1
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Cernera S, Long S, Kelberman M, Hegland KW, Hicks J, Smith-Hublou M, Taylor B, Mou Y, de Hemptinne C, Johnson KA, Cagle JN, Moore K, Foote KD, Okun MS, Gunduz A. Responsive Versus Continuous Deep Brain Stimulation for Speech in Essential Tremor: A Pilot Study. Mov Disord 2024. [PMID: 38877761 DOI: 10.1002/mds.29865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 03/25/2024] [Accepted: 05/10/2024] [Indexed: 06/16/2024] Open
Abstract
BACKGROUND Responsive deep brain stimulation (rDBS) uses physiological signals to deliver stimulation when needed. rDBS is hypothesized to reduce stimulation-induced speech effects associated with continuous DBS (cDBS) in patients with essential tremor (ET). OBJECTIVE To determine if rDBS reduces cDBS speech-related side effects while maintaining tremor suppression. METHODS Eight ET participants with thalamic DBS underwent unilateral rDBS. Both speech evaluations and tremor severity were assessed across three conditions (DBS OFF, cDBS ON, and rDBS ON). Speech was analyzed using intelligibility ratings. Tremor severity was scored using the Fahn-Tolosa-Marin Tremor Rating Scale (TRS). RESULTS During unilateral cDBS, participants experienced reduced speech intelligibility (P = 0.025) compared to DBS OFF. rDBS was not associated with a deterioration of intelligibility. Both rDBS (P = 0.026) and cDBS (P = 0.038) improved the contralateral TRS score compared to DBS OFF. CONCLUSIONS rDBS maintained speech intelligibility without loss of tremor suppression. A larger prospective chronic study of rDBS in ET is justified. © 2024 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Stephanie Cernera
- J. Crayton Pruitt Department of Biomedical Engineering, Gainesville, Florida, USA
| | - Sarah Long
- J. Crayton Pruitt Department of Biomedical Engineering, Gainesville, Florida, USA
| | - Madison Kelberman
- J. Crayton Pruitt Department of Biomedical Engineering, Gainesville, Florida, USA
| | - Karen W Hegland
- Department of Speech, Language, and Hearing Sciences, Norman Fixel Institute for Neurological Diseases, Gainesville, Florida, USA
| | - Julie Hicks
- Department of Neurologic Rehabilitation, Stanford Neuroscience Health Center, Palo Alto, California, USA
| | - May Smith-Hublou
- Department of Speech, Language, and Hearing Sciences, Norman Fixel Institute for Neurological Diseases, Gainesville, Florida, USA
| | - Bryn Taylor
- Department of Speech, Language, and Hearing Sciences, Norman Fixel Institute for Neurological Diseases, Gainesville, Florida, USA
| | - Yuhan Mou
- Department of Speech, Language, and Hearing Sciences, Norman Fixel Institute for Neurological Diseases, Gainesville, Florida, USA
| | - Coralie de Hemptinne
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, Gainesville, Florida, USA
| | - Kara A Johnson
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, Gainesville, Florida, USA
| | - Jackson N Cagle
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, Gainesville, Florida, USA
| | - Kathryn Moore
- Department of Neurology, Duke University, Durham, North Carolina, USA
| | - Kelly D Foote
- Department of Neurosurgery, Norman Fixel Institute for Neurological Diseases, Gainesville, Florida, USA
| | - Michael S Okun
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, Gainesville, Florida, USA
| | - Aysegul Gunduz
- J. Crayton Pruitt Department of Biomedical Engineering, Gainesville, Florida, USA
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, Gainesville, Florida, USA
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2
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Yang JC, Yang AI, Gross RE. Sensing-Enabled Deep Brain Stimulation in Epilepsy. Neurosurg Clin N Am 2024; 35:119-123. [PMID: 38000835 DOI: 10.1016/j.nec.2023.08.005] [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] [Indexed: 11/26/2023]
Abstract
Deep brain stimulation has demonstrated efficacy in reducing seizure frequency in patients with drug-resistant epilepsy who may otherwise not be candidates for other surgical procedures. Recently, a clinical device that can monitor neural activity in the form of local field potentials around the deep brain stimulator lead implant site has been introduced. While this technology has been clinically adopted in other disorders treated with deep brain stimulation, such as Parkinson's disease, its application in epilepsy remains unclear. Previous research using investigational devices has suggested that specific frequency bands may correlate with clinical response to deep brain stimulation in epilepsy, but features of the clinical device may prevent its use. The authors present their experience with using this technology in epilepsy patients and describe some of its limitations. Ultimately, novel biomarkers will need to be identified to elucidate how neural activity at deep brain stimulation sites may change with clinical response.
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Affiliation(s)
- Jimmy C Yang
- Department of Neurological Surgery, The Ohio State University College of Medicine, Columbus, OH, USA; Department of Neurosurgery, Emory University, 1365 Clifton Road NE, Suite B6200, Atlanta, GA 30322, USA.
| | - Andrew I Yang
- Department of Neurosurgery, Emory University, 1365 Clifton Road NE, Suite B6200, Atlanta, GA 30322, USA
| | - Robert E Gross
- Department of Neurosurgery, Emory University, 1365 Clifton Road NE, Suite B6200, Atlanta, GA 30322, USA; Department of Neurology, Emory University School of Medicine, 1365 Clifton Road NE, Suite B6200, Atlanta, GA 30322
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3
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Bou Assi E, Schindler K, de Bézenac C, Denison T, Desai S, Keller SS, Lemoine É, Rahimi A, Shoaran M, Rummel C. From basic sciences and engineering to epileptology: A translational approach. Epilepsia 2023; 64 Suppl 3:S72-S84. [PMID: 36861368 DOI: 10.1111/epi.17566] [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: 02/20/2023] [Revised: 02/23/2023] [Accepted: 02/28/2023] [Indexed: 03/03/2023]
Abstract
Collaborative efforts between basic scientists, engineers, and clinicians are enabling translational epileptology. In this article, we summarize the recent advances presented at the International Conference for Technology and Analysis of Seizures (ICTALS 2022): (1) novel developments of structural magnetic resonance imaging; (2) latest electroencephalography signal-processing applications; (3) big data for the development of clinical tools; (4) the emerging field of hyperdimensional computing; (5) the new generation of artificial intelligence (AI)-enabled neuroprostheses; and (6) the use of collaborative platforms to facilitate epilepsy research translation. We highlight the promise of AI reported in recent investigations and the need for multicenter data-sharing initiatives.
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Affiliation(s)
- Elie Bou Assi
- Department of Neuroscience, Université de Montréal, Montréal, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Canada
| | - Kaspar Schindler
- Department of Neurology, Inselspital, Sleep-Wake-Epilepsy-Center, Bern University Hospital, Bern University, Bern, Switzerland
| | - Christophe de Bézenac
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
- The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
- Department of Engineering Science, University of Oxford, Oxford, UK
| | | | - Simon S Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
- The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Émile Lemoine
- Centre de Recherche du CHUM (CRCHUM), Montréal, Canada
- Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, Canada
| | | | - Mahsa Shoaran
- Institute of Electrical and Micro Engineering, Neuro-X Institute, EPFL, Lausanne, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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4
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Ouyang W, Lu W, Zhang Y, Liu Y, Kim JU, Shen H, Wu Y, Luan H, Kilner K, Lee SP, Lu Y, Yang Y, Wang J, Yu Y, Wegener AJ, Moreno JA, Xie Z, Wu Y, Won SM, Kwon K, Wu C, Bai W, Guo H, Liu TL, Bai H, Monti G, Zhu J, Madhvapathy SR, Trueb J, Stanslaski M, Higbee-Dempsey EM, Stepien I, Ghoreishi-Haack N, Haney CR, Kim TI, Huang Y, Ghaffari R, Banks AR, Jhou TC, Good CH, Rogers JA. A wireless and battery-less implant for multimodal closed-loop neuromodulation in small animals. Nat Biomed Eng 2023; 7:1252-1269. [PMID: 37106153 DOI: 10.1038/s41551-023-01029-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 03/26/2023] [Indexed: 04/29/2023]
Abstract
Fully implantable wireless systems for the recording and modulation of neural circuits that do not require physical tethers or batteries allow for studies that demand the use of unconstrained and freely behaving animals in isolation or in social groups. Moreover, feedback-control algorithms that can be executed within such devices without the need for remote computing eliminate virtual tethers and any associated latencies. Here we report a wireless and battery-less technology of this type, implanted subdermally along the back of freely moving small animals, for the autonomous recording of electroencephalograms, electromyograms and body temperature, and for closed-loop neuromodulation via optogenetics and pharmacology. The device incorporates a system-on-a-chip with Bluetooth Low Energy for data transmission and a compressed deep-learning module for autonomous operation, that offers neurorecording capabilities matching those of gold-standard wired systems. We also show the use of the implant in studies of sleep-wake regulation and for the programmable closed-loop pharmacological suppression of epileptic seizures via feedback from electroencephalography. The technology can support a broader range of applications in neuroscience and in biomedical research with small animals.
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Affiliation(s)
- Wei Ouyang
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
| | - Wei Lu
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
| | - Yamin Zhang
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
| | - Yiming Liu
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA
| | - Jong Uk Kim
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Haixu Shen
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
| | - Yunyun Wu
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
| | - Haiwen Luan
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
| | | | - Stephen P Lee
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
- Neurolux Inc., Northfield, IL, USA
| | - Yinsheng Lu
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
| | - Yiyuan Yang
- Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA
| | - Jin Wang
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
| | | | - Amy J Wegener
- US Army Research Laboratory, Aberdeen Proving Ground, MD, USA
- US Army Medical Research Institute of Chemical Defense, Aberdeen Proving Ground, MD, USA
| | - Justin A Moreno
- US Army Research Laboratory, Aberdeen Proving Ground, MD, USA
- US Army Medical Research Institute of Chemical Defense, Aberdeen Proving Ground, MD, USA
- SURVICE Engineering, Belcamp, MD, USA
| | - Zhaoqian Xie
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
- Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA
- Department of Civil and Environmental Engineering, Northwestern University, Evanston, IL, USA
| | - Yixin Wu
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
| | - Sang Min Won
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Kyeongha Kwon
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Changsheng Wu
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
| | - Wubin Bai
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
- Department of Applied Physical Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hexia Guo
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
| | - Tzu-Li Liu
- Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA
| | - Hedan Bai
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
| | - Giuditta Monti
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
| | - Jason Zhu
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA
- Department of Chemistry, Northwestern University, Evanston, IL, USA
| | - Surabhi R Madhvapathy
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
| | - Jacob Trueb
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
| | | | | | - Iwona Stepien
- Developmental Therapeutics Core, Northwestern University, Evanston, IL, USA
| | | | - Chad R Haney
- Center for Advanced Molecular Imaging, Northwestern University, Evanston, IL, USA
| | - Tae-Il Kim
- School of Chemical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
- Biomedical Institute for Convergence at SKKU (BICS), Sungkyunkwan University (SKKU), Suwon, Republic of Korea
| | - Yonggang Huang
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
- Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA
- Department of Civil and Environmental Engineering, Northwestern University, Evanston, IL, USA
| | - Roozbeh Ghaffari
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
- Neurolux Inc., Northfield, IL, USA
| | - Anthony R Banks
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA
- Neurolux Inc., Northfield, IL, USA
| | - Thomas C Jhou
- Department of Neurosciences, Medical University of South Carolina, Charleston, SC, USA.
| | - Cameron H Good
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA.
- Neurolux Inc., Northfield, IL, USA.
- US Army Research Laboratory, Aberdeen Proving Ground, MD, USA.
- US Army Medical Research Institute of Chemical Defense, Aberdeen Proving Ground, MD, USA.
| | - John A Rogers
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA.
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA.
- Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA.
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA.
- Department of Chemistry, Northwestern University, Evanston, IL, USA.
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Evanston, IL, USA.
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5
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Shirvalkar P, Prosky J, Chin G, Ahmadipour P, Sani OG, Desai M, Schmitgen A, Dawes H, Shanechi MM, Starr PA, Chang EF. First-in-human prediction of chronic pain state using intracranial neural biomarkers. Nat Neurosci 2023; 26:1090-1099. [PMID: 37217725 PMCID: PMC10330878 DOI: 10.1038/s41593-023-01338-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 04/18/2023] [Indexed: 05/24/2023]
Abstract
Chronic pain syndromes are often refractory to treatment and cause substantial suffering and disability. Pain severity is often measured through subjective report, while objective biomarkers that may guide diagnosis and treatment are lacking. Also, which brain activity underlies chronic pain on clinically relevant timescales, or how this relates to acute pain, remains unclear. Here four individuals with refractory neuropathic pain were implanted with chronic intracranial electrodes in the anterior cingulate cortex and orbitofrontal cortex (OFC). Participants reported pain metrics coincident with ambulatory, direct neural recordings obtained multiple times daily over months. We successfully predicted intraindividual chronic pain severity scores from neural activity with high sensitivity using machine learning methods. Chronic pain decoding relied on sustained power changes from the OFC, which tended to differ from transient patterns of activity associated with acute, evoked pain states during a task. Thus, intracranial OFC signals can be used to predict spontaneous, chronic pain state in patients.
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Affiliation(s)
- Prasad Shirvalkar
- UCSF Department of Anesthesiology and Perioperative Care, Division of Pain Medicine, University of California San Francisco, San Francisco, CA, USA.
- UCSF Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
- UCSF Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA.
- UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.
| | - Jordan Prosky
- UCSF Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
- UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Gregory Chin
- UCSF Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Parima Ahmadipour
- Departments of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Omid G Sani
- Departments of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Maansi Desai
- Department of Speech, Language, and Hearing Sciences, The University of Texas at Austin, Austin, TX, USA
| | - Ashlyn Schmitgen
- UCSF Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
- UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Heather Dawes
- UCSF Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
- UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Maryam M Shanechi
- Departments of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Philip A Starr
- UCSF Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
- UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
- UCSF Department of Physiology, University of California San Francisco, San Francisco, CA, USA
| | - Edward F Chang
- UCSF Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
- UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
- UCSF Department of Physiology, University of California San Francisco, San Francisco, CA, USA
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6
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Stanslaski SR, Case MA, Giftakis JE, Raike RS, Stypulkowski PH. Long Term Performance of a Bi-Directional Neural Interface for Deep Brain Stimulation and Recording. Front Hum Neurosci 2022; 16:916627. [PMID: 35754768 PMCID: PMC9218069 DOI: 10.3389/fnhum.2022.916627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 05/16/2022] [Indexed: 11/24/2022] Open
Abstract
Background: In prior reports, we described the design and initial performance of a fully implantable, bi-directional neural interface system for use in deep brain and other neurostimulation applications. Here we provide an update on the chronic, long-term neural sensing performance of the system using traditional 4-contact leads and extend those results to include directional 8-contact leads. Methods: Seven ovine subjects were implanted with deep brain stimulation (DBS) leads at different nodes within the Circuit of Papez: four with unilateral leads in the anterior nucleus of the thalamus and hippocampus; two with bilateral fornix leads, and one with bilateral hippocampal leads. The leads were connected to either an Activa PC+S® (Medtronic) or Percept PC°ledR (Medtronic) deep brain stimulation and recording device. Spontaneous local field potentials (LFPs), evoked potentials (EPs), LFP response to stimulation, and electrode impedances were monitored chronically for periods of up to five years in these subjects. Results: The morphology, amplitude, and latencies of chronic hippocampal EPs evoked by thalamic stimulation remained stable over the duration of the study. Similarly, LFPs showed consistent spectral peaks with expected variation in absolute magnitude dependent upon behavioral state and other factors, but no systematic degradation of signal quality over time. Electrode impedances remained within expected ranges with little variation following an initial stabilization period. Coupled neural activity between the two nodes within the Papez circuit could be observed in synchronized recordings up to 5 years post-implant. The magnitude of passive LFP power recorded from directional electrode segments was indicative of the contacts that produced the greatest stimulation-induced changes in LFP power within the Papez network. Conclusion: The implanted device performed as designed, providing the ability to chronically stimulate and record neural activity within this network for up to 5 years of follow-up.
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7
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Lu C, Amundsen-Huffmaster SL, Louie KH, Petrucci MN, Palnitkar T, Patriat R, Harel N, Park MC, Vitek JL, MacKinnon CD, Cooper SE. Modulation of Beta Oscillations in the Pallidum During Externally Cued Gait. FRONTIERS IN SIGNAL PROCESSING 2022; 2. [PMID: 35663826 PMCID: PMC9164277 DOI: 10.3389/frsip.2022.813509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Freezing of gait (FOG) is a particularly debilitating symptom of Parkinson’s disease (PD) and is often refractory to treatment. A striking feature of FOG is that external sensory cues can be used to overcome freezing and improve gait. Local field potentials (LFPs) recorded from the subthalamic nucleus (STN) and globus pallidus (GP) show that beta-band power modulates with gait phase. In the STN, beta-band oscillations are modulated by external cues, but it is unknown if this relationship holds in the globus pallidus (GP). Here we report LFP data recorded from the left GP, using a Medtronic PC + S device, in a 68-year-old man with PD and FOG during treadmill walking. A “stepping stone” task was used during which stepping was cued using visual targets of constant color or targets that unpredictably changed color, requiring a step length adjustment. Gait performance was quantified using measures of treadmill ground reaction forces and center of pressure and body kinematics from video monitoring. Beta-band power (12–30 Hz) and number of freezing episodes were measured. Cues which unpredictably changed color improved FOG more than conventional cues and were associated with greater modulation of beta-band power in phase with gait. This preliminary finding suggests that cueing-induced improvement of FOG may relate to beta-band modulation.
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Affiliation(s)
- Chiahao Lu
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
- Correspondence: Chiahao Lu,
| | | | - Kenneth H. Louie
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Matthew N. Petrucci
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
| | - Tara Palnitkar
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
- Department of Radiology, University of Minnesota, Minneapolis, MN, United States
| | - Remi Patriat
- Department of Radiology, University of Minnesota, Minneapolis, MN, United States
| | - Noam Harel
- Department of Radiology, University of Minnesota, Minneapolis, MN, United States
| | - Michael C. Park
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN, United States
| | - Jerrold L. Vitek
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
| | - Colum D. MacKinnon
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
| | - Scott E. Cooper
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
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8
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Zamora M, Toth R, Morgante F, Ottaway J, Gillbe T, Martin S, Lamb G, Noone T, Benjaber M, Nairac Z, Sehgal D, Constandinou TG, Herron J, Aziz TZ, Gillbe I, Green AL, Pereira EAC, Denison T. DyNeuMo Mk-1: Design and pilot validation of an investigational motion-adaptive neurostimulator with integrated chronotherapy. Exp Neurol 2022; 351:113977. [PMID: 35016994 PMCID: PMC7612891 DOI: 10.1016/j.expneurol.2022.113977] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 12/20/2021] [Accepted: 01/06/2022] [Indexed: 11/19/2022]
Abstract
There is growing interest in using adaptive neuromodulation to provide a more personalized therapy experience that might improve patient outcomes. Current implant technology, however, can be limited in its adaptive algorithm capability. To enable exploration of adaptive algorithms with chronic implants, we designed and validated the 'Picostim DyNeuMo Mk-1' (DyNeuMo Mk-1 for short), a fully-implantable, adaptive research stimulator that titrates stimulation based on circadian rhythms (e.g. sleep, wake) and the patient's movement state (e.g. posture, activity, shock, free-fall). The design leverages off-the-shelf consumer technology that provides inertial sensing with low-power, high reliability, and relatively modest cost. The DyNeuMo Mk-1 system was designed, manufactured and verified using ISO 13485 design controls, including ISO 14971 risk management techniques to ensure patient safety, while enabling novel algorithms. The system was validated for an intended use case in movement disorders under an emergency-device authorization from the Medicines and Healthcare Products Regulatory Agency (MHRA). The algorithm configurability and expanded stimulation parameter space allows for a number of applications to be explored in both central and peripheral applications. Intended applications include adaptive stimulation for movement disorders, synchronizing stimulation with circadian patterns, and reacting to transient inertial events such as posture changes, general activity, and walking. With appropriate design controls in place, first-in-human research trials are now being prepared to explore the utility of automated motion-adaptive algorithms.
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Affiliation(s)
- Mayela Zamora
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford, United Kingdom; MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
| | - Robert Toth
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Francesca Morgante
- Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St George's, University of London, London, United Kingdom; Department of Neurosurgery, Atkinson Morley Regional Neurosciences Centre, St George's Hospital, London, United Kingdom
| | | | - Tom Gillbe
- Bioinduction Ltd., Bristol, United Kingdom
| | - Sean Martin
- Department of Neurosurgery, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Guy Lamb
- Bioinduction Ltd., Bristol, United Kingdom
| | - Tara Noone
- Bioinduction Ltd., Bristol, United Kingdom
| | - Moaad Benjaber
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford, United Kingdom; MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Zachary Nairac
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford, United Kingdom
| | - Devang Sehgal
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford, United Kingdom
| | - Timothy G Constandinou
- Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom; Care Research and Technology Centre, UK Dementia Research Institute, London, United Kingdom
| | - Jeffrey Herron
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - Tipu Z Aziz
- Department of Neurosurgery, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | | | - Alexander L Green
- Department of Neurosurgery, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Erlick A C Pereira
- Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St George's, University of London, London, United Kingdom; Department of Neurosurgery, Atkinson Morley Regional Neurosciences Centre, St George's Hospital, London, United Kingdom
| | - Timothy Denison
- Institute of Biomedical Engineering, Department of Engineering Sciences, University of Oxford, Oxford, United Kingdom; MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
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9
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Kathiravelu P, Arnold M, Fleischer J, Yao Y, Awasthi S, Goel AK, Branen A, Sarikhani P, Kumar G, Kothare MV, Mahmoudi B. CONTROL-CORE: A Framework for Simulation and Design of Closed-Loop Peripheral Neuromodulation Control Systems. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2022; 10:36268-36285. [PMID: 36199437 PMCID: PMC9531851 DOI: 10.1109/access.2022.3161471] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Closed-loop Vagus Nerve Stimulation (VNS) based on physiological feedback signals is a promising approach to regulate organ functions and develop therapeutic devices. Designing closed-loop neurostimulation systems requires simulation environments and computing infrastructures that support i) modeling the physiological responses of organs under neuromodulation, also known as physiological models, and ii) the interaction between the physiological models and the neuromodulation control algorithms. However, existing simulation platforms do not support closed-loop VNS control systems modeling without extensive rewriting of computer code and manual deployment and configuration of programs. The CONTROL-CORE project aims to develop a flexible software platform for designing and implementing closed-loop VNS systems. This paper proposes the software architecture and the elements of the CONTROL-CORE platform that allow the interaction between a controller and a physiological model in feedback. CONTROL-CORE facilitates modular simulation and deployment of closed-loop peripheral neuromodulation control systems, spanning multiple organizations securely and concurrently. CONTROL-CORE allows simulations to run on different operating systems, be developed in various programming languages (such as Matlab, Python, C++, and Verilog), and be run locally, in containers, and in a distributed fashion. The CONTROL-CORE platform allows users to create tools and testbenches to facilitate sophisticated simulation experiments. We tested the CONTROL-CORE platform in the context of closed-loop control of cardiac physiological models, including pulsatile and nonpulsatile rat models. These were tested using various controllers such as Model Predictive Control and Long-Short-Term Memory based controllers. Our wide range of use cases and evaluations show the performance, flexibility, and usability of the CONTROL-CORE platform.
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Affiliation(s)
| | - Mark Arnold
- Department of Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Jake Fleischer
- Department of Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Yuyu Yao
- Department of Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Shubham Awasthi
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu 632014, India
| | - Aviral Kumar Goel
- Department of Computer Science and Information Systems, Birla Institute of Technology and Science, Pilani, K. K. Birla Goa Campus, Sancoale, Goa 403726, India
| | - Andrew Branen
- Department of Chemical and Materials Engineering, San José State University, San Jose, CA 95192, USA
| | - Parisa Sarikhani
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, USA
| | - Gautam Kumar
- Department of Chemical and Materials Engineering, San José State University, San Jose, CA 95192, USA
| | - Mayuresh V Kothare
- Department of Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Babak Mahmoudi
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, USA
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
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10
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Widge AS, Ellard KK, Paulk AC, Basu I, Yousefi A, Zorowitz S, Gilmour A, Afzal A, Deckersbach T, Cash SS, Kramer MA, Eden UT, Dougherty DD, Eskandar EN. Treating Refractory Mental Illness With Closed-Loop Brain Stimulation: Progress Towards a Patient-Specific Transdiagnostic Approach. FOCUS (AMERICAN PSYCHIATRIC PUBLISHING) 2022; 20:137-151. [PMID: 35746936 PMCID: PMC9063604 DOI: 10.1176/appi.focus.20102] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2015] [Accepted: 07/25/2016] [Indexed: 01/03/2023]
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11
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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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12
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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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13
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Molina R, Hass CJ, Cernera S, Sowalsky K, Schmitt AC, Roper JA, Martinez-Ramirez D, Opri E, Hess CW, Eisinger RS, Foote KD, Gunduz A, Okun MS. Closed-Loop Deep Brain Stimulation to Treat Medication-Refractory Freezing of Gait in Parkinson's Disease. Front Hum Neurosci 2021; 15:633655. [PMID: 33732122 PMCID: PMC7959768 DOI: 10.3389/fnhum.2021.633655] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 01/19/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Treating medication-refractory freezing of gait (FoG) in Parkinson’s disease (PD) remains challenging despite several trials reporting improvements in motor symptoms using subthalamic nucleus or globus pallidus internus (GPi) deep brain stimulation (DBS). Pedunculopontine nucleus (PPN) region DBS has been used for medication-refractory FoG, with mixed findings. FoG, as a paroxysmal phenomenon, provides an ideal framework for the possibility of closed-loop DBS (CL-DBS). Methods: In this clinical trial (NCT02318927), five subjects with medication-refractory FoG underwent bilateral GPi DBS implantation to address levodopa-responsive PD symptoms with open-loop stimulation. Additionally, PPN DBS leads were implanted for CL-DBS to treat FoG. The primary outcome of the study was a 40% improvement in medication-refractory FoG in 60% of subjects at 6 months when “on” PPN CL-DBS. Secondary outcomes included device feasibility to gauge the recruitment potential of this four-lead DBS approach for a potentially larger clinical trial. Safety was judged based on adverse events and explantation rate. Findings: The feasibility of this approach was demonstrated as we recruited five subjects with both “on” and “off” medication freezing. The safety for this population of patients receiving four DBS leads was suboptimal and associated with a high explantation rate of 40%. The primary clinical outcome in three of the five subjects was achieved at 6 months. However, the group analysis of the primary clinical outcome did not reveal any benefit. Interpretation: This study of a human PPN CL-DBS trial in medication-refractory FoG showed feasibility in recruitment, suboptimal safety, and a heterogeneous clinical effect in FoG outcomes.
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Affiliation(s)
- Rene Molina
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States.,Norman Fixel Institute for Neurological Diseases and The Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Chris J Hass
- Norman Fixel Institute for Neurological Diseases and The Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States.,Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, United States
| | - Stephanie Cernera
- Norman Fixel Institute for Neurological Diseases and The Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States.,J. Crayton Pruitt Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Kristen Sowalsky
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, United States
| | - Abigail C Schmitt
- Norman Fixel Institute for Neurological Diseases and The Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States.,Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, United States
| | - Jaimie A Roper
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, United States
| | | | - Enrico Opri
- Norman Fixel Institute for Neurological Diseases and The Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States.,J. Crayton Pruitt Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Christopher W Hess
- Norman Fixel Institute for Neurological Diseases and The Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States.,Department of Neurology, University of Florida, Gainesville, FL, United States
| | - Robert S Eisinger
- Norman Fixel Institute for Neurological Diseases and The Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States.,Department of Neuroscience, University of Florida, Gainesville, FL, United States
| | - Kelly D Foote
- Norman Fixel Institute for Neurological Diseases and The Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States.,Department of Neurosurgery, University of Florida, Gainesville, FL, United States
| | - Aysegul Gunduz
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States.,Norman Fixel Institute for Neurological Diseases and The Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States.,J. Crayton Pruitt Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Michael S Okun
- Norman Fixel Institute for Neurological Diseases and The Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States.,Department of Neurology, University of Florida, Gainesville, FL, United States.,Department of Neurosurgery, University of Florida, Gainesville, FL, United States
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14
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Powell MP, Anso J, Gilron R, Provenza NR, Allawala AB, Sliva DD, Bijanki KR, Oswalt D, Adkinson J, Pouratian N, Sheth SA, Goodman WK, Jones SR, Starr PA, Borton DA. NeuroDAC: an open-source arbitrary biosignal waveform generator. J Neural Eng 2021; 18:10.1088/1741-2552/abc7f0. [PMID: 33152715 PMCID: PMC8096859 DOI: 10.1088/1741-2552/abc7f0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 11/05/2020] [Indexed: 11/12/2022]
Abstract
Objective.Researchers are developing biomedical devices with embedded closed-loop algorithms for providing advanced adaptive therapies. As these devices become more capable and algorithms become more complex, tasked with integrating and interpreting multi-channel, multi-modal electrophysiological signals, there is a need for flexible bench-top testing and prototyping. We present a methodology for leveraging off-the-shelf audio equipment to construct a biosignal waveform generator capable of streaming pre-recorded biosignals from a host computer. By re-playing known, well-characterized, but physiologically relevant real-world biosignals into a device under test, researchers can evaluate their systems without the need for expensivein vivoexperiments.Approach.An open-source design based on the proposed methodology is described and validated, the NeuroDAC. NeuroDAC allows for 8 independent channels of biosignal playback using a simple, custom designed attenuation and buffering circuit. Applications can communicate with the device over a USB interface using standard audio drivers. On-board analog amplitude adjustment is used to maximize the dynamic range for a given signal and can be independently tuned for each channel.Main results.Low noise component selection yields a no-signal noise floor of just 5.35 ± 0.063. NeuroDAC's frequency response is characterized with a high pass -3 dB rolloff at 0.57 Hz, and is capable of accurately reproducing a wide assortment of biosignals ranging from EMG, EEG, and ECG to extracellularly recorded neural activity. We also present an application example using the device to test embedded algorithms on a closed-loop neural modulation device, the Medtronic RC+S.Significance.By making the design of NeuroDAC open-source we aim to present an accessible tool for rapidly prototyping new biomedical devices and algorithms than can be easily modified based on individual testing needs.ClinicalTrials.gov Identifiers: NCT04281134, NCT03437928, NCT03582891.
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Affiliation(s)
- M P Powell
- School of Engineering, Brown University, Providence, RI, United States of America
- Carney Institute for Brain Science, Brown University, Providence, RI, United States of America
| | - J Anso
- Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, United States of America
| | - R Gilron
- Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, United States of America
| | - N R Provenza
- School of Engineering, Brown University, Providence, RI, United States of America
- Carney Institute for Brain Science, Brown University, Providence, RI, United States of America
- The Charles Stark Draper Laboratory, Inc., Cambridge, MA, United States of America
| | - A B Allawala
- School of Engineering, Brown University, Providence, RI, United States of America
- Carney Institute for Brain Science, Brown University, Providence, RI, United States of America
| | - D D Sliva
- Carney Institute for Brain Science, Brown University, Providence, RI, United States of America
- Department of Neuroscience, Brown University, Providence, RI, United States of America
| | - K R Bijanki
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States of America
| | - D Oswalt
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States of America
| | - J Adkinson
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States of America
| | - N Pouratian
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States of America
| | - S A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States of America
| | - W K Goodman
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States of America
| | - S R Jones
- Carney Institute for Brain Science, Brown University, Providence, RI, United States of America
- Department of Neuroscience, Brown University, Providence, RI, United States of America
| | - P A Starr
- Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, United States of America
| | - D A Borton
- School of Engineering, Brown University, Providence, RI, United States of America
- Carney Institute for Brain Science, Brown University, Providence, RI, United States of America
- VA RR&D Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, Providence, RI, United States of America
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15
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Borton DA, Dawes HE, Worrell GA, Starr PA, Denison TJ. Developing Collaborative Platforms to Advance Neurotechnology and Its Translation. Neuron 2020; 108:286-301. [PMID: 33120024 PMCID: PMC7610607 DOI: 10.1016/j.neuron.2020.10.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 09/02/2020] [Accepted: 10/02/2020] [Indexed: 12/19/2022]
Abstract
Neurotechnological devices are failing to deliver on their therapeutic promise because of the time it takes to translate them from bench to clinic. In this Perspective, we reflect on lessons learned from medical device successes and failures and consider how such lessons might shape a strategic vision for translating neurotechnologies in the future. We articulate how the intentional design and deployment of "scientific platforms," from the technology stack of hardware and software through the supporting ecosystem, could catalyze a new wave of innovation, discovery, and therapy. We also identify specific actions that could promote future neurotechnology roadmaps and industrial-academic-government collaborative activities. We believe that community-supported neurotechnology platforms will prove to be transformational in accelerating ideas from bench to bedside, maximizing scientific discovery and improving patient care.
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Affiliation(s)
- David A Borton
- School of Engineering and the Carney Institute for Brain Science, Brown University, Providence, RI 02906, USA; VA RR&D Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, Providence, RI, USA
| | - Heather E Dawes
- Department of Neurological Surgery, UCSF, San Francisco, CA 94143, USA; Weill Institute for Neurosciences, UCSF, San Francisco, CA 94143, USA
| | - Gregory A Worrell
- Department of Neurology, Mayo Clinic, Rochester, MN 55902, USA; Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55902, USA
| | - Philip A Starr
- Department of Neurological Surgery, UCSF, San Francisco, CA 94143, USA; Weill Institute for Neurosciences, UCSF, San Francisco, CA 94143, USA
| | - Timothy J Denison
- Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK; MRC Brain Network Dynamics Unit, University of Oxford, Oxford OX3 7DQ, UK.
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16
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Vissani M, Isaias IU, Mazzoni A. Deep brain stimulation: a review of the open neural engineering challenges. J Neural Eng 2020; 17:051002. [PMID: 33052884 DOI: 10.1088/1741-2552/abb581] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Deep brain stimulation (DBS) is an established and valid therapy for a variety of pathological conditions ranging from motor to cognitive disorders. Still, much of the DBS-related mechanism of action is far from being understood, and there are several side effects of DBS whose origin is unclear. In the last years DBS limitations have been tackled by a variety of approaches, including adaptive deep brain stimulation (aDBS), a technique that relies on using chronically implanted electrodes on 'sensing mode' to detect the neural markers of specific motor symptoms and to deliver on-demand or modulate the stimulation parameters accordingly. Here we will review the state of the art of the several approaches to improve DBS and summarize the main challenges toward the development of an effective aDBS therapy. APPROACH We discuss models of basal ganglia disorders pathogenesis, hardware and software improvements for conventional DBS, and candidate neural and non-neural features and related control strategies for aDBS. MAIN RESULTS We identify then the main operative challenges toward optimal DBS such as (i) accurate target localization, (ii) increased spatial resolution of stimulation, (iii) development of in silico tests for DBS, (iv) identification of specific motor symptoms biomarkers, in particular (v) assessing how LFP oscillations relate to behavioral disfunctions, and (vi) clarify how stimulation affects the cortico-basal-ganglia-thalamic network to (vii) design optimal stimulation patterns. SIGNIFICANCE This roadmap will lead neural engineers novel to the field toward the most relevant open issues of DBS, while the in-depth readers might find a careful comparison of advantages and drawbacks of the most recent attempts to improve DBS-related neuromodulatory strategies.
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Affiliation(s)
- Matteo Vissani
- The BioRobotics Institute, Scuola Superiore Sant'Anna, 56025 Pisa, Italy. Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56025 Pisa, Italy
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17
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Landin K, Benjaber M, Jamshed F, Stagg C, Denison T. Technology Integration Methods for Bi-directional Brain-computer Interfaces and XR-based Interventions. CONFERENCE PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS 2020; 2020:3695-3701. [PMID: 33707935 PMCID: PMC7116886 DOI: 10.1109/smc42975.2020.9282993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Brain stimulation therapies have been established as effective treatments for Parkinson's disease, essential tremor, and epilepsy, as well as having high diagnostic and therapeutic potential in a wide range of neurological and psychiatric conditions. Novel interventions such as extended reality (XR), video games and exergames that can improve physiological and cognitive functioning are also emerging as targets for therapeutic and rehabilitative treatments. Previous studies have proposed specific applications involving non-invasive brain stimulation (NIBS) and virtual environments, but to date these have been uni-directional and restricted to specific applications or proprietary hardware. Here, we describe technology integration methods that enable invasive and non-invasive brain stimulation devices to interface with a cross-platform game engine and development platform for creating bi-directional brain-computer interfaces (BCI) and XR-based interventions. Furthermore, we present a highly-modifiable software framework and methods for integrating deep brain stimulation (DBS) in 2D, 3D, virtual and mixed reality applications, as well as extensible applications for BCI integration in wireless systems. The source code and integrated brain stimulation applications are available online at https://github.com/oxfordbioelectronics/brain-stim-game.
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Affiliation(s)
- Kei Landin
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
| | - Moaad Benjaber
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
| | - Fawad Jamshed
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
| | - Charlotte Stagg
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
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18
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Molina R, Hass CJ, Sowalsky K, Schmitt AC, Opri E, Roper JA, Martinez-Ramirez D, Hess CW, Foote KD, Okun MS, Gunduz A. Neurophysiological Correlates of Gait in the Human Basal Ganglia and the PPN Region in Parkinson's Disease. Front Hum Neurosci 2020; 14:194. [PMID: 32581744 PMCID: PMC7287013 DOI: 10.3389/fnhum.2020.00194] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 04/29/2020] [Indexed: 11/13/2022] Open
Abstract
This study aimed to characterize the neurophysiological correlates of gait in the human pedunculopontine nucleus (PPN) region and the globus pallidus internus (GPi) in Parkinson's disease (PD) cohort. Though much is known about the PPN region through animal studies, there are limited physiological recordings from ambulatory humans. The PPN has recently garnered interest as a potential deep brain stimulation (DBS) target for improving gait and freezing of gait (FoG) in PD. We used bidirectional neurostimulators to record from the human PPN region and GPi in a small cohort of severely affected PD subjects with FoG despite optimized dopaminergic medications. Five subjects, with confirmed on-dopaminergic medication FoG, were implanted with bilateral GPi and bilateral PPN region DBS electrodes. Electrophysiological recordings were obtained during various gait tasks for 5 months postoperatively in both the off- and on-medication conditions (obtained during the no stimulation condition). The results revealed suppression of low beta power in the GPi and a 1-8 Hz modulation in the PPN region which correlated with human gait. The PPN feature correlated with walking speed. GPi beta desynchronization and PPN low-frequency synchronization were observed as subjects progressed from rest to ambulatory tasks. Our findings add to our understanding of the neurophysiology underpinning gait and will likely contribute to the development of novel therapies for abnormal gait in PD. Clinical Trial Registration: Clinicaltrials.gov identifier; NCT02318927.
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Affiliation(s)
- Rene Molina
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States.,Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Chris J Hass
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States.,Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, United States
| | - Kristen Sowalsky
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States.,Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, United States
| | - Abigail C Schmitt
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States.,Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, United States
| | - Enrico Opri
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States.,J. Crayton Pruitt Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Jaime A Roper
- School of Kinesiology, Auburn University, Auburn, AL, United States
| | | | - Christopher W Hess
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States.,Department of Neurology, University of Florida, Gainesville, FL, United States
| | - Kelly D Foote
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States.,Department of Neurosurgery, University of Florida, Gainesville, FL, United States
| | - Michael S Okun
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States.,Department of Neurology, University of Florida, Gainesville, FL, United States
| | - Aysegul Gunduz
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States.,Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States.,J. Crayton Pruitt Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States.,Department of Neurology, University of Florida, Gainesville, FL, United States
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19
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Hoang KB, Turner DA. The Emerging Role of Biomarkers in Adaptive Modulation of Clinical Brain Stimulation. Neurosurgery 2020; 85:E430-E439. [PMID: 30957145 DOI: 10.1093/neuros/nyz096] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Accepted: 03/01/2019] [Indexed: 11/14/2022] Open
Abstract
Therapeutic brain stimulation has proven efficacious for treatment of nervous system diseases, exerting widespread influence via disease-specific neural networks. Activation or suppression of neural networks could theoretically be assessed by either clinical symptom modification (ie, tremor, rigidity, seizures) or development of specific biomarkers linked to treatment of symptomatic disease states. For example, biomarkers indicative of disease state could aid improved intraoperative localization of electrode position, optimize device efficacy or efficiency through dynamic control, and eventually serve to guide automatic adjustment of stimulation settings. Biomarkers to control either extracranial or intracranial stimulation span from continuous physiological brain activity, intermittent pathological activity, and triggered local phenomena or potentials, to wearable devices, blood flow, biochemical or cardiac signals, temperature perturbations, optical or magnetic resonance imaging changes, or optogenetic signals. The goal of this review is to update new approaches to implement control of stimulation through relevant biomarkers. Critical questions include whether adaptive systems adjusted through biomarkers can optimize efficiency and eventually efficacy, serve as inputs for stimulation adjustment, and consequently broaden our fundamental understanding of abnormal neural networks in pathologic states. Neurosurgeons are at the forefront of translating and developing biomarkers embedded within improved brain stimulation systems. Thus, criteria for developing and validating biomarkers for clinical use are important for the adaptation of device approaches into clinical practice.
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Affiliation(s)
- Kimberly B Hoang
- Department of Neurosurgery, MD Anderson Cancer Center, Houston, Texas
| | - Dennis A Turner
- Departments of Neurosurgery, Duke University Medical Center, Durham, North Carolina.,Department of Neurobiology, Duke University Medical Center, Durham, North Carolina.,Department of Biomedical Engineering, Duke University, Durham, North Carolina
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20
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Little S, Brown P. Debugging Adaptive Deep Brain Stimulation for Parkinson's Disease. Mov Disord 2020; 35:555-561. [PMID: 32039501 PMCID: PMC7166127 DOI: 10.1002/mds.27996] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 01/22/2020] [Accepted: 01/27/2020] [Indexed: 12/20/2022] Open
Abstract
Deep brain stimulation (DBS) is a successful treatment for patients with Parkinson's disease. In adaptive DBS, stimulation is titrated according to feedback about clinical state and underlying pathophysiology. This contrasts with conventional stimulation, which is fixed and continuous. In acute trials, adaptive stimulation matches the efficacy of conventional stimulation while delivering about half the electrical energy. The latter means potentially fewer side-effects. The next step is to determine the long-term efficacy, efficiency, and side-effect profile of adaptive stimulation, and chronic trials are currently being considered by the medical devices industry. However, there are several different approaches to adaptive DBS, and several possible limitations have been highlighted. Here we review the findings to date to ascertain how and who to stimulate in chronic trials designed to establish the long-term utility of adaptive DBS. © 2020 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Simon Little
- Department of Movement Disorders and Neuromodulation, University of California San Francisco, San Francisco, California, USA
| | - Peter Brown
- Medical Research Council Brain Network Dynamics Unit at the University of Oxford, Oxford, United Kingdom
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21
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Ashourvan A, Pequito S, Khambhati AN, Mikhail F, Baldassano SN, Davis KA, Lucas TH, Vettel JM, Litt B, Pappas GJ, Bassett DS. Model-based design for seizure control by stimulation. J Neural Eng 2020; 17:026009. [PMID: 32103826 PMCID: PMC8341467 DOI: 10.1088/1741-2552/ab7a4e] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
OBJECTIVE Current brain stimulation paradigms are largely empirical rather than theoretical. An opportunity exists to improve upon their modest effectiveness in closed-loop control strategies with the development of theoretically grounded, model-based designs. APPROACH Inspired by this need, here we couple experimental data and mathematical modeling with a control-theoretic strategy for seizure termination. We begin by exercising a dynamical systems approach to model seizures (n = 94) recorded using intracranial EEG (iEEG) from 21 patients with medication-resistant, localization-related epilepsy. MAIN RESULTS Although each patient's seizures displayed unique spatial and temporal patterns, their evolution can be parsimoniously characterized by the same model form. Idiosyncracies of the model can inform individualized intervention strategies, specifically in iEEG samples with well-localized seizure onset zones. Temporal fluctuations in the spatial profiles of the oscillatory modes show that seizure onset marks a transition into a regime in which the underlying system supports prolonged rhythmic and focal activity. Based on these observations, we propose a control-theoretic strategy that aims to stabilize ictal activity using static output feedback for linear time-invariant switching systems. Finally, we demonstrate in silico that our proposed strategy allows us to dampen the emerging focal oscillatory sources using only a small set of electrodes. SIGNIFICANCE Our integrative study informs the development of modulation and control algorithms for neurostimulation that could improve the effectiveness of implantable, closed-loop anti-epileptic devices.
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Affiliation(s)
- Arian Ashourvan
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, United States of America. U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005, United States of America
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22
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Abstract
The technological ability to capture electrophysiological activity of populations of cortical neurons through chronic implantable devices has led to significant advancements in the field of brain-computer interfaces. Recent progress in the field has been driven by developments in integrated microelectronics, wireless communications, materials science, and computational neuroscience. Here, we review major device development landmarks in the arena of neural interfaces from FDA-approved clinical systems to prototype head-mounted and fully implantable wireless systems for multi-channel neural recording. Additionally, we provide an outlook toward next-generation, highly miniaturized technologies for minimally invasive, vastly parallel neural interfaces for naturalistic, closed-loop neuroprostheses.
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23
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Freudenburg ZV, Branco MP, Leinders S, van der Vijgh BH, Pels EGM, Denison T, van den Berg LH, Miller KJ, Aarnoutse EJ, Ramsey NF, Vansteensel MJ. Sensorimotor ECoG Signal Features for BCI Control: A Comparison Between People With Locked-In Syndrome and Able-Bodied Controls. Front Neurosci 2019; 13:1058. [PMID: 31680806 PMCID: PMC6805728 DOI: 10.3389/fnins.2019.01058] [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: 02/28/2019] [Accepted: 09/20/2019] [Indexed: 01/10/2023] Open
Abstract
The sensorimotor cortex is a frequently targeted brain area for the development of Brain-Computer Interfaces (BCIs) for communication in people with severe paralysis and communication problems (locked-in syndrome; LIS). It is widely acknowledged that this area displays an increase in high-frequency band (HFB) power and a decrease in the power of the low frequency band (LFB) during movement of, for example, the hand. Upon termination of hand movement, activity in the LFB band typically shows a short increase (rebound). The ability to modulate the neural signal in the sensorimotor cortex by imagining or attempting to move is crucial for the implementation of sensorimotor BCI in people who are unable to execute movements. This may not always be self-evident, since the most common causes of LIS, amyotrophic lateral sclerosis (ALS) and brain stem stroke, are associated with significant damage to the brain, potentially affecting the generation of baseline neural activity in the sensorimotor cortex and the modulation thereof by imagined or attempted hand movement. In the Utrecht NeuroProsthesis (UNP) study, a participant with LIS caused by ALS and a participant with LIS due to brain stem stroke were implanted with a fully implantable BCI, including subdural electrocorticography (ECoG) electrodes over the sensorimotor area, with the purpose of achieving ECoG-BCI-based communication. We noted differences between these participants in the spectral power changes generated by attempted movement of the hand. To better understand the nature and origin of these differences, we compared the baseline spectral features and task-induced modulation of the neural signal of the LIS participants, with those of a group of able-bodied people with epilepsy who received a subchronic implant with ECoG electrodes for diagnostic purposes. Our data show that baseline LFB oscillatory components and changes generated in the LFB power of the sensorimotor cortex by (attempted) hand movement differ between participants, despite consistent HFB responses in this area. We conclude that the etiology of LIS may have significant effects on the LFB spectral components in the sensorimotor cortex, which is relevant for the development of communication-BCIs for this population.
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Affiliation(s)
- Zachary V Freudenburg
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Utrecht, Netherlands
| | - Mariana P Branco
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Utrecht, Netherlands
| | - Sacha Leinders
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Utrecht, Netherlands
| | - Benny H van der Vijgh
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Utrecht, Netherlands
| | - Elmar G M Pels
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Utrecht, Netherlands
| | - Timothy Denison
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Leonard H van den Berg
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Utrecht, Netherlands
| | - Kai J Miller
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Utrecht, Netherlands
| | - Erik J Aarnoutse
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Utrecht, Netherlands
| | - Nick F Ramsey
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Utrecht, Netherlands
| | - Mariska J Vansteensel
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, Utrecht, Netherlands
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24
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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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25
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Huang Y, Cheeran B, Green AL, Denison TJ, Aziz TZ. Applying a Sensing-Enabled System for Ensuring Safe Anterior Cingulate Deep Brain Stimulation for Pain. Brain Sci 2019; 9:brainsci9070150. [PMID: 31247982 PMCID: PMC6680545 DOI: 10.3390/brainsci9070150] [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: 05/22/2019] [Revised: 06/17/2019] [Accepted: 06/25/2019] [Indexed: 12/18/2022] Open
Abstract
Deep brain stimulation (DBS) of the anterior cingulate cortex (ACC) was offered to chronic pain patients who had exhausted medical and surgical options. However, several patients developed recurrent seizures. This work was conducted to assess the effect of ACC stimulation on the brain activity and to guide safe DBS programming. A sensing-enabled neurostimulator (Activa PC + S) allowing wireless recording through the stimulating electrodes was chronically implanted in three patients. Stimulation patterns with different amplitude levels and variable ramping rates were tested to investigate whether these patterns could provide pain relief without triggering after-discharges (ADs) within local field potentials (LFPs) recorded in the ACC. In the absence of ramping, AD activity was detected following stimulation at amplitude levels below those used in chronic therapy. Adjustment of stimulus cycling patterns, by slowly ramping on/off (8-s ramp duration), was able to prevent ADs at higher amplitude levels while maintaining effective pain relief. The absence of AD activity confirmed from the implant was correlated with the absence of clinical seizures. We propose that AD activity in the ACC could be a biomarker for the likelihood of seizures in these patients, and the application of sensing-enabled techniques has the potential to advance safer brain stimulation therapies, especially in novel targets.
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Affiliation(s)
- Yongzhi Huang
- Oxford Functional Neurosurgery Group, Nuffield Departments of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK
| | - Binith Cheeran
- Oxford Functional Neurosurgery Group, Nuffield Departments of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK
| | - Alexander L Green
- Oxford Functional Neurosurgery Group, Nuffield Departments of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK
| | - Timothy J Denison
- Institute of Biomedical Engineering, University of Oxford, Oxford OX3 7DQ, UK.
| | - Tipu Z Aziz
- Oxford Functional Neurosurgery Group, Nuffield Departments of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK.
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26
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Gunduz A, Opri E, Gilron R, Kremen V, Worrell G, Starr P, Leyde K, Denison T. Adding wisdom to 'smart' bioelectronic systems: a design framework for physiologic control including practical examples. ACTA ACUST UNITED AC 2019; 2:29-41. [PMID: 33868718 PMCID: PMC7610621 DOI: 10.2217/bem-2019-0008] [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] [Indexed: 02/07/2023]
Abstract
This perspective provides an overview of how risk can be effectively considered in physiological control loops that strive for semi-to-fully automated operation. The perspective first introduces the motivation, user needs and framework for the design of a physiological closed-loop controller. Then, we discuss specific risk areas and use examples from historical medical devices to illustrate the key concepts. Finally, we provide a design overview of an adaptive bidirectional brain–machine interface, currently undergoing human clinical studies, to synthesize the design principles in an exemplar application.
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Affiliation(s)
- Aysegul Gunduz
- Department of Biomedical Engineering, University of Florida Gainesville, Gainesville, FL 32611, USA
| | - Enrico Opri
- Department of Biomedical Engineering, University of Florida Gainesville, Gainesville, FL 32611, USA
| | - Ro'ee Gilron
- School of Medicine, University of California San Francisco, San Francisco CA 94143, USA
| | - Vaclav Kremen
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Gregory Worrell
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Phil Starr
- School of Medicine, University of California San Francisco, San Francisco CA 94143, USA
| | - Kent Leyde
- Cadence Neuroscience Inc, Sammamish, WA 98074, USA
| | - Timothy Denison
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, UK
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27
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Hirschmann J, Abbasi O, Storzer L, Butz M, Hartmann CJ, Wojtecki L, Schnitzler A. Longitudinal Recordings Reveal Transient Increase of Alpha/Low-Beta Power in the Subthalamic Nucleus Associated With the Onset of Parkinsonian Rest Tremor. Front Neurol 2019; 10:145. [PMID: 30899240 PMCID: PMC6416159 DOI: 10.3389/fneur.2019.00145] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Accepted: 02/05/2019] [Indexed: 11/23/2022] Open
Abstract
Functional magnetic resonance imaging studies suggest that different subcortico-cortical circuits control different aspects of Parkinsonian rest tremor. The basal ganglia were proposed to drive tremor onset, and the cerebellum was suggested to be responsible for tremor maintenance (“dimmer-switch” hypothesis). Although several electrophysiological correlates of tremor have been described, it is currently unclear whether any of these is specific to tremor onset or maintenance. In this study, we present data from a single patient measured repeatedly within 2 years after implantation of a deep brain stimulation (DBS) system capable of recording brain activity from the target. Local field potentials (LFPs) from the subthalamic nucleus and the scalp electroencephalogram were recorded 1 week, 3 months, 6 months, 1 year, and 2 years after surgery. Importantly, the patient suffered from severe rest tremor of the lower limbs, which could be interrupted voluntarily by repositioning the feet. This provided the unique opportunity to record many tremor onsets in succession. We found that tremor onset and tremor maintenance were characterized by distinct modulations of subthalamic oscillations. Alpha/low-beta power increased transiently immediately after tremor onset. In contrast, beta power was continuously suppressed during tremor maintenance. Tremor maintenance was additionally associated with subthalamic and cortical power increases around individual tremor frequency. To our knowledge, this is the first evidence of distinct subthalamic LFP modulations in tremor onset and tremor maintenance. Our observations suggest the existence of an acceleration signal for Parkinsonian rest tremor in the basal ganglia, in line with the “dimmer-switch” hypothesis.
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Affiliation(s)
- Jan Hirschmann
- Medical Faculty, Institute of Clinical Neuroscience and Medical Psychology, Heinrich Heine University, Düsseldorf, Germany
| | - Omid Abbasi
- Medical Faculty, Institute of Clinical Neuroscience and Medical Psychology, Heinrich Heine University, Düsseldorf, Germany
| | - Lena Storzer
- Medical Faculty, Institute of Clinical Neuroscience and Medical Psychology, Heinrich Heine University, Düsseldorf, Germany
| | - Markus Butz
- Medical Faculty, Institute of Clinical Neuroscience and Medical Psychology, Heinrich Heine University, Düsseldorf, Germany
| | - Christian J Hartmann
- Medical Faculty, Institute of Clinical Neuroscience and Medical Psychology, Heinrich Heine University, Düsseldorf, Germany.,Medical Faculty, Center for Movement Disorders and Neuromodulation, Heinrich Heine University, Düsseldorf, Germany
| | - Lars Wojtecki
- Medical Faculty, Institute of Clinical Neuroscience and Medical Psychology, Heinrich Heine University, Düsseldorf, Germany.,Medical Faculty, Center for Movement Disorders and Neuromodulation, Heinrich Heine University, Düsseldorf, Germany
| | - Alfons Schnitzler
- Medical Faculty, Institute of Clinical Neuroscience and Medical Psychology, Heinrich Heine University, Düsseldorf, Germany.,Medical Faculty, Center for Movement Disorders and Neuromodulation, Heinrich Heine University, Düsseldorf, Germany
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28
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Provenza NR, Matteson ER, Allawala AB, Barrios-Anderson A, Sheth SA, Viswanathan A, McIngvale E, Storch EA, Frank MJ, McLaughlin NCR, Cohn JF, Goodman WK, Borton DA. The Case for Adaptive Neuromodulation to Treat Severe Intractable Mental Disorders. Front Neurosci 2019; 13:152. [PMID: 30890909 PMCID: PMC6412779 DOI: 10.3389/fnins.2019.00152] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Accepted: 02/11/2019] [Indexed: 12/20/2022] Open
Abstract
Mental disorders are a leading cause of disability worldwide, and available treatments have limited efficacy for severe cases unresponsive to conventional therapies. Neurosurgical interventions, such as lesioning procedures, have shown success in treating refractory cases of mental illness, but may have irreversible side effects. Neuromodulation therapies, specifically Deep Brain Stimulation (DBS), may offer similar therapeutic benefits using a reversible (explantable) and adjustable platform. Early DBS trials have been promising, however, pivotal clinical trials have failed to date. These failures may be attributed to targeting, patient selection, or the “open-loop” nature of DBS, where stimulation parameters are chosen ad hoc during infrequent visits to the clinician’s office that take place weeks to months apart. Further, the tonic continuous stimulation fails to address the dynamic nature of mental illness; symptoms often fluctuate over minutes to days. Additionally, stimulation-based interventions can cause undesirable effects if applied when not needed. A responsive, adaptive DBS (aDBS) system may improve efficacy by titrating stimulation parameters in response to neural signatures (i.e., biomarkers) related to symptoms and side effects. Here, we present rationale for the development of a responsive DBS system for treatment of refractory mental illness, detail a strategic approach for identification of electrophysiological and behavioral biomarkers of mental illness, and discuss opportunities for future technological developments that may harness aDBS to deliver improved therapy.
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Affiliation(s)
- Nicole R Provenza
- Brown University School of Engineering, Providence, RI, United States.,Charles Stark Draper Laboratory, Cambridge, MA, United States
| | - Evan R Matteson
- Brown University School of Engineering, Providence, RI, United States
| | - Anusha B Allawala
- Brown University School of Engineering, Providence, RI, United States
| | - Adriel Barrios-Anderson
- Psychiatric Neurosurgery Program at Butler Hospital, The Warren Alpert Medical School of Brown University, Providence, RI, United States
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Ashwin Viswanathan
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Elizabeth McIngvale
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Eric A Storch
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Michael J Frank
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI, United States.,Department of Psychology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Nicole C R McLaughlin
- Psychiatric Neurosurgery Program at Butler Hospital, The Warren Alpert Medical School of Brown University, Providence, RI, United States
| | - Jeffrey F Cohn
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Wayne K Goodman
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - David A Borton
- Brown University School of Engineering, Providence, RI, United States.,Carney Institute for Brain Science, Brown University, Providence, RI, United States.,Department of Veterans Affairs, Providence Medical Center, Center for Neurorestoration and Neurotechnology, Providence, RI, United States
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29
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Neumann WJ, Turner RS, Blankertz B, Mitchell T, Kühn AA, Richardson RM. Toward Electrophysiology-Based Intelligent Adaptive Deep Brain Stimulation for Movement Disorders. Neurotherapeutics 2019; 16:105-118. [PMID: 30607748 PMCID: PMC6361070 DOI: 10.1007/s13311-018-00705-0] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Deep brain stimulation (DBS) represents one of the major clinical breakthroughs in the age of translational neuroscience. In 1987, Benabid and colleagues demonstrated that high-frequency stimulation can mimic the effects of ablative neurosurgery in Parkinson's disease (PD), while offering two key advantages to previous procedures: adjustability and reversibility. Deep brain stimulation is now an established therapeutic approach that robustly alleviates symptoms in patients with movement disorders, such as Parkinson's disease, essential tremor, and dystonia, who present with inadequate or adverse responses to medication. Currently, stimulation electrodes are implanted in specific target regions of the basal ganglia-thalamic circuit and stimulation pulses are delivered chronically. To achieve optimal therapeutic effect, stimulation frequency, amplitude, and pulse width must be adjusted on a patient-specific basis by a movement disorders specialist. The finding that pathological neural activity can be sampled directly from the target region using the DBS electrode has inspired a novel DBS paradigm: closed-loop adaptive DBS (aDBS). The goal of this strategy is to identify pathological and physiologically normal patterns of neuronal activity that can be used to adapt stimulation parameters to the concurrent therapeutic demand. This review will give detailed insight into potential biomarkers and discuss next-generation strategies, implementing advances in artificial intelligence, to further elevate the therapeutic potential of DBS by capitalizing on its modifiable nature. Development of intelligent aDBS, with an ability to deliver highly personalized treatment regimens and to create symptom-specific therapeutic strategies in real-time, could allow for significant further improvements in the quality of life for movement disorders patients with DBS that ultimately could outperform traditional drug treatment.
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Affiliation(s)
- Wolf-Julian Neumann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Campus Charite Mitte, Chariteplatz 1, 10117, Berlin, Germany.
| | - Robert S Turner
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Benjamin Blankertz
- Department of Computer Science, Technische Universität Berlin, Berlin, Germany
| | - Tom Mitchell
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Andrea A Kühn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Campus Charite Mitte, Chariteplatz 1, 10117, Berlin, Germany
- Berlin School of Mind and Brain, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Neurocure, Centre of Excellence, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - R Mark Richardson
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
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30
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Stanslaski S, Herron J, Chouinard T, Bourget D, Isaacson B, Kremen V, Opri E, Drew W, Brinkmann BH, Gunduz A, Adamski T, Worrell GA, Denison T. A Chronically Implantable Neural Coprocessor for Investigating the Treatment of Neurological Disorders. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2018; 12:1230-1245. [PMID: 30418885 PMCID: PMC6415546 DOI: 10.1109/tbcas.2018.2880148] [Citation(s) in RCA: 100] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Developing new tools to better understand disorders of the nervous system, with a goal to more effectively treat them, is an active area of bioelectronic medicine research. Future tools must be flexible and configurable, given the evolving understanding of both neuromodulation mechanisms and how to configure a system for optimal clinical outcomes. We describe a system, the Summit RC+S "neural coprocessor," that attempts to bring the capability and flexibility of a microprocessor to a prosthesis embedded within the nervous system. This paper describes the updated system architecture for the Summit RC+S system, the five custom integrated circuits required for bi-directional neural interfacing, the supporting firmware/software ecosystem, and the verification and validation activities to prepare for human implantation. Emphasis is placed on design changes motivated by experience with the CE-marked Activa PC+S research tool; specifically, enhancement of sense-stim performance for improved bi-directional communication to the nervous system, implementation of rechargeable technology to extend device longevity, and application of MICS-band telemetry for algorithm development and data management. The technology was validated in a chronic treatment paradigm for canines with naturally occurring epilepsy, including free ambulation in the home environment, which represents a typical use case for future human protocols.
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Peh WYX, Raczkowska MN, Teh Y, Alam M, Thakor NV, Yen SC. Closed-loop stimulation of the pelvic nerve for optimal micturition. J Neural Eng 2018; 15:066009. [PMID: 30181427 DOI: 10.1088/1741-2552/aadee9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
OBJECTIVE Neural stimulation to restore bladder function has traditionally relied on open-loop approaches that used pre-set parameters, which do not adapt to suboptimal outcomes. The goal of this study was to examine the effectiveness of a novel closed-loop stimulation paradigm for improving micturition or bladder voiding. APPROACH We compared the voiding efficiency obtained with this closed-loop framework against open-loop stimulation paradigms in anesthetized rats. The bladder pressures that preceded voiding, and the minimum current amplitudes for stimulating the pelvic nerves to evoke bladder contractions, were first calibrated for each animal. An automated closed-loop system was used to initiate voiding upon bladder fullness, adapt the stimulation current by using real-time bladder pressure changes to classify voiding outcomes, and halt stimulation when the bladder had been emptied or when the safe stimulation limit was reached. MAIN RESULTS In vivo testing demonstrated that the closed-loop system achieved high voiding efficiency or VE (75.7% ± 3.07%, mean ± standard error of the mean) and outperformed open-loop systems with either conserved number of stimulation epochs (63.2% ± 4.90% VE) or conserved charge injected (32.0% ± 1.70% VE). Post-hoc analyses suggest that the classification algorithm can be further improved with data from additional closed-loop experiments. SIGNIFICANCE This novel approach may be applied to an implantable device for treating underactive bladder (<60% VE), especially in cases where under- or over-stimulation of the nerve is a concern.
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Affiliation(s)
- Wendy Yen Xian Peh
- Singapore Institute for Neurotechnology, National University of Singapore, 28 Medical Drive, #05-02, Singapore 117456, Singapore
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Swann NC, de Hemptinne C, Thompson MC, Miocinovic S, Miller AM, Gilron R, Ostrem JL, Chizeck HJ, Starr PA. Adaptive deep brain stimulation for Parkinson's disease using motor cortex sensing. J Neural Eng 2018; 15:046006. [PMID: 29741160 PMCID: PMC6021210 DOI: 10.1088/1741-2552/aabc9b] [Citation(s) in RCA: 225] [Impact Index Per Article: 37.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Contemporary deep brain stimulation (DBS) for Parkinson's disease is delivered continuously, and adjustments based on patient's changing symptoms must be made manually by a trained clinician. Patients may be subjected to energy intensive settings at times when they are not needed, possibly resulting in stimulation-induced adverse effects, such as dyskinesia. One solution is 'adaptive' DBS, in which stimulation is modified in real time based on neural signals that co-vary with the severity of motor signs or of stimulation-induced adverse effects. Here we show the feasibility of adaptive DBS using a fully implanted neural prosthesis. APPROACH We demonstrate adaptive deep brain stimulation in two patients with Parkinson's disease using a fully implanted neural prosthesis that is enabled to utilize brain sensing to control stimulation amplitude (Activa PC + S). We used a cortical narrowband gamma (60-90 Hz) oscillation related to dyskinesia to decrease stimulation voltage when gamma oscillatory activity is high (indicating dyskinesia) and increase stimulation voltage when it is low. MAIN RESULTS We demonstrate the feasibility of 'adaptive deep brain stimulation' in two patients with Parkinson's disease. In short term in-clinic testing, energy savings were substantial (38%-45%), and therapeutic efficacy was maintained. SIGNIFICANCE This is the first demonstration of adaptive DBS in Parkinson's disease using a fully implanted device and neural sensing. Our approach is distinct from other strategies utilizing basal ganglia signals for feedback control.
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Affiliation(s)
- Nicole C Swann
- Departments of Neurological Surgery, University of California, San Franciso, CA, United States of America. Department of Human Physiology, University of Oregon, Eugene, OR, United States of America
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Pulliam CL, Peterson EJ, Herron JA, Denison T. Designing Neuromodulation Devices for Feedback Control. Neuromodulation 2018. [DOI: 10.1016/b978-0-12-805353-9.00023-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Hoang KB, Cassar IR, Grill WM, Turner DA. Biomarkers and Stimulation Algorithms for Adaptive Brain Stimulation. Front Neurosci 2017; 11:564. [PMID: 29066947 PMCID: PMC5641319 DOI: 10.3389/fnins.2017.00564] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Accepted: 09/25/2017] [Indexed: 11/29/2022] Open
Abstract
The goal of this review is to describe in what ways feedback or adaptive stimulation may be delivered and adjusted based on relevant biomarkers. Specific treatment mechanisms underlying therapeutic brain stimulation remain unclear, in spite of the demonstrated efficacy in a number of nervous system diseases. Brain stimulation appears to exert widespread influence over specific neural networks that are relevant to specific disease entities. In awake patients, activation or suppression of these neural networks can be assessed by either symptom alleviation (i.e., tremor, rigidity, seizures) or physiological criteria, which may be predictive of expected symptomatic treatment. Secondary verification of network activation through specific biomarkers that are linked to symptomatic disease improvement may be useful for several reasons. For example, these biomarkers could aid optimal intraoperative localization, possibly improve efficacy or efficiency (i.e., reduced power needs), and provide long-term adaptive automatic adjustment of stimulation parameters. Possible biomarkers for use in portable or implanted devices span from ongoing physiological brain activity, evoked local field potentials (LFPs), and intermittent pathological activity, to wearable devices, biochemical, blood flow, optical, or magnetic resonance imaging (MRI) changes, temperature changes, or optogenetic signals. First, however, potential biomarkers must be correlated directly with symptom or disease treatment and network activation. Although numerous biomarkers are under consideration for a variety of stimulation indications the feasibility of these approaches has yet to be fully determined. Particularly, there are critical questions whether the use of adaptive systems can improve efficacy over continuous stimulation, facilitate adjustment of stimulation interventions and improve our understanding of the role of abnormal network function in disease mechanisms.
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Affiliation(s)
- Kimberly B. Hoang
- Department of Neurosurgery, Duke University, Durham, NC, United States
| | - Isaac R. Cassar
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Warren M. Grill
- Department of Neurosurgery, Duke University, Durham, NC, United States
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
- Department of Neurobiology, Duke University Medical Center, Duke University, Durham, NC, United States
| | - Dennis A. Turner
- Department of Neurosurgery, Duke University, Durham, NC, United States
- Department of Neurobiology, Duke University Medical Center, Duke University, Durham, NC, United States
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Karimi-Bidhendi A, Malekzadeh-Arasteh O, Lee MC, McCrimmon CM, Wang PT, Mahajan A, Liu CY, Nenadic Z, Do AH, Heydari P. CMOS Ultralow Power Brain Signal Acquisition Front-Ends: Design and Human Testing. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2017; 11:1111-1122. [PMID: 28783638 PMCID: PMC6508959 DOI: 10.1109/tbcas.2017.2723607] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Two brain signal acquisition (BSA) front-ends incorporating two CMOS ultralow power, low-noise amplifier arrays and serializers operating in mosfet weak inversion region are presented. To boost the amplifier's gain for a given current budget, cross-coupled-pair active load topology is used in the first stages of these two amplifiers. These two BSA front-ends are fabricated in 130 and 180 nm CMOS processes, occupying 5.45 mm 2 and 0.352 mm 2 of die areas, respectively (excluding pad rings). The CMOS 130-nm amplifier array is comprised of 64 elements, where each amplifier element consumes 0.216 μW from 0.4 V supply, has input-referred noise voltage (IRNoise) of 2.19 μV[Formula: see text] corresponding to a power efficiency factor (PEF) of 11.7, and occupies 0.044 mm 2 of die area. The CMOS 180 nm amplifier array employs 4 elements, where each element consumes 0.69 μW from 0.6 V supply with IRNoise of 2.3 μV[Formula: see text] (corresponding to a PEF of 31.3) and 0.051 mm 2 of die area. Noninvasive electroencephalographic and invasive electrocorticographic signals were recorded real time directly on able-bodied human subjects, showing feasibility of using these analog front-ends for future fully implantable BSA and brain- computer interface systems.
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Lee HM, Howell B, Grill WM, Ghovanloo M. Stimulation Efficiency With Decaying Exponential Waveforms in a Wirelessly Powered Switched-Capacitor Discharge Stimulation System. IEEE Trans Biomed Eng 2017; 65:1095-1106. [PMID: 28829301 DOI: 10.1109/tbme.2017.2741107] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The purpose of this study was to test the feasibility of using a switched-capacitor discharge stimulation (SCDS) system for electrical stimulation, and, subsequently, determine the overall energy saved compared to a conventional stimulator. We have constructed a computational model by pairing an image-based volume conductor model of the cat head with cable models of corticospinal tract (CST) axons and quantified the theoretical stimulation efficiency of rectangular and decaying exponential waveforms, produced by conventional and SCDS systems, respectively. Subsequently, the model predictions were tested in vivo by activating axons in the posterior internal capsule and recording evoked electromyography (EMG) in the contralateral upper arm muscles. Compared to rectangular waveforms, decaying exponential waveforms with time constants >500 μs were predicted to require 2%-4% less stimulus energy to activate directly models of CST axons and 0.4%-2% less stimulus energy to evoke EMG activity in vivo. Using the calculated wireless input energy of the stimulation system and the measured stimulus energies required to evoke EMG activity, we predict that an SCDS implantable pulse generator (IPG) will require 40% less input energy than a conventional IPG to activate target neural elements. A wireless SCDS IPG that is more energy efficient than a conventional IPG will reduce the size of an implant, require that less wireless energy be transmitted through the skin, and extend the lifetime of the battery in the external power transmitter.
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Sarma AA, Crocker B, Cash SS, Truccolo W. A modular, closed-loop platform for intracranial stimulation in people with neurological disorders. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:3139-3142. [PMID: 28268973 DOI: 10.1109/embc.2016.7591394] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Neuromodulation systems based on electrical stimulation can be used to investigate, probe, and potentially treat a range of neurological disorders. The effects of ongoing neural state and dynamics on stimulation response, and of stimulation parameters on neural state, have broad implications for the development of closed-loop neuro-modulation approaches. We describe the development of a modular, low-latency platform for pre-clinical, closed-loop neuromodulation studies with human participants. We illustrate the uses of the platform in a stimulation case study with a person with epilepsy undergoing neuro-monitoring prior to resective surgery. We demonstrate the efficacy of the system by tracking interictal epileptiform discharges in the local field potential to trigger intracranial electrical stimulation, and show that the response to stimulation depends on the neural state.
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Broccard FD, Joshi S, Wang J, Cauwenberghs G. Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems. J Neural Eng 2017; 14:041002. [PMID: 28573983 DOI: 10.1088/1741-2552/aa67a9] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
OBJECTIVE Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. APPROACH This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. MAIN RESULTS Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. SIGNIFICANCE Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a computational tool for investigating fundamental questions related to neural dynamics, the sophistication of current neuromorphic systems now allows direct interfaces with large neuronal networks and circuits, resulting in potentially interesting clinical applications for neuroengineering systems, neuroprosthetics and neurorehabilitation.
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Affiliation(s)
- Frédéric D Broccard
- Institute for Neural Computation, UC San Diego, United States of America. Department of Bioengineering, UC San Diego, United States of America
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Meidahl AC, Tinkhauser G, Herz DM, Cagnan H, Debarros J, Brown P. Adaptive Deep Brain Stimulation for Movement Disorders: The Long Road to Clinical Therapy. Mov Disord 2017; 32:810-819. [PMID: 28597557 PMCID: PMC5482397 DOI: 10.1002/mds.27022] [Citation(s) in RCA: 128] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Revised: 03/06/2017] [Accepted: 03/19/2017] [Indexed: 11/07/2022] Open
Abstract
Continuous high-frequency DBS is an established treatment for essential tremor and Parkinson's disease. Current developments focus on trying to widen the therapeutic window of DBS. Adaptive DBS (aDBS), where stimulation is dynamically controlled by feedback from biomarkers of pathological brain circuit activity, is one such development. Relevant biomarkers may be central, such as local field potential activity, or peripheral, such as inertial tremor data. Moreover, stimulation may be directed by the amplitude or the phase (timing) of the biomarker signal. In this review, we evaluate existing aDBS studies as proof-of-principle, discuss their limitations, most of which stem from their acute nature, and propose what is needed to take aDBS into a chronic setting. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Anders Christian Meidahl
- Medical Research Council Brain Network Dynamics Unit at the University of OxfordOxfordUK
- Nuffield Department of Clinical Neurosciences, John Radcliffe HospitalUniversity of OxfordOxfordUK
| | - Gerd Tinkhauser
- Medical Research Council Brain Network Dynamics Unit at the University of OxfordOxfordUK
- Nuffield Department of Clinical Neurosciences, John Radcliffe HospitalUniversity of OxfordOxfordUK
- Department of NeurologyBern University Hospital and University of BernBernSwitzerland
| | - Damian Marc Herz
- Nuffield Department of Clinical Neurosciences, John Radcliffe HospitalUniversity of OxfordOxfordUK
| | - Hayriye Cagnan
- Medical Research Council Brain Network Dynamics Unit at the University of OxfordOxfordUK
- Nuffield Department of Clinical Neurosciences, John Radcliffe HospitalUniversity of OxfordOxfordUK
- Institute of NeurologyUniversity College LondonLondonUK
| | - Jean Debarros
- Medical Research Council Brain Network Dynamics Unit at the University of OxfordOxfordUK
- Nuffield Department of Clinical Neurosciences, John Radcliffe HospitalUniversity of OxfordOxfordUK
| | - Peter Brown
- Medical Research Council Brain Network Dynamics Unit at the University of OxfordOxfordUK
- Nuffield Department of Clinical Neurosciences, John Radcliffe HospitalUniversity of OxfordOxfordUK
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Herron JA, Thompson MC, Brown T, Chizeck HJ, Ojemann JG, Ko AL. Cortical Brain-Computer Interface for Closed-Loop Deep Brain Stimulation. IEEE Trans Neural Syst Rehabil Eng 2017; 25:2180-2187. [PMID: 28541211 DOI: 10.1109/tnsre.2017.2705661] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Essential tremor is the most common neurological movement disorder. This progressive disease causes uncontrollable rhythmic motions-most often affecting the patient'sdominant upper extremity-thatoccur during volitional movement and make it difficult for the patient to perform everyday tasks. Medication may also become ineffective as the disorder progresses. For many patients, deep brain stimulation (DBS) of the thalamus is an effective means of treating this condition when medication fails. In current use, however, clinicians set the patient's stimulator to apply stimulation at all times-whether it is needed or not. This practice leads to excess power use, and more rapid depletion of batteries that require surgical replacement. In this paper, for the first time, neural sensing of movement (using chronically implanted cortical electrodes) is used to enable or disable stimulation for tremor. Therapeutic stimulation is delivered onlywhen the patient is actively using their effected limb, thereby reducing the total stimulation applied, and potentially extending the lifetime of surgically implanted batteries. This paper, which involves both implanted and external subsystems, paves the way for fully-implanted closed-loop DBS in the future.
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Wang CF, Yang SH, Lin SH, Chen PC, Lo YC, Pan HC, Lai HY, Liao LD, Lin HC, Chen HY, Huang WC, Huang WJ, Chen YY. A proof-of-principle simulation for closed-loop control based on preexisting experimental thalamic DBS-enhanced instrumental learning. Brain Stimul 2017; 10:672-683. [DOI: 10.1016/j.brs.2017.02.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 01/14/2017] [Accepted: 02/17/2017] [Indexed: 12/23/2022] Open
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Swann NC, de Hemptinne C, Miocinovic S, Qasim S, Ostrem JL, Galifianakis NB, Luciano MS, Wang SS, Ziman N, Taylor R, Starr PA. Chronic multisite brain recordings from a totally implantable bidirectional neural interface: experience in 5 patients with Parkinson's disease. J Neurosurg 2017; 128:605-616. [PMID: 28409730 DOI: 10.3171/2016.11.jns161162] [Citation(s) in RCA: 81] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
OBJECTIVE Dysfunction of distributed neural networks underlies many brain disorders. The development of neuromodulation therapies depends on a better understanding of these networks. Invasive human brain recordings have a favorable temporal and spatial resolution for the analysis of network phenomena but have generally been limited to acute intraoperative recording or short-term recording through temporarily externalized leads. Here, the authors describe their initial experience with an investigational, first-generation, totally implantable, bidirectional neural interface that allows both continuous therapeutic stimulation and recording of field potentials at multiple sites in a neural network. METHODS Under a physician-sponsored US Food and Drug Administration investigational device exemption, 5 patients with Parkinson's disease were implanted with the Activa PC+S system (Medtronic Inc.). The device was attached to a quadripolar lead placed in the subdural space over motor cortex, for electrocorticography potential recordings, and to a quadripolar lead in the subthalamic nucleus (STN), for both therapeutic stimulation and recording of local field potentials. Recordings from the brain of each patient were performed at multiple time points over a 1-year period. RESULTS There were no serious surgical complications or interruptions in deep brain stimulation therapy. Signals in both the cortex and the STN were relatively stable over time, despite a gradual increase in electrode impedance. Canonical movement-related changes in specific frequency bands in the motor cortex were identified in most but not all recordings. CONCLUSIONS The acquisition of chronic multisite field potentials in humans is feasible. The device performance characteristics described here may inform the design of the next generation of totally implantable neural interfaces. This research tool provides a platform for translating discoveries in brain network dynamics to improved neurostimulation paradigms. Clinical trial registration no.: NCT01934296 (clinicaltrials.gov).
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Philip A Starr
- Departments of1Neurological Surgery and.,3Kavli Institute for Fundamental Neuroscience; and.,4Graduate Program in Neuroscience, University of California, San Francisco, California
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Tinkhauser G, Pogosyan A, Little S, Beudel M, Herz DM, Tan H, Brown P. The modulatory effect of adaptive deep brain stimulation on beta bursts in Parkinson's disease. Brain 2017; 140:1053-1067. [PMID: 28334851 PMCID: PMC5382944 DOI: 10.1093/brain/awx010] [Citation(s) in RCA: 254] [Impact Index Per Article: 36.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2016] [Revised: 12/05/2016] [Accepted: 12/07/2016] [Indexed: 11/13/2022] Open
Abstract
Adaptive deep brain stimulation uses feedback about the state of neural circuits to control stimulation rather than delivering fixed stimulation all the time, as currently performed. In patients with Parkinson's disease, elevations in beta activity (13-35 Hz) in the subthalamic nucleus have been demonstrated to correlate with clinical impairment and have provided the basis for feedback control in trials of adaptive deep brain stimulation. These pilot studies have suggested that adaptive deep brain stimulation may potentially be more effective, efficient and selective than conventional deep brain stimulation, implying mechanistic differences between the two approaches. Here we test the hypothesis that such differences arise through differential effects on the temporal dynamics of beta activity. The latter is not constantly increased in Parkinson's disease, but comes in bursts of different durations and amplitudes. We demonstrate that the amplitude of beta activity in the subthalamic nucleus increases in proportion to burst duration, consistent with progressively increasing synchronization. Effective adaptive deep brain stimulation truncated long beta bursts shifting the distribution of burst duration away from long duration with large amplitude towards short duration, lower amplitude bursts. Critically, bursts with shorter duration are negatively and bursts with longer duration positively correlated with the motor impairment off stimulation. Conventional deep brain stimulation did not change the distribution of burst durations. Although both adaptive and conventional deep brain stimulation suppressed mean beta activity amplitude compared to the unstimulated state, this was achieved by a selective effect on burst duration during adaptive deep brain stimulation, whereas conventional deep brain stimulation globally suppressed beta activity. We posit that the relatively selective effect of adaptive deep brain stimulation provides a rationale for why this approach could be more efficacious than conventional continuous deep brain stimulation in the treatment of Parkinson's disease, and helps inform how adaptive deep brain stimulation might best be delivered.
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Affiliation(s)
- Gerd Tinkhauser
- Medical Research Council Brain Network Dynamics Unit at the University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Department of Neurology, Bern University Hospital and University of Bern, Switzerland
| | - Alek Pogosyan
- Medical Research Council Brain Network Dynamics Unit at the University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Simon Little
- Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, London, UK
| | - Martijn Beudel
- University Medical Center Groningen, 9700 RB Groningen, The Netherlands
| | - Damian M. Herz
- Medical Research Council Brain Network Dynamics Unit at the University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Huiling Tan
- Medical Research Council Brain Network Dynamics Unit at the University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Peter Brown
- Medical Research Council Brain Network Dynamics Unit at the University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
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Liu W, Wang PM, Lo YK. Towards Closed-Loop Neuromodulation: A Wireless Miniaturized Neural Implant SoC. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2017; 10194. [PMID: 30410205 DOI: 10.1117/12.2263566] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
This work reports a platform technology toward the development of closed-loop neuromodulation. A neural implant based on the SoC developed in our laboratory is used as an example to illustrate the necessary functionalities for the efficacious implantable system. We also present an example of using the system to investigate the epidural stimulation for partial motor function recovery after spinal cord injury in a rat model. This hardware-software co-design tool demonstrate its promising potential towards an effective closed-loop neuromodulation for various biomedical applications.
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Affiliation(s)
- Wentai Liu
- University of California, Los Angeles, Los Angeles, CA, USA 90095
| | - Po-Min Wang
- University of California, Los Angeles, Los Angeles, CA, USA 90095
| | - Yi-Kai Lo
- University of California, Los Angeles, Los Angeles, CA, USA 90095
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Karamintziou SD, Custódio AL, Piallat B, Polosan M, Chabardès S, Stathis PG, Tagaris GA, Sakas DE, Polychronaki GE, Tsirogiannis GL, David O, Nikita KS. Algorithmic design of a noise-resistant and efficient closed-loop deep brain stimulation system: A computational approach. PLoS One 2017; 12:e0171458. [PMID: 28222198 PMCID: PMC5319757 DOI: 10.1371/journal.pone.0171458] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Accepted: 01/20/2017] [Indexed: 11/19/2022] Open
Abstract
Advances in the field of closed-loop neuromodulation call for analysis and modeling approaches capable of confronting challenges related to the complex neuronal response to stimulation and the presence of strong internal and measurement noise in neural recordings. Here we elaborate on the algorithmic aspects of a noise-resistant closed-loop subthalamic nucleus deep brain stimulation system for advanced Parkinson’s disease and treatment-refractory obsessive-compulsive disorder, ensuring remarkable performance in terms of both efficiency and selectivity of stimulation, as well as in terms of computational speed. First, we propose an efficient method drawn from dynamical systems theory, for the reliable assessment of significant nonlinear coupling between beta and high-frequency subthalamic neuronal activity, as a biomarker for feedback control. Further, we present a model-based strategy through which optimal parameters of stimulation for minimum energy desynchronizing control of neuronal activity are being identified. The strategy integrates stochastic modeling and derivative-free optimization of neural dynamics based on quadratic modeling. On the basis of numerical simulations, we demonstrate the potential of the presented modeling approach to identify, at a relatively low computational cost, stimulation settings potentially associated with a significantly higher degree of efficiency and selectivity compared with stimulation settings determined post-operatively. Our data reinforce the hypothesis that model-based control strategies are crucial for the design of novel stimulation protocols at the backstage of clinical applications.
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Affiliation(s)
- Sofia D. Karamintziou
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
- Department of Mechanical Engineering, University of California, Riverside, California, United States of America
- * E-mail: (SDK); (KSN)
| | | | - Brigitte Piallat
- Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, France
- Inserm, U1216, Grenoble, France
| | - Mircea Polosan
- Inserm, U1216, Grenoble, France
- Department of Psychiatry, University Hospital of Grenoble, Grenoble, France
| | - Stéphan Chabardès
- Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, France
- Inserm, U1216, Grenoble, France
- Department of Neurosurgery, University Hospital of Grenoble, Grenoble, France
| | | | - George A. Tagaris
- Department of Neurology, ‘G. Gennimatas’ General Hospital of Athens, Athens, Greece
| | - Damianos E. Sakas
- Department of Neurosurgery, University of Athens Medical School, ‘Evangelismos’ General Hospital, Athens, Greece
| | - Georgia E. Polychronaki
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - George L. Tsirogiannis
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Olivier David
- Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, France
- Inserm, U1216, Grenoble, France
| | - Konstantina S. Nikita
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
- * E-mail: (SDK); (KSN)
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Widge AS, Ellard KK, Paulk AC, Basu I, Yousefi A, Zorowitz S, Gilmour A, Afzal A, Deckersbach T, Cash SS, Kramer MA, Eden UT, Dougherty DD, Eskandar EN. Treating refractory mental illness with closed-loop brain stimulation: Progress towards a patient-specific transdiagnostic approach. Exp Neurol 2017; 287:461-472. [PMID: 27485972 DOI: 10.1016/j.expneurol.2016.07.021] [Citation(s) in RCA: 79] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2015] [Accepted: 07/25/2016] [Indexed: 12/24/2022]
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Ritaccio AL, Williams J, Denison T, Foster BL, Starr PA, Gunduz A, Zijlmans M, Schalk G. Proceedings of the Eighth International Workshop on Advances in Electrocorticography. Epilepsy Behav 2016; 64:248-252. [PMID: 27780085 PMCID: PMC5323263 DOI: 10.1016/j.yebeh.2016.08.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Accepted: 08/19/2016] [Indexed: 01/26/2023]
Abstract
Excerpted proceedings of the Eighth International Workshop on Advances in Electrocorticography (ECoG), which convened October 15-16, 2015 in Chicago, IL, are presented. The workshop series has become the foremost gathering to present current basic and clinical research in subdural brain signal recording and analysis.
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Affiliation(s)
| | | | - Tim Denison
- Medtronic Neuromodulation, Minneapolis, MN, USA
| | | | | | | | - Maeike Zijlmans
- University Medical Center Utrecht, Utrecht, The Netherlands,Stichting Epilepsie Instellingen Nederland, Heemstede, The Netherlands
| | - Gerwin Schalk
- Albany Medical College, Albany, NY, USA,Wadsworth Center, New York State Department of Health, Albany, NY, USA
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Vassanelli S, Mahmud M. Trends and Challenges in Neuroengineering: Toward "Intelligent" Neuroprostheses through Brain-"Brain Inspired Systems" Communication. Front Neurosci 2016; 10:438. [PMID: 27721741 PMCID: PMC5034009 DOI: 10.3389/fnins.2016.00438] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2016] [Accepted: 09/09/2016] [Indexed: 11/30/2022] Open
Abstract
Future technologies aiming at restoring and enhancing organs function will intimately rely on near-physiological and energy-efficient communication between living and artificial biomimetic systems. Interfacing brain-inspired devices with the real brain is at the forefront of such emerging field, with the term "neurobiohybrids" indicating all those systems where such interaction is established. We argue that achieving a "high-level" communication and functional synergy between natural and artificial neuronal networks in vivo, will allow the development of a heterogeneous world of neurobiohybrids, which will include "living robots" but will also embrace "intelligent" neuroprostheses for augmentation of brain function. The societal and economical impact of intelligent neuroprostheses is likely to be potentially strong, as they will offer novel therapeutic perspectives for a number of diseases, and going beyond classical pharmaceutical schemes. However, they will unavoidably raise fundamental ethical questions on the intermingling between man and machine and more specifically, on how deeply it should be allowed that brain processing is affected by implanted "intelligent" artificial systems. Following this perspective, we provide the reader with insights on ongoing developments and trends in the field of neurobiohybrids. We address the topic also from a "community building" perspective, showing through a quantitative bibliographic analysis, how scientists working on the engineering of brain-inspired devices and brain-machine interfaces are increasing their interactions. We foresee that such trend preludes to a formidable technological and scientific revolution in brain-machine communication and to the opening of new avenues for restoring or even augmenting brain function for therapeutic purposes.
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Affiliation(s)
- Stefano Vassanelli
- NeuroChip Laboratory, Department of Biomedical Sciences, University of PadovaPadova, Italy
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Khambhati AN, Davis KA, Lucas TH, Litt B, Bassett DS. Virtual Cortical Resection Reveals Push-Pull Network Control Preceding Seizure Evolution. Neuron 2016; 91:1170-1182. [PMID: 27568515 PMCID: PMC5017915 DOI: 10.1016/j.neuron.2016.07.039] [Citation(s) in RCA: 131] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Revised: 05/31/2016] [Accepted: 07/22/2016] [Indexed: 12/17/2022]
Abstract
In ∼20 million people with drug-resistant epilepsy, focal seizures originating in dysfunctional brain networks will often evolve and spread to surrounding tissue, disrupting function in otherwise normal brain regions. To identify network control mechanisms that regulate seizure spread, we developed a novel tool for pinpointing brain regions that facilitate synchronization in the epileptic network. Our method measures the impact of virtually resecting putative control regions on synchronization in a validated model of the human epileptic network. By applying our technique to time-varying functional networks, we identified brain regions whose topological role is to synchronize or desynchronize the epileptic network. Our results suggest that greater antagonistic push-pull interaction between synchronizing and desynchronizing brain regions better constrains seizure spread. These methods, while applied here to epilepsy, are generalizable to other brain networks and have wide applicability in isolating and mapping functional drivers of brain dynamics in health and disease.
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Affiliation(s)
- Ankit N Khambhati
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kathryn A Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Timothy H Lucas
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Brian Litt
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
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50
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Lee GW, Zambetta F, Li X, Paolini AG. Utilising reinforcement learning to develop strategies for driving auditory neural implants. J Neural Eng 2016; 13:046027. [PMID: 27432803 DOI: 10.1088/1741-2560/13/4/046027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE In this paper we propose a novel application of reinforcement learning to the area of auditory neural stimulation. We aim to develop a simulation environment which is based off real neurological responses to auditory and electrical stimulation in the cochlear nucleus (CN) and inferior colliculus (IC) of an animal model. Using this simulator we implement closed loop reinforcement learning algorithms to determine which methods are most effective at learning effective acoustic neural stimulation strategies. APPROACH By recording a comprehensive set of acoustic frequency presentations and neural responses from a set of animals we created a large database of neural responses to acoustic stimulation. Extensive electrical stimulation in the CN and the recording of neural responses in the IC provides a mapping of how the auditory system responds to electrical stimuli. The combined dataset is used as the foundation for the simulator, which is used to implement and test learning algorithms. MAIN RESULTS Reinforcement learning, utilising a modified n-Armed Bandit solution, is implemented to demonstrate the model's function. We show the ability to effectively learn stimulation patterns which mimic the cochlea's ability to covert acoustic frequencies to neural activity. Time taken to learn effective replication using neural stimulation takes less than 20 min under continuous testing. SIGNIFICANCE These results show the utility of reinforcement learning in the field of neural stimulation. These results can be coupled with existing sound processing technologies to develop new auditory prosthetics that are adaptable to the recipients current auditory pathway. The same process can theoretically be abstracted to other sensory and motor systems to develop similar electrical replication of neural signals.
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Affiliation(s)
- Geoffrey W Lee
- School of Computer Science and Information Technology, RMIT University, Melbourne 3000, Australia
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