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Siddique MAB, Zhang Y, An H. Monitoring time domain characteristics of Parkinson's disease using 3D memristive neuromorphic system. Front Comput Neurosci 2023; 17:1274575. [PMID: 38162516 PMCID: PMC10754992 DOI: 10.3389/fncom.2023.1274575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 11/06/2023] [Indexed: 01/03/2024] Open
Abstract
Introduction Parkinson's disease (PD) is a neurodegenerative disorder affecting millions of patients. Closed-Loop Deep Brain Stimulation (CL-DBS) is a therapy that can alleviate the symptoms of PD. The CL-DBS system consists of an electrode sending electrical stimulation signals to a specific region of the brain and a battery-powered stimulator implanted in the chest. The electrical stimuli in CL-DBS systems need to be adjusted in real-time in accordance with the state of PD symptoms. Therefore, fast and precise monitoring of PD symptoms is a critical function for CL-DBS systems. However, the current CL-DBS techniques suffer from high computational demands for real-time PD symptom monitoring, which are not feasible for implanted and wearable medical devices. Methods In this paper, we present an energy-efficient neuromorphic PD symptom detector using memristive three-dimensional integrated circuits (3D-ICs). The excessive oscillation at beta frequencies (13-35 Hz) at the subthalamic nucleus (STN) is used as a biomarker of PD symptoms. Results Simulation results demonstrate that our neuromorphic PD detector, implemented with an 8-layer spiking Long Short-Term Memory (S-LSTM), excels in recognizing PD symptoms, achieving a training accuracy of 99.74% and a validation accuracy of 99.52% for a 75%-25% data split. Furthermore, we evaluated the improvement of our neuromorphic CL-DBS detector using NeuroSIM. The chip area, latency, energy, and power consumption of our CL-DBS detector were reduced by 47.4%, 66.63%, 65.6%, and 67.5%, respectively, for monolithic 3D-ICs. Similarly, for heterogeneous 3D-ICs, employing memristive synapses to replace traditional Static Random Access Memory (SRAM) resulted in reductions of 44.8%, 64.75%, 65.28%, and 67.7% in chip area, latency, and power usage. Discussion This study introduces a novel approach for PD symptom evaluation by directly utilizing spiking signals from neural activities in the time domain. This method significantly reduces the time and energy required for signal conversion compared to traditional frequency domain approaches. The study pioneers the use of neuromorphic computing and memristors in designing CL-DBS systems, surpassing SRAM-based designs in chip design area, latency, and energy efficiency. Lastly, the proposed neuromorphic PD detector demonstrates high resilience to timing variations in brain neural signals, as confirmed by robustness analysis.
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Affiliation(s)
- Md Abu Bakr Siddique
- Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, United States
| | - Yan Zhang
- Department of Biological Sciences, Michigan Technological University, Houghton, MI, United States
| | - Hongyu An
- Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, United States
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Averna A, Arlotti M, Rosa M, Chabardès S, Seigneuret E, Priori A, Moro E, Meoni S. Pallidal and Cortical Oscillations in Freely Moving Patients With Dystonia. Neuromodulation 2023; 26:1661-1667. [PMID: 34328685 DOI: 10.1111/ner.13503] [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: 02/07/2021] [Revised: 06/15/2021] [Accepted: 06/21/2021] [Indexed: 11/30/2022]
Abstract
OBJECTIVES To evaluate the correlation between the pallidal local field potentials (LFPs) activity and the cortical oscillations (at rest and during several motor tasks) in two freely moving patients with generalized dystonia and pallidal deep brain stimulation (DBS). MATERIALS AND METHODS Two women with isolated generalized dystonia were selected for bilateral globus pallidus internus (GPi) DBS. After the electrodes' implantation, cortical activity was recorded by a portable electroencephalography (EEG) system simultaneously with GPi LFPs activity, during several motor tasks, gait, and rest condition. Recordings were not performed during stimulation. EEG and LFPs signals relative to each specific movement were coupled together and grouped in neck/upper limbs movements and gait. Power spectral density (PSD), EEG-LFP coherence (through envelope of imaginary coherence operator), and 1/f exponent of LFP-PSD background were calculated. RESULTS In both patients, the pallidal LFPs PSD at rest was characterized by prominent 4-12 Hz activity. Voluntary movements increased activity in the theta (θ) band (4-7 Hz) compared to rest, in both LFPs and EEG signals. Gait induced a drastic raise of θ activity in both patients' pallidal activity, less marked for the EEG signal. A coherence peak within the 8-13 Hz range was found between pallidal LFPs and EEG recorded at rest. CONCLUSIONS Neck/upper limbs voluntary movements and gait suppressed the GPi-LFPs-cortical-EEG coherence and differently impacted both EEG and LFPs low frequency activity. These findings suggest a selective modulation of the cortico-basal ganglia network activity in dystonia.
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Affiliation(s)
- Alberto Averna
- "Aldo Ravelli" Center for Nanotechnology and Neurostimulation, University of Milan, Milan, Italy
| | - Mattia Arlotti
- Clinical Center for Neurotechnologies, Neuromodulation, and Movement Disorders, Fondazione IRCCS Ca'Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Manuela Rosa
- Clinical Center for Neurotechnologies, Neuromodulation, and Movement Disorders, Fondazione IRCCS Ca'Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Stéphan Chabardès
- Université Grenoble Alpes, INSERM, U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, Grenoble, France; Division of Neurosurgery, Grenoble Alpes University Hospital Center, Grenoble, France
| | - Eric Seigneuret
- Université Grenoble Alpes, INSERM, U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, Grenoble, France; Division of Neurosurgery, Grenoble Alpes University Hospital Center, Grenoble, France
| | - Alberto Priori
- "Aldo Ravelli" Center for Nanotechnology and Neurostimulation, University of Milan, Milan, Italy; Neurology, Department of Health Sciences, San Paolo University Hospital, Azienda Socio Sanitaria Territoriale Santi Paolo e Carlo, University of Milan Medical School, Milan, Italy
| | - Elena Moro
- Université Grenoble Alpes, INSERM, U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, Grenoble, France; Movement Disorders Unit, Division of Neurology, CHU Grenoble Alpes, Grenoble, France
| | - Sara Meoni
- "Aldo Ravelli" Center for Nanotechnology and Neurostimulation, University of Milan, Milan, Italy; Université Grenoble Alpes, INSERM, U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, Grenoble, France; Movement Disorders Unit, Division of Neurology, CHU Grenoble Alpes, Grenoble, France.
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Oliveira AM, Coelho L, Carvalho E, Ferreira-Pinto MJ, Vaz R, Aguiar P. Machine learning for adaptive deep brain stimulation in Parkinson's disease: closing the loop. J Neurol 2023; 270:5313-5326. [PMID: 37530789 PMCID: PMC10576725 DOI: 10.1007/s00415-023-11873-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 07/08/2023] [Accepted: 07/10/2023] [Indexed: 08/03/2023]
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disease bearing a severe social and economic impact. So far, there is no known disease modifying therapy and the current available treatments are symptom oriented. Deep Brain Stimulation (DBS) is established as an effective treatment for PD, however current systems lag behind today's technological potential. Adaptive DBS, where stimulation parameters depend on the patient's physiological state, emerges as an important step towards "smart" DBS, a strategy that enables adaptive stimulation and personalized therapy. This new strategy is facilitated by currently available neurotechnologies allowing the simultaneous monitoring of multiple signals, providing relevant physiological information. Advanced computational models and analytical methods are an important tool to explore the richness of the available data and identify signal properties to close the loop in DBS. To tackle this challenge, machine learning (ML) methods applied to DBS have gained popularity due to their ability to make good predictions in the presence of multiple variables and subtle patterns. ML based approaches are being explored at different fronts such as the identification of electrophysiological biomarkers and the development of personalized control systems, leading to effective symptom relief. In this review, we explore how ML can help overcome the challenges in the development of closed-loop DBS, particularly its role in the search for effective electrophysiology biomarkers. Promising results demonstrate ML potential for supporting a new generation of adaptive DBS, with better management of stimulation delivery, resulting in more efficient and patient-tailored treatments.
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Affiliation(s)
- Andreia M Oliveira
- Faculdade de Engenharia da Universidade do Porto, Porto, Portugal
- Neuroengineering and Computational Neuroscience Lab, Instituto de Investigação e Inovação da Universidade do Porto, Porto, Portugal
| | - Luis Coelho
- Instituto Superior de Engenharia do Porto, Porto, Portugal
| | - Eduardo Carvalho
- Neuroengineering and Computational Neuroscience Lab, Instituto de Investigação e Inovação da Universidade do Porto, Porto, Portugal
- ICBAS-School of Medicine and Biomedical Sciences, University of Porto, Porto, Portugal
| | - Manuel J Ferreira-Pinto
- Centro Hospitalar Universitário de São João, Porto, Portugal
- Faculdade de Medicina da Universidade do Porto, Porto, Portugal
| | - Rui Vaz
- Centro Hospitalar Universitário de São João, Porto, Portugal
- Faculdade de Medicina da Universidade do Porto, Porto, Portugal
| | - Paulo Aguiar
- Faculdade de Engenharia da Universidade do Porto, Porto, Portugal.
- Neuroengineering and Computational Neuroscience Lab, Instituto de Investigação e Inovação da Universidade do Porto, Porto, Portugal.
- Faculdade de Medicina da Universidade do Porto, Porto, Portugal.
- i3S-Instituto de Investigação e Inovação em Saúde, Rua Alfredo Allen, 208, 4200-135, Porto, Portugal.
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Popa RC, Serban CA, Barborica A, Zagrean AM, Buiu O, Dumbravescu N, Paslaru AC, Obreja C, Pachiu C, Stoian M, Marculescu C, Radoi A, Vulpe S, Ion M. Functional Enhancement and Characterization of an Electrophysiological Mapping Electrode Probe with Carbonic, Directional Macrocontacts. SENSORS (BASEL, SWITZERLAND) 2023; 23:7497. [PMID: 37687953 PMCID: PMC10490806 DOI: 10.3390/s23177497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/16/2023] [Accepted: 08/21/2023] [Indexed: 09/10/2023]
Abstract
Electrophysiological mapping (EM) using acute electrode probes is a common procedure performed during functional neurosurgery. Due to their constructive specificities, the EM probes are lagging in innovative enhancements. This work addressed complementing a clinically employed EM probe with carbonic and circumferentially segmented macrocontacts that are operable both for neurophysiological sensing ("recording") of local field potentials (LFP) and for test stimulation. This paper illustrates in-depth the development that is based on the direct writing of functional materials. The unconventional fabrication processes were optimized on planar geometry and then transferred to the cylindrically thin probe body. We report and discuss the constructive concept and architecture of the probe, characteristics of the electrochemical interface deduced from voltammetry and chronopotentiometry, and the results of in vitro and in vivo recording and pulse stimulation tests. Two- and three-directional macrocontacts were added on probes having shanks of 550 and 770 μm diameters and 10-23 cm lengths. The graphitic material presents a ~2.7 V wide, almost symmetric water electrolysis window, and an ultra-capacitive charge transfer. When tested with clinically relevant 150 μs biphasic current pulses, the interfacial polarization stayed safely away from the water window for pulse amplitudes up to 9 mA (135 μC/cm2). The in vivo experiments on adult rat models confirmed the high-quality sensing of LFPs. Additionally, the in vivo-prevailing increase in the electrode impedance and overpotential are discussed and modeled by an ionic mobility-reducing spongiform structure; this restricted diffusion model gives new applicative insight into the in vivo-uprisen stimulation overpotential.
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Affiliation(s)
- Radu C. Popa
- National Institute for R&D in Microtechnologies–IMT Bucharest, 077190 Bucharest, Romania; (O.B.); (N.D.); (C.O.); (C.P.); (M.S.); (C.M.); (A.R.); (S.V.); (M.I.)
| | - Cosmin-Andrei Serban
- Termobit Prod Srl, 020281 Bucharest, Romania; (C.-A.S.); (A.B.)
- Fhc, Inc., Bowdoin, ME 04287, USA
- Faculty of Physics, University of Bucharest, 077125 Magurele, Romania
| | - Andrei Barborica
- Termobit Prod Srl, 020281 Bucharest, Romania; (C.-A.S.); (A.B.)
- Fhc, Inc., Bowdoin, ME 04287, USA
- Faculty of Physics, University of Bucharest, 077125 Magurele, Romania
| | - Ana-Maria Zagrean
- Physiology and Neuroscience Department, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (A.-M.Z.); (A.-C.P.)
| | - Octavian Buiu
- National Institute for R&D in Microtechnologies–IMT Bucharest, 077190 Bucharest, Romania; (O.B.); (N.D.); (C.O.); (C.P.); (M.S.); (C.M.); (A.R.); (S.V.); (M.I.)
| | - Niculae Dumbravescu
- National Institute for R&D in Microtechnologies–IMT Bucharest, 077190 Bucharest, Romania; (O.B.); (N.D.); (C.O.); (C.P.); (M.S.); (C.M.); (A.R.); (S.V.); (M.I.)
| | - Alexandru-Catalin Paslaru
- Physiology and Neuroscience Department, “Carol Davila” University of Medicine and Pharmacy, 050474 Bucharest, Romania; (A.-M.Z.); (A.-C.P.)
| | - Cosmin Obreja
- National Institute for R&D in Microtechnologies–IMT Bucharest, 077190 Bucharest, Romania; (O.B.); (N.D.); (C.O.); (C.P.); (M.S.); (C.M.); (A.R.); (S.V.); (M.I.)
| | - Cristina Pachiu
- National Institute for R&D in Microtechnologies–IMT Bucharest, 077190 Bucharest, Romania; (O.B.); (N.D.); (C.O.); (C.P.); (M.S.); (C.M.); (A.R.); (S.V.); (M.I.)
| | - Marius Stoian
- National Institute for R&D in Microtechnologies–IMT Bucharest, 077190 Bucharest, Romania; (O.B.); (N.D.); (C.O.); (C.P.); (M.S.); (C.M.); (A.R.); (S.V.); (M.I.)
| | - Catalin Marculescu
- National Institute for R&D in Microtechnologies–IMT Bucharest, 077190 Bucharest, Romania; (O.B.); (N.D.); (C.O.); (C.P.); (M.S.); (C.M.); (A.R.); (S.V.); (M.I.)
| | - Antonio Radoi
- National Institute for R&D in Microtechnologies–IMT Bucharest, 077190 Bucharest, Romania; (O.B.); (N.D.); (C.O.); (C.P.); (M.S.); (C.M.); (A.R.); (S.V.); (M.I.)
| | - Silviu Vulpe
- National Institute for R&D in Microtechnologies–IMT Bucharest, 077190 Bucharest, Romania; (O.B.); (N.D.); (C.O.); (C.P.); (M.S.); (C.M.); (A.R.); (S.V.); (M.I.)
| | - Marian Ion
- National Institute for R&D in Microtechnologies–IMT Bucharest, 077190 Bucharest, Romania; (O.B.); (N.D.); (C.O.); (C.P.); (M.S.); (C.M.); (A.R.); (S.V.); (M.I.)
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Bahador N, Saha J, Rezaei MR, Utpal S, Ghahremani A, Chen R, Lankarany M. Robust Removal of Slow Artifactual Dynamics Induced by Deep Brain Stimulation in Local Field Potential Recordings Using SVD-Based Adaptive Filtering. Bioengineering (Basel) 2023; 10:719. [PMID: 37370650 DOI: 10.3390/bioengineering10060719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 06/07/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
Deep brain stimulation (DBS) is widely used as a treatment option for patients with movement disorders. In addition to its clinical impact, DBS has been utilized in the field of cognitive neuroscience, wherein the answers to several fundamental questions underpinning the mechanisms of neuromodulation in decision making rely on the ways in which a burst of DBS pulses, usually delivered at a clinical frequency, i.e., 130 Hz, perturb participants' choices. It was observed that neural activities recorded during DBS were contaminated with large artifacts, which lasts for a few milliseconds, as well as a low-frequency (slow) signal (~1-2 Hz) that can persist for hundreds of milliseconds. While the focus of most of methods for removing DBS artifacts was on the former, the artifact removal capabilities of the slow signal have not been addressed. In this work, we propose a new method based on combining singular value decomposition (SVD) and normalized adaptive filtering to remove both large (fast) and slow artifacts in local field potentials, recorded during a cognitive task in which bursts of DBS were utilized. Using synthetic data, we show that our proposed algorithm outperforms four commonly used techniques in the literature, namely, (1) normalized least mean square adaptive filtering, (2) optimal FIR Wiener filtering, (3) Gaussian model matching, and (4) moving average. The algorithm's capabilities are further demonstrated by its ability to effectively remove DBS artifacts in local field potentials recorded from the subthalamic nucleus during a verbal Stroop task, highlighting its utility in real-world applications.
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Affiliation(s)
- Nooshin Bahador
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON M5S 2E8, Canada
| | - Josh Saha
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- Department of Electrical and Computer Engineering, University of Waterloo, Toronto, ON N2L 3G1, Canada
| | - Mohammad R Rezaei
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON M5S 2E8, Canada
| | - Saha Utpal
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
| | - Ayda Ghahremani
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- School of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Robert Chen
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- Department of Medicine, Division of Neurology, University of Toronto, Toronto, ON M5S 2E8, Canada
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network (UHN), Toronto, ON M5G 2A2, Canada
| | - Milad Lankarany
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON M5S 2E8, Canada
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network (UHN), Toronto, ON M5G 2A2, Canada
- Department of Physiology, University of Toronto, Toronto, ON M5S 2E8, Canada
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An Q, Yin Z, Ma R, Fan H, Xu Y, Gan Y, Gao Y, Meng F, Yang A, Jiang Y, Zhu G, Zhang J. Adaptive deep brain stimulation for Parkinson's disease: looking back at the past decade on motor outcomes. J Neurol 2023; 270:1371-1387. [PMID: 36471098 DOI: 10.1007/s00415-022-11495-z] [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: 07/30/2022] [Revised: 11/15/2022] [Accepted: 11/17/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Adaptive deep brain stimulation (aDBS) has been reported to be an effective treatment for motor symptoms in patients with Parkinson's disease (PD). However, it remains unclear whether and in which motor domain aDBS provides greater/less benefits than conventional DBS (cDBS). OBJECTIVE To conduct a meta-analysis and systematic review to explore the improvement of the motor symptoms of PD patients undergoing aDBS and the comparison between aDBS and cDBS. METHODS Nineteen studies from PubMed, Embase, and the Cochrane Library database were eligible for the main analysis. Twelve studies used quantitative plus qualitative analysis; seven studies were only qualitatively analyzed. The efficacy of aDBS was evaluated and compared to cDBS through overall motor function improvements, changes in symptoms of rigidity-bradykinesia, dyskinesia, tremor, and speech function, and total electrical energy delivered (TEED). The overall motor improvement and TEED were investigated through meta-analyses, while other variables were investigated by systematic review. RESULTS Quantitative analysis showed that aDBS, with a reduction of TEED (55% of that of cDBS), significantly improved motor functions (33.9%, p < 0.01) and may be superior to cDBS in overall motor improvement (p = 0.002). However, significant publication bias was detected regarding the superiority (p = 0.006, Egger's test). In the qualitative analysis, rigidity-bradykinesia, dyskinesia, and speech function outcomes after aDBS and cDBS were comparable. Beta-based aDBS may not be as efficient as cDBS for tremor control. CONCLUSIONS aDBS can effectively relieve the clinical symptoms of advanced PD as did cDBS, at least in acute trials, delivering less stimulation than cDBS. Specific symptoms including tremor and axial disability remain to be compared between aDBS and cDBS in long-term studies.
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Affiliation(s)
- Qi An
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, 100070, Beijing, China
| | - Zixiao Yin
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, 100070, Beijing, China
| | - Ruoyu Ma
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, 100070, Beijing, China
| | - Houyou Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, 100070, Beijing, China
| | - Yichen Xu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, 100070, Beijing, China
| | - Yifei Gan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, 100070, Beijing, China
| | - Yuan Gao
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, 100070, Beijing, China
| | - Fangang Meng
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, 100070, Beijing, China.,Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, 100070, Beijing, China
| | - Anchao Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, 100070, Beijing, China
| | - Yin Jiang
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, 100070, Beijing, China
| | - Guanyu Zhu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, 100070, Beijing, China.
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, 100070, Beijing, China. .,Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, 100070, Beijing, China. .,Beijing Key Laboratory of Neurostimulation, Beijing, China.
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Belkacem AN, Jamil N, Khalid S, Alnajjar F. On closed-loop brain stimulation systems for improving the quality of life of patients with neurological disorders. Front Hum Neurosci 2023; 17:1085173. [PMID: 37033911 PMCID: PMC10076878 DOI: 10.3389/fnhum.2023.1085173] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 03/06/2023] [Indexed: 04/11/2023] Open
Abstract
Emerging brain technologies have significantly transformed human life in recent decades. For instance, the closed-loop brain-computer interface (BCI) is an advanced software-hardware system that interprets electrical signals from neurons, allowing communication with and control of the environment. The system then transmits these signals as controlled commands and provides feedback to the brain to execute specific tasks. This paper analyzes and presents the latest research on closed-loop BCI that utilizes electric/magnetic stimulation, optogenetic, and sonogenetic techniques. These techniques have demonstrated great potential in improving the quality of life for patients suffering from neurodegenerative or psychiatric diseases. We provide a comprehensive and systematic review of research on the modalities of closed-loop BCI in recent decades. To achieve this, the authors used a set of defined criteria to shortlist studies from well-known research databases into categories of brain stimulation techniques. These categories include deep brain stimulation, transcranial magnetic stimulation, transcranial direct-current stimulation, transcranial alternating-current stimulation, and optogenetics. These techniques have been useful in treating a wide range of disorders, such as Alzheimer's and Parkinson's disease, dementia, and depression. In total, 76 studies were shortlisted and analyzed to illustrate how closed-loop BCI can considerably improve, enhance, and restore specific brain functions. The analysis revealed that literature in the area has not adequately covered closed-loop BCI in the context of cognitive neural prosthetics and implanted neural devices. However, the authors demonstrate that the applications of closed-loop BCI are highly beneficial, and the technology is continually evolving to improve the lives of individuals with various ailments, including those with sensory-motor issues or cognitive deficiencies. By utilizing emerging techniques of stimulation, closed-loop BCI can safely improve patients' cognitive and affective skills, resulting in better healthcare outcomes.
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Affiliation(s)
- Abdelkader Nasreddine Belkacem
- Department of Computer and Network Engineering, College of Information Technology, UAE University, Al-Ain, United Arab Emirates
- *Correspondence: Abdelkader Nasreddine Belkacem
| | - Nuraini Jamil
- Department of Computer Science and Software Engineering, College of Information Technology, UAE University, Al-Ain, United Arab Emirates
| | - Sumayya Khalid
- Department of Computer Science and Software Engineering, College of Information Technology, UAE University, Al-Ain, United Arab Emirates
| | - Fady Alnajjar
- Department of Computer Science and Software Engineering, College of Information Technology, UAE University, Al-Ain, United Arab Emirates
- Center for Brain Science, RIKEN, Saitama, Japan
- Fady Alnajjar
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Wang K, Wang J, Zhu Y, Li H, Liu C, Fietkiewicz C, Loparo KA. Adaptive closed-loop control strategy inhibiting pathological basal ganglia oscillations. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Stanslaski SR, Case MA, Giftakis JE, Raike RS, Stypulkowski PH. Long Term Performance of a Bi-Directional Neural Interface for Deep Brain Stimulation and Recording. Front Hum Neurosci 2022; 16:916627. [PMID: 35754768 PMCID: PMC9218069 DOI: 10.3389/fnhum.2022.916627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 05/16/2022] [Indexed: 11/24/2022] Open
Abstract
Background: In prior reports, we described the design and initial performance of a fully implantable, bi-directional neural interface system for use in deep brain and other neurostimulation applications. Here we provide an update on the chronic, long-term neural sensing performance of the system using traditional 4-contact leads and extend those results to include directional 8-contact leads. Methods: Seven ovine subjects were implanted with deep brain stimulation (DBS) leads at different nodes within the Circuit of Papez: four with unilateral leads in the anterior nucleus of the thalamus and hippocampus; two with bilateral fornix leads, and one with bilateral hippocampal leads. The leads were connected to either an Activa PC+S® (Medtronic) or Percept PC°ledR (Medtronic) deep brain stimulation and recording device. Spontaneous local field potentials (LFPs), evoked potentials (EPs), LFP response to stimulation, and electrode impedances were monitored chronically for periods of up to five years in these subjects. Results: The morphology, amplitude, and latencies of chronic hippocampal EPs evoked by thalamic stimulation remained stable over the duration of the study. Similarly, LFPs showed consistent spectral peaks with expected variation in absolute magnitude dependent upon behavioral state and other factors, but no systematic degradation of signal quality over time. Electrode impedances remained within expected ranges with little variation following an initial stabilization period. Coupled neural activity between the two nodes within the Papez circuit could be observed in synchronized recordings up to 5 years post-implant. The magnitude of passive LFP power recorded from directional electrode segments was indicative of the contacts that produced the greatest stimulation-induced changes in LFP power within the Papez network. Conclusion: The implanted device performed as designed, providing the ability to chronically stimulate and record neural activity within this network for up to 5 years of follow-up.
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Marceglia S, Conti C, Svanidze O, Foffani G, Lozano AM, Moro E, Volkmann J, Arlotti M, Rossi L, Priori A. Double-blind cross-over pilot trial protocol to evaluate the safety and preliminary efficacy of long-term adaptive deep brain stimulation in patients with Parkinson's disease. BMJ Open 2022; 12:e049955. [PMID: 34980610 PMCID: PMC8724732 DOI: 10.1136/bmjopen-2021-049955] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
INTRODUCTION After several years of brain-sensing technology development and proof-of-concept studies, adaptive deep brain stimulation (aDBS) is ready to better treat Parkinson's disease (PD) using aDBS-capable implantable pulse generators (IPGs). New aDBS devices are capable of continuous sensing of neuronal activity from the subthalamic nucleus (STN) and contemporaneous stimulation automatically adapted to match the patient's clinical state estimated from the analysis of STN activity using proprietary algorithms. Specific studies are necessary to assess superiority of aDBS vs conventional DBS (cDBS) therapy. This protocol describes an original innovative multicentre international study aimed to assess safety and efficacy of aDBS vs cDBS using a new generation of DBS IPG in PD (AlphaDBS system by Newronika SpA, Milan, Italy). METHODS The study involves six investigational sites (in Italy, Poland and The Netherlands). The primary objective will be to evaluate the safety and tolerability of the AlphaDBS System, when used in cDBS and aDBS mode. Secondary objective will be to evaluate the potential efficacy of aDBS. After eligibility screening, 15 patients with PD already implanted with DBS systems and in need of battery replacement will be randomised to enter a two-phase protocol, including a 'short-term follow-up' (2 days experimental sessions during hospitalisation, 1 day per each mode) and a 'long-term follow-up' (1 month at home, 15 days per each mode). ETHICS AND DISSEMINATION The trial was approved as premarket study by the Italian, Polish, and Dutch Competent Authorities: Bioethics Committee at National Oncology Institute of Maria Skłodowska-Curie-National Research Institute in Warsaw; Comitato Etico Milano Area 2; Comitato Etico IRCCS Istituto Neurologico C. Besta; Comitato Etico interaziendale AOUC Città della Salute e della Scienza-AO Ordine Mauriziano di Torino-ASL Città di Torino; De Medisch Ethisch Toetsingscommissie van Maastricht UMC. The study started enrolling patients in January 2021. TRIAL REGISTRATION NUMBER NCT04681534.
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Affiliation(s)
- Sara Marceglia
- Dipartimento di Ingegneria e Architettura, Università degli Studi di Trieste, Trieste, Italy
- UO Neurofisiopatologia, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | | | | | - Guglielmo Foffani
- Fundación del Hospital Nacional de Parapléjicos para la Investigación y la Integración, Toledo, Spain
- CINAC, Hospital Universitario HM Puerta del Sur, Universidad CEU-San Pablo, Móstoles, Madrid, Spain
| | - Andres M Lozano
- Division of Neurosurgery, Department of Surgery, Toronto Western Hospital, University of Toronto, Toronto, Ontario, Canada
- Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
| | - Elena Moro
- Grenoble Institute of Neurosciences, INSERM U1216, University Grenoble Alpes, Grenoble, France
| | - Jens Volkmann
- Department of Neurology, University of Wurzburg, Würzburg, Germany
| | | | | | - Alberto Priori
- ASST Santi Paolo e Carlo, Milano, Italy
- Aldo Ravelli Research Center for Neurotechnology and Experimental Neurotherapeutics, Department of Health Sciences, University of Milan, Milan, Italy
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11
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Arlotti M, Colombo M, Bonfanti A, Mandat T, Lanotte MM, Pirola E, Borellini L, Rampini P, Eleopra R, Rinaldo S, Romito L, Janssen MLF, Priori A, Marceglia S. A New Implantable Closed-Loop Clinical Neural Interface: First Application in Parkinson's Disease. Front Neurosci 2021; 15:763235. [PMID: 34949982 PMCID: PMC8689059 DOI: 10.3389/fnins.2021.763235] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 11/04/2021] [Indexed: 11/13/2022] Open
Abstract
Deep brain stimulation (DBS) is used for the treatment of movement disorders, including Parkinson’s disease, dystonia, and essential tremor, and has shown clinical benefits in other brain disorders. A natural path for the improvement of this technique is to continuously observe the stimulation effects on patient symptoms and neurophysiological markers. This requires the evolution of conventional deep brain stimulators to bidirectional interfaces, able to record, process, store, and wirelessly communicate neural signals in a robust and reliable fashion. Here, we present the architecture, design, and first use of an implantable stimulation and sensing interface (AlphaDBSR System) characterized by artifact-free recording and distributed data management protocols. Its application in three patients with Parkinson’s disease (clinical trial n. NCT04681534) is shown as a proof of functioning of a clinically viable implanted brain-computer interface (BCI) for adaptive DBS. Reliable artifact free-recordings, and chronic long-term data and neural signal management are in place.
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Affiliation(s)
| | | | - Andrea Bonfanti
- Newronika SpA, Milan, Italy.,Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| | - Tomasz Mandat
- Narodowy Instytut Onkologii im. Marii Skłodowskiej-Curie, Warsaw, Poland
| | - Michele Maria Lanotte
- Department of Neuroscience, University of Torino, Torino, Italy.,AOU Città della Salute e della Scienza, Molinette Hospital, Turin, Italy
| | - Elena Pirola
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Linda Borellini
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Paolo Rampini
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Roberto Eleopra
- Movement Disorders Unit, Department of Clinical Neurosciences, Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy
| | - Sara Rinaldo
- Movement Disorders Unit, Department of Clinical Neurosciences, Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy
| | - Luigi Romito
- Movement Disorders Unit, Department of Clinical Neurosciences, Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy
| | - Marcus L F Janssen
- Department of Neurology and Clinical Neurophysiology, Maastricht University Medical Center, Maastricht, Netherlands.,Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Alberto Priori
- Department of Health Sciences, Aldo Ravelli Research Center for Neurotechnology and Experimental Neurotherapeutics, University of Milan, Milan, Italy
| | - Sara Marceglia
- Dipartimento di Ingegneria e Architettura, Università degli Studi di Trieste, Trieste, Italy
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Marceglia S, Guidetti M, Harmsen IE, Loh A, Meoni S, Foffani G, Lozano AM, Volkmann J, Moro E, Priori A. Deep brain stimulation: is it time to change gears by closing the loop? J Neural Eng 2021; 18. [PMID: 34678794 DOI: 10.1088/1741-2552/ac3267] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 10/22/2021] [Indexed: 11/12/2022]
Abstract
Objective.Adaptive deep brain stimulation (aDBS) is a form of invasive stimulation that was conceived to overcome the technical limitations of traditional DBS, which delivers continuous stimulation of the target structure without considering patients' symptoms or status in real-time. Instead, aDBS delivers on-demand, contingency-based stimulation. So far, aDBS has been tested in several neurological conditions, and will be soon extensively studied to translate it into clinical practice. However, an exhaustive description of technical aspects is still missing.Approach.in this topical review, we summarize the knowledge about the current (and future) aDBS approach and control algorithms to deliver the stimulation, as reference for a deeper undestending of aDBS model.Main results.We discuss the conceptual and functional model of aDBS, which is based on the sensing module (that assesses the feedback variable), the control module (which interpretes the variable and elaborates the new stimulation parameters), and the stimulation module (that controls the delivery of stimulation), considering both the historical perspective and the state-of-the-art of available biomarkers.Significance.aDBS modulates neuronal circuits based on clinically relevant biofeedback signals in real-time. First developed in the mid-2000s, many groups have worked on improving closed-loop DBS technology. The field is now at a point in conducting large-scale randomized clinical trials to translate aDBS into clinical practice. As we move towards implanting brain-computer interfaces in patients, it will be important to understand the technical aspects of aDBS.
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Affiliation(s)
- Sara Marceglia
- Department of Engineering and Architecture, University of Trieste, 34127 Trieste, Italy
| | - Matteo Guidetti
- Aldo Ravelli Research Center for Neurotechnology and Experimental Neurotherapeutics, Department of Health Sciences, University of Milan, 20142 Milan, Italy.,Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy
| | - Irene E Harmsen
- Division of Neurosurgery, Department of Surgery, Toronto Western Hospital, University of Toronto, Toronto, Ontario, Canada.,Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
| | - Aaron Loh
- Division of Neurosurgery, Department of Surgery, Toronto Western Hospital, University of Toronto, Toronto, Ontario, Canada.,Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
| | - Sara Meoni
- Aldo Ravelli Research Center for Neurotechnology and Experimental Neurotherapeutics, Department of Health Sciences, University of Milan, 20142 Milan, Italy.,Movement Disorders Unit, Division of Neurology, CHU Grenoble Alpes, Grenoble, France.,Grenoble Institute of Neurosciences, INSERM U1216, University Grenoble Alpes, Grenoble, France
| | - Guglielmo Foffani
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain.,Hospital Nacional de Parapléjicos, SESCAM, Toledo, Spain
| | - Andres M Lozano
- Division of Neurosurgery, Department of Surgery, Toronto Western Hospital, University of Toronto, Toronto, Ontario, Canada.,Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
| | - Jens Volkmann
- Department of Neurology, University of Wurzburg, Wurzburg, Germany
| | - Elena Moro
- Movement Disorders Unit, Division of Neurology, CHU Grenoble Alpes, Grenoble, France.,Grenoble Institute of Neurosciences, INSERM U1216, University Grenoble Alpes, Grenoble, France
| | - Alberto Priori
- Aldo Ravelli Research Center for Neurotechnology and Experimental Neurotherapeutics, Department of Health Sciences, University of Milan, 20142 Milan, Italy.,ASST Santi Paolo e Carlo, 20142 Milan, Italy
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13
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Priori A, Maiorana N, Dini M, Guidetti M, Marceglia S, Ferrucci R. Adaptive deep brain stimulation (aDBS). INTERNATIONAL REVIEW OF NEUROBIOLOGY 2021; 159:111-127. [PMID: 34446243 DOI: 10.1016/bs.irn.2021.06.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Deep brain stimulation is an established technique for the treatment of movement disorders related to neurodegenerative diseases such as Parkinson's disease (PD) and essential tremor (ET). Its application seems also feasible for the treatment of neuropsychiatric disorders such as treatment resistant depression (TRD) and Tourette's syndrome (TS). In a typical deep brain stimulation system, the amount of current delivered to the patients is constant and regulated by the physician. Conversely, an adaptive deep brain stimulation system (aDBS) is a closed loop system that adjusts the stimulation parameters according to biomarkers which reflect the patient's clinical state. In this chapter, we examined the main issues related to aDBS systems, which are both clinical and technological in nature. From a clinical point of view, we have reported the major findings related to symptoms management using aDBS and principal findings in animal models, showing that the implementation of closed loop adaptive deep brain stimulation can ameliorate symptom management in neurodegenerative disorders. From the technological point of view, we reported the major advances related to aDBS system design and implementation, such as noise filtering methods, biomarkers recording and processing to adjust pulse delivery. To date, aDBS systems represent a major evolution in brain stimulation, further developments are needed to maximize the efficacy of this technique and to expand its use in a wide range of neuropsychiatric disorders.
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Affiliation(s)
- Alberto Priori
- Department of Health Science, Aldo Ravelli Center, University of Milan, Milan, Italy.
| | - Natale Maiorana
- Department of Health Science, Aldo Ravelli Center, University of Milan, Milan, Italy
| | - Michelangelo Dini
- Department of Health Science, Aldo Ravelli Center, University of Milan, Milan, Italy
| | - Matteo Guidetti
- Department of Health Science, Aldo Ravelli Center, University of Milan, Milan, Italy
| | - Sara Marceglia
- Department of Engineering and Architecture, University of Trieste, Trieste, Italy
| | - Roberta Ferrucci
- Department of Health Science, Aldo Ravelli Center, University of Milan, Milan, Italy
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Guidetti M, Marceglia S, Loh A, Harmsen IE, Meoni S, Foffani G, Lozano AM, Moro E, Volkmann J, Priori A. Clinical perspectives of adaptive deep brain stimulation. Brain Stimul 2021; 14:1238-1247. [PMID: 34371211 DOI: 10.1016/j.brs.2021.07.063] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 06/01/2021] [Accepted: 07/31/2021] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND The application of stimulators implanted directly over deep brain structures (i.e., deep brain stimulation, DBS) was developed in the late 1980s and has since become a mainstream option to treat several neurological conditions. Conventional DBS involves the continuous stimulation of the target structure, which is an approach that cannot adapt to patients' changing symptoms or functional status in real-time. At the beginning of 2000, a more sophisticated form of stimulation was conceived to overcome these limitations. Adaptive deep brain stimulation (aDBS) employs on-demand, contingency-based stimulation to stimulate only when needed. So far, aDBS has been tested in several pathological conditions in animal and human models. OBJECTIVE To review the current findings obtained from application of aDBS to animal and human models that highlights effects on motor, cognitive and psychiatric behaviors. FINDINGS while aDBS has shown promising results in the treatment of Parkinson's disease and essential tremor, the possibility of its use in less common DBS indications, such as cognitive and psychiatric disorders (Alzheimer's disease, obsessive-compulsive disorder, post-traumatic stress disorder) is still challenging. CONCLUSIONS While aDBS seems to be effective to treat movement disorders (Parkinson's disease and essential tremor), its role in cognitive and psychiatric disorders is to be determined, although neurophysiological assumptions are promising.
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Affiliation(s)
- Matteo Guidetti
- Aldo Ravelli Research Center for Neurotechnology and Experimental Neurotherapeutics, Department of Health Sciences, University of Milan, Via Antonio di Rudinì, 8, 20142, Milan, Italy; Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133, Milan, Italy.
| | - Sara Marceglia
- Department of Engineering and Architecture, University of Trieste, 34127, Trieste, Italy.
| | - Aaron Loh
- Division of Neurosurgery, Department of Surgery, Toronto Western Hospital, University of Toronto, Toronto, Ontario, Canada; Krembil Research Institute, University Health Network, Toronto, Ontario, Canada.
| | - Irene E Harmsen
- Division of Neurosurgery, Department of Surgery, Toronto Western Hospital, University of Toronto, Toronto, Ontario, Canada; Krembil Research Institute, University Health Network, Toronto, Ontario, Canada.
| | - Sara Meoni
- Aldo Ravelli Research Center for Neurotechnology and Experimental Neurotherapeutics, Department of Health Sciences, University of Milan, Via Antonio di Rudinì, 8, 20142, Milan, Italy; Movement Disorders Unit, Division of Neurology, CHU Grenoble Alpes, Grenoble, France; Grenoble Institute of Neurosciences, INSERM U1216, University Grenoble Alpes, Grenoble, France.
| | - Guglielmo Foffani
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain; Hospital Nacional de Parapléjicos, SESCAM, Toledo, Spain.
| | - Andres M Lozano
- Division of Neurosurgery, Department of Surgery, Toronto Western Hospital, University of Toronto, Toronto, Ontario, Canada; Krembil Research Institute, University Health Network, Toronto, Ontario, Canada.
| | - Elena Moro
- Movement Disorders Unit, Division of Neurology, CHU Grenoble Alpes, Grenoble, France; Grenoble Institute of Neurosciences, INSERM U1216, University Grenoble Alpes, Grenoble, France.
| | - Jens Volkmann
- Department of Neurology, University of Wurzburg, Germany.
| | - Alberto Priori
- Aldo Ravelli Research Center for Neurotechnology and Experimental Neurotherapeutics, Department of Health Sciences, University of Milan, Via Antonio di Rudinì, 8, 20142, Milan, Italy; ASST Santi Paolo e Carlo, Milan, Italy.
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15
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Merola A, Singh J, Reeves K, Changizi B, Goetz S, Rossi L, Pallavaram S, Carcieri S, Harel N, Shaikhouni A, Sammartino F, Krishna V, Verhagen L, Dalm B. New Frontiers for Deep Brain Stimulation: Directionality, Sensing Technologies, Remote Programming, Robotic Stereotactic Assistance, Asleep Procedures, and Connectomics. Front Neurol 2021; 12:694747. [PMID: 34367055 PMCID: PMC8340024 DOI: 10.3389/fneur.2021.694747] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 06/14/2021] [Indexed: 11/21/2022] Open
Abstract
Over the last few years, while expanding its clinical indications from movement disorders to epilepsy and psychiatry, the field of deep brain stimulation (DBS) has seen significant innovations. Hardware developments have introduced directional leads to stimulate specific brain targets and sensing electrodes to determine optimal settings via feedback from local field potentials. In addition, variable-frequency stimulation and asynchronous high-frequency pulse trains have introduced new programming paradigms to efficiently desynchronize pathological neural circuitry and regulate dysfunctional brain networks not responsive to conventional settings. Overall, these innovations have provided clinicians with more anatomically accurate programming and closed-looped feedback to identify optimal strategies for neuromodulation. Simultaneously, software developments have simplified programming algorithms, introduced platforms for DBS remote management via telemedicine, and tools for estimating the volume of tissue activated within and outside the DBS targets. Finally, the surgical accuracy has improved thanks to intraoperative magnetic resonance or computerized tomography guidance, network-based imaging for DBS planning and targeting, and robotic-assisted surgery for ultra-accurate, millimetric lead placement. These technological and imaging advances have collectively optimized DBS outcomes and allowed “asleep” DBS procedures. Still, the short- and long-term outcomes of different implantable devices, surgical techniques, and asleep vs. awake procedures remain to be clarified. This expert review summarizes and critically discusses these recent innovations and their potential impact on the DBS field.
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Affiliation(s)
- Aristide Merola
- Department of Neurology, The Ohio State University Wexner Medical Center, Columbus, OH, United States
| | - Jaysingh Singh
- Department of Neurology, The Ohio State University Wexner Medical Center, Columbus, OH, United States
| | - Kevin Reeves
- Department of Psychiatry, The Ohio State University Wexner Medical Center, Columbus, OH, United States
| | - Barbara Changizi
- Department of Neurology, The Ohio State University Wexner Medical Center, Columbus, OH, United States
| | - Steven Goetz
- Medtronic PLC Neuromodulation, Minneapolis, MN, United States
| | | | | | | | - Noam Harel
- Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN, United States
| | - Ammar Shaikhouni
- Department of Neurosurgery, The Ohio State University Wexner Medical Center, Columbus, OH, United States
| | - Francesco Sammartino
- Department of Neurosurgery, The Ohio State University Wexner Medical Center, Columbus, OH, United States
| | - Vibhor Krishna
- Department of Neurosurgery, The Ohio State University Wexner Medical Center, Columbus, OH, United States
| | - Leo Verhagen
- Movement Disorder Section, Department of Neurological Sciences, Rush University, Chicago, IL, United States
| | - Brian Dalm
- Department of Neurosurgery, The Ohio State University Wexner Medical Center, Columbus, OH, United States
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Sirica D, Hewitt AL, Tarolli CG, Weber MT, Zimmerman C, Santiago A, Wensel A, Mink JW, Lizárraga KJ. Neurophysiological biomarkers to optimize deep brain stimulation in movement disorders. Neurodegener Dis Manag 2021; 11:315-328. [PMID: 34261338 PMCID: PMC8977945 DOI: 10.2217/nmt-2021-0002] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Intraoperative neurophysiological information could increase accuracy of surgical deep brain stimulation (DBS) lead placement. Subsequently, DBS therapy could be optimized by specifically targeting pathological activity. In Parkinson’s disease, local field potentials (LFPs) excessively synchronized in the beta band (13–35 Hz) correlate with akinetic-rigid symptoms and their response to DBS therapy, particularly low beta band suppression (13–20 Hz) and high frequency gamma facilitation (35–250 Hz). In dystonia, LFPs abnormally synchronize in the theta/alpha (4–13 Hz), beta and gamma (60–90 Hz) bands. Phasic dystonic symptoms and their response to DBS correlate with changes in theta/alpha synchronization. In essential tremor, LFPs excessively synchronize in the theta/alpha and beta bands. Adaptive DBS systems will individualize pathological characteristics of neurophysiological signals to automatically deliver therapeutic DBS pulses of specific spatial and temporal parameters.
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Affiliation(s)
- Daniel Sirica
- Motor Physiology & Neuromodulation Program, Division of Movement Disorders, Department of Neurology, University of Rochester, Rochester, NY 14618, USA
| | - Angela L Hewitt
- Motor Physiology & Neuromodulation Program, Division of Movement Disorders, Department of Neurology, University of Rochester, Rochester, NY 14618, USA.,Division of Child Neurology, Department of Neurology, University of Rochester, Rochester, NY 14623, USA
| | - Christopher G Tarolli
- Motor Physiology & Neuromodulation Program, Division of Movement Disorders, Department of Neurology, University of Rochester, Rochester, NY 14618, USA.,Center for Health & Technology (CHeT), University of Rochester, Rochester, NY 14642, USA
| | - Miriam T Weber
- Motor Physiology & Neuromodulation Program, Division of Movement Disorders, Department of Neurology, University of Rochester, Rochester, NY 14618, USA
| | - Carol Zimmerman
- Motor Physiology & Neuromodulation Program, Division of Movement Disorders, Department of Neurology, University of Rochester, Rochester, NY 14618, USA
| | - Aida Santiago
- Motor Physiology & Neuromodulation Program, Division of Movement Disorders, Department of Neurology, University of Rochester, Rochester, NY 14618, USA
| | - Andrew Wensel
- Motor Physiology & Neuromodulation Program, Division of Movement Disorders, Department of Neurology, University of Rochester, Rochester, NY 14618, USA.,Department of Neurosurgery, University of Rochester, Rochester, NY 14618, USA
| | - Jonathan W Mink
- Motor Physiology & Neuromodulation Program, Division of Movement Disorders, Department of Neurology, University of Rochester, Rochester, NY 14618, USA.,Division of Child Neurology, Department of Neurology, University of Rochester, Rochester, NY 14623, USA
| | - Karlo J Lizárraga
- Motor Physiology & Neuromodulation Program, Division of Movement Disorders, Department of Neurology, University of Rochester, Rochester, NY 14618, USA.,Center for Health & Technology (CHeT), University of Rochester, Rochester, NY 14642, USA
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Marceglia S, Prenassi M, Galbiati TF, Porta M, Zekaj E, Priori A, Servello D. Thalamic Local Field Potentials Are Related to Long-Term DBS Effects in Tourette Syndrome. Front Neurol 2021; 12:578324. [PMID: 33658970 PMCID: PMC7917178 DOI: 10.3389/fneur.2021.578324] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 01/07/2021] [Indexed: 12/13/2022] Open
Abstract
Background: Local field potential (LFP) recordings helped to clarify the pathophysiology of Tourette syndrome (TS) and to define new strategies for deep brain stimulation (DBS) treatment for refractory TS, based on the delivery of stimulation in accordance with changes in the electrical activity of the DBS target area. However, there is little evidence on the relationship between LFP pattern and DBS outcomes in TS. Objective: To investigate the relationship between LFP oscillations and DBS effects on tics and on obsessive compulsive behavior (OCB) comorbidities. Methods: We retrospectively analyzed clinical data and LFP recordings from 17 patients treated with DBS of the centromedian-parafascicular/ventralis oralis (CM-Pf/VO) complex, and followed for more several years after DBS in the treating center. In these patients, LFPs were recorded either in the acute setting (3–5 days after DBS electrode implant) or in the chronic setting (during impulse generator replacement surgery). LFP oscillations were correlated with the Yale Global Tic Severity Scale (YGTSS) and the Yale–Brown Obsessive–Compulsive Scale (Y-BOCS) collected at baseline (before DBS surgery), 1 year after DBS, and at the last follow-up available. Results: We found that, at baseline, in the acute setting, the power of the oscillations included in the 5–15-Hz band, previously identified as TS biomarker, is correlated with the pathophysiology of tics, being significantly correlated with total YGTSS before DBS (Spearman's ρ = 0.701, p = 0.011). The power in the 5–15-Hz band was also correlated with the improvement in Y-BOCS after 1 year of DBS (Spearman's ρ = −0.587, p = 0.045), thus suggesting a relationship with the DBS effects on OCB comorbidities. Conclusions: Our observations confirm that the low-frequency (5–15-Hz) band is a significant biomarker of TS, being related to the severity of tics and, also to the long-term response on OCBs. This represents a step toward both the understanding of the mechanisms underlying DBS effects in TS and the development of adaptive DBS strategies.
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Affiliation(s)
- Sara Marceglia
- Dipartimento di Ingegneria e Architettura, Università degli Studi di Trieste, Trieste, Italy.,Unità Operativa Neurofisiopatologia, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Ca'Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Marco Prenassi
- Dipartimento di Ingegneria e Architettura, Università degli Studi di Trieste, Trieste, Italy.,Unità Operativa Neurofisiopatologia, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Ca'Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Tommaso F Galbiati
- Functional Neurosurgery Unit, Istituto di Ricovero e Cura a Carattere Scientifico Galeazzi Hospital, Milan, Italy
| | - Mauro Porta
- Functional Neurosurgery Unit, Istituto di Ricovero e Cura a Carattere Scientifico Galeazzi Hospital, Milan, Italy
| | - Edvin Zekaj
- Functional Neurosurgery Unit, Istituto di Ricovero e Cura a Carattere Scientifico Galeazzi Hospital, Milan, Italy.,"Aldo Ravelli" Research Center for Neurotechnology and Experimental Brain Therapeutics, University of Milan Medical School, Milan, Italy
| | - Alberto Priori
- "Aldo Ravelli" Research Center for Neurotechnology and Experimental Brain Therapeutics, University of Milan Medical School, Milan, Italy
| | - Domenico Servello
- Functional Neurosurgery Unit, Istituto di Ricovero e Cura a Carattere Scientifico Galeazzi Hospital, Milan, Italy
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18
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Özkurt TE, Akram H, Zrinzo L, Limousin P, Foltynie T, Oswal A, Litvak V. Identification of nonlinear features in cortical and subcortical signals of Parkinson's Disease patients via a novel efficient measure. Neuroimage 2020; 223:117356. [PMID: 32916287 PMCID: PMC8417768 DOI: 10.1016/j.neuroimage.2020.117356] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 07/31/2020] [Accepted: 09/04/2020] [Indexed: 11/25/2022] Open
Abstract
This study offers a novel and efficient measure based on a higher order version of autocorrelative signal memory that can identify nonlinearities in a single time series. The suggested method was applied to simultaneously recorded subthalamic nucleus (STN) local field potentials (LFP) and magnetoencephalography (MEG) from fourteen Parkinson's Disease (PD) patients who underwent surgery for deep brain stimulation. Recordings were obtained during rest for both OFF and ON dopaminergic medication states. We analyzed the bilateral LFP channels that had the maximum beta power in the OFF state and the cortical sources that had the maximum coherence with the selected LFP channels in the alpha band. Our findings revealed the inherent nonlinearity in the PD data as subcortical high beta (20-30 Hz) band and cortical alpha (8-12 Hz) band activities. While the former was discernible without medication (p=0.015), the latter was induced upon the dopaminergic medication (p<6.10-4). The degree of subthalamic nonlinearity was correlated with contralateral tremor severity (r=0.45, p=0.02). Conversely, for the cortical signals nonlinearity was present for the ON medication state with a peak in the alpha band and correlated with contralateral akinesia and rigidity (r=0.46, p=0.02). This correlation appeared to be independent from that of alpha power and the two measures combined explained 34 % of the variance in contralateral akinesia scores. Our findings suggest that particular frequency bands and brain regions display nonlinear features closely associated with distinct motor symptoms and functions.
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Affiliation(s)
- Tolga Esat Özkurt
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK; Middle East Technical University, Department of Health Informatics, Graduate School of Informatics, Ankara, Turkey.
| | - Harith Akram
- Department of Clinical and Movement Neurosciences, UCL Institute of Neurology and The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Ludvic Zrinzo
- Department of Clinical and Movement Neurosciences, UCL Institute of Neurology and The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Patricia Limousin
- Department of Clinical and Movement Neurosciences, UCL Institute of Neurology and The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Tom Foltynie
- Department of Clinical and Movement Neurosciences, UCL Institute of Neurology and The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Ashwini Oswal
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK; Department of Clinical Neurology, John Radcliffe Hospital, Oxford, UK
| | - Vladimir Litvak
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK
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19
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Vissani M, Isaias IU, Mazzoni A. Deep brain stimulation: a review of the open neural engineering challenges. J Neural Eng 2020; 17:051002. [PMID: 33052884 DOI: 10.1088/1741-2552/abb581] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Deep brain stimulation (DBS) is an established and valid therapy for a variety of pathological conditions ranging from motor to cognitive disorders. Still, much of the DBS-related mechanism of action is far from being understood, and there are several side effects of DBS whose origin is unclear. In the last years DBS limitations have been tackled by a variety of approaches, including adaptive deep brain stimulation (aDBS), a technique that relies on using chronically implanted electrodes on 'sensing mode' to detect the neural markers of specific motor symptoms and to deliver on-demand or modulate the stimulation parameters accordingly. Here we will review the state of the art of the several approaches to improve DBS and summarize the main challenges toward the development of an effective aDBS therapy. APPROACH We discuss models of basal ganglia disorders pathogenesis, hardware and software improvements for conventional DBS, and candidate neural and non-neural features and related control strategies for aDBS. MAIN RESULTS We identify then the main operative challenges toward optimal DBS such as (i) accurate target localization, (ii) increased spatial resolution of stimulation, (iii) development of in silico tests for DBS, (iv) identification of specific motor symptoms biomarkers, in particular (v) assessing how LFP oscillations relate to behavioral disfunctions, and (vi) clarify how stimulation affects the cortico-basal-ganglia-thalamic network to (vii) design optimal stimulation patterns. SIGNIFICANCE This roadmap will lead neural engineers novel to the field toward the most relevant open issues of DBS, while the in-depth readers might find a careful comparison of advantages and drawbacks of the most recent attempts to improve DBS-related neuromodulatory strategies.
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Affiliation(s)
- Matteo Vissani
- The BioRobotics Institute, Scuola Superiore Sant'Anna, 56025 Pisa, Italy. Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56025 Pisa, Italy
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20
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Sorkhabi MM, Benjaber M, Brown P, Denison T. Physiological Artifacts and the Implications for Brain-Machine-Interface Design. CONFERENCE PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS 2020; 2020:1498-1504. [PMID: 33479560 PMCID: PMC7116608 DOI: 10.1109/smc42975.2020.9283328] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
The accurate measurement of brain activity by Brain-Machine-Interfaces (BMI) and closed-loop Deep Brain Stimulators (DBS) is one of the most important steps in communicating between the brain and subsequent processing blocks. In conventional chest-mounted systems, frequently used in DBS, a significant amount of artifact can be induced in the sensing interface, often as a common-mode signal applied between the case and the sensing electrodes. Attenuating this common-mode signal can be a serious challenge in these systems due to finite common-mode-rejection-ratio (CMRR) capability in the interface. Emerging BMI and DBS devices are being developed which can mount on the skull. Mounting the system on the cranial region can potentially suppress these induced physiological signals by limiting the artifact amplitude. In this study, we model the effect of artifacts by focusing on cardiac activity, using a current- source dipole model in a torso-shaped volume conductor. Performing finite element simulation with the different DBS architectures, we estimate the ECG common mode artifacts for several device architectures. Using this model helps define the overall requirements for the total system CMRR to maintain resolution of brain activity. The results of the simulations estimate that the cardiac artifacts for skull-mounted systems will have a significantly lower effect than non-cranial systems that include the pectoral region. It is expected that with a pectoral mounted device, a minimum of 60-80 dB CMRR is required to suppress the ECG artifact, depending on device placement relative to the cardiac dipole, while in cranially mounted devices, a 0 dB CMRR is sufficient, in the worst-case scenario. In addition, the model suggests existing commercial devices could optimize performance with a right-hand side placement. The methods used for estimating cardiac artifacts can be extended to other sources such as motion/muscle sources. The susceptibility of the device to artifacts has significant implications for the practical translation of closed-loop DBS and BMI, including the choice of biomarkers, the system design requirements, and the surgical placement of the device relative to artifact sources.
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Affiliation(s)
| | - Moaad Benjaber
- MRC Brain Network Dynamics Unit University of Oxford Oxford, UK
| | - Peter Brown
- MRC Brain Network Dynamics Unit University of Oxford Oxford, UK
| | - Timothy Denison
- MRC Brain Network Dynamics Unit and Department of Engineering Science University of Oxford Oxford, UK
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21
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Debarros J, Gaignon L, He S, Pogosyan A, Benjaber M, Denison T, Brown P, Tan H. Artefact-free recording of local field potentials with simultaneous stimulation for closed-loop Deep-Brain Stimulation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:3367-3370. [PMID: 33018726 DOI: 10.1109/embc44109.2020.9176665] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Continuous high frequency Deep Brain Stimulation (DBS) is a standard therapy for several neurological disorders. Closed-loop DBS is expected to further improve treatment by providing adaptive, on-demand therapy. Local field potentials (LFPs) recorded from the stimulation electrodes are the most often used feedback signal in closed-loop DBS. However, closed-loop DBS based on LFPs requires simultaneous recording and stimulating, which remains a challenge due to persistent stimulation artefacts that distort underlying LFP biomarkers. Here we first investigate the nature of the stimulation-induced artefacts and review several techniques that have been proposed to deal with them. Then we propose a new method to synchronize the sampling clock with the stimulation pulse so that the stimulation artefacts are never sampled, while at the same time the Nyquist-Shannon theorem is satisfied for uninterrupted LFP recording. Test results show that this method achieves true uninterrupted artefact-free LFP recording over a wide frequency band and for a wide range of stimulation frequencies.Clinical relevance-The method proposed here provides continuous and artefact-free recording of LFPs close to the stimulation target, and thereby facilitates the implementation of more advanced closed-loop DBS using LFPs as feedback.
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22
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Fleming JE, Orłowski J, Lowery MM, Chaillet A. Self-Tuning Deep Brain Stimulation Controller for Suppression of Beta Oscillations: Analytical Derivation and Numerical Validation. Front Neurosci 2020; 14:639. [PMID: 32694975 PMCID: PMC7339866 DOI: 10.3389/fnins.2020.00639] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 05/25/2020] [Indexed: 01/06/2023] Open
Abstract
Closed-loop control strategies for deep brain stimulation (DBS) in Parkinson's disease offer the potential to provide more effective control of patient symptoms and fewer side effects than continuous stimulation, while reducing battery consumption. Most of the closed-loop methods proposed and tested to-date rely on controller parameters, such as controller gains, that remain constant over time. While the controller may operate effectively close to the operating point for which it is set, providing benefits when compared to conventional open-loop DBS, it may perform sub-optimally if the operating conditions evolve. Such changes may result from, for example, diurnal variation in symptoms, disease progression or changes in the properties of the electrode-tissue interface. In contrast, an adaptive or “self-tuning” control mechanism has the potential to accommodate slowly varying changes in system properties over a period of days, months, or years. Such an adaptive mechanism would automatically adjust the controller parameters to maintain the desired performance while limiting side effects, despite changes in the system operating point. In this paper, two neural modeling approaches are utilized to derive and test an adaptive control scheme for closed-loop DBS, whereby the gain of a feedback controller is continuously adjusted to sustain suppression of pathological beta-band oscillatory activity at a desired target level. First, the controller is derived based on a simplified firing-rate model of the reciprocally connected subthalamic nucleus (STN) and globus pallidus (GPe). Its efficacy is shown both when pathological oscillations are generated endogenously within the STN-GPe network and when they arise in response to exogenous cortical STN inputs. To account for more realistic biological features, the control scheme is then tested in a physiologically detailed model of the cortical basal ganglia network, comprised of individual conductance-based spiking neurons, and simulates the coupled DBS electric field and STN local field potential. Compared to proportional feedback methods without gain adaptation, the proposed adaptive controller was able to suppress beta-band oscillations with less power consumption, even as the properties of the controlled system evolve over time due to alterations in the target for beta suppression, beta fluctuations and variations in the electrode impedance.
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Affiliation(s)
- John E Fleming
- Neuromuscular Systems Laboratory, UCD School of Electrical & Electronic Engineering, University College Dublin, Dublin, Ireland
| | - Jakub Orłowski
- Laboratoire des Signaux et Systèmes, Université Paris-Saclay, CNRS, CentraleSupélec, Gif-sur-Yvette, France
| | - Madeleine M Lowery
- Neuromuscular Systems Laboratory, UCD School of Electrical & Electronic Engineering, University College Dublin, Dublin, Ireland
| | - Antoine Chaillet
- Laboratoire des Signaux et Systèmes, Université Paris-Saclay, CNRS, CentraleSupélec, Gif-sur-Yvette, France.,Institut Universitaire de France, Paris, France
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23
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Iturrate I, Chavarriaga R, Millán JDR. General principles of machine learning for brain-computer interfacing. HANDBOOK OF CLINICAL NEUROLOGY 2020; 168:311-328. [PMID: 32164862 DOI: 10.1016/b978-0-444-63934-9.00023-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Brain-computer interfaces (BCIs) are systems that translate brain activity patterns into commands that can be executed by an artificial device. This enables the possibility of controlling devices such as a prosthetic arm or exoskeleton, a wheelchair, typewriting applications, or games directly by modulating our brain activity. For this purpose, BCI systems rely on signal processing and machine learning algorithms to decode the brain activity. This chapter provides an overview of the main steps required to do such a process, including signal preprocessing, feature extraction and selection, and decoding. Given the large amount of possible methods that can be used for these processes, a comprehensive review of them is beyond the scope of this chapter, and it is focused instead on the general principles that should be taken into account, as well as discussing good practices on how these methods should be applied and evaluated for proper design of reliable BCI systems.
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Affiliation(s)
- Iñaki Iturrate
- Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Ricardo Chavarriaga
- Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland; Institute of Applied Information Technology (InIT), Zurich University of Applied Sciences ZHAW, Winterthur, Switzerland.
| | - José Del R Millán
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States; Department of Neurology, The University of Texas at Austin, Austin, TX, United States
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24
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Piña-Fuentes D, Beudel M, Little S, van Zijl J, Elting JW, Oterdoom DLM, van Egmond ME, van Dijk JMC, Tijssen MAJ. Toward adaptive deep brain stimulation for dystonia. Neurosurg Focus 2019; 45:E3. [PMID: 30064317 DOI: 10.3171/2018.5.focus18155] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The presence of abnormal neural oscillations within the cortico-basal ganglia-thalamo-cortical (CBGTC) network has emerged as one of the current principal theories to explain the pathophysiology of movement disorders. In theory, these oscillations can be used as biomarkers and thereby serve as a feedback signal to control the delivery of deep brain stimulation (DBS). This new form of DBS, dependent on different characteristics of pathological oscillations, is called adaptive DBS (aDBS), and it has already been applied in patients with Parkinson's disease. In this review, the authors summarize the scientific research to date on pathological oscillations in dystonia and address potential biomarkers that might be used as a feedback signal for controlling aDBS in patients with dystonia.
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Affiliation(s)
- Dan Piña-Fuentes
- Departments of1Neurosurgery and.,2Neurology, University Medical Center Groningen, University of Groningen
| | - Martijn Beudel
- 2Neurology, University Medical Center Groningen, University of Groningen.,3Department of Neurology, Isala Klinieken, Zwolle, The Netherlands; and
| | - Simon Little
- 4Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London, United Kingdom
| | - Jonathan van Zijl
- 2Neurology, University Medical Center Groningen, University of Groningen
| | - Jan Willem Elting
- 2Neurology, University Medical Center Groningen, University of Groningen
| | | | | | | | - Marina A J Tijssen
- 2Neurology, University Medical Center Groningen, University of Groningen
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25
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Petkos K, Guiho T, Degenaar P, Jackson A, Brown P, Denison T, Drakakis EM. A high-performance 4 nV (√Hz) -1 analog front-end architecture for artefact suppression in local field potential recordings during deep brain stimulation. J Neural Eng 2019; 16:066003. [PMID: 31151118 PMCID: PMC6877351 DOI: 10.1088/1741-2552/ab2610] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Recording of local field potentials (LFPs) during deep brain stimulation (DBS) is necessary to investigate the instantaneous brain response to stimulation, minimize time delays for closed-loop neurostimulation and maximise the available neural data. To our knowledge, existing recording systems lack the ability to provide artefact-free high-frequency (>100 Hz) LFP recordings during DBS in real time primarily because of the contamination of the neural signals of interest by the stimulation artefacts. APPROACH To solve this problem, we designed and developed a novel, low-noise and versatile analog front-end (AFE) that uses a high-order (8th) analog Chebyshev notch filter to suppress the artefacts originating from the stimulation frequency. After defining the system requirements for concurrent LFP recording and DBS artefact suppression, we assessed the performance of the realised AFE by conducting both in vitro and in vivo experiments using unipolar and bipolar DBS (monophasic pulses, amplitude ranging from 3 to 6 V peak-to-peak, frequency 140 Hz and pulse width 100 µs). A full performance comparison between the proposed AFE and an identical AFE, equipped with an 8th order analog Bessel notch filter, was also conducted. MAIN RESULTS A high-performance, 4 nV ([Formula: see text])-1 AFE that is capable of recording nV-scale signals was designed in accordance with the imposed specifications. Under both in vitro and in vivo experimental conditions, the proposed AFE provided real-time, low-noise and artefact-free LFP recordings (in the frequency range 0.5-250 Hz) during stimulation. Its sensing and stimulation artefact suppression capabilities outperformed the capabilities of the AFE equipped with the Bessel notch filter. SIGNIFICANCE The designed AFE can precisely record LFP signals, in and without the presence of either unipolar or bipolar DBS, which renders it as a functional and practical AFE architecture to be utilised in a wide range of applications and environments. This work paves the way for the development of externalized research tools for closed-loop neuromodulation that use low- and higher-frequency LFPs as control signals.
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Affiliation(s)
- Konstantinos Petkos
- Department of Bioengineering, Imperial College London, London, United Kingdom. Center for Neurotechnology, Imperial College London, London, United Kingdom
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26
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Parastarfeizabadi M, Kouzani AZ. A Miniature Dual-Biomarker-Based Sensing and Conditioning Device for Closed-Loop DBS. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2019; 7:2000308. [PMID: 31667027 PMCID: PMC6752632 DOI: 10.1109/jtehm.2019.2937776] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2018] [Revised: 05/03/2019] [Accepted: 08/20/2019] [Indexed: 01/15/2023]
Abstract
In this paper, a dual-biomarker-based neural sensing and conditioning device is proposed for closing the feedback loop in deep brain stimulation devices. The device explores both local field potentials (LFPs) and action potentials (APs) as measured biomarkers. It includes two channels, each having four main parts: (1) a pre-amplifier with built-in low-pass filter, (2) a ground shifting circuit, (3) an amplifier with low-pass function, and (4) a high-pass filter. The design specifications include miniature-size, light-weight, and 100 dB gain in the LFP and AP channels. This device has been validated through bench and in-vitro tests. The bench tests have been performed using different sinusoidal signals and pre-recorded neural signals. The in-vitro tests have been conducted in the saline solution that mimics the brain environment. The total weight of the device including a 3 V coin battery, and battery holder is 1.2 g. The diameter of the device is 11.2 mm. The device can be used to concurrently sense LFPs and APs for closing the feedback loop in closed-loop deep brain stimulation systems. It provides a tetherless head-mountable platform suitable for pre-clinical trials.
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27
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Sinclair NC, Fallon JB, Bulluss KJ, Thevathasan W, McDermott HJ. On the neural basis of deep brain stimulation evoked resonant activity. Biomed Phys Eng Express 2019. [DOI: 10.1088/2057-1976/ab366e] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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28
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Deep brain stimulation for Parkinson's disease modulates high-frequency evoked and spontaneous neural activity. Neurobiol Dis 2019; 130:104522. [PMID: 31276793 DOI: 10.1016/j.nbd.2019.104522] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 06/11/2019] [Accepted: 07/01/2019] [Indexed: 01/23/2023] Open
Abstract
Deep brain stimulation is an established therapy for Parkinson's disease; however, its effectiveness is hindered by limited understanding of therapeutic mechanisms and the lack of a robust feedback signal for tailoring stimulation. We recently reported that subthalamic nucleus deep brain stimulation evokes a neural response resembling a decaying high-frequency (200-500 Hz) oscillation that typically has a duration of at least 10 ms and is localizable to the dorsal sub-region. As the morphology of this response suggests a propensity for the underlying neural circuitry to oscillate at a particular frequency, we have named it evoked resonant neural activity. Here, we determine whether this evoked activity is modulated by therapeutic stimulation - a critical attribute of a feedback signal. Furthermore, we investigated whether any related changes occurred in spontaneous local field potentials. Evoked and spontaneous neural activity was intraoperatively recorded from 19 subthalamic nuclei in patients with Parkinson's disease. Recordings were obtained before therapeutic stimulation and during 130 Hz stimulation at increasing amplitudes (0.67-3.38 mA), 'washout' of therapeutic effects, and non-therapeutic 20 Hz stimulation. Therapeutic efficacy was assessed using clinical bradykinesia and rigidity scores. The frequency and amplitude of evoked resonant neural activity varied with the level of 130 Hz stimulation (p < .001). This modulation coincided with improvement in bradykinesia and rigidity (p < .001), and correlated with spontaneous beta band suppression (p < .001). Evoked neural activity occupied a similar frequency band to spontaneous high-frequency oscillations (200-400 Hz), both of which decreased to around twice the 130 Hz stimulation rate. Non-therapeutic stimulation at 20 Hz evoked, but did not modulate, resonant activity. These results indicate that therapeutic deep brain stimulation alters the frequency of evoked and spontaneous oscillations recorded in the subthalamic nucleus that are likely generated by loops within the cortico-basal ganglia-thalamo-cortical network. Evoked resonant neural activity therefore has potential as a tool for providing insight into brain network function and has key attributes of a dynamic feedback signal for optimizing therapy.
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29
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Chiang CH, Viventi J. Artefact-free wireless closed-loop device. Nat Biomed Eng 2019; 3:3-4. [DOI: 10.1038/s41551-018-0340-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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30
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Parastarfeizabadi M, Kouzani AZ, Beckinghausen J, Lin T, Sillitoe RV. A Programmable Multi-biomarker Neural Sensor for Closed-loop DBS. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2018; 7:230-244. [PMID: 30976472 PMCID: PMC6453143 DOI: 10.1109/access.2018.2885336] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Most of the current closed-loop DBS devices use a single biomarker in their feedback loop which may limit their performance and applications. This paper presents design, fabrication, and validation of a programmable multi-biomarker neural sensor which can be integrated into closed-loop DBS devices. The device is capable of sensing a combination of low-frequency (7-45 Hz), and high-frequency (200-1000 Hz) neural signals. The signals can be amplified with a digitally programmable gain within the range 50-100 dB. The neural signals can be stored into a local memory for processing and validation. The sensing and storage functions are implemented via a combination of analog and digital circuits involving preamplifiers, filters, programmable post-amplifiers, microcontroller, digital potentiometer, and flash memory. The device is fabricated, and its performance is validated through: (i) bench tests using sinusoidal and pre-recorded neural signals, (ii) in-vitro tests using pre-recorded neural signals in saline solution, and (iii) in-vivo tests by recording neural signals from freely-moving laboratory mice. The animals were implanted with a PlasticsOne electrode, and recording was conducted after recovery from the electrode implantation surgery. The experimental results are presented and discussed confirming the successful operation of the device. The size and weight of the device enable tetherless back-mountable use in pre-clinical trials.
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Affiliation(s)
| | - Abbas Z. Kouzani
- School of Engineering, Deakin University, Geelong, VIC 3216, Australia
| | - Jaclyn Beckinghausen
- Department of Pathology and Immunology, Department of Neuroscience, and Jan and Dan Duncan Neurological Research Institute of Texas Children’s Hospital, 1250 Moursund Street, Suite 1325, Houston Texas 77030, USA
| | - Tao Lin
- Department of Pathology and Immunology, and Jan and Dan Duncan Neurological Research Institute of Texas Children’s Hospital, 1250 Moursund Street, Suite 1325, Houston Texas 77030, USA
| | - Roy V. Sillitoe
- Department of Pathology and Immunology, Department of Neuroscience, Program in Developmental Biology, Baylor College of Medicine, and Jan and Dan Duncan Neurological Research Institute of Texas Children’s Hospital, 1250 Moursund Street, Suite 1325, Houston Texas 77030, USA
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31
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Zhao D, Sun Q, Cheng S, He M, Chen X, Hou X. Extraction of Parkinson’s Disease-Related Features from Local Field Potentials for Adaptive Deep Brain Stimulation. NEUROPHYSIOLOGY+ 2018. [DOI: 10.1007/s11062-018-9717-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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32
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YOUNG D, WILLETT F, MEMBERG WD, MURPHY B, WALTER B, SWEET J, MILLER J, HOCHBERG LR, KIRSCH RF, AJIBOYE AB. Signal processing methods for reducing artifacts in microelectrode brain recordings caused by functional electrical stimulation. J Neural Eng 2018; 15:026014. [PMID: 29199642 PMCID: PMC5818316 DOI: 10.1088/1741-2552/aa9ee8] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
OBJECTIVE Functional electrical stimulation (FES) is a promising technology for restoring movement to paralyzed limbs. Intracortical brain-computer interfaces (iBCIs) have enabled intuitive control over virtual and robotic movements, and more recently over upper extremity FES neuroprostheses. However, electrical stimulation of muscles creates artifacts in intracortical microelectrode recordings that could degrade iBCI performance. Here, we investigate methods for reducing the cortically recorded artifacts that result from peripheral electrical stimulation. APPROACH One participant in the BrainGate2 pilot clinical trial had two intracortical microelectrode arrays placed in the motor cortex, and thirty-six stimulating intramuscular electrodes placed in the muscles of the contralateral limb. We characterized intracortically recorded electrical artifacts during both intramuscular and surface stimulation. We compared the performance of three artifact reduction methods: blanking, common average reference (CAR) and linear regression reference (LRR), which creates channel-specific reference signals, composed of weighted sums of other channels. MAIN RESULTS Electrical artifacts resulting from surface stimulation were 175 × larger than baseline neural recordings (which were 110 µV peak-to-peak), while intramuscular stimulation artifacts were only 4 × larger. The artifact waveforms were highly consistent across electrodes within each array. Application of LRR reduced artifact magnitudes to less than 10 µV and largely preserved the original neural feature values used for decoding. Unmitigated stimulation artifacts decreased iBCI decoding performance, but performance was almost completely recovered using LRR, which outperformed CAR and blanking and extracted useful neural information during stimulation artifact periods. SIGNIFICANCE The LRR method was effective at reducing electrical artifacts resulting from both intramuscular and surface FES, and almost completely restored iBCI decoding performance (>90% recovery for surface stimulation and full recovery for intramuscular stimulation). The results demonstrate that FES-induced artifacts can be easily mitigated in FES + iBCI systems by using LRR for artifact reduction, and suggest that the LRR method may also be useful in other noise reduction applications.
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Affiliation(s)
- D. YOUNG
- Case Western Reserve Univ., Cleveland, OH
- FES Ctr. of Excellence, Rehab. R&D Service, Louis Stokes Cleveland Dept. of VA Med. Ctr., Cleveland, OH
| | - F. WILLETT
- Case Western Reserve Univ., Cleveland, OH
- FES Ctr. of Excellence, Rehab. R&D Service, Louis Stokes Cleveland Dept. of VA Med. Ctr., Cleveland, OH
- Dept of Neurosurgery, Stanford University, Stanford, CA
| | - W. D. MEMBERG
- Case Western Reserve Univ., Cleveland, OH
- FES Ctr. of Excellence, Rehab. R&D Service, Louis Stokes Cleveland Dept. of VA Med. Ctr., Cleveland, OH
| | - B. MURPHY
- Case Western Reserve Univ., Cleveland, OH
| | - B. WALTER
- Neurol., UH Cleveland Med. Ctr., Cleveland, OH
- Neurol., CWRU Sch. of Med., Cleveland, OH
| | - J. SWEET
- Neurosurg., UH Cleveland Med. Ctr., Cleveland, OH
- Neurolog. Surgery, CWRU Sch. of Med., Cleveland, OH
| | - J. MILLER
- Neurosurg., UH Cleveland Med. Ctr., Cleveland, OH
- Neurolog. Surgery, CWRU Sch. of Med., Cleveland, OH
| | - L. R. HOCHBERG
- Ctr. for Neurorestoration and Neurotechnology, Rehab. R&D Service, Dept. of VA Medical Center, Providence RI
- Sch. of Engin., Brown Univ., Providence, RI
- Neurol., Massachusetts Gen. Hosp., Boston, MA
- Neurol., Harvard Med. Sch., Boston, MA
- Inst. For Brain Sci., Brown Univ., Providence, RI
| | - R. F. KIRSCH
- Case Western Reserve Univ., Cleveland, OH
- FES Ctr. of Excellence, Rehab. R&D Service, Louis Stokes Cleveland Dept. of VA Med. Ctr., Cleveland, OH
| | - A. B. AJIBOYE
- Case Western Reserve Univ., Cleveland, OH
- FES Ctr. of Excellence, Rehab. R&D Service, Louis Stokes Cleveland Dept. of VA Med. Ctr., Cleveland, OH
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Harmsen IE, Rowland NC, Wennberg RA, Lozano AM. Characterizing the effects of deep brain stimulation with magnetoencephalography: A review. Brain Stimul 2018; 11:481-491. [PMID: 29331287 DOI: 10.1016/j.brs.2017.12.016] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2017] [Revised: 12/26/2017] [Accepted: 12/28/2017] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Deep brain stimulation (DBS) is an important form of neuromodulation that is being applied to patients with motor, mood, or cognitive circuit disorders. Despite the efficacy and widespread use of DBS, the precise mechanisms by which it works remain unknown. Over the last decade, magnetoencephalography (MEG) has become an important functional neuroimaging technique used to study DBS. OBJECTIVE This review summarizes the literature related to the use of MEG to characterize the effects of DBS. METHODS Peer reviewed literature on DBS-MEG was obtained by searching the publicly accessible literature databases available on PubMed. The abstracts of all reports were scanned and publications which combined DBS-MEG in human subjects were selected for review. RESULTS A total of 32 publications met the selection criteria, and included studies which applied DBS for Parkinson's disease, dystonia, chronic pain, phantom limb pain, cluster headache, and epilepsy. DBS-MEG studies provided valuable insights into network connectivity, pathological coupling, and the modulatory effects of DBS. CONCLUSIONS As DBS-MEG research continues to develop, we can expect to gain a better understanding of diverse pathophysiological networks and their response to DBS. This knowledge will improve treatment efficacy, reduce side-effects, reveal optimal surgical targets, and advance the development of closed-loop neuromodulation.
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Affiliation(s)
- Irene E Harmsen
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada; Toronto Western Research Institute, Krembil Discovery Tower, University Health Network, Toronto, Ontario, Canada.
| | - Nathan C Rowland
- Department of Neurosurgery, Medical University of South Carolina, Charleston, SC, USA
| | - Richard A Wennberg
- Mitchell Goldhar Magnetoencephalography Unit, Krembil Neuroscience Centre, Toronto Western Hospital, Toronto, Ontario, Canada; Division of Neurology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Andres M Lozano
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada; Toronto Western Research Institute, Krembil Discovery Tower, University Health Network, Toronto, Ontario, Canada
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Marceglia S, Rosa M, Servello D, Porta M, Barbieri S, Moro E, Priori A. Adaptive Deep Brain Stimulation (aDBS) for Tourette Syndrome. Brain Sci 2017; 8:E4. [PMID: 29295486 PMCID: PMC5789335 DOI: 10.3390/brainsci8010004] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Revised: 12/12/2017] [Accepted: 12/13/2017] [Indexed: 12/16/2022] Open
Abstract
Deep brain stimulation (DBS) has emerged as a novel therapy for the treatment of several movement and neuropsychiatric disorders, and may also be suitable for the treatment of Tourette syndrome (TS). The main DBS targets used to date in patients with TS are located within the basal ganglia-thalamo-cortical circuit involved in the pathophysiology of this syndrome. They include the ventralis oralis/centromedian-parafascicular (Vo/CM-Pf) nucleus of the thalamus and the nucleus accumbens. Current DBS treatments deliver continuous electrical stimulation and are not designed to adapt to the patient's symptoms, thereby contributing to unwanted side effects. Moreover, continuous DBS can lead to rapid battery depletion, which necessitates frequent battery replacement surgeries. Adaptive deep brain stimulation (aDBS), which is controlled based on neurophysiological biomarkers, is considered one of the most promising approaches to optimize clinical benefits and to limit the side effects of DBS. aDBS consists of a closed-loop system designed to measure and analyse a control variable reflecting the patient's clinical condition and to modify on-line stimulation settings to improve treatment efficacy. Local field potentials (LFPs), which are sums of pre- and post-synaptic activity arising from large neuronal populations, directly recorded from electrodes implanted for DBS can theoretically represent a reliable correlate of clinical status in patients with TS. The well-established LFP-clinical correlations in patients with Parkinson's disease reported in the last few years provide the rationale for developing and implementing new aDBS devices whose efficacies are under evaluation in humans. Only a few studies have investigated LFP activity recorded from DBS target structures and the relationship of this activity to clinical symptoms in TS. Here, we review the available literature supporting the feasibility of an LFP-based aDBS approach in patients with TS. In addition, to increase such knowledge, we report explorative findings regarding LFP data recently acquired and analysed in patients with TS after DBS electrode implantation at rest, during voluntary and involuntary movements (tics), and during ongoing DBS. Data available up to now suggest that patients with TS have oscillatory patterns specifically associated with the part of the brain they are recorded from, and thereby with clinical manifestations. The Vo/CM-Pf nucleus of the thalamus is involved in movement execution and the pathophysiology of TS. Moreover, the oscillatory patterns in TS are specifically modulated by DBS treatment, as reflected by improvements in TS symptoms. These findings suggest that LFPs recorded from DBS targets may be used to control new aDBS devices capable of adaptive stimulation responsive to the symptoms of TS.
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Affiliation(s)
- Sara Marceglia
- Clinical Center for Neurostimulation, Neurotechnology and Movement Disorders, Fondazione Istituto Ricovero e Cura a Carattere Scientifico (IRCCS) Ca' Granda, Ospedale Maggiore Policlinico, Milan 20122, Italy.
- Dipartimento di Ingegneria e Architettura, Università degli Studi di Trieste, Trieste 34127, Italy.
| | - Manuela Rosa
- Clinical Center for Neurostimulation, Neurotechnology and Movement Disorders, Fondazione Istituto Ricovero e Cura a Carattere Scientifico (IRCCS) Ca' Granda, Ospedale Maggiore Policlinico, Milan 20122, Italy.
| | - Domenico Servello
- Functional Neurosurgery Unit, Galeazzi Hospital and Tourette Center, Milan 20161, Italy.
| | - Mauro Porta
- Functional Neurosurgery Unit, Galeazzi Hospital and Tourette Center, Milan 20161, Italy.
| | - Sergio Barbieri
- Clinical Center for Neurostimulation, Neurotechnology and Movement Disorders, Fondazione Istituto Ricovero e Cura a Carattere Scientifico (IRCCS) Ca' Granda, Ospedale Maggiore Policlinico, Milan 20122, Italy.
| | - Elena Moro
- Division of Neurology, Centre Hospitalier Universitaire de Grenoble, CS 10217, 38043 Grenoble, France.
| | - Alberto Priori
- "Aldo Ravelli" Center for Neurotechnology and Experimental Brain Therapeutics, University of Milan, Milan 20142 , Italy.
- Department of Health Sciences, University of Milan & ASST Santi Paolo e Carlo, Milan 20142, Italy.
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Parastarfeizabadi M, Kouzani AZ. Advances in closed-loop deep brain stimulation devices. J Neuroeng Rehabil 2017; 14:79. [PMID: 28800738 PMCID: PMC5553781 DOI: 10.1186/s12984-017-0295-1] [Citation(s) in RCA: 113] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Accepted: 08/04/2017] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Millions of patients around the world are affected by neurological and psychiatric disorders. Deep brain stimulation (DBS) is a device-based therapy that could have fewer side-effects and higher efficiencies in drug-resistant patients compared to other therapeutic options such as pharmacological approaches. Thus far, several efforts have been made to incorporate a feedback loop into DBS devices to make them operate in a closed-loop manner. METHODS This paper presents a comprehensive investigation into the existing research-based and commercial closed-loop DBS devices. It describes a brief history of closed-loop DBS techniques, biomarkers and algorithms used for closing the feedback loop, components of the current research-based and commercial closed-loop DBS devices, and advancements and challenges in this field of research. This review also includes a comparison of the closed-loop DBS devices and provides the future directions of this area of research. RESULTS Although we are in the early stages of the closed-loop DBS approach, there have been fruitful efforts in design and development of closed-loop DBS devices. To date, only one commercial closed-loop DBS device has been manufactured. However, this system does not have an intelligent and patient dependent control algorithm. A closed-loop DBS device requires a control algorithm to learn and optimize the stimulation parameters according to the brain clinical state. CONCLUSIONS The promising clinical effects of open-loop DBS have been demonstrated, indicating DBS as a pioneer technology and treatment option to serve neurological patients. However, like other commercial devices, DBS needs to be automated and modernized.
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Affiliation(s)
| | - Abbas Z. Kouzani
- School of Engineering, Deakin University, Waurn Ponds, VIC 3216 Australia
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Qian X, Chen Y, Feng Y, Ma B, Hao H, Li L. A platform for long-term monitoring the deep brain rhythms. Biomed Phys Eng Express 2017. [DOI: 10.1088/2057-1976/aa50d6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Risk of Infection After Local Field Potential Recording from Externalized Deep Brain Stimulation Leads in Parkinson's Disease. World Neurosurg 2017; 97:64-69. [DOI: 10.1016/j.wneu.2016.09.069] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Revised: 09/14/2016] [Accepted: 09/16/2016] [Indexed: 11/22/2022]
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Qian X, Chen Y, Feng Y, Ma B, Hao H, Li L. A Method for Removal of Deep Brain Stimulation Artifact From Local Field Potentials. IEEE Trans Neural Syst Rehabil Eng 2016; 25:2217-2226. [PMID: 28113981 DOI: 10.1109/tnsre.2016.2613412] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This paper presents a signal processing method for the electrophysiology simultaneously recorded during deep brain stimulation (DBS) as a research tool. Regarding the local field potential (LFP) signals recorded during stimulation, a novel method was proposed for removal of stimulation artifacts caused by the much stronger stimulating pulse compared to typical LFP. This artifact suppression method was tested and evaluated in an in vitro situation. The results indicate that the stimulation artifacts are well suppressed by this method. Secondly, this method was tested in vivo in Parkinson's disease (PD) patients. It was used to process the LFP signals recorded intraoperatively from PD patients to preliminarily explore the quantitative dependencies of beta band synchronization variations in the subthalamic nucleus (STNs) on the applied DBS parameters, including stimulation voltage, frequency and pulse width. The results confirm that DBS therapy can suppress excessive beta frequency activity and that the degree of attenuation increases with increasing DBS voltage within a range of 1-3 V and increasing DBS frequency within a range of 60-120 Hz. The proposed artifact suppression method provides technical support for exploring the direct effect of electrical stimulation on the brain activities.
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Yang AC, Meng DW, Liu HG, Shi L, Zhang K, Qiao H, Yang LC, Hao HW, Li LM, Zhang JG. The ability of anterior thalamic signals to predict seizures in temporal lobe epilepsy in kainate-treated rats. Epilepsia 2016; 57:1369-76. [PMID: 27481634 DOI: 10.1111/epi.13469] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/16/2016] [Indexed: 11/28/2022]
Abstract
OBJECTIVE To analyze the local field potential (LFP) of the anterior nucleus of the thalamus (ANT) of epileptic rats using the Generic Osorio-Frei algorithm (GOFA), and to determine the ability of the ANT LFP to predict clinical seizures in temporal lobe epilepsy. METHODS GOFA is an advanced real-time technique used to detect and predict seizures. In this article, GOFA was utilized to process the electrical signals of ANT and the motor cortex recorded in 12 rat models of temporal lobe epilepsy (TLE) induced via the injection of kainic acid into the unilateral hippocampus. The electroencephalography (EEG) data included (1) 161 clinical seizures (each contained a 10-min segment) involving the ANT and cortical regions and (2) one hundred three 10-min segments of randomly selected interictal (no seizure) data. RESULTS Minimal false-positives (0.51 ± 0.36/h) and no false-negatives were detected based on the ANT LFP data processed using GOFA. In ANT LFP, the delay from electrographic onset (EO) to automated onset (AO) was 1.24 ± 0.47 s, and the delay from AO to clinical onset (CO) was 7.73 ± 3.23 s. The AO time occurred significantly earlier in the ANT than in the cortex (p = 0.001). In 75.2% of the clinical onsets predicted by ANT LFP, it was 1.37 ± 0.82 s ahead of the prediction of cortical potentials (CPs), and the remainder were 0.84 ± 0.31 s slower than the prediction of CPs. SIGNIFICANCE ANT LFP appears to be an optimal option for the prediction of seizures in temporal lobe epilepsy. It was possible to upgrade the responsive neurostimulation system to emit electrical stimulation in response to the prediction of epileptic seizures based on the changes in the ANT LFP.
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Affiliation(s)
- An-Chao Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Da-Wei Meng
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Huan-Guang Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lin Shi
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Hui Qiao
- Department of Electrophysiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Lin-Chang Yang
- Institute of Man-Machine and Environmental Engineering, School of Aerospace, Tsinghua University, Beijing, China
| | - Hong-Wei Hao
- Institute of Man-Machine and Environmental Engineering, School of Aerospace, Tsinghua University, Beijing, China
| | - Lu-Ming Li
- Institute of Man-Machine and Environmental Engineering, School of Aerospace, Tsinghua University, Beijing, China
| | - Jian-Guo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
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Yang Z, Xu J, Nguyen AT, Wu T, Zhao W, Tam WK. Neuronix enables continuous, simultaneous neural recording and electrical microstimulation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2016:4451-4454. [PMID: 28269266 DOI: 10.1109/embc.2016.7591715] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
This paper reports a novel neurotechnology (Neuronix) and its validation through experiments. It is a miniature system-on-chip (SoC) that allows recording with simultaneous electrical microstimulation. This function has not been demonstrated before and enables precise, closed-loop neuromodulation. Neuronix represents recent advancement in brain technology and applies to both animal research and clinical applications.
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Oswal A, Beudel M, Zrinzo L, Limousin P, Hariz M, Foltynie T, Litvak V, Brown P. Deep brain stimulation modulates synchrony within spatially and spectrally distinct resting state networks in Parkinson's disease. Brain 2016; 139:1482-96. [PMID: 27017189 PMCID: PMC4845255 DOI: 10.1093/brain/aww048] [Citation(s) in RCA: 171] [Impact Index Per Article: 21.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Accepted: 01/25/2016] [Indexed: 11/13/2022] Open
Abstract
Chronic dopamine depletion in Parkinson's disease leads to progressive motor and cognitive impairment, which is associated with the emergence of characteristic patterns of synchronous oscillatory activity within cortico-basal-ganglia circuits. Deep brain stimulation of the subthalamic nucleus is an effective treatment for Parkinson's disease, but its influence on synchronous activity in cortico-basal-ganglia loops remains to be fully characterized. Here, we demonstrate that deep brain stimulation selectively suppresses certain spatially and spectrally segregated resting state subthalamic nucleus-cortical networks. To this end we used a validated and novel approach for performing simultaneous recordings of the subthalamic nucleus and cortex using magnetoencephalography (during concurrent subthalamic nucleus deep brain stimulation). Our results highlight that clinically effective subthalamic nucleus deep brain stimulation suppresses synchrony locally within the subthalamic nucleus in the low beta oscillatory range and furthermore that the degree of this suppression correlates with clinical motor improvement. Moreover, deep brain stimulation relatively selectively suppressed synchronization of activity between the subthalamic nucleus and mesial premotor regions, including the supplementary motor areas. These mesial premotor regions were predominantly coupled to the subthalamic nucleus in the high beta frequency range, but the degree of deep brain stimulation-associated suppression in their coupling to the subthalamic nucleus was not found to correlate with motor improvement. Beta band coupling between the subthalamic nucleus and lateral motor areas was not influenced by deep brain stimulation. Motor cortical coupling with subthalamic nucleus predominantly involved driving of the subthalamic nucleus, with those drives in the higher beta frequency band having much shorter net delays to subthalamic nucleus than those in the lower beta band. These observations raise the possibility that cortical connectivity with the subthalamic nucleus in the high and low beta bands may reflect coupling mediated predominantly by the hyperdirect and indirect pathways to subthalamic nucleus, respectively, and that subthalamic nucleus deep brain stimulation predominantly suppresses the former. Yet only the change in strength of local subthalamic nucleus oscillations correlates with the degree of improvement during deep brain stimulation, compatible with the current view that a strengthened hyperdirect pathway is a prerequisite for locally generated beta activity but that it is the severity of the latter that may determine or index motor impairment.
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Affiliation(s)
- Ashwini Oswal
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Oxford, UK Medical Research Council Brain Network Dynamics Unit, University of Oxford, UK Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, 12 Queen Square, London, UK
| | - Martijn Beudel
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Oxford, UK Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London, UK University Medical Centre Groningen, Department of Neurology, University of Groningen, The Netherlands
| | - Ludvic Zrinzo
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London, UK
| | - Patricia Limousin
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London, UK
| | - Marwan Hariz
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London, UK
| | - Tom Foltynie
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London, UK
| | - Vladimir Litvak
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, 12 Queen Square, London, UK
| | - Peter Brown
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Oxford, UK Medical Research Council Brain Network Dynamics Unit, University of Oxford, UK
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Oswal A, Jha A, Neal S, Reid A, Bradbury D, Aston P, Limousin P, Foltynie T, Zrinzo L, Brown P, Litvak V. Analysis of simultaneous MEG and intracranial LFP recordings during Deep Brain Stimulation: a protocol and experimental validation. J Neurosci Methods 2015; 261:29-46. [PMID: 26698227 PMCID: PMC4758829 DOI: 10.1016/j.jneumeth.2015.11.029] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2015] [Accepted: 11/30/2015] [Indexed: 11/17/2022]
Abstract
Setup for MEG and intracranial recordings during Deep Brain Stimulation is described. Phantom experiment showed correct recovery of oscillatory sources despite artefacts. The method is applied to real data from a patient with Parkinson's Disease. Cortico-subthalamic coherence profiles on and off stimulation were comparable.
Background Deep Brain Stimulation (DBS) is an effective treatment for several neurological and psychiatric disorders. In order to gain insights into the therapeutic mechanisms of DBS and to advance future therapies a better understanding of the effects of DBS on large-scale brain networks is required. New method In this paper, we describe an experimental protocol and analysis pipeline for simultaneously performing DBS and intracranial local field potential (LFP) recordings at a target brain region during concurrent magnetoencephalography (MEG) measurement. Firstly we describe a phantom setup that allowed us to precisely characterise the MEG artefacts that occurred during DBS at clinical settings. Results Using the phantom recordings we demonstrate that with MEG beamforming it is possible to recover oscillatory activity synchronised to a reference channel, despite the presence of high amplitude artefacts evoked by DBS. Finally, we highlight the applicability of these methods by illustrating in a single patient with Parkinson's disease (PD), that changes in cortical-subthalamic nucleus coupling can be induced by DBS. Comparison with existing approaches To our knowledge this paper provides the first technical description of a recording and analysis pipeline for combining simultaneous cortical recordings using MEG, with intracranial LFP recordings of a target brain nucleus during DBS.
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Affiliation(s)
- Ashwini Oswal
- Wellcome Trust Centre for Neuroimaging, 12 Queen Square, London, UK; Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Oxford, UK
| | - Ashwani Jha
- Sobell Department of Motor Neuroscience and Movement Disorders, Institute of Neurology, Queen Square, London, UK
| | - Spencer Neal
- Sobell Department of Motor Neuroscience and Movement Disorders, Institute of Neurology, Queen Square, London, UK
| | - Alphonso Reid
- Wellcome Trust Centre for Neuroimaging, 12 Queen Square, London, UK
| | - David Bradbury
- Wellcome Trust Centre for Neuroimaging, 12 Queen Square, London, UK
| | - Peter Aston
- Wellcome Trust Centre for Neuroimaging, 12 Queen Square, London, UK
| | - Patricia Limousin
- Sobell Department of Motor Neuroscience and Movement Disorders, Institute of Neurology, Queen Square, London, UK
| | - Tom Foltynie
- Sobell Department of Motor Neuroscience and Movement Disorders, Institute of Neurology, Queen Square, London, UK
| | - Ludvic Zrinzo
- Sobell Department of Motor Neuroscience and Movement Disorders, Institute of Neurology, Queen Square, London, UK
| | - Peter Brown
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Oxford, UK
| | - Vladimir Litvak
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Oxford, UK.
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Connolly AT, Muralidharan A, Hendrix C, Johnson L, Gupta R, Stanslaski S, Denison T, Baker KB, Vitek JL, Johnson MD. Local field potential recordings in a non-human primate model of Parkinsons disease using the Activa PC + S neurostimulator. J Neural Eng 2015; 12:066012. [PMID: 26469737 DOI: 10.1088/1741-2560/12/6/066012] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Using the Medtronic Activa® PC + S system, this study investigated how passive joint manipulation, reaching behavior, and deep brain stimulation (DBS) modulate local field potential (LFP) activity in the subthalamic nucleus (STN) and globus pallidus (GP). APPROACH Five non-human primates were implanted unilaterally with one or more DBS leads. LFPs were collected in montage recordings during resting state conditions and during motor tasks that facilitate the expression of parkinsonian motor signs. These recordings were made in the naïve state in one subject, in the parkinsonian state in two subjects, and in both naïve and parkinsonian states in two subjects. MAIN RESULTS LFPs measured at rest were consistent over time for a given recording location and parkinsonian state in a given subject; however, LFPs were highly variable between subjects, between and within recording locations, and across parkinsonian states. LFPs in both naïve and parkinsonian states across all recorded nuclei contained a spectral peak in the beta band (10-30 Hz). Moreover, the spectral content of recorded LFPs was modulated by passive and active movement of the subjects' limbs. LFPs recorded during a cued-reaching task displayed task-related beta desynchronization in STN and GP. The bidirectional capabilities of the Activa® PC + S also allowed for recording LFPs while delivering DBS. The therapeutic effect of STN DBS on parkinsonian rigidity outlasted stimulation for 30-60 s, but there was no correlation with beta band power. SIGNIFICANCE This study emphasizes (1) the variability in spontaneous LFPs amongst subjects and (2) the value of using the Activa® PC + S system to record neural data in the context of behavioral tasks that allow one to evaluate a subject's symptomatology.
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Chu P, Muller R, Koralek A, Carmena JM, Rabaey JM, Gambini S. Equalization for intracortical microstimulation artifact reduction. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2013:245-8. [PMID: 24109670 DOI: 10.1109/embc.2013.6609483] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
We present a method for decreasing the duration of artifacts present during intra-cortical microstimulation (ICMS) recordings by using techniques developed for digital communications. We replace the traditional monophasic or biphasic current stimulation pulse with a patterned pulse stream produced by a Zero Forcing Equalizer (ZFE) filter after characterizing the artifact as a communications channel. The results find that using the ZFE stimulus has the potential to reduce artifact width by more than 70%. Considerations for the hardware implementation of the equalizer are presented.
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Kale RP, Kouzani AZ, Walder K, Berk M, Tye SJ. Evolution of optogenetic microdevices. NEUROPHOTONICS 2015; 2:031206. [PMID: 26158015 PMCID: PMC4481025 DOI: 10.1117/1.nph.2.3.031206] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Accepted: 05/27/2015] [Indexed: 05/30/2023]
Abstract
Implementation of optogenetic techniques is a recent addition to the neuroscientists' preclinical research arsenal, helping to expose the intricate connectivity of the brain and allowing for on-demand direct modulation of specific neural pathways. Developing an optogenetic system requires thorough investigation of the optogenetic technique and of previously fabricated devices, which this review accommodates. Many experiments utilize bench-top systems that are bulky, expensive, and necessitate tethering to the animal. However, these bench-top systems can make use of power-demanding technologies, such as concurrent electrical recording. Newer portable microdevices and implantable systems carried by freely moving animals are being fabricated that take advantage of wireless energy harvesting to power a system and allow for natural movements that are vital for behavioral testing and analysis. An investigation of the evolution of tethered, portable, and implantable optogenetic microdevices is presented, and an analysis of benefits and detriments of each system, including optical power output, device dimensions, electrode width, and weight is given. Opsins, light sources, and optical fiber coupling are also discussed to optimize device parameters and maximize efficiency from the light source to the fiber, respectively. These attributes are important considerations when designing and developing improved optogenetic microdevices.
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Affiliation(s)
- Rajas P. Kale
- Deakin University School of Engineering, Faculty of Science, Engineering, and Built Environment, 75 Pigdons Road, Waurn Ponds, Victoria 3216, Australia
- Mayo Clinic Department of Psychiatry and Psychology, 200 First Street SW, Rochester, Minnesota 55905, United States
| | - Abbas Z. Kouzani
- Deakin University School of Engineering, Faculty of Science, Engineering, and Built Environment, 75 Pigdons Road, Waurn Ponds, Victoria 3216, Australia
| | - Ken Walder
- Deakin University School of Medicine, 75 Pigdons Road, Waurn Ponds, Victoria 3216, Australia
| | - Michael Berk
- Deakin University, IMPACT Strategic Research Centre, Faulty of Health, School of Medicine, Barwon Health, Geelong, Victoria, Australia
- Orygen, National Centre of Excellence in Youth Mental Health, Department of Psychiatry, 35 Poplar Road, Parkville, Victoria 3052, Australia
- University of Melbourne, Florey Institute of Neuroscience and Mental Health, 30 Royal Parade, Parkville Victoria 3052, Australia
| | - Susannah J. Tye
- Mayo Clinic Department of Psychiatry and Psychology, 200 First Street SW, Rochester, Minnesota 55905, United States
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Priori A. Technology for deep brain stimulation at a gallop. Mov Disord 2015; 30:1206-12. [PMID: 26011821 PMCID: PMC4736681 DOI: 10.1002/mds.26253] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2014] [Revised: 03/13/2015] [Accepted: 03/30/2015] [Indexed: 11/16/2022] Open
Affiliation(s)
- Alberto Priori
- Clinical Center for Neurostimulation, Neurotechnology and Movement Disorders, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, and University of Milan, Italy
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Wu H, Ghekiere H, Beeckmans D, Tambuyzer T, van Kuyck K, Aerts JM, Nuttin B. Conceptualization and validation of an open-source closed-loop deep brain stimulation system in rat. Sci Rep 2015; 4:9921. [PMID: 25897892 PMCID: PMC4404680 DOI: 10.1038/srep09921] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Accepted: 03/20/2015] [Indexed: 11/24/2022] Open
Abstract
Conventional deep brain stimulation (DBS) applies constant electrical stimulation to specific brain regions to treat neurological disorders. Closed-loop DBS with real-time feedback is gaining attention in recent years, after proved more effective than conventional DBS in terms of pathological symptom control clinically. Here we demonstrate the conceptualization and validation of a closed-loop DBS system using open-source hardware. We used hippocampal theta oscillations as system input, and electrical stimulation in the mesencephalic reticular formation (mRt) as controller output. It is well documented that hippocampal theta oscillations are highly related to locomotion, while electrical stimulation in the mRt induces freezing. We used an Arduino open-source microcontroller between input and output sources. This allowed us to use hippocampal local field potentials (LFPs) to steer electrical stimulation in the mRt. Our results showed that closed-loop DBS significantly suppressed locomotion compared to no stimulation, and required on average only 56% of the stimulation used in open-loop DBS to reach similar effects. The main advantages of open-source hardware include wide selection and availability, high customizability, and affordability. Our open-source closed-loop DBS system is effective, and warrants further research using open-source hardware for closed-loop neuromodulation.
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Affiliation(s)
- Hemmings Wu
- Research Group Experimental Neurosurgery and Neuroanatomy, KU Leuven, Leuven, Belgium
| | - Hartwin Ghekiere
- Department of Biosystems, M3-BIORES: Measure, Model & Manage Bioresponses, KU Leuven, Leuven, Belgium
| | - Dorien Beeckmans
- Research Group Experimental Neurosurgery and Neuroanatomy, KU Leuven, Leuven, Belgium
| | - Tim Tambuyzer
- Department of Biosystems, M3-BIORES: Measure, Model & Manage Bioresponses, KU Leuven, Leuven, Belgium
| | - Kris van Kuyck
- Research Group Experimental Neurosurgery and Neuroanatomy, KU Leuven, Leuven, Belgium
| | - Jean-Marie Aerts
- Department of Biosystems, M3-BIORES: Measure, Model & Manage Bioresponses, KU Leuven, Leuven, Belgium
| | - Bart Nuttin
- Research Group Experimental Neurosurgery and Neuroanatomy, KU Leuven, Leuven, Belgium.,Department of Neurosurgery, University Hospitals Leuven, Leuven, Belgium
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Beudel M, Little S, Pogosyan A, Ashkan K, Foltynie T, Limousin P, Zrinzo L, Hariz M, Bogdanovic M, Cheeran B, Green AL, Aziz T, Thevathasan W, Brown P. Tremor Reduction by Deep Brain Stimulation Is Associated With Gamma Power Suppression in Parkinson's Disease. Neuromodulation 2015; 18:349-54. [PMID: 25879998 PMCID: PMC4829100 DOI: 10.1111/ner.12297] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Objectives Rest tremor is a cardinal symptom of Parkinson's disease (PD), and is readily suppressed by deep brain stimulation (DBS) of the subthalamic nucleus (STN). The therapeutic effect of the latter on bradykinesia and rigidity has been associated with the suppression of exaggerated beta (13–30 Hz) band synchronization in the vicinity of the stimulating electrode, but there is no correlation between beta suppression and tremor amplitude. In the present study, we investigate whether tremor suppression is related to suppression of activities at other frequencies. Materials and Methods We recorded hand tremor and contralateral local field potential (LFP) activity from DBS electrodes during stimulation of the STN in 15 hemispheres in 11 patients with PD. DBS was applied with increasing voltages starting at 0.5 V until tremor suppression was achieved or until 4.5 V was reached. Results Tremor was reduced to 48.9% ± 10.9% of that without DBS once stimulation reached 2.5–3 V (t14 = −4.667, p < 0.001). There was a parallel suppression of low gamma (31–45 Hz) power to 92.5% ± 3% (t14 = −2.348, p = 0.034). This was not seen over a band containing tremor frequencies and their harmonic (4–12 Hz), or over the beta band. Moreover, low gamma power correlated with tremor severity (mean r = 0.43 ± 0.14, p = 0.008) within subjects. This was not the case for LFP power in the other two bands. Conclusions Our findings support a relationship between low gamma oscillations and PD tremor, and reinforce the principle that the subthalamic LFP is a rich signal that may contain information about the severity of multiple different Parkinsonian features.
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Affiliation(s)
- Martijn Beudel
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK.,Department of Neurology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Simon Little
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Alek Pogosyan
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Keyoumars Ashkan
- Department of Neurosurgery, Kings College Hospital, Kings College London, London, UK
| | - Thomas Foltynie
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience & Movement Disorders, UCL Institute of Neurology, Queen Square, London, UK
| | - Patricia Limousin
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience & Movement Disorders, UCL Institute of Neurology, Queen Square, London, UK
| | - Ludvic Zrinzo
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience & Movement Disorders, UCL Institute of Neurology, Queen Square, London, UK
| | - Marwan Hariz
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience & Movement Disorders, UCL Institute of Neurology, Queen Square, London, UK
| | - Marko Bogdanovic
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Binith Cheeran
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Alexander L Green
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Tipu Aziz
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Wesley Thevathasan
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK.,Melbourne Brain Centre, Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Melbourne, Victoria, Australia.,The Bionics Institute, Melbourne, Victoria, Australia
| | - Peter Brown
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
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Marceglia S, Rossi E, Rosa M, Cogiamanian F, Rossi L, Bertolasi L, Vogrig A, Pinciroli F, Barbieri S, Priori A. Web-based telemonitoring and delivery of caregiver support for patients with Parkinson disease after deep brain stimulation: protocol. JMIR Res Protoc 2015; 4:e30. [PMID: 25803512 PMCID: PMC4376163 DOI: 10.2196/resprot.4044] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2014] [Accepted: 11/25/2014] [Indexed: 11/25/2022] Open
Abstract
Background The increasing number of patients, the high costs of management, and the chronic progress of the disease that prevents patients from performing even simple daily activities make Parkinson disease (PD) a complex pathology with a high impact on society. In particular, patients implanted with deep brain stimulation (DBS) electrodes face a highly fragile stabilization period, requiring specific support at home. However, DBS patients are followed usually by untrained personnel (caregivers or family), without specific care pathways and supporting systems. Objective This projects aims to (1) create a reference consensus guideline and a shared requirements set for the homecare and monitoring of DBS patients, (2) define a set of biomarkers that provides alarms to caregivers for continuous home monitoring, and (3) implement an information system architecture allowing communication between health care professionals and caregivers and improving the quality of care for DBS patients. Methods The definitions of the consensus care pathway and of caregiver needs will be obtained by analyzing the current practices for patient follow-up through focus groups and structured interviews involving health care professionals, patients, and caregivers. The results of this analysis will be represented in a formal graphical model of the process of DBS patient care at home. To define the neurophysiological biomarkers to be used to raise alarms during the monitoring process, neurosignals will be acquired from DBS electrodes through a new experimental system that records while DBS is turned ON and transmits signals by radiofrequency. Motor, cognitive, and behavioral protocols will be used to study possible feedback/alarms to be provided by the system. Finally, a set of mobile apps to support the caregiver at home in managing and monitoring the patient will be developed and tested in the community of caregivers that participated in the focus groups. The set of developed apps will be connected to the already existing WebBioBank Web-based platform allowing health care professionals to manage patient electronic health records and neurophysiological signals. New modules in the WebBioBank platform will be implemented to allow integration and data exchange with mobile health apps. Results The results of this project will provide a novel approach to long-term evaluation of patients with chronic, severe conditions in the homecare environment, based on caregiver empowerment and tailored applications developed according to consensus care pathways established by clinicians. Conclusions The creation of a direct communication channel between health care professionals and caregivers can benefit large communities of patients and would represent a scalable experience in integrating data and information coming from a clinical setting to those in home monitoring.
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Affiliation(s)
- Sara Marceglia
- Clinical Center for Neurostimulation, Neurotechnology, and Movement Disorders, Fondazione IRCCS Ca'Granda Ospedale Maggiore Policlinico, Milan, Italy.
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