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Tian Y, Saradhi S, Bello E, Johnson MD, D’Eleuterio G, Popovic MR, Lankarany M. Model-based closed-loop control of thalamic deep brain stimulation. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1356653. [PMID: 38650608 PMCID: PMC11033853 DOI: 10.3389/fnetp.2024.1356653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 03/18/2024] [Indexed: 04/25/2024]
Abstract
Introduction: Closed-loop control of deep brain stimulation (DBS) is beneficial for effective and automatic treatment of various neurological disorders like Parkinson's disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists' expertise and patients' experience. Continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system uses a feedback biomarker/signal to track worsening (or improving) of patients' symptoms and offers several advantages compared to the open-loop DBS system. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying DBS or symptoms, e.g., how DBS modulates dynamics of synaptic plasticity. Methods: In this work, we propose a computational framework for development of a model-based DBS controller where a neural model can describe the relationship between DBS and neural activity and a polynomial-based approximation can estimate the relationship between neural and behavioral activities. A controller is used in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. By using the proposed computational framework, these DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given electromyography (EMG) recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed neural model simulation in which physiological mechanisms underlying Vim-DBS are linked to symptomatic changes in EMG signals. By using a proportional-integral-derivative (PID) controller, we showed that a closed-loop system can track EMG signals and adjust the stimulation frequency of Vim-DBS so that the power of EMG reaches a desired control target. Results and discussion: We demonstrated that the model-based DBS frequency aligns well with that used in clinical studies. Our model-based closed-loop system is adaptable to different control targets and can potentially be used for different diseases and personalized systems.
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Affiliation(s)
- Yupeng Tian
- Krembil Brain Institute—University Health Network, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada
| | - Srikar Saradhi
- Krembil Brain Institute—University Health Network, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Edward Bello
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Matthew D. Johnson
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | | | - Milos R. Popovic
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada
- Center for Advancing Neurotechnological Innovation to Application, University Health Network and University of Toronto, Toronto, ON, Canada
| | - Milad Lankarany
- Krembil Brain Institute—University Health Network, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada
- Center for Advancing Neurotechnological Innovation to Application, University Health Network and University of Toronto, Toronto, ON, Canada
- Department of Physiology, University of Toronto, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
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Chao-Chia Lu D, Boulay C, Chan ADC, Sachs AJ. A Systematic Review of Neurophysiology-Based Localization Techniques Used in Deep Brain Stimulation Surgery of the Subthalamic Nucleus. Neuromodulation 2024; 27:409-421. [PMID: 37462595 DOI: 10.1016/j.neurom.2023.02.081] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 01/13/2023] [Accepted: 02/09/2023] [Indexed: 04/05/2024]
Abstract
OBJECTIVE This systematic review is conducted to identify, compare, and analyze neurophysiological feature selection, extraction, and classification to provide a comprehensive reference on neurophysiology-based subthalamic nucleus (STN) localization. MATERIALS AND METHODS The review was carried out using the methods and guidelines of the Kitchenham systematic review and provides an in-depth analysis on methods proposed on STN localization discussed in the literature between 2000 and 2021. Three research questions were formulated, and 115 publications were identified to answer the questions. RESULTS The three research questions formulated are answered using the literature found on the respective topics. This review discussed the technologies used in past research, and the performance of the state-of-the-art techniques is also reviewed. CONCLUSION This systematic review provides a comprehensive reference on neurophysiology-based STN localization by reviewing the research questions other new researchers may also have.
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Affiliation(s)
| | | | | | - Adam J Sachs
- The Ottawa Hospital Research Institute, Ottawa, ON, Canada
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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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Branco LRF, Viswanathan A, Tarakad A, Ince NF. Construction of semi-supervised spatial projections to identify the source of beta- and high frequency oscillations in Parkinson's disease. INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING : [PROCEEDINGS]. INTERNATIONAL IEEE EMBS CONFERENCE ON NEURAL ENGINEERING 2023; 2023:10.1109/ner52421.2023.10123890. [PMID: 37601420 PMCID: PMC10440159 DOI: 10.1109/ner52421.2023.10123890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
Abstract
Traditional deep brain stimulation (DBS) treatment for Parkinson's disease (PD) targets the placement of DBS leads into subthalamic nucleus (STN). Extraction of neurobiomarkers from STN local field potential activity can be used for the optimization of DBS. Beta (12-30 Hz) and high frequency oscillations (200-450 Hz, HFO) of STN and their phase-amplitude coupling have been previously correlated with symptom severity in PD. The typical approach is to take bipolar derivations of electrode contacts in order to enhance recordings of local brain activity and suppress noise levels. This approach can often cancel the signals in correlated neighboring contacts and create ambiguity in which monopolar contact to select for the identification of the main source of the oscillatory signal. To improve local specificity and help identify the source of beta and HFO in terms of electrode contact, we propose a semi supervised blind-source separation method. This approach presents a novel perspective to investigate electrophysiology by projecting the recorded channels into a subspace of virtual channels. We show the contribution of each channel to the identified source and correlate the spatial information with imaging and postoperative programming parameters. We anticipate such a source identification strategy can be used in the future to investigate the distribution of beta and HFO on individual contacts of the DBS lead and can improve the interpretation of these signals.
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Affiliation(s)
- Luciano R F Branco
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
| | - Ashwin Viswanathan
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Arjun Tarakad
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Nuri F Ince
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
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Giridharan N, Katlowitz KA, Anand A, Gadot R, Najera RA, Shofty B, Snyder R, Larrinaga C, Prablek M, Karas PJ, Viswanathan A, Sheth SA. Robot-Assisted Deep Brain Stimulation: High Accuracy and Streamlined Workflow. Oper Neurosurg (Hagerstown) 2022; 23:254-260. [PMID: 35972090 DOI: 10.1227/ons.0000000000000298] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 04/03/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND A number of stereotactic platforms are available for performing deep brain stimulation (DBS) lead implantation. Robot-assisted stereotaxy has emerged more recently demonstrating comparable accuracy and shorter operating room times compared with conventional frame-based systems. OBJECTIVE To compare the accuracy of our streamlined robotic DBS workflow with data in the literature from frame-based and frameless systems. METHODS We retrospectively reviewed 126 consecutive DBS lead placement procedures using a robotic stereotactic platform. Indications included Parkinson disease (n = 94), essential tremor (n = 21), obsessive compulsive disorder (n = 7), and dystonia (n = 4). Procedures were performed using a stereotactic frame for fixation and the frame pins as skull fiducials for robot registration. We used intraoperative fluoroscopic computed tomography for registration and postplacement verification. RESULTS The mean radial error for the target point was 1.06 mm (SD: 0.55 mm, range 0.04-2.80 mm) on intraoperative fluoroscopic computed tomography. The mean operative time for an asleep, bilateral implant without implantable pulse generator placement was 238 minutes (SD: 52 minutes), and skin-to-skin procedure time was 116 minutes (SD: 42 minutes). CONCLUSION We describe a streamlined workflow for DBS lead placement using robot-assisted stereotaxy with a comparable accuracy profile. Obviating the need for checking and switching coordinates, as is standard for frame-based DBS, also reduces the chance for human error and facilitates training.
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Affiliation(s)
- Nisha Giridharan
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA
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Foffani G, Alegre M. Brain oscillations and Parkinson disease. HANDBOOK OF CLINICAL NEUROLOGY 2022; 184:259-271. [PMID: 35034740 DOI: 10.1016/b978-0-12-819410-2.00014-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Brain oscillations have been associated with Parkinson's disease (PD) for a long time mainly due to the fundamental oscillatory nature of parkinsonian rest tremor. Over the years, this association has been extended to frequencies well above that of tremor, largely owing to the opportunities offered by deep brain stimulation (DBS) to record electrical activity directly from the patients' basal ganglia. This chapter reviews the results of research on brain oscillations in PD focusing on theta (4-7Hz), beta (13-35Hz), gamma (70-80Hz) and high-frequency oscillations (200-400Hz). For each of these oscillations, we describe localization and interaction with brain structures and between frequencies, changes due to dopamine intake, task-related modulation, and clinical relevance. The study of brain oscillations will also help to dissect the mechanisms of action of DBS. Overall, the chapter tentatively depicts PD in terms of "oscillopathy."
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Affiliation(s)
- Guglielmo Foffani
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain; Neural Bioengineering, Hospital Nacional de Parapléjicos, SESCAM, Toledo, Spain; CIBERNED, Instituto de Salud Carlos III, Madrid, Spain.
| | - Manuel Alegre
- Clinical Neurophysiology Section, Clínica Universidad de Navarra, Pamplona, Spain; Systems Neuroscience Lab, Program of Neuroscience, CIMA, Universidad de Navarra, Pamplona, Spain; IdisNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain.
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Velasco S, Branco L, Abosch A, Ince NF. The Entropy of Adaptively Segmented Beta Oscillations Predict Motor Improvement in Patients with Parkinsons Disease. IEEE Trans Biomed Eng 2022; 69:2333-2341. [PMID: 35025735 DOI: 10.1109/tbme.2022.3142716] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Beta bursts of local fields potentials (LFPs) recorded from subthalamic nucleus (STN) have been recently proposed as a new temporal feature for patients with Parkinsons disease (PD). We introduce a new technique for the adaptive time-domain segmentation of STN-LFP recordings such that the constructed time segments are proportional to the duration of stationary beta activity. We investigated whether the spectral entropy of the adaptively captured beta oscillations can describe the improvement in motor signs following dopaminergic medication. METHODS STN-LFP recordings from externalized chronic deep brain stimulation (DBS) leads were obtained in 9 PD patients. During this monitoring, each patient underwent 3 medication intake cycles where short acting agents (L-DOPA equivalent dose) were administered. We analyzed 2-minute resting state LFP data in each OFF and L-DOPA-induced ON medication states and constructed time domain segmentation of LFP signal in which the length segmentations are adapted to time-varying nature of the oscillatory activity. RESULTS Adaptively constructed segments were noted to be significantly longer in OFF- and shorter in ON-state (p<0.001). Interestingly, in the OFF state, the peak frequency of long beta bursts (>375ms) was in the low range (12-23Hz) of the beta spectrum, whereas shorter beta bursts (<375ms) were widespread in the 13-30Hz band. Measured clinical improvement was highly correlated with the difference in the spectral entropy of beta bursts between OFF and ON states (r=-0.83, p<0.01). CONCLUSION AND SIGNIFICANCE Our findings suggest that beta oscillations can be adaptively segmented without the use of a predetermined amplitude threshold, thereby allowing for objective quantification of burst itself. Compared to the shorter ones, longer oscillations with duration 375ms were highly correlated with the clinical improvement, supporting a pathological role for them. Overall, these findings coupled with our adaptive approach could enable the quantitative use of temporal dynamics of beta activity in assessing severity of PD and improvements in motor features.
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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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Jimenez-Shahed J. Device profile of the percept PC deep brain stimulation system for the treatment of Parkinson's disease and related disorders. Expert Rev Med Devices 2021; 18:319-332. [PMID: 33765395 DOI: 10.1080/17434440.2021.1909471] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
INTRODUCTION Several software and hardware advances in the field of deep brain stimulation (DBS) have been realized in recent years and devices from three manufacturers are available. The Percept™ PC platform (Medtronic, Inc.) enables brain sensing, the latest innovation. Clinicians should be familiar with the differences in devices, and with the latest technologies to deliver optimized patient care.Areas covered: In this device profile, the sensing capabilities of the Percept™ PC platform are described, and the system capabilities are differentiated from other available platforms. The development of the preceding Activa™ PC+S research platform, an investigational device to simultaneously sense brain signals and provide therapeutic stimulation, is provided to place Percept™ PC in the appropriate context.Expert opinion: Percept™ PC offers unique sensing and diary functions as a means to refine therapeutic stimulation, track symptoms and correlate them to neurophysiologic characteristics. Additional features enhance the patient experience with DBS, including 3 T magnetic resonance imaging compatibility, wireless telemetry, a smaller and thinner battery profile, and increased battery longevity. Future work will be needed to illustrate the clinical utility and added value of using sensing to optimize DBS therapy. Patients implanted with Percept™ PC will have ready access to future technology developments.
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Affiliation(s)
- Joohi Jimenez-Shahed
- Movement Disorders Neuromodulation & Brain Circuit Therapeutics, Neurology and Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, USA
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Ozturk M, Viswanathan A, Sheth SA, Ince NF. Electroceutically induced subthalamic high-frequency oscillations and evoked compound activity may explain the mechanism of therapeutic stimulation in Parkinson's disease. Commun Biol 2021; 4:393. [PMID: 33758361 PMCID: PMC7988171 DOI: 10.1038/s42003-021-01915-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 02/23/2021] [Indexed: 01/31/2023] Open
Abstract
Despite having remarkable utility in treating movement disorders, the lack of understanding of the underlying mechanisms of high-frequency deep brain stimulation (DBS) is a main challenge in choosing personalized stimulation parameters. Here we investigate the modulations in local field potentials induced by electrical stimulation of the subthalamic nucleus (STN) at therapeutic and non-therapeutic frequencies in Parkinson's disease patients undergoing DBS surgery. We find that therapeutic high-frequency stimulation (130-180 Hz) induces high-frequency oscillations (~300 Hz, HFO) similar to those observed with pharmacological treatment. Along with HFOs, we also observed evoked compound activity (ECA) after each stimulation pulse. While ECA was observed in both therapeutic and non-therapeutic (20 Hz) stimulation, the HFOs were induced only with therapeutic frequencies, and the associated ECA were significantly more resonant. The relative degree of enhancement in the HFO power was related to the interaction of stimulation pulse with the phase of ECA. We propose that high-frequency STN-DBS tunes the neural oscillations to their healthy/treated state, similar to pharmacological treatment, and the stimulation frequency to maximize these oscillations can be inferred from the phase of ECA waveforms of individual subjects. The induced HFOs can, therefore, be utilized as a marker of successful re-calibration of the dysfunctional circuit generating PD symptoms.
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Affiliation(s)
- Musa Ozturk
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
| | - Ashwin Viswanathan
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Nuri F Ince
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA.
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Watts J, Khojandi A, Shylo O, Ramdhani RA. Machine Learning's Application in Deep Brain Stimulation for Parkinson's Disease: A Review. Brain Sci 2020; 10:E809. [PMID: 33139614 PMCID: PMC7694006 DOI: 10.3390/brainsci10110809] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 10/16/2020] [Accepted: 10/29/2020] [Indexed: 01/07/2023] Open
Abstract
Deep brain stimulation (DBS) is a surgical treatment for advanced Parkinson's disease (PD) that has undergone technological evolution that parallels an expansion in clinical phenotyping, neurophysiology, and neuroimaging of the disease state. Machine learning (ML) has been successfully used in a wide range of healthcare problems, including DBS. As computational power increases and more data become available, the application of ML in DBS is expected to grow. We review the literature of ML in DBS and discuss future opportunities for such applications. Specifically, we perform a comprehensive review of the literature from PubMed, the Institute for Scientific Information's Web of Science, Cochrane Database of Systematic Reviews, and Institute of Electrical and Electronics Engineers' (IEEE) Xplore Digital Library for ML applications in DBS. These studies are broadly placed in the following categories: (1) DBS candidate selection; (2) programming optimization; (3) surgical targeting; and (4) insights into DBS mechanisms. For each category, we provide and contextualize the current body of research and discuss potential future directions for the application of ML in DBS.
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Affiliation(s)
- Jeremy Watts
- Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA; (J.W.); (A.K.); (O.S.)
| | - Anahita Khojandi
- Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA; (J.W.); (A.K.); (O.S.)
| | - Oleg Shylo
- Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA; (J.W.); (A.K.); (O.S.)
| | - Ritesh A. Ramdhani
- Department of Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA
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