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Teo YX, Lee RE, Nurzaman SG, Tan CP, Chan PY. Action tremor features discovery for essential tremor and Parkinson's disease with explainable multilayer BiLSTM. Comput Biol Med 2024; 180:108957. [PMID: 39098236 DOI: 10.1016/j.compbiomed.2024.108957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 07/04/2024] [Accepted: 07/26/2024] [Indexed: 08/06/2024]
Abstract
The tremors of Parkinson's disease (PD) and essential tremor (ET) are known to have overlapping characteristics that make it complicated for clinicians to distinguish them. While deep learning is robust in detecting features unnoticeable to humans, an opaque trained model is impractical in clinical scenarios as coincidental correlations in the training data may be used by the model to make classifications, which may result in misdiagnosis. This work aims to overcome the aforementioned challenge of deep learning models by introducing a multilayer BiLSTM network with explainable AI (XAI) that can better explain tremulous characteristics and quantify the respective discovered important regions in tremor differentiation. The proposed network classifies PD, ET, and normal tremors during drinking actions and derives the contribution from tremor characteristics, (i.e., time, frequency, amplitude, and actions) utilized in the classification task. The analysis shows that the XAI-BiLSTM marks the regions with high tremor amplitude as important in classification, which is verified by a high correlation between relevance distribution and tremor displacement amplitude. The XAI-BiLSTM discovered that the transition phases from arm resting to lifting (during the drinking cycle) is the most important action to classify tremors. Additionally, the XAI-BiLSTM reveals frequency ranges that only contribute to the classification of one tremor class, which may be the potential distinctive feature to overcome the overlapping frequencies problem. By revealing critical timing and frequency patterns unique to PD and ET tremors, this proposed XAI-BiLSTM model enables clinicians to make more informed classifications, potentially reducing misclassification rates and improving treatment outcomes.
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Affiliation(s)
- Yu Xuan Teo
- Department of Electrical & Robotics Engineering, School of Engineering, Monash University Malaysia, Malaysia.
| | - Rui En Lee
- Department of Electrical & Robotics Engineering, School of Engineering, Monash University Malaysia, Malaysia.
| | - Surya Girinatha Nurzaman
- Department of Mechanical Engineering, School of Engineering, Monash University Malaysia, Bandar Sunway, Malaysia.
| | - Chee Pin Tan
- Department of Electrical & Robotics Engineering, School of Engineering, Monash University Malaysia, Malaysia.
| | - Ping Yi Chan
- Department of Electrical & Robotics Engineering, School of Engineering, Monash University Malaysia, Malaysia.
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Radcliffe EM, Baumgartner AJ, Kern DS, Al Borno M, Ojemann S, Kramer DR, Thompson JA. Oscillatory beta dynamics inform biomarker-driven treatment optimization for Parkinson's disease. J Neurophysiol 2023; 129:1492-1504. [PMID: 37198135 DOI: 10.1152/jn.00055.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 04/23/2023] [Accepted: 05/17/2023] [Indexed: 05/19/2023] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder characterized by loss of dopaminergic neurons and dysregulation of the basal ganglia. Cardinal motor symptoms include bradykinesia, rigidity, and tremor. Deep brain stimulation (DBS) of select subcortical nuclei is standard of care for medication-refractory PD. Conventional open-loop DBS delivers continuous stimulation with fixed parameters that do not account for a patient's dynamic activity state or medication cycle. In comparison, closed-loop DBS, or adaptive DBS (aDBS), adjusts stimulation based on biomarker feedback that correlates with clinical state. Recent work has identified several neurophysiological biomarkers in local field potential recordings from PD patients, the most promising of which are 1) elevated beta (∼13-30 Hz) power in the subthalamic nucleus (STN), 2) increased beta synchrony throughout basal ganglia-thalamocortical circuits, notably observed as coupling between the STN beta phase and cortical broadband gamma (∼50-200 Hz) amplitude, and 3) prolonged beta bursts in the STN and cortex. In this review, we highlight relevant frequency and time domain features of STN beta measured in PD patients and summarize how spectral beta power, oscillatory beta synchrony, phase-amplitude coupling, and temporal beta bursting inform PD pathology, neurosurgical targeting, and DBS therapy. We then review how STN beta dynamics inform predictive, biomarker-driven aDBS approaches for optimizing PD treatment. We therefore provide clinically useful and actionable insight that can be applied toward aDBS implementation for PD.
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Affiliation(s)
- Erin M Radcliffe
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
| | - Alexander J Baumgartner
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
| | - Drew S Kern
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
| | - Mazen Al Borno
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Department of Computer Science and Engineering, University of Colorado Denver, Denver, Colorado, United States
| | - Steven Ojemann
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
| | - Daniel R Kramer
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
| | - John A Thompson
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
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Paulk AC, Zelmann R, Crocker B, Widge AS, Dougherty DD, Eskandar EN, Weisholtz DS, Richardson RM, Cosgrove GR, Williams ZM, Cash SS. Local and distant cortical responses to single pulse intracranial stimulation in the human brain are differentially modulated by specific stimulation parameters. Brain Stimul 2022; 15:491-508. [PMID: 35247646 PMCID: PMC8985164 DOI: 10.1016/j.brs.2022.02.017] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 02/23/2022] [Accepted: 02/24/2022] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Electrical neuromodulation via direct electrical stimulation (DES) is an increasingly common therapy for a wide variety of neuropsychiatric diseases. Unfortunately, therapeutic efficacy is inconsistent, likely due to our limited understanding of the relationship between the massive stimulation parameter space and brain tissue responses. OBJECTIVE To better understand how different parameters induce varied neural responses, we systematically examined single pulse-induced cortico-cortico evoked potentials (CCEP) as a function of stimulation amplitude, duration, brain region, and whether grey or white matter was stimulated. METHODS We measured voltage peak amplitudes and area under the curve (AUC) of intracranially recorded stimulation responses as a function of distance from the stimulation site, pulse width, current injected, location relative to grey and white matter, and brain region stimulated (N = 52, n = 719 stimulation sites). RESULTS Increasing stimulation pulse width increased responses near the stimulation location. Increasing stimulation amplitude (current) increased both evoked amplitudes and AUC nonlinearly. Locally (<15 mm), stimulation at the boundary between grey and white matter induced larger responses. In contrast, for distant sites (>15 mm), white matter stimulation consistently produced larger responses than stimulation in or near grey matter. The stimulation location-response curves followed different trends for cingulate, lateral frontal, and lateral temporal cortical stimulation. CONCLUSION These results demonstrate that a stronger local response may require stimulation in the grey-white boundary while stimulation in the white matter could be needed for network activation. Thus, stimulation parameters tailored for a specific anatomical-functional outcome may be key to advancing neuromodulatory therapy.
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Affiliation(s)
- Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
| | - Rina Zelmann
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Britni Crocker
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Harvard-MIT Health Sciences and Technology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Alik S Widge
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, 02129, USA
| | - Darin D Dougherty
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, 02129, USA
| | - Emad N Eskandar
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Daniel S Weisholtz
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, 02114, USA
| | - R Mark Richardson
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - G Rees Cosgrove
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, 02114, USA
| | - Ziv M Williams
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
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Wenger N, Vogt A, Skrobot M, Garulli EL, Kabaoglu B, Salchow-Hömmen C, Schauer T, Kroneberg D, Schuhmann M, Ip CW, Harms C, Endres M, Isaias I, Tovote P, Blum R. Rodent models for gait network disorders in Parkinson's disease - a translational perspective. Exp Neurol 2022; 352:114011. [PMID: 35176273 DOI: 10.1016/j.expneurol.2022.114011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 01/23/2022] [Accepted: 02/10/2022] [Indexed: 11/26/2022]
Abstract
Gait impairments in Parkinson's disease remain a scientific and therapeutic challenge. The advent of new deep brain stimulation (DBS) devices capable of recording brain activity from chronically implanted electrodes has fostered new studies of gait in freely moving patients. The hope is to identify gait-related neural biomarkers and improve therapy using closed-loop DBS. In this context, animal models offer the opportunity to investigate gait network activity at multiple biological scales and address unresolved questions from clinical research. Yet, the contribution of rodent models to the development of future neuromodulation therapies will rely on translational validity. In this review, we summarize the most effective strategies to model parkinsonian gait in rodents. We discuss how clinical observations have inspired targeted brain lesions in animal models, and whether resulting motor deficits and network oscillations match recent findings in humans. Gait impairments with hypo-, bradykinesia and altered limb rhythmicity were successfully modelled in rodents. However, clear evidence for the presence of freezing of gait was missing. The identification of reliable neural biomarkers for gait impairments has remained challenging in both animals and humans. Moving forward, we expect that the ongoing investigation of circuit specific neuromodulation strategies in animal models will lead to future optimizations of gait therapy in Parkinson's disease.
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Affiliation(s)
- Nikolaus Wenger
- Department of Neurology with experimental Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany; Berlin Institute of Health, Germany.
| | - Arend Vogt
- Department of Neurology with experimental Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Matej Skrobot
- Department of Neurology with experimental Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Elisa L Garulli
- Department of Neurology with experimental Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Burce Kabaoglu
- Department of Neurology with experimental Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Christina Salchow-Hömmen
- Department of Neurology with experimental Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Thomas Schauer
- Technische Universität Berlin, Control Systems Group, 10587 Berlin, Germany
| | - Daniel Kroneberg
- Department of Neurology with experimental Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany; Berlin Institute of Health, Germany
| | - Michael Schuhmann
- Department of Neurology, University Hospital of Würzburg, Josef-Schneider-Straße 11, 97080 Wuerzburg, Germany
| | - Chi Wang Ip
- Department of Neurology, University Hospital of Würzburg, Josef-Schneider-Straße 11, 97080 Wuerzburg, Germany
| | - Christoph Harms
- Department of Neurology with experimental Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany; Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin, Germany
| | - Matthias Endres
- Department of Neurology with experimental Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany; Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin, Germany; DZHK (German Center for Cardiovascular Research), Berlin Site, Germany; DZNE (German Center for Neurodegenerative Disease), Berlin Site, Germany
| | - Ioannis Isaias
- Department of Neurology, University Hospital of Würzburg, Josef-Schneider-Straße 11, 97080 Wuerzburg, Germany
| | - Philip Tovote
- Institute of Clinical Neurobiology, University Hospital Wuerzburg, Versbacher Str. 5, 97078 Wuerzburg, Germany; Center for Mental Health, University of Wuerzburg, Margarete-Höppel-Platz 1, 97080 Wuerzburg, Germany
| | - Robert Blum
- Department of Neurology, University Hospital of Würzburg, Josef-Schneider-Straße 11, 97080 Wuerzburg, Germany
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di Biase L, Tinkhauser G, Martin Moraud E, Caminiti ML, Pecoraro PM, Di Lazzaro V. Adaptive, personalized closed-loop therapy for Parkinson's disease: biochemical, neurophysiological, and wearable sensing systems. Expert Rev Neurother 2021; 21:1371-1388. [PMID: 34736368 DOI: 10.1080/14737175.2021.2000392] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
INTRODUCTION Motor complication management is one of the main unmet needs in Parkinson's disease patients. AREAS COVERED Among the most promising emerging approaches for handling motor complications in Parkinson's disease, adaptive deep brain stimulation strategies operating in closed-loop have emerged as pivotal to deliver sustained, near-to-physiological inputs to dysfunctional basal ganglia-cortical circuits over time. Existing sensing systems that can provide feedback signals to close the loop include biochemical-, neurophysiological- or wearable-sensors. Biochemical sensing allows to directly monitor the pharmacokinetic and pharmacodynamic of antiparkinsonian drugs and metabolites. Neurophysiological sensing relies on neurotechnologies to sense cortical or subcortical brain activity and extract real-time correlates of symptom intensity or symptom control during DBS. A more direct representation of the symptom state, particularly the phenomenological differentiation and quantification of motor symptoms, can be realized via wearable sensor technology. EXPERT OPINION Biochemical, neurophysiologic, and wearable-based biomarkers are promising technological tools that either individually or in combination could guide adaptive therapy for Parkinson's disease motor symptoms in the future.
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Affiliation(s)
- Lazzaro di Biase
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico Di Roma, Rome, Italy.,Brain Innovations Lab, Università Campus Bio-Medico Di Roma, Rome, Italy
| | - Gerd Tinkhauser
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Eduardo Martin Moraud
- Department of Clinical Neurosciences, Lausanne University Hospital (Chuv) and University of Lausanne (Unil), Lausanne, Switzerland.,Defitech Center for Interventional Neurotherapies (.neurorestore), Lausanne University Hospital and Swiss Federal Institute of Technology (Epfl), Lausanne, Switzerland
| | - Maria Letizia Caminiti
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico Di Roma, Rome, Italy
| | - Pasquale Maria Pecoraro
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico Di Roma, Rome, Italy
| | - Vincenzo Di Lazzaro
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico Di Roma, Rome, Italy
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Intraoperative neural signals predict rapid antidepressant effects of deep brain stimulation. Transl Psychiatry 2021; 11:551. [PMID: 34728599 PMCID: PMC8563808 DOI: 10.1038/s41398-021-01669-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 09/14/2021] [Accepted: 10/07/2021] [Indexed: 12/31/2022] Open
Abstract
Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) is a promising intervention for treatment-resistant depression (TRD). Despite the failure of a clinical trial, multiple case series have described encouraging results, especially with the introduction of improved surgical protocols. Recent evidence further suggests that tractography targeting and intraoperative exposure to stimulation enhances early antidepressant effects that further evolve with ongoing chronic DBS. Accelerating treatment gains is critical to the care of this at-risk population, and identification of intraoperative electrophysiological biomarkers of early antidepressant effects will help guide future treatment protocols. Eight patients underwent intraoperative electrophysiological recording when bilateral DBS leads were implanted in the SCC using a connectomic approach at the site previously shown to optimize 6-month treatment outcomes. A machine learning classification method was used to discriminate between intracranial local field potentials (LFPs) recorded at baseline (stimulation-naïve) and after the first exposure to SCC DBS during surgical procedures. Spectral inputs (theta, 4-8 Hz; alpha, 9-12 Hz; beta, 13-30 Hz) to the model were then evaluated for importance to classifier success and tested as predictors of the antidepressant response. A decline in depression scores by 45.6% was observed after 1 week and this early antidepressant response correlated with a decrease in SCC LFP beta power, which most contributed to classifier success. Intraoperative exposure to therapeutic stimulation may result in an acute decrease in symptoms of depression following SCC DBS surgery. The correlation of symptom improvement with an intraoperative reduction in SCC beta power suggests this electrophysiological finding as a biomarker for treatment optimization.
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Sisterson ND, Carlson AA, Rutishauser U, Mamelak AN, Flagg M, Pouratian N, Salimpour Y, Anderson WS, Richardson RM. Electrocorticography During Deep Brain Stimulation Surgery: Safety Experience From 4 Centers Within the National Institute of Neurological Disorders and Stroke Research Opportunities in Human Consortium. Neurosurgery 2021; 88:E420-E426. [PMID: 33575799 DOI: 10.1093/neuros/nyaa592] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 11/20/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Intraoperative research during deep brain stimulation (DBS) surgery has enabled major advances in understanding movement disorders pathophysiology and potential mechanisms for therapeutic benefit. In particular, over the last decade, recording electrocorticography (ECoG) from the cortical surface, simultaneously with subcortical recordings, has become an important research tool for assessing basal ganglia-thalamocortical circuit physiology. OBJECTIVE To provide confirmation of the safety of performing ECoG during DBS surgery, using data from centers involved in 2 BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative-funded basic human neuroscience projects. METHODS Data were collected separately at 4 centers. The primary endpoint was complication rate, defined as any intraoperative event, infection, or postoperative magnetic resonance imaging abnormality requiring clinical follow-up. Complication rates for explanatory variables were compared using point biserial correlations and Fisher exact tests. RESULTS A total of 367 DBS surgeries involving ECoG were reviewed. No cortical hemorrhages were observed. Seven complications occurred: 4 intraparenchymal hemorrhages and 3 infections (complication rate of 1.91%; CI = 0.77%-3.89%). The placement of 2 separate ECoG research electrodes through a single burr hole (84 cases) did not result in a significantly different rate of complications, compared to placement of a single electrode (3.6% vs 1.5%; P = .4). Research data were obtained successfully in 350 surgeries (95.4%). CONCLUSION Combined with the single report previously available, which described no ECoG-related complications in a single-center cohort of 200 cases, these findings suggest that research ECOG during DBS surgery did not significantly alter complication rates.
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Affiliation(s)
- Nathaniel D Sisterson
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - April A Carlson
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Ueli Rutishauser
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Computation and Neural Systems, Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
| | - Adam N Mamelak
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Mitchell Flagg
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, California, USA
| | - Nader Pouratian
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, California, USA
| | - Yousef Salimpour
- Department of Neurological Surgery, Johns Hopkins University, Baltimore, Maryland, USA
| | - William S Anderson
- Department of Neurological Surgery, Johns Hopkins University, Baltimore, Maryland, USA
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
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