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Jiao D, Xu L, Gu Z, Yan H, Shen D, Gu X. Pathogenesis, diagnosis, and treatment of epilepsy: electromagnetic stimulation-mediated neuromodulation therapy and new technologies. Neural Regen Res 2025; 20:917-935. [PMID: 38989927 DOI: 10.4103/nrr.nrr-d-23-01444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 01/18/2024] [Indexed: 07/12/2024] Open
Abstract
Epilepsy is a severe, relapsing, and multifactorial neurological disorder. Studies regarding the accurate diagnosis, prognosis, and in-depth pathogenesis are crucial for the precise and effective treatment of epilepsy. The pathogenesis of epilepsy is complex and involves alterations in variables such as gene expression, protein expression, ion channel activity, energy metabolites, and gut microbiota composition. Satisfactory results are lacking for conventional treatments for epilepsy. Surgical resection of lesions, drug therapy, and non-drug interventions are mainly used in clinical practice to treat pain associated with epilepsy. Non-pharmacological treatments, such as a ketogenic diet, gene therapy for nerve regeneration, and neural regulation, are currently areas of research focus. This review provides a comprehensive overview of the pathogenesis, diagnostic methods, and treatments of epilepsy. It also elaborates on the theoretical basis, treatment modes, and effects of invasive nerve stimulation in neurotherapy, including percutaneous vagus nerve stimulation, deep brain electrical stimulation, repetitive nerve electrical stimulation, in addition to non-invasive transcranial magnetic stimulation and transcranial direct current stimulation. Numerous studies have shown that electromagnetic stimulation-mediated neuromodulation therapy can markedly improve neurological function and reduce the frequency of epileptic seizures. Additionally, many new technologies for the diagnosis and treatment of epilepsy are being explored. However, current research is mainly focused on analyzing patients' clinical manifestations and exploring relevant diagnostic and treatment methods to study the pathogenesis at a molecular level, which has led to a lack of consensus regarding the mechanisms related to the disease.
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Affiliation(s)
- Dian Jiao
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Lai Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Zhen Gu
- Key Laboratory of Advanced Drug Delivery Systems of Zhejiang Province, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Hua Yan
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Dingding Shen
- Affiliated Hospital of Nantong University, Nantong, Jiangsu Province, China
| | - Xiaosong Gu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
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Steina A, Sure S, Butz M, Vesper J, Schnitzler A, Hirschmann J. Mapping Subcortico-Cortical Coupling-A Comparison of Thalamic and Subthalamic Oscillations. Mov Disord 2024; 39:684-693. [PMID: 38380765 DOI: 10.1002/mds.29730] [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: 06/27/2023] [Revised: 11/29/2023] [Accepted: 01/08/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND The ventral intermediate nucleus of the thalamus (VIM) is an effective target for deep brain stimulation in tremor patients. Despite its therapeutic importance, its oscillatory coupling to cortical areas has rarely been investigated in humans. OBJECTIVES The objective of this study was to identify the cortical areas coupled to the VIM in patients with essential tremor. METHODS We combined resting-state magnetoencephalography with local field potential recordings from the VIM of 19 essential tremor patients. Whole-brain maps of VIM-cortex coherence in several frequency bands were constructed using beamforming and compared with corresponding maps of subthalamic nucleus (STN) coherence based on data from 19 patients with Parkinson's disease. In addition, we computed spectral Granger causality. RESULTS The topographies of VIM-cortex and STN-cortex coherence were very similar overall but differed quantitatively. Both nuclei were coupled to the ipsilateral sensorimotor cortex in the high-beta band; to the sensorimotor cortex, brainstem, and cerebellum in the low-beta band; and to the temporal cortex, brainstem, and cerebellum in the alpha band. High-beta coherence to sensorimotor cortex was stronger for the STN (P = 0.014), whereas low-beta coherence to the brainstem was stronger for the VIM (P = 0.017). Although the STN was driven by cortical activity in the high-beta band, the VIM led the sensorimotor cortex in the alpha band. CONCLUSIONS Thalamo-cortical coupling is spatially and spectrally organized. The overall similar topographies of VIM-cortex and STN-cortex coherence suggest that functional connections are not necessarily unique to one subcortical structure but might reflect larger frequency-specific networks involving VIM and STN to a different degree. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Alexandra Steina
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Sarah Sure
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Markus Butz
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Jan Vesper
- Department of Functional Neurosurgery and Stereotaxy, Neurosurgical Clinic, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Jan Hirschmann
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
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Neumann WJ. Cortical brain signals improve decoding of movement and tremor for clinical brain computer interfaces. Clin Neurophysiol 2024; 157:143-145. [PMID: 38097414 DOI: 10.1016/j.clinph.2023.11.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 11/27/2023] [Indexed: 01/13/2024]
Affiliation(s)
- Wolf-Julian Neumann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Chariteplatz 1, 10117 Berlin, Berlin, Germany.
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Todorov D, Schnitzler A, Hirschmann J. Parkinsonian rest tremor can be distinguished from voluntary hand movements based on subthalamic and cortical activity. Clin Neurophysiol 2024; 157:146-155. [PMID: 38030516 DOI: 10.1016/j.clinph.2023.10.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 10/19/2023] [Accepted: 10/31/2023] [Indexed: 12/01/2023]
Abstract
OBJECTIVE To distinguish Parkinsonian rest tremor and different voluntary hand movements by analyzing brain activity. METHODS We re-analyzed magnetoencephalography and local field potential recordings from the subthalamic nucleus of six patients with Parkinson's disease. Data were obtained after withdrawal from dopaminergic medication (Med Off) and after administration of levodopa (Med On). Using gradient-boosted tree learning, we classified epochs as tremor, fist-clenching, forearm extension or tremor-free rest. RESULTS Subthalamic activity alone was insufficient for distinguishing the four different motor states (balanced accuracy mean: 38%, std: 7%). The combination of cortical and subthalamic features, in contrast, allowed for a much more accurate classification (balanced accuracy mean: 75%, std: 17%). Adding a single cortical area improved balanced accuracy by 17% on average, as compared to classification based on subthalamic activity alone. In most patients, the most informative cortical areas were sensorimotor cortical regions. Decoding performance was similar in Med On and Med Off. CONCLUSIONS Electrophysiological recordings allow for distinguishing several motor states, provided that cortical signals are monitored in addition to subthalamic activity. SIGNIFICANCE By combining cortical recordings, subcortical recordings and machine learning, adaptive deep brain stimulation systems might be able to detect tremor specifically and to respond adequately to several motor states.
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Affiliation(s)
- Dmitrii Todorov
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany; Centre de Recherche en Neurosciences de Lyon - Inserm U1028, 69675 Bron, France; Centre de Recerca Matemática, Campus UAB edifici C, 08193 Bellaterra, Barcelona, Spain
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany; Center for Movement Disorders and Neuromodulation, Department of Neurology Medical Faculty, Heinrich Heine University, 40225 Düsseldorf, Germany
| | - Jan Hirschmann
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany.
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Bi Z. Cognition of Time and Thinking Beyond. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1455:171-195. [PMID: 38918352 DOI: 10.1007/978-3-031-60183-5_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
A common research protocol in cognitive neuroscience is to train subjects to perform deliberately designed experiments while recording brain activity, with the aim of understanding the brain mechanisms underlying cognition. However, how the results of this protocol of research can be applied in technology is seldom discussed. Here, I review the studies on time processing of the brain as examples of this research protocol, as well as two main application areas of neuroscience (neuroengineering and brain-inspired artificial intelligence). Time processing is a fundamental dimension of cognition, and time is also an indispensable dimension of any real-world signal to be processed in technology. Therefore, one may expect that the studies of time processing in cognition profoundly influence brain-related technology. Surprisingly, I found that the results from cognitive studies on timing processing are hardly helpful in solving practical problems. This awkward situation may be due to the lack of generalizability of the results of cognitive studies, which are under well-controlled laboratory conditions, to real-life situations. This lack of generalizability may be rooted in the fundamental unknowability of the world (including cognition). Overall, this paper questions and criticizes the usefulness and prospect of the abovementioned research protocol of cognitive neuroscience. I then give three suggestions for future research. First, to improve the generalizability of research, it is better to study brain activity under real-life conditions instead of in well-controlled laboratory experiments. Second, to overcome the unknowability of the world, we can engineer an easily accessible surrogate of the object under investigation, so that we can predict the behavior of the object under investigation by experimenting on the surrogate. Third, the paper calls for technology-oriented research, with the aim of technology creation instead of knowledge discovery.
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Affiliation(s)
- Zedong Bi
- Lingang Laboratory, Shanghai, China.
- Institute for Future, Qingdao University, Qingdao, China.
- School of Automation, Shandong Key Laboratory of Industrial Control Technology, Qingdao University, Qingdao, China.
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Guehl D, Guillaud E, Langbour N, Doat E, Auzou N, Courtin E, Branchard O, Engelhardt J, Benazzouz A, Eusebio A, Cuny E, Burbaud P. Usefulness of thalamic beta activity for closed-loop therapy in essential tremor. Sci Rep 2023; 13:22332. [PMID: 38102180 PMCID: PMC10724233 DOI: 10.1038/s41598-023-49511-5] [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: 05/18/2023] [Accepted: 12/08/2023] [Indexed: 12/17/2023] Open
Abstract
A partial loss of effectiveness of deep brain stimulation of the ventral intermediate nucleus of the thalamus (VIM) has been reported in some patients with essential tremor (ET), possibly due to habituation to permanent stimulation. This study focused on the evolution of VIM local-field potentials (LFPs) data over time to assess the long-term feasibility of closed-loop therapy based on thalamic activity. We performed recordings of thalamic LFPs in 10 patients with severe ET using the ACTIVA™ PC + S (Medtronic plc.) allowing both recordings and stimulation in the same region. Particular attention was paid to describing the evolution of LFPs over time from 3 to 24 months after surgery when the stimulation was Off. We demonstrated a significant decrease in high-beta LFPs amplitude during movements inducing tremor in comparison to the rest condition 3 months after surgery (1.91 ± 0.89 at rest vs. 1.27 ± 1.37 µV2/Hz during posture/action for N = 8/10 patients; p = 0.010), 12 months after surgery (2.92 ± 1.75 at rest vs. 2.12 ± 1.78 µV2/Hz during posture/action for N = 7/10 patients; p = 0.014) and 24 months after surgery (2.32 ± 0.35 at rest vs 0.75 ± 0.78 µV2/Hz during posture/action for 4/6 patients; p = 0.017). Among the patients who exhibited a significant decrease of high-beta LFP amplitude when stimulation was Off, this phenomenon was observed at least twice during the follow-up. Although the extent of this decrease in high-beta LFPs amplitude during movements inducing tremor may vary over time, this thalamic biomarker of movement could potentially be usable for closed-loop therapy in the long term.
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Affiliation(s)
- Dominique Guehl
- Service de Neurophysiologie Clinique de l'enfant et de l'adulte, Hôpital Pellegrin, Pôle des Neurosciences Cliniques, CHU de Bordeaux, Bordeaux, France.
- Institut des Maladies Neurodégénératives, Univ. Bordeaux, CNRS, IMN, UMR 5293, F-33000, Bordeaux, France.
| | - Etienne Guillaud
- Institute of Cognitive and Integrative Neurosciences, Univ. Bordeaux, CNRS, INCIA, UMR 5287, F-33000, Bordeaux, France
| | - Nicolas Langbour
- Centre de Recherche en Psychiatrie, CH de la Milétrie, 86000, Poitiers, France
| | - Emilie Doat
- Institute of Cognitive and Integrative Neurosciences, Univ. Bordeaux, CNRS, INCIA, UMR 5287, F-33000, Bordeaux, France
| | - Nicolas Auzou
- Institut des Maladies Neurodégénératives Clinique (IMNc), Pôle des Neurosciences Cliniques, CHU de Bordeaux, Bordeaux, France
| | - Edouard Courtin
- Service de Neurophysiologie Clinique de l'enfant et de l'adulte, Hôpital Pellegrin, Pôle des Neurosciences Cliniques, CHU de Bordeaux, Bordeaux, France
| | | | | | - Abdelhamid Benazzouz
- Institut des Maladies Neurodégénératives, Univ. Bordeaux, CNRS, IMN, UMR 5293, F-33000, Bordeaux, France
| | - Alexandre Eusebio
- Department of Neurology and Movement Disorders, APHM, Hôpitaux Universitaire de Marseille, Marseille, France
- Institut de Neurosciences de la Timone, UMR 7289, Aix Marseille Univ, CNRS, Marseille, France
| | - Emmanuel Cuny
- Service de Neurochirurgie, CHU de Bordeaux, Bordeaux, France
| | - Pierre Burbaud
- Service de Neurophysiologie Clinique de l'enfant et de l'adulte, Hôpital Pellegrin, Pôle des Neurosciences Cliniques, CHU de Bordeaux, Bordeaux, France
- Institut des Maladies Neurodégénératives, Univ. Bordeaux, CNRS, IMN, UMR 5293, F-33000, Bordeaux, France
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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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Hariz M, Cif L, Blomstedt P. Thirty Years of Global Deep Brain Stimulation: "Plus ça change, plus c'est la même chose"? Stereotact Funct Neurosurg 2023; 101:395-406. [PMID: 37844558 DOI: 10.1159/000533430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 07/31/2023] [Indexed: 10/18/2023]
Abstract
BACKGROUND The advent of deep brain stimulation (DBS) of the subthalamic nucleus (STN) for Parkinson's disease 30 years ago has ushered a global breakthrough of DBS as a universal method for therapy and research in wide areas of neurology and psychiatry. The literature of the last three decades has described numerous concepts and practices of DBS, often branded as novelties or discoveries. However, reading the contemporary publications often elicits a sense of déjà vu in relation to several methods, attributes, and practices of DBS. Here, we review various applications and techniques of the modern-era DBS and compare them with practices of the past. SUMMARY Compared with modern literature, publications of the old-era functional stereotactic neurosurgery, including old-era DBS, show that from the very beginning multidisciplinarity and teamwork were often prevalent and insisted upon, ethical concerns were recognized, brain circuitries and rational for brain targets were discussed, surgical indications were similar, closed-loop stimulation was attempted, evaluations of surgical results were debated, and controversies were common. Thus, it appears that virtually everything done today in the field of DBS bears resemblance to old-time practices, or has been done before, albeit with partly other tools and techniques. Movement disorders remain the main indications for modern DBS as was the case for lesional surgery and old-era DBS. The novelties today consist of the STN as the dominant target for DBS, the tremendous advances in computerized brain imaging, the sophistication and versatility of implantable DBS hardware, and the large potential for research. KEY MESSAGES Many aspects of contemporary DBS bear strong resemblance to practices of the past. The dominant clinical indications remain movement disorders with virtually the same brain targets as in the past, with one exception: the STN. Other novel brain targets - that are so far subject to DBS trials - are the pedunculopontine nucleus for gait freezing, the anteromedial internal pallidum for Gilles de la Tourette and the fornix for Alzheimer's disease. The major innovations and novelties compared to the past concern mainly the unmatched level of research activity, its high degree of sponsorship, and the outstanding advances in technology that have enabled multimodal brain imaging and the miniaturization, versatility, and sophistication of implantable hardware. The greatest benefit for patients today, compared to the past, is the higher level of precision and safety of DBS, and of all functional stereotactic neurosurgery.
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Affiliation(s)
- Marwan Hariz
- Department of Clinical Neuroscience, Umeå University, Umeå, Sweden
- UCL Institute of Neurology, Queen Square, London, UK
| | - Laura Cif
- Laboratoire de Recherche en Neurosciences Cliniques, Montpellier, France
| | - Patric Blomstedt
- Department of Clinical Neuroscience, Umeå University, Umeå, Sweden
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Ferreira Felloni Borges Y, Cheyuo C, Lozano AM, Fasano A. Essential Tremor - Deep Brain Stimulation vs. Focused Ultrasound. Expert Rev Neurother 2023; 23:603-619. [PMID: 37288812 DOI: 10.1080/14737175.2023.2221789] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 06/01/2023] [Indexed: 06/09/2023]
Abstract
INTRODUCTION Essential Tremor (ET) is one of the most common tremor syndromes typically presented as action tremor, affecting mainly the upper limbs. In at least 30-50% of patients, tremor interferes with quality of life, does not respond to first-line therapies and/or intolerable adverse effects may occur. Therefore, surgery may be considered. AREAS COVERED In this review, the authors discuss and compare unilateral ventral intermedius nucleus deep brain stimulation (VIM DBS) and bilateral DBS with Magnetic Resonance-guided Focused Ultrasound (MRgFUS) thalamotomy, which comprises focused acoustic energy generating ablation under real-time MRI guidance. Discussion includes their impact on tremor reduction and their potential complications. Finally, the authors provide their expert opinion. EXPERT OPINION DBS is adjustable, potentially reversible and allows bilateral treatments; however, it is invasive requires hardware implantation, and has higher surgical risks. Instead, MRgFUS is less invasive, less expensive, and requires no hardware maintenance. Beyond these technical differences, the decision should also involve the patient, family, and caregivers.
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Affiliation(s)
- Yuri Ferreira Felloni Borges
- Edmond J. Safra Program in Parkinson's Disease, Division of Neurology, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, UHN, University of Toronto, Toronto, ON, Canada
| | - Cletus Cheyuo
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Andres M Lozano
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
- Krembil Brain Institute, Toronto, ON, Canada
| | - Alfonso Fasano
- Edmond J. Safra Program in Parkinson's Disease, Division of Neurology, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, UHN, University of Toronto, Toronto, ON, Canada
- Krembil Brain Institute, Toronto, ON, Canada
- Center for Advancing Neurotechnological Innovation to Application (CRANIA), Toronto, ON, Canada
- Department of Parkinson's Disease & Movement Disorders Rehabilitation, Moriggia-Pelascini Hospital, Gravedona Ed Uniti, Como, Italy
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Martineau T, He S, Vaidyanathan R, Tan H. Hyper-parameter tuning and feature extraction for asynchronous action detection from sub-thalamic nucleus local field potentials. Front Hum Neurosci 2023; 17:1111590. [PMID: 37292583 PMCID: PMC10244770 DOI: 10.3389/fnhum.2023.1111590] [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: 11/29/2022] [Accepted: 05/04/2023] [Indexed: 06/10/2023] Open
Abstract
Introduction Decoding brain states from subcortical local field potentials (LFPs) indicative of activities such as voluntary movement, tremor, or sleep stages, holds significant potential in treating neurodegenerative disorders and offers new paradigms in brain-computer interface (BCI). Identified states can serve as control signals in coupled human-machine systems, e.g., to regulate deep brain stimulation (DBS) therapy or control prosthetic limbs. However, the behavior, performance, and efficiency of LFP decoders depend on an array of design and calibration settings encapsulated into a single set of hyper-parameters. Although methods exist to tune hyper-parameters automatically, decoders are typically found through exhaustive trial-and-error, manual search, and intuitive experience. Methods This study introduces a Bayesian optimization (BO) approach to hyper-parameter tuning, applicable through feature extraction, channel selection, classification, and stage transition stages of the entire decoding pipeline. The optimization method is compared with five real-time feature extraction methods paired with four classifiers to decode voluntary movement asynchronously based on LFPs recorded with DBS electrodes implanted in the subthalamic nucleus of Parkinson's disease patients. Results Detection performance, measured as the geometric mean between classifier specificity and sensitivity, is automatically optimized. BO demonstrates improved decoding performance from initial parameter setting across all methods. The best decoders achieve a maximum performance of 0.74 ± 0.06 (mean ± SD across all participants) sensitivity-specificity geometric mean. In addition, parameter relevance is determined using the BO surrogate models. Discussion Hyper-parameters tend to be sub-optimally fixed across different users rather than individually adjusted or even specifically set for a decoding task. The relevance of each parameter to the optimization problem and comparisons between algorithms can also be difficult to track with the evolution of the decoding problem. We believe that the proposed decoding pipeline and BO approach is a promising solution to such challenges surrounding hyper-parameter tuning and that the study's findings can inform future design iterations of neural decoders for adaptive DBS and BCI.
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Affiliation(s)
- Thomas Martineau
- Biomechatronics Group, Department of Mechanical Engineering, Imperial College London, London, United Kingdom
| | - Shenghong He
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Ravi Vaidyanathan
- Biomechatronics Group, Department of Mechanical Engineering, Imperial College London, London, United Kingdom
- UK Dementia Research Institute-Care Research and Technology, Imperial College London, London, United Kingdom
| | - Huiling Tan
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
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11
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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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12
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Peterson V, Merk T, Bush A, Nikulin V, Kühn AA, Neumann WJ, Richardson RM. Movement decoding using spatio-spectral features of cortical and subcortical local field potentials. Exp Neurol 2023; 359:114261. [PMID: 36349662 DOI: 10.1016/j.expneurol.2022.114261] [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: 01/31/2022] [Revised: 09/26/2022] [Accepted: 10/25/2022] [Indexed: 12/30/2022]
Abstract
The first commercially sensing enabled deep brain stimulation (DBS) devices for the treatment of movement disorders have recently become available. In the future, such devices could leverage machine learning based brain signal decoding strategies to individualize and adapt therapy in real-time. As multi-channel recordings become available, spatial information may provide an additional advantage for informing machine learning models. To investigate this concept, we compared decoding performances from single channels vs. spatial filtering techniques using intracerebral multitarget electrophysiology in Parkinson's disease patients undergoing DBS implantation. We investigated the feasibility of spatial filtering in invasive neurophysiology and the putative utility of combined cortical ECoG and subthalamic local field potential signals for decoding grip-force, a well-defined and continuous motor readout. We found that adding spatial information to the model can improve decoding (6% gain in decoding), but the spatial patterns and additional benefit was highly individual. Beyond decoding performance results, spatial filters and patterns can be used to obtain meaningful neurophysiological information about the brain networks involved in target behavior. Our results highlight the importance of individualized approaches for brain signal decoding, for which multielectrode recordings and spatial filtering can improve precision medicine approaches for clinical brain computer interfaces.
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Affiliation(s)
- Victoria Peterson
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, USA.
| | - Timon Merk
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Alan Bush
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Vadim Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Andrea A Kühn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Wolf-Julian Neumann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, USA
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13
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Merk T, Peterson V, Köhler R, Haufe S, Richardson RM, Neumann WJ. Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation. Exp Neurol 2022; 351:113993. [PMID: 35104499 PMCID: PMC10521329 DOI: 10.1016/j.expneurol.2022.113993] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 11/18/2021] [Accepted: 01/22/2022] [Indexed: 12/30/2022]
Abstract
Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.
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Affiliation(s)
- Timon Merk
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Chariteplatz 1, 10117 Berlin, Germany
| | - Victoria Peterson
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, United States
| | - Richard Köhler
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Chariteplatz 1, 10117 Berlin, Germany
| | - Stefan Haufe
- Berlin Center for Advanced Neuroimaging (BCAN), Charité - Universitätsmedizin Berlin, Chariteplatz 1, 10117 Berlin, Germany
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, United States
| | - Wolf-Julian Neumann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Chariteplatz 1, 10117 Berlin, Germany.
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14
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Shetty N. Essential Tremor-Do We Have Better Therapeutics? A Review of Recent Advances and Future Directions. Curr Neurol Neurosci Rep 2022; 22:197-208. [PMID: 35235170 DOI: 10.1007/s11910-022-01185-8] [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] [Accepted: 01/18/2022] [Indexed: 11/30/2022]
Abstract
PURPOSE OF REVIEW Essential tremor (ET) is a very common condition that significantly impacts quality of life. Current medical treatments are quite limited, and while surgical treatments like deep brain stimulation (DBS) can be very effective, they come with their own limitations as well as procedural risks. This article reviews updates on recent advances and future directions in the treatment of ET. RECENT FINDINGS A new generation of pharmacologic agents specifically designed for ET is in clinical trials. Advances in DBS technology continue to improve this therapy. MRI-guided focused ultrasound (MRgFUS) is now an approved noninvasive ablative treatment for ET that is effective and shows potential for continuing improvement. The first peripheral stimulation device for ET has also now been approved. This article reviews updates on the treatment of ET, encompassing pharmacologic agents in clinical trials, DBS, MRgFUS, and noninvasive stimulation therapies. Recent treatment advances and future directions of development show a great deal of promise for ET therapeutics.
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Affiliation(s)
- Neil Shetty
- Parkinson's Disease and Movement Disorders Center, Department of Neurology, Northwestern University Feinberg School of Medicine, Abbott Hall, 11th Floor, 710 N. Lake Shore Drive, Chicago, IL, 60611, USA.
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15
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Remote measurement and home monitoring of tremor. J Neurol Sci 2022; 435:120201. [DOI: 10.1016/j.jns.2022.120201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 11/21/2021] [Accepted: 02/17/2022] [Indexed: 11/15/2022]
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16
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Tinkhauser G, Moraud EM. Controlling Clinical States Governed by Different Temporal Dynamics With Closed-Loop Deep Brain Stimulation: A Principled Framework. Front Neurosci 2021; 15:734186. [PMID: 34858126 PMCID: PMC8632004 DOI: 10.3389/fnins.2021.734186] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 10/18/2021] [Indexed: 02/05/2023] Open
Abstract
Closed-loop strategies for deep brain stimulation (DBS) are paving the way for improving the efficacy of existing neuromodulation therapies across neurological disorders. Unlike continuous DBS, closed-loop DBS approaches (cl-DBS) optimize the delivery of stimulation in the temporal domain. However, clinical and neurophysiological manifestations exhibit highly diverse temporal properties and evolve over multiple time-constants. Moreover, throughout the day, patients are engaged in different activities such as walking, talking, or sleeping that may require specific therapeutic adjustments. This broad range of temporal properties, along with inter-dependencies affecting parallel manifestations, need to be integrated in the development of therapies to achieve a sustained, optimized control of multiple symptoms over time. This requires an extended view on future cl-DBS design. Here we propose a conceptual framework to guide the development of multi-objective therapies embedding parallel control loops. Its modular organization allows to optimize the personalization of cl-DBS therapies to heterogeneous patient profiles. We provide an overview of clinical states and symptoms, as well as putative electrophysiological biomarkers that may be integrated within this structure. This integrative framework may guide future developments and become an integral part of next-generation precision medicine instruments.
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Affiliation(s)
- Gerd Tinkhauser
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Eduardo Martin Moraud
- Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland.,Defitech Center for Interventional Neurotherapies (.NeuroRestore), Ecole Polytechnique Fédérale de Lausanne and Lausanne University Hospital, Lausanne, Switzerland
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17
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Peralta M, Jannin P, Baxter JSH. Machine learning in deep brain stimulation: A systematic review. Artif Intell Med 2021; 122:102198. [PMID: 34823832 DOI: 10.1016/j.artmed.2021.102198] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 09/23/2021] [Accepted: 10/12/2021] [Indexed: 11/16/2022]
Abstract
Deep Brain Stimulation (DBS) is an increasingly common therapy for a large range of neurological disorders, such as abnormal movement disorders. The effectiveness of DBS in terms of controlling patient symptomatology has made this procedure increasingly used over the past few decades. Concurrently, the popularity of Machine Learning (ML), a subfield of artificial intelligence, has skyrocketed and its influence has more recently extended to medical domains such as neurosurgery. Despite its growing research interest, there has yet to be a literature review specifically on the use of ML in DBS. We have followed a fully systematic methodology to obtain a corpus of 73 papers. In each paper, we identified the clinical application, the type/amount of data used, the method employed, and the validation strategy, further decomposed into 12 different sub-categories. The papers overall illustrated some existing trends in how ML is used in the context of DBS, including the breath of the problem domain and evolving techniques, as well as common frameworks and limitations. This systematic review analyzes at a broad level how ML have been recently used to address clinical problems on DBS, giving insight into how these new computational methods are helping to push the state-of-the-art of functional neurosurgery. DBS clinical workflow is complex, involves many specialists, and raises several clinical issues which have partly been addressed with artificial intelligence. However, several areas remain and those that have been recently addressed with ML are by no means considered "solved" by the community nor are they closed to new and evolving methods.
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Affiliation(s)
- Maxime Peralta
- Univ Rennes, Inserm, LTSI - UMR 1099, F-35000 Rennes, France
| | - Pierre Jannin
- Univ Rennes, Inserm, LTSI - UMR 1099, F-35000 Rennes, France
| | - John S H Baxter
- Univ Rennes, Inserm, LTSI - UMR 1099, F-35000 Rennes, France.
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18
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Danilov GV, Shifrin MA, Kotik KV, Ishankulov TA, Orlov YN, Kulikov AS, Potapov AA. Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives. Sovrem Tekhnologii Med 2021; 12:111-118. [PMID: 34796024 PMCID: PMC8596229 DOI: 10.17691/stm2020.12.6.12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Indexed: 12/29/2022] Open
Abstract
The current increase in the number of publications on the use of artificial intelligence (AI) technologies in neurosurgery indicates a new trend in clinical neuroscience. The aim of the study was to conduct a systematic literature review to highlight the main directions and trends in the use of AI in neurosurgery.
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Affiliation(s)
- G V Danilov
- Scientific Board Secretary; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia; Head of the Laboratory of Biomedical Informatics and Artificial Intelligence; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - M A Shifrin
- Scientific Consultant, Laboratory of Biomedical Informatics and Artificial Intelligence; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - K V Kotik
- Physics Engineer, Laboratory of Biomedical Informatics and Artificial Intelligence; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - T A Ishankulov
- Engineer, Laboratory of Biomedical Informatics and Artificial Intelligence; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - Yu N Orlov
- Head of the Department of Computational Physics and Kinetic Equations; Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, 4 Miusskaya Sq., Moscow, 125047, Russia
| | - A S Kulikov
- Staff Anesthesiologist; N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
| | - A A Potapov
- Professor, Academician of the Russian Academy of Sciences, Chief Scientific Supervisor N.N. Burdenko National Medical Research Center for Neurosurgery, Ministry of Health of the Russian Federation, 16, 4 Tverskaya-Yamskaya St., Moscow, 125047, Russia
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19
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Eight-hours conventional versus adaptive deep brain stimulation of the subthalamic nucleus in Parkinson's disease. NPJ PARKINSONS DISEASE 2021; 7:88. [PMID: 34584095 PMCID: PMC8478873 DOI: 10.1038/s41531-021-00229-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 07/23/2021] [Indexed: 12/04/2022]
Abstract
This study compares the effects on motor symptoms between conventional deep brain stimulation (cDBS) and closed-loop adaptive deep brain stimulation (aDBS) in patients with Parkinson’s Disease. The aDBS stimulation is controlled by the power in the beta band (12–35 Hz) of local field potentials recorded directly by subthalamic nucleus electrodes. Eight subjects were assessed in two 8-h stimulation sessions (first day, cDBS; second day, aDBS) with regular levodopa intake and during normal daily activities. The Unified Parkinson’s Disease Rating Scale (UPDRS) part III scores, the Rush scale for dyskinesias, and the total electrical energy delivered to the tissues per second (TEEDs) were significantly lower in the aDBS session (relative UPDRS mean, cDBS: 0.46 ± 0.05, aDBS: 0.33 ± 0.04, p = 0.015; UPDRS part III rigidity subset mean, cDBS: 2.9143 ± 0.6551 and aDBS: 2.1429 ± 0.5010, p = 0.034; UPDRS part III standard deviation cDBS: 2.95, aDBS: 2.68; p = 0.047; Rush scale, cDBS 2.79 ± 0.39 versus aDBS 1.57 ± 0.23, p = 0.037; cDBS TEEDs mean: 28.75 ± 3.36 µj s−1, aDBS TEEDs mean: 16.47 ± 3.33, p = 0.032 Wilcoxon’s sign rank test). This work further supports the safety and effectiveness of aDBS stimulation compared to cDBS in a daily session, both in terms of motor performance and TEED to the patient.
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20
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Wearable sensor-driven responsive deep brain stimulation for essential tremor. Brain Stimul 2021; 14:1434-1443. [PMID: 34547503 DOI: 10.1016/j.brs.2021.09.002] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/28/2021] [Accepted: 09/11/2021] [Indexed: 10/20/2022] Open
Abstract
BACKGROUND Deep brain stimulation (DBS) is an effective surgical therapy for individuals with essential tremor (ET). However, DBS operates continuously, resulting in adverse effects such as postural instability or dysarthria. Continuous DBS (cDBS) also presents important practical issues including limited battery life of the implantable neurostimulator (INS). Collectively, these shortcomings impact optimal therapeutic benefit in ET. OBJECTIVE The goal of the study was to establish a physiology-driven responsive DBS (rDBS) system to provide targeted and personalized therapy based on electromyography (EMG) signals. METHODS Ten participants with ET underwent rDBS using Nexus-D, a Medtronic telemetry wand that acts as a direct conduit to the INS by modulating stimulation voltage. Two different rDBS paradigms were tested: one driven by one EMG (single-sensor) and another driven by two or more EMGs (multi-sensor). The feature(s) used in the rDBS algorithms was the pow2er in the participant's tremor frequency band derived from the sensors controlling stimulation. Both algorithms were trained on kinetic and postural data collected during DBS off and cDBS states. RESULTS Using established clinical scales and objective measurements of tremor severity, we confirm that both rDBS paradigms deliver equivalent clinical benefit as cDBS. Moreover, both EMG-driven rDBS paradigms delivered less total electrical energy translating to an increase in the battery life of the INS. CONCLUSIONS The results of this study verify that EMG-driven rDBS provides clinically equivalent tremor suppression compared to cDBS, while delivering less total electrical energy. Controlling stimulation using a dynamic rDBS paradigm can mitigate limitations of traditional cDBS systems.
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21
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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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22
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Bočková M, Rektor I. Electrophysiological biomarkers for deep brain stimulation outcomes in movement disorders: state of the art and future challenges. J Neural Transm (Vienna) 2021; 128:1169-1175. [PMID: 34245367 DOI: 10.1007/s00702-021-02381-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 07/02/2021] [Indexed: 11/25/2022]
Abstract
Several neurological diseases are accompanied by rhythmic oscillatory dysfunctions in various frequency ranges and disturbed cross-frequency relationships on regional, interregional, and whole brain levels. Knowledge of these disease-specific oscillopathies is important mainly in the context of deep brain stimulation (DBS) therapy. Electrophysiological biomarkers have been used as input signals for adaptive DBS (aDBS) as well as preoperative outcome predictors. As movement disorders, particularly Parkinson's disease (PD), are among the most frequent DBS indications, the current research of DBS is the most advanced in the movement disorders field. We reviewed the literature published mainly between 2010 and 2020 to identify the most important findings concerning the current evolution of electrophysiological biomarkers in DBS and to address future challenges for prospective research.
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Affiliation(s)
- Martina Bočková
- Central European Institute of Technology (CEITEC), Brain and Mind Research Program, Masaryk University, Brno, Czech Republic
- Movement Disorders Center, First Department of Neurology, Masaryk University School of Medicine, St. Anne's Hospital, Pekařská 53, 656 91, Brno, Czech Republic
| | - Ivan Rektor
- Central European Institute of Technology (CEITEC), Brain and Mind Research Program, Masaryk University, Brno, Czech Republic.
- Movement Disorders Center, First Department of Neurology, Masaryk University School of Medicine, St. Anne's Hospital, Pekařská 53, 656 91, Brno, Czech Republic.
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23
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Opri E, Cernera S, Molina R, Eisinger RS, Cagle JN, Almeida L, Denison T, Okun MS, Foote KD, Gunduz A. Chronic embedded cortico-thalamic closed-loop deep brain stimulation for the treatment of essential tremor. Sci Transl Med 2021; 12:12/572/eaay7680. [PMID: 33268512 DOI: 10.1126/scitranslmed.aay7680] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 01/14/2020] [Accepted: 08/25/2020] [Indexed: 11/02/2022]
Abstract
Deep brain stimulation (DBS) is an approved therapy for the treatment of medically refractory and severe movement disorders. However, most existing neurostimulators can only apply continuous stimulation [open-loop DBS (OL-DBS)], ignoring patient behavior and environmental factors, which consequently leads to an inefficient therapy, thus limiting the therapeutic window. Here, we established the feasibility of a self-adjusting therapeutic DBS [closed-loop DBS (CL-DBS)], fully embedded in a chronic investigational neurostimulator (Activa PC + S), for three patients affected by essential tremor (ET) enrolled in a longitudinal (6 months) within-subject crossover protocol (DBS OFF, OL-DBS, and CL-DBS). Most patients with ET experience involuntary limb tremor during goal-directed movements, but not during rest. Hence, the proposed CL-DBS paradigm explored the efficacy of modulating the stimulation amplitude based on patient-specific motor behavior, suppressing the pathological tremor on-demand based on a cortical electrode detecting upper limb motor activity. Here, we demonstrated how the proposed stimulation paradigm was able to achieve clinical efficacy and tremor suppression comparable with OL-DBS in a range of movements (cup reaching, proximal and distal posture, water pouring, and writing) while having a consistent reduction in energy delivery. The proposed paradigm is an important step toward a behaviorally modulated fully embedded DBS system, capable of delivering stimulation only when needed, and potentially mitigating pitfalls of OL-DBS, such as DBS-induced side effects and premature device replacement.
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Affiliation(s)
- Enrico Opri
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA.
| | - Stephanie Cernera
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Rene Molina
- Electrical and Computer Engineering, University of Florida, Gainesville, FL 32603, USA
| | - Robert S Eisinger
- Norman Fixel Institute for Neurological Diseases at UF Health, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL 32608, USA
| | - Jackson N Cagle
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Leonardo Almeida
- Norman Fixel Institute for Neurological Diseases at UF Health, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL 32608, USA
| | - Timothy Denison
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
| | - Michael S Okun
- Norman Fixel Institute for Neurological Diseases at UF Health, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL 32608, USA
| | - Kelly D Foote
- Norman Fixel Institute for Neurological Diseases at UF Health, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL 32608, USA
| | - Aysegul Gunduz
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA.,Electrical and Computer Engineering, University of Florida, Gainesville, FL 32603, USA.,Norman Fixel Institute for Neurological Diseases at UF Health, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL 32608, USA
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24
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Latorre A, Rocchi L, Sadnicka A. The Expanding Horizon of Neural Stimulation for Hyperkinetic Movement Disorders. Front Neurol 2021; 12:669690. [PMID: 34054710 PMCID: PMC8160223 DOI: 10.3389/fneur.2021.669690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 04/19/2021] [Indexed: 11/13/2022] Open
Abstract
Novel methods of neural stimulation are transforming the management of hyperkinetic movement disorders. In this review the diversity of approach available is showcased. We first describe the most commonly used features that can be extracted from oscillatory activity of the central nervous system, and how these can be combined with an expanding range of non-invasive and invasive brain stimulation techniques. We then shift our focus to the periphery using tremor and Tourette's syndrome to illustrate the utility of peripheral biomarkers and interventions. Finally, we discuss current innovations which are changing the landscape of stimulation strategy by integrating technological advances and the use of machine learning to drive optimization.
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Affiliation(s)
- Anna Latorre
- Department of Clinical and Movement Neurosciences, University College London, London, United Kingdom
| | - Lorenzo Rocchi
- Department of Clinical and Movement Neurosciences, University College London, London, United Kingdom.,Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
| | - Anna Sadnicka
- Department of Clinical and Movement Neurosciences, University College London, London, United Kingdom.,Motor Control and Neuromodulation Group, St George's University of London, London, United Kingdom
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25
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He S, Baig F, Mostofi A, Pogosyan A, Debarros J, Green AL, Aziz TZ, Pereira E, Brown P, Tan H. Closed-Loop Deep Brain Stimulation for Essential Tremor Based on Thalamic Local Field Potentials. Mov Disord 2021; 36:863-873. [PMID: 33547859 PMCID: PMC7610625 DOI: 10.1002/mds.28513] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 12/07/2020] [Accepted: 01/06/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND High-frequency thalamic stimulation is an effective therapy for essential tremor, which mainly affects voluntary movements and/or sustained postures. However, continuous stimulation may deliver unnecessary current to the brain due to the intermittent nature of the tremor. OBJECTIVE We proposed to close the loop of thalamic stimulation by detecting tremor-provoking movement states using local field potentials recorded from the same electrodes implanted for stimulation, so that the stimulation is only delivered when necessary. METHODS Eight patients with essential tremor participated in this study. Patient-specific support vector machine classifiers were first trained using data recorded while the patient performed tremor-provoking movements. Then, the trained models were applied in real-time to detect these movements and triggered the delivery of stimulation. RESULTS Using the proposed method, stimulation was switched on for 80.37 ± 7.06% of the time when tremor-evoking movements were present. In comparison, the stimulation was switched on for 12.71 ± 7.06% of the time when the patients were at rest and tremor-free. Compared with continuous stimulation, a similar amount of tremor suppression was achieved while only delivering 36.62 ± 13.49% of the energy used in continuous stimulation. CONCLUSIONS The results suggest that responsive thalamic stimulation for essential tremor based on tremor-provoking movement detection can be achieved without any requirement for external sensors or additional electrocorticography strips. Further research is required to investigate whether the decoding model is stable across time and generalizable to the variety of activities patients may engage with in everyday life. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Shenghong He
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Fahd Baig
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.,Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St. George's, University of London, Oxford, UK
| | - Abteen Mostofi
- Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St. George's, University of London, Oxford, UK
| | - Alek Pogosyan
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Jean Debarros
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Alexander L Green
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Tipu Z Aziz
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Erlick Pereira
- Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St. George's, University of London, Oxford, UK
| | - Peter Brown
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Huiling Tan
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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26
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Neumann WJ, Memarian Sorkhabi M, Benjaber M, Feldmann LK, Saryyeva A, Krauss JK, Contarino MF, Sieger T, Jech R, Tinkhauser G, Pollo C, Palmisano C, Isaias IU, Cummins DD, Little SJ, Starr PA, Kokkinos V, Gerd-Helge S, Herrington T, Brown P, Richardson RM, Kühn AA, Denison T. The sensitivity of ECG contamination to surgical implantation site in brain computer interfaces. Brain Stimul 2021; 14:1301-1306. [PMID: 34428554 PMCID: PMC8460992 DOI: 10.1016/j.brs.2021.08.016] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 08/05/2021] [Accepted: 08/19/2021] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Brain sensing devices are approved today for Parkinson's, essential tremor, and epilepsy therapies. Clinical decisions for implants are often influenced by the premise that patients will benefit from using sensing technology. However, artifacts, such as ECG contamination, can render such treatments unreliable. Therefore, clinicians need to understand how surgical decisions may affect artifact probability. OBJECTIVES Investigate neural signal contamination with ECG activity in sensing enabled neurostimulation systems, and in particular clinical choices such as implant location that impact signal fidelity. METHODS Electric field modeling and empirical signals from 85 patients were used to investigate the relationship between implant location and ECG contamination. RESULTS The impact on neural recordings depends on the difference between ECG signal and noise floor of the electrophysiological recording. Empirically, we demonstrate that severe ECG contamination was more than 3.2x higher in left-sided subclavicular implants (48.3%), when compared to right-sided implants (15.3%). Cranial implants did not show ECG contamination. CONCLUSIONS Given the relative frequency of corrupted neural signals, we conclude that implant location will impact the ability of brain sensing devices to be used for "closed-loop" algorithms. Clinical adjustments such as implant location can significantly affect signal integrity and need consideration.
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Affiliation(s)
- Wolf-Julian Neumann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Chariteplatz 1, 10117, Berlin, Germany.
| | - Majid Memarian Sorkhabi
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
| | - Moaad Benjaber
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
| | - Lucia K Feldmann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Chariteplatz 1, 10117, Berlin, Germany
| | - Assel Saryyeva
- Department of Neurosurgery, Medizinische Hochschule Hannover, Hannover, Germany
| | - Joachim K Krauss
- Department of Neurosurgery, Medizinische Hochschule Hannover, Hannover, Germany
| | - Maria Fiorella Contarino
- Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands; Department of Neurology, Haga Teaching Hospital, The Hague, the Netherlands
| | - Tomas Sieger
- Department of Neurology, Charles University, 1st Faculty of Medicine and General University Hospital, Prague, Czech Republic
| | - Robert Jech
- Department of Neurology, Charles University, 1st Faculty of Medicine and General University Hospital, Prague, Czech Republic
| | - Gerd Tinkhauser
- 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
| | - Chiara Palmisano
- Department of Neurology, University Hospital of Würzburg and Julius Maximilian University of Würzburg, Würzburg, Germany
| | - Ioannis U Isaias
- Department of Neurology, University Hospital of Würzburg and Julius Maximilian University of Würzburg, Würzburg, Germany
| | - Daniel D Cummins
- Department of Neurology, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Simon J Little
- Department of Neurology, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Philip A Starr
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Vasileios Kokkinos
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Schneider Gerd-Helge
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Chariteplatz 1, 10117, Berlin, Germany
| | - Todd Herrington
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Peter Brown
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrea A Kühn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Chariteplatz 1, 10117, Berlin, Germany
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
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27
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Zelmann R, Paulk AC, Basu I, Sarma A, Yousefi A, Crocker B, Eskandar E, Williams Z, Cosgrove GR, Weisholtz DS, Dougherty DD, Truccolo W, Widge AS, Cash SS. CLoSES: A platform for closed-loop intracranial stimulation in humans. Neuroimage 2020; 223:117314. [PMID: 32882382 PMCID: PMC7805582 DOI: 10.1016/j.neuroimage.2020.117314] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 07/15/2020] [Accepted: 08/25/2020] [Indexed: 01/02/2023] Open
Abstract
Targeted interrogation of brain networks through invasive brain stimulation has become an increasingly important research tool as well as therapeutic modality. The majority of work with this emerging capability has been focused on open-loop approaches. Closed-loop techniques, however, could improve neuromodulatory therapies and research investigations by optimizing stimulation approaches using neurally informed, personalized targets. Implementing closed-loop systems is challenging particularly with regard to applying consistent strategies considering inter-individual variability. In particular, during intracranial epilepsy monitoring, where much of this research is currently progressing, electrodes are implanted exclusively for clinical reasons. Thus, detection and stimulation sites must be participant- and task-specific. The system must run in parallel with clinical systems, integrate seamlessly with existing setups, and ensure safety features are in place. In other words, a robust, yet flexible platform is required to perform different tests with a single participant and to comply with clinical requirements. In order to investigate closed-loop stimulation for research and therapeutic use, we developed a Closed-Loop System for Electrical Stimulation (CLoSES) that computes neural features which are then used in a decision algorithm to trigger stimulation in near real-time. To summarize CLoSES, intracranial electroencephalography (iEEG) signals are acquired, band-pass filtered, and local and network features are continuously computed. If target features are detected (e.g. above a preset threshold for a certain duration), stimulation is triggered. Not only could the system trigger stimulation while detecting real-time neural features, but we incorporated a pipeline wherein we used an encoder/decoder model to estimate a hidden cognitive state from the neural features. CLoSES provides a flexible platform to implement a variety of closed-loop experimental paradigms in humans. CLoSES has been successfully used with twelve patients implanted with depth electrodes in the epilepsy monitoring unit. During cognitive tasks (N=5), stimulation in closed loop modified a cognitive hidden state on a trial by trial basis. Sleep spindle oscillations (N=6) and sharp transient epileptic activity (N=9) were detected in near real-time, and stimulation was applied during the event or at specified delays (N=3). In addition, we measured the capabilities of the CLoSES system. Total latency was related to the characteristics of the event being detected, with tens of milliseconds for epileptic activity and hundreds of milliseconds for spindle detection. Stepwise latency, the actual duration of each continuous step, was within the specified fixed-step duration and increased linearly with the number of channels and features. We anticipate that probing neural dynamics and interaction between brain states and stimulation responses with CLoSES will lead to novel insights into the mechanism of normal and pathological brain activity, the discovery and evaluation of potential electrographic biomarkers of neurological and psychiatric disorders, and the development and testing of patient-specific stimulation targets and control signals before implanting a therapeutic device.
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Affiliation(s)
- Rina Zelmann
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
| | - Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Ishita Basu
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA; Department of Neurosurgery, University of Cincinnati, OH, USA
| | - Anish Sarma
- Department of Neuroscience, Brown University, Providence, RI, USA
| | - Ali Yousefi
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA; Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Britni Crocker
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Emad Eskandar
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA; Department of Neurosurgery, Albert Einstein College of Medicine, NY, USA
| | - Ziv Williams
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - G Rees Cosgrove
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Darin D Dougherty
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Wilson Truccolo
- Department of Neuroscience, Brown University, Providence, RI, USA
| | - Alik S Widge
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA; Department of Psychiatry, University of Minnesota, MI, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
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28
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Krauss JK, Lipsman N, Aziz T, Boutet A, Brown P, Chang JW, Davidson B, Grill WM, Hariz MI, Horn A, Schulder M, Mammis A, Tass PA, Volkmann J, Lozano AM. Technology of deep brain stimulation: current status and future directions. Nat Rev Neurol 2020; 17:75-87. [PMID: 33244188 DOI: 10.1038/s41582-020-00426-z] [Citation(s) in RCA: 271] [Impact Index Per Article: 67.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/08/2020] [Indexed: 01/20/2023]
Abstract
Deep brain stimulation (DBS) is a neurosurgical procedure that allows targeted circuit-based neuromodulation. DBS is a standard of care in Parkinson disease, essential tremor and dystonia, and is also under active investigation for other conditions linked to pathological circuitry, including major depressive disorder and Alzheimer disease. Modern DBS systems, borrowed from the cardiac field, consist of an intracranial electrode, an extension wire and a pulse generator, and have evolved slowly over the past two decades. Advances in engineering and imaging along with an improved understanding of brain disorders are poised to reshape how DBS is viewed and delivered to patients. Breakthroughs in electrode and battery designs, stimulation paradigms, closed-loop and on-demand stimulation, and sensing technologies are expected to enhance the efficacy and tolerability of DBS. In this Review, we provide a comprehensive overview of the technical development of DBS, from its origins to its future. Understanding the evolution of DBS technology helps put the currently available systems in perspective and allows us to predict the next major technological advances and hurdles in the field.
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Affiliation(s)
- Joachim K Krauss
- Department of Neurosurgery, Hannover Medical School, Hannover, Germany
| | - Nir Lipsman
- Department of Neurosurgery, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Tipu Aziz
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Alexandre Boutet
- Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Peter Brown
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK
| | - Jin Woo Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, South Korea
| | - Benjamin Davidson
- Department of Neurosurgery, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Marwan I Hariz
- Department of Clinical Neuroscience, University of Umea, Umea, Sweden
| | - Andreas Horn
- Department of Neurology, Movement Disorders and Neuromodulation Section, Charité Medicine University of Berlin, Berlin, Germany
| | - Michael Schulder
- Department of Neurosurgery, Zucker School of Medicine at Hofstra/Northwell, New York, NY, USA
| | - Antonios Mammis
- Department of Neurosurgery, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Peter A Tass
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Jens Volkmann
- Department of Neurosurgery, Hannover Medical School, Hannover, Germany.,Department of Neurology, University Hospital of Würzburg, Würzburg, Germany
| | - Andres M Lozano
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada.
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29
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Burns MR, Chiu SY, Patel B, Mitropanopoulos SG, Wong JK, Ramirez-Zamora A. Advances and Future Directions of Neuromodulation in Neurologic Disorders. Neurol Clin 2020; 39:71-85. [PMID: 33223090 DOI: 10.1016/j.ncl.2020.09.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
"Deep brain stimulation is a safe and effective therapy for the management of a variety of neurologic conditions with Food and Drug Administration or humanitarian exception approval for Parkinson disease, dystonia, tremor, and obsessive-compulsive disorder. Advances in neurophysiology, neuroimaging, and technology have driven increasing interest in the potential benefits of neurostimulation in other neuropsychiatric conditions including dementia, depression, pain, Tourette syndrome, and epilepsy, among others. New anatomic or combined targets are being investigated in these conditions to improve symptoms refractory to medications or standard stimulation."
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Affiliation(s)
- Matthew R Burns
- The Fixel Institute for Neurological Diseases, Department of Neurology, The University of Florida, 3009 Williston Road, Gainesville, FL 32608, USA
| | - Shannon Y Chiu
- The Fixel Institute for Neurological Diseases, Department of Neurology, The University of Florida, 3009 Williston Road, Gainesville, FL 32608, USA
| | - Bhavana Patel
- The Fixel Institute for Neurological Diseases, Department of Neurology, The University of Florida, 3009 Williston Road, Gainesville, FL 32608, USA
| | - Sotiris G Mitropanopoulos
- The Fixel Institute for Neurological Diseases, Department of Neurology, The University of Florida, 3009 Williston Road, Gainesville, FL 32608, USA
| | - Joshua K Wong
- The Fixel Institute for Neurological Diseases, Department of Neurology, The University of Florida, 3009 Williston Road, Gainesville, FL 32608, USA
| | - Adolfo Ramirez-Zamora
- The Fixel Institute for Neurological Diseases, Department of Neurology, The University of Florida, 3009 Williston Road, Gainesville, FL 32608, USA.
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30
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Castaño-Candamil S, Ferleger BI, Haddock A, Cooper SS, Herron J, Ko A, Chizeck HJ, Tangermann M. A Pilot Study on Data-Driven Adaptive Deep Brain Stimulation in Chronically Implanted Essential Tremor Patients. Front Hum Neurosci 2020; 14:541625. [PMID: 33250727 PMCID: PMC7674800 DOI: 10.3389/fnhum.2020.541625] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 10/15/2020] [Indexed: 11/13/2022] Open
Abstract
Deep brain stimulation (DBS) is an established therapy for Parkinson's disease (PD) and essential-tremor (ET). In adaptive DBS (aDBS) systems, online tuning of stimulation parameters as a function of neural signals may improve treatment efficacy and reduce side-effects. State-of-the-art aDBS systems use symptom surrogates derived from neural signals-so-called neural markers (NMs)-defined on the patient-group level, and control strategies assuming stationarity of symptoms and NMs. We aim at improving these aDBS systems with (1) a data-driven approach for identifying patient- and session-specific NMs and (2) a control strategy coping with short-term non-stationary dynamics. The two building blocks are implemented as follows: (1) The data-driven NMs are based on a machine learning model estimating tremor intensity from electrocorticographic signals. (2) The control strategy accounts for local variability of tremor statistics. Our study with three chronically implanted ET patients amounted to five online sessions. Tremor quantified from accelerometer data shows that symptom suppression is at least equivalent to that of a continuous DBS strategy in 3 out-of 4 online tests, while considerably reducing net stimulation (at least 24%). In the remaining online test, symptom suppression was not significantly different from either the continuous strategy or the no treatment condition. We introduce a novel aDBS system for ET. It is the first aDBS system based on (1) a machine learning model to identify session-specific NMs, and (2) a control strategy coping with short-term non-stationary dynamics. We show the suitability of our aDBS approach for ET, which opens the door to its further study in a larger patient population.
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Affiliation(s)
- Sebastián Castaño-Candamil
- Brain State Decoding Lab, Department of Computer Science, BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg im Breisgau, Germany
| | - Benjamin I Ferleger
- BioRobotics Lab, Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Andrew Haddock
- BioRobotics Lab, Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Sarah S Cooper
- BioRobotics Lab, Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Jeffrey Herron
- Department of Neurological Surgery, University of Washington Medical Center, Seattle, WA, United States
| | - Andrew Ko
- Department of Neurological Surgery, University of Washington Medical Center, Seattle, WA, United States
| | - Howard J Chizeck
- BioRobotics Lab, Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Michael Tangermann
- Brain State Decoding Lab, Department of Computer Science, BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg im Breisgau, Germany.,Autonomous Intelligent Systems, Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany.,Artificial Cognitive Systems Lab, Artificial Intelligence Department, Faculty of Social Sciences, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
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31
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Wang KL, Ren Q, Chiu S, Patel B, Meng FG, Hu W, Shukla AW. Deep brain stimulation and other surgical modalities for the management of essential tremor. Expert Rev Med Devices 2020; 17:817-833. [PMID: 33081571 DOI: 10.1080/17434440.2020.1806709] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
INTRODUCTION Surgical treatments are considered for essential tremor (ET) when patients do not respond to oral pharmacological therapies. These treatments mainly comprise radiofrequency (RF) thalamotomy, gamma knife radiosurgery (GKRS), deep brain stimulation (DBS), and focused ultrasound (FUS) procedures. AREAS COVERED We reviewed the strengths and weaknesses of each procedure and clinical outcomes for 7 RF studies (n = 85), 11 GKRS (n = 477), 33 DBS (n = 1061), and 13 FUS studies (n = 368). A formal comparison was not possible given the heterogeneity in studies. Improvements were about 42%-90% RF, 10%-79% GKRS, 45%-83% DBS, 42%-83% FUS at short-term follow-up (<12 months) and were about 54%-82% RF, 11%-84% GKRS, 18%-92% DBS, and 42%-80% FUS at long-term follow-up (>12 months). EXPERT OPINION We found DBS with inherent advantages of being an adjustable and reversible procedure as the most frequently employed surgical procedure for control of ET symptoms. FUS is a promising procedure but has limited applicability for unilateral control of symptoms. RF is invasive, and GKRS has unpredictable delayed effects. Each of these surgical modalities has advantages and limitations that need consideration when selecting a treatment for the ET patients.
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Affiliation(s)
- Kai-Liang Wang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University , Beijing, China.,Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University , Beijing, China
| | - Qianwei Ren
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University , Beijing, China
| | - Shannon Chiu
- Department of Neurology, University of Florida College of Medicine , Gainesville, FL, USA
| | - Bhavana Patel
- Department of Neurology, University of Florida College of Medicine , Gainesville, FL, USA
| | - Fan-Gang Meng
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University , Beijing, China
| | - Wei Hu
- Department of Neurology, University of Florida College of Medicine , Gainesville, FL, USA
| | - Aparna Wagle Shukla
- Department of Neurology, University of Florida College of Medicine , Gainesville, FL, USA
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32
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Martineau T, He S, Vaidyanathan R, Brown P, Tan H. Optimizing Time-Frequency Feature Extraction and Channel Selection through Gradient Backpropagation to Improve Action Decoding based on Subthalamic Local Field Potentials. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:3023-3026. [PMID: 33018642 DOI: 10.1109/embc44109.2020.9175885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Neural oscillating patterns, or time-frequency features, predicting voluntary motor intention, can be extracted from the local field potentials (LFPs) recorded from the sub-thalamic nucleus (STN) or thalamus of human patients implanted with deep brain stimulation (DBS) electrodes for the treatment of movement disorders. This paper investigates the optimization of signal conditioning processes using deep learning to augment time-frequency feature extraction from LFP signals, with the aim of improving the performance of real-time decoding of voluntary motor states. A brain-computer interface (BCI) pipeline capable of continuously classifying discrete pinch grip states from LFPs was designed in Pytorch, a deep learning framework. The pipeline was implemented offline on LFPs recorded from 5 different patients bilaterally implanted with DBS electrodes. Optimizing channel combination in different frequency bands and frequency domain feature extraction demonstrated improved classification accuracy of pinch grip detection and laterality of the pinch (either pinch of the left hand or pinch of the right hand). Overall, the optimized BCI pipeline achieved a maximal average classification accuracy of 79.67±10.02% when detecting all pinches and 67.06±10.14% when considering the laterality of the pinch.
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Debarros J, Gaignon L, He S, Pogosyan A, Benjaber M, Denison T, Brown P, Tan H. Artefact-free recording of local field potentials with simultaneous stimulation for closed-loop Deep-Brain Stimulation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:3367-3370. [PMID: 33018726 DOI: 10.1109/embc44109.2020.9176665] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Continuous high frequency Deep Brain Stimulation (DBS) is a standard therapy for several neurological disorders. Closed-loop DBS is expected to further improve treatment by providing adaptive, on-demand therapy. Local field potentials (LFPs) recorded from the stimulation electrodes are the most often used feedback signal in closed-loop DBS. However, closed-loop DBS based on LFPs requires simultaneous recording and stimulating, which remains a challenge due to persistent stimulation artefacts that distort underlying LFP biomarkers. Here we first investigate the nature of the stimulation-induced artefacts and review several techniques that have been proposed to deal with them. Then we propose a new method to synchronize the sampling clock with the stimulation pulse so that the stimulation artefacts are never sampled, while at the same time the Nyquist-Shannon theorem is satisfied for uninterrupted LFP recording. Test results show that this method achieves true uninterrupted artefact-free LFP recording over a wide frequency band and for a wide range of stimulation frequencies.Clinical relevance-The method proposed here provides continuous and artefact-free recording of LFPs close to the stimulation target, and thereby facilitates the implementation of more advanced closed-loop DBS using LFPs as feedback.
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He S, Debarros J, Khawaldeh S, Pogosyan A, Mostofi A, Baig F, Pereira E, Brown P, Tan H. Closed-loop DBS triggered by real-time movement and tremor decoding based on thalamic LFPs for essential tremor. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:3602-3605. [PMID: 33018782 DOI: 10.1109/embc44109.2020.9175433] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
High frequency Deep Brain Stimulation (DBS) targeting the motor thalamus is an effective therapy for essential tremor (ET). However, since tremor mainly affects periods of voluntary movements and sustained postures in ET, conventional continuous stimulation may deliver unnecessary current to the brain. Here we tried to decode movement states based on local field potentials (LFPs) recorded from motor thalamus and zona incerta in real-time to trigger the switching on and off of DBS in three patients with ET. Patient-specific models were first identified using thalamic LFPs recorded while the patient performed movements that tended to trigger tremor in everyday life. During the real-time test, LFPs were continuously recorded to decode movements and tremor, and the detection triggered stimulation. Results show that voluntary movements can be detected with a mean sensitivity ranging from 76.8% to 88.6% and a false positive rate ranging from 16.0% to 23.1% Postural tremor was detected with similar accuracy. The closed-loop DBS triggered by tremor detection suppressed intention tremor by 90.5% with a false positive rate of 20.3%.Clinical Relevance- This is the first study on closed-loop DBS triggered by real-time movement and tremor decoding based solely on thalamic LFPs. The results suggest that responsive DBS based on movement and tremor detection can be achieved without any requirement for external sensors or additional electrocorticography strips.
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Wozny TA, Wang DD, Starr PA. Simultaneous cortical and subcortical recordings in humans with movement disorders: Acute and chronic paradigms. Neuroimage 2020; 217:116904. [PMID: 32387742 DOI: 10.1016/j.neuroimage.2020.116904] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 04/22/2020] [Accepted: 04/29/2020] [Indexed: 11/20/2022] Open
Abstract
Invasive basal ganglia recordings in humans have significantly advanced our understanding of the neurophysiology of movement disorders. A recent technical advance has been the addition of electrocorticography to basal ganglia recording, for evaluating distributed motor networks. Here we review the rationale, results, and ethics of this multisite recording technique in movement disorders, as well as its application in chronic recording paradigms utilizing implantable neural interfaces that include a sensing function.
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Affiliation(s)
- Thomas A Wozny
- Department of Neurological Surgery, University of California, 505 Parnassus Avenue, San Francisco, CA, 94143, USA.
| | - Doris D Wang
- Department of Neurological Surgery, University of California, 505 Parnassus Avenue, San Francisco, CA, 94143, USA
| | - Philip A Starr
- Department of Neurological Surgery, University of California, 505 Parnassus Avenue, San Francisco, CA, 94143, USA
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Gaidica M, Hurst A, Cyr C, Leventhal DK. Interactions Between Motor Thalamic Field Potentials and Single-Unit Spiking Are Correlated With Behavior in Rats. Front Neural Circuits 2020; 14:52. [PMID: 32922268 PMCID: PMC7457120 DOI: 10.3389/fncir.2020.00052] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 07/16/2020] [Indexed: 11/30/2022] Open
Abstract
Field potential (FP) oscillations are believed to coordinate brain activity over large spatiotemporal scales, with specific features (e.g., phase and power) in discrete frequency bands correlated with motor output. Furthermore, complex correlations between oscillations in distinct frequency bands (phase-amplitude, amplitude-amplitude, and phase-phase coupling) are commonly observed. However, the mechanisms underlying FP-behavior correlations and cross-frequency coupling remain unknown. The thalamus plays a central role in generating many circuit-level neural oscillations, and single-unit activity in motor thalamus (Mthal) is correlated with behavioral output. We, therefore, hypothesized that motor thalamic spiking coordinates motor system FPs and underlies FP-behavior correlations. To investigate this possibility, we recorded wideband motor thalamic (Mthal) electrophysiology as healthy rats performed a two-alternative forced-choice task. Delta (1–4 Hz), beta (13–30 Hz), low gamma (30–70 Hz), and high gamma (70–200 Hz) power were strongly modulated by task performance. As in the cortex, the delta phase was correlated with beta/low gamma power and reaction time. Most interestingly, subpopulations of Mthal neurons defined by their relationship to the behavior exhibited distinct relationships with FP features. Specifically, neurons whose activity was correlated with action selection and movement speed were entrained to delta oscillations. Furthermore, changes in their activity anticipated power fluctuations in beta/low gamma bands. These complex relationships suggest mechanisms for commonly observed FP-FP and spike-FP correlations, as well as subcortical influences on motor output.
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Affiliation(s)
- Matt Gaidica
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, United States
| | - Amy Hurst
- Department of Neurology, University of Michigan, Ann Arbor, MI, United States
| | - Christopher Cyr
- Department of Neurology, University of Michigan, Ann Arbor, MI, United States
| | - Daniel K Leventhal
- Department of Neurology, University of Michigan, Ann Arbor, MI, United States.,Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States.,Parkinson Disease Foundation Research Center of Excellence, University of Michigan, Ann Arbor, MI, United States.,Department of Neurology, VA Ann Arbor Health System, Ann Arbor, MI, United States
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Castaño-Candamil S, Piroth T, Reinacher P, Sajonz B, Coenen VA, Tangermann M. Identifying controllable cortical neural markers with machine learning for adaptive deep brain stimulation in Parkinson's disease. Neuroimage Clin 2020; 28:102376. [PMID: 32889400 PMCID: PMC7479445 DOI: 10.1016/j.nicl.2020.102376] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 07/17/2020] [Accepted: 08/04/2020] [Indexed: 12/24/2022]
Abstract
The identification of oscillatory neural markers of Parkinson's disease (PD) can contribute not only to the understanding of functional mechanisms of the disorder, but may also serve in adaptive deep brain stimulation (DBS) systems. These systems seek online adaptation of stimulation parameters in closed-loop as a function of neural markers, aiming at improving treatment's efficacy and reducing side effects. Typically, the identification of PD neural markers is based on group-level studies. Due to the heterogeneity of symptoms across patients, however, such group-level neural markers, like the beta band power of the subthalamic nucleus, are not present in every patient or not informative about every patient's motor state. Instead, individual neural markers may be preferable for providing a personalized solution for the adaptation of stimulation parameters. Fortunately, data-driven bottom-up approaches based on machine learning may be utilized. These approaches have been developed and applied successfully in the field of brain-computer interfaces with the goal of providing individuals with means of communication and control. In our contribution, we present results obtained with a novel supervised data-driven identification of neural markers of hand motor performance based on a supervised machine learning model. Data of 16 experimental sessions obtained from seven PD patients undergoing DBS therapy show that the supervised patient-specific neural markers provide improved decoding accuracy of hand motor performance, compared to group-level neural markers reported in the literature. We observed that the individual markers are sensitive to DBS therapy and thus, may represent controllable variables in an adaptive DBS system.
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Affiliation(s)
- Sebastián Castaño-Candamil
- Brain State Decoding Lab (BrainLinks-BrainTools), Dept. of Computer Science at the University of Freiburg, Germany.
| | - Tobias Piroth
- Kantonsspital Aarau, with the Faculty of Medicine at the University of Freiburg, and with the Dept. of Neurology and Neurophysiology at the University Medical Center, Freiburg, Germany
| | - Peter Reinacher
- Faculty of Medicine at the University of Freiburg, and with the Dept of Stereotactic and Functional Neurosurgery at the University Medical Center, Freiburg, Germany
| | - Bastian Sajonz
- Faculty of Medicine at the University of Freiburg, and with the Dept of Stereotactic and Functional Neurosurgery at the University Medical Center, Freiburg, Germany
| | - Volker A Coenen
- Faculty of Medicine at the University of Freiburg, and with the Dept of Stereotactic and Functional Neurosurgery at the University Medical Center, Freiburg, Germany
| | - Michael Tangermann
- Brain State Decoding Lab (BrainLinks-BrainTools) and Autonomous Intelligent Systems, Dept. of Computer Science at the University of Freiburg, Germany; Artificial Cognitive Systems Lab, Artificial Intelligence Dept., Donders Institute for Brain, Cognition and Behaviour, Faculty of Social Sciences, Radboud University, Nijmegen, The Netherlands.
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Acute effects of adaptive Deep Brain Stimulation in Parkinson's disease. Brain Stimul 2020; 13:1507-1516. [PMID: 32738409 PMCID: PMC7116216 DOI: 10.1016/j.brs.2020.07.016] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 07/19/2020] [Accepted: 07/23/2020] [Indexed: 02/08/2023] Open
Abstract
Background Beta-based adaptive Deep Brain Stimulation (aDBS) is effective in Parkinson’s disease (PD), when assessed in the immediate post-implantation phase. However, the potential benefits of aDBS in patients with electrodes chronically implanted, in whom changes due to the microlesion effect have disappeared, are yet to be assessed. Methods To determine the acute effectiveness and side-effect profile of aDBS in PD compared to conventional continuous DBS (cDBS) and no stimulation (NoStim), years after DBS implantation, 13 PD patients undergoing battery replacement were pseudo-randomised in a crossover fashion, into three conditions (NoStim, aDBS or cDBS), with a 2-min interval between them. Patient videos were blindly evaluated using a short version of the Unified Parkinson’s Disease Rating Scale (subUPDRS), and the Speech Intelligibility Test (SIT). Results Mean disease duration was 16 years, and the mean time since DBS-implantation was 6.9 years. subUPDRS scores (11 patients tested) were significantly lower both in aDBS (p = <.001), and cDBS (p = .001), when compared to NoStim. Bradykinesia subscores were significantly lower in aDBS (p = .002), and did not achieve significance during cDBS (p = .08), when compared to NoStim. Two patients demonstrated re-emerging tremor during aDBS. SIT scores of patients who presented stimulation-induced dysarthria significantly worsened in cDBS (p = .009), but not in aDBS (p = .407), when compared to NoStim. Overall, stimulation was applied 48.8% of the time during aDBS. Conclusion Beta-based aDBS is effective in PD patients with bradykinetic phenotypes, delivers less stimulation than cDBS, and potentially has a more favourable speech side-effect profile. Patients with prominent tremor may require a modified adaptive strategy.
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Essential tremor: New advances. Clin Park Relat Disord 2019; 3:100031. [PMID: 34316617 PMCID: PMC8298793 DOI: 10.1016/j.prdoa.2019.100031] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 11/30/2019] [Accepted: 12/18/2019] [Indexed: 01/15/2023] Open
Abstract
Background Essential Tremor (ET) is one of the most common movement disorders but many controversies still exist in regards to its definition and pathophysiology. In view of the recent published criteria by the Tremor Task Force of the International Parkinson's and Movement Disorders Society (IPMDS), we intended to analyze if this has changed our view of ET and if new developments have arisen since. Methods A Medline search for English-written articles was done on June 15, 2019 using the keyword "Essential Tremor". Publications from November 2017 (publication date of the new tremor classification) were taken into account. Reviews, letters and original studies relevant to the subject were selected and reviewed according to the following themes: clinical characteristics, epidemiology, genetics, pathology, biomarkers and treatment. Results Out of 132 publications the most relevant articles were selected and reviewed (total of 65 articles). The great majority of these studies focused on surgical treatments (new targets, new technologies) while relatively few articles addressed epidemiology, pathology and pathophysiology. Conclusions The use of the new classification is not commonly used still, excepting more recent studies on therapeutics. This is in keeping with diverse opinions and criticisms reported by the IPMDS task force members themselves. One important change has been validating ET as a heterogeneous condition and defining the ET-plus category. We propose a further sub-group classification derived from the new definition of ET-plus.
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Aristieta A, Ruiz-Ortega J, Morera-Herreras T, Miguelez C, Ugedo L. Acute L-DOPA administration reverses changes in firing pattern and low frequency oscillatory activity in the entopeduncular nucleus from long term L-DOPA treated 6-OHDA-lesioned rats. Exp Neurol 2019; 322:113036. [DOI: 10.1016/j.expneurol.2019.113036] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 08/12/2019] [Accepted: 08/14/2019] [Indexed: 01/06/2023]
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Krack P, Volkmann J, Tinkhauser G, Deuschl G. Deep Brain Stimulation in Movement Disorders: From Experimental Surgery to Evidence‐Based Therapy. Mov Disord 2019; 34:1795-1810. [DOI: 10.1002/mds.27860] [Citation(s) in RCA: 89] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 08/01/2019] [Accepted: 08/19/2019] [Indexed: 12/21/2022] Open
Affiliation(s)
- Paul Krack
- Department of Neurology Bern University Hospital and University of Bern Bern Switzerland
| | - Jens Volkmann
- Department of Neurology University Hospital and Julius‐Maximilian‐University Wuerzburg Germany
| | - Gerd Tinkhauser
- Department of Neurology Bern University Hospital and University of Bern Bern Switzerland
| | - Günther Deuschl
- Department of Neurology University Hospital Schleswig Holstein (UKSH), Kiel Campus; Christian‐Albrechts‐University Kiel Germany
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Cagnan H, Denison T, McIntyre C, Brown P. Emerging technologies for improved deep brain stimulation. Nat Biotechnol 2019; 37:1024-1033. [PMID: 31477926 PMCID: PMC6877347 DOI: 10.1038/s41587-019-0244-6] [Citation(s) in RCA: 120] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 07/26/2019] [Indexed: 12/18/2022]
Abstract
Deep brain stimulation (DBS) is an effective treatment for common movement disorders and has been used to modulate neural activity through delivery of electrical stimulation to key brain structures. The long-term efficacy of stimulation in treating disorders, such as Parkinson's disease and essential tremor, has encouraged its application to a wide range of neurological and psychiatric conditions. Nevertheless, adoption of DBS remains limited, even in Parkinson's disease. Recent failed clinical trials of DBS in major depression, and modest treatment outcomes in dementia and epilepsy, are spurring further development. These improvements focus on interaction with disease circuits through complementary, spatially and temporally specific approaches. Spatial specificity is promoted by the use of segmented electrodes and field steering, and temporal specificity involves the delivery of patterned stimulation, mostly controlled through disease-related feedback. Underpinning these developments are new insights into brain structure-function relationships and aberrant circuit dynamics, including new methods with which to assess and refine the clinical effects of stimulation.
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Affiliation(s)
- Hayriye Cagnan
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK.
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Department of Engineering Sciences, University of Oxford, Oxford, UK
| | - Cameron McIntyre
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Peter Brown
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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Comment on the letter to editor: Closed loop stimulation for tremor was invented in 1980. Brain Stimul 2019; 12:1074. [PMID: 30979642 DOI: 10.1016/j.brs.2019.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 04/02/2019] [Indexed: 11/20/2022] Open
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Blomstedt P, Hariz M. Closed loop stimulation for tremor was invented in 1980. Brain Stimul 2019; 12:1072-1073. [PMID: 30979640 DOI: 10.1016/j.brs.2019.03.075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 03/25/2019] [Indexed: 11/30/2022] Open
Affiliation(s)
- Patric Blomstedt
- Department of Clinical Neuroscience, Umeå University, University Hospital, 90185, Umeå, Sweden.
| | - Marwan Hariz
- Department of Clinical Neuroscience, Umeå University, Umeå, Sweden; Unit of Functional Neurosurgery, UCL Institute of Neurology, Queen Square, London, UK.
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Hocker D, Park IM. Myopic control of neural dynamics. PLoS Comput Biol 2019; 15:e1006854. [PMID: 30856171 PMCID: PMC6428347 DOI: 10.1371/journal.pcbi.1006854] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 03/21/2019] [Accepted: 02/07/2019] [Indexed: 01/29/2023] Open
Abstract
Manipulating the dynamics of neural systems through targeted stimulation is a frontier of research and clinical neuroscience; however, the control schemes considered for neural systems are mismatched for the unique needs of manipulating neural dynamics. An appropriate control method should respect the variability in neural systems, incorporating moment to moment “input” to the neural dynamics and behaving based on the current neural state, irrespective of the past trajectory. We propose such a controller under a nonlinear state-space feedback framework that steers one dynamical system to function as through it were another dynamical system entirely. This “myopic” controller is formulated through a novel variant of a model reference control cost that manipulates dynamics in a short-sighted manner that only sets a target trajectory of a single time step into the future (hence its myopic nature), which omits the need to pre-calculate a rigid and computationally costly neural feedback control solution. To demonstrate the breadth of this control’s utility, two examples with distinctly different applications in neuroscience are studied. First, we show the myopic control’s utility to probe the causal link between dynamics and behavior for cognitive processes by transforming a winner-take-all decision-making system to operate as a robust neural integrator of evidence. Second, an unhealthy motor-like system containing an unwanted beta-oscillation spiral attractor is controlled to function as a healthy motor system, a relevant clinical example for neurological disorders. Stimulating a neural system and observing its effect through simultaneous observation offers the promise to better understand how neural systems perform computations, as well as for the treatment of neurological disorders. A powerful perspective for understanding a neural system’s behavior undergoing stimulation is to conceptualize them as dynamical systems, which considers the global effect that stimulation has on the brain, rather than only assessing what impact it has on the recorded signal from the brain. With this more comprehensive perspective comes a central challenge of determining what requirements need to be satisfied to harness neural observations and then stimulate to make one dynamical system function as another one entirely. This could lead to applications such as neural stimulators that make a diseased brain behave like its healthy counterpart, or to make a neural system previously capable of only hasty decision making to wait and accumulate more evidence for a more informed decision. In this work we explore the implications of this new perspective on neural stimulation and derive a simple prescription for using neural observations to inform stimulation protocol that makes one neural system behave like another one.
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Affiliation(s)
- David Hocker
- Department of Neurobiology and Behavior Stony Brook University, Stony Brook, New York, United States of America
| | - Il Memming Park
- Department of Neurobiology and Behavior Stony Brook University, Stony Brook, New York, United States of America
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, United States of America
- Institute for Advanced Computational Science, Stony Brook University, Stony Brook, New York, United States of America
- * E-mail:
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