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Wilkins KB, Petrucci MN, Lambert EF, Melbourne JA, Gala AS, Akella P, Parisi L, Cui C, Kehnemouyi YM, Hoffman SL, Aditham S, Diep C, Dorris HJ, Parker JE, Herron JA, Bronte-Stewart HM. Beta Burst-Driven Adaptive Deep Brain Stimulation Improves Gait Impairment and Freezing of Gait in Parkinson's Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.26.24309418. [PMID: 38978669 PMCID: PMC11230310 DOI: 10.1101/2024.06.26.24309418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease (PD) that is often refractory to medication. Pathological prolonged beta bursts within the subthalamic nucleus (STN) are associated with both worse impairment and freezing behavior in PD, which are improved with deep brain stimulation (DBS). The goal of the current study was to investigate the feasibility, safety, and tolerability of beta burst-driven adaptive DBS (aDBS) for FOG in PD. Methods Seven individuals with PD were implanted with the investigational Summit™ RC+S DBS system (Medtronic, PLC) with leads placed bilaterally in the STN. A PC-in-the-loop architecture was used to adjust stimulation amplitude in real-time based on the observed beta burst durations in the STN. Participants performed either a harnessed stepping-in-place task or a free walking turning and barrier course, as well as clinical motor assessments and instrumented measures of bradykinesia, OFF stimulation, on aDBS, continuous DBS (cDBS), or random intermittent DBS (iDBS). Results Beta burst driven aDBS was successfully implemented and deemed safe and tolerable in all seven participants. Gait metrics such as overall percent time freezing and mean peak shank angular velocity improved from OFF to aDBS and showed similar efficacy as cDBS. Similar improvements were also seen for overall clinical motor impairment, including tremor, as well as quantitative metrics of bradykinesia. Conclusion Beta burst driven adaptive DBS was feasible, safe, and tolerable in individuals with PD with gait impairment and FOG.
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O'Sullivan KP, Orazem ME, Otto KJ, Butson CR, Baker JL. Electrical rejuvenation of chronically implanted macroelectrodes in nonhuman primates. J Neural Eng 2024; 21:036056. [PMID: 38862007 DOI: 10.1088/1741-2552/ad5703] [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: 12/21/2023] [Accepted: 06/11/2024] [Indexed: 06/13/2024]
Abstract
Objective.Electrodes chronically implanted in the brain undergo complex changes over time that can lower the signal to noise ratio (SNR) of recorded signals and reduce the amount of energy delivered to the tissue during therapeutic stimulation, both of which are relevant for the development of robust, closed-loop control systems. Several factors have been identified that link changes in the electrode-tissue interface (ETI) to increased impedance and degraded performance in micro- and macro-electrodes. Previous studies have demonstrated that brief pulses applied every few days can restore SNR to near baseline levels during microelectrode recordings in rodents, a process referred to as electrical rejuvenation. However, electrical rejuvenation has not been tested in clinically relevant macroelectrode designs in large animal models, which could serve as preliminary data for translation of this technique. Here, several variations of this approach were tested to characterize parameters for optimization.Approach. Alternating-current (AC) and direct-current (DC) electrical rejuvenation methods were explored in three electrode types, chronically implanted in two adult male nonhuman primates (NHP) (Macaca mulatta), which included epidural electrocorticography (ECoG) electrodes and penetrating deep-brain stimulation (DBS) electrodes. Electrochemical impedance spectroscopy (EIS) was performed before and after each rejuvenation paradigm as a gold standard measure of impedance, as well as at subsequent intervals to longitudinally track the evolution of the ETI. Stochastic error modeling was performed to assess the standard deviation of the impedance data, and consistency with the Kramers-Kronig relations was assessed to evaluate the stationarity of EIS measurement.Main results. AC and DC rejuvenation were found to quickly reduce impedance and minimize the tissue component of the ETI on all three electrode types, with DC and low-frequency AC producing the largest impedance drops and reduction of the tissue component in Nyquist plots. The effects of a single rejuvenation session were found to last from several days to over 1 week, and all rejuvenation pulses induced no observable changes to the animals' behavior.Significance. These results demonstrate the effectiveness of electrical rejuvenation for diminishing the impact of chronic ETI changes in NHP with clinically relevant macroelectrode designs.
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Affiliation(s)
- K P O'Sullivan
- Department of Biomedical Engineering, University of Utah, 36 S Wasatch Dr, Salt Lake City, UT 84112, United States of America
| | - M E Orazem
- Department of Chemical Engineering, Herbert Wertheim College of Engineering, University of Florida, 1030 Center Drive PO Box, Gainesville, FL 116005, United States of America
| | - K J Otto
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, 1275 Center Drive, Gainesville, FL NEB 363, United States of America
- Department of Neuroscience, University of Florida, Gainesville, FL, United States of America
- Department of Neurology, University of Florida, Gainesville, FL, United States of America
- Department of Materials Science and Engineering, University of Florida, Gainesville, FL, United States of America
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States of America
| | - C R Butson
- Department of Biomedical Engineering, University of Utah, 36 S Wasatch Dr, Salt Lake City, UT 84112, United States of America
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, 1275 Center Drive, Gainesville, FL NEB 363, United States of America
- Norman Fixel Institute for Neurological Diseases, University of Florida, 3009 Williston Road, Gainesville, FL 32608, United States of America
| | - J L Baker
- Brain and Mind Research Institute, Weil Cornell Medical College, 407 E 61st St, New York, NY 10065, United States of America
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Cummins DD, Sandoval-Pistorius SS, Cernera S, Fernandez-Gajardo R, Hammer LH, Starr PA. Physiological effects of dual target DBS in an individual with Parkinson's disease and a sensing-enabled pulse generator. Parkinsonism Relat Disord 2024; 122:106089. [PMID: 38460490 DOI: 10.1016/j.parkreldis.2024.106089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 02/28/2024] [Accepted: 03/03/2024] [Indexed: 03/11/2024]
Abstract
INTRODUCTION Deep brain stimulation (DBS) of the subthalamic nucleus (STN) or globus pallidus (GP) is an established therapy for Parkinson's disease (PD). Novel DBS devices can record local field potential (LFP) physiomarkers from the STN or GP. While beta (13-30 Hz) and gamma (40-90 Hz) STN and GP LFP oscillations correlate with PD motor severity and with therapeutic effects of treatments, STN-GP interactions in electrophysiology in patients with PD are not well characterized. METHODS Simultaneous bilateral STN and GP LFPs were recorded in a patient with PD who received bilateral STN-DBS and GP-DBS. Power spectra in each target and STN-GP coherence were assessed in various ON- and OFF-levodopa and DBS states, both at rest and with voluntary movement. RESULTS OFF-levodopa and OFF-DBS, beta peaks were present at bilateral STN and GP, coincident with prominent STN-GP beta coherence. Levodopa and dual-target-DBS (simultaneous STN-DBS and GP-DBS) completely suppressed STN-GP coherence. Finely-tuned gamma (FTG) activity at half the stimulation frequency (62.5 Hz) was seen in the STN during GP-DBS at rest. To assess the effects of movement on FTG activity, we recorded LFPs during instructed movement. We observed FTG activity in bilateral GP and bilateral STN during contralateral body movements while on GP-DBS and ON-levodopa. No FTG was seen with STN-DBS or dual-target-DBS. CONCLUSION Dual-target-DBS and levodopa suppressed STN-GP coherence. FTG throughout the basal ganglia was induced by GP-DBS in the presence of levodopa and movement. This bilateral STN-FTG and GP-FTG corresponded with the least severe bradykinesia state, suggesting a pro-kinetic role for FTG.
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Affiliation(s)
- Daniel D Cummins
- School of Medicine, University of California San Francisco, 533 Parnassus Ave, San Francisco, CA, 94143, United States.
| | - Stephanie S Sandoval-Pistorius
- Department of Neurological Surgery, University of California San Francisco, 505 Parnassus Ave, Rm M779, San Francisco, CA, 94143, United States
| | - Stephanie Cernera
- Department of Neurological Surgery, University of California San Francisco, 505 Parnassus Ave, Rm M779, San Francisco, CA, 94143, United States
| | - Rodrigo Fernandez-Gajardo
- Department of Neurological Surgery, University of California San Francisco, 505 Parnassus Ave, Rm M779, San Francisco, CA, 94143, United States
| | - Lauren H Hammer
- Department of Neurology, University of California San Francisco, 1651 4th Street, East Care Center, San Francisco, CA, 94143, United States
| | - Philip A Starr
- School of Medicine, University of California San Francisco, 533 Parnassus Ave, San Francisco, CA, 94143, United States; Department of Neurological Surgery, University of California San Francisco, 505 Parnassus Ave, Rm M779, San Francisco, CA, 94143, United States
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Kumar G, Zhou Z, Wang Z, Kwan KM, Tin C, Ma CHE. Real-time field-programmable gate array-based closed-loop deep brain stimulation platform targeting cerebellar circuitry rescues motor deficits in a mouse model of cerebellar ataxia. CNS Neurosci Ther 2024; 30:e14638. [PMID: 38488445 PMCID: PMC10941591 DOI: 10.1111/cns.14638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 01/09/2024] [Accepted: 02/01/2024] [Indexed: 03/18/2024] Open
Abstract
AIMS The open-loop nature of conventional deep brain stimulation (DBS) produces continuous and excessive stimulation to patients which contributes largely to increased prevalence of adverse side effects. Cerebellar ataxia is characterized by abnormal Purkinje cells (PCs) dendritic arborization, loss of PCs and motor coordination, and muscle weakness with no effective treatment. We aim to develop a real-time field-programmable gate array (FPGA) prototype targeting the deep cerebellar nuclei (DCN) to close the loop for ataxia using conditional double knockout mice with deletion of PC-specific LIM homeobox (Lhx)1 and Lhx5, resulting in abnormal dendritic arborization and motor deficits. METHODS We implanted multielectrode array in the DCN and muscles of ataxia mice. The beneficial effect of open-loop DCN-DBS or closed-loop DCN-DBS was compared by motor behavioral assessments, electromyography (EMG), and neural activities (neurospike and electroencephalogram) in freely moving mice. FPGA board, which performed complex real-time computation, was used for closed-loop DCN-DBS system. RESULTS Closed-loop DCN-DBS was triggered only when symptomatic muscle EMG was detected in a real-time manner, which restored motor activities, electroencephalogram activities and neurospike properties completely in ataxia mice. Closed-loop DCN-DBS was more effective than an open-loop paradigm as it reduced the frequency of DBS. CONCLUSION Our real-time FPGA-based DCN-DBS system could be a potential clinical strategy for alleviating cerebellar ataxia and other movement disorders.
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Affiliation(s)
- Gajendra Kumar
- Department of NeuroscienceCity University of Hong KongHong KongHong Kong SAR
| | - Zhanhong Zhou
- Department of Biomedical EngineeringCity University of Hong KongHong KongHong Kong SAR
| | - Zhihua Wang
- Department of Biomedical EngineeringCity University of Hong KongHong KongHong Kong SAR
| | - Kin Ming Kwan
- School of Life Sciences, Center for Cell and Developmental Biology and State Key Laboratory of AgrobiotechnologyThe Chinese University of Hong KongHong KongHong Kong SAR
| | - Chung Tin
- Department of Biomedical EngineeringCity University of Hong KongHong KongHong Kong SAR
| | - Chi Him Eddie Ma
- Department of NeuroscienceCity University of Hong KongHong KongHong Kong SAR
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O'Keeffe AB, Merla A, Ashkan K. Deep brain stimulation of the subthalamic nucleus in Parkinson disease 2013-2023: where are we a further 10 years on? Br J Neurosurg 2024:1-9. [PMID: 38323603 DOI: 10.1080/02688697.2024.2311128] [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: 08/14/2023] [Accepted: 01/23/2024] [Indexed: 02/08/2024]
Abstract
Deep brain stimulation has been in clinical use for 30 years and during that time it has changed markedly from a small-scale treatment employed by only a few highly specialized centers into a widespread keystone approach to the management of disorders such as Parkinson's disease. In the intervening decades, many of the broad principles of deep brain stimulation have remained unchanged, that of electrode insertion into stereotactically targeted brain nuclei, however the underlying technology and understanding around the approach have progressed markedly. Some of the most significant advances have taken place over the last decade with the advent of artificial intelligence, directional electrodes, stimulation/recording implantable pulse generators and the potential for remote programming among many other innovations. New therapeutic targets are being assessed for their potential benefits and a surge in the number of deep brain stimulation implantations has given birth to a flourishing scientific literature surrounding the pathophysiology of brain disorders such as Parkinson's disease. Here we outline the developments of the last decade and look to the future of deep brain stimulation to attempt to discern some of the most promising lines of inquiry in this fast-paced and rapidly evolving field.
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Affiliation(s)
| | - Anca Merla
- King's College Hospital Department of Neurosurgery, Kings College Hospital, Denmark
| | - Keyoumars Ashkan
- King's College Hospital Department of Neurosurgery, Kings College Hospital, Denmark
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6
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Sellers KK, Cohen JL, Khambhati AN, Fan JM, Lee AM, Chang EF, Krystal AD. Closed-loop neurostimulation for the treatment of psychiatric disorders. Neuropsychopharmacology 2024; 49:163-178. [PMID: 37369777 PMCID: PMC10700557 DOI: 10.1038/s41386-023-01631-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/31/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023]
Abstract
Despite increasing prevalence and huge personal and societal burden, psychiatric diseases still lack treatments which can control symptoms for a large fraction of patients. Increasing insight into the neurobiology underlying these diseases has demonstrated wide-ranging aberrant activity and functioning in multiple brain circuits and networks. Together with varied presentation and symptoms, this makes one-size-fits-all treatment a challenge. There has been a resurgence of interest in the use of neurostimulation as a treatment for psychiatric diseases. Initial studies using continuous open-loop stimulation, in which clinicians adjusted stimulation parameters during patient visits, showed promise but also mixed results. Given the periodic nature and fluctuations of symptoms often observed in psychiatric illnesses, the use of device-driven closed-loop stimulation may provide more effective therapy. The use of a biomarker, which is correlated with specific symptoms, to deliver stimulation only during symptomatic periods allows for the personalized therapy needed for such heterogeneous disorders. Here, we provide the reader with background motivating the use of closed-loop neurostimulation for the treatment of psychiatric disorders. We review foundational studies of open- and closed-loop neurostimulation for neuropsychiatric indications, focusing on deep brain stimulation, and discuss key considerations when designing and implementing closed-loop neurostimulation.
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Affiliation(s)
- Kristin K Sellers
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Joshua L Cohen
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Ankit N Khambhati
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Joline M Fan
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, CA, USA
| | - A Moses Lee
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Andrew D Krystal
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA.
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA.
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7
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He S, Baig F, Merla A, Torrecillos F, Perera A, Wiest C, Debarros J, Benjaber M, Hart MG, Ricciardi L, Morgante F, Hasegawa H, Samuel M, Edwards M, Denison T, Pogosyan A, Ashkan K, Pereira E, Tan H. Beta-triggered adaptive deep brain stimulation during reaching movement in Parkinson's disease. Brain 2023; 146:5015-5030. [PMID: 37433037 PMCID: PMC10690014 DOI: 10.1093/brain/awad233] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 05/30/2023] [Accepted: 06/28/2023] [Indexed: 07/13/2023] Open
Abstract
Subthalamic nucleus (STN) beta-triggered adaptive deep brain stimulation (ADBS) has been shown to provide clinical improvement comparable to conventional continuous DBS (CDBS) with less energy delivered to the brain and less stimulation induced side effects. However, several questions remain unanswered. First, there is a normal physiological reduction of STN beta band power just prior to and during voluntary movement. ADBS systems will therefore reduce or cease stimulation during movement in people with Parkinson's disease and could therefore compromise motor performance compared to CDBS. Second, beta power was smoothed and estimated over a time period of 400 ms in most previous ADBS studies, but a shorter smoothing period could have the advantage of being more sensitive to changes in beta power, which could enhance motor performance. In this study, we addressed these two questions by evaluating the effectiveness of STN beta-triggered ADBS using a standard 400 ms and a shorter 200 ms smoothing window during reaching movements. Results from 13 people with Parkinson's disease showed that reducing the smoothing window for quantifying beta did lead to shortened beta burst durations by increasing the number of beta bursts shorter than 200 ms and more frequent switching on/off of the stimulator but had no behavioural effects. Both ADBS and CDBS improved motor performance to an equivalent extent compared to no DBS. Secondary analysis revealed that there were independent effects of a decrease in beta power and an increase in gamma power in predicting faster movement speed, while a decrease in beta event related desynchronization (ERD) predicted quicker movement initiation. CDBS suppressed both beta and gamma more than ADBS, whereas beta ERD was reduced to a similar level during CDBS and ADBS compared with no DBS, which together explained the achieved similar performance improvement in reaching movements during CDBS and ADBS. In addition, ADBS significantly improved tremor compared with no DBS but was not as effective as CDBS. These results suggest that STN beta-triggered ADBS is effective in improving motor performance during reaching movements in people with Parkinson's disease, and that shortening of the smoothing window does not result in any additional behavioural benefit. When developing ADBS systems for Parkinson's disease, it might not be necessary to track very fast beta dynamics; combining beta, gamma, and information from motor decoding might be more beneficial with additional biomarkers needed for optimal treatment of tremor.
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Affiliation(s)
- Shenghong He
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Fahd Baig
- Neurosciences Research Centre, St George’s, University of London & St George’s University Hospitals NHS Foundation Trust, Institute of Molecular and Clinical Sciences, Cranmer Terrace, London SW17 0QT, UK
| | - Anca Merla
- Department of Neurosurgery, King’s College Hospital NHS Foundation Trust, London SE5 9RS, UK
| | - Flavie Torrecillos
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Andrea Perera
- Department of Neurosurgery, King’s College Hospital NHS Foundation Trust, London SE5 9RS, UK
| | - Christoph Wiest
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Jean Debarros
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Moaad Benjaber
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Michael G Hart
- Neurosciences Research Centre, St George’s, University of London & St George’s University Hospitals NHS Foundation Trust, Institute of Molecular and Clinical Sciences, Cranmer Terrace, London SW17 0QT, UK
| | - Lucia Ricciardi
- Neurosciences Research Centre, St George’s, University of London & St George’s University Hospitals NHS Foundation Trust, Institute of Molecular and Clinical Sciences, Cranmer Terrace, London SW17 0QT, UK
| | - Francesca Morgante
- Neurosciences Research Centre, St George’s, University of London & St George’s University Hospitals NHS Foundation Trust, Institute of Molecular and Clinical Sciences, Cranmer Terrace, London SW17 0QT, UK
| | - Harutomo Hasegawa
- Department of Neurosurgery, King’s College Hospital NHS Foundation Trust, London SE5 9RS, UK
| | - Michael Samuel
- Department of Neurology, King’s College Hospital NHS Foundation Trust, London, SE5 9RS, UK
| | - Mark Edwards
- Department of Clinical and Basic Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London WC2R 2LS, UK
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Alek Pogosyan
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Keyoumars Ashkan
- Department of Neurosurgery, King’s College Hospital NHS Foundation Trust, London SE5 9RS, UK
| | - Erlick Pereira
- Neurosciences Research Centre, St George’s, University of London & St George’s University Hospitals NHS Foundation Trust, Institute of Molecular and Clinical Sciences, Cranmer Terrace, London SW17 0QT, UK
| | - Huiling Tan
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
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Wilkins KB, Melbourne JA, Akella P, Bronte-Stewart HM. Unraveling the complexities of programming neural adaptive deep brain stimulation in Parkinson's disease. Front Hum Neurosci 2023; 17:1310393. [PMID: 38094147 PMCID: PMC10716917 DOI: 10.3389/fnhum.2023.1310393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 11/09/2023] [Indexed: 02/01/2024] Open
Abstract
Over the past three decades, deep brain stimulation (DBS) for Parkinson's disease (PD) has been applied in a continuous open loop fashion, unresponsive to changes in a given patient's state or symptoms over the course of a day. Advances in recent neurostimulator technology enable the possibility for closed loop adaptive DBS (aDBS) for PD as a treatment option in the near future in which stimulation adjusts in a demand-based manner. Although aDBS offers great clinical potential for treatment of motor symptoms, it also brings with it the need for better understanding how to implement it in order to maximize its benefits. In this perspective, we outline considerations for programing several key parameters for aDBS based on our experience across several aDBS-capable research neurostimulators. At its core, aDBS hinges on successful identification of relevant biomarkers that can be measured reliably in real-time working in cohesion with a control policy that governs stimulation adaption. However, auxiliary parameters such as the window in which stimulation is allowed to adapt, as well as the rate it changes, can be just as impactful on performance and vary depending on the control policy and patient. A standardize protocol for programming aDBS will be crucial to ensuring its effective application in clinical practice.
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Affiliation(s)
- Kevin B. Wilkins
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Jillian A. Melbourne
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Pranav Akella
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Helen M. Bronte-Stewart
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States
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9
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Smyth C, Anjum MF, Ravi S, Denison T, Starr P, Little S. Adaptive Deep Brain Stimulation for sleep stage targeting in Parkinson's disease. Brain Stimul 2023; 16:1292-1296. [PMID: 37567463 PMCID: PMC10835741 DOI: 10.1016/j.brs.2023.08.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 05/21/2023] [Accepted: 08/07/2023] [Indexed: 08/13/2023] Open
Abstract
BACKGROUND Sleep dysfunction is disabling in people with Parkinson's disease and is linked to worse motor and non-motor outcomes. Sleep-specific adaptive Deep Brain Stimulation has the potential to target pathophysiologies of sleep. OBJECTIVE Develop an adaptive Deep Brain Stimulation algorithm that modulates stimulation parameters in response to intracranially classified sleep stages. METHODS We performed at-home, multi-night intracranial electrocorticography and polysomnogram recordings to train personalized linear classifiers for discriminating the N3 NREM sleep stage. Classifiers were embedded into investigational Deep Brain Stimulators for N3 specific adaptive DBS. RESULTS We report high specificity of embedded, autonomous, intracranial electrocorticography N3 sleep stage classification across two participants and provide proof-of-principle of successful sleep stage specific adaptive Deep Brain Stimulation. CONCLUSION Multi-night cortico-basal recordings and sleep specific adaptive Deep Brain Stimulation provide an experimental framework to investigate sleep pathophysiology and mechanistic interactions with stimulation, towards the development of therapeutic neurostimulation paradigms directly targeting sleep dysfunction.
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Affiliation(s)
- Clay Smyth
- Department of Bioengineering, University of California, San Francisco, UCSF Byers Hall Box 2520, 1700 Fourth St Ste 203, San Francisco, CA, 94143, United States.
| | - Md Fahim Anjum
- Department of Neurology, University of California, San Francisco, Eighth Floor, 400 Parnassus Ave, San Francisco, CA, 94143, United States.
| | - Shravanan Ravi
- Department of Neurology, University of California, San Francisco, Eighth Floor, 400 Parnassus Ave, San Francisco, CA, 94143, United States.
| | - Timothy Denison
- Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK.
| | - Philip Starr
- Department of Neurosurgery, University of California, San Francisco, Eighth Floor, 400 Parnassus Ave, San Francisco, CA, 94143, United States.
| | - Simon Little
- Department of Neurology, University of California, San Francisco, Eighth Floor, 400 Parnassus Ave, San Francisco, CA, 94143, United States.
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10
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Alarie ME, Provenza NR, Herron JA, Asaad WF. Automated artifact injection into sensing-capable brain modulation devices for neural-behavioral synchronization and the influence of device state. Brain Stimul 2023; 16:1358-1360. [PMID: 37690601 DOI: 10.1016/j.brs.2023.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 09/06/2023] [Indexed: 09/12/2023] Open
Affiliation(s)
- Michaela E Alarie
- Center for Biomedical Engineering, Brown University, Providence, RI, United States; Carney Institute for Brain Science, Brown University, Providence, RI, United States.
| | - Nicole R Provenza
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Jeffrey A Herron
- Department of Neurological Surgery, University of Washington, Seattle, WA, United States
| | - Wael F Asaad
- Carney Institute for Brain Science, Brown University, Providence, RI, United States; Departments of Neurosurgery & Neuroscience, Brown University, Providence, RI, United States; Norman Prince Neurosciences Institute, Rhode Island Hospital, Providence, RI, United States
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11
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Oehrn CR, Cernera S, Hammer LH, Shcherbakova M, Yao J, Hahn A, Wang S, Ostrem JL, Little S, Starr PA. Personalized chronic adaptive deep brain stimulation outperforms conventional stimulation in Parkinson's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.03.23293450. [PMID: 37649907 PMCID: PMC10463549 DOI: 10.1101/2023.08.03.23293450] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Deep brain stimulation is a widely used therapy for Parkinson's disease (PD) but currently lacks dynamic responsiveness to changing clinical and neural states. Feedback control has the potential to improve therapeutic effectiveness, but optimal control strategy and additional benefits of "adaptive" neurostimulation are unclear. We implemented adaptive subthalamic nucleus stimulation, controlled by subthalamic or cortical signals, in three PD patients (five hemispheres) during normal daily life. We identified neurophysiological biomarkers of residual motor fluctuations using data-driven analyses of field potentials over a wide frequency range and varying stimulation amplitudes. Narrowband gamma oscillations (65-70 Hz) at either site emerged as the best control signal for sensing during stimulation. A blinded, randomized trial demonstrated improved motor symptoms and quality of life compared to clinically optimized standard stimulation. Our approach highlights the promise of personalized adaptive neurostimulation based on data-driven selection of control signals and may be applied to other neurological disorders.
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Affiliation(s)
- Carina R Oehrn
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Stephanie Cernera
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Lauren H Hammer
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
| | - Maria Shcherbakova
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Jiaang Yao
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- University of California, Berkeley - University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA, USA
| | - Amelia Hahn
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Sarah Wang
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Jill L Ostrem
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Simon Little
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
- University of California, Berkeley - University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Philip A Starr
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- University of California, Berkeley - University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
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12
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Fleming JE, Benjaber M, Toth R, Zamora M, Landin K, Kavoosi A, Ottoway J, Gillbe T, Piper RJ, Noone T, Campbell H, Gillbe I, Kaliakatsos M, Tisdall M, Valentín A, Denison T. An embedded intracranial seizure monitor for objective outcome measurements and rhythm identification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-6. [PMID: 38083730 PMCID: PMC7615373 DOI: 10.1109/embc40787.2023.10340850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Providing clinicians with objective outcomes of neuromodulation therapy is a key unmet need, especially in emerging areas such as epilepsy and mood disorders. These diseases have episodic behavior and circadian/multidien rhythm characteristics that are difficult to capture in short clinical follow-ups. This work presents preliminary validation evidence for an implantable neuromodulation system with integrated physiological event monitoring, with an initial focus on seizure tracking for epilepsy. The system was developed to address currently unmet requirements for patients undergoing neuromodulation therapy for neurological disorders, specifically the ability to sense physiological data during stimulation and track events with seconds-level granularity. The system incorporates an interactive software tool to enable optimal configuration of the signal processing chain on an embedded implantable device (the Picostim-DyNeuMo Mk-2) including data ingestion from the device loop recorder, event labeling, generation of filter and classification parameters, as well as summary statistics. When the monitor parameters are optimized, the user can wirelessly update the system for chronic event tracking. The simulated performance of the device was assessed using an in silico model with human data to predict the real-time device performance at tracking recorded seizure activity. The in silico performance was then compared against its performance in an in vitro model to capture the full environmental constraints such as sensing during stimulation at the tissue electrode interface. In vitro modeling demonstrated comparable results to the in silico model, providing verification of the software tool and model. This study provides validation evidence of the suitability of the proposed system for tracking longitudinal seizure activity. Given its flexibility, the event monitor can be adapted to track other disorders with episodic and rhythmic symptoms represented by bioelectrical behavior.Clinical relevance-An implantable neuromodulation system is presented that enables chronic tracking of physiological events in disease. This physiological monitor provides the basis for longitudinal assessments of therapy outcomes for patients, such as those with epilepsy where objective identification of patient seizure activity and rhythms might help guide therapy optimization. The system is configurable for other disease states such as Parkinson's disease and mood disorders.
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Affiliation(s)
- John E. Fleming
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | - Moaad Benjaber
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | - Robert Toth
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | - Mayela Zamora
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | - Kei Landin
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | - Ali Kavoosi
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
| | | | | | - Rory J. Piper
- Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
- Department of Neurosurgery, Great Ormond Street Hospital, London WC1N 3JH, UK
| | | | | | | | - Marios Kaliakatsos
- Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
| | - Martin Tisdall
- Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
- Department of Neurosurgery, Great Ormond Street Hospital, London WC1N 3JH, UK
| | - Antonio Valentín
- Department of Basic and Clinical Neuroscience, King’s College London, London SE5 9RT, UK
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford OX3 7DQ, UK
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13
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Stangl M, Maoz SL, Suthana N. Mobile cognition: imaging the human brain in the 'real world'. Nat Rev Neurosci 2023; 24:347-362. [PMID: 37046077 PMCID: PMC10642288 DOI: 10.1038/s41583-023-00692-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/03/2023] [Indexed: 04/14/2023]
Abstract
Cognitive neuroscience studies in humans have enabled decades of impactful discoveries but have primarily been limited to recording the brain activity of immobile participants in a laboratory setting. In recent years, advances in neuroimaging technologies have enabled recordings of human brain activity to be obtained during freely moving behaviours in the real world. Here, we propose that these mobile neuroimaging methods can provide unique insights into the neural mechanisms of human cognition and contribute to the development of novel treatments for neurological and psychiatric disorders. We further discuss the challenges associated with studying naturalistic human behaviours in complex real-world settings as well as strategies for overcoming them. We conclude that mobile neuroimaging methods have the potential to bring about a new era of cognitive neuroscience in which neural mechanisms can be studied with increased ecological validity and with the ability to address questions about natural behaviour and cognitive processes in humans engaged in dynamic real-world experiences.
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Affiliation(s)
- Matthias Stangl
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behaviour, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Sabrina L Maoz
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Nanthia Suthana
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behaviour, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA.
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14
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Neumann WJ, Gilron R, Little S, Tinkhauser G. Adaptive Deep Brain Stimulation: From Experimental Evidence Toward Practical Implementation. Mov Disord 2023. [PMID: 37148553 DOI: 10.1002/mds.29415] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 03/27/2023] [Accepted: 04/05/2023] [Indexed: 05/08/2023] Open
Abstract
Closed-loop adaptive deep brain stimulation (aDBS) can deliver individualized therapy at an unprecedented temporal precision for neurological disorders. This has the potential to lead to a breakthrough in neurotechnology, but the translation to clinical practice remains a significant challenge. Via bidirectional implantable brain-computer-interfaces that have become commercially available, aDBS can now sense and selectively modulate pathophysiological brain circuit activity. Pilot studies investigating different aDBS control strategies showed promising results, but the short experimental study designs have not yet supported individualized analyses of patient-specific factors in biomarker and therapeutic response dynamics. Notwithstanding the clear theoretical advantages of a patient-tailored approach, these new stimulation possibilities open a vast and mostly unexplored parameter space, leading to practical hurdles in the implementation and development of clinical trials. Therefore, a thorough understanding of the neurophysiological and neurotechnological aspects related to aDBS is crucial to develop evidence-based treatment regimens for clinical practice. Therapeutic success of aDBS will depend on the integrated development of strategies for feedback signal identification, artifact mitigation, signal processing, and control policy adjustment, for precise stimulation delivery tailored to individual patients. The present review introduces the reader to the neurophysiological foundation of aDBS for Parkinson's disease (PD) and other network disorders, explains currently available aDBS control policies, and highlights practical pitfalls and difficulties to be addressed in the upcoming years. Finally, it highlights the importance of interdisciplinary clinical neurotechnological research within and across DBS centers, toward an individualized patient-centered approach to invasive brain stimulation. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Wolf-Julian Neumann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | | | - Simon Little
- Movement Disorders and Neuromodulation Centre, University of California San Francisco, San Francisco, California, USA
| | - Gerd Tinkhauser
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland
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15
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Averna A, Debove I, Nowacki A, Peterman K, Duchet B, Sousa M, Bernasconi E, Alva L, Lachenmayer ML, Schuepbach M, Pollo C, Krack P, Nguyen TAK, Tinkhauser G. Spectral Topography of the Subthalamic Nucleus to Inform Next-Generation Deep Brain Stimulation. Mov Disord 2023; 38:818-830. [PMID: 36987385 PMCID: PMC7615852 DOI: 10.1002/mds.29381] [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: 11/01/2022] [Revised: 01/13/2023] [Accepted: 02/27/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND The landscape of neurophysiological symptoms and behavioral biomarkers in basal ganglia signals for movement disorders is expanding. The clinical translation of sensing-based deep brain stimulation (DBS) also requires a thorough understanding of the anatomical organization of spectral biomarkers within the subthalamic nucleus (STN). OBJECTIVES The aims were to systematically investigate the spectral topography, including a wide range of sub-bands in STN local field potentials (LFP) of Parkinson's disease (PD) patients, and to evaluate its predictive performance for clinical response to DBS. METHODS STN-LFPs were recorded from 70 PD patients (130 hemispheres) awake and at rest using multicontact DBS electrodes. A comprehensive spatial characterization, including hot spot localization and focality estimation, was performed for multiple sub-bands (delta, theta, alpha, low-beta, high-beta, low-gamma, high-gamma, and fast-gamma (FG) as well as low- and fast high-frequency oscillations [HFO]) and compared to the clinical hot spot for rigidity response to DBS. A spectral biomarker map was established and used to predict the clinical response to DBS. RESULTS The STN shows a heterogeneous topographic distribution of different spectral biomarkers, with the strongest segregation in the inferior-superior axis. Relative to the superiorly localized beta hot spot, HFOs (FG, slow HFO) were localized up to 2 mm more inferiorly. Beta oscillations are spatially more spread compared to other sub-bands. Both the spatial proximity of contacts to the beta hot spot and the distance to higher-frequency hot spots were predictive for the best rigidity response to DBS. CONCLUSIONS The spatial segregation and properties of spectral biomarkers within the DBS target structure can additionally be informative for the implementation of next-generation sensing-based DBS. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Alberto Averna
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Ines Debove
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Andreas Nowacki
- Department of Neurosurgery, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Katrin Peterman
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Benoit Duchet
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom
| | - Mário Sousa
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Elena Bernasconi
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Laura Alva
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Martin L. Lachenmayer
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland
| | | | - Claudio Pollo
- Department of Neurosurgery, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Paul Krack
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Thuy-Anh K. Nguyen
- Department of Neurosurgery, Bern University Hospital and University of Bern, Bern, Switzerland
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Gerd Tinkhauser
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland
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16
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Widge AS. Closed-Loop Deep Brain Stimulation for Psychiatric Disorders. Harv Rev Psychiatry 2023; 31:162-171. [PMID: 37171475 PMCID: PMC10188203 DOI: 10.1097/hrp.0000000000000367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
ABSTRACT Deep brain stimulation (DBS) is a well-established approach to treating medication-refractory neurological disorders and holds promise for treating psychiatric disorders. Despite strong open-label results in extremely refractory patients, DBS has struggled to meet endpoints in randomized controlled trials. A major challenge is stimulation "dosing"-DBS systems have many adjustable parameters, and clinicians receive little feedback on whether they have chosen the correct parameters for an individual patient. Multiple groups have proposed closed loop technologies as a solution. These systems sense electrical activity, identify markers of an (un)desired state, then automatically deliver or adjust stimulation to alter that electrical state. Closed loop DBS has been successfully deployed in movement disorders and epilepsy. The availability of that technology, as well as advances in opportunities for invasive research with neurosurgical patients, has yielded multiple pilot demonstrations in psychiatric illness. Those demonstrations split into two schools of thought, one rooted in well-established diagnoses and symptom scales, the other in the more experimental Research Domain Criteria (RDoC) framework. Both are promising, and both are limited by the boundaries of current stimulation technology. They are in turn driving advances in implantable recording hardware, signal processing, and stimulation paradigms. The combination of these advances is likely to change both our understanding of psychiatric neurobiology and our treatment toolbox, though the timeframe may be limited by the realities of implantable device development.
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Affiliation(s)
- Alik S Widge
- From the Department of Psychiatry & Behavioral Sciences and Medical Discovery Team on Addictions, University of Minnesota
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17
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Chandrabhatla AS, Pomeraniec IJ, Horgan TM, Wat EK, Ksendzovsky A. Landscape and future directions of machine learning applications in closed-loop brain stimulation. NPJ Digit Med 2023; 6:79. [PMID: 37106034 PMCID: PMC10140375 DOI: 10.1038/s41746-023-00779-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 02/17/2023] [Indexed: 04/29/2023] Open
Abstract
Brain stimulation (BStim) encompasses multiple modalities (e.g., deep brain stimulation, responsive neurostimulation) that utilize electrodes implanted in deep brain structures to treat neurological disorders. Currently, BStim is primarily used to treat movement disorders such as Parkinson's, though indications are expanding to include neuropsychiatric disorders like depression and schizophrenia. Traditional BStim systems are "open-loop" and deliver constant electrical stimulation based on manually-determined parameters. Advancements in BStim have enabled development of "closed-loop" systems that analyze neural biomarkers (e.g., local field potentials in the sub-thalamic nucleus) and adjust electrical modulation in a dynamic, patient-specific, and energy efficient manner. These closed-loop systems enable real-time, context-specific stimulation adjustment to reduce symptom burden. Machine learning (ML) has emerged as a vital component in designing these closed-loop systems as ML models can predict / identify presence of disease symptoms based on neural activity and adaptively learn to modulate stimulation. We queried the US National Library of Medicine PubMed database to understand the role of ML in developing closed-loop BStim systems to treat epilepsy, movement disorders, and neuropsychiatric disorders. Both neural and non-neural network ML algorithms have successfully been leveraged to create closed-loop systems that perform comparably to open-loop systems. For disorders in which the underlying neural pathophysiology is relatively well understood (e.g., Parkinson's, essential tremor), most work has involved refining ML models that can classify neural signals as aberrant or normal. The same is seen for epilepsy, where most current research has focused on identifying optimal ML model design and integrating closed-loop systems into existing devices. For neuropsychiatric disorders, where the underlying pathologic neural circuitry is still being investigated, research is focused on identifying biomarkers (e.g., local field potentials from brain nuclei) that ML models can use to identify onset of symptoms and stratify severity of disease.
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Affiliation(s)
- Anirudha S Chandrabhatla
- School of Medicine, University of Virginia Health Sciences Center, Charlottesville, VA, 22903, USA
| | - I Jonathan Pomeraniec
- Surgical Neurology Branch, National Institutes of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA.
- Department of Neurosurgery, University of Virginia Health Sciences Center, Charlottesville, VA, 22903, USA.
| | - Taylor M Horgan
- School of Medicine, University of Virginia Health Sciences Center, Charlottesville, VA, 22903, USA
| | - Elizabeth K Wat
- School of Medicine, University of Virginia Health Sciences Center, Charlottesville, VA, 22903, USA
| | - Alexander Ksendzovsky
- Department of Neurosurgery, University of Maryland Medical System, Baltimore, MD, 21201, USA
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18
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Bangel KA, Bais M, Eijsker N, Schuurman PR, van den Munckhof P, Figee M, Smit DJA, Denys D. Acute effects of deep brain stimulation on brain function in obsessive-compulsive disorder. Clin Neurophysiol 2023; 148:109-117. [PMID: 36774324 DOI: 10.1016/j.clinph.2022.12.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 12/08/2022] [Accepted: 12/24/2022] [Indexed: 01/24/2023]
Abstract
OBJECTIVE Deep brain stimulation (DBS) is an effective treatment for refractory obsessive-compulsive disorder (OCD) yet neural markers of optimized stimulation parameters are largely unknown. We aimed to describe (sub-)cortical electrophysiological responses to acute DBS at various voltages in OCD. METHODS We explored how DBS doses between 3-5 V delivered to the ventral anterior limb of the internal capsule of five OCD patients affected electroencephalograms and intracranial local field potentials (LFPs). We focused on theta power/ phase-stability, given their previously established role in DBS for OCD. RESULTS Cortical theta power and theta phase-stability did not increase significantly with DBS voltage. DBS-induced theta power peaks were seen at the previously defined individualized therapeutic voltage. Although LFP power generally increased with DBS voltages, this occurred mostly in frequency peaks that overlapped with stimulation artifacts limiting its interpretability. Though highly idiosyncratic, three subjects showed significant acute DBS effects on electroencephalogram theta power and four subjects showed significant carry-over effects (pre-vs post DBS, unstimulated) on LFP and electroencephalogram theta power. CONCLUSIONS Our findings challenge the presence of a consistent dose-response relationship between stimulation voltage and brain activity. SIGNIFICANCE Theta power may be investigated further as a neurophysiological marker to aid personalized DBS voltage optimization in OCD.
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Affiliation(s)
- Katrin A Bangel
- Amsterdam University Medical Centers, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, the Netherlands; Institute of Neuroscience, The Medical School, Newcastle University, NE2 4HH, UK; Department of Medical Physics and Clinical Engineering, Royal Victoria Infirmary, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE1 4LP, UK; Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, the Netherlands
| | - Melisse Bais
- Amsterdam University Medical Centers, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Nadine Eijsker
- Amsterdam University Medical Centers, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, the Netherlands; Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, the Netherlands
| | - P Richard Schuurman
- Amsterdam University Medical Centers, University of Amsterdam, Department of Neurosurgery, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Pepijn van den Munckhof
- Amsterdam University Medical Centers, University of Amsterdam, Department of Neurosurgery, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Martijn Figee
- Amsterdam University Medical Centers, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, the Netherlands; Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, the Netherlands; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, NY, USA
| | - Dirk J A Smit
- Amsterdam University Medical Centers, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, the Netherlands; Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, the Netherlands.
| | - Damiaan Denys
- Amsterdam University Medical Centers, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, the Netherlands; Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, the Netherlands; The Netherlands Institute for Neuroscience, Amsterdam, the Netherlands
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Effects of Contralateral Deep Brain Stimulation and Levodopa on Subthalamic Nucleus Oscillatory Activity and Phase-Amplitude Coupling. Neuromodulation 2023; 26:310-319. [PMID: 36513587 DOI: 10.1016/j.neurom.2022.11.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/14/2022] [Accepted: 11/07/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND The modulatory effects of medication and deep brain stimulation (DBS) on subthalamic nucleus (STN) neural activity in Parkinson's disease have been widely studied. However, effects on the contralateral side to the stimulated STN, in particular, changes in local field potential (LFP) oscillatory activity and phase-amplitude coupling (PAC), have not yet been reported. OBJECTIVE The aim of this study was to examine changes in STN LFP activity across a range of frequency bands and STN PAC for different combinations of DBS and medication on/off on the side contralateral to the applied stimulation. MATERIALS AND METHODS We examined STN LFPs that were recorded using externalized leads from eight parkinsonian patients during unilateral DBS from the side contralateral to the stimulation. LFP spectral power in alpha (5 to ∼13 Hz), low beta (13 to ∼20 Hz), high beta (20-30 Hz), and high gamma plus high-frequency oscillation (high gamma+HFO) (100-400 Hz) bands were estimated for different combinations of medication and unilateral stimulation (off/on). PAC between beta and high gamma+HFO in the STN LFPs was also investigated. The effect of the condition was examined using linear mixed models. RESULTS PAC in the STN LFP was reduced by DBS when compared to the baseline condition (no medication and stimulation). Medication had no significant effect on PAC. Alpha power decreased with DBS, both alone and when combined with medication. Beta power decreased with DBS, medication, and DBS and medication combined. High gamma+HFO power increased during the application of contralateral DBS and was unaltered by medication. CONCLUSIONS The results provide new insights into the effects of DBS and levodopa on STN LFP PAC and oscillatory activity on the side contralateral to stimulation. These may have important implications in understanding mechanisms underlying motor improvements with DBS, including changes on both contralateral and ipsilateral sides, while suggesting a possible role for contralateral sensing during unilateral DBS.
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Tiruvadi V, James S, Howell B, Obatusin M, Crowell A, Riva-Posse P, Gross RE, McIntyre CC, Mayberg HS, Butera R. Mitigating Mismatch Compression in Differential Local Field Potentials. IEEE Trans Neural Syst Rehabil Eng 2023; 31:68-77. [PMID: 36288215 PMCID: PMC10784110 DOI: 10.1109/tnsre.2022.3217469] [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] [Indexed: 11/06/2022]
Abstract
Deep brain stimulation (DBS) devices capable of measuring differential local field potentials ( ∂ LFP) enable neural recordings alongside clinical therapy. Efforts to identify oscillatory correlates of various brain disorders, or disease readouts, are growing but must proceed carefully to ensure readouts are not distorted by brain environment. In this report we identified, characterized, and mitigated a major source of distortion in ∂ LFP that we introduce as mismatch compression (MC). Using in vivo, in silico, and in vitro models of MC, we showed that impedance mismatches in the two recording electrodes can yield incomplete rejection of stimulation artifact and subsequent gain compression that distorts oscillatory power. We then developed and validated an opensource mitigation pipeline that mitigates the distortions arising from MC. This work enables more reliable oscillatory readouts for adaptive DBS applications.
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21
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Cortico-Subthalamic Field Potentials Support Classification of the Natural Gait Cycle in Parkinson's Disease and Reveal Individualized Spectral Signatures. eNeuro 2022; 9:ENEURO.0325-22.2022. [PMID: 36270803 PMCID: PMC9663205 DOI: 10.1523/eneuro.0325-22.2022] [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: 08/15/2022] [Revised: 10/10/2022] [Accepted: 10/17/2022] [Indexed: 12/24/2022] Open
Abstract
The ability of humans to coordinate stereotyped, alternating movements between the two legs during bipedal walking is a complex motor behavior that requires precisely timed activities across multiple nodes of the supraspinal network. Understanding of the neural network dynamics that underlie natural walking in humans is limited. We investigated cortical and subthalamic neural activities during overground walking and evaluated spectral biomarkers to decode the gait cycle in three patients with Parkinson's disease without gait disturbances. Patients were implanted with chronic bilateral deep brain stimulation (DBS) leads in the subthalamic nucleus (STN) and electrocorticography paddles overlaying the primary motor and somatosensory cortices. Local field potentials were recorded from these areas while the participants performed overground walking and synchronized to external gait kinematic sensors. We found that the STN displays increased low-frequency (4-12 Hz) spectral power during the period before contralateral leg swing. Furthermore, STN shows increased theta frequency (4-8 Hz) coherence with the primary motor through the initiation and early phase of contralateral leg swing. Additional analysis revealed that each patient had specific frequency bands that could detect a significant difference between left and right initial leg swing. Our findings indicate that there are alternating spectral changes between the two hemispheres in accordance with the gait cycle. In addition, we identified patient-specific, gait-related biomarkers in both the STN and cortical areas at discrete frequency bands that may be used to drive adaptive DBS to improve gait dysfunction in patients with Parkinson's disease.
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Alarie ME, Provenza NR, Avendano-Ortega M, McKay SA, Waite AS, Mathura RK, Herron JA, Sheth SA, Borton DA, Goodman WK. Artifact characterization and mitigation techniques during concurrent sensing and stimulation using bidirectional deep brain stimulation platforms. Front Hum Neurosci 2022; 16:1016379. [PMID: 36337849 PMCID: PMC9626519 DOI: 10.3389/fnhum.2022.1016379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 10/03/2022] [Indexed: 11/30/2022] Open
Abstract
Bidirectional deep brain stimulation (DBS) platforms have enabled a surge in hours of recordings in naturalistic environments, allowing further insight into neurological and psychiatric disease states. However, high amplitude, high frequency stimulation generates artifacts that contaminate neural signals and hinder our ability to interpret the data. This is especially true in psychiatric disorders, for which high amplitude stimulation is commonly applied to deep brain structures where the native neural activity is miniscule in comparison. Here, we characterized artifact sources in recordings from a bidirectional DBS platform, the Medtronic Summit RC + S, with the goal of optimizing recording configurations to improve signal to noise ratio (SNR). Data were collected from three subjects in a clinical trial of DBS for obsessive-compulsive disorder. Stimulation was provided bilaterally to the ventral capsule/ventral striatum (VC/VS) using two independent implantable neurostimulators. We first manipulated DBS amplitude within safe limits (2–5.3 mA) to characterize the impact of stimulation artifacts on neural recordings. We found that high amplitude stimulation produces slew overflow, defined as exceeding the rate of change that the analog to digital converter can accurately measure. Overflow led to expanded spectral distortion of the stimulation artifact, with a six fold increase in the bandwidth of the 150.6 Hz stimulation artifact from 147–153 to 140–180 Hz. By increasing sense blank values during high amplitude stimulation, we reduced overflow by as much as 30% and improved artifact distortion, reducing the bandwidth from 140–180 Hz artifact to 147–153 Hz. We also identified artifacts that shifted in frequency through modulation of telemetry parameters. We found that telemetry ratio changes led to predictable shifts in the center-frequencies of the associated artifacts, allowing us to proactively shift the artifacts outside of our frequency range of interest. Overall, the artifact characterization methods and results described here enable increased data interpretability and unconstrained biomarker exploration using data collected from bidirectional DBS devices.
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Affiliation(s)
| | - Nicole R. Provenza
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Michelle Avendano-Ortega
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Sarah A. McKay
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Ayan S. Waite
- Brown University School of Engineering, Providence, RI, United States
| | - Raissa K. Mathura
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Jeffrey A. Herron
- Department of Neurological Surgery, University of Washington, Seattle, WA, United States
| | - Sameer A. Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - David A. Borton
- Brown University School of Engineering, Providence, RI, United States
- Department of Veterans Affairs, Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence, RI, United States
| | - Wayne K. Goodman
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
- *Correspondence: Wayne K. Goodman,
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Kavoosi A, Toth R, Benjaber M, Zamora M, Valentin A, Sharott A, Denison T. Computationally efficient neural network classifiers for next generation closed loop neuromodulation therapy - a case study in epilepsy. 2022 44TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) 2022; 2022:288-291. [PMID: 36085909 PMCID: PMC7613668 DOI: 10.1109/embc48229.2022.9871793] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
This work explores the potential utility of neural network classifiers for real-time classification of field-potential based biomarkers in next-generation responsive neuromodulation systems. Compared to classical filter-based classifiers, neural networks offer an ease of patient-specific parameter tuning, promising to reduce the burden of programming on clinicians. The paper explores a compact, feed-forward neural network architecture of only dozens of units for seizure-state classification in refractory epilepsy. The proposed classifier offers comparable accuracy to filterclassifiers on clinician-labeled data, while reducing detection latency. As a trade-off to classical methods, the paper focuses on keeping the complexity of the architecture minimal, to accommodate the on-board computational constraints of implantable pulse generator systems. Clinical relevance—A neural network-based classifier is presented for responsive neurostimulation, with comparable accuracy to classical methods at reduced latency.
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Affiliation(s)
- Ali Kavoosi
- University of Oxford,Brain Network Dynamics Unit,Department of Pharmacology,Oxford,United Kingdom,OX1 3TH
| | - Robert Toth
- Institute of Biomedical Engineering, University of Oxford,Old Road Campus Research Building,Department of Engineering Sciences,Oxford,United Kingdom,OX3 7DQ
| | - Moaad Benjaber
- University of Oxford,Brain Network Dynamics Unit,Department of Pharmacology,Oxford,United Kingdom,OX1 3TH
| | - Mayela Zamora
- University of Oxford,Brain Network Dynamics Unit,Department of Pharmacology,Oxford,United Kingdom,OX1 3TH
| | - Antonio Valentin
- King's College London,Department of Basic and Clinical Neuroscience,London,United Kingdom,SE5 9RT
| | - Andrew Sharott
- Institute of Biomedical Engineering, University of Oxford,Old Road Campus Research Building,Department of Engineering Sciences,Oxford,United Kingdom,OX3 7DQ
| | - Timothy Denison
- University of Oxford,Brain Network Dynamics Unit,Department of Pharmacology,Oxford,United Kingdom,OX1 3TH
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24
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Tinkhauser G. The present and future role of clinical neurophysiology for Deep Brain Stimulation. Clin Neurophysiol 2022; 140:161-162. [PMID: 35717329 DOI: 10.1016/j.clinph.2022.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 05/25/2022] [Indexed: 11/30/2022]
Affiliation(s)
- Gerd Tinkhauser
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland.
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