficits in people with PD.
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Affiliation(s)
- Tomislav Milekovic
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
- Department of Fundamental Neuroscience, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Eduardo Martin Moraud
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
| | - Nicolo Macellari
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
| | - Charlotte Moerman
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
| | - Flavio Raschellà
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- NeuroX Institute, School of Bioengineering, EPFL, Lausanne, Switzerland
| | - Shiqi Sun
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
| | - Matthew G Perich
- Department of Fundamental Neuroscience, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Camille Varescon
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
| | - Robin Demesmaeker
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
| | - Alice Bruel
- Institute of Bioengineering, School of Engineering, EPFL, Lausanne, Switzerland
| | - Léa N Bole-Feysot
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
| | - Giuseppe Schiavone
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Laboratory for Soft Bioelectronic Interfaces (LSBI), NeuroX Institute, EPFL, Lausanne, Switzerland
| | - Elvira Pirondini
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Rehab and Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA
| | - Cheng YunLong
- Motac Neuroscience, UK-M15 6WE, Manchester, UK
- China Academy of Medical Sciences, Beijing, China
- Institute of Laboratory Animal Sciences, Beijing, China
| | - Li Hao
- Motac Neuroscience, UK-M15 6WE, Manchester, UK
- China Academy of Medical Sciences, Beijing, China
- Institute of Laboratory Animal Sciences, Beijing, China
| | - Andrea Galvez
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
| | - Sergio Daniel Hernandez-Charpak
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
| | - Gregory Dumont
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
| | - Jimmy Ravier
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
| | - Camille G Le Goff-Mignardot
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
| | - Jean-Baptiste Mignardot
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
| | - Gaia Carparelli
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
| | - Cathal Harte
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
| | - Nicolas Hankov
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
| | - Viviana Aureli
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
| | | | | | - David Borton
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
- School of Engineering, Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - Jean Laurens
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Isabelle Vollenweider
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
| | - Simon Borgognon
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
| | - François Bourre
- Université de Bordeaux, Institut des Maladies Neurodégénératives, UMR 5293, Bordeaux, France
- CNRS, Institut des Maladies Neurodégénératives, UMR 5293, Bordeaux, France
| | - Michel Goillandeau
- Université de Bordeaux, Institut des Maladies Neurodégénératives, UMR 5293, Bordeaux, France
- CNRS, Institut des Maladies Neurodégénératives, UMR 5293, Bordeaux, France
| | - Wai Kin D Ko
- Motac Neuroscience, UK-M15 6WE, Manchester, UK
- China Academy of Medical Sciences, Beijing, China
- Institute of Laboratory Animal Sciences, Beijing, China
| | - Laurent Petit
- Université de Bordeaux, Institut des Maladies Neurodégénératives, UMR 5293, Bordeaux, France
- CNRS, Institut des Maladies Neurodégénératives, UMR 5293, Bordeaux, France
| | - Qin Li
- Motac Neuroscience, UK-M15 6WE, Manchester, UK
- China Academy of Medical Sciences, Beijing, China
- Institute of Laboratory Animal Sciences, Beijing, China
| | | | | | - Maria Yaroshinsky
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Jean-Baptiste Ledoux
- Department of Diagnostic and Interventional Radiology, CHUV/UNIL, Lausanne, Switzerland
| | - Fabio Becce
- Department of Diagnostic and Interventional Radiology, CHUV/UNIL, Lausanne, Switzerland
| | | | - Julien F Bally
- Department of Neurology, CHUV/UNIL, Lausanne, Switzerland
| | | | - Dominique Guehl
- Université de Bordeaux, Institut des Maladies Neurodégénératives, UMR 5293, Bordeaux, France
- CNRS, Institut des Maladies Neurodégénératives, UMR 5293, Bordeaux, France
| | - Auke Ijspeert
- Institute of Bioengineering, School of Engineering, EPFL, Lausanne, Switzerland
| | - Marco Capogrosso
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
- Rehab and Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jordan W Squair
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
| | - Leonie Asboth
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland
- Department of Neurosurgery, CHUV, Lausanne, Switzerland
| | - Philip A Starr
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Doris D Wang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Stéphanie P Lacour
- NeuroX Institute, School of Bioengineering, EPFL, Lausanne, Switzerland
- Laboratory for Soft Bioelectronic Interfaces (LSBI), NeuroX Institute, EPFL, Lausanne, Switzerland
| | - Silvestro Micera
- NeuroX Institute, School of Bioengineering, EPFL, Lausanne, Switzerland
- Department of Excellence in Robotics and AI, Biorobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Chuan Qin
- China Academy of Medical Sciences, Beijing, China
| | - Jocelyne Bloch
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland.
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland.
- Department of Neurosurgery, CHUV, Lausanne, Switzerland.
| | - Erwan Bezard
- Motac Neuroscience, UK-M15 6WE, Manchester, UK.
- China Academy of Medical Sciences, Beijing, China.
- Institute of Laboratory Animal Sciences, Beijing, China.
- Université de Bordeaux, Institut des Maladies Neurodégénératives, UMR 5293, Bordeaux, France.
- CNRS, Institut des Maladies Neurodégénératives, UMR 5293, Bordeaux, France.
| | - G Courtine
- NeuroX Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland.
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
- NeuroRestore, Defitech Center for Interventional Neurotherapies, EPFL/CHUV/UNIL, Lausanne, Switzerland.
- Department of Neurosurgery, CHUV, Lausanne, Switzerland.
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13
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Wendt K, Sorkhabi MM, O'Shea J, Denison T, van Rheede J. Influence of time of day on resting motor threshold in clinical TMS practice. Clin Neurophysiol 2023; 155:65-67. [PMID: 37738899 PMCID: PMC7615892 DOI: 10.1016/j.clinph.2023.08.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 08/10/2023] [Indexed: 09/24/2023]
Affiliation(s)
- Karen Wendt
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK; Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK.
| | | | - Jacinta O'Shea
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity (OHBA), University of Oxford Department of Psychiatry, Warneford Hospital, Warneford Lane, Oxford, UK
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX1 3TH, UK; Department of Engineering Science, University of Oxford, Oxford, OX1 3PJ, UK
| | - Joram van Rheede
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
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Kavoosi A, Mitchell MP, Kariyawasam R, Fleming JE, Lewis P, Johansen-Berg H, Cagnan H, Denison T. MorpheusNet: Resource efficient sleep stage classifier for embedded on-line systems. Conf Proc IEEE Int Conf Syst Man Cybern 2023; 2023:2315-2320. [PMID: 38384281 PMCID: PMC7615658 DOI: 10.1109/smc53992.2023.10394274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Sleep Stage Classification (SSC) is a labor-intensive task, requiring experts to examine hours of electrophysiological recordings for manual classification. This is a limiting factor when it comes to leveraging sleep stages for therapeutic purposes. With increasing affordability and expansion of wearable devices, automating SSC may enable deployment of sleep-based therapies at scale. Deep Learning has gained increasing attention as a potential method to automate this process. Previous research has shown accuracy comparable to manual expert scores. However, previous approaches require sizable amount of memory and computational resources. This constrains the ability to classify in real time and deploy models on the edge. To address this gap, we aim to provide a model capable of predicting sleep stages in real-time, without requiring access to external computational sources (e.g., mobile phone, cloud). The algorithm is power efficient to enable use on embedded battery powered systems. Our compact sleep stage classifier can be deployed on most off-the-shelf microcontrollers (MCU) with constrained hardware settings. This is due to the memory footprint of our approach requiring significantly fewer operations. The model was tested on three publicly available data bases and achieved performance comparable to the state of the art, whilst reducing model complexity by orders of magnitude (up to 280 times smaller compared to state of the art). We further optimized the model with quantization of parameters to 8 bits with only an average drop of 0.95% in accuracy. When implemented in firmware, the quantized model achieves a latency of 1.6 seconds on an Arm Cortex-M4 processor, allowing its use for on-line SSC-based therapies.
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Affiliation(s)
- Ali Kavoosi
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
| | - Morgan P. Mitchell
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | | | - John E. Fleming
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
| | - Penny Lewis
- Brain Research Imaging Centre, Cardiff University, Cardiff, UK
| | - Heidi Johansen-Berg
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Hayriye Cagnan
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
- Department of Engineering Sciences, University of Oxford, Oxford, UK
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15
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Deli A, Zamora M, Fleming JE, Divanbeighi Zand A, Benjaber M, Green AL, Denison T. Bioelectronic Zeitgebers: targeted neuromodulation to re-establish circadian rhythms. Conf Proc IEEE Int Conf Syst Man Cybern 2023; 2023:2301-2308. [PMID: 38343562 PMCID: PMC7615625 DOI: 10.1109/smc53992.2023.10394632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/16/2024]
Abstract
Existing neurostimulation systems implanted for the treatment of neurodegenerative disorders generally deliver invariable therapy parameters, regardless of phase of the sleep/wake cycle. However, there is considerable evidence that brain activity in these conditions varies according to this cycle, with discrete patterns of dysfunction linked to loss of circadian rhythmicity, worse clinical outcomes and impaired patient quality of life. We present a targeted concept of circadian neuromodulation using a novel device platform. This system utilises stimulation of circuits important in sleep and wake regulation, delivering bioelectronic cues (Zeitgebers) aimed at entraining rhythms to more physiological patterns in a personalised and fully configurable manner. Preliminary evidence from its first use in a clinical trial setting, with brainstem arousal circuits as a surgical target, further supports its promising impact on sleep/wake pathology. Data included in this paper highlight its versatility and effectiveness on two different patient phenotypes. In addition to exploring acute and long-term electrophysiological and behavioural effects, we also discuss current caveats and future feature improvements of our proposed system, as well as its potential applicability in modifying disease progression in future therapies.
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Affiliation(s)
- Alceste Deli
- Functional Neurosurgery Group, Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK
| | - Mayela Zamora
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | - John E. Fleming
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - Amir Divanbeighi Zand
- Functional Neurosurgery Group, Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK
| | - Moaad Benjaber
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - Alexander L. Green
- Functional Neurosurgery Group, Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK
| | - Timothy Denison
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
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16
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Mivalt F, Kremen V, Sladky V, Cui J, Gregg NM, Balzekas I, Marks V, St Louis EK, Croarkin P, Lundstrom BN, Nelson N, Kim J, Hermes D, Messina S, Worrell S, Richner T, Brinkmann BH, Denison T, Miller KJ, Van Gompel J, Stead M, Worrell GA. Impedance Rhythms in Human Limbic System. J Neurosci 2023; 43:6653-6666. [PMID: 37620157 PMCID: PMC10538585 DOI: 10.1523/jneurosci.0241-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 08/09/2023] [Accepted: 08/14/2023] [Indexed: 08/26/2023] Open
Abstract
The impedance is a fundamental electrical property of brain tissue, playing a crucial role in shaping the characteristics of local field potentials, the extent of ephaptic coupling, and the volume of tissue activated by externally applied electrical brain stimulation. We tracked brain impedance, sleep-wake behavioral state, and epileptiform activity in five people with epilepsy living in their natural environment using an investigational device. The study identified impedance oscillations that span hours to weeks in the amygdala, hippocampus, and anterior nucleus thalamus. The impedance in these limbic brain regions exhibit multiscale cycles with ultradian (∼1.5-1.7 h), circadian (∼21.6-26.4 h), and infradian (∼20-33 d) periods. The ultradian and circadian period cycles are driven by sleep-wake state transitions between wakefulness, nonrapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. Limbic brain tissue impedance reaches a minimum value in NREM sleep, intermediate values in REM sleep, and rises through the day during wakefulness, reaching a maximum in the early evening before sleep onset. Infradian (∼20-33 d) impedance cycles were not associated with a distinct behavioral correlate. Brain tissue impedance is known to strongly depend on the extracellular space (ECS) volume, and the findings reported here are consistent with sleep-wake-dependent ECS volume changes recently observed in the rodent cortex related to the brain glymphatic system. We hypothesize that human limbic brain ECS changes during sleep-wake state transitions underlie the observed multiscale impedance cycles. Impedance is a simple electrophysiological biomarker that could prove useful for tracking ECS dynamics in human health, disease, and therapy.SIGNIFICANCE STATEMENT The electrical impedance in limbic brain structures (amygdala, hippocampus, anterior nucleus thalamus) is shown to exhibit oscillations over multiple timescales. We observe that impedance oscillations with ultradian and circadian periodicities are associated with transitions between wakefulness, NREM, and REM sleep states. There are also impedance oscillations spanning multiple weeks that do not have a clear behavioral correlate and whose origin remains unclear. These multiscale impedance oscillations will have an impact on extracellular ionic currents that give rise to local field potentials, ephaptic coupling, and the tissue activated by electrical brain stimulation. The approach for measuring tissue impedance using perturbational electrical currents is an established engineering technique that may be useful for tracking ECS volume.
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Affiliation(s)
- Filip Mivalt
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, 61600 Brno, Czech Republic
- International Clinical Research Center, St. Anne's University Hospital, 60200 Brno, Czech Republic
| | - Vaclav Kremen
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University, 16000 Prague, Czech Republic
| | - Vladimir Sladky
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
- International Clinical Research Center, St. Anne's University Hospital, 60200 Brno, Czech Republic
- Faculty of Biomedical Engineering, Czech Technical University, 16000 Prague, Czech Republic
| | - Jie Cui
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Nicholas M Gregg
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Irena Balzekas
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota 55905
| | - Victoria Marks
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota 55905
| | - Erik K St Louis
- Center for Sleep Medicine, Departments of Neurology and Medicine, Divisions of Sleep Neurology and Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, Minnesota 55905
| | | | - Brian Nils Lundstrom
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Noelle Nelson
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Jiwon Kim
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota 55905
| | - Steven Messina
- Department of Radiology, Mayo Clinic Rochester, Minnesota 55905
| | - Samuel Worrell
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Thomas Richner
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Benjamin H Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota 55905
| | - Timothy Denison
- Department of Engineering Science, Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Kai J Miller
- Department of Neurologic Surgery, Mayo Clinic, Rochester, Minnesota 55905
| | - Jamie Van Gompel
- Department of Neurologic Surgery, Mayo Clinic, Rochester, Minnesota 55905
| | - Matthew Stead
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Gregory A Worrell
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota 55905
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17
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Smyth C, Anjum MF, Ravi S, Denison T, Starr P, Little S. Adaptive Deep Brain Stimulation for sleep stage targeting in Parkinson's disease. Brain Stimul 2023; 16:1292-1296. [PMID: 37567463 PMCID: PMC10835741 DOI: 10.1016/j.brs.2023.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 05/21/2023] [Accepted: 08/07/2023] [Indexed: 08/13/2023] Open
Abstract
BACKGROUND Sleep dysfunction is disabling in people with Parkinson's disease and is linked to worse motor and non-motor outcomes. Sleep-specific adaptive Deep Brain Stimulation has the potential to target pathophysiologies of sleep. OBJECTIVE Develop an adaptive Deep Brain Stimulation algorithm that modulates stimulation parameters in response to intracranially classified sleep stages. METHODS We performed at-home, multi-night intracranial electrocorticography and polysomnogram recordings to train personalized linear classifiers for discriminating the N3 NREM sleep stage. Classifiers were embedded into investigational Deep Brain Stimulators for N3 specific adaptive DBS. RESULTS We report high specificity of embedded, autonomous, intracranial electrocorticography N3 sleep stage classification across two participants and provide proof-of-principle of successful sleep stage specific adaptive Deep Brain Stimulation. CONCLUSION Multi-night cortico-basal recordings and sleep specific adaptive Deep Brain Stimulation provide an experimental framework to investigate sleep pathophysiology and mechanistic interactions with stimulation, towards the development of therapeutic neurostimulation paradigms directly targeting sleep dysfunction.
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Affiliation(s)
- Clay Smyth
- Department of Bioengineering, University of California, San Francisco, UCSF Byers Hall Box 2520, 1700 Fourth St Ste 203, San Francisco, CA, 94143, United States.
| | - Md Fahim Anjum
- Department of Neurology, University of California, San Francisco, Eighth Floor, 400 Parnassus Ave, San Francisco, CA, 94143, United States.
| | - Shravanan Ravi
- Department of Neurology, University of California, San Francisco, Eighth Floor, 400 Parnassus Ave, San Francisco, CA, 94143, United States.
| | - Timothy Denison
- Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK.
| | - Philip Starr
- Department of Neurosurgery, University of California, San Francisco, Eighth Floor, 400 Parnassus Ave, San Francisco, CA, 94143, United States.
| | - Simon Little
- Department of Neurology, University of California, San Francisco, Eighth Floor, 400 Parnassus Ave, San Francisco, CA, 94143, United States.
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18
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Sermon JJ, Olaru M, Ansó J, Cernera S, Little S, Shcherbakova M, Bogacz R, Starr PA, Denison T, Duchet B. Sub-harmonic entrainment of cortical gamma oscillations to deep brain stimulation in Parkinson's disease: Model based predictions and validation in three human subjects. Brain Stimul 2023; 16:1412-1424. [PMID: 37683763 PMCID: PMC10635843 DOI: 10.1016/j.brs.2023.08.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 08/28/2023] [Accepted: 08/30/2023] [Indexed: 09/10/2023] Open
Abstract
OBJECTIVES The exact mechanisms of deep brain stimulation (DBS) are still an active area of investigation, in spite of its clinical successes. This is due in part to the lack of understanding of the effects of stimulation on neuronal rhythms. Entrainment of brain oscillations has been hypothesised as a potential mechanism of neuromodulation. A better understanding of entrainment might further inform existing methods of continuous DBS, and help refine algorithms for adaptive methods. The purpose of this study is to develop and test a theoretical framework to predict entrainment of cortical rhythms to DBS across a wide range of stimulation parameters. MATERIALS AND METHODS We fit a model of interacting neural populations to selected features characterising PD patients' off-stimulation finely-tuned gamma rhythm recorded through electrocorticography. Using the fitted models, we predict basal ganglia DBS parameters that would result in 1:2 entrainment, a special case of sub-harmonic entrainment observed in patients and predicted by theory. RESULTS We show that the neural circuit models fitted to patient data exhibit 1:2 entrainment when stimulation is provided across a range of stimulation parameters. Furthermore, we verify key features of the region of 1:2 entrainment in the stimulation frequency/amplitude space with follow-up recordings from the same patients, such as the loss of 1:2 entrainment above certain stimulation amplitudes. CONCLUSION Our results reveal that continuous, constant frequency DBS in patients may lead to nonlinear patterns of neuronal entrainment across stimulation parameters, and that these responses can be predicted by modelling. Should entrainment prove to be an important mechanism of therapeutic stimulation, our modelling framework may reduce the parameter space that clinicians must consider when programming devices for optimal benefit.
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Affiliation(s)
- James J Sermon
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK; MRC Brain Networks Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Maria Olaru
- Department of Neurological Surgery, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Juan Ansó
- Department of Neurological Surgery, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Stephanie Cernera
- Department of Neurological Surgery, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Simon Little
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Maria Shcherbakova
- Department of Neurological Surgery, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Rafal Bogacz
- MRC Brain Networks Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Philip A Starr
- Department of Neurological Surgery, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Timothy Denison
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK; MRC Brain Networks Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Benoit Duchet
- MRC Brain Networks Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
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19
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Mivalt F, Sladky V, Worrell S, Gregg NM, Balzekas I, Kim I, Chang SY, Montonye DR, Duque-Lopez A, Krakorova M, Pridalova T, Lepkova K, Brinkmann BH, Miller KJ, Van Gompel JJ, Denison T, Kaufmann TJ, Messina SA, St. Louis EK, Kremen V, Worrell GA. Automated sleep classification with chronic neural implants in freely behaving canines. J Neural Eng 2023; 20:10.1088/1741-2552/aced21. [PMID: 37536320 PMCID: PMC10480092 DOI: 10.1088/1741-2552/aced21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 08/03/2023] [Indexed: 08/05/2023]
Abstract
Objective.Long-term intracranial electroencephalography (iEEG) in freely behaving animals provides valuable electrophysiological information and when correlated with animal behavior is useful for investigating brain function.Approach.Here we develop and validate an automated iEEG-based sleep-wake classifier for canines using expert sleep labels derived from simultaneous video, accelerometry, scalp electroencephalography (EEG) and iEEG monitoring. The video, scalp EEG, and accelerometry recordings were manually scored by a board-certified sleep expert into sleep-wake state categories: awake, rapid-eye-movement (REM) sleep, and three non-REM sleep categories (NREM1, 2, 3). The expert labels were used to train, validate, and test a fully automated iEEG sleep-wake classifier in freely behaving canines.Main results. The iEEG-based classifier achieved an overall classification accuracy of 0.878 ± 0.055 and a Cohen's Kappa score of 0.786 ± 0.090. Subsequently, we used the automated iEEG-based classifier to investigate sleep over multiple weeks in freely behaving canines. The results show that the dogs spend a significant amount of the day sleeping, but the characteristics of daytime nap sleep differ from night-time sleep in three key characteristics: during the day, there are fewer NREM sleep cycles (10.81 ± 2.34 cycles per day vs. 22.39 ± 3.88 cycles per night;p< 0.001), shorter NREM cycle durations (13.83 ± 8.50 min per day vs. 15.09 ± 8.55 min per night;p< 0.001), and dogs spend a greater proportion of sleep time in NREM sleep and less time in REM sleep compared to night-time sleep (NREM 0.88 ± 0.09, REM 0.12 ± 0.09 per day vs. NREM 0.80 ± 0.08, REM 0.20 ± 0.08 per night;p< 0.001).Significance.These results support the feasibility and accuracy of automated iEEG sleep-wake classifiers for canine behavior investigations.
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Affiliation(s)
- Filip Mivalt
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
| | - Vladimir Sladky
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic
| | - Samuel Worrell
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
| | - Nicholas M. Gregg
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
| | - Irena Balzekas
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
- Mayo Clinic School of Medicine and the Mayo Clinic Medical Scientist Training Program, Rochester, MN, United States of America
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, United States of America
| | - Inyong Kim
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
| | - Su-youne Chang
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, United States of America
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States of America
| | - Daniel R. Montonye
- Department of Comparative Medicine, Mayo Clinic, Rochester, MN, United States of America
| | - Andrea Duque-Lopez
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
| | - Martina Krakorova
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
| | - Tereza Pridalova
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
| | - Kamila Lepkova
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic
| | - Benjamin H. Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States of America
| | - Kai J. Miller
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, United States of America
| | - Jamie J. Van Gompel
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, United States of America
| | - Timothy Denison
- Department of Engineering Science, Oxford University, Oxford, United Kingdom
| | - Timothy J. Kaufmann
- Department of Neuroradiology, Mayo Clinic, Rochester, MN, United States of America
| | - Steven A. Messina
- Department of Neuroradiology, Mayo Clinic, Rochester, MN, United States of America
| | - Erik K St. Louis
- Center for Sleep Medicine, Departments of Neurology and Medicine, Divisions of Sleep Neurology & Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN
| | - Vaclav Kremen
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States of America
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
| | - Gregory A. Worrell
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States of America
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States of America
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Fleming JE, Benjaber M, Toth R, Zamora M, Landin K, Kavoosi A, Ottoway J, Gillbe T, Piper RJ, Noone T, Campbell H, Gillbe I, Kaliakatsos M, Tisdall M, Valentín A, Denison T. An embedded intracranial seizure monitor for objective outcome measurements and rhythm identification. Annu Int Conf IEEE Eng Med Biol Soc 2023; 2023:1-6. [PMID: 38083730 PMCID: PMC7615373 DOI: 10.1109/embc40787.2023.10340850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Providing clinicians with objective outcomes of neuromodulation therapy is a key unmet need, especially in emerging areas such as epilepsy and mood disorders. These diseases have episodic behavior and circadian/multidien rhythm characteristics that are difficult to capture in short clinical follow-ups. This work presents preliminary validation evidence for an implantable neuromodulation system with integrated physiological event monitoring, with an initial focus on seizure tracking for epilepsy. The system was developed to address currently unmet requirements for patients undergoing neuromodulation therapy for neurological disorders, specifically the ability to sense physiological data during stimulation and track events with seconds-level granularity. The system incorporates an interactive software tool to enable optimal configuration of the signal processing chain on an embedded implantable device (the Picostim-DyNeuMo Mk-2) including data ingestion from the device loop recorder, event labeling, generation of filter and classification parameters, as well as summary statistics. When the monitor parameters are optimized, the user can wirelessly update the system for chronic event tracking. The simulated performance of the device was assessed using an in silico model with human data to predict the real-time device performance at tracking recorded seizure activity. The in silico performance was then compared against its performance in an in vitro model to capture the full environmental constraints such as sensing during stimulation at the tissue electrode interface. In vitro modeling demonstrated comparable results to the in silico model, providing verification of the software tool and model. This study provides validation evidence of the suitability of the proposed system for tracking longitudinal seizure activity. Given its flexibility, the event monitor can be adapted to track other disorders with episodic and rhythmic symptoms represented by bioelectrical behavior.Clinical relevance-An implantable neuromodulation system is presented that enables chronic tracking of physiological events in disease. This physiological monitor provides the basis for longitudinal assessments of therapy outcomes for patients, such as those with epilepsy where objective identification of patient seizure activity and rhythms might help guide therapy optimization. The system is configurable for other disease states such as Parkinson's disease and mood disorders.
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Affiliation(s)
- John E. Fleming
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | - Moaad Benjaber
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | - Robert Toth
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | - Mayela Zamora
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | - Kei Landin
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | - Ali Kavoosi
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | | | | | - Rory J. Piper
- Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
- Department of Neurosurgery, Great Ormond Street Hospital, London WC1N 3JH, UK
| | | | | | | | - Marios Kaliakatsos
- Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
| | - Martin Tisdall
- Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
- Department of Neurosurgery, Great Ormond Street Hospital, London WC1N 3JH, UK
| | - Antonio Valentín
- Department of Basic and Clinical Neuroscience, King’s College London, London SE5 9RT, UK
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
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21
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Wendt K, Sorkhabi MM, Stagg CJ, Fleming MK, Denison T, O'Shea J. The effect of pulse shape in theta-burst stimulation: Monophasic vs biphasic TMS. Brain Stimul 2023; 16:1178-1185. [PMID: 37543172 PMCID: PMC10444700 DOI: 10.1016/j.brs.2023.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 07/30/2023] [Accepted: 08/02/2023] [Indexed: 08/07/2023] Open
Abstract
BACKGROUND Intermittent theta-burst stimulation (i) (TBS) is a transcranial magnetic stimulation (TMS) plasticity protocol. Conventionally, TBS is applied using biphasic pulses due to hardware limitations. However, monophasic pulses are hypothesised to recruit cortical neurons more selectively than biphasic pulses, predicting stronger plasticity effects. Monophasic and biphasic TBS can be generated using a custom-made pulse-width modulation-based TMS device (pTMS). OBJECTIVE Using pTMS, we tested the hypothesis that monophasic iTBS would induce a stronger plasticity effect than biphasic, measured as induced increases in motor corticospinal excitability. METHODS In a repeated-measures design, thirty healthy volunteers participated in three separate sessions, where monophasic and biphasic iTBS was applied to the primary motor cortex (M1 condition) or the vertex (control condition). Plasticity was quantified as increases in motor corticospinal excitability after versus before iTBS, by comparing peak-to-peak amplitudes of motor evoked potentials (MEP) measured at baseline and over 60 min after iTBS. RESULTS Both monophasic and biphasic M1 iTBS led to significant increases in MEP amplitude. As predicted, linear mixed effects (LME) models showed that the iTBS condition had a significant effect on the MEP amplitude (χ2 (1) = 27.615, p < 0.001) with monophasic iTBS leading to significantly stronger plasticity than biphasic iTBS (t (693) = 2.311, p = 0.021). Control vertex iTBS had no effect. CONCLUSIONS In this study, monophasic iTBS induced a stronger motor corticospinal excitability increase than biphasic within participants. This greater physiological effect suggests that monophasic iTBS may also have potential for greater functional impact, of interest for future fundamental and clinical applications of TBS.
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Affiliation(s)
- Karen Wendt
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX1 3TH, UK; Department of Engineering Science, University of Oxford, Oxford, OX1 3PJ, UK.
| | - Majid Memarian Sorkhabi
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX1 3TH, UK
| | - Charlotte J Stagg
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX1 3TH, UK; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Melanie K Fleming
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX1 3TH, UK; Department of Engineering Science, University of Oxford, Oxford, OX1 3PJ, UK
| | - Jacinta O'Shea
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity (OHBA), University of Oxford Department of Psychiatry, Warneford Hospital, Warneford Lane, Oxford, UK
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Sorkhabi MM, Wendt K, O'Shea J, Denison T. Corrigendum to "Pulse width modulation-based TMS: Primary motor cortex responses compared to conventional monophasic stimuli" [Brain Stimulat 15 (2022) 980-983]. Brain Stimul 2023; 16:1221. [PMID: 37105841 DOI: 10.1016/j.brs.2023.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023] Open
Affiliation(s)
- Majid Memarian Sorkhabi
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX1 3TH, UK.
| | - Karen Wendt
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX1 3TH, UK; Department of Engineering Science, University of Oxford, Oxford, OX1 3PJ, UK
| | - Jacinta O'Shea
- Wellcome Centre for Integrative Neuroimaging (WIN), Oxford Centre for Human Brain Activity (OHBA), University of Oxford Department of Psychiatry, Warneford Hospital, Warneford Lane, Oxford, UK
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX1 3TH, UK; Department of Engineering Science, University of Oxford, Oxford, OX1 3PJ, UK
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Ali K, Wendt K, Sorkhabi MM, Benjaber M, Denison T, Rogers DJ. xTMS: A Pulse Generator for Exploring Transcranial Magnetic Stimulation Therapies. Conf Proc (IEEE Appl Power Electron Conf Expo) 2023; 2023:1875-1880. [PMID: 37342241 PMCID: PMC7614672 DOI: 10.1109/apec43580.2023.10131554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/22/2023]
Abstract
A cascaded H-bridge based pulse generator for transcranial magnetic stimulation is introduced. The system demonstrates complete flexibility for producing different shape, duration, direction, and rate of repetition of stimulus pulses within its electrical limits, and can emulate all commercial and research systems available to-date in this application space. An offline model predictive control algorithm, used to generate pulses and sequences, shows superior performance compared to conventional carrier-based pulse width modulation. A fully functioning laboratory prototype delivers up to 1.5 kV, 6 kA pulses, and is ready to be used as a research tool for the exploration of transcranial magnetic stimulation therapies by leveraging the many degrees-of-freedom offered by the design.
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Affiliation(s)
- Kawsar Ali
- Department o fEngineering Science, University of Oxford, UK
| | - Karen Wendt
- Department o fEngineering Science, University of Oxford, UK
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | | | - Moaad Benjaber
- Department o fEngineering Science, University of Oxford, UK
| | - Timothy Denison
- Department o fEngineering Science, University of Oxford, UK
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
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Deli A, Toth R, Zamora M, Divanbeighi Zand AP, Green AL, Denison T. The Design of Brainstem Interfaces: Characterisation of Physiological Artefacts and Implications for Closed-loop Algorithms. Int IEEE EMBS Conf Neural Eng 2023; 2023:10123850. [PMID: 37249946 PMCID: PMC7614576 DOI: 10.1109/ner52421.2023.10123850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Surgical neuromodulation through implantable devices allows for stimulation delivery to subcortical regions, crucial for symptom control in many debilitating neurological conditions. Novel closed-loop algorithms deliver therapy tailor-made to endogenous physiological activity, however rely on precise sensing of signals such as subcortical oscillations. The frequency of such intrinsic activity can vary depending on subcortical target nucleus, while factors such as regional anatomy may also contribute to variability in sensing signals. While artefact parameters have been explored in more 'standard' and commonly used targets (such as the basal ganglia, which are implanted in movement disorders), characterisation in novel candidate nuclei is still under investigation. One such important area is the brainstem, which contains nuclei crucial for arousal and autonomic regulation. The brainstem provides additional implantation targets for treatment indications in disorders of consciousness and sleep, yet poses distinct anatomical challenges compared to central subcortical targets. Here we investigate the region-specific artefacts encountered during activity and rest while streaming data from brainstem implants with a cranially-mounted device in two patients. Such artefacts result from this complex anatomical environment and its interactions with physiological parameters such as head movement and cardiac functions. The implications of the micromotion-induced artefacts, and potential mitigation, are then considered for future closed-loop stimulation methods.
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Affiliation(s)
- Alceste Deli
- Department of Neurosurgery, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
| | - Robert Toth
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - Mayela Zamora
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK
| | | | - Alexander L. Green
- Department of Neurosurgery, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK
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25
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Baker JL, Toth R, Deli A, Zamora M, Fleming JE, Benjaber M, Goerzen D, Ryou JW, Purpura KP, Schiff ND, Denison T. Regulation of arousal and performance of a healthy non-human primate using closed-loop central thalamic deep brain stimulation. Int IEEE EMBS Conf Neural Eng 2023; 2023:10123754. [PMID: 37228786 PMCID: PMC7614571 DOI: 10.1109/ner52421.2023.10123754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Application of closed-loop approaches in systems neuroscience and brain-computer interfaces holds great promise for revolutionizing our understanding of the brain and for developing novel neuromodulation strategies to restore lost function. The anterior forebrain mesocircuit (AFM) of the mammalian brain is hypothesized to underlie arousal regulation of the cortex and striatum, and support cognitive functions during wakefulness. Dysfunction of arousal regulation is hypothesized to contribute to cognitive dysfunctions in various neurological disorders, and most prominently in patients following traumatic brain injury (TBI). Several clinical studies have explored the use of daily central thalamic deep brain stimulation (CT-DBS) within the AFM to restore consciousness and executive attention in TBI patients. In this study, we explored the use of closed-loop CT-DBS in order to episodically regulate arousal of the AFM of a healthy non-human primate (NHP) with the goal of restoring behavioral performance. We used pupillometry and near real-time analysis of ECoG signals to episodically initiate closed-loop CT-DBS and here we report on our ability to enhance arousal and restore the animal's performance. The initial computer based approach was then experimentally validated using a customized clinical-grade DBS device, the DyNeuMo-X, a bi-directional research platform used for rapidly testing closed-loop DBS. The successful implementation of the DyNeuMo-X in a healthy NHP supports ongoing clinical trials employing the internal DyNeuMo system (NCT05437393, NCT05197816) and our goal of developing and accelerating the deployment of novel neuromodulation approaches to treat cognitive dysfunction in patients with structural brain injuries and other etiologies.
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Affiliation(s)
- Jonathan L. Baker
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
| | - Robert Toth
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, OX1 3TH, UK
| | - Alceste Deli
- Department of Neurosurgery, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
| | - Mayela Zamora
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK
| | - John E. Fleming
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK
| | - Moaad Benjaber
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK
| | - Dana Goerzen
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
| | - Jae-Wook Ryou
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
| | - Keith P. Purpura
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
| | - Nicholas D. Schiff
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK
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26
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Duchet B, Sermon JJ, Weerasinghe G, Denison T, Bogacz R. How to entrain a selected neuronal rhythm but not others: open-loop dithered brain stimulation for selective entrainment. J Neural Eng 2023; 20:10.1088/1741-2552/acbc4a. [PMID: 36880684 PMCID: PMC7614323 DOI: 10.1088/1741-2552/acbc4a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 02/15/2023] [Indexed: 03/08/2023]
Abstract
Objective.While brain stimulation therapies such as deep brain stimulation for Parkinson's disease (PD) can be effective, they have yet to reach their full potential across neurological disorders. Entraining neuronal rhythms using rhythmic brain stimulation has been suggested as a new therapeutic mechanism to restore neurotypical behaviour in conditions such as chronic pain, depression, and Alzheimer's disease. However, theoretical and experimental evidence indicate that brain stimulation can also entrain neuronal rhythms at sub- and super-harmonics, far from the stimulation frequency. Crucially, these counterintuitive effects could be harmful to patients, for example by triggering debilitating involuntary movements in PD. We therefore seek a principled approach to selectively promote rhythms close to the stimulation frequency, while avoiding potential harmful effects by preventing entrainment at sub- and super-harmonics.Approach.Our open-loop approach to selective entrainment, dithered stimulation, consists in adding white noise to the stimulation period.Main results.We theoretically establish the ability of dithered stimulation to selectively entrain a given brain rhythm, and verify its efficacy in simulations of uncoupled neural oscillators, and networks of coupled neural oscillators. Furthermore, we show that dithered stimulation can be implemented in neurostimulators with limited capabilities by toggling within a finite set of stimulation frequencies.Significance.Likely implementable across a variety of existing brain stimulation devices, dithering-based selective entrainment has potential to enable new brain stimulation therapies, as well as new neuroscientific research exploiting its ability to modulate higher-order entrainment.
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Affiliation(s)
- Benoit Duchet
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom.,MRC Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom
| | - James J Sermon
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom.,MRC Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom.,Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford,Oxford, United Kingdom
| | - Gihan Weerasinghe
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom.,MRC Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom
| | - Timothy Denison
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom.,MRC Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom.,Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford,Oxford, United Kingdom
| | - Rafal Bogacz
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom.,MRC Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom
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27
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Wiest C, He S, Duchet B, Pogosyan A, Benjaber M, Denison T, Hasegawa H, Ashkan K, Baig F, Bertaina I, Morgante F, Pereira EA, Torrecillos F, Tan H. Evoked resonant neural activity in subthalamic local field potentials reflects basal ganglia network dynamics. Neurobiol Dis 2023; 178:106019. [PMID: 36706929 PMCID: PMC7614125 DOI: 10.1016/j.nbd.2023.106019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/11/2023] [Accepted: 01/23/2023] [Indexed: 01/25/2023] Open
Abstract
Evoked resonant neural activity (ERNA) is induced by subthalamic deep brain stimulation (DBS) and was recently suggested as a marker of lead placement and contact selection in Parkinson's disease. Yet, its underlying mechanisms and how it is modulated by stimulation parameters are unclear. Here, we recorded local field potentials from 27 Parkinson's disease patients, while leads were externalised to scrutinise the ERNA. First, we show that ERNA in the time series waveform and spectrogram likely represent the same activity, which was contested before. Second, our results show that the ERNA has fast and slow dynamics during stimulation, consistent with the synaptic failure hypothesis. Third, we show that ERNA parameters are modulated by different DBS frequencies, intensities, medication states and stimulation modes (continuous DBS vs. adaptive DBS). These results suggest the ERNA might prove useful as a predictor of the best DBS frequency and lowest effective intensity in addition to contact selection. Changes with levodopa and DBS mode suggest that the ERNA may indicate the state of the cortico-basal ganglia circuit making it a putative biomarker to track clinical state in adaptive DBS.
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Affiliation(s)
- Christoph Wiest
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Shenghong He
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Benoit Duchet
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Alek Pogosyan
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Moaad Benjaber
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Timothy Denison
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Harutomo Hasegawa
- Department of Neurosurgery, King’s College Hospital, Denmark Hill, London, UK
| | - Keyoumars Ashkan
- Department of Neurosurgery, King’s College Hospital, Denmark Hill, London, UK
| | - Fahd Baig
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK,Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George’s, University of London, London, UK
| | - Ilaria Bertaina
- Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George’s, University of London, London, UK,Neurology Department, Neurocenter of Southern Switzerland, Ente Ospedaliero Cantonale, Lugano, Switzerland
| | - Francesca Morgante
- Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George’s, University of London, London, UK
| | - Erlick A. Pereira
- Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George’s, University of London, London, UK
| | - Flavie Torrecillos
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Huiling Tan
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
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Mitchell P, Lee SCM, Yoo PE, Morokoff A, Sharma RP, Williams DL, MacIsaac C, Howard ME, Irving L, Vrljic I, Williams C, Bush S, Balabanski AH, Drummond KJ, Desmond P, Weber D, Denison T, Mathers S, O’Brien TJ, Mocco J, Grayden DB, Liebeskind DS, Opie NL, Oxley TJ, Campbell BCV. Assessment of Safety of a Fully Implanted Endovascular Brain-Computer Interface for Severe Paralysis in 4 Patients: The Stentrode With Thought-Controlled Digital Switch (SWITCH) Study. JAMA Neurol 2023; 80:270-278. [PMID: 36622685 PMCID: PMC9857731 DOI: 10.1001/jamaneurol.2022.4847] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 10/18/2022] [Indexed: 01/10/2023]
Abstract
Importance Brain-computer interface (BCI) implants have previously required craniotomy to deliver penetrating or surface electrodes to the brain. Whether a minimally invasive endovascular technique to deliver recording electrodes through the jugular vein to superior sagittal sinus is safe and feasible is unknown. Objective To assess the safety of an endovascular BCI and feasibility of using the system to control a computer by thought. Design, Setting, and Participants The Stentrode With Thought-Controlled Digital Switch (SWITCH) study, a single-center, prospective, first in-human study, evaluated 5 patients with severe bilateral upper-limb paralysis, with a follow-up of 12 months. From a referred sample, 4 patients with amyotrophic lateral sclerosis and 1 with primary lateral sclerosis met inclusion criteria and were enrolled in the study. Surgical procedures and follow-up visits were performed at the Royal Melbourne Hospital, Parkville, Australia. Training sessions were performed at patients' homes and at a university clinic. The study start date was May 27, 2019, and final follow-up was completed January 9, 2022. Interventions Recording devices were delivered via catheter and connected to subcutaneous electronic units. Devices communicated wirelessly to an external device for personal computer control. Main Outcomes and Measures The primary safety end point was device-related serious adverse events resulting in death or permanent increased disability. Secondary end points were blood vessel occlusion and device migration. Exploratory end points were signal fidelity and stability over 12 months, number of distinct commands created by neuronal activity, and use of system for digital device control. Results Of 4 patients included in analyses, all were male, and the mean (SD) age was 61 (17) years. Patients with preserved motor cortex activity and suitable venous anatomy were implanted. Each completed 12-month follow-up with no serious adverse events and no vessel occlusion or device migration. Mean (SD) signal bandwidth was 233 (16) Hz and was stable throughout study in all 4 patients (SD range across all sessions, 7-32 Hz). At least 5 attempted movement types were decoded offline, and each patient successfully controlled a computer with the BCI. Conclusions and Relevance Endovascular access to the sensorimotor cortex is an alternative to placing BCI electrodes in or on the dura by open-brain surgery. These final safety and feasibility data from the first in-human SWITCH study indicate that it is possible to record neural signals from a blood vessel. The favorable safety profile could promote wider and more rapid translation of BCI to people with paralysis. Trial Registration ClinicalTrials.gov Identifier: NCT03834857.
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Affiliation(s)
- Peter Mitchell
- Department of Radiology, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
| | - Sarah C. M. Lee
- Neurology, Calvary Healthcare Bethlehem, Parkdale, Australia
| | | | - Andrew Morokoff
- Parkville Neurosurgery, The University of Melbourne, Royal Melbourne Hospital, Parkville, Australia
| | - Rahul P. Sharma
- Stanford Healthcare Cardiovascular Medicine, Stanford University, Stanford, California
| | - Daryl L. Williams
- Department of Anaesthesia and Pain Management, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
| | - Christopher MacIsaac
- Intensive Care Department, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
| | - Mark E. Howard
- Victorian Respiratory Support Service, Austin Health, Heidelberg, Australia
| | - Lou Irving
- Peter MacCallum Cancer Centre, The University of Melbourne, The Royal Melbourne Hospital, Melbourne, Australia
| | - Ivan Vrljic
- Department of Radiology, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
| | - Cameron Williams
- Department of Neurology, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
| | - Steven Bush
- Department of Neurology, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
| | - Anna H. Balabanski
- Department of Neurology, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
- Melbourne Brain Centre, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
- Department of Neuroscience, Alfred Brain, Alfred Health, Melbourne, Australia
| | - Katharine J. Drummond
- Department of Neurosurgery, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
| | - Patricia Desmond
- Department of Radiology, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
| | - Douglas Weber
- Department of Biomedical Engineering, College of Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Timothy Denison
- Institute of Biomedical Engineering, The University of Oxford, Oxford, United Kingdom
| | - Susan Mathers
- Neurology, Calvary Healthcare Bethlehem, Parkdale, Australia
| | - Terence J. O’Brien
- Department of Neurology, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
- Department of Medicine, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
- Department of Neuroscience, The Central Clinical School, Monash University and Alfred Health, Melbourne, Australia
| | - J. Mocco
- Department of Neurosurgery, Klingenstein Clinical Center, The Mount Sinai Hospital, New York, New York
| | - David B. Grayden
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Parkville, Australia
| | - David S. Liebeskind
- UCLA Comprehensive Stroke Center, Department of Neurology, University of California, Los Angeles
| | - Nicholas L. Opie
- Vascular Bionics Laboratory, Department of Medicine, The University of Melbourne, Melbourne, Australia
- Synchron, Carlton, Australia
| | - Thomas J. Oxley
- Synchron Inc, New York, New York
- Vascular Bionics Laboratory, Department of Medicine, The University of Melbourne, Melbourne, Australia
| | - Bruce C. V. Campbell
- Department of Neurology, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
- Melbourne Brain Centre, The University of Melbourne, The Royal Melbourne Hospital, Parkville, Australia
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29
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Wendt K, Sorkhabi MM, O’Shea J, Denison T. Emerging non-invasive technologies to stimulate the brain more effectively. Brain Stimul 2023. [DOI: 10.1016/j.brs.2023.01.181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023] Open
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30
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Sorkhabi MM, Wendt K, Granstrom E, Gregory D, Rees S, Denison T. A Pilot Study Leveraging Large Scale Datasets from Internet-Connected Transcranial Magnetic Stimulators: Circadian Modulation of Cortical Excitability. Brain Stimul 2023. [DOI: 10.1016/j.brs.2023.01.615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023] Open
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31
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van Rheede JJ, Feldmann LK, Busch JL, Fleming JE, Mathiopoulou V, Denison T, Sharott A, Kühn AA. Diurnal modulation of subthalamic beta oscillatory power in Parkinson’s disease patients during deep brain stimulation. NPJ Parkinsons Dis 2022; 8:88. [PMID: 35804160 PMCID: PMC9270436 DOI: 10.1038/s41531-022-00350-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 06/10/2022] [Indexed: 11/09/2022] Open
Abstract
Beta-band activity in the subthalamic local field potential (LFP) is correlated with Parkinson’s disease (PD) symptom severity and is the therapeutic target of deep brain stimulation (DBS). While beta fluctuations in PD patients are well characterized on shorter timescales, it is not known how beta activity evolves around the diurnal cycle, outside a clinical setting. Here, we obtained chronic recordings (34 ± 13 days) of subthalamic beta power in PD patients implanted with the Percept DBS device during high-frequency DBS and analysed their diurnal properties as well as sensitivity to artifacts. Time of day explained 41 ± 9% of the variance in beta power (p < 0.001 in all patients), with increased beta during the day and reduced beta at night. Certain movements affected LFP quality, which may have contributed to diurnal patterns in some patients. Future DBS algorithms may benefit from taking such diurnal and artifactual fluctuations in beta power into account.
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Piper RJ, Richardson RM, Worrell G, Carmichael DW, Baldeweg T, Litt B, Denison T, Tisdall MM. Towards network-guided neuromodulation for epilepsy. Brain 2022; 145:3347-3362. [PMID: 35771657 PMCID: PMC9586548 DOI: 10.1093/brain/awac234] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 05/30/2022] [Accepted: 06/16/2022] [Indexed: 11/30/2022] Open
Abstract
Epilepsy is well-recognized as a disorder of brain networks. There is a growing body of research to identify critical nodes within dynamic epileptic networks with the aim to target therapies that halt the onset and propagation of seizures. In parallel, intracranial neuromodulation, including deep brain stimulation and responsive neurostimulation, are well-established and expanding as therapies to reduce seizures in adults with focal-onset epilepsy; and there is emerging evidence for their efficacy in children and generalized-onset seizure disorders. The convergence of these advancing fields is driving an era of 'network-guided neuromodulation' for epilepsy. In this review, we distil the current literature on network mechanisms underlying neurostimulation for epilepsy. We discuss the modulation of key 'propagation points' in the epileptogenic network, focusing primarily on thalamic nuclei targeted in current clinical practice. These include (i) the anterior nucleus of thalamus, now a clinically approved and targeted site for open loop stimulation, and increasingly targeted for responsive neurostimulation; and (ii) the centromedian nucleus of the thalamus, a target for both deep brain stimulation and responsive neurostimulation in generalized-onset epilepsies. We discuss briefly the networks associated with other emerging neuromodulation targets, such as the pulvinar of the thalamus, piriform cortex, septal area, subthalamic nucleus, cerebellum and others. We report synergistic findings garnered from multiple modalities of investigation that have revealed structural and functional networks associated with these propagation points - including scalp and invasive EEG, and diffusion and functional MRI. We also report on intracranial recordings from implanted devices which provide us data on the dynamic networks we are aiming to modulate. Finally, we review the continuing evolution of network-guided neuromodulation for epilepsy to accelerate progress towards two translational goals: (i) to use pre-surgical network analyses to determine patient candidacy for neurostimulation for epilepsy by providing network biomarkers that predict efficacy; and (ii) to deliver precise, personalized and effective antiepileptic stimulation to prevent and arrest seizure propagation through mapping and modulation of each patients' individual epileptogenic networks.
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Affiliation(s)
- Rory J Piper
- Department of Neurosurgery, Great Ormond Street Hospital, London, UK
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | | | | | - Torsten Baldeweg
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Brian Litt
- Department of Neurology and Bioengineering, University of Pennsylvania, Philadelphia, USA
| | | | - Martin M Tisdall
- Department of Neurosurgery, Great Ormond Street Hospital, London, UK
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
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Piper R, Fleming J, Valentin A, Kaliakatsos M, Tisdall M, Denison T. PO091 / #502 INTRACRANIAL NEUROMODULATION DEVICES FOR CHILDREN: LESSONS LEARNED AND FUTURE OPPORTUNITIES. Neuromodulation 2022. [DOI: 10.1016/j.neurom.2022.08.265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Kavoosi A, Toth R, Benjaber M, Zamora M, Valentin A, Sharott A, Denison T. Computationally efficient neural network classifiers for next generation closed loop neuromodulation therapy - a case study in epilepsy. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2022; 2022:288-291. [PMID: 36085909 PMCID: PMC7613668 DOI: 10.1109/embc48229.2022.9871793] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
This work explores the potential utility of neural network classifiers for real-time classification of field-potential based biomarkers in next-generation responsive neuromodulation systems. Compared to classical filter-based classifiers, neural networks offer an ease of patient-specific parameter tuning, promising to reduce the burden of programming on clinicians. The paper explores a compact, feed-forward neural network architecture of only dozens of units for seizure-state classification in refractory epilepsy. The proposed classifier offers comparable accuracy to filterclassifiers on clinician-labeled data, while reducing detection latency. As a trade-off to classical methods, the paper focuses on keeping the complexity of the architecture minimal, to accommodate the on-board computational constraints of implantable pulse generator systems. Clinical relevance—A neural network-based classifier is presented for responsive neurostimulation, with comparable accuracy to classical methods at reduced latency.
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Affiliation(s)
- Ali Kavoosi
- University of Oxford,Brain Network Dynamics Unit,Department of Pharmacology,Oxford,United Kingdom,OX1 3TH
| | - Robert Toth
- Institute of Biomedical Engineering, University of Oxford,Old Road Campus Research Building,Department of Engineering Sciences,Oxford,United Kingdom,OX3 7DQ
| | - Moaad Benjaber
- University of Oxford,Brain Network Dynamics Unit,Department of Pharmacology,Oxford,United Kingdom,OX1 3TH
| | - Mayela Zamora
- University of Oxford,Brain Network Dynamics Unit,Department of Pharmacology,Oxford,United Kingdom,OX1 3TH
| | - Antonio Valentin
- King's College London,Department of Basic and Clinical Neuroscience,London,United Kingdom,SE5 9RT
| | - Andrew Sharott
- Institute of Biomedical Engineering, University of Oxford,Old Road Campus Research Building,Department of Engineering Sciences,Oxford,United Kingdom,OX3 7DQ
| | - Timothy Denison
- University of Oxford,Brain Network Dynamics Unit,Department of Pharmacology,Oxford,United Kingdom,OX1 3TH
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Radyte E, Wendt K, Sorkhabi MM, O'Shea J, Denison T. Relative Comparison of Non-Invasive Brain Stimulation Methods for Modulating Deep Brain Targets. Annu Int Conf IEEE Eng Med Biol Soc 2022; 2022:1715-1718. [PMID: 36085882 DOI: 10.1109/embc48229.2022.9871476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
This study models and investigates whether temporally interfering electric fields (TI EFs) could function as an effective non-invasive brain stimulation (NIBS) method for deep brain structure targeting in humans, relevant for psychiatric applications. Here, electric fields off- and on-target are modelled and compared with other common NIBS modalities (tACS, TMS). Additionally, local effects of the field strength are modelled on single-compartment neuronal models. While TI EFs are able to effectively reach deep brain targets, the ratio of off- to on-target stimulation remains high and comparable to other NIBS and may result in off-target neural blocks. Clinical Relevance- This study builds on earlier work and demonstrates some of the challenges -such as off-target conduction blocks- of applying TI EFs for targeting deep brain structures important in understanding the potential of treating neuropsychiatric conditions in the future.
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Memarian Sorkhabi M, Wendt K, O'Shea J, Denison T. Pulse width modulation-based TMS: Primary motor cortex responses compared to conventional monophasic stimuli. Brain Stimul 2022; 15:980-983. [PMID: 35792319 DOI: 10.1016/j.brs.2022.06.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 06/26/2022] [Accepted: 06/28/2022] [Indexed: 11/20/2022] Open
Affiliation(s)
- Majid Memarian Sorkhabi
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX1 3TH, UK.
| | - Karen Wendt
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX1 3TH, UK; Department of Engineering Science, University of Oxford, Oxford, OX1 3PJ, UK
| | - Jacinta O'Shea
- Wellcome Centre for Integrative Neuroimaging (WIN), Oxford Centre for Human Brain Activity (OHBA), University of Oxford Department of Psychiatry, Warneford Hospital, Warneford Lane, Oxford, UK
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX1 3TH, UK; Department of Engineering Science, University of Oxford, Oxford, OX1 3PJ, UK
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Nowakowska M, Üçal M, Charalambous M, Bhatti SFM, Denison T, Meller S, Worrell GA, Potschka H, Volk HA. Neurostimulation as a Method of Treatment and a Preventive Measure in Canine Drug-Resistant Epilepsy: Current State and Future Prospects. Front Vet Sci 2022; 9:889561. [PMID: 35782557 PMCID: PMC9244381 DOI: 10.3389/fvets.2022.889561] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 05/23/2022] [Indexed: 11/28/2022] Open
Abstract
Modulation of neuronal activity for seizure control using various methods of neurostimulation is a rapidly developing field in epileptology, especially in treatment of refractory epilepsy. Promising results in human clinical practice, such as diminished seizure burden, reduced incidence of sudden unexplained death in epilepsy, and improved quality of life has brought neurostimulation into the focus of veterinary medicine as a therapeutic option. This article provides a comprehensive review of available neurostimulation methods for seizure management in drug-resistant epilepsy in canine patients. Recent progress in non-invasive modalities, such as repetitive transcranial magnetic stimulation and transcutaneous vagus nerve stimulation is highlighted. We further discuss potential future advances and their plausible application as means for preventing epileptogenesis in dogs.
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Affiliation(s)
- Marta Nowakowska
- Research Unit of Experimental Neurotraumatology, Department of Neurosurgery, Medical University of Graz, Graz, Austria
| | - Muammer Üçal
- Research Unit of Experimental Neurotraumatology, Department of Neurosurgery, Medical University of Graz, Graz, Austria
| | - Marios Charalambous
- Department of Small Animal Medicine and Surgery, University of Veterinary Medicine Hannover, Hanover, Germany
| | - Sofie F. M. Bhatti
- Small Animal Department, Faculty of Veterinary Medicine, Small Animal Teaching Hospital, Ghent University, Merelbeke, Belgium
| | - Timothy Denison
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | - Sebastian Meller
- Department of Small Animal Medicine and Surgery, University of Veterinary Medicine Hannover, Hanover, Germany
| | | | - Heidrun Potschka
- Faculty of Veterinary Medicine, Institute of Pharmacology, Toxicology and Pharmacy, Ludwig-Maximilians-University, Munich, Germany
| | - Holger A. Volk
- Department of Small Animal Medicine and Surgery, University of Veterinary Medicine Hannover, Hanover, Germany
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Sladky V, Nejedly P, Mivalt F, Brinkmann BH, Kim I, St. Louis EK, Gregg NM, Lundstrom BN, Crowe CM, Attia TP, Crepeau D, Balzekas I, Marks VS, Wheeler LP, Cimbalnik J, Cook M, Janca R, Sturges BK, Leyde K, Miller KJ, Van Gompel JJ, Denison T, Worrell GA, Kremen V. Distributed brain co-processor for tracking spikes, seizures and behavior during electrical brain stimulation. Brain Commun 2022; 4:fcac115. [PMID: 35755635 PMCID: PMC9217965 DOI: 10.1093/braincomms/fcac115] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 01/12/2022] [Accepted: 05/05/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioral inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behavior and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a hand-held device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes, and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from 9 humans and 8 canines with epilepsy, and then implemented prospectively in out-of-sample testing in 2 pet canines and 4 humans with drug resistant epilepsy living in their natural environments. Accurate seizure diaries are needed as the primary clinical outcome measure of epilepsy therapy and to guide brain stimulation optimization. The brain co-processor system described here enables tracking interictal epileptiform spikes, seizures, and correlation with patient behavioral reports. In the future correlation of spikes and seizures with behavior will allow more detailed investigation of the clinical impact of spikes and seizures on patients.
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Affiliation(s)
- Vladimir Sladky
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic
| | - Petr Nejedly
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
- The Czech Academy of Sciences, Institute of Scientific Instruments, Brno, Czech Republic
| | - Filip Mivalt
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
- Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic
| | - Benjamin H. Brinkmann
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Inyong Kim
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
| | - Erik K. St. Louis
- Center for Sleep Medicine, Departments of Neurology and Medicine, Divisions of Sleep Neurology & Pul-monary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA
| | - Nicholas M. Gregg
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
| | - Brian N. Lundstrom
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
| | - Chelsea M. Crowe
- Department of Veterinary Clinical Sciences, University of California, Davis, CA, USA
| | - Tal Pal Attia
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
| | - Daniel Crepeau
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
| | - Irena Balzekas
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
- Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, MN, USA
- Mayo Clinic School of Medicine and the Mayo Clinic Medical Scientist Training Program, MN, USA
| | - Victoria S. Marks
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
- Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, MN, USA
| | - Lydia P. Wheeler
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
- Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, MN, USA
| | - Jan Cimbalnik
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
| | - Mark Cook
- Department of Neurology, Royal Melbourne Hospital, Melbourne, Australia
| | - Radek Janca
- Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
- Second Faculty of Medicine, Motol University Hospital, Charles University, Prague, Czech Republic
| | - Beverly K. Sturges
- Department of Veterinary Clinical Sciences, University of California, Davis, CA, USA
| | | | - Kai J. Miller
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA
| | | | | | - Gregory A. Worrell
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Vaclav Kremen
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Roch-ester, MN, USA
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
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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: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 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.
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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.
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Sorkhabi MM, Denison T. A neurostimulator system for real, sham, and multi-target transcranial magnetic stimulation. J Neural Eng 2022; 19:10.1088/1741-2552/ac60c9. [PMID: 35325879 PMCID: PMC7614713 DOI: 10.1088/1741-2552/ac60c9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 03/24/2022] [Indexed: 11/11/2022]
Abstract
Objective.Transcranial magnetic stimulation (TMS) is a clinically effective therapeutic instrument used to modulate neural activity. Despite three decades of research, two challenging issues remain, the possibility of changing the (a) stimulated spot and (b) stimulation type (real or sham) without physically moving the coil. In this study, a second-generation programmable TMS device with advanced stimulus shaping is introduced that uses a five-level cascaded H-bridge inverter and phase-shifted pulse-width modulation. The principal idea of this research is to obtain real, sham, and multi-locus stimulation using the same TMS system.Approach.We propose a two-channel modulation-based magnetic pulse generator and a novel coil arrangement, consisting of two circular coils with a physical distance of 20 mm between the coils and a control method for modifying the effective stimulus intensity, which leads to the live steerability of the target and type of stimulation.Main results.Based on the measured system performance, the stimulation profile can be steered ±20 mm along a line from the centroid of the coil locations by modifying the modulation index.Significance.The proposed system supports electronic control of the stimulation spot without physical coil movement, resulting in tunable modulation of targets, which is a crucial step towards automated TMS machines.
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Affiliation(s)
- Majid Memarian Sorkhabi
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX1 3TH, UK
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX1 3TH, UK
- Department of Engineering Science, University of Oxford, Oxford, OX1 3PJ, UK
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Mcclelland V, Valentin A, Foddai E, Denison T, Lin J. HP08: Deep Brain Stimulation Evoked Potentials in Children with dystonia. Clin Neurophysiol 2022. [DOI: 10.1016/j.clinph.2021.11.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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42
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Mivalt F, Kremen V, Sladky V, Balzekas I, Nejedly P, Gregg N, Lundstrom B, Lepkova K, Pridalova T, Brinkmann BH, Jurak P, Van Gompel JJ, Miller K, Denison T, Louis ES, Worrell GA. Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans. J Neural Eng 2022; 19:10.1088/1741-2552/ac4bfd. [PMID: 35038687 PMCID: PMC9070680 DOI: 10.1088/1741-2552/ac4bfd] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 01/17/2022] [Indexed: 11/11/2022]
Abstract
Objective.Electrical deep brain stimulation (DBS) is an established treatment for patients with drug-resistant epilepsy. Sleep disorders are common in people with epilepsy, and DBS may actually further disturb normal sleep patterns and sleep quality. Novel implantable devices capable of DBS and streaming of continuous intracranial electroencephalography (iEEG) signals enable detailed assessments of therapy efficacy and tracking of sleep related comorbidities. Here, we investigate the feasibility of automated sleep classification using continuous iEEG data recorded from Papez's circuit in four patients with drug resistant mesial temporal lobe epilepsy using an investigational implantable sensing and stimulation device with electrodes implanted in bilateral hippocampus (HPC) and anterior nucleus of thalamus (ANT).Approach.The iEEG recorded from HPC is used to classify sleep during concurrent DBS targeting ANT. Simultaneous polysomnography (PSG) and sensing from HPC were used to train, validate and test an automated classifier for a range of ANT DBS frequencies: no stimulation, 2 Hz, 7 Hz, and high frequency (>100 Hz).Main results.We show that it is possible to build a patient specific automated sleep staging classifier using power in band features extracted from one HPC iEEG sensing channel. The patient specific classifiers performed well under all thalamic DBS frequencies with an average F1-score 0.894, and provided viable classification into awake and major sleep categories, rapid eye movement (REM) and non-REM. We retrospectively analyzed classification performance with gold-standard PSG annotations, and then prospectively deployed the classifier on chronic continuous iEEG data spanning multiple months to characterize sleep patterns in ambulatory patients living in their home environment.Significance.The ability to continuously track behavioral state and fully characterize sleep should prove useful for optimizing DBS for epilepsy and associated sleep, cognitive and mood comorbidities.
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Affiliation(s)
- Filip Mivalt
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czechia
| | - Vaclav Kremen
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
| | - Vladimir Sladky
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
| | - Irena Balzekas
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Mayo Clinic School of Medicine and the Mayo Clinic Medical Scientist Training Program, Rochester, MN, USA
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, USA
| | - Petr Nejedly
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- The Czech Academy of Sciences, Institute of Scientific Instruments, Brno, Czech Republic
| | - Nick Gregg
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Brian Lundstrom
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Kamila Lepkova
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic
| | - Tereza Pridalova
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czechia
- International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
| | - Benjamin H. Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Pavel Jurak
- The Czech Academy of Sciences, Institute of Scientific Instruments, Brno, Czech Republic
| | | | - Kai Miller
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, USA
| | - Timothy Denison
- Department of Biomedical Engineering, Oxford University, Oxford, UK
| | - Erik St Louis
- Center for Sleep Medicine, Departments of Neurology and Medicine, Divisions of Sleep Neurology & Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN
| | - Gregory A. Worrell
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
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Beale R, Rosendo JB, Bergeles C, Beverly A, Camporota L, Castrejón-Pita AA, Crockett DC, Cronin JN, Denison T, East S, Edwardes C, Farmery AD, Fele F, Fisk J, Fuenteslópez CV, Garstka M, Goulart P, Heaysman C, Hussain A, Jha P, Kempf I, Kumar AS, Möslein A, Orr ACJ, Ourselin S, Salisbury D, Seneci C, Staruch R, Steel H, Thompson M, Tran MC, Vitiello V, Xochicale M, Zhou F, Formenti F, Kirk T. OxVent: Design and evaluation of a rapidly-manufactured Covid-19 ventilator. EBioMedicine 2022; 76:103868. [PMID: 35172957 PMCID: PMC8842095 DOI: 10.1016/j.ebiom.2022.103868] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 12/08/2021] [Accepted: 01/21/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND The manufacturing of any standard mechanical ventilator cannot rapidly be upscaled to several thousand units per week, largely due to supply chain limitations. The aim of this study was to design, verify and perform a pre-clinical evaluation of a mechanical ventilator based on components not required for standard ventilators, and that met the specifications provided by the Medicines and Healthcare Products Regulatory Agency (MHRA) for rapidly-manufactured ventilator systems (RMVS). METHODS The design utilises closed-loop negative feedback control, with real-time monitoring and alarms. Using a standard test lung, we determined the difference between delivered and target tidal volume (VT) at respiratory rates between 20 and 29 breaths per minute, and the ventilator's ability to deliver consistent VT during continuous operation for >14 days (RMVS specification). Additionally, four anaesthetised domestic pigs (3 male-1 female) were studied before and after lung injury to provide evidence of the ventilator's functionality, and ability to support spontaneous breathing. FINDINGS Continuous operation lasted 23 days, when the greatest difference between delivered and target VT was 10% at inspiratory flow rates >825 mL/s. In the pre-clinical evaluation, the VT difference was -1 (-90 to 88) mL [mean (LoA)], and positive end-expiratory pressure (PEEP) difference was -2 (-8 to 4) cmH2O. VT delivery being triggered by pressures below PEEP demonstrated spontaneous ventilation support. INTERPRETATION The mechanical ventilator presented meets the MHRA therapy standards for RMVS and, being based on largely available components, can be manufactured at scale. FUNDING Work supported by Wellcome/EPSRC Centre for Medical Engineering,King's Together Fund and Oxford University.
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Affiliation(s)
- Richard Beale
- Centre for Human and Applied Physiological Sciences, King's College London, UK; Intensive Care Unit, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | | | - Christos Bergeles
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Anair Beverly
- Department of Engineering Science, University of Oxford, UK
| | - Luigi Camporota
- Centre for Human and Applied Physiological Sciences, King's College London, UK; Intensive Care Unit, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | | | - Douglas C Crockett
- Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Milton Keynes University Hospital NHS Foundation Trust, Milton Keynes, UK
| | - John N Cronin
- Centre for Human and Applied Physiological Sciences, King's College London, UK; Department of Anaesthesia, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Timothy Denison
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK
| | - Sebastian East
- Department of Engineering Science, University of Oxford, UK
| | | | - Andrew D Farmery
- Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Filiberto Fele
- Department of Engineering Science, University of Oxford, UK
| | - James Fisk
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK
| | - Carla V Fuenteslópez
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK
| | | | - Paul Goulart
- Department of Engineering Science, University of Oxford, UK
| | - Clare Heaysman
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | | | - Prashant Jha
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Idris Kempf
- Department of Engineering Science, University of Oxford, UK
| | | | - Annika Möslein
- Department of Engineering Science, University of Oxford, UK
| | - Andrew C J Orr
- Department of Engineering Science, University of Oxford, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - David Salisbury
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK
| | - Carlo Seneci
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Robert Staruch
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK; Nuffield Department of Orthopaedic, Rheumatology and Musculoskeletal Sciences, University of Oxford, UK; The Academic Department of Military Surgery and Trauma, Birmingham, UK
| | - Harrison Steel
- Department of Engineering Science, University of Oxford, UK
| | - Mark Thompson
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK
| | - Minh C Tran
- Department of Engineering Science, University of Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Valentina Vitiello
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Miguel Xochicale
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Feibiao Zhou
- Department of Engineering Science, University of Oxford, UK
| | - Federico Formenti
- Centre for Human and Applied Physiological Sciences, King's College London, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Department of Biomechanics, University of Nebraska Omaha, Omaha, NE, USA.
| | - Thomas Kirk
- Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK.
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Wendt K, Denison T, Foster G, Krinke L, Thomson A, Wilson S, Widge AS. Physiologically informed neuromodulation. J Neurol Sci 2021; 434:120121. [PMID: 34998239 PMCID: PMC8976285 DOI: 10.1016/j.jns.2021.120121] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 12/19/2021] [Accepted: 12/21/2021] [Indexed: 01/09/2023]
Abstract
The rapid evolution of neuromodulation techniques includes an increasing amount of research into stimulation paradigms that are guided by patients' neurophysiology, to increase efficacy and responder rates. Treatment personalisation and target engagement have shown to be effective in fields such as Parkinson's disease, and closed-loop paradigms have been successfully implemented in cardiac defibrillators. Promising avenues are being explored for physiologically informed neuromodulation in psychiatry. Matching the stimulation frequency to individual brain rhythms has shown some promise in transcranial magnetic stimulation (TMS). Matching the phase of those rhythms may further enhance neuroplasticity, for instance when combining TMS with electroencephalographic (EEG) recordings. Resting-state EEG and event-related potentials may be useful to demonstrate connectivity between stimulation sites and connected areas. These techniques are available today to the psychiatrist to diagnose underlying sleep disorders, epilepsy, or lesions as contributing factors to the cause of depression. These technologies may also be useful in assessing the patient's brain network status prior to deciding on treatment options. Ongoing research using invasive recordings may allow for future identification of mood biomarkers and network structure. A core limitation is that biomarker research may currently be limited by the internal heterogeneity of psychiatric disorders according to the current DSM-based classifications. New approaches are being developed and may soon be validated. Finally, care must be taken when incorporating closed-loop capabilities into neuromodulation systems, by ensuring the safe operation of the system and understanding the physiological dynamics. Neurophysiological tools are rapidly evolving and will likely define the next generation of neuromodulation therapies.
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Affiliation(s)
- Karen Wendt
- Department of Engineering Science and MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK.
| | - Timothy Denison
- Department of Engineering Science and MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
| | - Gaynor Foster
- Welcony Inc., Plymouth, MN, United States of America
| | - Lothar Krinke
- Welcony Inc., Plymouth, MN, United States of America; Department of Neuroscience, School of Medicine, West Virginia University, Morgantown, WV, United States of America
| | - Alix Thomson
- Welcony Inc., Plymouth, MN, United States of America
| | - Saydra Wilson
- Department of Psychiatry and Behavioral Sciences, University of Minnesota-Twin Cities, Minneapolis, MN, United States of America
| | - Alik S Widge
- Department of Psychiatry and Behavioral Sciences, University of Minnesota-Twin Cities, Minneapolis, MN, United States of America; Medical Discovery Team on Additions, University of Minnesota, Minneapolis, MN, United States of America
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45
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Wendt K, Sorkhabi MM, O'Shea J, Cagnan H, Denison T. Comparison between the modelled response of primary motor cortex neurons to pulse-width modulated and conventional TMS stimuli. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:6058-6061. [PMID: 34892498 DOI: 10.1109/embc46164.2021.9629605] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In this study, the neural response to pulse-width modulated (PWM) transcranial magnetic stimulation (TMS) is estimated using a computational neural model which simulates the response of cortical neurons to TMS. The recently introduced programmable TMS uses PWM to approximate conventional resonance-based TMS pulses by fast switching between voltage levels. The effect of such stimulation on the six cortical layers is modelled by estimating the activation threshold of the neurons. Modelling results are compared between the novel device and that of conventional TMS stimuli generated by Magstim stimulators. The neural responses to the PWM pulses and the conventional stimuli show a high correlation, which validates the use of pulse-width modulated pulses in TMS.Clinical Relevance- This computational modelling study demonstrates an equivalent effect of PWM and conventional TMS pulses on the nervous system which paves the way to more flexibility in exploring and choosing stimulation parameters for TMS treatment.
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46
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Peterken F, Benjaber M, Doherty S, Perkins T, Creasey G, Donaldson N, Andrews B, Denison T. Adapting the Finetech-Brindley Sacral Anterior Root Stimulator for Bioelectronic Medicine . Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:6406-6411. [PMID: 34892578 DOI: 10.1109/embc46164.2021.9630995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The Finetech-Brindley Sacral Anterior Root Stimulator (SARS) is a low cost and reliable system. The architecture has been used for various bioelectric treatments, including several thousand implanted systems for restoring bladder function following spinal cord injury (SCI). Extending the operational frequency range would expand the capability of the system; enabling, for example, the exploration of eliminating the rhizotomy through an electrical nerve block. The distributed architecture of the SARS system enables stimulation parameters to be adjusted without modifying the implant design or manufacturing. To explore the design degrees-of-freedom, a circuit simulation was created and validated using a modified SARS system that supported stimulation frequencies up to 600 Hz. The simulation was also used to explore high frequency (up to 30kHz) behaviour, and to determine the constraints on charge delivered at the higher rates. A key constraint found was the DC blocking capacitors, designed originally for low frequency operation, not fully discharging within a shortened stimulation period. Within these current implant constraints, we demonstrate the potential capability for higher frequency operation that is consistent with presynaptic stimulation block, and also define targeted circuit improvements for future extension of stimulation capability.
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47
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Sorkhabi MM, Gingell F, Wendt K, Benjaber M, Ali K, Rogers DJ, Denison T. Design Analysis and Circuit Topology Optimization for Programmable Magnetic Neurostimulator. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:6384-6389. [PMID: 34892573 DOI: 10.1109/embc46164.2021.9630915] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Transcranial magnetic stimulation (TMS) is a form of non-invasive brain stimulation commonly used to modulate neural activity. Despite three decades of examination, the generation of flexible magnetic pulses is still a challenging technical question. It has been revealed that the characteristics of pulses influence the bio-physiology of neuromodulation. In this study, a second-generation programmable TMS (xTMS) equipment with advanced stimulus shaping is introduced that uses cascaded H-bridge inverters and a phase-shifted pulse-width modulation (PWM). A low-pass RC filter model is used to estimate stimulated neural behavior, which helps to design the magnetic pulse generator, according to neural dynamics. The proposed device can generate highly adjustable magnetic pulses, in terms of waveform, polarity and pattern. We present experimental measurements of different stimuli waveforms, such as monophasic, biphasic and polyphasic shapes with peak coil current and the delivered energy of up to 6 kA and 250 J, respectively. The modular and scalable design idea presented here is a potential solution for generating arbitrary and highly customizable magnetic pulses and transferring repetitive paradigms.
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48
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Zamora M, Meller S, Kajin F, Sermon JJ, Toth R, Benjaber M, Dijk DJ, Bogacz R, Worrell GA, Valentin A, Duchet B, Volk HA, Denison T. Case Report: Embedding "Digital Chronotherapy" Into Medical Devices-A Canine Validation for Controlling Status Epilepticus Through Multi-Scale Rhythmic Brain Stimulation. Front Neurosci 2021; 15:734265. [PMID: 34630021 PMCID: PMC8498587 DOI: 10.3389/fnins.2021.734265] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 08/24/2021] [Indexed: 01/15/2023] Open
Abstract
Circadian and other physiological rhythms play a key role in both normal homeostasis and disease processes. Such is the case of circadian and infradian seizure patterns observed in epilepsy. However, these rhythms are not fully exploited in the design of active implantable medical devices. In this paper we explore a new implantable stimulator that implements chronotherapy as a feedforward input to supplement both open-loop and closed-loop methods. This integrated algorithm allows for stimulation to be adjusted to the ultradian, circadian and infradian patterns observed in patients through slowly-varying temporal adjustments of stimulation and algorithm sub-components, while also enabling adaption of stimulation based on immediate physiological needs such as a breakthrough seizure or change of posture. Embedded physiological sensors in the stimulator can be used to refine the baseline stimulation circadian pattern as a "digital zeitgeber," i.e., a source of stimulus that entrains or synchronizes the subject's natural rhythms. This algorithmic approach is tested on a canine with severe drug-resistant idiopathic generalized epilepsy exhibiting a characteristic diurnal pattern correlated with sleep-wake cycles. Prior to implantation, the canine's cluster seizures evolved to status epilepticus (SE) and required emergency pharmacological intervention. The cranially-mounted system was fully-implanted bilaterally into the centromedian nucleus of the thalamus. Using combinations of time-based modulation, thalamocortical rhythm-specific tuning of frequency parameters as well as fast-adaptive modes based on activity, the canine experienced no further SE events post-implant as of the time of writing (7 months). Importantly, no significant cluster seizures have been observed either, allowing the reduction of rescue medication. The use of digitally-enabled chronotherapy as a feedforward signal to augment adaptive neurostimulators could prove a useful algorithmic method in conditions where sensitivity to temporal patterns are characteristics of the disease state, providing a novel mechanism for tailoring a more patient-specific therapy approach.
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Affiliation(s)
- Mayela Zamora
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Sebastian Meller
- Department of Small Animal Medicine and Surgery, University of Veterinary Medicine Hannover, Hanover, Germany
| | - Filip Kajin
- Department of Small Animal Medicine and Surgery, University of Veterinary Medicine Hannover, Hanover, Germany
| | - James J. Sermon
- Institute of Biomedical Engineering, Department of Engineering Science, 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
| | - Moaad Benjaber
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Derk-Jan Dijk
- Surrey Sleep Research Centre, University of Surrey, Guildford, United Kingdom
- UK Dementia Research Institute, Care Research and Technology Centre, Imperial College London and The University of Surrey, Guildford, United Kingdom
| | - Rafal Bogacz
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | | | - Antonio Valentin
- Department of Clinical Neurophysiology, King's College Hospital NHS Trust, London, United Kingdom
| | - Benoit Duchet
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Holger A. Volk
- Department of Small Animal Medicine and Surgery, University of Veterinary Medicine Hannover, Hanover, Germany
| | - Timothy Denison
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
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Gilron R, Little S, Perrone R, Wilt R, de Hemptinne C, Yaroshinsky MS, Racine CA, Wang SS, Ostrem JL, Larson PS, Wang DD, Galifianakis NB, Bledsoe IO, San Luciano M, Dawes HE, Worrell GA, Kremen V, Borton DA, Denison T, Starr PA. Long-term wireless streaming of neural recordings for circuit discovery and adaptive stimulation in individuals with Parkinson's disease. Nat Biotechnol 2021; 39:1078-1085. [PMID: 33941932 PMCID: PMC8434942 DOI: 10.1038/s41587-021-00897-5] [Citation(s) in RCA: 134] [Impact Index Per Article: 44.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 03/16/2021] [Indexed: 02/08/2023]
Abstract
Neural recordings using invasive devices in humans can elucidate the circuits underlying brain disorders, but have so far been limited to short recordings from externalized brain leads in a hospital setting or from implanted sensing devices that provide only intermittent, brief streaming of time series data. Here, we report the use of an implantable two-way neural interface for wireless, multichannel streaming of field potentials in five individuals with Parkinson's disease (PD) for up to 15 months after implantation. Bilateral four-channel motor cortex and basal ganglia field potentials streamed at home for over 2,600 h were paired with behavioral data from wearable monitors for the neural decoding of states of inadequate or excessive movement. We validated individual-specific neurophysiological biomarkers during normal daily activities and used those patterns for adaptive deep brain stimulation (DBS). This technological approach may be widely applicable to brain disorders treatable by invasive neuromodulation.
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Affiliation(s)
- Ro'ee Gilron
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA.
| | - Simon Little
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Randy Perrone
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Robert Wilt
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Coralie de Hemptinne
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Maria S Yaroshinsky
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Caroline A Racine
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Sarah S Wang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Jill L Ostrem
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Paul S Larson
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Doris D Wang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Nick B Galifianakis
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Ian O Bledsoe
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Marta San Luciano
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Heather E Dawes
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Gregory A Worrell
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Vaclav Kremen
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - David A Borton
- School of Engineering and Carney Institute, Brown University, Providence, RI, USA
| | - Timothy Denison
- Department of Engineering Science, University of Oxford and MRC Brain Network Dynamics Unit, Oxford, UK
| | - Philip A Starr
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
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50
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Pal Attia T, Crepeau D, Kremen V, Nasseri M, Guragain H, Steele SW, Sladky V, Nejedly P, Mivalt F, Herron JA, Stead M, Denison T, Worrell GA, Brinkmann BH. Epilepsy Personal Assistant Device-A Mobile Platform for Brain State, Dense Behavioral and Physiology Tracking and Controlling Adaptive Stimulation. Front Neurol 2021; 12:704170. [PMID: 34393981 PMCID: PMC8358117 DOI: 10.3389/fneur.2021.704170] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 06/21/2021] [Indexed: 12/04/2022] Open
Abstract
Epilepsy is one of the most common neurological disorders, and it affects almost 1% of the population worldwide. Many people living with epilepsy continue to have seizures despite anti-epileptic medication therapy, surgical treatments, and neuromodulation therapy. The unpredictability of seizures is one of the most disabling aspects of epilepsy. Furthermore, epilepsy is associated with sleep, cognitive, and psychiatric comorbidities, which significantly impact the quality of life. Seizure predictions could potentially be used to adjust neuromodulation therapy to prevent the onset of a seizure and empower patients to avoid sensitive activities during high-risk periods. Long-term objective data is needed to provide a clearer view of brain electrical activity and an objective measure of the efficacy of therapeutic measures for optimal epilepsy care. While neuromodulation devices offer the potential for acquiring long-term data, available devices provide very little information regarding brain activity and therapy effectiveness. Also, seizure diaries kept by patients or caregivers are subjective and have been shown to be unreliable, in particular for patients with memory-impairing seizures. This paper describes the design, architecture, and development of the Mayo Epilepsy Personal Assistant Device (EPAD). The EPAD has bi-directional connectivity to the implanted investigational Medtronic Summit RC+STM device to implement intracranial EEG and physiological monitoring, processing, and control of the overall system and wearable devices streaming physiological time-series signals. In order to mitigate risk and comply with regulatory requirements, we developed a Quality Management System (QMS) to define the development process of the EPAD system, including Risk Analysis, Verification, Validation, and protocol mitigations. Extensive verification and validation testing were performed on thirteen canines and benchtop systems. The system is now under a first-in-human trial as part of the US FDA Investigational Device Exemption given in 2018 to study modulated responsive and predictive stimulation using the Mayo EPAD system and investigational Medtronic Summit RC+STM in ten patients with non-resectable dominant or bilateral mesial temporal lobe epilepsy. The EPAD system coupled with an implanted device capable of EEG telemetry represents a next-generation solution to optimizing neuromodulation therapy.
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Affiliation(s)
- Tal Pal Attia
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Daniel Crepeau
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Vaclav Kremen
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Cognitive Systems and Neurosciences, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czechia
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States
| | - Mona Nasseri
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- School of Engineering, University of North Florida, Jacksonville, FL, United States
| | - Hari Guragain
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Steven W. Steele
- Division of Engineering, Mayo Clinic, Rochester, MN, United States
| | - Vladimir Sladky
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czechia
| | - Petr Nejedly
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Filip Mivalt
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Jeffrey A. Herron
- Department of Neurological Surgery, University of Washington, Seattle, WA, United States
| | - Matt Stead
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States
| | - Timothy Denison
- Engineering Sciences and Clinical Neurosciences, Oxford University, Oxford, United Kingdom
| | - Gregory A. Worrell
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States
| | - Benjamin H. Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States
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