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Davidson B, Bhattacharya A, Sarica C, Darmani G, Raies N, Chen R, Lozano AM. Neuromodulation techniques - From non-invasive brain stimulation to deep brain stimulation. Neurotherapeutics 2024; 21:e00330. [PMID: 38340524 PMCID: PMC11103220 DOI: 10.1016/j.neurot.2024.e00330] [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: 10/11/2023] [Revised: 01/14/2024] [Accepted: 01/28/2024] [Indexed: 02/12/2024] Open
Abstract
Over the past 30 years, the field of neuromodulation has witnessed remarkable advancements. These developments encompass a spectrum of techniques, both non-invasive and invasive, that possess the ability to both probe and influence the central nervous system. In many cases neuromodulation therapies have been adopted into standard care treatments. Transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), and transcranial ultrasound stimulation (TUS) are the most common non-invasive methods in use today. Deep brain stimulation (DBS), spinal cord stimulation (SCS), and vagus nerve stimulation (VNS), are leading surgical methods for neuromodulation. Ongoing active clinical trials using are uncovering novel applications and paradigms for these interventions.
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Affiliation(s)
- Benjamin Davidson
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada
| | | | - Can Sarica
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada; Krembil Research Institute, University Health Network, Toronto, ON, Canada
| | - Ghazaleh Darmani
- Krembil Research Institute, University Health Network, Toronto, ON, Canada
| | - Nasem Raies
- Krembil Research Institute, University Health Network, Toronto, ON, Canada
| | - Robert Chen
- Krembil Research Institute, University Health Network, Toronto, ON, Canada; Edmond J. Safra Program in Parkinson's Disease Morton and Gloria Shulman Movement Disorders Clinic, Division of Neurology, University of Toronto, Toronto, ON, Canada
| | - Andres M Lozano
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada; Krembil Research Institute, University Health Network, Toronto, ON, Canada.
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Sandoval-Pistorius SS, Hacker ML, Waters AC, Wang J, Provenza NR, de Hemptinne C, Johnson KA, Morrison MA, Cernera S. Advances in Deep Brain Stimulation: From Mechanisms to Applications. J Neurosci 2023; 43:7575-7586. [PMID: 37940596 PMCID: PMC10634582 DOI: 10.1523/jneurosci.1427-23.2023] [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: 07/27/2023] [Revised: 08/14/2023] [Accepted: 08/15/2023] [Indexed: 11/10/2023] Open
Abstract
Deep brain stimulation (DBS) is an effective therapy for various neurologic and neuropsychiatric disorders, involving chronic implantation of electrodes into target brain regions for electrical stimulation delivery. Despite its safety and efficacy, DBS remains an underutilized therapy. Advances in the field of DBS, including in technology, mechanistic understanding, and applications have the potential to expand access and use of DBS, while also improving clinical outcomes. Developments in DBS technology, such as MRI compatibility and bidirectional DBS systems capable of sensing neural activity while providing therapeutic stimulation, have enabled advances in our understanding of DBS mechanisms and its application. In this review, we summarize recent work exploring DBS modulation of target networks. We also cover current work focusing on improved programming and the development of novel stimulation paradigms that go beyond current standards of DBS, many of which are enabled by sensing-enabled DBS systems and have the potential to expand access to DBS.
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Affiliation(s)
| | - Mallory L Hacker
- Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee 37232
| | - Allison C Waters
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York 10029
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| | - Jing Wang
- Department of Neurology, University of Minnesota, Minneapolis, Minnesota 55455
| | - Nicole R Provenza
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas 77030
| | - Coralie de Hemptinne
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, Florida 32608
| | - Kara A Johnson
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, Florida 32608
| | - Melanie A Morrison
- Department of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, California 94143
| | - Stephanie Cernera
- Department of Neurological Surgery, University of California-San Francisco, San Francisco, California 94143
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3
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Alarie ME, Provenza NR, Herron JA, Asaad WF. Automated artifact injection into sensing-capable brain modulation devices for neural-behavioral synchronization and the influence of device state. Brain Stimul 2023; 16:1358-1360. [PMID: 37690601 DOI: 10.1016/j.brs.2023.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 09/06/2023] [Indexed: 09/12/2023] Open
Affiliation(s)
- Michaela E Alarie
- Center for Biomedical Engineering, Brown University, Providence, RI, United States; Carney Institute for Brain Science, Brown University, Providence, RI, United States.
| | - Nicole R Provenza
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Jeffrey A Herron
- Department of Neurological Surgery, University of Washington, Seattle, WA, United States
| | - Wael F Asaad
- Carney Institute for Brain Science, Brown University, Providence, RI, United States; Departments of Neurosurgery & Neuroscience, Brown University, Providence, RI, United States; Norman Prince Neurosciences Institute, Rhode Island Hospital, Providence, RI, United States
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Najera RA, Mahavadi AK, Khan AU, Boddeti U, Del Bene VA, Walker HC, Bentley JN. Alternative patterns of deep brain stimulation in neurologic and neuropsychiatric disorders. Front Neuroinform 2023; 17:1156818. [PMID: 37415779 PMCID: PMC10320008 DOI: 10.3389/fninf.2023.1156818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 06/06/2023] [Indexed: 07/08/2023] Open
Abstract
Deep brain stimulation (DBS) is a widely used clinical therapy that modulates neuronal firing in subcortical structures, eliciting downstream network effects. Its effectiveness is determined by electrode geometry and location as well as adjustable stimulation parameters including pulse width, interstimulus interval, frequency, and amplitude. These parameters are often determined empirically during clinical or intraoperative programming and can be altered to an almost unlimited number of combinations. Conventional high-frequency stimulation uses a continuous high-frequency square-wave pulse (typically 130-160 Hz), but other stimulation patterns may prove efficacious, such as continuous or bursting theta-frequencies, variable frequencies, and coordinated reset stimulation. Here we summarize the current landscape and potential clinical applications for novel stimulation patterns.
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Affiliation(s)
- Ricardo A. Najera
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Anil K. Mahavadi
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Anas U. Khan
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Ujwal Boddeti
- Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Victor A. Del Bene
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Harrison C. Walker
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - J. Nicole Bentley
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, AL, United States
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Stangl M, Maoz SL, Suthana N. Mobile cognition: imaging the human brain in the 'real world'. Nat Rev Neurosci 2023; 24:347-362. [PMID: 37046077 PMCID: PMC10642288 DOI: 10.1038/s41583-023-00692-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/03/2023] [Indexed: 04/14/2023]
Abstract
Cognitive neuroscience studies in humans have enabled decades of impactful discoveries but have primarily been limited to recording the brain activity of immobile participants in a laboratory setting. In recent years, advances in neuroimaging technologies have enabled recordings of human brain activity to be obtained during freely moving behaviours in the real world. Here, we propose that these mobile neuroimaging methods can provide unique insights into the neural mechanisms of human cognition and contribute to the development of novel treatments for neurological and psychiatric disorders. We further discuss the challenges associated with studying naturalistic human behaviours in complex real-world settings as well as strategies for overcoming them. We conclude that mobile neuroimaging methods have the potential to bring about a new era of cognitive neuroscience in which neural mechanisms can be studied with increased ecological validity and with the ability to address questions about natural behaviour and cognitive processes in humans engaged in dynamic real-world experiences.
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Affiliation(s)
- Matthias Stangl
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behaviour, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Sabrina L Maoz
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA
| | - Nanthia Suthana
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behaviour, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA.
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Zhang R, Nie Y, Dai W, Wang S, Geng X. Balance between pallidal neural oscillations correlated with dystonic activity and severity. Neurobiol Dis 2023:106178. [PMID: 37268239 DOI: 10.1016/j.nbd.2023.106178] [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: 03/23/2023] [Revised: 05/14/2023] [Accepted: 05/28/2023] [Indexed: 06/04/2023] Open
Abstract
BACKGROUND AND OBJECTIVE The balance between neural oscillations provides valuable insights into the organisation of neural oscillations related to brain states, which may play important roles in dystonia. We aim to investigate the relationship of the balance in the globus pallidus internus (GPi) with the dystonic severity under different muscular contraction conditions. METHODS Twenty-one patients with dystonia were recruited. All of them underwent bilateral GPi implantation, and local field potentials (LFPs) from the GPi were recorded via simultaneous surface electromyography. The power spectral ratio between neural oscillations was computed as the measure of neural balance. This ratio was calculated under high and low dystonic muscular contraction conditions, and its correlation with the dystonic severity was assessed using clinical scores. RESULTS The power spectral of the pallidal LFPs peaked in the theta and alpha bands. Within participant comparison showed that the power spectral of the theta oscillations significantly increased during high muscle contraction compared with that during low contraction. The power spectral ratios between the theta and alpha, theta and low beta, and theta and high gamma oscillations were significantly higher during high contraction than during low contraction. The total score and motor score were associated with the power spectral ratio between the low and high beta oscillations, which was correlated with the dystonic severity both during high and low contractions. The power spectral ratios between the low beta and low gamma and between the low beta and high gamma oscillations showed a significantly positive correlation with the total score during both high and low contractions; a correlation with the motor scale score was found only during high contraction. Meanwhile, the power spectral ratio between the theta and alpha oscillations during low contraction showed a significantly negative correlation with the total score. The power spectral ratios between the alpha and high beta, alpha and low gamma, and alpha and high gamma oscillations were significantly correlated with the dystonic severity only during low contraction. CONCLUSION The balance between neural oscillations, as quantified by the power ratio between specific frequency bands, differed between the high and low muscular contraction conditions and was correlated with the dystonic severity. The balance between the low and high beta oscillations was correlated with the dystonic severity during both conditions, making this parameter a new possible biomarker for closed-loop deep brain stimulation in patients with dystonia.
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Affiliation(s)
- Ruili Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Yingnan Nie
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wen Dai
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Shouyan Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, Shanghai, China; Shanghai Engineering Research Center of AI & Robotics, Fudan University, Shanghai, China; Engineering Research Center of AI & Robotics, Ministry of Education, Fudan University, Shanghai, China
| | - Xinyi Geng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, Shanghai, China.
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Fischer P, Piña-Fuentes D, Kassavetis P, Sadnicka A. Physiology of dystonia: Human studies. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2023; 169:137-162. [PMID: 37482391 DOI: 10.1016/bs.irn.2023.05.007] [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: 07/25/2023]
Abstract
In this chapter, we discuss neurophysiological techniques that have been used in the study of dystonia. We examine traditional disease models such as inhibition and excessive plasticity and review the evidence that these play a causal role in pathophysiology. We then review the evidence for sensory and peripheral influences within pathophysiology and look at an emergent literature that tries to probe how oscillatory brain activity may be linked to dystonia pathophysiology.
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Affiliation(s)
- Petra Fischer
- School of Physiology, Pharmacology and Neuroscience, University of Bristol, Biomedical Sciences Building, University Walk, Bristol, United Kingdom
| | - Dan Piña-Fuentes
- Department of Neurology, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, Meibergdreef 9, Amsterdam, The Netherlands; Department of Neurology, OLVG, Amsterdam, The Netherlands
| | | | - Anna Sadnicka
- Motor Control and Movement Disorders Group, St George's University of London, London, United Kingdom; Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, United Kingdom.
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Neumann WJ, Gilron R, Little S, Tinkhauser G. Adaptive Deep Brain Stimulation: From Experimental Evidence Toward Practical Implementation. Mov Disord 2023. [PMID: 37148553 DOI: 10.1002/mds.29415] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 03/27/2023] [Accepted: 04/05/2023] [Indexed: 05/08/2023] Open
Abstract
Closed-loop adaptive deep brain stimulation (aDBS) can deliver individualized therapy at an unprecedented temporal precision for neurological disorders. This has the potential to lead to a breakthrough in neurotechnology, but the translation to clinical practice remains a significant challenge. Via bidirectional implantable brain-computer-interfaces that have become commercially available, aDBS can now sense and selectively modulate pathophysiological brain circuit activity. Pilot studies investigating different aDBS control strategies showed promising results, but the short experimental study designs have not yet supported individualized analyses of patient-specific factors in biomarker and therapeutic response dynamics. Notwithstanding the clear theoretical advantages of a patient-tailored approach, these new stimulation possibilities open a vast and mostly unexplored parameter space, leading to practical hurdles in the implementation and development of clinical trials. Therefore, a thorough understanding of the neurophysiological and neurotechnological aspects related to aDBS is crucial to develop evidence-based treatment regimens for clinical practice. Therapeutic success of aDBS will depend on the integrated development of strategies for feedback signal identification, artifact mitigation, signal processing, and control policy adjustment, for precise stimulation delivery tailored to individual patients. The present review introduces the reader to the neurophysiological foundation of aDBS for Parkinson's disease (PD) and other network disorders, explains currently available aDBS control policies, and highlights practical pitfalls and difficulties to be addressed in the upcoming years. Finally, it highlights the importance of interdisciplinary clinical neurotechnological research within and across DBS centers, toward an individualized patient-centered approach to invasive brain stimulation. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Wolf-Julian Neumann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | | | - Simon Little
- Movement Disorders and Neuromodulation Centre, University of California San Francisco, San Francisco, California, USA
| | - Gerd Tinkhauser
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland
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Neumann WJ, Horn A, Kühn AA. Insights and opportunities for deep brain stimulation as a brain circuit intervention. Trends Neurosci 2023; 46:472-487. [PMID: 37105806 DOI: 10.1016/j.tins.2023.03.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 03/13/2023] [Accepted: 03/17/2023] [Indexed: 04/29/2023]
Abstract
Deep brain stimulation (DBS) is an effective treatment and has provided unique insights into the dynamic circuit architecture of brain disorders. This Review illustrates our current understanding of the pathophysiology of movement disorders and their underlying brain circuits that are modulated with DBS. It proposes principles of pathological network synchronization patterns like beta activity (13-35 Hz) in Parkinson's disease. We describe alterations from microscale including local synaptic activity via modulation of mesoscale hypersynchronization to changes in whole-brain macroscale connectivity. Finally, an outlook on advances for clinical innovations in next-generation neurotechnology is provided: from preoperative connectomic targeting to feedback controlled closed-loop adaptive DBS as individualized network-specific brain circuit interventions.
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Affiliation(s)
- Wolf-Julian Neumann
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience, Humboldt Universität zu Berlin, Berlin, Germany
| | - Andreas Horn
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience, Humboldt Universität zu Berlin, Berlin, Germany; Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA; MGH Neurosurgery & Center for Neurotechnology and Neurorecovery at MGH Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Andrea A Kühn
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience, Humboldt Universität zu Berlin, Berlin, Germany; NeuroCure Clinical Research Centre, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany; DZNE, German Center for Degenerative Diseases, Berlin, Germany.
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10
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Lofredi R, Scheller U, Mindermann A, Feldmann LK, Krauss JK, Saryyeva A, Schneider GH, Kühn AA. Pallidal Beta Activity Is Linked to Stimulation-Induced Slowness in Dystonia. Mov Disord 2023. [PMID: 36807626 DOI: 10.1002/mds.29347] [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: 09/08/2022] [Accepted: 01/30/2023] [Indexed: 02/22/2023] Open
Abstract
BACKGROUND Pallidal deep brain stimulation (DBS) effectively alleviates symptoms in dystonia patients, but may induce movement slowness as a side-effect. In Parkinson's disease, hypokinetic symptoms have been associated with increased beta oscillations (13-30 Hz). We hypothesize that this pattern is symptom-specific, thus accompanying DBS-induced slowness in dystonia. METHODS In 6 dystonia patients, pallidal rest recordings with a sensing-enabled DBS device were performed and tapping speed was assessed using marker-less pose estimation over 5 time points following cessation of DBS. RESULTS After cessation of pallidal stimulation, movement speed increased over time (P < 0.01). A linear mixed-effects model revealed that pallidal beta activity explained 77% of the variance in movement speed across patients (P = 0.01). CONCLUSIONS The association between beta oscillations and slowness across disease entities provides further evidence for symptom-specific oscillatory patterns in the motor circuit. Our findings might help DBS therapy improvements, as DBS-devices able to adapt to beta oscillations are already commercially available. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Roxanne Lofredi
- Department of Neurology, Movement Disorders and Neuromodulation Unit, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute of Health, Berlin, Germany
| | - Ute Scheller
- Department of Neurology, Movement Disorders and Neuromodulation Unit, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Department of Neurology, Georg-August-Universität Göttingen, Göttingen, Germany
| | - Aurika Mindermann
- Department of Neurology, Movement Disorders and Neuromodulation Unit, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Lucia K Feldmann
- Department of Neurology, Movement Disorders and Neuromodulation Unit, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute of Health, Berlin, Germany
| | - Joachim K Krauss
- Department of Neurosurgery, Medizinische Hochschule Hannover, Hannover, Germany
| | - Assel Saryyeva
- Department of Neurosurgery, Medizinische Hochschule Hannover, Hannover, Germany
| | - Gerd-Helge Schneider
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Andrea A Kühn
- Department of Neurology, Movement Disorders and Neuromodulation Unit, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Exzellenzcluster - NeuroCure, Charité - Universitätsmedizin Berlin, Berlin, Germany
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11
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A systematic review of local field potential physiomarkers in Parkinson's disease: from clinical correlations to adaptive deep brain stimulation algorithms. J Neurol 2023; 270:1162-1177. [PMID: 36209243 PMCID: PMC9886603 DOI: 10.1007/s00415-022-11388-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 09/16/2022] [Indexed: 02/03/2023]
Abstract
Deep brain stimulation (DBS) treatment has proven effective in suppressing symptoms of rigidity, bradykinesia, and tremor in Parkinson's disease. Still, patients may suffer from disabling fluctuations in motor and non-motor symptom severity during the day. Conventional DBS treatment consists of continuous stimulation but can potentially be further optimised by adapting stimulation settings to the presence or absence of symptoms through closed-loop control. This critically relies on the use of 'physiomarkers' extracted from (neuro)physiological signals. Ideal physiomarkers for adaptive DBS (aDBS) are indicative of symptom severity, detectable in every patient, and technically suitable for implementation. In the last decades, much effort has been put into the detection of local field potential (LFP) physiomarkers and in their use in clinical practice. We conducted a research synthesis of the correlations that have been reported between LFP signal features and one or more specific PD motor symptoms. Features based on the spectral beta band (~ 13 to 30 Hz) explained ~ 17% of individual variability in bradykinesia and rigidity symptom severity. Limitations of beta band oscillations as physiomarker are discussed, and strategies for further improvement of aDBS are explored.
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12
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Alarie ME, Provenza NR, Avendano-Ortega M, McKay SA, Waite AS, Mathura RK, Herron JA, Sheth SA, Borton DA, Goodman WK. Artifact characterization and mitigation techniques during concurrent sensing and stimulation using bidirectional deep brain stimulation platforms. Front Hum Neurosci 2022; 16:1016379. [PMID: 36337849 PMCID: PMC9626519 DOI: 10.3389/fnhum.2022.1016379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 10/03/2022] [Indexed: 11/30/2022] Open
Abstract
Bidirectional deep brain stimulation (DBS) platforms have enabled a surge in hours of recordings in naturalistic environments, allowing further insight into neurological and psychiatric disease states. However, high amplitude, high frequency stimulation generates artifacts that contaminate neural signals and hinder our ability to interpret the data. This is especially true in psychiatric disorders, for which high amplitude stimulation is commonly applied to deep brain structures where the native neural activity is miniscule in comparison. Here, we characterized artifact sources in recordings from a bidirectional DBS platform, the Medtronic Summit RC + S, with the goal of optimizing recording configurations to improve signal to noise ratio (SNR). Data were collected from three subjects in a clinical trial of DBS for obsessive-compulsive disorder. Stimulation was provided bilaterally to the ventral capsule/ventral striatum (VC/VS) using two independent implantable neurostimulators. We first manipulated DBS amplitude within safe limits (2–5.3 mA) to characterize the impact of stimulation artifacts on neural recordings. We found that high amplitude stimulation produces slew overflow, defined as exceeding the rate of change that the analog to digital converter can accurately measure. Overflow led to expanded spectral distortion of the stimulation artifact, with a six fold increase in the bandwidth of the 150.6 Hz stimulation artifact from 147–153 to 140–180 Hz. By increasing sense blank values during high amplitude stimulation, we reduced overflow by as much as 30% and improved artifact distortion, reducing the bandwidth from 140–180 Hz artifact to 147–153 Hz. We also identified artifacts that shifted in frequency through modulation of telemetry parameters. We found that telemetry ratio changes led to predictable shifts in the center-frequencies of the associated artifacts, allowing us to proactively shift the artifacts outside of our frequency range of interest. Overall, the artifact characterization methods and results described here enable increased data interpretability and unconstrained biomarker exploration using data collected from bidirectional DBS devices.
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Affiliation(s)
| | - Nicole R. Provenza
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Michelle Avendano-Ortega
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Sarah A. McKay
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Ayan S. Waite
- Brown University School of Engineering, Providence, RI, United States
| | - Raissa K. Mathura
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Jeffrey A. Herron
- Department of Neurological Surgery, University of Washington, Seattle, WA, United States
| | - Sameer A. Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - David A. Borton
- Brown University School of Engineering, Providence, RI, United States
- Department of Veterans Affairs, Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence, RI, United States
| | - Wayne K. Goodman
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
- *Correspondence: Wayne K. Goodman,
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13
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Dastin-van Rijn EM, Provenza NR, Vogt GS, Avendano-Ortega M, Sheth SA, Goodman WK, Harrison MT, Borton DA. PELP: Accounting for Missing Data in Neural Time Series by Periodic Estimation of Lost Packets. Front Hum Neurosci 2022; 16:934063. [PMID: 35874161 PMCID: PMC9301255 DOI: 10.3389/fnhum.2022.934063] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 06/06/2022] [Indexed: 11/13/2022] Open
Abstract
Recent advances in wireless data transmission technology have the potential to revolutionize clinical neuroscience. Today sensing-capable electrical stimulators, known as "bidirectional devices", are used to acquire chronic brain activity from humans in natural environments. However, with wireless transmission come potential failures in data transmission, and not all available devices correctly account for missing data or provide precise timing for when data losses occur. Our inability to precisely reconstruct time-domain neural signals makes it difficult to apply subsequent neural signal processing techniques and analyses. Here, our goal was to accurately reconstruct time-domain neural signals impacted by data loss during wireless transmission. Towards this end, we developed a method termed Periodic Estimation of Lost Packets (PELP). PELP leverages the highly periodic nature of stimulation artifacts to precisely determine when data losses occur. Using simulated stimulation waveforms added to human EEG data, we show that PELP is robust to a range of stimulation waveforms and noise characteristics. Then, we applied PELP to local field potential (LFP) recordings collected using an implantable, bidirectional DBS platform operating at various telemetry bandwidths. By effectively accounting for the timing of missing data, PELP enables the analysis of neural time series data collected via wireless transmission-a prerequisite for better understanding the brain-behavior relationships underlying neurological and psychiatric disorders.
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Affiliation(s)
- Evan M Dastin-van Rijn
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Nicole R Provenza
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Gregory S Vogt
- Department of Psychological and Brain Sciences, Texas A&M University, College Station, TX, United States
| | - Michelle Avendano-Ortega
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Wayne K Goodman
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Matthew T Harrison
- Division of Applied Mathematics, Brown University, Providence, RI, United States
| | - David A Borton
- School of Engineering, Brown University, Providence, RI, United States.,Carney Institute for Brain Science, Brown University, Providence, RI, United States.,Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, United States Department of Veterans Affairs, Providence, RI, United States
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14
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Rauschenberger L, Güttler C, Volkmann J, Kühn AA, Ip CW, Lofredi R. A translational perspective on pathophysiological changes of oscillatory activity in dystonia and parkinsonism. Exp Neurol 2022; 355:114140. [PMID: 35690132 DOI: 10.1016/j.expneurol.2022.114140] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 05/14/2022] [Accepted: 06/03/2022] [Indexed: 11/19/2022]
Abstract
Intracerebral recordings from movement disorders patients undergoing deep brain stimulation have allowed the identification of pathophysiological patterns in oscillatory activity that correlate with symptom severity. Changes in oscillatory synchrony occur within and across brain areas, matching the classification of movement disorders as network disorders. However, the underlying mechanisms of oscillatory changes are difficult to assess in patients, as experimental interventions are technically limited and ethically problematic. This is why animal models play an important role in neurophysiological research of movement disorders. In this review, we highlight the contributions of translational research to the mechanistic understanding of pathological changes in oscillatory activity, with a focus on parkinsonism and dystonia, while addressing the limitations of current findings and proposing possible future directions.
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Affiliation(s)
- Lisa Rauschenberger
- Department of Neurology, University Hospital of Würzburg, Josef-Schneider-Straße 11, 97080 Würzburg, Germany
| | - Christopher Güttler
- Department of Neurology, Movement Disorders and Neuromodulation Unit, Campus Charité Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Jens Volkmann
- Department of Neurology, University Hospital of Würzburg, Josef-Schneider-Straße 11, 97080 Würzburg, Germany
| | - Andrea A Kühn
- Department of Neurology, Movement Disorders and Neuromodulation Unit, Campus Charité Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience, Humboldt-Universität, Berlin, Germany; NeuroCure, Exzellenzcluster, Charité-Universitätsmedizin Berlin, Berlin, Germany; DZNE, German Center for Neurodegenerative Diseases, Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Chi Wang Ip
- Department of Neurology, University Hospital of Würzburg, Josef-Schneider-Straße 11, 97080 Würzburg, Germany
| | - Roxanne Lofredi
- Department of Neurology, Movement Disorders and Neuromodulation Unit, Campus Charité Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany; Berlin Institute of Health (BIH), Berlin, Germany.
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15
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Ansó J, Benjaber M, Parks B, Parker S, Oehrn CR, Petrucci M, Gilron R, Little S, Wilt R, Bronte-Stewart H, Gunduz A, Borton D, Starr PA, Denison T. Concurrent stimulation and sensing in bi-directional brain interfaces: a multi-site translational experience. J Neural Eng 2022; 19:10.1088/1741-2552/ac59a3. [PMID: 35234664 PMCID: PMC9095704 DOI: 10.1088/1741-2552/ac59a3] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 03/01/2022] [Indexed: 11/12/2022]
Abstract
Objective. To provide a design analysis and guidance framework for the implementation of concurrent stimulation and sensing during adaptive deep brain stimulation (aDBS) with particular emphasis on artifact mitigations.Approach. We defined a general architecture of feedback-enabled devices, identified key components in the signal chain which might result in unwanted artifacts and proposed methods that might ultimately enable improved aDBS therapies. We gathered data from research subjects chronically-implanted with an investigational aDBS system, Summit RC + S, to characterize and explore artifact mitigations arising from concurrent stimulation and sensing. We then used a prototype investigational implantable device, DyNeuMo, and a bench-setup that accounts for tissue-electrode properties, to confirm our observations and verify mitigations. The strategies to reduce transient stimulation artifacts and improve performance during aDBS were confirmed in a chronic implant using updated configuration settings.Main results.We derived and validated a 'checklist' of configuration settings to improve system performance and areas for future device improvement. Key considerations for the configuration include (a) active instead of passive recharge, (b) sense-channel blanking in the amplifier, (c) high-pass filter settings, (d) tissue-electrode impedance mismatch management, (e) time-frequency trade-offs in the classifier, (f) algorithm blanking and transition rate limits. Without proper channel configuration, the aDBS algorithm was susceptible to limit-cycles of oscillating stimulation independent of physiological state. By applying the checklist, we could optimize each block's performance characteristics within the overall system. With system-level optimization, a 'fast' aDBS prototype algorithm was demonstrated to be feasible without reentrant loops, and with noise performance suitable for subcortical brain circuits.Significance. We present a framework to study sources and propose mitigations of artifacts in devices that provide chronic aDBS. This work highlights the trade-offs in performance as novel sensing devices translate to the clinic. Finding the appropriate balance of constraints is imperative for successful translation of aDBS therapies.Clinical trial:Institutional Review Board and Investigational Device Exemption numbers: NCT02649166/IRB201501021 (University of Florida), NCT04043403/IRB52548 (Stanford University), NCT03582891/IRB1824454 (University of California San Francisco). IDE #180 097.
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Affiliation(s)
- Juan Ansó
- Department of Neurological Surgery, University of California, San Francisco, CA, United States of America
- Shared first author
| | - Moaad Benjaber
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Shared first author
| | - Brandon Parks
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States of America
- Shared first author
| | - Samuel Parker
- School of Engineering and Carney Institute, Brown University, Providence, RI, United States of America
| | - Carina Renate Oehrn
- Department of Neurological Surgery, University of California, San Francisco, CA, United States of America
| | - Matthew Petrucci
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Ro’ee Gilron
- Department of Neurological Surgery, University of California, San Francisco, CA, United States of America
| | - Simon Little
- Department of Neurology, University of California San Francisco, San Francisco, CA, United States of America
| | - Robert Wilt
- Department of Neurological Surgery, University of California, San Francisco, CA, United States of America
| | - Helen Bronte-Stewart
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Aysegul Gunduz
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States of America
| | - David Borton
- School of Engineering and Carney Institute, Brown University, Providence, RI, United States of America
| | - Philip A Starr
- Department of Neurological Surgery, University of California, San Francisco, CA, United States of America
- Shared senior author
| | - Timothy Denison
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
- Shared senior author
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16
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Swinnen BEKS, Buijink AW, Piña-Fuentes D, de Bie RMA, Beudel M. Diving into the Subcortex: The Potential of Chronic Subcortical Sensing for Unravelling Basal Ganglia Function and Optimization of Deep Brain STIMULATION. Neuroimage 2022; 254:119147. [PMID: 35346837 DOI: 10.1016/j.neuroimage.2022.119147] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 03/23/2022] [Accepted: 03/24/2022] [Indexed: 11/18/2022] Open
Abstract
Subcortical structures are a relative neurophysiological 'terra incognita' owing to their location within the skull. While perioperative subcortical sensing has been performed for more than 20 years, the neurophysiology of the basal ganglia in the home setting has remained almost unexplored. However, with the recent advent of implantable pulse generators (IPG) that are able to record neural activity, the opportunity to chronically record local field potentials (LFPs) directly from electrodes implanted for deep brain stimulation opens up. This allows for a breakthrough of chronic subcortical sensing into fundamental research and clinical practice. In this review an extensive overview of the current state of subcortical sensing is provided. The widespread potential of chronic subcortical sensing for investigational and clinical use is discussed. Finally, status and future perspectives of the most promising application of chronic subcortical sensing -i.e., adaptive deep brain stimulation (aDBS)- are discussed in the context of movement disorders. The development of aDBS based on both chronic subcortical and cortical sensing has the potential to dramatically change clinical practice and the life of patients with movement disorders. However, several barriers still stand in the way of clinical implementation. Advancements regarding IPG and lead technology, physiomarkers, and aDBS algorithms as well as harnessing artificial intelligence, multimodality and sensing in the naturalistic setting are needed to bring aDBS to clinical practice.
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Affiliation(s)
- Bart E K S Swinnen
- Department of Neurology and Clinical Neurophysiology, Amsterdam University Medical, Centers, Amsterdam Neuroscience, University of Amsterdam, PO Box 22660, Amsterdam 1100DD, the Netherland.
| | - Arthur W Buijink
- Department of Neurology and Clinical Neurophysiology, Amsterdam University Medical, Centers, Amsterdam Neuroscience, University of Amsterdam, PO Box 22660, Amsterdam 1100DD, the Netherland
| | - Dan Piña-Fuentes
- Department of Neurology and Clinical Neurophysiology, Amsterdam University Medical, Centers, Amsterdam Neuroscience, University of Amsterdam, PO Box 22660, Amsterdam 1100DD, the Netherland
| | - Rob M A de Bie
- Department of Neurology and Clinical Neurophysiology, Amsterdam University Medical, Centers, Amsterdam Neuroscience, University of Amsterdam, PO Box 22660, Amsterdam 1100DD, the Netherland
| | - Martijn Beudel
- Department of Neurology and Clinical Neurophysiology, Amsterdam University Medical, Centers, Amsterdam Neuroscience, University of Amsterdam, PO Box 22660, Amsterdam 1100DD, the Netherland
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17
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Arlotti M, Colombo M, Bonfanti A, Mandat T, Lanotte MM, Pirola E, Borellini L, Rampini P, Eleopra R, Rinaldo S, Romito L, Janssen MLF, Priori A, Marceglia S. A New Implantable Closed-Loop Clinical Neural Interface: First Application in Parkinson's Disease. Front Neurosci 2021; 15:763235. [PMID: 34949982 PMCID: PMC8689059 DOI: 10.3389/fnins.2021.763235] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 11/04/2021] [Indexed: 11/13/2022] Open
Abstract
Deep brain stimulation (DBS) is used for the treatment of movement disorders, including Parkinson’s disease, dystonia, and essential tremor, and has shown clinical benefits in other brain disorders. A natural path for the improvement of this technique is to continuously observe the stimulation effects on patient symptoms and neurophysiological markers. This requires the evolution of conventional deep brain stimulators to bidirectional interfaces, able to record, process, store, and wirelessly communicate neural signals in a robust and reliable fashion. Here, we present the architecture, design, and first use of an implantable stimulation and sensing interface (AlphaDBSR System) characterized by artifact-free recording and distributed data management protocols. Its application in three patients with Parkinson’s disease (clinical trial n. NCT04681534) is shown as a proof of functioning of a clinically viable implanted brain-computer interface (BCI) for adaptive DBS. Reliable artifact free-recordings, and chronic long-term data and neural signal management are in place.
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Affiliation(s)
| | | | - Andrea Bonfanti
- Newronika SpA, Milan, Italy.,Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| | - Tomasz Mandat
- Narodowy Instytut Onkologii im. Marii Skłodowskiej-Curie, Warsaw, Poland
| | - Michele Maria Lanotte
- Department of Neuroscience, University of Torino, Torino, Italy.,AOU Città della Salute e della Scienza, Molinette Hospital, Turin, Italy
| | - Elena Pirola
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Linda Borellini
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Paolo Rampini
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Roberto Eleopra
- Movement Disorders Unit, Department of Clinical Neurosciences, Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy
| | - Sara Rinaldo
- Movement Disorders Unit, Department of Clinical Neurosciences, Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy
| | - Luigi Romito
- Movement Disorders Unit, Department of Clinical Neurosciences, Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy
| | - Marcus L F Janssen
- Department of Neurology and Clinical Neurophysiology, Maastricht University Medical Center, Maastricht, Netherlands.,Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Alberto Priori
- Department of Health Sciences, Aldo Ravelli Research Center for Neurotechnology and Experimental Neurotherapeutics, University of Milan, Milan, Italy
| | - Sara Marceglia
- Dipartimento di Ingegneria e Architettura, Università degli Studi di Trieste, Trieste, Italy
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18
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Fra̧czek TM, Ferleger BI, Brown TE, Thompson MC, Haddock AJ, Houston BC, Ojemann JG, Ko AL, Herron JA, Chizeck HJ. Closing the Loop With Cortical Sensing: The Development of Adaptive Deep Brain Stimulation for Essential Tremor Using the Activa PC+S. Front Neurosci 2021; 15:749705. [PMID: 34955714 PMCID: PMC8695120 DOI: 10.3389/fnins.2021.749705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 11/04/2021] [Indexed: 11/25/2022] Open
Abstract
Deep Brain Stimulation (DBS) is an important tool in the treatment of pharmacologically resistant neurological movement disorders such as essential tremor (ET) and Parkinson's disease (PD). However, the open-loop design of current systems may be holding back the true potential of invasive neuromodulation. In the last decade we have seen an explosion of activity in the use of feedback to "close the loop" on neuromodulation in the form of adaptive DBS (aDBS) systems that can respond to the patient's therapeutic needs. In this paper we summarize the accomplishments of a 5-year study at the University of Washington in the use of neural feedback from an electrocorticography strip placed over the sensorimotor cortex. We document our progress from an initial proof of hardware all the way to a fully implanted adaptive stimulation system that leverages machine-learning approaches to simplify the programming process. In certain cases, our systems out-performed current open-loop approaches in both power consumption and symptom suppression. Throughout this effort, we collaborated with neuroethicists to capture patient experiences and take them into account whilst developing ethical aDBS approaches. Based on our results we identify several key areas for future work. "Graded" aDBS will allow the system to smoothly tune the stimulation level to symptom severity, and frequent automatic calibration of the algorithm will allow aDBS to adapt to the time-varying dynamics of the disease without additional input from a clinician. Additionally, robust computational models of the pathophysiology of ET will allow stimulation to be optimized to the nuances of an individual patient's symptoms. We also outline the unique advantages of using cortical electrodes for control and the remaining hardware limitations that need to be overcome to facilitate further development in this field. Over the course of this study we have verified the potential of fully-implanted, cortically driven aDBS as a feasibly translatable treatment for pharmacologically resistant ET.
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Affiliation(s)
- Tomasz M. Fra̧czek
- Neuroscience Program, University of Washington, Seattle, WA, United States
| | - Benjamin I. Ferleger
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Timothy E. Brown
- Department of Philosophy, University of Washington, Seattle, WA, United States
| | - Margaret C. Thompson
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Andrew J. Haddock
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Brady C. Houston
- Neuroscience Program, University of Washington, Seattle, WA, United States
| | - Jeffrey G. Ojemann
- Department of Neurological Surgery, University of Washington, Seattle, WA, United States
| | - Andrew L. Ko
- Department of Neurological Surgery, University of Washington, Seattle, WA, United States
| | - Jeffrey A. Herron
- Department of Neurological Surgery, University of Washington, Seattle, WA, United States
| | - Howard J. Chizeck
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
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19
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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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20
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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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