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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] [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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Fleming JE, Senneff S, Lowery MM. Multivariable closed-loop control of deep brain stimulation for Parkinson's disease. J Neural Eng 2023; 20:056029. [PMID: 37733003 DOI: 10.1088/1741-2552/acfbfa] [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: 06/28/2022] [Accepted: 09/21/2023] [Indexed: 09/22/2023]
Abstract
Objective. Closed-loop deep brain stimulation (DBS) methods for Parkinson's disease (PD) to-date modulate either stimulation amplitude or frequency to control a single biomarker. While good performance has been demonstrated for symptoms that are correlated with the chosen biomarker, suboptimal regulation can occur for uncorrelated symptoms or when the relationship between biomarker and symptom varies. Control of stimulation-induced side-effects is typically not considered.Approach.A multivariable control architecture is presented to selectively target suppression of either tremor or subthalamic nucleus beta band oscillations. DBS pulse amplitude and duration are modulated to maintain amplitude below a threshold and avoid stimulation of distal large diameter axons associated with stimulation-induced side effects. A supervisor selects between a bank of controllers which modulate DBS pulse amplitude to control rest tremor or beta activity depending on the level of muscle electromyographic (EMG) activity detected. A secondary controller limits pulse amplitude and modulates pulse duration to target smaller diameter axons lying close to the electrode. The control architecture was investigated in a computational model of the PD motor network which simulated the cortico-basal ganglia network, motoneuron pool, EMG and muscle force signals.Main results.Good control of both rest tremor and beta activity was observed with reduced power delivered when compared with conventional open loop stimulation, The supervisor avoided over- or under-stimulation which occurred when using a single controller tuned to one biomarker. When DBS amplitude was constrained, the secondary controller maintained the efficacy of stimulation by increasing pulse duration to compensate for reduced amplitude. Dual parameter control delivered effective control of the target biomarkers, with additional savings in the power delivered.Significance.Non-linear multivariable control can enable targeted suppression of motor symptoms for PD patients. Moreover, dual parameter control facilitates automatic regulation of the stimulation therapeutic dosage to prevent overstimulation, whilst providing additional power savings.
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Affiliation(s)
- John E Fleming
- Neuromuscular Systems Laboratory, UCD School of Electrical & Electronic Engineering, University College Dublin, Dublin, Ireland
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, United Kingdom
| | - Sageanne Senneff
- Neuromuscular Systems Laboratory, UCD School of Electrical & Electronic Engineering, University College Dublin, Dublin, Ireland
| | - Madeleine M Lowery
- Neuromuscular Systems Laboratory, UCD School of Electrical & Electronic Engineering, University College Dublin, Dublin, Ireland
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Ferreira Felloni Borges Y, Cheyuo C, Lozano AM, Fasano A. Essential Tremor - Deep Brain Stimulation vs. Focused Ultrasound. Expert Rev Neurother 2023; 23:603-619. [PMID: 37288812 DOI: 10.1080/14737175.2023.2221789] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 06/01/2023] [Indexed: 06/09/2023]
Abstract
INTRODUCTION Essential Tremor (ET) is one of the most common tremor syndromes typically presented as action tremor, affecting mainly the upper limbs. In at least 30-50% of patients, tremor interferes with quality of life, does not respond to first-line therapies and/or intolerable adverse effects may occur. Therefore, surgery may be considered. AREAS COVERED In this review, the authors discuss and compare unilateral ventral intermedius nucleus deep brain stimulation (VIM DBS) and bilateral DBS with Magnetic Resonance-guided Focused Ultrasound (MRgFUS) thalamotomy, which comprises focused acoustic energy generating ablation under real-time MRI guidance. Discussion includes their impact on tremor reduction and their potential complications. Finally, the authors provide their expert opinion. EXPERT OPINION DBS is adjustable, potentially reversible and allows bilateral treatments; however, it is invasive requires hardware implantation, and has higher surgical risks. Instead, MRgFUS is less invasive, less expensive, and requires no hardware maintenance. Beyond these technical differences, the decision should also involve the patient, family, and caregivers.
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Affiliation(s)
- Yuri Ferreira Felloni Borges
- Edmond J. Safra Program in Parkinson's Disease, Division of Neurology, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, UHN, University of Toronto, Toronto, ON, Canada
| | - Cletus Cheyuo
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Andres M Lozano
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
- Krembil Brain Institute, Toronto, ON, Canada
| | - Alfonso Fasano
- Edmond J. Safra Program in Parkinson's Disease, Division of Neurology, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, UHN, University of Toronto, Toronto, ON, Canada
- Krembil Brain Institute, Toronto, ON, Canada
- Center for Advancing Neurotechnological Innovation to Application (CRANIA), Toronto, ON, Canada
- Department of Parkinson's Disease & Movement Disorders Rehabilitation, Moriggia-Pelascini Hospital, Gravedona Ed Uniti, Como, Italy
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Wong JK, Mayberg HS, Wang DD, Richardson RM, Halpern CH, Krinke L, Arlotti M, Rossi L, Priori A, Marceglia S, Gilron R, Cavanagh JF, Judy JW, Miocinovic S, Devergnas AD, Sillitoe RV, Cernera S, Oehrn CR, Gunduz A, Goodman WK, Petersen EA, Bronte-Stewart H, Raike RS, Malekmohammadi M, Greene D, Heiden P, Tan H, Volkmann J, Voon V, Li L, Sah P, Coyne T, Silburn PA, Kubu CS, Wexler A, Chandler J, Provenza NR, Heilbronner SR, Luciano MS, Rozell CJ, Fox MD, de Hemptinne C, Henderson JM, Sheth SA, Okun MS. Proceedings of the 10th annual deep brain stimulation think tank: Advances in cutting edge technologies, artificial intelligence, neuromodulation, neuroethics, interventional psychiatry, and women in neuromodulation. Front Hum Neurosci 2023; 16:1084782. [PMID: 36819295 PMCID: PMC9933515 DOI: 10.3389/fnhum.2022.1084782] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/12/2022] [Indexed: 02/05/2023] Open
Abstract
The deep brain stimulation (DBS) Think Tank X was held on August 17-19, 2022 in Orlando FL. The session organizers and moderators were all women with the theme women in neuromodulation. Dr. Helen Mayberg from Mt. Sinai, NY was the keynote speaker. She discussed milestones and her experiences in developing depression DBS. The DBS Think Tank was founded in 2012 and provides an open platform where clinicians, engineers and researchers (from industry and academia) can freely discuss current and emerging DBS technologies as well as the logistical and ethical issues facing the field. The consensus among the DBS Think Tank X speakers was that DBS has continued to expand in scope however several indications have reached the "trough of disillusionment." DBS for depression was considered as "re-emerging" and approaching a slope of enlightenment. DBS for depression will soon re-enter clinical trials. The group estimated that globally more than 244,000 DBS devices have been implanted for neurological and neuropsychiatric disorders. This year's meeting was focused on advances in the following areas: neuromodulation in Europe, Asia, and Australia; cutting-edge technologies, closed loop DBS, DBS tele-health, neuroethics, lesion therapy, interventional psychiatry, and adaptive DBS.
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Affiliation(s)
- Joshua K. Wong
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Helen S. Mayberg
- Department of Neurology, Neurosurgery, Psychiatry, and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Doris D. Wang
- Department of Neurological Surgery, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - R. Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Casey H. Halpern
- Richards Medical Research Laboratories, Department of Neurosurgery, Perelman School of Medicine, Pennsylvania Hospital, University of Pennsylvania, Philadelphia, PA, United States
| | - Lothar Krinke
- Newronika, Goose Creek, SC, United States
- Department of Neuroscience, West Virginia University, Morgantown, WV, United States
| | | | | | | | | | | | - James F. Cavanagh
- Department of Psychology, University of New Mexico, Albuquerque, NM, United States
| | - Jack W. Judy
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States
| | - Svjetlana Miocinovic
- Department of Neurology, School of Medicine, Emory University, Atlanta, GA, United States
| | - Annaelle D. Devergnas
- Department of Neurology, School of Medicine, Emory University, Atlanta, GA, United States
| | - Roy V. Sillitoe
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, United States
| | - Stephanie Cernera
- Department of Neurological Surgery, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Carina R. Oehrn
- Department of Neurological Surgery, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Aysegul Gunduz
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Wayne K. Goodman
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Erika A. Petersen
- Department of Neurosurgery, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Helen Bronte-Stewart
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Robert S. Raike
- Restorative Therapies Group Implantables, Research, and Core Technology, Medtronic Inc., Minneapolis, MN, United States
| | | | - David Greene
- NeuroPace, Inc., Mountain View, CA, United States
| | - Petra Heiden
- Department of Stereotactic and Functional Neurosurgery, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Huiling Tan
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jens Volkmann
- Department of Neurology, University of Würzburg, Würzburg, Germany
| | - Valerie Voon
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Luming Li
- National Engineering Research Center of Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Pankaj Sah
- Queensland Brain Institute, University of Queensland, St Lucia, QLD, Australia
| | - Terry Coyne
- Queensland Brain Institute, University of Queensland, St Lucia, QLD, Australia
| | - Peter A. Silburn
- Queensland Brain Institute, University of Queensland, St Lucia, QLD, Australia
| | - Cynthia S. Kubu
- Department of Neurology, Cleveland Clinic, Cleveland, OH, United States
| | - Anna Wexler
- Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia, PA, United States
| | - Jennifer Chandler
- Centre for Health Law, Policy, and Ethics, Faculty of Law, University of Ottawa, Ottawa, ON, Canada
| | - Nicole R. Provenza
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Sarah R. Heilbronner
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, United States
| | - Marta San Luciano
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Christopher J. Rozell
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Michael D. Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, Radiology, and Neurosurgery, Brigham and Women’s Hospital, Boston, MA, United States
| | - Coralie de Hemptinne
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Jaimie M. Henderson
- Department of Neurosurgery, Stanford University, Stanford, CA, United States
| | - Sameer A. Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Michael S. Okun
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
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Stieve BJ, Richner TJ, Krook-Magnuson C, Netoff TI, Krook-Magnuson E. Optimization of closed-loop electrical stimulation enables robust cerebellar-directed seizure control. Brain 2023; 146:91-108. [PMID: 35136942 DOI: 10.1093/brain/awac051] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 12/17/2021] [Accepted: 01/11/2022] [Indexed: 01/11/2023] Open
Abstract
Additional treatment options for temporal lobe epilepsy are needed, and potential interventions targeting the cerebellum are of interest. Previous animal work has shown strong inhibition of hippocampal seizures through on-demand optogenetic manipulation of the cerebellum. However, decades of work examining electrical stimulation-a more immediately translatable approach-targeting the cerebellum has produced very mixed results. We were therefore interested in exploring the impact that stimulation parameters may have on seizure outcomes. Using a mouse model of temporal lobe epilepsy, we conducted on-demand electrical stimulation of the cerebellar cortex, and varied stimulation charge, frequency and pulse width, resulting in over 1000 different potential combinations of settings. To explore this parameter space in an efficient, data-driven, manner, we utilized Bayesian optimization with Gaussian process regression, implemented in MATLAB with an Expected Improvement Plus acquisition function. We examined three different fitting conditions and two different electrode orientations. Following the optimization process, we conducted additional on-demand experiments to test the effectiveness of selected settings. Regardless of experimental setup, we found that Bayesian optimization allowed identification of effective intervention settings. Additionally, generally similar optimal settings were identified across animals, suggesting that personalized optimization may not always be necessary. While optimal settings were effective, stimulation with settings predicted from the Gaussian process regression to be ineffective failed to provide seizure control. Taken together, our results provide a blueprint for exploration of a large parameter space for seizure control and illustrate that robust inhibition of seizures can be achieved with electrical stimulation of the cerebellum, but only if the correct stimulation parameters are used.
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Affiliation(s)
- Bethany J Stieve
- Graduate Program in Neuroscience, University of Minnesota, Minneapolis 55455, USA
| | - Thomas J Richner
- Department of Biomedical Engineering, University of Minnesota, Minneapolis 55455, USA.,Department of Neuroscience, University of Minnesota, Minneapolis 55455, USA
| | | | - Theoden I Netoff
- Graduate Program in Neuroscience, University of Minnesota, Minneapolis 55455, USA.,Department of Biomedical Engineering, University of Minnesota, Minneapolis 55455, USA
| | - Esther Krook-Magnuson
- Graduate Program in Neuroscience, University of Minnesota, Minneapolis 55455, USA.,Department of Neuroscience, University of Minnesota, Minneapolis 55455, USA
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Sarikhani P, Ferleger B, Mitchell K, Ostrem J, Herron J, Mahmoudi B, Miocinovic S. Automated deep brain stimulation programming with safety constraints for tremor suppression in patients with Parkinson's Disease and essential tremor. J Neural Eng 2022; 19. [PMID: 35921806 DOI: 10.1088/1741-2552/ac86a2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 08/03/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVES Deep brain stimulation programming for movement disorders requires systematic fine tuning of stimulation parameters to ameliorate tremor and other symptoms while avoiding side effects. DBS programming can be a time-consuming process and requires clinical expertise to assess response to DBS to optimize therapy for each patient. In this study, we describe and evaluate an automated, closed-loop, and patient-specific framework for DBS programming that measures tremor using a smartwatch and automatically changes DBS parameters based on the recommendations from a closed-loop optimization algorithm thus eliminating the need for an expert clinician. APPROACH Bayesian optimization which is a sample-efficient global optimization method was used as the core of this DBS programming framework to adaptively learn each patient's response to DBS and suggest the next best settings to be evaluated. Input from a clinician was used initially to define a maximum safe amplitude, but we also implemented 'safe Bayesian optimization' to automatically discover tolerable exploration boundaries. RESULTS We tested the system in 15 patients (9 with Parkinson's disease and 6 with essential tremor). Tremor suppression at best automated settings was statistically comparable to previously established clinical settings. The optimization algorithm converged after testing 15.1±0.7 settings when maximum safe exploration boundaries were predefined, and 17.7±4.9 when the algorithm itself determined safe exploration boundaries. SIGNIFICANCE We demonstrate that fully automated DBS programming framework for treatment of tremor is efficient and safe while providing outcomes comparable to that achieved by expert clinicians.
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Affiliation(s)
- Parisa Sarikhani
- Emory University, 101 Woodruff Cir, Suite 4137, Atlanta, Georgia, 30322-1007, UNITED STATES
| | - Benjamin Ferleger
- University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, Pennsylvania, 19104-6243, UNITED STATES
| | - Kyle Mitchell
- Neurology, Duke University, 932 Morreene Rd, Durham, North Carolina, 2770, UNITED STATES
| | - Jill Ostrem
- Neurology, University of California, San Francisco, 1651 Fourth St., Suite 232, San Francisco, California, 94158, UNITED STATES
| | - Jeffrey Herron
- Electrical Engineering, University of Washington, 185 Stevens Way, Room AE100R, Campus Box 352500, Seattle, Washington, 98195, UNITED STATES
| | - Babak Mahmoudi
- Biomedical Informatics, Emory University, 101 Woodruff Cir, Atlanta, Georgia, 30322, UNITED STATES
| | - Svjetlana Miocinovic
- Neurology, Emory University, 12 Executive Park Drive Northeast, Atlanta, Georgia, 30329, UNITED STATES
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Song J, Liu S, Lin H. Model-based quantitative optimization of deep brain stimulation and prediction of parkinson's states. Neuroscience 2022; 498:105-124. [PMID: 35750111 DOI: 10.1016/j.neuroscience.2022.05.019] [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: 01/05/2022] [Revised: 05/01/2022] [Accepted: 05/16/2022] [Indexed: 10/18/2022]
Abstract
Although the exact etiology of Parkinson's disease (PD) is still unknown, there are a variety of treatments available to alleviate its symptoms according to the development stage of PD. Deep brain stimulation (DBS), the most common surgical treatment for advanced PD, accurately locates and implants stimulating electrodes at specific targets in the brain to deliver high-frequency electrical stimulation that alters the excitability of the corresponding nuclei. However, for different patients and stages of PD development, there exists a choice of the optimal DBS protocol. In this paper, we propose a quantitative method (multi-dimensional feature indexes) to determine the stimulation pattern, stimulation parameters, and target of DBS from the perspective of the network model. On the other hand, based on this method, the development of PD can be predicted so that timely treatment can be given to patients. Simulation results show that, first, different network states can be distinguished by extracting features of the firing activity of neuronal populations within the basal ganglia network system. Secondly, the optimal DBS treatment can be selected by comparing the feature indexes vectors of the pre- and post-state of the network after the action of different modes of DBS. Lastly, the evolution of the network state from normal to pathological is simulated. The critical point of network state transitions is determined. These results provide a quantitative and qualitative method for determining the optimal regimen for DBS for PD, which is helpful for clinical practice.
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Affiliation(s)
- Jian Song
- School of mathematics, South China University of technology, Guangzhou, China.
| | - Shenquan Liu
- School of mathematics, South China University of technology, Guangzhou, China.
| | - Hui Lin
- Department of Precision Instruments, Tsinghua University, Beijing, China.
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Frey J, Cagle J, Johnson KA, Wong JK, Hilliard JD, Butson CR, Okun MS, de Hemptinne C. Past, Present, and Future of Deep Brain Stimulation: Hardware, Software, Imaging, Physiology and Novel Approaches. Front Neurol 2022; 13:825178. [PMID: 35356461 PMCID: PMC8959612 DOI: 10.3389/fneur.2022.825178] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 02/04/2022] [Indexed: 11/13/2022] Open
Abstract
Deep brain stimulation (DBS) has advanced treatment options for a variety of neurologic and neuropsychiatric conditions. As the technology for DBS continues to progress, treatment efficacy will continue to improve and disease indications will expand. Hardware advances such as longer-lasting batteries will reduce the frequency of battery replacement and segmented leads will facilitate improvements in the effectiveness of stimulation and have the potential to minimize stimulation side effects. Targeting advances such as specialized imaging sequences and “connectomics” will facilitate improved accuracy for lead positioning and trajectory planning. Software advances such as closed-loop stimulation and remote programming will enable DBS to be a more personalized and accessible technology. The future of DBS continues to be promising and holds the potential to further improve quality of life. In this review we will address the past, present and future of DBS.
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Affiliation(s)
- Jessica Frey
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Jackson Cagle
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Kara A. Johnson
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Joshua K. Wong
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Justin D. Hilliard
- Department of Neurosurgery, University of Florida, Gainesville, FL, United States
| | - Christopher R. Butson
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
- Department of Neurosurgery, University of Florida, Gainesville, FL, United States
| | - Michael S. Okun
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Coralie de Hemptinne
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
- *Correspondence: Coralie de Hemptinne
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Fra̧czek TM, Ferleger BI, Brown TE, Thompson MC, Haddock AJ, Houston BC, Ojemann JG, Ko AL, Herron JA, Chizeck HJ. Closing the Loop With Cortical Sensing: The Development of Adaptive Deep Brain Stimulation for Essential Tremor Using the Activa PC+S. Front Neurosci 2021; 15:749705. [PMID: 34955714 PMCID: PMC8695120 DOI: 10.3389/fnins.2021.749705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 11/04/2021] [Indexed: 11/25/2022] Open
Abstract
Deep Brain Stimulation (DBS) is an important tool in the treatment of pharmacologically resistant neurological movement disorders such as essential tremor (ET) and Parkinson's disease (PD). However, the open-loop design of current systems may be holding back the true potential of invasive neuromodulation. In the last decade we have seen an explosion of activity in the use of feedback to "close the loop" on neuromodulation in the form of adaptive DBS (aDBS) systems that can respond to the patient's therapeutic needs. In this paper we summarize the accomplishments of a 5-year study at the University of Washington in the use of neural feedback from an electrocorticography strip placed over the sensorimotor cortex. We document our progress from an initial proof of hardware all the way to a fully implanted adaptive stimulation system that leverages machine-learning approaches to simplify the programming process. In certain cases, our systems out-performed current open-loop approaches in both power consumption and symptom suppression. Throughout this effort, we collaborated with neuroethicists to capture patient experiences and take them into account whilst developing ethical aDBS approaches. Based on our results we identify several key areas for future work. "Graded" aDBS will allow the system to smoothly tune the stimulation level to symptom severity, and frequent automatic calibration of the algorithm will allow aDBS to adapt to the time-varying dynamics of the disease without additional input from a clinician. Additionally, robust computational models of the pathophysiology of ET will allow stimulation to be optimized to the nuances of an individual patient's symptoms. We also outline the unique advantages of using cortical electrodes for control and the remaining hardware limitations that need to be overcome to facilitate further development in this field. Over the course of this study we have verified the potential of fully-implanted, cortically driven aDBS as a feasibly translatable treatment for pharmacologically resistant ET.
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Affiliation(s)
- Tomasz M. Fra̧czek
- Neuroscience Program, University of Washington, Seattle, WA, United States
| | - Benjamin I. Ferleger
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Timothy E. Brown
- Department of Philosophy, University of Washington, Seattle, WA, United States
| | - Margaret C. Thompson
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Andrew J. Haddock
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Brady C. Houston
- Neuroscience Program, University of Washington, Seattle, WA, United States
| | - Jeffrey G. Ojemann
- Department of Neurological Surgery, University of Washington, Seattle, WA, United States
| | - Andrew L. Ko
- Department of Neurological Surgery, University of Washington, Seattle, WA, United States
| | - Jeffrey A. Herron
- Department of Neurological Surgery, University of Washington, Seattle, WA, United States
| | - Howard J. Chizeck
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
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Wenzel GR, Roediger J, Brücke C, Marcelino ALDA, Gülke E, Pötter-Nerger M, Scholtes H, Wynants K, Juárez Paz LM, Kühn AA. CLOVER-DBS: Algorithm-Guided Deep Brain Stimulation-Programming Based on External Sensor Feedback Evaluated in a Prospective, Randomized, Crossover, Double-Blind, Two-Center Study. JOURNAL OF PARKINSONS DISEASE 2021; 11:1887-1899. [PMID: 34151855 DOI: 10.3233/jpd-202480] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Recent technological advances in deep brain stimulation (DBS) (e.g., directional leads, multiple independent current sources) lead to increasing DBS-optimization burden. Techniques to streamline and facilitate programming could leverage these innovations. OBJECTIVE We evaluated clinical effectiveness of algorithm-guided DBS-programming based on wearable-sensor-feedback compared to standard-of-care DBS-settings in a prospective, randomized, crossover, double-blind study in two German DBS centers. METHODS For 23 Parkinson's disease patients with clinically effective DBS, new algorithm-guided DBS-settings were determined and compared to previously established standard-of-care DBS-settings using UPDRS-III and motion-sensor-assessment. Clinical and imaging data with lead-localizations were analyzed to evaluate characteristics of algorithm-derived programming compared to standard-of-care. Six different versions of the algorithm were evaluated during the study and 10 subjects programmed with uniform algorithm-version were analyzed as a subgroup. RESULTS Algorithm-guided and standard-of-care DBS-settings effectively reduced motor symptoms compared to off-stimulation-state. UPDRS-III scores were reduced significantly more with standard-of-care settings as compared to algorithm-guided programming with heterogenous algorithm versions in the entire cohort. A subgroup with the latest algorithm version showed no significant differences in UPDRS-III achieved by the two programming-methods. Comparing active contacts in standard-of-care and algorithm-guided DBS-settings, contacts in the latter had larger location variability and were farther away from a literature-based optimal stimulation target. CONCLUSION Algorithm-guided programming may be a reasonable approach to replace monopolar review, enable less trained health-professionals to achieve satisfactory DBS-programming results, or potentially reduce time needed for programming. Larger studies and further improvements of algorithm-guided programming are needed to confirm these results.
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Affiliation(s)
- Gregor R Wenzel
- Department of Neurology, Movement disorders & Neuromodulation section, Charité -University Medicine Berlin, Germany
| | - Jan Roediger
- Department of Neurology, Movement disorders & Neuromodulation section, Charité -University Medicine Berlin, Germany.,Einstein Center for Neurosciences Berlin, Charité -University Medicine Berlin, Germany
| | - Christof Brücke
- Department of Neurology, Movement disorders & Neuromodulation section, Charité -University Medicine Berlin, Germany
| | - Ana Luísa de A Marcelino
- Department of Neurology, Movement disorders & Neuromodulation section, Charité -University Medicine Berlin, Germany
| | - Eileen Gülke
- Department of Neurology, Universitätsklinikum Hamburg-Eppendorf, Germany
| | | | | | | | | | - Andrea A Kühn
- Department of Neurology, Movement disorders & Neuromodulation section, Charité -University Medicine Berlin, Germany
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11
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Connolly MJ, Cole ER, Isbaine F, de Hemptinne C, Starr PA, Willie JT, Gross RE, Miocinovic S. Multi-objective data-driven optimization for improving deep brain stimulation in Parkinson's disease. J Neural Eng 2021; 18. [PMID: 33862604 DOI: 10.1088/1741-2552/abf8ca] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 04/16/2021] [Indexed: 11/12/2022]
Abstract
Objective.Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease (PD) but its success depends on a time-consuming process of trial-and-error to identify the optimal stimulation settings for each individual patient. Data-driven optimization algorithms have been proposed to efficiently find the stimulation setting that maximizes a quantitative biomarker of symptom relief. However, these algorithms cannot efficiently take into account stimulation settings that may control symptoms but also cause side effects. Here we demonstrate how multi-objective data-driven optimization can be used to find the optimal trade-off between maximizing symptom relief and minimizing side effects.Approach.Cortical and motor evoked potential data collected from PD patients during intraoperative stimulation of the subthalamic nucleus were used to construct a framework for designing and prototyping data-driven multi-objective optimization algorithms. Using this framework, we explored how these techniques can be applied clinically, and characterized the design features critical for solving this optimization problem. Our two optimization objectives were to maximize cortical evoked potentials, a putative biomarker of therapeutic benefit, and to minimize motor potentials, a biomarker of motor side effects.Main Results.Using thisin silicodesign framework, we demonstrated how the optimal trade-off between two objectives can substantially reduce the stimulation parameter space by 61 ± 19%. The best algorithm for identifying the optimal trade-off between the two objectives was a Bayesian optimization approach with an area under the receiver operating characteristic curve of up to 0.94 ± 0.02, which was possible with the use of a surrogate model and a well-tuned acquisition function to efficiently select which stimulation settings to sample.Significance.These findings show that multi-objective optimization is a promising approach for identifying the optimal trade-off between symptom relief and side effects in DBS. Moreover, these approaches can be readily extended to newly discovered biomarkers, adapted to DBS for disorders beyond PD, and can scale with the development of more complex DBS devices.
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Affiliation(s)
- Mark J Connolly
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, United States of America
| | - Eric R Cole
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, United States of America
| | - Faical Isbaine
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA 30322, United States of America
| | - Coralie de Hemptinne
- Department of Neurology, University of Florida, Gainesville, FL 32608, United States of America
| | - Phillip A Starr
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, United States of America
| | - Jon T Willie
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Robert E Gross
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, United States of America.,Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA 30322, United States of America.,Department of Neurology, Emory University School of Medicine, 12 Executive Park Drive North East, Atlanta, GA 30322, United States of America
| | - Svjetlana Miocinovic
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, United States of America.,Department of Neurology, Emory University School of Medicine, 12 Executive Park Drive North East, Atlanta, GA 30322, United States of America
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12
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He S, Baig F, Mostofi A, Pogosyan A, Debarros J, Green AL, Aziz TZ, Pereira E, Brown P, Tan H. Closed-Loop Deep Brain Stimulation for Essential Tremor Based on Thalamic Local Field Potentials. Mov Disord 2021; 36:863-873. [PMID: 33547859 PMCID: PMC7610625 DOI: 10.1002/mds.28513] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 12/07/2020] [Accepted: 01/06/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND High-frequency thalamic stimulation is an effective therapy for essential tremor, which mainly affects voluntary movements and/or sustained postures. However, continuous stimulation may deliver unnecessary current to the brain due to the intermittent nature of the tremor. OBJECTIVE We proposed to close the loop of thalamic stimulation by detecting tremor-provoking movement states using local field potentials recorded from the same electrodes implanted for stimulation, so that the stimulation is only delivered when necessary. METHODS Eight patients with essential tremor participated in this study. Patient-specific support vector machine classifiers were first trained using data recorded while the patient performed tremor-provoking movements. Then, the trained models were applied in real-time to detect these movements and triggered the delivery of stimulation. RESULTS Using the proposed method, stimulation was switched on for 80.37 ± 7.06% of the time when tremor-evoking movements were present. In comparison, the stimulation was switched on for 12.71 ± 7.06% of the time when the patients were at rest and tremor-free. Compared with continuous stimulation, a similar amount of tremor suppression was achieved while only delivering 36.62 ± 13.49% of the energy used in continuous stimulation. CONCLUSIONS The results suggest that responsive thalamic stimulation for essential tremor based on tremor-provoking movement detection can be achieved without any requirement for external sensors or additional electrocorticography strips. Further research is required to investigate whether the decoding model is stable across time and generalizable to the variety of activities patients may engage with in everyday life. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Shenghong He
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Fahd Baig
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.,Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St. George's, University of London, Oxford, UK
| | - Abteen Mostofi
- Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St. George's, University of London, Oxford, UK
| | - Alek Pogosyan
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Jean Debarros
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Alexander L Green
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Tipu Z Aziz
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Erlick Pereira
- Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St. George's, University of London, Oxford, UK
| | - Peter Brown
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Huiling Tan
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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13
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Folador JP, Vieira MF, Pereira AA, Andrade ADO. Open-source data management system for Parkinson's disease follow-up. PeerJ Comput Sci 2021; 7:e396. [PMID: 33817042 PMCID: PMC7959639 DOI: 10.7717/peerj-cs.396] [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: 11/05/2020] [Accepted: 01/26/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Parkinson's disease (PD) is a neurodegenerative condition of the central nervous system that causes motor and non-motor dysfunctions. The disease affects 1% of the world population over 60 years and remains cureless. Knowledge and monitoring of PD are essential to provide better living conditions for patients. Thus, diagnostic exams and monitoring of the disease can generate a large amount of data from a given patient. This study proposes the development and usability evaluation of an integrated system, which can be used in clinical and research settings to manage biomedical data collected from PD patients. METHODS A system, so-called Sistema Integrado de Dados Biomédicos (SIDABI) (Integrated Biomedical Data System), was designed following the model-view-controller (MVC) standard. A modularized architecture was created in which all the other modules are connected to a central security module. Thirty-six examiners evaluated the system usability through the System Usability Scale (SUS). The agreement between examiners was measured by Kendall's coefficient with a significance level of 1%. RESULTS The free and open-source web-based system was implemented using modularized and responsive methods to adapt the system features on multiple platforms. The mean SUS score was 82.99 ± 13.97 points. The overall agreement was 70.2%, as measured by Kendall's coefficient (p < 0.001). CONCLUSION According to the SUS scores, the developed system has good usability. The system proposed here can help researchers to organize and share information, avoiding data loss and fragmentation. Furthermore, it can help in the follow-up of PD patients, in the training of professionals involved in the treatment of the disorder, and in studies that aim to find hidden correlations in data.
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Affiliation(s)
- João Paulo Folador
- Centre for Innovation and Technology Assessment in Health, Postgraduate Program in Electrical and Biomedical Engineering, Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia, Minas Gerais, Brazil
| | - Marcus Fraga Vieira
- Bioengineering and Biomechanics Laboratory, Federal University of Goiás, Goiânia, Goiás, Brazil
| | - Adriano Alves Pereira
- Centre for Innovation and Technology Assessment in Health, Postgraduate Program in Electrical and Biomedical Engineering, Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia, Minas Gerais, Brazil
| | - Adriano de Oliveira Andrade
- Centre for Innovation and Technology Assessment in Health, Postgraduate Program in Electrical and Biomedical Engineering, Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia, Minas Gerais, Brazil
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14
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He S, Mostofi A, Syed E, Torrecillos F, Tinkhauser G, Fischer P, Pogosyan A, Hasegawa H, Li Y, Ashkan K, Pereira E, Brown P, Tan H. Subthalamic beta-targeted neurofeedback speeds up movement initiation but increases tremor in Parkinsonian patients. eLife 2020; 9:e60979. [PMID: 33205752 PMCID: PMC7695453 DOI: 10.7554/elife.60979] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Accepted: 11/16/2020] [Indexed: 12/17/2022] Open
Abstract
Previous studies have explored neurofeedback training for Parkinsonian patients to suppress beta oscillations in the subthalamic nucleus (STN). However, its impacts on movements and Parkinsonian tremor are unclear. We developed a neurofeedback paradigm targeting STN beta bursts and investigated whether neurofeedback training could improve motor initiation in Parkinson's disease compared to passive observation. Our task additionally allowed us to test which endogenous changes in oscillatory STN activities are associated with trial-to-trial motor performance. Neurofeedback training reduced beta synchrony and increased gamma activity within the STN, and reduced beta band coupling between the STN and motor cortex. These changes were accompanied by reduced reaction times in subsequently cued movements. However, in Parkinsonian patients with pre-existing symptoms of tremor, successful volitional beta suppression was associated with an amplification of tremor which correlated with theta band activity in STN local field potentials, suggesting an additional cross-frequency interaction between STN beta and theta activities.
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Affiliation(s)
- Shenghong He
- MRC Brain Network Dynamics Unit at the University of OxfordOxfordUnited Kingdom
- Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Abteen Mostofi
- Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St George’s University of LondonLondonUnited Kingdom
| | - Emilie Syed
- MRC Brain Network Dynamics Unit at the University of OxfordOxfordUnited Kingdom
| | - Flavie Torrecillos
- MRC Brain Network Dynamics Unit at the University of OxfordOxfordUnited Kingdom
- Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Gerd Tinkhauser
- MRC Brain Network Dynamics Unit at the University of OxfordOxfordUnited Kingdom
- Department of Neurology, Bern University Hospital and University of BernBernSwitzerland
| | - Petra Fischer
- MRC Brain Network Dynamics Unit at the University of OxfordOxfordUnited Kingdom
- Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Alek Pogosyan
- MRC Brain Network Dynamics Unit at the University of OxfordOxfordUnited Kingdom
- Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Harutomo Hasegawa
- Department of Neurosurgery, King's College Hospital NHS Foundation Trust, King's Health PartnersLondonUnited Kingdom
| | - Yuanqing Li
- School of Automation Science and Engineering, South China University of TechnologyGuangzhouChina
| | - Keyoumars Ashkan
- Department of Neurosurgery, King's College Hospital NHS Foundation Trust, King's Health PartnersLondonUnited Kingdom
| | - Erlick Pereira
- Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St George’s University of LondonLondonUnited Kingdom
| | - Peter Brown
- MRC Brain Network Dynamics Unit at the University of OxfordOxfordUnited Kingdom
- Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Huiling Tan
- MRC Brain Network Dynamics Unit at the University of OxfordOxfordUnited Kingdom
- Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
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15
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Castaño-Candamil S, Ferleger BI, Haddock A, Cooper SS, Herron J, Ko A, Chizeck HJ, Tangermann M. A Pilot Study on Data-Driven Adaptive Deep Brain Stimulation in Chronically Implanted Essential Tremor Patients. Front Hum Neurosci 2020; 14:541625. [PMID: 33250727 PMCID: PMC7674800 DOI: 10.3389/fnhum.2020.541625] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 10/15/2020] [Indexed: 11/13/2022] Open
Abstract
Deep brain stimulation (DBS) is an established therapy for Parkinson's disease (PD) and essential-tremor (ET). In adaptive DBS (aDBS) systems, online tuning of stimulation parameters as a function of neural signals may improve treatment efficacy and reduce side-effects. State-of-the-art aDBS systems use symptom surrogates derived from neural signals-so-called neural markers (NMs)-defined on the patient-group level, and control strategies assuming stationarity of symptoms and NMs. We aim at improving these aDBS systems with (1) a data-driven approach for identifying patient- and session-specific NMs and (2) a control strategy coping with short-term non-stationary dynamics. The two building blocks are implemented as follows: (1) The data-driven NMs are based on a machine learning model estimating tremor intensity from electrocorticographic signals. (2) The control strategy accounts for local variability of tremor statistics. Our study with three chronically implanted ET patients amounted to five online sessions. Tremor quantified from accelerometer data shows that symptom suppression is at least equivalent to that of a continuous DBS strategy in 3 out-of 4 online tests, while considerably reducing net stimulation (at least 24%). In the remaining online test, symptom suppression was not significantly different from either the continuous strategy or the no treatment condition. We introduce a novel aDBS system for ET. It is the first aDBS system based on (1) a machine learning model to identify session-specific NMs, and (2) a control strategy coping with short-term non-stationary dynamics. We show the suitability of our aDBS approach for ET, which opens the door to its further study in a larger patient population.
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Affiliation(s)
- Sebastián Castaño-Candamil
- Brain State Decoding Lab, Department of Computer Science, BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg im Breisgau, Germany
| | - Benjamin I Ferleger
- BioRobotics Lab, Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Andrew Haddock
- BioRobotics Lab, Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Sarah S Cooper
- BioRobotics Lab, Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Jeffrey Herron
- Department of Neurological Surgery, University of Washington Medical Center, Seattle, WA, United States
| | - Andrew Ko
- Department of Neurological Surgery, University of Washington Medical Center, Seattle, WA, United States
| | - Howard J Chizeck
- BioRobotics Lab, Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Michael Tangermann
- Brain State Decoding Lab, Department of Computer Science, BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg im Breisgau, Germany.,Autonomous Intelligent Systems, Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany.,Artificial Cognitive Systems Lab, Artificial Intelligence Department, Faculty of Social Sciences, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
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16
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Wang KL, Ren Q, Chiu S, Patel B, Meng FG, Hu W, Shukla AW. Deep brain stimulation and other surgical modalities for the management of essential tremor. Expert Rev Med Devices 2020; 17:817-833. [PMID: 33081571 DOI: 10.1080/17434440.2020.1806709] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
INTRODUCTION Surgical treatments are considered for essential tremor (ET) when patients do not respond to oral pharmacological therapies. These treatments mainly comprise radiofrequency (RF) thalamotomy, gamma knife radiosurgery (GKRS), deep brain stimulation (DBS), and focused ultrasound (FUS) procedures. AREAS COVERED We reviewed the strengths and weaknesses of each procedure and clinical outcomes for 7 RF studies (n = 85), 11 GKRS (n = 477), 33 DBS (n = 1061), and 13 FUS studies (n = 368). A formal comparison was not possible given the heterogeneity in studies. Improvements were about 42%-90% RF, 10%-79% GKRS, 45%-83% DBS, 42%-83% FUS at short-term follow-up (<12 months) and were about 54%-82% RF, 11%-84% GKRS, 18%-92% DBS, and 42%-80% FUS at long-term follow-up (>12 months). EXPERT OPINION We found DBS with inherent advantages of being an adjustable and reversible procedure as the most frequently employed surgical procedure for control of ET symptoms. FUS is a promising procedure but has limited applicability for unilateral control of symptoms. RF is invasive, and GKRS has unpredictable delayed effects. Each of these surgical modalities has advantages and limitations that need consideration when selecting a treatment for the ET patients.
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Affiliation(s)
- Kai-Liang Wang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University , Beijing, China.,Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University , Beijing, China
| | - Qianwei Ren
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University , Beijing, China
| | - Shannon Chiu
- Department of Neurology, University of Florida College of Medicine , Gainesville, FL, USA
| | - Bhavana Patel
- Department of Neurology, University of Florida College of Medicine , Gainesville, FL, USA
| | - Fan-Gang Meng
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University , Beijing, China
| | - Wei Hu
- Department of Neurology, University of Florida College of Medicine , Gainesville, FL, USA
| | - Aparna Wagle Shukla
- Department of Neurology, University of Florida College of Medicine , Gainesville, FL, USA
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17
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Ferleger BI, Sonnet KS, Morriss TH, Ko AL, Chizeck HJ, Herron JA. A tablet- and mobile-based application for remote diagnosis and analysis of movement disorder symptoms. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:5588-5591. [PMID: 33019244 DOI: 10.1109/embc44109.2020.9176044] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
One significant hindrance to effective diagnosis of movement disorders (MDs) and analysis of their progression is the requirement for patients to conduct tests in the presence of a clinician. Here is presented a pilot study for diagnosis of essential tremor (ET), the world's most common MD, through analysis of a tablet- or mobile-based drawing task that may be selected at will, with the spiral- and line-drawing tasks of the Fahn-Tolosa-Marin tremor rating scale serving as our task in this work. This system replaces the need for pen-and-paper drawing tests while permitting advanced quantitative analysis of drawing smoothness, pressure applied, and other measures. Data is securely recorded and stored in the cloud, from which all analysis was conducted remotely. This will enable longitudinal analysis of patient disease progression without the need for excessive clinical visits. Several features were extracted and recursive feature elimination applied to rank the features' individual contribution to our classifier. Maximum cross-validated classification accuracy on a preliminary sample set was 98.3%. Future work will involve collecting healthy subject data from an age-controlled population and extending this diagnostic application to additional conditions, as well as incorporating regression-based symptom severity analysis. This highly promising new technology has the potential to substantially alleviate the demands placed on both clinicians and patients by bringing MD treatment more into line with the era of personalized medicine.
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18
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Huo W, Angeles P, Tai YF, Pavese N, Wilson S, Hu MT, Vaidyanathan R. A Heterogeneous Sensing Suite for Multisymptom Quantification of Parkinson's Disease. IEEE Trans Neural Syst Rehabil Eng 2020; 28:1397-1406. [PMID: 32305925 DOI: 10.1109/tnsre.2020.2978197] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disease affecting millions worldwide. Bespoke subject-specific treatment (medication or deep brain stimulation (DBS)) is critical for management, yet depends on precise assessment cardinal PD symptoms - bradykinesia, rigidity and tremor. Clinician diagnosis is the basis of treatment, yet it allows only a cross-sectional assessment of symptoms which can vary on an hourly basis and is liable to inter- and intra-rater subjectivity across human examiners. Automated symptomatic assessment has attracted significant interest to optimise treatment regimens between clinician visits, however, no wearable has the capacity to simultaneously assess all three cardinal symptoms. Challenges in the measurement of rigidity, mapping muscle activity out-of-clinic and sensor fusion have inhibited translation. In this study, we address all through a novel wearable sensor system and machine learning algorithms. The sensor system is composed of a force-sensor, three inertial measurement units (IMUs) and four custom mechanomyography (MMG) sensors. The system was tested in its capacity to predict Unified Parkinson's Disease Rating Scale (UPDRS) scores based on quantitative assessment of bradykinesia, rigidity and tremor in PD patients. 23 PD patients were tested with the sensor system in parallel with exams conducted by treating clinicians and 10 healthy subjects were recruited as a comparison control group. Results prove the system accurately predicts UPDRS scores for all symptoms (85.4% match on average with physician assessment) and discriminates between healthy subjects and PD patients (96.6% on average). MMG features can also be used for remote monitoring of severity and fluctuations in PD symptoms out-of-clinic. This closed-loop feedback system enables individually tailored and regularly updated treatment, facilitating better outcomes for a very large patient population.
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19
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Misra S, Islam N, Pal R. Long-Term Alleviation of Parkinsonian Resting Tremor Using Wireless Optogenetic Nanonetworks. IEEE Trans Nanobioscience 2020; 19:403-409. [PMID: 32167904 DOI: 10.1109/tnb.2020.2979781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Resting tremor is one of the major symptoms of Parkinson's disease, which causes havoc in motor functions of the body, has its genesis in communication impairments in the subthalamic nucleus of the basal ganglia. The modern sophisticated surgical treatments, including electrical deep brain stimulation do not yield satisfactory results due to their inability to provide long-term cure and minimize side effects, such as discomfort and increased infection rates. In this work, we propose a novel system based on the emerging communication technology of wireless optogenetic networks of neural dusts to provide a long-term solution for the alleviation of resting tremor. Interfaced with neural dusts, each of the subthalamic nucleus neurons can be controlled and stimulated by the ultrasonic waves which are transmitted from a single/multiple subdural transducer(s) that are placed in the dura mater of the brain. Moreover, in order to address the challenging tasks of charging and addressing each of the neural dusts, we propose a protocol, named as Single Time Instant addressing Protocol, which outperforms the state-of-the-art parallel charging protocol. The basic idea of our protocol is that it selects most frequently occurring spike patterns in a single time instant and assigns the pattern with an ultrasonic frequency. With the improved efficiency of Single Time Instant addressing Protocol validated with empirical datasets, the proposed system is expected to revolutionize the way of treatment of parkinsonian resting tremor.
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20
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Abstract
Surgery in Parkinson disease is effective for a select group of patients when optimal medical management is not sufficient. Functional neurosurgery can be used as either a salvage therapy in patients with disabling symptoms or to maintain quality of life and independence before progression to severe disability in high-functioning patients. With recent technological advancements in imaging and targeting as well as novel neuromodulation paradigms, there are numerous options for targeted brain lesions and deep brain stimulation. Surgical decision making and postoperative management in Parkinson disease therefore often requires a multidisciplinary team effort with neurology, neurosurgery, neuropsychology, and psychiatry.
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Affiliation(s)
- Kyle T Mitchell
- Duke University Movement Disorders Center, DUMC 3333, 932 Morreene Road, Durham, NC 27705, USA.
| | - Jill L Ostrem
- UCSF Movement Disorders and Neuromodulation Center, 1635 Divisadero Street Suite 520, Box 1838, San Francisco, CA 94115, USA
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21
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Local Field Potentials and ECoG. Stereotact Funct Neurosurg 2020. [DOI: 10.1007/978-3-030-34906-6_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Sarikhani P, Miocinovic S, Mahmoudi B. Towards automated patient-specific optimization of deep brain stimulation for movement disorders. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2019:6159-6162. [PMID: 31947249 DOI: 10.1109/embc.2019.8857736] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
In this paper we present a simulation framework for automated optimization of deep brain stimulation (DBS) parameters based on the hand kinematics signal as the feedback signal, in patients with essential tremor. We used Gaussian Process regression (GPR) models to develop patient-specific models for predicting the effect of DBS on the hand kinematics using the clinical data that was recorded during DBS programming. In this framework, we characterized the performance of a Bayesian Optimization method to identify the optimal DBS parameters that maximized the clinical efficacy. Our results demonstrate the feasibility of using black-box optimization methods for automated identification of optimal DBS parameters in clinical settings.
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