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Bougea A. Digital biomarkers in Parkinson's disease. Adv Clin Chem 2024; 123:221-253. [PMID: 39181623 DOI: 10.1016/bs.acc.2024.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Digital biomarker (DB) assessments provide objective measures of daily life tasks and thus hold promise to improve diagnosis and monitoring of Parkinson's disease (PD) patients especially those with advanced stages. Data from DB studies can be used in advanced analytics such as Artificial Intelligence and Machine Learning to improve monitoring, treatment and outcomes. Although early development of inertial sensors as accelerometers and gyroscopes in smartphones provided encouraging results, the use of DB remains limited due to lack of standards, harmonization and consensus for analytical as well as clinical validation. Accordingly, a number of clinical trials have been developed to evaluate the performance of DB vs traditional assessment tools with the goal of monitoring disease progression, improving quality of life and outcomes. Herein, we update current evidence on the use of DB in PD and highlight potential benefits and limitations and provide suggestions for future research study.
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Affiliation(s)
- Anastasia Bougea
- Department of Neurology, Medical School, Aeginition Hospital, National and Kapodistrian University of Athens, Athens, Greece.
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Moreau C, Rouaud T, Grabli D, Benatru I, Remy P, Marques AR, Drapier S, Mariani LL, Roze E, Devos D, Dupont G, Bereau M, Fabbri M. Overview on wearable sensors for the management of Parkinson's disease. NPJ Parkinsons Dis 2023; 9:153. [PMID: 37919332 PMCID: PMC10622581 DOI: 10.1038/s41531-023-00585-y] [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: 05/25/2023] [Accepted: 10/02/2023] [Indexed: 11/04/2023] Open
Abstract
Parkinson's disease (PD) is affecting about 1.2 million patients in Europe with a prevalence that is expected to have an exponential increment, in the next decades. This epidemiological evolution will be challenged by the low number of neurologists able to deliver expert care for PD. As PD is better recognized, there is an increasing demand from patients for rigorous control of their symptoms and for therapeutic education. In addition, the highly variable nature of symtoms between patients and the fluctuations within the same patient requires innovative tools to help doctors and patients monitor the disease in their usual living environment and adapt treatment in a more relevant way. Nowadays, there are various body-worn sensors (BWS) proposed to monitor parkinsonian clinical features, such as motor fluctuations, dyskinesia, tremor, bradykinesia, freezing of gait (FoG) or gait disturbances. BWS have been used as add-on tool for patients' management or research purpose. Here, we propose a practical anthology, summarizing the characteristics of the most used BWS for PD patients in Europe, focusing on their role as tools to improve treatment management. Consideration regarding the use of technology to monitor non-motor features is also included. BWS obviously offer new opportunities for improving management strategy in PD but their precise scope of use in daily routine care should be clarified.
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Affiliation(s)
- Caroline Moreau
- Department of Neurology, Parkinson's disease expert Center, Lille University, INSERM UMRS_1172, University Hospital Center, Lille, France
- The French Ns-Park Network, Paris, France
| | - Tiphaine Rouaud
- The French Ns-Park Network, Paris, France
- CHU Nantes, Centre Expert Parkinson, Department of Neurology, Nantes, F-44093, France
| | - David Grabli
- The French Ns-Park Network, Paris, France
- Assistance Publique Hôpitaux de Paris, Department of Neurology, CIC Neurosciences, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Paris, France
| | - Isabelle Benatru
- The French Ns-Park Network, Paris, France
- Department of Neurology, University Hospital of Poitiers, Poitiers, France
- INSERM, CHU de Poitiers, University of Poitiers, Centre d'Investigation Clinique CIC1402, Poitiers, France
| | - Philippe Remy
- The French Ns-Park Network, Paris, France
- Centre Expert Parkinson, NS-Park/FCRIN Network, CHU Henri Mondor, AP-HP, Equipe NPI, IMRB, INSERM et Faculté de Santé UPE-C, Créteil, FranceService de neurologie, hôpital Henri-Mondor, AP-HP, Créteil, France
| | - Ana-Raquel Marques
- The French Ns-Park Network, Paris, France
- Université Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand University Hospital, Neurology department, Clermont-Ferrand, France
| | - Sophie Drapier
- The French Ns-Park Network, Paris, France
- Pontchaillou University Hospital, Department of Neurology, CIC INSERM 1414, Rennes, France
| | - Louise-Laure Mariani
- The French Ns-Park Network, Paris, France
- Assistance Publique Hôpitaux de Paris, Department of Neurology, CIC Neurosciences, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Paris, France
| | - Emmanuel Roze
- The French Ns-Park Network, Paris, France
- Assistance Publique Hôpitaux de Paris, Department of Neurology, CIC Neurosciences, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Paris, France
| | - David Devos
- The French Ns-Park Network, Paris, France
- Parkinson's Disease Centre of Excellence, Department of Medical Pharmacology, Univ. Lille, INSERM; CHU Lille, U1172 - Degenerative & Vascular Cognitive Disorders, LICEND, NS-Park Network, F-59000, Lille, France
| | - Gwendoline Dupont
- The French Ns-Park Network, Paris, France
- Centre hospitalier universitaire François Mitterrand, Département de Neurologie, Université de Bourgogne, Dijon, France
| | - Matthieu Bereau
- The French Ns-Park Network, Paris, France
- Service de neurologie, université de Franche-Comté, CHRU de Besançon, 25030, Besançon, France
| | - Margherita Fabbri
- The French Ns-Park Network, Paris, France.
- Department of Neurosciences, Clinical Investigation Center CIC 1436, Parkinson Toulouse Expert Centre, NS-Park/FCRIN Network and NeuroToul COEN Center, Toulouse University Hospital, INSERM, University of Toulouse 3, Toulouse, France.
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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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Duffley G, Szabo A, Lutz BJ, Mahoney-Rafferty EC, Hess CW, Ramirez-Zamora A, Zeilman P, Foote KD, Chiu S, Pourfar MH, Goas Cnp C, Wood JL, Haq IU, Siddiqui MS, Afshari M, Heiry M, Choi J, Volz M, Ostrem JL, San Luciano M, Niemann N, Billnitzer A, Savitt D, Tarakad A, Jimenez-Shahed J, Aquino CC, Okun MS, Butson CR. Interactive mobile application for Parkinson's disease deep brain stimulation (MAP DBS): An open-label, multicenter, randomized, controlled clinical trial. Parkinsonism Relat Disord 2023; 109:105346. [PMID: 36966051 PMCID: PMC11265292 DOI: 10.1016/j.parkreldis.2023.105346] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 02/20/2023] [Accepted: 02/23/2023] [Indexed: 03/17/2023]
Abstract
INTRODUCTION Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease (PD), but its efficacy is tied to DBS programming, which is often time consuming and burdensome for patients, caregivers, and clinicians. Our aim is to test whether the Mobile Application for PD DBS (MAP DBS), a clinical decision support system, can improve programming. METHODS We conducted an open-label, 1:1 randomized, controlled, multicenter clinical trial comparing six months of SOC standard of care (SOC) to six months of MAP DBS-aided programming. We enrolled patients between 30 and 80 years old who received DBS to treat idiopathic PD at six expert centers across the United States. The primary outcome was time spent DBS programming and secondary outcomes measured changes in motor symptoms, caregiver strain and medication requirements. RESULTS We found a significant reduction in initial visit time (SOC: 43.8 ± 28.9 min n = 37, MAP DBS: 27.4 ± 13.0 min n = 35, p = 0.001). We did not find a significant difference in total programming time between the groups over the 6-month study duration. MAP DBS-aided patients experienced a significantly larger reduction in UPDRS III on-medication scores (-7.0 ± 7.9) compared to SOC (-2.7 ± 6.9, p = 0.01) at six months. CONCLUSION MAP DBS was well tolerated and improves key aspects of DBS programming time and clinical efficacy.
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Affiliation(s)
- Gordon Duffley
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Aniko Szabo
- Division of Biostatistics, Institute for Health & Equity, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Barbara J Lutz
- School of Nursing, University of North Carolina-Wilmington, Wilmington, NC, USA
| | - Emily C Mahoney-Rafferty
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Christopher W Hess
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Adolfo Ramirez-Zamora
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Pamela Zeilman
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Kelly D Foote
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Shannon Chiu
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Michael H Pourfar
- Center for Neuromodulation, New York University Langone Medical Center, New York, NY, USA
| | - Clarisse Goas Cnp
- Department of Neurology, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Jennifer L Wood
- Department of Neurology, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Ihtsham U Haq
- Department of Neurology, University of Miami, Miami, FL, USA
| | - Mustafa S Siddiqui
- Department of Neurology, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Mitra Afshari
- Department of Neurological Sciences, Section of Movement Disorders, Rush University, Chicago, IL, USA
| | - Melissa Heiry
- Weill Institute of Neurosciences, UCSF Movement Disorder and Neuromodulation Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Jennifer Choi
- Weill Institute of Neurosciences, UCSF Movement Disorder and Neuromodulation Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Monica Volz
- Weill Institute of Neurosciences, UCSF Movement Disorder and Neuromodulation Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Jill L Ostrem
- Weill Institute of Neurosciences, UCSF Movement Disorder and Neuromodulation Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Marta San Luciano
- Weill Institute of Neurosciences, UCSF Movement Disorder and Neuromodulation Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Nicki Niemann
- Department of Neurology, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Andrew Billnitzer
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Daniel Savitt
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Arjun Tarakad
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Joohi Jimenez-Shahed
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Camila C Aquino
- Department of Neurology, University of Utah, Salt Lake City, UT, USA; Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Michael S Okun
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Christopher R Butson
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA; Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA; Department of Neurology, University of Utah, Salt Lake City, UT, USA; Departments of Neurosurgery, and Psychiatry, University of Utah, Salt Lake City, UT, USA.
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Bremm RP, Berthold C, Krüger R, Koch KP, Gonçalves J, Hertel F. Therapeutic maps for a sensor-based evaluation of deep brain stimulation programming. BIOMED ENG-BIOMED TE 2021; 66:603-611. [PMID: 34727584 DOI: 10.1515/bmt-2020-0210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 10/01/2021] [Indexed: 11/15/2022]
Abstract
Programming in deep brain stimulation (DBS) is a labour-intensive process for treating advanced motor symptoms. Specifically for patients with medication-refractory tremor in multiple sclerosis (MS). Wearable sensors are able to detect some manifestations of pathological signs, such as intention tremor in MS. However, methods are needed to visualise the response of tremor to DBS parameter changes in a clinical setting while patients perform the motor task finger-to-nose. To this end, we attended DBS programming sessions of a MS patient and intention tremor was effectively quantified by acceleration amplitude and frequency. A new method is introduced which results in the generation of therapeutic maps for a systematic review of the programming procedure in DBS. The maps visualise the combination of tremor acceleration power, clinical rating scores, total electrical energy delivered to the brain and possible side effects. Therapeutic maps have not yet been employed and could lead to a certain degree of standardisation for more objective decisions about DBS settings. The maps provide a base for future research on visualisation tools to assist physicians who frequently encounter patients for DBS therapy.
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Affiliation(s)
- Rene Peter Bremm
- National Department of Neurosurgery, Centre Hospitalier de Luxembourg, Luxembourg (City), Luxembourg
- Interventional Neuroscience, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Christophe Berthold
- National Department of Neurosurgery, Centre Hospitalier de Luxembourg, Luxembourg (City), Luxembourg
| | - Rejko Krüger
- Translational Neuroscience, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Klaus Peter Koch
- Department of Electrical Engineering, Trier University of Applied Sciences, Trier, Germany
| | - Jorge Gonçalves
- Systems Control, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Frank Hertel
- National Department of Neurosurgery, Centre Hospitalier de Luxembourg, Luxembourg (City), Luxembourg
- Interventional Neuroscience, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
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Duffley G, Lutz BJ, Szabo A, Wright A, Hess CW, Ramirez-Zamora A, Zeilman P, Chiu S, Foote KD, Okun MS, Butson CR. Home Health Management of Parkinson Disease Deep Brain Stimulation: A Randomized Clinical Trial. JAMA Neurol 2021; 78:972-981. [PMID: 34180949 DOI: 10.1001/jamaneurol.2021.1910] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Importance The travel required to receive deep brain stimulation (DBS) programming causes substantial burden on patients and limits who can access DBS therapy. Objective To evaluate the efficacy of home health DBS postoperative management in an effort to reduce travel burden and improve access. Design, Settings, and Participants This open-label randomized clinical trial was conducted at University of Florida Health from November 2017 to April 2020. Eligible participants had a diagnosis of Parkinson disease (PD) and were scheduled to receive DBS independently of the study. Consenting participants were randomized 1:1 to receive either standard of care or home health postoperative DBS management for 6 months after surgery. Primary caregivers, usually spouses, were also enrolled to assess caregiver strain. Interventions The home health postoperative management was conducted by a home health nurse who chose DBS settings with the aid of the iPad-based Mobile Application for PD DBS system. Prior to the study, the home health nurse had no experience providing DBS care. Main Outcomes and Measures The primary outcome was the number of times each patient traveled to the movement disorders clinic during the study period. Secondary outcomes included changes from baseline on the Unified Parkinson's Disease Rating Scale part III. Results Approximately 75 patients per year were scheduled for DBS. Of the patients who met inclusion criteria over the entire study duration, 45 either declined or were excluded for various reasons. Of the 44 patients enrolled, 19 of 21 randomized patients receiving the standard of care (mean [SD] age, 64.1 [10.0] years; 11 men) and 23 of 23 randomized patients receiving home health who underwent a minimum of 1 postoperative management visit (mean [SD] age, 65.0 [10.9] years; 13 men) were included in analysis. The primary outcome revealed that patients randomized to home health had significantly fewer clinic visits than the patients in the standard of care arm (mean [SD], 0.4 [0.8] visits vs 4.8 [0.4] visits; P < .001). We found no significant differences between the groups in the secondary outcomes measuring the efficacy of DBS. No adverse events occurred in association with the study procedure or devices. Conclusions and Relevance This study provides evidence supporting the safety and feasibility of postoperative home health DBS management. Trial Registration ClinicalTrials.gov Identifier: NCT02474459.
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Affiliation(s)
- Gordon Duffley
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City.,Department of Biomedical Engineering, University of Utah, Salt Lake City
| | - Barbara J Lutz
- School of Nursing, University of North Carolina-Wilmington, Wilmington
| | - Aniko Szabo
- Division of Biostatistics, Institute for Health & Equity, Medical College of Wisconsin, Milwaukee
| | - Adrienne Wright
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville
| | - Christopher W Hess
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville
| | - Adolfo Ramirez-Zamora
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville
| | - Pamela Zeilman
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville
| | - Shannon Chiu
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville
| | - Kelly D Foote
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville
| | - Michael S Okun
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville
| | - Christopher R Butson
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City.,Department of Biomedical Engineering, University of Utah, Salt Lake City.,Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville.,Departments of Neurology, Neurosurgery, and Psychiatry, University of Utah, Salt Lake City
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Closed-loop programming using external responses for deep brain stimulation in Parkinson's disease. Parkinsonism Relat Disord 2021; 84:47-51. [PMID: 33556765 DOI: 10.1016/j.parkreldis.2021.01.023] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 01/25/2021] [Accepted: 01/26/2021] [Indexed: 11/22/2022]
Abstract
INTRODUCTION Deep brain stimulation (DBS) is an established treatment for Parkinson's disease (PD). Clinicians face various challenges in adjusting stimulation parameters and configurations in clinical DBS settings owing to inexperience, time constraints, and recent advances in DBS technology that have expanded the number of possible contact configurations. We aimed to assess the efficacy of a closed-loop algorithm (CLA) for the DBS-programming method using external motion sensor-based motor assessments in patients with PD. METHODS In this randomized, double-blind, crossover study, we enrolled 12 patients who underwent eight-ring-contact DBS lead implantations bilaterally in the subthalamic nucleus. The DBS settings of the participants were programmed using a standard of care (SOC) and CLA method. The clinical effects of both programming methods were assessed in a randomized crossover fashion. The outcomes were evaluated using the Unified Parkinson's Disease Scale part III (UPDRS-III) and sensor-based scores for baseline (medication-off/stimulation-off) and both programming methods. The number of programming steps required for each programming method was also recorded. RESULTS The UPDRS-III scores and sensor-based scores were significantly improved by SOC and CLA settings compared to the baseline. No statistical difference was observed between SOC and CLA. The programming steps were significantly reduced in the CLA settings compared to those in the SOC. No serious adverse events were observed. CONCLUSION CLA can optimize DBS settings prospectively with similar therapeutic benefits as that of the SOC and reduce the number of programming steps. Automated optimization of DBS settings would reduce the burden of programming for both clinicians and patients.
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Rissanen SM, Koivu M, Hartikainen P, Pekkonen E. Ambulatory surface electromyography with accelerometry for evaluating daily motor fluctuations in Parkinson's disease. Clin Neurophysiol 2020; 132:469-479. [PMID: 33450567 DOI: 10.1016/j.clinph.2020.11.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 11/13/2020] [Accepted: 11/18/2020] [Indexed: 11/17/2022]
Abstract
OBJECTIVE To evaluate motor fluctuations in patients with advanced Parkinson's disease (PD) using a small-sized wearable device for surface electromyography (EMG) with accelerometry (ACC) for 24 hours. METHODS Seven PD patients with medication were measured once, and nine patients with directional deep brain stimulation (dDBS) twice: before and after the dDBS reprogramming. EMG and ACC parameters were compared with clinical rating scores and patients' home diaries. RESULTS The combination of EMG and ACC parameters (first principal component PC1) correlated significantly with patient's condition as quantified by the motor score of Unified Parkinson's Disease Rating Scale and it changed significantly with dDBS reprogramming in line with decreased PD symptoms. Monitoring data detected in comparison with the home diaries: 91 % concordance with tremor, 76 % with rigidity, and 74 % with dyskinesia. In the DBS group, the wake-up time with abnormal neuromuscular function was reduced with reprogramming in all except one patient based on measurements. CONCLUSIONS A wearable device measuring simultaneously both muscle activity and motion can provide continuous and dynamic information about patient's condition and motor fluctuations at home. SIGNIFICANCE The present method may help to modify pharmacologic management and DBS treatment in advanced PD.
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Affiliation(s)
- Saara M Rissanen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
| | - Maija Koivu
- Department of Neurology, Helsinki University Hospital and Department of Clinical Neurosciences (Neurology), University of Helsinki, Helsinki, Finland
| | - Päivi Hartikainen
- Neurology Outpatient Clinic, Kuopio University Hospital, Kuopio, Finland
| | - Eero Pekkonen
- Department of Neurology, Helsinki University Hospital and Department of Clinical Neurosciences (Neurology), University of Helsinki, Helsinki, Finland
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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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10
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Haddock A, Mitchell KT, Miller A, Ostrem JL, Chizeck HJ, Miocinovic S. Automated Deep Brain Stimulation Programming for Tremor. IEEE Trans Neural Syst Rehabil Eng 2018; 26:1618-1625. [PMID: 29994714 DOI: 10.1109/tnsre.2018.2852222] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Deep brain stimulation (DBS) programming, the systematic selection of fixed electrical stimulation parameters that deliver maximal therapeutic benefit while limiting side effects, poses several challenges in the treatment of movement disorders. DBS programming requires the expertise of trained neurologists or nurses who assess patient symptoms according to standardized clinical rating scales and use patient reports of DBS-related side effects to adjust stimulation parameters and optimize therapy. In this paper, we describe and validate an automated software platform for DBS programming for tremor associated with Parkinson's disease and essential tremor. DBS parameters are changed automatically through a direct computer interface with implanted neurostimulators. Each tested DBS setting is ranked according to its effect on tremor, which is assessed using smartwatch inertial measurement unit data, and side effects, which are reported through a user interface. Blinded neurologist assessments showed the automated programming method performed at least as well as clinician mediated programming in selecting the optimal settings for tremor therapy. This proof of concept study describes a novel DBS programming paradigm that may improve programming efficiency and outcomes, increase access to programming outside specialty clinics, and aid in the development of adaptive and closed-loop DBS strategies.
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11
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Quantification of Finger-Tapping Angle Based on Wearable Sensors. SENSORS 2017; 17:s17020203. [PMID: 28125051 PMCID: PMC5336005 DOI: 10.3390/s17020203] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2016] [Revised: 01/15/2017] [Accepted: 01/16/2017] [Indexed: 11/17/2022]
Abstract
We propose a novel simple method for quantitative and qualitative finger-tapping assessment based on miniature inertial sensors (3D gyroscopes) placed on the thumb and index-finger. We propose a simplified description of the finger tapping by using a single angle, describing rotation around a dominant axis. The method was verified on twelve subjects, who performed various tapping tasks, mimicking impaired patterns. The obtained tapping angles were compared with results of a motion capture camera system, demonstrating excellent accuracy. The root-mean-square (RMS) error between the two sets of data is, on average, below 4°, and the intraclass correlation coefficient is, on average, greater than 0.972. Data obtained by the proposed method may be used together with scores from clinical tests to enable a better diagnostic. Along with hardware simplicity, this makes the proposed method a promising candidate for use in clinical practice. Furthermore, our definition of the tapping angle can be applied to all tapping assessment systems.
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12
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Johnson LA, Nebeck SD, Muralidharan A, Johnson MD, Baker KB, Vitek JL. Closed-Loop Deep Brain Stimulation Effects on Parkinsonian Motor Symptoms in a Non-Human Primate - Is Beta Enough? Brain Stimul 2016; 9:892-896. [PMID: 27401045 PMCID: PMC5143196 DOI: 10.1016/j.brs.2016.06.051] [Citation(s) in RCA: 79] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Revised: 06/02/2016] [Accepted: 06/14/2016] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Incorporating feedback controls based on real-time measures of pathological brain activity may improve deep brain stimulation (DBS) approaches for the treatment of Parkinson's disease (PD). Excessive beta oscillations in subthalamic nucleus (STN) local field potentials (LFP) have been proposed as a potential biomarker for closed-loop DBS (CL-DBS). OBJECTIVE In a non-human primate PD model we compared CL-DBS, which delivered stimulation only when STN LFP beta activity was elevated, to traditional continuous DBS (tDBS). METHODS Therapeutic effects of CL-DBS and tDBS relative to the Off-DBS condition were evaluated via a clinical rating scale and objective measures of movement speed during a cued reaching task. RESULTS CL-DBS was comparable to tDBS at reducing rigidity, while reducing the amount of time DBS was on by ≈50%; however, only tDBS improved bradykinesia during the reaching behavior. This was likely due to reach-related reductions in beta amplitude that influence the timing and duration of stimulation in the CL-DBS condition. CONCLUSION These results illustrate the potential utility of closed-loop DBS devices for PD based on STN beta LFP levels. They also point to possible consequences in behavioral tasks when restricting real-time sensing to a single LFP frequency that itself is modulated during performance of such tasks. The present study provides data that suggest alternate algorithms or more than one physiological biomarker may be required to optimize the performance of behavioral tasks and demonstrates the value of using multiple objective measures when evaluating the efficacy of closed-loop DBS systems.
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Affiliation(s)
- Luke A. Johnson
- Department of Neurology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Shane D. Nebeck
- Department of Neurology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Abirami Muralidharan
- Department of Neurology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Matthew D. Johnson
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Kenneth B. Baker
- Department of Neurology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Jerrold L. Vitek
- Department of Neurology, University of Minnesota, Minneapolis, Minnesota, United States of America
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Shah A, Coste J, Lemaire JJ, Taub E, Schüpbach WMM, Pollo C, Schkommodau E, Guzman R, Hemm-Ode S. Intraoperative acceleration measurements to quantify improvement in tremor during deep brain stimulation surgery. Med Biol Eng Comput 2016; 55:845-858. [PMID: 27631560 DOI: 10.1007/s11517-016-1559-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2016] [Accepted: 08/08/2016] [Indexed: 11/25/2022]
Abstract
Deep brain stimulation (DBS) surgery is extensively used in the treatment of movement disorders. Nevertheless, methods to evaluate the clinical response during intraoperative stimulation tests to identify the optimal position for the implantation of the chronic DBS lead remain subjective. In this paper, we describe a new, versatile method for quantitative intraoperative evaluation of improvement in tremor with an acceleration sensor that is mounted on the patient's wrist during surgery. At each anatomical test position, the improvement in tremor compared to the initial tremor is estimated on the basis of extracted outcome measures. This method was tested on 15 tremor patients undergoing DBS surgery in two centers. Data from 359 stimulation tests were acquired. Our results suggest that accelerometric evaluation detects tremor changes more sensitively than subjective visual ratings. The effective stimulation current amplitudes identified from the quantitative data (1.1 ± 0.8 mA) are lower than those identified by visual evaluation (1.7 ± 0.8 mA) for similar improvement in tremor. Additionally, if these data had been used to choose the chronic implant position of the DBS lead, 15 of the 26 choices would have been different. These results show that our method of accelerometric evaluation can potentially improve DBS targeting.
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Affiliation(s)
- Ashesh Shah
- Institute for Medical and Analytical Technologies, University of Applied Sciences and Arts Northwestern Switzerland, Gruendenstrasse 40, 4132, Muttenz, Switzerland
| | - Jérôme Coste
- Image-Guided Clinical Neuroscience and Connectomics (EA 7282), Université Clermont Auvergne, Clermont-Ferrand, France.,Service de Neurochirurgie, CHU Clermont-Ferrand, Clermont-Ferrand, France
| | - Jean-Jacques Lemaire
- Image-Guided Clinical Neuroscience and Connectomics (EA 7282), Université Clermont Auvergne, Clermont-Ferrand, France.,Service de Neurochirurgie, CHU Clermont-Ferrand, Clermont-Ferrand, France
| | - Ethan Taub
- Departments of Neurosurgery and Biomedicine, University of Basel, Basel, Switzerland
| | - W M Michael Schüpbach
- Department of Neurology, University Hospital Bern and University of Bern, Bern, Switzerland.,Assistance Publique Hôpitaux de Paris, Institut National de Santé et en Recherche Médicale, Institut du Cerveau et de la Moelle Epinière, Centre d'Investigation Clinique 1422, Département de Neurologie, Hôpital Pitié-Salpêtrière, 75013, Paris, France
| | - Claudio Pollo
- Department of Neurosurgery, University Hospital Bern, Bern, Switzerland
| | - Erik Schkommodau
- Institute for Medical and Analytical Technologies, University of Applied Sciences and Arts Northwestern Switzerland, Gruendenstrasse 40, 4132, Muttenz, Switzerland
| | - Raphael Guzman
- Departments of Neurosurgery and Biomedicine, University of Basel, Basel, Switzerland
| | - Simone Hemm-Ode
- Institute for Medical and Analytical Technologies, University of Applied Sciences and Arts Northwestern Switzerland, Gruendenstrasse 40, 4132, Muttenz, Switzerland.
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14
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Delrobaei M, Tran S, Gilmore G, McIsaac K, Jog M. Characterization of multi-joint upper limb movements in a single task to assess bradykinesia. J Neurol Sci 2016; 368:337-42. [DOI: 10.1016/j.jns.2016.07.056] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2016] [Revised: 07/07/2016] [Accepted: 07/25/2016] [Indexed: 10/21/2022]
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15
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Haubenberger D, Abbruzzese G, Bain PG, Bajaj N, Benito-León J, Bhatia KP, Deuschl G, Forjaz MJ, Hallett M, Louis ED, Lyons KE, Mestre TA, Raethjen J, Stamelou M, Tan EK, Testa CM, Elble RJ. Transducer-based evaluation of tremor. Mov Disord 2016; 31:1327-36. [PMID: 27273470 DOI: 10.1002/mds.26671] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2015] [Revised: 04/04/2016] [Accepted: 04/07/2016] [Indexed: 11/11/2022] Open
Abstract
The International Parkinson and Movement Disorder Society established a task force on tremor that reviewed the use of transducer-based measures in the quantification and characterization of tremor. Studies of accelerometry, electromyography, activity monitoring, gyroscopy, digitizing tablet-based measures, vocal acoustic analysis, and several other transducer-based methods were identified by searching PubMed.gov. The availability, use, acceptability, reliability, validity, and responsiveness were reviewed for each measure using the following criteria: (1) used in the assessment of tremor; (2) used in published studies by people other than the developers; and (3) adequate clinimetric testing. Accelerometry, gyroscopy, electromyography, and digitizing tablet-based measures fulfilled all three criteria. Compared to rating scales, transducers are far more sensitive to changes in tremor amplitude and frequency, but they do not appear to be more capable of detecting a change that exceeds random variability in tremor amplitude (minimum detectable change). The use of transducer-based measures requires careful attention to their limitations and validity in a particular clinical or research setting. © 2016 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Dietrich Haubenberger
- Clinical Trials Unit, Office of the Clinical Director, National Institutes of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA.
| | | | - Peter G Bain
- Department of Neurology, Imperial College London, Charing Cross Hospital, London, United Kingdom
| | - Nin Bajaj
- Department of Neurology, Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom
| | - Julián Benito-León
- Department of Neurology, University Hospital "12 de Octubre", Madrid, Spain.,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain.,Department of Medicine, Complutense University, Madrid, Spain
| | - Kailash P Bhatia
- Sobell Department for Movement Neuroscience, UCL, Institute of Neurology, Queen Square, London, United Kingdom
| | - Günther Deuschl
- Department of Neurology, Christian-Albrechts-University Kiel, Kiel, Germany
| | - Maria João Forjaz
- National School of Public Health and Red de Investigación en Servicios Sanitarios y Enfermedades Crónicas (REDISSEC), Carlos III Institute of Health, Madrid, Spain
| | - Mark Hallett
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
| | - Elan D Louis
- Departments of Neurology and Chronic Disease Epidemiology, Yale School of Medicine and Yale School of Public Health, Yale University, New Haven, Connecticut, USA
| | - Kelly E Lyons
- University of Kansas Medical Center, Kansas City, Kansas
| | - Tiago A Mestre
- Parkinson's disease and Movement Disorders Center, Division of Neurology, Department of Medicine, University of Ottawa, The Ottawa Hospital Research Institute, Ottawa Brain and Mind Research Institute, Ottawa, Ontario, Canada
| | - Jan Raethjen
- Department of Neurology, Christian-Albrechts-University Kiel, Kiel, Germany
| | - Maria Stamelou
- Neurology Department, University of Athens, Greece and Neurology Department, Philipps University, Marburg, Germany
| | - Eng-King Tan
- Department of Neurology, National Neuroscience Institute (SGH campus), Duke NUS Medical School, Singapore General Hospital, Singapore
| | - Claudia M Testa
- Department of Neurology and Parkinson's and Movement Disorders Center, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Rodger J Elble
- Department of Neurology, Southern Illinois University School of Medicine, Springfield, Illinois, USA
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16
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Hickey P, Stacy M. Deep Brain Stimulation: A Paradigm Shifting Approach to Treat Parkinson's Disease. Front Neurosci 2016; 10:173. [PMID: 27199637 PMCID: PMC4848307 DOI: 10.3389/fnins.2016.00173] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Accepted: 04/04/2016] [Indexed: 11/13/2022] Open
Abstract
Parkinson disease (PD) is a chronic and progressive movement disorder classically characterized by slowed voluntary movements, resting tremor, muscle rigidity, and impaired gait and balance. Medical treatment is highly successful early on, though the majority of people experience significant complications in later stages. In advanced PD, when medications no longer adequately control motor symptoms, deep brain stimulation (DBS) offers a powerful therapeutic alternative. DBS involves the surgical implantation of one or more electrodes into specific areas of the brain, which modulate or disrupt abnormal patterns of neural signaling within the targeted region. Outcomes are often dramatic following DBS, with improvements in motor function and reductions motor complications having been repeatedly demonstrated. Given such robust responses, emerging indications for DBS are being investigated. In parallel with expansions of therapeutic scope, advancements within the areas of neurosurgical technique and the precision of stimulation delivery have recently broadened as well. This review focuses on the revolutionary addition of DBS to the therapeutic armamentarium for PD, and summarizes the technological advancements in the areas of neuroimaging and biomedical engineering intended to improve targeting, programming, and overall management.
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Affiliation(s)
- Patrick Hickey
- Department of Neurology, Duke University Medical CenterDurham, NC, USA
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17
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Signal features of surface electromyography in advanced Parkinson’s disease during different settings of deep brain stimulation. Clin Neurophysiol 2015; 126:2290-8. [DOI: 10.1016/j.clinph.2015.01.021] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2014] [Revised: 01/14/2015] [Accepted: 01/21/2015] [Indexed: 11/19/2022]
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18
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Heldman DA, Pulliam CL, Urrea Mendoza E, Gartner M, Giuffrida JP, Montgomery EB, Espay AJ, Revilla FJ. Computer-Guided Deep Brain Stimulation Programming for Parkinson's Disease. Neuromodulation 2015; 19:127-32. [PMID: 26621764 DOI: 10.1111/ner.12372] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Revised: 09/22/2015] [Accepted: 10/12/2015] [Indexed: 11/27/2022]
Abstract
OBJECTIVE Pilot study to evaluate computer-guided deep brain stimulation (DBS) programming designed to optimize stimulation settings using objective motion sensor-based motor assessments. MATERIALS AND METHODS Seven subjects (five males; 54-71 years) with Parkinson's disease (PD) and recently implanted DBS systems participated in this pilot study. Within two months of lead implantation, the subject returned to the clinic to undergo computer-guided programming and parameter selection. A motion sensor was placed on the index finger of the more affected hand. Software guided a monopolar survey during which monopolar stimulation on each contact was iteratively increased followed by an automated assessment of tremor and bradykinesia. After completing assessments at each setting, a software algorithm determined stimulation settings designed to minimize symptom severities, side effects, and battery usage. RESULTS Optimal DBS settings were chosen based on average severity of motor symptoms measured by the motion sensor. Settings chosen by the software algorithm identified a therapeutic window and improved tremor and bradykinesia by an average of 35.7% compared with baseline in the "off" state (p < 0.01). CONCLUSIONS Motion sensor-based computer-guided DBS programming identified stimulation parameters that significantly improved tremor and bradykinesia with minimal clinician involvement. Automated motion sensor-based mapping is worthy of further investigation and may one day serve to extend programming to populations without access to specialized DBS centers.
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Affiliation(s)
| | | | | | | | | | | | | | - Fredy J Revilla
- University of Cincinnati, Cincinnati, OH, USA.,Greenville Health System, University of South Carolina School of Medicine-Greenville, Greenville, SC, USA
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19
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Desautels TA, Choe J, Gad P, Nandra MS, Roy RR, Zhong H, Tai YC, Edgerton VR, Burdick JW. An Active Learning Algorithm for Control of Epidural Electrostimulation. IEEE Trans Biomed Eng 2015; 62:2443-2455. [PMID: 25974925 PMCID: PMC4617183 DOI: 10.1109/tbme.2015.2431911] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Epidural electrostimulation has shown promise for spinal cord injury therapy. However, finding effective stimuli on the multi-electrode stimulating arrays employed requires a laborious manual search of a vast space for each patient. Widespread clinical application of these techniques would be greatly facilitated by an autonomous, algorithmic system which choses stimuli to simultaneously deliver effective therapy and explore this space. We propose a method based on GP-BUCB, a Gaussian process bandit algorithm. In n = 4 spinally transected rats, we implant epidural electrode arrays and examine the algorithm's performance in selecting bipolar stimuli to elicit specified muscle responses. These responses are compared with temporally interleaved intra-animal stimulus selections by a human expert. GP-BUCB successfully controlled the spinal electrostimulation preparation in 37 testing sessions, selecting 670 stimuli. These sessions included sustained autonomous operations (ten-session duration). Delivered performance with respect to the specified metric was as good as or better than that of the human expert. Despite receiving no information as to anatomically likely locations of effective stimuli, GP-BUCB also consistently discovered such a pattern. Further, GP-BUCB was able to extrapolate from previous sessions' results to make predictions about performance in new testing sessions, while remaining sufficiently flexible to capture temporal variability. These results provide validation for applying automated stimulus selection methods to the problem of spinal cord injury therapy.
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Affiliation(s)
- Thomas A Desautels
- California Institute of Technology, Pasadena, CA 91125, USA. He is now with the Gatsby Computational Neuroscience Unit, University College London, London WC1N 3AR UK
| | - Jaehoon Choe
- University of California, Los Angeles, Los Angeles, CA, USA. J. Choe is now with HRL Laboratories, LLC, Malibu, CA, USA
| | - Parag Gad
- University of California, Los Angeles, Los Angeles, CA, USA. J. Choe is now with HRL Laboratories, LLC, Malibu, CA, USA
| | | | - Roland R Roy
- University of California, Los Angeles, Los Angeles, CA, USA. J. Choe is now with HRL Laboratories, LLC, Malibu, CA, USA
| | - Hui Zhong
- University of California, Los Angeles, Los Angeles, CA, USA. J. Choe is now with HRL Laboratories, LLC, Malibu, CA, USA
| | - Yu-Chong Tai
- California Institute of Technology, Pasadena, CA, USA
| | - V Reggie Edgerton
- University of California, Los Angeles, Los Angeles, CA, USA. J. Choe is now with HRL Laboratories, LLC, Malibu, CA, USA
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Motion sensor strategies for automated optimization of deep brain stimulation in Parkinson's disease. Parkinsonism Relat Disord 2015; 21:378-82. [PMID: 25703990 DOI: 10.1016/j.parkreldis.2015.01.018] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2014] [Revised: 01/23/2015] [Accepted: 01/30/2015] [Indexed: 11/24/2022]
Abstract
BACKGROUND Deep brain stimulation (DBS) is a well-established treatment for Parkinson's disease (PD). Optimization of DBS settings can be a challenge due to the number of variables that must be considered, including presence of multiple motor signs, side effects, and battery life. METHODS Nine PD subjects visited the clinic for programming at approximately 1, 2, and 4 months post-surgery. During each session, various stimulation settings were assessed and subjects performed motor tasks while wearing a motion sensor to quantify tremor and bradykinesia. At the end of each session, a clinician determined final stimulation settings using standard practices. Sensor-based ratings of motor symptom severities collected during programming were then used to develop two automated programming algorithms--one to optimize symptom benefit and another to optimize battery life. Therapeutic benefit was compared between the final clinician-determined DBS settings and those calculated by the automated algorithm. RESULTS Settings determined using the symptom optimization algorithm would have reduced motor symptoms by an additional 13 percentage points when compared to clinician settings, typically at the expense of increased stimulation amplitude. By adding a battery life constraint, the algorithm would have been able to decrease stimulation amplitude by an average of 50% while maintaining the level of therapeutic benefit observed using clinician settings for a subset of programming sessions. CONCLUSIONS Objective assessment in DBS programming can identify settings that improve symptoms or obtain similar benefit as clinicians with improvement in battery life. Both options have the potential to improve post-operative patient outcomes.
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21
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Origins and suppression of oscillations in a computational model of Parkinson's disease. J Comput Neurosci 2014; 37:505-21. [PMID: 25099916 DOI: 10.1007/s10827-014-0523-7] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2014] [Revised: 07/22/2014] [Accepted: 07/25/2014] [Indexed: 01/09/2023]
Abstract
Efficacy of deep brain stimulation (DBS) for motor signs of Parkinson's disease (PD) depends in part on post-operative programming of stimulus parameters. There is a need for a systematic approach to tuning parameters based on patient physiology. We used a physiologically realistic computational model of the basal ganglia network to investigate the emergence of a 34 Hz oscillation in the PD state and its optimal suppression with DBS. Discrete time transfer functions were fit to post-stimulus time histograms (PSTHs) collected in open-loop, by simulating the pharmacological block of synaptic connections, to describe the behavior of the basal ganglia nuclei. These functions were then connected to create a mean-field model of the closed-loop system, which was analyzed to determine the origin of the emergent 34 Hz pathological oscillation. This analysis determined that the oscillation could emerge from the coupling between the globus pallidus external (GPe) and subthalamic nucleus (STN). When coupled, the two resonate with each other in the PD state but not in the healthy state. By characterizing how this oscillation is affected by subthreshold DBS pulses, we hypothesize that it is possible to predict stimulus frequencies capable of suppressing this oscillation. To characterize the response to the stimulus, we developed a new method for estimating phase response curves (PRCs) from population data. Using the population PRC we were able to predict frequencies that enhance and suppress the 34 Hz pathological oscillation. This provides a systematic approach to tuning DBS frequencies and could enable closed-loop tuning of stimulation parameters.
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22
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Gorniak SL, McIntyre CC, Alberts JL. Bimanual force coordination in Parkinson's disease patients with bilateral subthalamic deep brain stimulation. PLoS One 2013; 8:e78934. [PMID: 24244388 PMCID: PMC3823934 DOI: 10.1371/journal.pone.0078934] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2013] [Accepted: 09/25/2013] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVE Studies of bimanual actions similar to activities of daily living (ADLs) are currently lacking in evaluating fine motor control in Parkinson's disease patients implanted with bilateral subthalamic deep brain stimulators. We investigated basic time and force characteristics of a bimanual task that resembles performance of ADLs in a group of bilateral subthalamic deep brain stimulation (DBS) patients. METHODS Patients were evaluated in three different DBS parameter conditions off stimulation, on clinically derived stimulation parameters, and on settings derived from a patient-specific computational model. Model-based parameters were computed as a means to minimize spread of current to non-motor regions of the subthalamic nucleus via Cicerone Deep Brain Stimulation software. Patients were evaluated off parkinsonian medications in each stimulation condition. RESULTS The data indicate that DBS parameter state does not affect most aspects of fine motor control in ADL-like tasks; however, features such as increased grip force and grip symmetry varied with the stimulation state. In the absence of DBS parameters, patients exhibited significant grip force asymmetry. Overall UPDRS-III and UPDRS-III scores associated with hand function were lower while patients were experiencing clinically-derived or model-based parameters, as compared to the off-stimulation condition. CONCLUSION While bilateral subthalamic DBS has been shown to alleviate gross motor dysfunction, our results indicate that DBS may not provide the same magnitude of benefit to fine motor coordination.
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Affiliation(s)
- Stacey L. Gorniak
- Department of Health and Human Performance, University of Houston, Houston, Texas, United States of America
- Centers for Neuromotor and Biomechanics Research and Neuro-Engineering and Cognitive Science, University of Houston, Houston, Texas, United States of America
- * E-mail:
| | - Cameron C. McIntyre
- Department of Biomedical Engineering and Center for Neurological Restoration, Cleveland Clinic, Cleveland, Ohio, United States of America
- Cleveland Functional Electrical Stimulation Center, Louis Stokes Veterans Affairs Medical Center, Cleveland, Ohio, United States of America
| | - Jay L. Alberts
- Department of Biomedical Engineering and Center for Neurological Restoration, Cleveland Clinic, Cleveland, Ohio, United States of America
- Cleveland Functional Electrical Stimulation Center, Louis Stokes Veterans Affairs Medical Center, Cleveland, Ohio, United States of America
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Torres EB. The rates of change of the stochastic trajectories of acceleration variability are a good predictor of normal aging and of the stage of Parkinson's disease. Front Integr Neurosci 2013; 7:50. [PMID: 23882193 PMCID: PMC3713394 DOI: 10.3389/fnint.2013.00050] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2013] [Accepted: 06/21/2013] [Indexed: 11/13/2022] Open
Abstract
The accelerometer data from mobile smart phones provide stochastic trajectories that change over time. This rate of change is unique to each person and can be well-characterized by the continuous two-parameter family of Gamma probability distributions. Accordingly, on the Gamma plane each participant can be uniquely localized by the shape and the scale parameters of the Gamma probability distribution. The scatter of such points contains information that can unambiguously separate the normal controls (NC) from those patients with Parkinson's disease (PD) that are at a later stage of the disease. In general normal aging seems conducive of more predictable patterns of variation in the accelerometer data. Yet this trend breaks down in PD where the statistical signatures seem to be a more relevant predictor of the stage of the disease. Those patients at a later stage of the disease have more random and noisier patterns than those in the earlier stages, whose statistics resemble those of the older NC. Overall the peak rates of change of the stochastic trajectories of the accelerometer are a good predictor of the stage of PD and of the age of a "normally" aging individual.
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Affiliation(s)
- Elizabeth B Torres
- Psychology Department, Computer Science, Cognitive Science, Sensory Motor Integration, Rutgers University Piscataway, NJ, USA
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Rehan M, Hong KS. Modeling and automatic feedback control of tremor: adaptive estimation of deep brain stimulation. PLoS One 2013; 8:e62888. [PMID: 23638163 PMCID: PMC3634768 DOI: 10.1371/journal.pone.0062888] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2012] [Accepted: 03/26/2013] [Indexed: 11/18/2022] Open
Abstract
This paper discusses modeling and automatic feedback control of (postural and rest) tremor for adaptive-control-methodology-based estimation of deep brain stimulation (DBS) parameters. The simplest linear oscillator-based tremor model, between stimulation amplitude and tremor, is investigated by utilizing input-output knowledge. Further, a nonlinear generalization of the oscillator-based tremor model, useful for derivation of a control strategy involving incorporation of parametric-bound knowledge, is provided. Using the Lyapunov method, a robust adaptive output feedback control law, based on measurement of the tremor signal from the fingers of a patient, is formulated to estimate the stimulation amplitude required to control the tremor. By means of the proposed control strategy, an algorithm is developed for estimation of DBS parameters such as amplitude, frequency and pulse width, which provides a framework for development of an automatic clinical device for control of motor symptoms. The DBS parameter estimation results for the proposed control scheme are verified through numerical simulations.
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Affiliation(s)
- Muhammad Rehan
- Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, Pakistan.
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Pouratian N, Thakkar S, Kim W, Bronstein JM. Deep brain stimulation for the treatment of Parkinson's disease: efficacy and safety. Degener Neurol Neuromuscul Dis 2012; 2012. [PMID: 24298202 DOI: 10.2147/dnnd.s25750] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Deep brain stimulation (DBS) surgery has become increasingly utilized in the treatment of advanced Parkinson's disease. Over the past decade, a number of studies have demonstrated that DBS is superior to best medical management in appropriately selected patients. The primary targets for DBS in Parkinson's disease include the subthalamic nucleus and the internal segment of the globus pallidus, both of which improve the cardinal motor features in Parkinson's disease. Recent randomized studies have revealed that both targets are similarly effective in treating the motor symptoms of Parkinson's disease, but emerging evidence suggests that the globus pallidus may be the preferred target in many patients, based on differences in nonmotor outcomes. Here, we review appropriate patient selection, and the efficacy and safety of DBS therapy in Parkinson's disease. Best outcomes are achieved if the problems of the individual patient are considered when evaluating surgical candidates and considering whether the subthalamic nucleus or the globus pallidus internus should be targeted.
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Affiliation(s)
- Nader Pouratian
- Departments of Neurosurgery, David Geffen School of Medicine at UCLA (University of California, Los Angeles), Los Angeles ; Bioengineering, David Geffen School of Medicine at UCLA (University of California, Los Angeles), Los Angeles
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Whitmer D, de Solages C, Hill B, Yu H, Henderson JM, Bronte-Stewart H. High frequency deep brain stimulation attenuates subthalamic and cortical rhythms in Parkinson's disease. Front Hum Neurosci 2012; 6:155. [PMID: 22675296 PMCID: PMC3366347 DOI: 10.3389/fnhum.2012.00155] [Citation(s) in RCA: 180] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2011] [Accepted: 05/16/2012] [Indexed: 12/31/2022] Open
Abstract
Parkinson's disease (PD) is marked by excessive synchronous activity in the beta (8–35 Hz) band throughout the cortico-basal ganglia network. The optimal location of high frequency deep brain stimulation (HF DBS) within the subthalamic nucleus (STN) region and the location of maximal beta hypersynchrony are currently matters of debate. Additionally, the effect of STN HF DBS on neural synchrony in functionally connected regions of motor cortex is unknown and is of great interest. Scalp EEG studies demonstrated that stimulation of the STN can activate motor cortex antidromically, but the spatial specificity of this effect has not been examined. The present study examined the effect of STN HF DBS on neural synchrony within the cortico-basal ganglia network in patients with PD. We measured local field potentials dorsal to and within the STN of PD patients, and additionally in the motor cortex in a subset of these patients. We used diffusion tensor imaging (DTI) to guide the placement of subdural cortical surface electrodes over the DTI-identified origin of the hyperdirect pathway (HDP) between motor cortex and the STN. The results demonstrated that local beta power was attenuated during HF DBS both dorsal to and within the STN. The degree of attenuation was monotonic with increased DBS voltages in both locations, but this voltage-dependent effect was greater in the central STN than dorsal to the STN (p < 0.05). Cortical signals over the estimated origin of the HDP also demonstrated attenuation of beta hypersynchrony during DBS dorsal to or within STN, whereas signals from non-specific regions of motor cortex were not attenuated. The spatially-specific suppression of beta synchrony in the motor cortex support the hypothesis that DBS may treat Parkinsonism by reducing excessive synchrony in the functionally connected sensorimotor network.
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Affiliation(s)
- Diane Whitmer
- Department of Neurology and Neurological Sciences, Stanford University, Stanford CA, USA
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