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Siddique MAB, Zhang Y, An H. Monitoring time domain characteristics of Parkinson's disease using 3D memristive neuromorphic system. Front Comput Neurosci 2023; 17:1274575. [PMID: 38162516 PMCID: PMC10754992 DOI: 10.3389/fncom.2023.1274575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 11/06/2023] [Indexed: 01/03/2024] Open
Abstract
Introduction Parkinson's disease (PD) is a neurodegenerative disorder affecting millions of patients. Closed-Loop Deep Brain Stimulation (CL-DBS) is a therapy that can alleviate the symptoms of PD. The CL-DBS system consists of an electrode sending electrical stimulation signals to a specific region of the brain and a battery-powered stimulator implanted in the chest. The electrical stimuli in CL-DBS systems need to be adjusted in real-time in accordance with the state of PD symptoms. Therefore, fast and precise monitoring of PD symptoms is a critical function for CL-DBS systems. However, the current CL-DBS techniques suffer from high computational demands for real-time PD symptom monitoring, which are not feasible for implanted and wearable medical devices. Methods In this paper, we present an energy-efficient neuromorphic PD symptom detector using memristive three-dimensional integrated circuits (3D-ICs). The excessive oscillation at beta frequencies (13-35 Hz) at the subthalamic nucleus (STN) is used as a biomarker of PD symptoms. Results Simulation results demonstrate that our neuromorphic PD detector, implemented with an 8-layer spiking Long Short-Term Memory (S-LSTM), excels in recognizing PD symptoms, achieving a training accuracy of 99.74% and a validation accuracy of 99.52% for a 75%-25% data split. Furthermore, we evaluated the improvement of our neuromorphic CL-DBS detector using NeuroSIM. The chip area, latency, energy, and power consumption of our CL-DBS detector were reduced by 47.4%, 66.63%, 65.6%, and 67.5%, respectively, for monolithic 3D-ICs. Similarly, for heterogeneous 3D-ICs, employing memristive synapses to replace traditional Static Random Access Memory (SRAM) resulted in reductions of 44.8%, 64.75%, 65.28%, and 67.7% in chip area, latency, and power usage. Discussion This study introduces a novel approach for PD symptom evaluation by directly utilizing spiking signals from neural activities in the time domain. This method significantly reduces the time and energy required for signal conversion compared to traditional frequency domain approaches. The study pioneers the use of neuromorphic computing and memristors in designing CL-DBS systems, surpassing SRAM-based designs in chip design area, latency, and energy efficiency. Lastly, the proposed neuromorphic PD detector demonstrates high resilience to timing variations in brain neural signals, as confirmed by robustness analysis.
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Affiliation(s)
- Md Abu Bakr Siddique
- Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, United States
| | - Yan Zhang
- Department of Biological Sciences, Michigan Technological University, Houghton, MI, United States
| | - Hongyu An
- Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, United States
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Hariz M, Cif L, Blomstedt P. Thirty Years of Global Deep Brain Stimulation: "Plus ça change, plus c'est la même chose"? Stereotact Funct Neurosurg 2023; 101:395-406. [PMID: 37844558 DOI: 10.1159/000533430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 07/31/2023] [Indexed: 10/18/2023]
Abstract
BACKGROUND The advent of deep brain stimulation (DBS) of the subthalamic nucleus (STN) for Parkinson's disease 30 years ago has ushered a global breakthrough of DBS as a universal method for therapy and research in wide areas of neurology and psychiatry. The literature of the last three decades has described numerous concepts and practices of DBS, often branded as novelties or discoveries. However, reading the contemporary publications often elicits a sense of déjà vu in relation to several methods, attributes, and practices of DBS. Here, we review various applications and techniques of the modern-era DBS and compare them with practices of the past. SUMMARY Compared with modern literature, publications of the old-era functional stereotactic neurosurgery, including old-era DBS, show that from the very beginning multidisciplinarity and teamwork were often prevalent and insisted upon, ethical concerns were recognized, brain circuitries and rational for brain targets were discussed, surgical indications were similar, closed-loop stimulation was attempted, evaluations of surgical results were debated, and controversies were common. Thus, it appears that virtually everything done today in the field of DBS bears resemblance to old-time practices, or has been done before, albeit with partly other tools and techniques. Movement disorders remain the main indications for modern DBS as was the case for lesional surgery and old-era DBS. The novelties today consist of the STN as the dominant target for DBS, the tremendous advances in computerized brain imaging, the sophistication and versatility of implantable DBS hardware, and the large potential for research. KEY MESSAGES Many aspects of contemporary DBS bear strong resemblance to practices of the past. The dominant clinical indications remain movement disorders with virtually the same brain targets as in the past, with one exception: the STN. Other novel brain targets - that are so far subject to DBS trials - are the pedunculopontine nucleus for gait freezing, the anteromedial internal pallidum for Gilles de la Tourette and the fornix for Alzheimer's disease. The major innovations and novelties compared to the past concern mainly the unmatched level of research activity, its high degree of sponsorship, and the outstanding advances in technology that have enabled multimodal brain imaging and the miniaturization, versatility, and sophistication of implantable hardware. The greatest benefit for patients today, compared to the past, is the higher level of precision and safety of DBS, and of all functional stereotactic neurosurgery.
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Affiliation(s)
- Marwan Hariz
- Department of Clinical Neuroscience, Umeå University, Umeå, Sweden
- UCL Institute of Neurology, Queen Square, London, UK
| | - Laura Cif
- Laboratoire de Recherche en Neurosciences Cliniques, Montpellier, France
| | - Patric Blomstedt
- Department of Clinical Neuroscience, Umeå University, Umeå, Sweden
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Nawaz A, Hasan O, Jabeen S. Formal Verification of Deep Brain Stimulation Controllers for Parkinson's Disease Treatment. Neural Comput 2023; 35:671-698. [PMID: 36827600 DOI: 10.1162/neco_a_01569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 09/01/2022] [Indexed: 02/26/2023]
Abstract
Deep brain stimulation (DBS) is a widely accepted treatment for the Parkinson's disease (PD). Traditionally, it is done in an open-loop manner, where stimulation is always ON, irrespective of the patient needs. As a consequence, patients can feel some side effects due to the continuous high-frequency stimulation. Closed-loop DBS can address this problem as it allows adjusting stimulation according to the patient need. The selection of open- or closed-loop DBS and an optimal algorithm for closed-loop DBS are some of the main challenges in DBS controller design, and typically the decision is made through sampling based simulations. In this letter, we used model checking, a formal verification technique used to exhaustively explore the complete state space of a system, for analyzing DBS controllers. We analyze the timed automata of the open-loop and closed-loop DBS controllers in response to the basal ganglia (BG) model. Furthermore, we present a formal verification approach for the closed-loop DBS controllers using timed computation tree logic (TCTL) properties, that is, safety, liveness (the property that under certain conditions, some event will eventually occur), and deadlock freeness. We show that the closed-loop DBS significantly outperforms existing open-loop DBS controllers in terms of energy efficiency. Moreover, we formally analyze the closed-loop DBS for energy efficiency and time behavior with two algorithms, the constant update algorithm and the error prediction update algorithm. Our results demonstrate that the closed-loop DBS running the error prediction update algorithm is efficient in terms of time and energy as compared to the constant update algorithm.
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Affiliation(s)
- Arooj Nawaz
- School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad 44000, Pakistan
| | - Osman Hasan
- School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad 44000, Pakistan
| | - Shaista Jabeen
- Electrical and Computer Engineering Department, COMSATS University, Islamabad 45550, Pakistan
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Saalmann YB, Mofakham S, Mikell CB, Djuric PM. Microscale multicircuit brain stimulation: Achieving real-time brain state control for novel applications. CURRENT RESEARCH IN NEUROBIOLOGY 2022; 4:100071. [PMID: 36619175 PMCID: PMC9816916 DOI: 10.1016/j.crneur.2022.100071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 11/30/2022] [Accepted: 12/19/2022] [Indexed: 12/30/2022] Open
Abstract
Neurological and psychiatric disorders typically result from dysfunction across multiple neural circuits. Most of these disorders lack a satisfactory neuromodulation treatment. However, deep brain stimulation (DBS) has been successful in a limited number of disorders; DBS typically targets one or two brain areas with single contacts on relatively large electrodes, allowing for only coarse modulation of circuit function. Because of the dysfunction in distributed neural circuits - each requiring fine, tailored modulation - that characterizes most neuropsychiatric disorders, this approach holds limited promise. To develop the next generation of neuromodulation therapies, we will have to achieve fine-grained, closed-loop control over multiple neural circuits. Recent work has demonstrated spatial and frequency selectivity using microstimulation with many small, closely-spaced contacts, mimicking endogenous neural dynamics. Using custom electrode design and stimulation parameters, it should be possible to achieve bidirectional control over behavioral outcomes, such as increasing or decreasing arousal during central thalamic stimulation. Here, we discuss one possible approach, which we term microscale multicircuit brain stimulation (MMBS). We discuss how machine learning leverages behavioral and neural data to find optimal stimulation parameters across multiple contacts, to drive the brain towards desired states associated with behavioral goals. We expound a mathematical framework for MMBS, where behavioral and neural responses adjust the model in real-time, allowing us to adjust stimulation in real-time. These technologies will be critical to the development of the next generation of neurostimulation therapies, which will allow us to treat problems like disorders of consciousness and cognition.
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Affiliation(s)
- Yuri B. Saalmann
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA
- Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Sima Mofakham
- Department of Neurological Surgery, Stony Brook University Hospital, Stony Brook, NY, USA
- Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Charles B. Mikell
- Department of Neurological Surgery, Stony Brook University Hospital, Stony Brook, NY, USA
| | - Petar M. Djuric
- Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA
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Stevens I, Gilbert F. International Regulatory Standards for the Qualitative Measurement of Deep Brain Stimulation in Clinical Research. J Empir Res Hum Res Ethics 2022; 17:228-241. [DOI: 10.1177/15562646221094922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Deep brain stimulation (DBS) has progressed to become a promising treatment modality for neurologic and psychiatric disorders like epilepsy and major depressive disorder due to its growing personalization. Despite evidence pointing to the benefits of DBS if tested on these personalized qualitative metrics, rather than randomized-control trial quantitative standards, the evaluation of these novel devices appears to be based on the latter. This study surveyed the presence of this trend in the national regulatory guidelines of the prominent DBS researching countries. It was found that two governing bodies, in the European Union and Australia, acknowledged the option for qualitative measures. These findings support further development of national regulatory guidelines, so the neuroscientific community developing these neurotechnologies can better understand the impact their treatments have on patients.
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Affiliation(s)
- I. Stevens
- School of Humanities, University of Tasmania, Hobart, Tasmania, Australia
| | - F. Gilbert
- School of Humanities, University of Tasmania, Hobart, Tasmania, Australia
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Zuk P, Sanchez CE, Kostick K, Torgerson L, Muñoz KA, Hsu R, Kalwani L, Sierra-Mercado D, Robinson JO, Outram S, Koenig BA, Pereira S, McGuire AL, Lázaro-Muñoz G. Researcher Perspectives on Data Sharing in Deep Brain Stimulation. Front Hum Neurosci 2021; 14:578687. [PMID: 33424563 PMCID: PMC7793701 DOI: 10.3389/fnhum.2020.578687] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 11/16/2020] [Indexed: 01/21/2023] Open
Abstract
The expansion of research on deep brain stimulation (DBS) and adaptive DBS (aDBS) raises important neuroethics and policy questions related to data sharing. However, there has been little empirical research on the perspectives of experts developing these technologies. We conducted semi-structured, open-ended interviews with aDBS researchers regarding their data sharing practices and their perspectives on ethical and policy issues related to sharing. Researchers expressed support for and a commitment to sharing, with most saying that they were either sharing their data or would share in the future and that doing so was important for advancing the field. However, those who are sharing reported a variety of sharing partners, suggesting heterogeneity in sharing practices and lack of the broad sharing that would reflect principles of open science. Researchers described several concerns and barriers related to sharing, including privacy and confidentiality, the usability of shared data by others, ownership and control of data (including potential commercialization), and limited resources for sharing. They also suggested potential solutions to these challenges, including additional safeguards to address privacy issues, standardization and transparency in analysis to address issues of data usability, professional norms and heightened cooperation to address issues of ownership and control, and streamlining of data transmission to address resource limitations. Researchers also offered a range of views on the sensitivity of neural activity data (NAD) and data related to mental health in the context of sharing. These findings are an important input to deliberations by researchers, policymakers, neuroethicists, and other stakeholders as they navigate ethics and policy questions related to aDBS research.
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Affiliation(s)
- Peter Zuk
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| | - Clarissa E Sanchez
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| | - Kristin Kostick
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| | - Laura Torgerson
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| | - Katrina A Muñoz
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| | - Rebecca Hsu
- Evans School of Public Policy and Governance, University of Washington, Seattle, WA, United States
| | - Lavina Kalwani
- Department of Biosciences, Rice University, Houston, TX, United States
| | - Demetrio Sierra-Mercado
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States.,Department of Anatomy and Neurobiology, School of Medicine, University of Puerto Rico, San Juan, Puerto Rico
| | - Jill O Robinson
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| | - Simon Outram
- Program in Bioethics, University of California, San Francisco, San Francisco, CA, United States
| | - Barbara A Koenig
- Program in Bioethics, University of California, San Francisco, San Francisco, CA, United States
| | - Stacey Pereira
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| | - Amy L McGuire
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| | - Gabriel Lázaro-Muñoz
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
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Ramirez-Zamora A, Giordano J, Gunduz A, Alcantara J, Cagle JN, Cernera S, Difuntorum P, Eisinger RS, Gomez J, Long S, Parks B, Wong JK, Chiu S, Patel B, Grill WM, Walker HC, Little SJ, Gilron R, Tinkhauser G, Thevathasan W, Sinclair NC, Lozano AM, Foltynie T, Fasano A, Sheth SA, Scangos K, Sanger TD, Miller J, Brumback AC, Rajasethupathy P, McIntyre C, Schlachter L, Suthana N, Kubu C, Sankary LR, Herrera-Ferrá K, Goetz S, Cheeran B, Steinke GK, Hess C, Almeida L, Deeb W, Foote KD, Okun MS. Proceedings of the Seventh Annual Deep Brain Stimulation Think Tank: Advances in Neurophysiology, Adaptive DBS, Virtual Reality, Neuroethics and Technology. Front Hum Neurosci 2020; 14:54. [PMID: 32292333 PMCID: PMC7134196 DOI: 10.3389/fnhum.2020.00054] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 02/05/2020] [Indexed: 12/12/2022] Open
Abstract
The Seventh Annual Deep Brain Stimulation (DBS) Think Tank held on September 8th of 2019 addressed the most current: (1) use and utility of complex neurophysiological signals for development of adaptive neurostimulation to improve clinical outcomes; (2) Advancements in recent neuromodulation techniques to treat neuropsychiatric disorders; (3) New developments in optogenetics and DBS; (4) The use of augmented Virtual reality (VR) and neuromodulation; (5) commercially available technologies; and (6) ethical issues arising in and from research and use of DBS. These advances serve as both "markers of progress" and challenges and opportunities for ongoing address, engagement, and deliberation as we move to improve the functional capabilities and translational value of DBS. It is in this light that these proceedings are presented to inform the field and initiate ongoing discourse. As consistent with the intent, and spirit of this, and prior DBS Think Tanks, the overarching goal is to continue to develop multidisciplinary collaborations to rapidly advance the field and ultimately improve patient outcomes.
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Affiliation(s)
- Adolfo Ramirez-Zamora
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - James Giordano
- Departments of Neurology and Biochemistry, and Neuroethics Studies Program—Pellegrino Center for Clinical Bioethics, Georgetown University Medical Center, Washington, DC, United States
| | - Aysegul Gunduz
- J. Crayton Pruitt Family Department of Biomedical Engineering, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
- Department of Neurosurgery, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Jose Alcantara
- J. Crayton Pruitt Family Department of Biomedical Engineering, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
- Department of Neurosurgery, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Jackson N. Cagle
- J. Crayton Pruitt Family Department of Biomedical Engineering, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
- Department of Neurosurgery, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Stephanie Cernera
- J. Crayton Pruitt Family Department of Biomedical Engineering, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
- Department of Neurosurgery, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Parker Difuntorum
- J. Crayton Pruitt Family Department of Biomedical Engineering, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
- Department of Neurosurgery, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Robert S. Eisinger
- J. Crayton Pruitt Family Department of Biomedical Engineering, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
- Department of Neurosurgery, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Julieth Gomez
- J. Crayton Pruitt Family Department of Biomedical Engineering, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
- Department of Neurosurgery, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Sarah Long
- J. Crayton Pruitt Family Department of Biomedical Engineering, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
- Department of Neurosurgery, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Brandon Parks
- J. Crayton Pruitt Family Department of Biomedical Engineering, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
- Department of Neurosurgery, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Joshua K. Wong
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Shannon Chiu
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Bhavana Patel
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Warren M. Grill
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Harrison C. Walker
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Simon J. Little
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Ro’ee Gilron
- Graduate Program in Neuroscience, Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Gerd Tinkhauser
- Department of Neurology, Bern University Hospital and the University of Bern, Bern, Switzerland
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom
| | - Wesley Thevathasan
- Department of Neurology, The Royal Melbourne and Austin Hospitals, University of Melbourne, Melbourne, VIC, Australia
- Medical Bionics Department, University of Melbourne, East Melbourne, VIC, Australia
- Bionics Institute, East Melbourne, VIC, Australia
| | - Nicholas C. Sinclair
- Medical Bionics Department, University of Melbourne, East Melbourne, VIC, Australia
- Bionics Institute, East Melbourne, VIC, Australia
| | - Andres M. Lozano
- Department of Surgery, Division of Neurosurgery, University of Toronto, Toronto, ON, Canada
| | - Thomas Foltynie
- Institute of Neurology, University College London, London, United Kingdom
| | - Alfonso Fasano
- Edmond J. Safra Program in Parkinson’s Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, UHN, Toronto, ON, Canada
- Division of Neurology, University of Toronto, Krembil Brain Institute, Center for Advancing Neurotechnological Innovation to Application (CRANIA), Toronto, ON, Canada
| | - Sameer A. Sheth
- Department of Neurological Surgery, Baylor College of Medicine, Houston, TX, United States
| | - Katherine Scangos
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
| | - Terence D. Sanger
- Department of Biomedical Engineering, Neurology, Biokinesiology, University of Southern California, Los Angeles, CA, United States
| | - Jonathan Miller
- Case Western Reserve University School of Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Audrey C. Brumback
- Departments of Neurology and Pediatrics at Dell Medical School and the Center for Learning and Memory, University of Texas at Austin, Austin, TX, United States
| | - Priya Rajasethupathy
- Laboratory for Neural Dynamics and Cognition, Rockefeller University, New York, NY, United States
- Department of Bioengineering, Stanford University, Stanford, CA, United States
| | - Cameron McIntyre
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Leslie Schlachter
- Department of Neurosurgery, Mount Sinai Health System, New York, NY, United States
| | - Nanthia Suthana
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - Cynthia Kubu
- Department of Neurology, Cleveland Clinic, Cleveland, OH, United States
| | - Lauren R. Sankary
- Center for Bioethics, Cleveland Clinic, Cleveland, OH, United States
| | | | - Steven Goetz
- Medtronic Neuromodulation, Minneapolis, MN, United States
| | - Binith Cheeran
- Neuromodulation Division, Abbott, Plano, TX, United States
| | - G. Karl Steinke
- Boston Scientific Neuromodulation, Valencia, CA, United States
| | - Christopher Hess
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Leonardo Almeida
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Wissam Deeb
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Kelly D. Foote
- Department of Neurosurgery, Norman Fixel Institute for Neurological Diseases, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Michael S. Okun
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
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Villalobos J, McDermott HJ, McNeill P, Golod A, Rathi V, Bauquier SH, Fallon JB. Slim electrodes for improved targeting in deep brain stimulation. J Neural Eng 2020; 17:026008. [PMID: 32101807 DOI: 10.1088/1741-2552/ab7a51] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The efficacy of deep brain stimulation can be limited by factors including poor selectivity of stimulation, targeting error, and complications related to implant reliability and stability. We aimed to improve surgical outcomes by evaluating electrode leads with smaller diameter electrode and microelectrodes incorporated which can be used for assisting targeting. APPROACH Electrode arrays were constructed with two different diameters of 0.65 mm and the standard 1.3 mm. Micro-electrodes were incorporated into the slim electrode arrays for recording spiking neural activity. Arrays were bilaterally implanted into the medial geniculate body (MGB) in nine anaesthetised cats for 24-40 h using stereotactic techniques. Recordings of auditory evoked field potentials and multi-unit activity were obtained at 1 mm intervals along the electrode insertion track. Insertion trauma was evaluated histologically. MAIN RESULTS Evoked auditory field potentials were recorded from ring and micro-electrodes in the vicinity of the medial geniculate body. Spiking activity was recorded from 81% of the microelectrodes approaching the MGB. Histological examination showed localized surgical trauma along the implant. The extent of haemorrhage surrounding the track was measured and found to be significantly reduced with the slim electrodes (541 ± 455 µm vs. 827 ± 647 µm; P < 0.001). Scoring of the trauma, focusing on tissue disruption, haemorrhage, oedema of glial parenchyma and pyknosis, revealed a significantly lower trauma score for the slim electrodes (P < 0.0001). SIGNIFICANCE The slim electrodes reduced the extent of acute trauma, while still providing adequate electrode impedance for both stimulating and recording, and providing the option to target stimulate smaller volumes of tissue. The incorporation of microelectrodes into the electrode array may allow for a simplified, single-step surgical approach where confirmatory micro-targeting is done with the same lead used for permanent implantation.
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Affiliation(s)
- Joel Villalobos
- Bionics Institute, East Melbourne, Australia. Author to whom any correspondence should be addressed
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Xu W, Zhang C, Deeb W, Patel B, Wu Y, Voon V, Okun MS, Sun B. Deep brain stimulation for Tourette's syndrome. Transl Neurodegener 2020; 9:4. [PMID: 31956406 PMCID: PMC6956485 DOI: 10.1186/s40035-020-0183-7] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 01/05/2020] [Indexed: 01/11/2023] Open
Abstract
Tourette syndrome (TS) is a childhood-onset neuropsychiatric disorder characterized by the presence of multiple motor and vocal tics. TS usually co-occurs with one or multiple psychiatric disorders. Although behavioral and pharmacological treatments for TS are available, some patients do not respond to the available treatments. For these patients, TS is a severe, chronic, and disabling disorder. In recent years, deep brain stimulation (DBS) of basal ganglia-thalamocortical networks has emerged as a promising intervention for refractory TS with or without psychiatric comorbidities. Three major challenges need to be addressed to move the field of DBS treatment for TS forward: (1) patient and DBS target selection, (2) ethical concerns with treating pediatric patients, and (3) DBS treatment optimization and improvement of individual patient outcomes (motor and phonic tics, as well as functioning and quality of life). The Tourette Association of America and the American Academy of Neurology have recently released their recommendations regarding surgical treatment for refractory TS. Here, we describe the challenges, advancements, and promises of the use of DBS in the treatment of TS. We summarize the results of clinical studies and discuss the ethical issues involved in treating pediatric patients. Our aim is to provide a better understanding of the feasibility, safety, selection process, and clinical effectiveness of DBS treatment for select cases of severe and medically intractable TS.
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Affiliation(s)
- Wenying Xu
- 1Department of Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Ruijin Hospital, 197 Ruijin Er Road, Shanghai, 200025 China
| | - Chencheng Zhang
- 1Department of Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Ruijin Hospital, 197 Ruijin Er Road, Shanghai, 200025 China
| | - Wissam Deeb
- 2Norman Fixel Institute for Neurological Diseases, Department of Neurology, College of Medicine, University of Florida, Gainesville, FL 32608 USA
| | - Bhavana Patel
- 2Norman Fixel Institute for Neurological Diseases, Department of Neurology, College of Medicine, University of Florida, Gainesville, FL 32608 USA
| | - Yiwen Wu
- 3Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Valerie Voon
- 1Department of Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Ruijin Hospital, 197 Ruijin Er Road, Shanghai, 200025 China.,4Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Michael S Okun
- 2Norman Fixel Institute for Neurological Diseases, Department of Neurology, College of Medicine, University of Florida, Gainesville, FL 32608 USA
| | - Bomin Sun
- 1Department of Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Ruijin Hospital, 197 Ruijin Er Road, Shanghai, 200025 China
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11
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Petkos K, Koutsoftidis S, Guiho T, Degenaar P, Jackson A, Greenwald SE, Brown P, Denison T, Drakakis EM. A high-performance 8 nV/√Hz 8-channel wearable and wireless system for real-time monitoring of bioelectrical signals. J Neuroeng Rehabil 2019; 16:156. [PMID: 31823804 PMCID: PMC6905040 DOI: 10.1186/s12984-019-0629-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 11/26/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND It is widely accepted by the scientific community that bioelectrical signals, which can be used for the identification of neurophysiological biomarkers indicative of a diseased or pathological state, could direct patient treatment towards more effective therapeutic strategies. However, the design and realisation of an instrument that can precisely record weak bioelectrical signals in the presence of strong interference stemming from a noisy clinical environment is one of the most difficult challenges associated with the strategy of monitoring bioelectrical signals for diagnostic purposes. Moreover, since patients often have to cope with the problem of limited mobility being connected to bulky and mains-powered instruments, there is a growing demand for small-sized, high-performance and ambulatory biopotential acquisition systems in the Intensive Care Unit (ICU) and in High-dependency wards. Finally, to the best of our knowledge, there are no commercial, small, battery-powered, wearable and wireless recording-only instruments that claim the capability of recording electrocorticographic (ECoG) signals. METHODS To address this problem, we designed and developed a low-noise (8 nV/√Hz), eight-channel, battery-powered, wearable and wireless instrument (55 × 80 mm2). The performance of the realised instrument was assessed by conducting both ex vivo and in vivo experiments. RESULTS To provide ex vivo proof-of-function, a wide variety of high-quality bioelectrical signal recordings are reported, including electroencephalographic (EEG), electromyographic (EMG), electrocardiographic (ECG), acceleration signals, and muscle fasciculations. Low-noise in vivo recordings of weak local field potentials (LFPs), which were wirelessly acquired in real time using segmented deep brain stimulation (DBS) electrodes implanted in the thalamus of a non-human primate, are also presented. CONCLUSIONS The combination of desirable features and capabilities of this instrument, namely its small size (~one business card), its enhanced recording capabilities, its increased processing capabilities, its manufacturability (since it was designed using discrete off-the-shelf components), the wide bandwidth it offers (0.5-500 Hz) and the plurality of bioelectrical signals it can precisely record, render it a versatile and reliable tool to be utilized in a wide range of applications and environments.
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Affiliation(s)
- Konstantinos Petkos
- Department of Bioengineering, Imperial College London, Prince Consort Road, London, SW7 2AZ, UK
- Center for Neurotechnology, Imperial College London, Prince Consort Road, London, SW7 2AZ, UK
| | - Simos Koutsoftidis
- Department of Bioengineering, Imperial College London, Prince Consort Road, London, SW7 2AZ, UK
| | - Thomas Guiho
- Institute of Neuroscience, Newcastle University, Framlington Place, Newcastle upon Tyne, NE2 4HH, UK
| | - Patrick Degenaar
- School of Electrical & Electronic Engineering, Newcastle University, Merz Court, Newcastle upon Tyne, NE1 7RU, UK
| | - Andrew Jackson
- Institute of Neuroscience, Newcastle University, Framlington Place, Newcastle upon Tyne, NE2 4HH, UK
| | - Stephen E Greenwald
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, E1 2AT, UK
| | - Peter Brown
- MRC Brain Network Dynamics Unit, University of Oxford, Mansfield Road, Oxford, OX1 3TH, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Level 6, West Wing, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, University of Oxford, Mansfield Road, Oxford, OX1 3TH, UK
| | - Emmanuel M Drakakis
- Department of Bioengineering, Imperial College London, Prince Consort Road, London, SW7 2AZ, UK.
- Center for Neurotechnology, Imperial College London, Prince Consort Road, London, SW7 2AZ, UK.
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12
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Iacopino DG, Gagliardo C, Giugno A, Giammalva GR, Napoli A, Maugeri R, Graziano F, Valentino F, Cosentino G, D'Amelio M, Bartolotta TV, Catalano C, Fierro B, Midiri M, Lagalla R. Preliminary experience with a transcranial magnetic resonance-guided focused ultrasound surgery system integrated with a 1.5-T MRI unit in a series of patients with essential tremor and Parkinson's disease. Neurosurg Focus 2019; 44:E7. [PMID: 29385927 DOI: 10.3171/2017.11.focus17614] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
OBJECTIVE Transcranial magnetic resonance-guided focused ultrasound surgery (tcMRgFUS) is one of the emerging noninvasive technologies for the treatment of neurological disorders such as essential tremor (ET), idiopathic asymmetrical tremor-dominant Parkinson's disease (PD), and neuropathic pain. In this clinical series the authors present the preliminary results achieved with the world's first tcMRgFUS system integrated with a 1.5-T MRI unit. METHODS The authors describe the results of tcMRgFUS in a sample of patients with ET and with PD who underwent the procedure during the period from January 2015 to September 2017. A monolateral ventralis intermedius nucleus (VIM) thalamic ablation was performed in both ET and PD patients. In all the tcMRgFUS treatments, a 1.5-T MRI scanner was used for both planning and monitoring the procedure. RESULTS During the study period, a total of 26 patients underwent tcMRgFUS thalamic ablation for different movement disorders. Among these patients, 18 were diagnosed with ET and 4 were affected by PD. All patients with PD were treated using tcMRgFUS thalamic ablation and all completed the procedure. Among the 18 patients with ET, 13 successfully underwent tcMRgFUS, 4 aborted the procedure during ultrasound delivery, and 1 did not undergo the tcMRgFUS procedure after stereotactic frame placement. Two patients with ET were not included in the results because of the short follow-up duration at the time of this study. A monolateral VIM thalamic ablation in both ET and PD patients was performed. All the enrolled patients were evaluated before the treatment and 2 days after, with a clinical control of the treatment effectiveness using the graphic items of the Fahn-Tolosa-Marin tremor rating scale. A global reevaluation was performed 3 months (17/22 patients) and 6 months (11/22 patients) after the treatment; the reevaluation consisted of clinical questionnaires, neurological tests, and video recordings of the tests. All the ET and PD treated patients who completed the procedure showed an immediate amelioration of tremor severity, with no intra- or posttreatment severe permanent side effects. CONCLUSIONS Although this study reports on a small number of patients with a short follow-up duration, the tcMRgFUS procedure using a 1.5-T MRI unit resulted in a safe and effective treatment option for motor symptoms in patients with ET and PD. To the best of the authors' knowledge, this is the first clinical series in which thalamotomy was performed using tcMRgFUS integrated with a 1.5-T magnet.
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Affiliation(s)
- Domenico Gerardo Iacopino
- Unit of Neurosurgery, Department of Experimental Biomedicine and Clinical Neuroscience, University of Palermo
| | - Cesare Gagliardo
- Section of Radiological Sciences, Department of Biopathology and Medical Biotechnologies, University of Palermo
| | - Antonella Giugno
- Unit of Neurosurgery, Department of Experimental Biomedicine and Clinical Neuroscience, University of Palermo
| | - Giuseppe Roberto Giammalva
- Unit of Neurosurgery, Department of Experimental Biomedicine and Clinical Neuroscience, University of Palermo
| | - Alessandro Napoli
- Radiology Section, Department of Radiological, Oncological and Anatomopathological Sciences, "Sapienza" University of Rome; and
| | - Rosario Maugeri
- Unit of Neurosurgery, Department of Experimental Biomedicine and Clinical Neuroscience, University of Palermo
| | - Francesca Graziano
- Unit of Neurosurgery, Department of Experimental Biomedicine and Clinical Neuroscience, University of Palermo
| | - Francesca Valentino
- Unit of Neurology, Department of Experimental Biomedicine and Clinical Neuroscience, University of Palermo, Italy
| | - Giuseppe Cosentino
- Unit of Neurology, Department of Experimental Biomedicine and Clinical Neuroscience, University of Palermo, Italy
| | - Marco D'Amelio
- Unit of Neurology, Department of Experimental Biomedicine and Clinical Neuroscience, University of Palermo, Italy
| | - Tommaso Vincenzo Bartolotta
- Section of Radiological Sciences, Department of Biopathology and Medical Biotechnologies, University of Palermo
| | - Carlo Catalano
- Radiology Section, Department of Radiological, Oncological and Anatomopathological Sciences, "Sapienza" University of Rome; and
| | - Brigida Fierro
- Unit of Neurology, Department of Experimental Biomedicine and Clinical Neuroscience, University of Palermo, Italy
| | - Massimo Midiri
- Section of Radiological Sciences, Department of Biopathology and Medical Biotechnologies, University of Palermo
| | - Roberto Lagalla
- Section of Radiological Sciences, Department of Biopathology and Medical Biotechnologies, University of Palermo
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13
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Petkos K, Guiho T, Degenaar P, Jackson A, Brown P, Denison T, Drakakis EM. A high-performance 4 nV (√Hz) -1 analog front-end architecture for artefact suppression in local field potential recordings during deep brain stimulation. J Neural Eng 2019; 16:066003. [PMID: 31151118 PMCID: PMC6877351 DOI: 10.1088/1741-2552/ab2610] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Recording of local field potentials (LFPs) during deep brain stimulation (DBS) is necessary to investigate the instantaneous brain response to stimulation, minimize time delays for closed-loop neurostimulation and maximise the available neural data. To our knowledge, existing recording systems lack the ability to provide artefact-free high-frequency (>100 Hz) LFP recordings during DBS in real time primarily because of the contamination of the neural signals of interest by the stimulation artefacts. APPROACH To solve this problem, we designed and developed a novel, low-noise and versatile analog front-end (AFE) that uses a high-order (8th) analog Chebyshev notch filter to suppress the artefacts originating from the stimulation frequency. After defining the system requirements for concurrent LFP recording and DBS artefact suppression, we assessed the performance of the realised AFE by conducting both in vitro and in vivo experiments using unipolar and bipolar DBS (monophasic pulses, amplitude ranging from 3 to 6 V peak-to-peak, frequency 140 Hz and pulse width 100 µs). A full performance comparison between the proposed AFE and an identical AFE, equipped with an 8th order analog Bessel notch filter, was also conducted. MAIN RESULTS A high-performance, 4 nV ([Formula: see text])-1 AFE that is capable of recording nV-scale signals was designed in accordance with the imposed specifications. Under both in vitro and in vivo experimental conditions, the proposed AFE provided real-time, low-noise and artefact-free LFP recordings (in the frequency range 0.5-250 Hz) during stimulation. Its sensing and stimulation artefact suppression capabilities outperformed the capabilities of the AFE equipped with the Bessel notch filter. SIGNIFICANCE The designed AFE can precisely record LFP signals, in and without the presence of either unipolar or bipolar DBS, which renders it as a functional and practical AFE architecture to be utilised in a wide range of applications and environments. This work paves the way for the development of externalized research tools for closed-loop neuromodulation that use low- and higher-frequency LFPs as control signals.
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Affiliation(s)
- Konstantinos Petkos
- Department of Bioengineering, Imperial College London, London, United Kingdom. Center for Neurotechnology, Imperial College London, London, United Kingdom
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14
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Anderson DN, Osting B, Vorwerk J, Dorval AD, Butson CR. Optimized programming algorithm for cylindrical and directional deep brain stimulation electrodes. J Neural Eng 2019; 15:026005. [PMID: 29235446 DOI: 10.1088/1741-2552/aaa14b] [Citation(s) in RCA: 77] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Deep brain stimulation (DBS) is a growing treatment option for movement and psychiatric disorders. As DBS technology moves toward directional leads with increased numbers of smaller electrode contacts, trial-and-error methods of manual DBS programming are becoming too time-consuming for clinical feasibility. We propose an algorithm to automate DBS programming in near real-time for a wide range of DBS lead designs. APPROACH Magnetic resonance imaging and diffusion tensor imaging are used to build finite element models that include anisotropic conductivity. The algorithm maximizes activation of target tissue and utilizes the Hessian matrix of the electric potential to approximate activation of neurons in all directions. We demonstrate our algorithm's ability in an example programming case that targets the subthalamic nucleus (STN) for the treatment of Parkinson's disease for three lead designs: the Medtronic 3389 (four cylindrical contacts), the direct STNAcute (two cylindrical contacts, six directional contacts), and the Medtronic-Sapiens lead (40 directional contacts). MAIN RESULTS The optimization algorithm returns patient-specific contact configurations in near real-time-less than 10 s for even the most complex leads. When the lead was placed centrally in the target STN, the directional leads were able to activate over 50% of the region, whereas the Medtronic 3389 could activate only 40%. When the lead was placed 2 mm lateral to the target, the directional leads performed as well as they did in the central position, but the Medtronic 3389 activated only 2.9% of the STN. SIGNIFICANCE This DBS programming algorithm can be applied to cylindrical electrodes as well as novel directional leads that are too complex with modern technology to be manually programmed. This algorithm may reduce clinical programming time and encourage the use of directional leads, since they activate a larger volume of the target area than cylindrical electrodes in central and off-target lead placements.
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Affiliation(s)
- Daria Nesterovich Anderson
- Department of Bioengineering, University of Utah, Salt Lake City, UT, United States of America. Scientific Computing & Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United States of America
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15
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Su F, Kumaravelu K, Wang J, Grill WM. Model-Based Evaluation of Closed-Loop Deep Brain Stimulation Controller to Adapt to Dynamic Changes in Reference Signal. Front Neurosci 2019; 13:956. [PMID: 31551704 PMCID: PMC6746932 DOI: 10.3389/fnins.2019.00956] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Accepted: 08/26/2019] [Indexed: 12/19/2022] Open
Abstract
High-frequency deep brain stimulation (DBS) of the subthalamic nucleus (STN) is effective in suppressing the motor symptoms of Parkinson's disease (PD). Current clinically-deployed DBS technology operates in an open-loop fashion, i.e., fixed parameter high-frequency stimulation is delivered continuously, invariant to the needs or status of the patient. This poses two major challenges: (1) depletion of the stimulator battery due to the energy demands of continuous high-frequency stimulation, (2) high-frequency stimulation-induced side-effects. Closed-loop deep brain stimulation (CL DBS) may be effective in suppressing parkinsonian symptoms with stimulation parameters that require less energy and evoke fewer side effects than open loop DBS. However, the design of CL DBS comes with several challenges including the selection of an appropriate biomarker reflecting the symptoms of PD, setting a suitable reference signal, and implementing a controller to adapt to dynamic changes in the reference signal. Dynamic changes in beta oscillatory activity occur during the course of voluntary movement, and thus there may be a performance advantage to tracking such dynamic activity. We addressed these challenges by studying the performance of a closed-loop controller using a biophysically-based network model of the basal ganglia. The model-based evaluation consisted of two parts: (1) we implemented a Proportional-Integral (PI) controller to compute optimal DBS frequencies based on the magnitude of a dynamic reference signal, the oscillatory power in the beta band (13-35 Hz) recorded from model globus pallidus internus (GPi) neurons. (2) We coupled a linear auto-regressive model based mapping function with the Routh-Hurwitz stability analysis method to compute the parameters of the PI controller to track dynamic changes in the reference signal. The simulation results demonstrated successful tracking of both constant and dynamic beta oscillatory activity by the PI controller, and the PI controller followed dynamic changes in the reference signal, something that cannot be accomplished by constant open-loop DBS.
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Affiliation(s)
- Fei Su
- Department of Biomedical Engineering, Duke University, Durham, NC, United States.,School of Mechanical and Electrical Engineering, Shandong Agricultural University, Tai'an, China.,School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Karthik Kumaravelu
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Jiang Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
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16
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Segato A, Pieri V, Favaro A, Riva M, Falini A, De Momi E, Castellano A. Automated Steerable Path Planning for Deep Brain Stimulation Safeguarding Fiber Tracts and Deep Gray Matter Nuclei. Front Robot AI 2019; 6:70. [PMID: 33501085 PMCID: PMC7806057 DOI: 10.3389/frobt.2019.00070] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 07/18/2019] [Indexed: 12/20/2022] Open
Abstract
Deep Brain Stimulation (DBS) is a neurosurgical procedure consisting in the stereotactic implantation of stimulation electrodes to specific brain targets, such as deep gray matter nuclei. Current solutions to place the electrodes rely on rectilinear stereotactic trajectories (RTs) manually defined by surgeons, based on pre-operative images. An automatic path planner that accurately targets subthalamic nuclei (STN) and safeguards critical surrounding structures is still lacking. Also, robotically-driven curvilinear trajectories (CTs) computed on the basis of state-of-the-art neuroimaging would decrease DBS invasiveness, circumventing patient-specific obstacles. This work presents a new algorithm able to estimate a pool of DBS curvilinear trajectories for reaching a given deep target in the brain, in the context of the EU's Horizon EDEN2020 project. The prospect of automatically computing trajectory plans relying on sophisticated newly engineered steerable devices represents a breakthrough in the field of microsurgical robotics. By tailoring the paths according to single-patient anatomical constraints, as defined by advanced preoperative neuroimaging including diffusion MR tractography, this planner ensures a higher level of safety than the standard rectilinear approach. Ten healthy controls underwent Magnetic Resonance Imaging (MRI) on 3T scanner, including 3DT1-weighted sequences, 3Dhigh-resolution time-of-flight MR angiography (TOF-MRA) and high angular resolution diffusion MR sequences. A probabilistic q-ball residual-bootstrap MR tractography algorithm was used to reconstruct motor fibers, while the other deep gray matter nuclei surrounding STN and vessels were segmented on T1 and TOF-MRA images, respectively. These structures were labeled as obstacles. The reliability of the automated planner was evaluated; CTs were compared to RTs in terms of efficacy and safety. Targeting the anterior STN, CTs performed significantly better in maximizing the minimal distance from critical structures, by finding a tuned balance between all obstacles. Moreover, CTs resulted superior in reaching the center of mass (COM) of STN, as well as in optimizing the entry angle in STN and in the skull surface.
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Affiliation(s)
- Alice Segato
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Valentina Pieri
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, Milan, Italy
| | - Alberto Favaro
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Marco Riva
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Milan, Italy.,Unit of Oncological Neurosurgery, Humanitas Research Hospital, Rozzano, Italy
| | - Andrea Falini
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, Milan, Italy
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Antonella Castellano
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, Milan, Italy
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17
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Panuccio G, Semprini M, Natale L, Buccelli S, Colombi I, Chiappalone M. Progress in Neuroengineering for brain repair: New challenges and open issues. Brain Neurosci Adv 2018; 2:2398212818776475. [PMID: 32166141 PMCID: PMC7058228 DOI: 10.1177/2398212818776475] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Accepted: 04/19/2018] [Indexed: 01/01/2023] Open
Abstract
Background In recent years, biomedical devices have proven to be able to target also different neurological disorders. Given the rapid ageing of the population and the increase of invalidating diseases affecting the central nervous system, there is a growing demand for biomedical devices of immediate clinical use. However, to reach useful therapeutic results, these tools need a multidisciplinary approach and a continuous dialogue between neuroscience and engineering, a field that is named neuroengineering. This is because it is fundamental to understand how to read and perturb the neural code in order to produce a significant clinical outcome. Results In this review, we first highlight the importance of developing novel neurotechnological devices for brain repair and the major challenges expected in the next years. We describe the different types of brain repair strategies being developed in basic and clinical research and provide a brief overview of recent advances in artificial intelligence that have the potential to improve the devices themselves. We conclude by providing our perspective on their implementation to humans and the ethical issues that can arise. Conclusions Neuroengineering approaches promise to be at the core of future developments for clinical applications in brain repair, where the boundary between biology and artificial intelligence will become increasingly less pronounced.
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Affiliation(s)
- Gabriella Panuccio
- Department of Neuroscience and Brain Technologies (NBT), Istituto Italiano di Tecnologia (IIT), Genova, Italy
| | | | - Lorenzo Natale
- iCub Facility, Istituto Italiano di Tecnologia, Genova, Italy
| | - Stefano Buccelli
- Department of Neuroscience and Brain Technologies (NBT), Istituto Italiano di Tecnologia (IIT), Genova, Italy.,Rehab Technologies, Istituto Italiano di Tecnologia, Genova, Italy.,Dipartimento di Neuroscienze, riabilitazione, oftalmologia, genetica e scienze materno-infantili (DINOGMI), University of Genova, Genova, Italy
| | - Ilaria Colombi
- Department of Neuroscience and Brain Technologies (NBT), Istituto Italiano di Tecnologia (IIT), Genova, Italy.,Rehab Technologies, Istituto Italiano di Tecnologia, Genova, Italy.,Dipartimento di Neuroscienze, riabilitazione, oftalmologia, genetica e scienze materno-infantili (DINOGMI), University of Genova, Genova, Italy
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18
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Mohammed A, Bayford R, Demosthenous A. Toward adaptive deep brain stimulation in Parkinson's disease: a review. Neurodegener Dis Manag 2018; 8:115-136. [DOI: 10.2217/nmt-2017-0050] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Clinical deep brain stimulation (DBS) is now regarded as the therapeutic intervention of choice at the advanced stages of Parkinson's disease. However, some major challenges of DBS are stimulation induced side effects and limited pacemaker battery life. Side effects and shortening of pacemaker battery life are mainly as a result of continuous stimulation and poor stimulation focus. These drawbacks can be mitigated using adaptive DBS (aDBS) schemes. Side effects resulting from continuous stimulation can be reduced through adaptive control using closed-loop feedback, while those due to poor stimulation focus can be mitigated through spatial adaptation. Other advantages of aDBS include automatic, rather than manual, initial adjustment and programming, and long-term adjustments to maintain stimulation parameters with changes in patient's condition. Both result in improved efficacy. This review focuses on the major areas that are essential in driving technological advances for the various aDBS schemes. Their challenges, prospects and progress so far are analyzed. In addition, important advances and milestones in state-of-the-art aDBS schemes are highlighted – both for closed-loop adaption and spatial adaption. With perspectives and future potentials of DBS provided at the end.
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Affiliation(s)
- Ameer Mohammed
- Department of Electronic & Electrical Engineering, University College London, Torrington Place, London WC1E 7JE, UK
| | - Richard Bayford
- Department of Natural Sciences, Middlesex University, The Burroughs, London NW4 6BT, UK
| | - Andreas Demosthenous
- Department of Electronic & Electrical Engineering, University College London, Torrington Place, London WC1E 7JE, UK
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Abstract
Advancements in microfabrication has enabled manufacturing of microscopic neurostimulation electrodes with smaller footprint than ever possible. The smaller electrodes can potentially reduce tissue damage and allow better spatial resolution for neural stimulation. Although electrodes of any shape can easily be fabricated, substantial effort have been focused on identification and characterization of new materials and surface morphology for efficient charge injection, while maintaining simple circular or rectangular Euclidean electrode geometries. In this work we provide a systematic electrochemical evaluation of charge injection capacities of serpentine and fractal-shaped platinum microelectrodes and compare their performance with traditional circular microelectrodes. Our findings indicate that the increase in electrode perimeter leads to an increase in maximum charge injection capacity. Furthermore, we found that the electrode geometry can have even more significant impact on electrode performance than having a larger perimeter for a given surface area. The fractal-shaped microelectrodes, despite having smaller perimeter than other designs, demonstrated superior charge injection capacity. Our results suggest that electrode design can significantly affect both Faradaic and non-Faradaic electrochemical processes, which may be optimized to enable a more energy efficient design for neurostimulation.
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20
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Spatio-temporal dynamics of cortical drive to human subthalamic nucleus neurons in Parkinson's disease. Neurobiol Dis 2018; 112:49-62. [PMID: 29307661 PMCID: PMC5821899 DOI: 10.1016/j.nbd.2018.01.001] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Revised: 11/30/2017] [Accepted: 01/03/2018] [Indexed: 11/24/2022] Open
Abstract
Pathological synchronisation of beta frequency (12–35 Hz) oscillations between the subthalamic nucleus (STN) and cerebral cortex is thought to contribute to motor impairment in Parkinson's disease (PD). For this cortico-subthalamic oscillatory drive to be mechanistically important, it must influence the firing of STN neurons and, consequently, their downstream targets. Here, we examined the dynamics of synchronisation between STN LFPs and units with multiple cortical areas, measured using frontal ECoG, midline EEG and lateral EEG, during rest and movement. STN neurons lagged cortical signals recorded over midline (over premotor cortices) and frontal (over prefrontal cortices) with stable time delays, consistent with strong corticosubthalamic drive, and many neurons maintained these dynamics during movement. In contrast, most STN neurons desynchronised from lateral EEG signals (over primary motor cortices) during movement and those that did not had altered phase relations to the cortical signals. The strength of synchronisation between STN units and midline EEG in the high beta range (25–35 Hz) correlated positively with the severity of akinetic-rigid motor symptoms across patients. Together, these results suggest that sustained synchronisation of STN neurons to premotor-cortical beta oscillations play an important role in disrupting the normal coding of movement in PD. Multi-channel EEG with coincident STN single unit and local field potential recordings Variable time delays between beta oscillations in different cortical areas and STN neurons. Frontal/premotor cortical areas have most stable oscillatory synchronisation with STN neurons. Correlation between cortico-subthalamic beta-frequency synchronisation and clinical scores in PD.
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21
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Delbeke J, Hoffman L, Mols K, Braeken D, Prodanov D. And Then There Was Light: Perspectives of Optogenetics for Deep Brain Stimulation and Neuromodulation. Front Neurosci 2017; 11:663. [PMID: 29311765 PMCID: PMC5732983 DOI: 10.3389/fnins.2017.00663] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Accepted: 11/14/2017] [Indexed: 12/12/2022] Open
Abstract
Deep Brain Stimulation (DBS) has evolved into a well-accepted add-on treatment for patients with severe Parkinsons disease as well as for other chronic neurological conditions. The focal action of electrical stimulation can yield better responses and it exposes the patient to fewer side effects compared to pharmaceuticals distributed throughout the body toward the brain. On the other hand, the current practice of DBS is hampered by the relatively coarse level of neuromodulation achieved. Optogenetics, in contrast, offers the perspective of much more selective actions on the various physiological structures, provided that the stimulated cells are rendered sensitive to the action of light. Optogenetics has experienced tremendous progress since its first in vivo applications about 10 years ago. Recent advancements of viral vector technology for gene transfer substantially reduce vector-associated cytotoxicity and immune responses. This brings about the possibility to transfer this technology into the clinic as a possible alternative to DBS and neuromodulation. New paths could be opened toward a rich panel of clinical applications. Some technical issues still limit the long term use in humans but realistic perspectives quickly emerge. Despite a rapid accumulation of observations about patho-physiological mechanisms, it is still mostly serendipity and empiric adjustments that dictate clinical practice while more efficient logically designed interventions remain rather exceptional. Interestingly, it is also very much the neuro technology developed around optogenetics that offers the most promising tools to fill in the existing knowledge gaps about brain function in health and disease. The present review examines Parkinson's disease and refractory epilepsy as use cases for possible optogenetic stimulation therapies.
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Affiliation(s)
- Jean Delbeke
- LCEN3, Department of Neurology, Institute of Neuroscience, Ghent University, Ghent, Belgium
| | | | - Katrien Mols
- Neuroscience Research Flanders, Leuven, Belgium.,Life Science and Imaging, Imec, Leuven, Belgium
| | | | - Dimiter Prodanov
- Neuroscience Research Flanders, Leuven, Belgium.,Environment, Health and Safety, Imec, Leuven, Belgium
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22
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Su F, Wang J, Niu S, Li H, Deng B, Liu C, Wei X. Nonlinear predictive control for adaptive adjustments of deep brain stimulation parameters in basal ganglia-thalamic network. Neural Netw 2017; 98:283-295. [PMID: 29291546 DOI: 10.1016/j.neunet.2017.12.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 09/05/2017] [Accepted: 12/01/2017] [Indexed: 11/29/2022]
Abstract
The efficacy of deep brain stimulation (DBS) for Parkinson's disease (PD) depends in part on the post-operative programming of stimulation parameters. Closed-loop stimulation is one method to realize the frequent adjustment of stimulation parameters. This paper introduced the nonlinear predictive control method into the online adjustment of DBS amplitude and frequency. This approach was tested in a computational model of basal ganglia-thalamic network. The autoregressive Volterra model was used to identify the process model based on physiological data. Simulation results illustrated the efficiency of closed-loop stimulation methods (amplitude adjustment and frequency adjustment) in improving the relay reliability of thalamic neurons compared with the PD state. Besides, compared with the 130Hz constant DBS the closed-loop stimulation methods can significantly reduce the energy consumption. Through the analysis of inter-spike-intervals (ISIs) distribution of basal ganglia neurons, the evoked network activity by the closed-loop frequency adjustment stimulation was closer to the normal state.
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Affiliation(s)
- Fei Su
- School of Electrical and Information Engineering, Tianjin University, 300072, Tianjin, China.
| | - Jiang Wang
- School of Electrical and Information Engineering, Tianjin University, 300072, Tianjin, China.
| | - Shuangxia Niu
- School of Electrical Engineering, The Hong Kong Polytechnic University, 999077, Hong Kong, China.
| | - Huiyan Li
- School of Automation and Electrical Engineering, Tianjin University of Technology and Education, 300222, Tianjin, China.
| | - Bin Deng
- School of Electrical and Information Engineering, Tianjin University, 300072, Tianjin, China.
| | - Chen Liu
- School of Electrical and Information Engineering, Tianjin University, 300072, Tianjin, China.
| | - Xile Wei
- School of Electrical and Information Engineering, Tianjin University, 300072, Tianjin, China.
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van Dijk KJ, Verhagen R, Bour LJ, Heida C, Veltink PH. Avoiding Internal Capsule Stimulation With a New Eight-Channel Steering Deep Brain Stimulation Lead. Neuromodulation 2017; 21:553-561. [DOI: 10.1111/ner.12702] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Revised: 08/22/2017] [Accepted: 08/25/2017] [Indexed: 11/30/2022]
Affiliation(s)
- Kees J. van Dijk
- MIRA Institute for Biomedical Engineering and Technical Medicine, University of Twente; Enschede NL The Netherlands
| | - Rens Verhagen
- Department of Neurology/Clinical Neurophysiology; Academic Medical Center; Amsterdam NL The Netherlands
| | - Lo J. Bour
- Department of Neurology/Clinical Neurophysiology; Academic Medical Center; Amsterdam NL The Netherlands
| | - Ciska Heida
- MIRA Institute for Biomedical Engineering and Technical Medicine, University of Twente; Enschede NL The Netherlands
| | - Peter H. Veltink
- MIRA Institute for Biomedical Engineering and Technical Medicine, University of Twente; Enschede NL The Netherlands
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24
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Gunduz A, Foote KD, Okun MS. Reengineering deep brain stimulation for movement disorders: Emerging technologies. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2017; 4:97-105. [PMID: 29450404 DOI: 10.1016/j.cobme.2017.09.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Deep brain stimulation (DBS) is a neurosurgical technique, which consists of continuous delivery of an electrical pulse through chronically implanted electrodes connected to a neurostimulator, programmable in amplitude, pulse width, frequency, and stimulation channel. DBS is a promising treatment option for addressing severe and drug-resistant movement disorders. The success of DBS therapy is a combination of surgical implantation techniques, device technology, and clinical programming strategies. Changes in device settings require highly trained and experienced clinicians to achieve maximal therapeutic benefit for each targeted symptom, and optimization of stimulation parameters can take many visits. Thus, the development of innovative DBS technologies that can optimize the clinical implementation of DBS will lead to wider scale utilization. This review aims to present engineering approaches that have the potential to improve clinical outcomes of DBS, focusing on the development novel temporal patterns, innovative electrode designs, computational models to guide stimulation, closed-loop DBS, and remote programming.
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Affiliation(s)
- Aysegul Gunduz
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.,Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, USA
| | - Kelly D Foote
- Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, USA.,Department of Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Michael S Okun
- Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, USA.,Department of Neurology, University of Florida, Gainesville, FL, USA
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25
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Meidahl AC, Tinkhauser G, Herz DM, Cagnan H, Debarros J, Brown P. Adaptive Deep Brain Stimulation for Movement Disorders: The Long Road to Clinical Therapy. Mov Disord 2017; 32:810-819. [PMID: 28597557 PMCID: PMC5482397 DOI: 10.1002/mds.27022] [Citation(s) in RCA: 129] [Impact Index Per Article: 18.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Revised: 03/06/2017] [Accepted: 03/19/2017] [Indexed: 11/07/2022] Open
Abstract
Continuous high-frequency DBS is an established treatment for essential tremor and Parkinson's disease. Current developments focus on trying to widen the therapeutic window of DBS. Adaptive DBS (aDBS), where stimulation is dynamically controlled by feedback from biomarkers of pathological brain circuit activity, is one such development. Relevant biomarkers may be central, such as local field potential activity, or peripheral, such as inertial tremor data. Moreover, stimulation may be directed by the amplitude or the phase (timing) of the biomarker signal. In this review, we evaluate existing aDBS studies as proof-of-principle, discuss their limitations, most of which stem from their acute nature, and propose what is needed to take aDBS into a chronic setting. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Anders Christian Meidahl
- Medical Research Council Brain Network Dynamics Unit at the University of OxfordOxfordUK
- Nuffield Department of Clinical Neurosciences, John Radcliffe HospitalUniversity of OxfordOxfordUK
| | - Gerd Tinkhauser
- Medical Research Council Brain Network Dynamics Unit at the University of OxfordOxfordUK
- Nuffield Department of Clinical Neurosciences, John Radcliffe HospitalUniversity of OxfordOxfordUK
- Department of NeurologyBern University Hospital and University of BernBernSwitzerland
| | - Damian Marc Herz
- Nuffield Department of Clinical Neurosciences, John Radcliffe HospitalUniversity of OxfordOxfordUK
| | - Hayriye Cagnan
- Medical Research Council Brain Network Dynamics Unit at the University of OxfordOxfordUK
- Nuffield Department of Clinical Neurosciences, John Radcliffe HospitalUniversity of OxfordOxfordUK
- Institute of NeurologyUniversity College LondonLondonUK
| | - Jean Debarros
- Medical Research Council Brain Network Dynamics Unit at the University of OxfordOxfordUK
- Nuffield Department of Clinical Neurosciences, John Radcliffe HospitalUniversity of OxfordOxfordUK
| | - Peter Brown
- Medical Research Council Brain Network Dynamics Unit at the University of OxfordOxfordUK
- Nuffield Department of Clinical Neurosciences, John Radcliffe HospitalUniversity of OxfordOxfordUK
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26
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Beuter A. The Use of Neurocomputational Models as Alternatives to Animal Models in the Development of Electrical Brain Stimulation Treatments. Altern Lab Anim 2017; 45:91-99. [DOI: 10.1177/026119291704500203] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Recent publications call for more animal models to be used and more experiments to be performed, in order to better understand the mechanisms of neurodegenerative disorders, to improve human health, and to develop new brain stimulation treatments. In response to these calls, some limitations of the current animal models are examined by using Deep Brain Stimulation (DBS) in Parkinson's disease as an illustrative example. Without focusing on the arguments for or against animal experimentation, or on the history of DBS, the present paper argues that given recent technological and theoretical advances, the time has come to consider bioinspired computational modelling as a valid alternative to animal models, in order to design the next generation of human brain stimulation treatments. However, before computational neuroscience is fully integrated in the translational process and used as a substitute for animal models, several obstacles need to be overcome. These obstacles are examined in the context of institutional, financial, technological and behavioural lock-in. Recommendations include encouraging agreement to change long-term habitual practices, explaining what alternative models can achieve, considering economic stakes, simplifying administrative and regulatory constraints, and carefully examining possible conflicts of interest.
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Affiliation(s)
- Anne Beuter
- Institut Polytechnique de Bordeaux, Bordeaux, France
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27
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Morishita T, Higuchi MA, Saita K, Tsuboi Y, Abe H, Inoue T. Changes in Motor-Related Cortical Activity Following Deep Brain Stimulation for Parkinson's Disease Detected by Functional Near Infrared Spectroscopy: A Pilot Study. Front Hum Neurosci 2016; 10:629. [PMID: 28018196 PMCID: PMC5149535 DOI: 10.3389/fnhum.2016.00629] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 11/24/2016] [Indexed: 11/13/2022] Open
Abstract
It remains unclear how deep brain stimulation (DBS) modulates the global neuronal network involving cortical activity. We aimed to evaluate changes in cortical activity in six (two men; four women) patients with Parkinson’s disease (PD) who underwent unilateral globus pallidus interna (GPI) DBS surgery using a multi-channel near infrared spectroscopy (NIRS) system. As five of the patients were right-handed, DBS was performed on the left in these five cases. The mean age was 66.8 ± 4.0 years. The unified Parkinson’s disease rating scale (UPDRS) motor scores were evaluated at baseline and 1- and 6-month follow-up. Task-related NIRS experiments applying the block design were performed at baseline and 1-month follow-up. The mean of the total UPDRS motor score was 48.5 ± 11.1 in the off-medication state preoperatively. Postoperatively, total UPDRS motor scores improved to 26.8 ± 16.6 (p < 0.05) and 22.2 ± 8.6 (p < 0.05) at 1- and 6-month follow-up, respectively. A task-related NIRS experiment showed a postoperative increase in the cortical activity of the prefrontal cortex comparable to the preoperative state. To our knowledge, this is the first study to use a multi-channel NIRS system for PD patients treated with DBS. In this pilot study, we showed changes in motor-associated cortical activities following DBS surgery. Therapeutic DBS was concluded to have promoted the underlying neuronal network remodeling.
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Affiliation(s)
- Takashi Morishita
- Department of Neurosurgery, Faculty of Medicine, Fukuoka University Fukuoka, Japan
| | - Masa-Aki Higuchi
- Department of Neurology, Faculty of Medicine, Fukuoka University Fukuoka, Japan
| | - Kazuya Saita
- Department of Neurosurgery, Faculty of Medicine, Fukuoka UniversityFukuoka, Japan; Department of Rehabilitation, Faculty of Medicine, Fukuoka UniversityFukuoka, Japan
| | - Yoshio Tsuboi
- Department of Neurology, Faculty of Medicine, Fukuoka University Fukuoka, Japan
| | - Hiroshi Abe
- Department of Neurosurgery, Faculty of Medicine, Fukuoka University Fukuoka, Japan
| | - Tooru Inoue
- Department of Neurosurgery, Faculty of Medicine, Fukuoka University Fukuoka, Japan
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28
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Lee KE, Bhati MT, Halpern CH. A Commentary on Attitudes Towards Deep Brain Stimulation for Addiction. ACTA ACUST UNITED AC 2016; 1:1-3. [PMID: 28620655 DOI: 10.29245/2572.942x/2016/8.1093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Deep brain stimulation (DBS) has proven to be an effective treatment for neurologic disorders such as Parkinson's disease, and is currently being investigated as a therapy for psychiatric diseases such as addiction, major depressive disorder, and obsessive compulsive disorder. In this commentary, we review and discuss the findings presented in the Letter to the Editor entitled "Attitudes towards treating addiction with deep brain stimulation," written by Ali et al1. The survey presented in this Letter reported general approval for examining the effects of DBS on addictive disorders in a clinical trial, but highlighted critical areas of concern including informed consent, patient autonomy, appropriate medical practice, passing of clinical trial milestones, and implications on law enforcement.
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Affiliation(s)
- Karen E Lee
- Department of Neurosurgery, Stanford University 300 Pasteur Dr., Edwards Bldg. Stanford, CA 94305, USA
| | - Mahendra T Bhati
- Department of Neurosurgery, Stanford University 300 Pasteur Dr., Edwards Bldg. Stanford, CA 94305, USA
| | - Casey H Halpern
- Department of Neurosurgery, Stanford University 300 Pasteur Dr., Edwards Bldg. Stanford, CA 94305, USA
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29
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Akbarian-Tefaghi L, Zrinzo L, Foltynie T. The Use of Deep Brain Stimulation in Tourette Syndrome. Brain Sci 2016; 6:brainsci6030035. [PMID: 27548235 PMCID: PMC5039464 DOI: 10.3390/brainsci6030035] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2016] [Revised: 08/13/2016] [Accepted: 08/15/2016] [Indexed: 12/16/2022] Open
Abstract
Tourette syndrome (TS) is a childhood neurobehavioural disorder, characterised by the presence of motor and vocal tics, typically starting in childhood but persisting in around 20% of patients into adulthood. In those patients who do not respond to pharmacological or behavioural therapy, deep brain stimulation (DBS) may be a suitable option for potential symptom improvement. This manuscript attempts to summarise the outcomes of DBS at different targets, explore the possible mechanisms of action of DBS in TS, as well as the potential of adaptive DBS. There will also be a focus on the future challenges faced in designing optimized trials.
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Affiliation(s)
- Ladan Akbarian-Tefaghi
- Institute of Neurology, University College London (UCL), Queen Square, London WC1N 3BG, UK.
| | - Ludvic Zrinzo
- Sobell Department of Motor Neuroscience, University College London (UCL) Institute of Neurology, London WC1N 3BG, UK.
| | - Thomas Foltynie
- Sobell Department of Motor Neuroscience, University College London (UCL) Institute of Neurology, London WC1N 3BG, UK.
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