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Patrick EE, Fleeting CR, Patel DR, Casauay JT, Patel A, Shepherd H, Wong JK. Modeling the volume of tissue activated in deep brain stimulation and its clinical influence: a review. Front Hum Neurosci 2024; 18:1333183. [PMID: 38660012 PMCID: PMC11039793 DOI: 10.3389/fnhum.2024.1333183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 03/26/2024] [Indexed: 04/26/2024] Open
Abstract
Deep brain stimulation (DBS) is a neuromodulatory therapy that has been FDA approved for the treatment of various disorders, including but not limited to, movement disorders (e.g., Parkinson's disease and essential tremor), epilepsy, and obsessive-compulsive disorder. Computational methods for estimating the volume of tissue activated (VTA), coupled with brain imaging techniques, form the basis of models that are being generated from retrospective clinical studies for predicting DBS patient outcomes. For instance, VTA models are used to generate target-and network-based probabilistic stimulation maps that play a crucial role in predicting DBS treatment outcomes. This review defines the methods for calculation of tissue activation (or modulation) including ones that use heuristic and clinically derived estimates and more computationally involved ones that rely on finite-element methods and biophysical axon models. We define model parameters and provide a comparison of commercial, open-source, and academic simulation platforms available for integrated neuroimaging and neural activation prediction. In addition, we review clinical studies that use these modeling methods as a function of disease. By describing the tissue-activation modeling methods and highlighting their application in clinical studies, we provide the neural engineering and clinical neuromodulation communities with perspectives that may influence the adoption of modeling methods for future DBS studies.
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Affiliation(s)
- Erin E. Patrick
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States
| | - Chance R. Fleeting
- College of Medicine, University of Florida, Gainesville, FL, United States
| | - Drashti R. Patel
- College of Medicine, University of Florida, Gainesville, FL, United States
| | - Jed T. Casauay
- College of Medicine, University of Florida, Gainesville, FL, United States
| | - Aashay Patel
- College of Medicine, University of Florida, Gainesville, FL, United States
| | - Hunter Shepherd
- College of Medicine, University of Florida, Gainesville, FL, United States
| | - Joshua K. Wong
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
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Ng PR, Bush A, Vissani M, McIntyre CC, Richardson RM. Biophysical Principles and Computational Modeling of Deep Brain Stimulation. Neuromodulation 2024; 27:422-439. [PMID: 37204360 DOI: 10.1016/j.neurom.2023.04.471] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 04/02/2023] [Accepted: 04/24/2023] [Indexed: 05/20/2023]
Abstract
BACKGROUND Deep brain stimulation (DBS) has revolutionized the treatment of neurological disorders, yet the mechanisms of DBS are still under investigation. Computational models are important in silico tools for elucidating these underlying principles and potentially for personalizing DBS therapy to individual patients. The basic principles underlying neurostimulation computational models, however, are not well known in the clinical neuromodulation community. OBJECTIVE In this study, we present a tutorial on the derivation of computational models of DBS and outline the biophysical contributions of electrodes, stimulation parameters, and tissue substrates to the effects of DBS. RESULTS Given that many aspects of DBS are difficult to characterize experimentally, computational models have played an important role in understanding how material, size, shape, and contact segmentation influence device biocompatibility, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Neural activation is dictated by stimulation parameters including frequency, current vs voltage control, amplitude, pulse width, polarity configurations, and waveform. These parameters also affect the potential for tissue damage, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Activation of the neural substrate also is influenced by the encapsulation layer surrounding the electrode, the conductivity of the surrounding tissue, and the size and orientation of white matter fibers. These properties modulate the effects of the electric field and determine the ultimate therapeutic response. CONCLUSION This article describes biophysical principles that are useful for understanding the mechanisms of neurostimulation.
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Affiliation(s)
| | - Alan Bush
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Matteo Vissani
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Cameron C McIntyre
- Department of Biomedical Engineering, Duke University, Durham, NC, USA; Department of Neurosurgery, Duke University, Durham, NC, USA
| | - Robert Mark Richardson
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
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Galaz Prieto F, Samavaki M, Pursiainen S. Lattice layout and optimizer effect analysis for generating optimal transcranial electrical stimulation (tES) montages through the metaheuristic L1L1 method. Front Hum Neurosci 2024; 18:1201574. [PMID: 38487104 PMCID: PMC10937538 DOI: 10.3389/fnhum.2024.1201574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 02/16/2024] [Indexed: 03/17/2024] Open
Abstract
Introduction This study focuses on broadening the applicability of the metaheuristic L1-norm fitted and penalized (L1L1) optimization method in finding a current pattern for multichannel transcranial electrical stimulation (tES). The metaheuristic L1L1 optimization framework defines the tES montage via linear programming by maximizing or minimizing an objective function with respect to a pair of hyperparameters. Methods In this study, we explore the computational performance and reliability of different optimization packages, algorithms, and search methods in combination with the L1L1 method. The solvers from Matlab R2020b, MOSEK 9.0, Gurobi Optimizer, CVX's SeDuMi 1.3.5, and SDPT3 4.0 were employed to produce feasible results through different linear programming techniques, including Interior-Point (IP), Primal-Simplex (PS), and Dual-Simplex (DS) methods. To solve the metaheuristic optimization task of L1L1, we implement an exhaustive and recursive search along with a well-known heuristic direct search as a reference algorithm. Results Based on our results, and the given optimization task, Gurobi's IP was, overall, the preferable choice among Interior-Point while MOSEK's PS and DS packages were in the case of Simplex methods. These methods provided substantial computational time efficiency for solving the L1L1 method regardless of the applied search method. Discussion While the best-performing solvers show that the L1L1 method is suitable for maximizing either focality and intensity, a few of these solvers could not find a bipolar configuration. Part of the discrepancies between these methods can be explained by a different sensitivity with respect to parameter variation or the resolution of the lattice provided.
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Affiliation(s)
- Fernando Galaz Prieto
- Computing Sciences, Faculty of Information Technology, Tampere University, Tampere, Finland
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O'Keeffe AB, Merla A, Ashkan K. Deep brain stimulation of the subthalamic nucleus in Parkinson disease 2013-2023: where are we a further 10 years on? Br J Neurosurg 2024:1-9. [PMID: 38323603 DOI: 10.1080/02688697.2024.2311128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 01/23/2024] [Indexed: 02/08/2024]
Abstract
Deep brain stimulation has been in clinical use for 30 years and during that time it has changed markedly from a small-scale treatment employed by only a few highly specialized centers into a widespread keystone approach to the management of disorders such as Parkinson's disease. In the intervening decades, many of the broad principles of deep brain stimulation have remained unchanged, that of electrode insertion into stereotactically targeted brain nuclei, however the underlying technology and understanding around the approach have progressed markedly. Some of the most significant advances have taken place over the last decade with the advent of artificial intelligence, directional electrodes, stimulation/recording implantable pulse generators and the potential for remote programming among many other innovations. New therapeutic targets are being assessed for their potential benefits and a surge in the number of deep brain stimulation implantations has given birth to a flourishing scientific literature surrounding the pathophysiology of brain disorders such as Parkinson's disease. Here we outline the developments of the last decade and look to the future of deep brain stimulation to attempt to discern some of the most promising lines of inquiry in this fast-paced and rapidly evolving field.
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Affiliation(s)
| | - Anca Merla
- King's College Hospital Department of Neurosurgery, Kings College Hospital, Denmark
| | - Keyoumars Ashkan
- King's College Hospital Department of Neurosurgery, Kings College Hospital, Denmark
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Carl B, Bopp M, SAß B, Waldthaler J, Timmermann L, Nimsky C. Visualization of volume of tissue activated modeling in a clinical planning system for deep brain stimulation. J Neurosurg Sci 2024; 68:59-69. [PMID: 32031356 DOI: 10.23736/s0390-5616.19.04827-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
BACKGROUND Pathway activating models try to describe stimulation spread in deep brain stimulation (DBS). Volume of tissue activated (VTA) models are simplified model variants allowing faster and easier computation. Our study aimed to investigate, how VTA visualization can be integrated into a clinical workflow applying directional electrodes using a standard clinical DBS planning system. METHODS Twelve patients underwent DBS, using directional electrodes for bilateral subthalamic nucleus (STN) stimulation in Parkinson's disease. Preoperative 3T magnetic resonance imaging was used for automatic visualization of the STN outline, as well as for fiber tractography. Intraoperative computed tomography was used for automatic lead detection. The Guide XT software, closely integrated into the DBS planning software environment, was used for VTA calculation and visualization. RESULTS VTA visualization was possible in all cases. The percentage of VTA covering the STN volume ranged from 25% to 100% (mean: 60±25%) on the left side and from 0% to 98% (51±30%) on the right side. The mean coordinate of all VTA centers was: 12.6±1.2 mm lateral, 2.1±1.2 mm posterior, and 2.3±1.4 mm inferior in relation to the midcommissural point. Stimulation effects can be compared to the VTA visualization in relation to surrounding structures, potentially facilitating programming, which might be especially beneficial in case of suboptimal lead placement. CONCLUSIONS VTA visualization in a clinical planning system allows an intuitive adjustment of the stimulation parameters, supports programming, and enhances understanding of effects and side effects of DBS.
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Affiliation(s)
- Barbara Carl
- Department of Neurosurgery, University of Marburg, Marburg, Germany
- Department of Neurosurgery, Helios Dr. Horst Schmidt Kliniken, Wiesbaden, Germany
| | - Miriam Bopp
- Department of Neurosurgery, University of Marburg, Marburg, Germany
- Marburg Center for Mind, Brain and Behavior (MCMBB), Marburg, Germany
| | - Benjamin SAß
- Department of Neurosurgery, University of Marburg, Marburg, Germany
| | | | - Lars Timmermann
- Marburg Center for Mind, Brain and Behavior (MCMBB), Marburg, Germany
- Department of Neurology, University Marburg, Marburg, Germany
| | - Christopher Nimsky
- Department of Neurosurgery, University of Marburg, Marburg, Germany -
- Marburg Center for Mind, Brain and Behavior (MCMBB), Marburg, Germany
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Spiliotis K, Butenko K, Starke J, van Rienen U, Köhling R. Towards an optimised deep brain stimulation using a large-scale computational network and realistic volume conductor model. J Neural Eng 2024; 20:066045. [PMID: 37988747 DOI: 10.1088/1741-2552/ad0e7c] [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: 02/01/2023] [Accepted: 11/21/2023] [Indexed: 11/23/2023]
Abstract
Objective. Constructing a theoretical framework to improve deep brain stimulation (DBS) based on the neuronal spatiotemporal patterns of the stimulation-affected areas constitutes a primary target.Approach. We develop a large-scale biophysical network, paired with a realistic volume conductor model, to estimate theoretically efficacious stimulation protocols. Based on previously published anatomically defined structural connectivity, a biophysical basal ganglia-thalamo-cortical neuronal network is constructed using Hodgkin-Huxley dynamics. We define a new biomarker describing the thalamic spatiotemporal activity as a ratio of spiking vs. burst firing. The per cent activation of the different pathways is adapted in the simulation to minimise the differences of the biomarker with respect to its value under healthy conditions.Main results.This neuronal network reproduces spatiotemporal patterns that emerge in Parkinson's disease. Simulations of the fibre per cent activation for the defined biomarker propose desensitisation of pallido-thalamic synaptic efficacy, induced by high-frequency signals, as one possible crucial mechanism for DBS action. Based on this activation, we define both an optimal electrode position and stimulation protocol using pathway activation modelling.Significance. A key advantage of this research is that it combines different approaches, i.e. the spatiotemporal pattern with the electric field and axonal response modelling, to compute the optimal DBS protocol. By correlating the inherent network dynamics with the activation of white matter fibres, we obtain new insights into the DBS therapeutic action.
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Affiliation(s)
| | - Konstantin Butenko
- Institute of General Electrical Engineering, University of Rostock, Rostock, Germany
- Movement Disorders and Neuromodulation Unit, Department for Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Jens Starke
- Institute of Mathematics, University of Rostock, Rostock, Germany
| | - Ursula van Rienen
- Institute of General Electrical Engineering, University of Rostock, Rostock, Germany
- Department Life, Light and Matter, University of Rostock, Rostock, Germany
- Department of Ageing of Individuals and Society, University of Rostock, Rostock, Germany
| | - Rüdiger Köhling
- Department of Ageing of Individuals and Society, University of Rostock, Rostock, Germany
- Oscar-Langendorff-Institute of Physiology, Rostock University Medical Center, Rostock, Germany
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Ahn SH, Koh CS, Park M, Jun SB, Chang JW, Kim SJ, Jung HH, Jeong J. Liquid Crystal Polymer-Based Miniaturized Fully Implantable Deep Brain Stimulator. Polymers (Basel) 2023; 15:4439. [PMID: 38006163 PMCID: PMC10675735 DOI: 10.3390/polym15224439] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/22/2023] [Accepted: 10/27/2023] [Indexed: 11/26/2023] Open
Abstract
A significant challenge in improving the deep brain stimulation (DBS) system is the miniaturization of the device, aiming to integrate both the stimulator and the electrode into a compact unit with a wireless charging capability to reduce invasiveness. We present a miniaturized, fully implantable, and battery-free DBS system designed for rats, using a liquid crystal polymer (LCP), a biocompatible and long-term reliable material. The system integrates the simulator circuit, the receiver coil, and a 20 mm long depth-type microelectrode array in a dome-shaped LCP package that is 13 mm in diameter and 5 mm in height. Wireless powering and control via an inductive link enable device miniaturization, allowing for full implantation and, thus, the free behavior of untethered animals. The eight-channel stimulation electrode array was microfabricated on an LCP substrate to form a multilayered system substrate, which was monolithically encapsulated by a domed LCP lid using a specialized spot-welding process. The device functionality was validated via an in vivo animal experiment using a neuropathic pain model in rats. This experiment demonstrated an increase in the mechanical withdrawal threshold of the rats with microelectrical stimulation delivered using the fully implanted device, highlighting the effectiveness of the system.
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Affiliation(s)
- Seung-Hee Ahn
- Department of Electrical and Computer Engineering, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Chin Su Koh
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Minkyung Park
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Sang Beom Jun
- Department of Electronic and Electrical Engineering, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Jin Woo Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Sung June Kim
- Department of Electrical and Computer Engineering, College of Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Hyun Ho Jung
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Joonsoo Jeong
- School of Biomedical Convergence Engineering, Pusan National University, Yangsan 50612, Republic of Korea
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Mirzakhalili E, Rogers ER, Lempka SF. An optimization framework for targeted spinal cord stimulation. J Neural Eng 2023; 20:056026. [PMID: 37647885 PMCID: PMC10535048 DOI: 10.1088/1741-2552/acf522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/14/2023] [Accepted: 08/30/2023] [Indexed: 09/01/2023]
Abstract
Objective. Spinal cord stimulation (SCS) is a common neurostimulation therapy to manage chronic pain. Technological advances have produced new neurostimulation systems with expanded capabilities in an attempt to improve the clinical outcomes associated with SCS. However, these expanded capabilities have dramatically increased the number of possible stimulation parameters and made it intractable to efficiently explore this large parameter space within the context of standard clinical programming procedures. Therefore, in this study, we developed an optimization approach to define the optimal current amplitudes or fractions across individual contacts in an SCS electrode array(s).Approach. We developed an analytic method using the Lagrange multiplier method along with smoothing approximations. To test our optimization framework, we used a hybrid computational modeling approach that consisted of a finite element method model and multi-compartment models of axons and cells within the spinal cord. Moreover, we extended our approach to multi-objective optimization to explore the trade-off between activating regions of interest (ROIs) and regions of avoidance (ROAs).Main results. For simple ROIs, our framework suggested optimized configurations that resembled simple bipolar configurations. However, when we considered multi-objective optimization, our framework suggested nontrivial stimulation configurations that could be selected from Pareto fronts to target multiple ROIs or avoid ROAs.Significance. We developed an optimization framework for targeted SCS. Our method is analytic, which allows for the fast calculation of optimal solutions. For the first time, we provided a multi-objective approach for selective SCS. Through this approach, we were able to show that novel configurations can provide neural recruitment profiles that are not possible with conventional stimulation configurations (e.g. bipolar stimulation). Most importantly, once integrated with computational models that account for sources of interpatient variability (e.g. anatomy, electrode placement), our optimization framework can be utilized to provide stimulation settings tailored to the needs of individual patients.
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Affiliation(s)
- Ehsan Mirzakhalili
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, United States of America
| | - Evan R Rogers
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, United States of America
| | - Scott F Lempka
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, United States of America
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, United States of America
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Makaroff SN, Nummenmaa AR, Noetscher GM, Qi Z, McIntyre CC, Bingham CS. Influence of charges deposited on membranes of human hyperdirect pathway axons on depolarization during subthalamic deep brain stimulation. J Neural Eng 2023; 20:10.1088/1741-2552/ace5de. [PMID: 37429285 PMCID: PMC10542971 DOI: 10.1088/1741-2552/ace5de] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 07/10/2023] [Indexed: 07/12/2023]
Abstract
Objective.The motor hyperdirect pathway (HDP) is a key target in the treatment of Parkinson's disease with deep brain stimulation (DBS). Biophysical models of HDP DBS have been used to explore the mechanisms of stimulation. Built upon finite element method volume conductor solutions, such models are limited by a resolution mismatch, where the volume conductor is modeled at the macro scale, while the neural elements are at the micro scale. New techniques are needed to better integrate volume conductor models with neuron models.Approach.We simulated subthalamic DBS of the human HDP using finely meshed axon models to calculate surface charge deposition on insulting membranes of nonmyelinated axons. We converted the corresponding double layer extracellular problem to a single layer problem and applied the well-conditioned charge-based boundary element fast multipole method (BEM-FMM) with unconstrained numerical spatial resolution. Commonly used simplified estimations of membrane depolarization were compared with more realistic solutions.Main result.Neither centerline potential nor estimates of axon recruitment were impacted by the estimation method used except at axon bifurcations and hemispherical terminations. Local estimates of axon polarization were often much higher at bifurcations and terminations than at any other place along the axon and terminal arbor. Local average estimates of terminal electric field are higher by 10%-20%.Significance. Biophysical models of action potential initiation in the HDP suggest that axon terminations are often the lowest threshold elements for activation. The results of this study reinforce that hypothesis and suggest that this phenomenon is even more pronounced than previously realized.
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Affiliation(s)
- Sergey N Makaroff
- Electrical and Computer Engineering Department, Worcester Polytechnic Institution, Worcester, MA 01609, United States of America
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, United States of America
| | - Aapo R Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, United States of America
| | - Gregory M Noetscher
- Electrical and Computer Engineering Department, Worcester Polytechnic Institution, Worcester, MA 01609, United States of America
- ARMY DEVCOM-SC, General Greene Ave, Natick, MA 01760, United States of America
| | - Zhen Qi
- Electrical and Computer Engineering Department, Worcester Polytechnic Institution, Worcester, MA 01609, United States of America
| | - Cameron C McIntyre
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America
- Department of Neurosurgery, Duke University, Durham, NC 27710, United States of America
| | - Clayton S Bingham
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America
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Shindhelm AC, Thio BJ, Sinha SR. Modeling the Impact of Electrode/Tissue Geometry on Electrical Stimulation in Stereo-EEG. J Clin Neurophysiol 2023; 40:339-349. [PMID: 34482315 DOI: 10.1097/wnp.0000000000000892] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
PURPOSE Electrical stimulation through depth electrodes is used to map function and seizure onset during stereoelectroencephalography in patients undergoing evaluation for epilepsy surgery. Factors such as electrode design, location, and orientation are expected to impact effects of electrical stimulation. METHODS We developed a steady-state finite element model of brain tissue including five layers (skull through white matter) and an implanted electrode to explore the impact of electrode design and placement on the activation of brain tissue by electrical stimulation. We calculated electric potentials, current densities, and volume of tissue activated ( Volact ) in response to constant current bipolar stimulation. We modeled two depth electrode designs (3.5- and 4.43-mm intercontact spacing) and varied electrode location and orientation. RESULTS The electrode with greater intercontact spacing produced 8% to 23% larger Volact (1% to 16% considering only gray matter). Vertical displacement of the electrodes by half intercontact space increased Volact for upward displacement (6% to 83% for all brain tissue or -5% to 96% gray matter only) and decreased Volact (1% to 16% or 24% to 49% gray matter only) for downward displacement. Rotating the electrode in the tissue by 30° to 60° with respect to the vertical axis increased Volact by 30% to 90% (20%-48% gray matter only). CONCLUSIONS Location and orientation of depth electrodes with respect to surrounding brain tissue have a large impact on the amount of tissue activated during electrical stimulation mapping in stereoelectroencephalography. Electrode design has an impact, although modest for commonly used designs. Individualization of stimulation intensity at each location remains critical, especially for avoiding false-negative results.
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Affiliation(s)
- Alexis C Shindhelm
- Department of Neurology, Duke University Medical Center, Durham, North Carolina; and
| | - Brandon J Thio
- Department of Biomedical Engineering, Duke University, Durham, North Carolina
| | - Saurabh R Sinha
- Department of Neurology, Duke University Medical Center, Durham, North Carolina; and
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Munhoz RP, Albuainain G. Deep brain stimulation - New programming algorithms and teleprogramming. Expert Rev Neurother 2023; 23:467-478. [PMID: 37115193 DOI: 10.1080/14737175.2023.2208749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
INTRODUCTION Thanks to a variety of factors, the field of neuromodulation has evolved significantly over the past decade. Developments include new indications and innovations of hardware, software, and stimulation techniques leading to an expansion in scope and role of these techniques as powerful therapies. They also imply the realization that practical application involves new nuances that make patient selection, surgical technique and the programming process even more complex, requiring continuous education and an organized structured approach. AREAS COVERED In this review, the authors explore the developments in deep brain stimulation technology, including electrodes, implantable pulse generators, contact configurations (i.e, directional leads and independent current control), remote programming and sensing using local field potentials. EXPERT OPINION The innovations in the field of deep brain stimulation discussed in this review potentially provide increased effectiveness and flexibility not only to improve therapeutic response but also to address troubleshooting challenges seen in clinical practice. Directional leads and shorter pulse widths may broaden the therapeutic window of stimulation, avoiding current spread to structures that might trigger stimulation-related side effects. Similarly, independent control of current to individual contacts allows for the shaping of the electric field. Finally, sensing and remote programming represent important developments for more effective and individualized patient care.
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Affiliation(s)
- Renato Puppi Munhoz
- Morton and Gloria Shulman Movement Disorders Centre and the Edmond J. Safra Program in Parkinson's Disease, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
- Krembil Research Institute, Toronto, ON, M5T 2S8, Canada
| | - Ghadh Albuainain
- Morton and Gloria Shulman Movement Disorders Centre and the Edmond J. Safra Program in Parkinson's Disease, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
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Shen K, Chen O, Edmunds JL, Piech DK, Maharbiz MM. Translational opportunities and challenges of invasive electrodes for neural interfaces. Nat Biomed Eng 2023; 7:424-442. [PMID: 37081142 DOI: 10.1038/s41551-023-01021-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 02/15/2023] [Indexed: 04/22/2023]
Abstract
Invasive brain-machine interfaces can restore motor, sensory and cognitive functions. However, their clinical adoption has been hindered by the surgical risk of implantation and by suboptimal long-term reliability. In this Review, we highlight the opportunities and challenges of invasive technology for clinically relevant electrophysiology. Specifically, we discuss the characteristics of neural probes that are most likely to facilitate the clinical translation of invasive neural interfaces, describe the neural signals that can be acquired or produced by intracranial electrodes, the abiotic and biotic factors that contribute to their failure, and emerging neural-interface architectures.
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Affiliation(s)
- Konlin Shen
- University of California, Berkeley - University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA, USA.
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.
| | - Oliver Chen
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA
| | - Jordan L Edmunds
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA
| | - David K Piech
- University of California, Berkeley - University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA, USA
| | - Michel M Maharbiz
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA
- Department of Bioengineering, University of California, Berkeley, CA, USA
- Chan-Zuckerberg Biohub, San Francisco, CA, USA
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Duffley G, Szabo A, Lutz BJ, Mahoney-Rafferty EC, Hess CW, Ramirez-Zamora A, Zeilman P, Foote KD, Chiu S, Pourfar MH, Goas Cnp C, Wood JL, Haq IU, Siddiqui MS, Afshari M, Heiry M, Choi J, Volz M, Ostrem JL, San Luciano M, Niemann N, Billnitzer A, Savitt D, Tarakad A, Jimenez-Shahed J, Aquino CC, Okun MS, Butson CR. Interactive mobile application for Parkinson's disease deep brain stimulation (MAP DBS): An open-label, multicenter, randomized, controlled clinical trial. Parkinsonism Relat Disord 2023; 109:105346. [PMID: 36966051 PMCID: PMC11265292 DOI: 10.1016/j.parkreldis.2023.105346] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 02/20/2023] [Accepted: 02/23/2023] [Indexed: 03/17/2023]
Abstract
INTRODUCTION Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease (PD), but its efficacy is tied to DBS programming, which is often time consuming and burdensome for patients, caregivers, and clinicians. Our aim is to test whether the Mobile Application for PD DBS (MAP DBS), a clinical decision support system, can improve programming. METHODS We conducted an open-label, 1:1 randomized, controlled, multicenter clinical trial comparing six months of SOC standard of care (SOC) to six months of MAP DBS-aided programming. We enrolled patients between 30 and 80 years old who received DBS to treat idiopathic PD at six expert centers across the United States. The primary outcome was time spent DBS programming and secondary outcomes measured changes in motor symptoms, caregiver strain and medication requirements. RESULTS We found a significant reduction in initial visit time (SOC: 43.8 ± 28.9 min n = 37, MAP DBS: 27.4 ± 13.0 min n = 35, p = 0.001). We did not find a significant difference in total programming time between the groups over the 6-month study duration. MAP DBS-aided patients experienced a significantly larger reduction in UPDRS III on-medication scores (-7.0 ± 7.9) compared to SOC (-2.7 ± 6.9, p = 0.01) at six months. CONCLUSION MAP DBS was well tolerated and improves key aspects of DBS programming time and clinical efficacy.
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Affiliation(s)
- Gordon Duffley
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Aniko Szabo
- Division of Biostatistics, Institute for Health & Equity, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Barbara J Lutz
- School of Nursing, University of North Carolina-Wilmington, Wilmington, NC, USA
| | - Emily C Mahoney-Rafferty
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Christopher W Hess
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Adolfo Ramirez-Zamora
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Pamela Zeilman
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Kelly D Foote
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Shannon Chiu
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Michael H Pourfar
- Center for Neuromodulation, New York University Langone Medical Center, New York, NY, USA
| | - Clarisse Goas Cnp
- Department of Neurology, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Jennifer L Wood
- Department of Neurology, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Ihtsham U Haq
- Department of Neurology, University of Miami, Miami, FL, USA
| | - Mustafa S Siddiqui
- Department of Neurology, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Mitra Afshari
- Department of Neurological Sciences, Section of Movement Disorders, Rush University, Chicago, IL, USA
| | - Melissa Heiry
- Weill Institute of Neurosciences, UCSF Movement Disorder and Neuromodulation Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Jennifer Choi
- Weill Institute of Neurosciences, UCSF Movement Disorder and Neuromodulation Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Monica Volz
- Weill Institute of Neurosciences, UCSF Movement Disorder and Neuromodulation Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Jill L Ostrem
- Weill Institute of Neurosciences, UCSF Movement Disorder and Neuromodulation Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Marta San Luciano
- Weill Institute of Neurosciences, UCSF Movement Disorder and Neuromodulation Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Nicki Niemann
- Department of Neurology, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Andrew Billnitzer
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Daniel Savitt
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Arjun Tarakad
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Joohi Jimenez-Shahed
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Camila C Aquino
- Department of Neurology, University of Utah, Salt Lake City, UT, USA; Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Michael S Okun
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Christopher R Butson
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA; Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA; Department of Neurology, University of Utah, Salt Lake City, UT, USA; Departments of Neurosurgery, and Psychiatry, University of Utah, Salt Lake City, UT, USA.
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14
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Abouelseoud G, Abouelseoud Y, Shoukry A, Ismail N, Mekky J. A mixed integer linear programming framework for improving cortical vision prosthesis designs. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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15
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Baniasadi M, Petersen MV, Gonçalves J, Horn A, Vlasov V, Hertel F, Husch A. DBSegment: Fast and robust segmentation of deep brain structures considering domain generalization. Hum Brain Mapp 2023; 44:762-778. [PMID: 36250712 PMCID: PMC9842883 DOI: 10.1002/hbm.26097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/30/2022] [Accepted: 09/15/2022] [Indexed: 01/25/2023] Open
Abstract
Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state-of-the-art solutions follow a segmentation-by-registration approach, where subject magnetic resonance imaging (MRIs) are mapped to a template with well-defined segmentations. However, registration-based pipelines are time-consuming, thus, limiting their clinical use. This paper uses deep learning to provide a one-step, robust, and efficient deep brain segmentation solution directly in the native space. The method consists of a preprocessing step to conform all MRI images to the same orientation, followed by a convolutional neural network using the nnU-Net framework. We use a total of 14 datasets from both research and clinical collections. Of these, seven were used for training and validation and seven were retained for testing. We trained the network to segment 30 deep brain structures, as well as a brain mask, using labels generated from a registration-based approach. We evaluated the generalizability of the network by performing a leave-one-dataset-out cross-validation, and independent testing on unseen datasets. Furthermore, we assessed cross-domain transportability by evaluating the results separately on different domains. We achieved an average dice score similarity of 0.89 ± 0.04 on the test datasets when compared to the registration-based gold standard. On our test system, the computation time decreased from 43 min for a reference registration-based pipeline to 1.3 min. Our proposed method is fast, robust, and generalizes with high reliability. It can be extended to the segmentation of other brain structures. It is publicly available on GitHub, and as a pip package for convenient usage.
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Affiliation(s)
- Mehri Baniasadi
- National Department of Neurosurgery, Centre Hospitalier deLuxembourg Center for Systems Biomedicine, University of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Mikkel V. Petersen
- Department of Clinical Medicine, Center of Functionally Integrative NeuroscienceUniversity of AarhusAarhusDenmark
| | - Jorge Gonçalves
- Luxembourg Center for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Andreas Horn
- Neuromodulation and Movement Disorders Unit, Department of NeurologyCharité–Universitätsmedizin BerlinBerlinGermany
- MGH Neurosurgery and Center for Neurotechnology and Neurorecovery at MGH Neurology Massachusetts General HospitalHarvard Medical SchoolBostonUSA
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's HospitalHarvard Medical SchoolBostonUSA
| | - Vanja Vlasov
- Luxembourg Center for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Frank Hertel
- National Department of NeurosurgeryCentre Hospitalier de LuxembourgLuxembourg
| | - Andreas Husch
- Luxembourg Center for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
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16
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Ramanathan PV, Salas-Vega S, Shenai MB. Directional Deep Brain Stimulation-A Step in the Right Direction? A Systematic Review of the Clinical and Therapeutic Efficacy of Directional Deep Brain Stimulation in Parkinson Disease. World Neurosurg 2023; 170:54-63.e1. [PMID: 36435384 DOI: 10.1016/j.wneu.2022.11.085] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 11/18/2022] [Accepted: 11/19/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND The use of directional deep brain stimulation (dDBS) electrodes for the treatment of movement disorders such as Parkinson disease (PD) has become relatively widespread. However, the efficacy of dDBS relative to its omnidirectional deep brain stimulation (oDBS) counterpart is not well characterized. This systematic review aims to synthesize the literature comparing clinical and therapeutic outcomes of dDBS relative to oDBS in patients with PD. METHODS A systematic literature search for studies with comparative clinical outcome data between dDBS and oDBS was performed across the PubMed, Ovid MEDLINE, and Web of Science databases. Data including therapeutic window (TW) and surrogate measures and the Unified Parkinson's Disease Rating Scale score were collected and summarized across multiple time periods. RESULTS Ten studies met the eligibility criteria. Three of these studies evaluated motor performance in the form of Unified Parkinson's Disease Rating Scale III, with none finding differences between dDBS and oDBS. Two studies assessed quality-of-life measures with neither finding differences between dDBS and oDBS. TW or a surrogate measure was assessed in 6 studies; 5 studies found an increase or strong trend toward increase in dDBS relative to oDBS. CONCLUSIONS The current evidence, although limited by bias, does suggest that dDBS in the treatment of PD yields improvements in motor symptoms and quality of life that are comparable to oDBS; TW and surrogate measures are consistently improved in patients with PD under a directional configuration relative to omnidirectional.
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Affiliation(s)
| | | | - Mahesh B Shenai
- Department of Neurosurgery, INOVA Medical Group, Fairfax, Virginia, USA.
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17
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Nordenström S, Petermann K, Debove I, Nowacki A, Krack P, Pollo C, Nguyen TAK. Programming of subthalamic nucleus deep brain stimulation for Parkinson's disease with sweet spot-guided parameter suggestions. Front Hum Neurosci 2022; 16:925283. [PMID: 36393984 PMCID: PMC9663652 DOI: 10.3389/fnhum.2022.925283] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 09/30/2022] [Indexed: 10/24/2023] Open
Abstract
Deep Brain Stimulation (DBS) is an effective treatment for advanced Parkinson's disease. However, identifying stimulation parameters, such as contact and current amplitudes, is time-consuming based on trial and error. Directional leads add more stimulation options and render this process more challenging with a higher workload for neurologists and more discomfort for patients. In this study, a sweet spot-guided algorithm was developed that automatically suggested stimulation parameters. These suggestions were retrospectively compared to clinical monopolar reviews. A cohort of 24 Parkinson's disease patients underwent bilateral DBS implantation in the subthalamic nucleus at our center. First, the DBS' leads were reconstructed with the open-source toolbox Lead-DBS. Second, a sweet spot for rigidity reduction was set as the desired stimulation target for programming. This sweet spot and estimations of the volume of tissue activated were used to suggest (i) the best lead level, (ii) the best contact, and (iii) the effect thresholds for full therapeutic effect for each contact. To assess these sweet spot-guided suggestions, the clinical monopolar reviews were considered as ground truth. In addition, the sweet spot-guided suggestions for best lead level and best contact were compared against reconstruction-guided suggestions, which considered the lead location with respect to the subthalamic nucleus. Finally, a graphical user interface was developed as an add-on to Lead-DBS and is publicly available. With the interface, suggestions for all contacts of a lead can be generated in a few seconds. The accuracy for suggesting the best out of four lead levels was 56%. These sweet spot-guided suggestions were not significantly better than reconstruction-guided suggestions (p = 0.3). The accuracy for suggesting the best out of eight contacts was 41%. These sweet spot-guided suggestions were significantly better than reconstruction-guided suggestions (p < 0.001). The sweet spot-guided suggestions of each contact's effect threshold had a mean error of 1.2 mA. On an individual lead level, the suggestions can vary more with mean errors ranging from 0.3 to 4.8 mA. Further analysis is warranted to improve the sweet spot-guided suggestions and to account for more symptoms and stimulation-induced side effects.
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Affiliation(s)
- Simon Nordenström
- Department of Neurosurgery, University Hospital Bern, Bern, Switzerland
| | - Katrin Petermann
- Department of Neurology, University Hospital Bern, Bern, Switzerland
| | - Ines Debove
- Department of Neurology, University Hospital Bern, Bern, Switzerland
| | - Andreas Nowacki
- Department of Neurosurgery, University Hospital Bern, Bern, Switzerland
| | - Paul Krack
- Department of Neurology, University Hospital Bern, Bern, Switzerland
| | - Claudio Pollo
- Department of Neurosurgery, University Hospital Bern, Bern, Switzerland
| | - T. A. Khoa Nguyen
- Department of Neurosurgery, University Hospital Bern, Bern, Switzerland
- ARTORG Center for Biomedical Engineering Research, University Bern, Bern, Switzerland
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18
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Sui Y, Yu H, Zhang C, Chen Y, Jiang C, Li L. Deep brain-machine interfaces: sensing and modulating the human deep brain. Natl Sci Rev 2022; 9:nwac212. [PMID: 36644311 PMCID: PMC9834907 DOI: 10.1093/nsr/nwac212] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/02/2022] [Accepted: 10/04/2022] [Indexed: 01/18/2023] Open
Abstract
Different from conventional brain-machine interfaces that focus more on decoding the cerebral cortex, deep brain-machine interfaces enable interactions between external machines and deep brain structures. They sense and modulate deep brain neural activities, aiming at function restoration, device control and therapeutic improvements. In this article, we provide an overview of multiple deep brain recording and stimulation techniques that can serve as deep brain-machine interfaces. We highlight two widely used interface technologies, namely deep brain stimulation and stereotactic electroencephalography, for technical trends, clinical applications and brain connectivity research. We discuss the potential to develop closed-loop deep brain-machine interfaces and achieve more effective and applicable systems for the treatment of neurological and psychiatric disorders.
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Affiliation(s)
- Yanan Sui
- National Engineering Research Center of Neuromodulation, Tsinghua University, Beijing 100084, China
| | - Huiling Yu
- National Engineering Research Center of Neuromodulation, Tsinghua University, Beijing 100084, China
| | - Chen Zhang
- National Engineering Research Center of Neuromodulation, Tsinghua University, Beijing 100084, China
| | - Yue Chen
- National Engineering Research Center of Neuromodulation, Tsinghua University, Beijing 100084, China
| | - Changqing Jiang
- National Engineering Research Center of Neuromodulation, Tsinghua University, Beijing 100084, China
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19
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Shin U, Ding C, Zhu B, Vyza Y, Trouillet A, Revol ECM, Lacour SP, Shoaran M. NeuralTree: A 256-Channel 0.227-μJ/Class Versatile Neural Activity Classification and Closed-Loop Neuromodulation SoC. IEEE JOURNAL OF SOLID-STATE CIRCUITS 2022; 57:3243-3257. [PMID: 36744006 PMCID: PMC9897226 DOI: 10.1109/jssc.2022.3204508] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Closed-loop neural interfaces with on-chip machine learning can detect and suppress disease symptoms in neurological disorders or restore lost functions in paralyzed patients. While high-density neural recording can provide rich neural activity information for accurate disease-state detection, existing systems have low channel counts and poor scalability, which could limit their therapeutic efficacy. This work presents a highly scalable and versatile closed-loop neural interface SoC that can overcome these limitations. A 256-channel time-division multiplexed (TDM) front-end with a two-step fast-settling mixed-signal DC servo loop (DSL) is proposed to record high-spatial-resolution neural activity and perform channel-selective brain-state inference. A tree-structured neural network (NeuralTree) classification processor extracts a rich set of neural biomarkers in a patient- and disease-specific manner. Trained with an energy-aware learning algorithm, the NeuralTree classifier detects the symptoms of underlying disorders (e.g., epilepsy and movement disorders) at an optimal energy-accuracy trade-off. A 16-channel high-voltage (HV) compliant neurostimulator closes the therapeutic loop by delivering charge-balanced biphasic current pulses to the brain. The proposed SoC was fabricated in 65nm CMOS and achieved a 0.227μJ/class energy efficiency in a compact area of 0.014mm2/channel. The SoC was extensively verified on human electroencephalography (EEG) and intracranial EEG (iEEG) epilepsy datasets, obtaining 95.6%/94% sensitivity and 96.8%/96.9% specificity, respectively. In-vivo neural recordings using soft μECoG arrays and multi-domain biomarker extraction were further performed on a rat model of epilepsy. In addition, for the first time in literature, on-chip classification of rest-state tremor in Parkinson's disease (PD) from human local field potentials (LFPs) was demonstrated.
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Affiliation(s)
- Uisub Shin
- Institute of Electrical and Micro Engineering, EPFL, 1202 Geneva, Switzerland, and the School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Cong Ding
- Institute of Electrical and Micro Engineering and Center for Neuroprosthetics, EPFL, 1202 Geneva, Switzerland
| | - Bingzhao Zhu
- Institute of Electrical and Micro Engineering, EPFL, 1202 Geneva, Switzerland, and the School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA
| | - Yashwanth Vyza
- Institute of Electrical and Micro Engineering and Center for Neuroprosthetics, EPFL, 1202 Geneva, Switzerland
| | - Alix Trouillet
- Institute of Electrical and Micro Engineering and Center for Neuroprosthetics, EPFL, 1202 Geneva, Switzerland
| | - Emilie C M Revol
- Institute of Electrical and Micro Engineering and Center for Neuroprosthetics, EPFL, 1202 Geneva, Switzerland
| | - Stéphanie P Lacour
- Institute of Electrical and Micro Engineering and Center for Neuroprosthetics, EPFL, 1202 Geneva, Switzerland
| | - Mahsa Shoaran
- Institute of Electrical and Micro Engineering and Center for Neuroprosthetics, EPFL, 1202 Geneva, Switzerland
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20
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Campbell BA, Cho H, Faulhammer RM, Hogue O, Tsai JPC, Hussain MS, Machado AG, Baker KB. Stability and Effect of Parkinsonian State on Deep Brain Stimulation Cortical Evoked Potentials. Neuromodulation 2022; 25:804-816. [PMID: 34309115 PMCID: PMC10246651 DOI: 10.1111/ner.13508] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 06/09/2021] [Accepted: 07/06/2021] [Indexed: 11/29/2022]
Abstract
OBJECTIVES To characterize and compare the stability of cortical potentials evoked by deep brain stimulation (DBS) of the subthalamic nucleus (STN) across the naïve, parkinsonian, and pharmacologically treated parkinsonian states. To advance cortical potentials as possible biomarkers for DBS programming. MATERIALS AND METHODS Serial electrocorticographic (ECoG) recordings were made more than nine months from a single non-human primate instrumented with bilateral ECoG grids spanning anterior parietal to prefrontal cortex. Cortical evoked potentials (CEPs) were generated through time-lock averaging of the ECoG recordings to DBS pulses delivered unilaterally in the STN region using a chronically implanted, six-contact, scaled DBS lead. Recordings were made across the naïve followed by mild and moderate parkinsonian conditions achieved by staged injections of the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) neurotoxin. In addition to characterizing the spatial distribution and stability of the response within each state, changes in the amplitude and latency of CEP components as well as in the frequency content were examined in relation to parkinsonian severity and dopamine replacement. RESULTS In the naïve state, the STN DBS CEP presented as a multiphase response maximal over M1 cortex, with components attributable to physiological activity distinguishable from stimulus artifact as early as 0.45-0.75 msec poststimulation. When delivered using therapeutically effective parameters in the parkinsonian state, the CEP was highly stable across multiple recording sessions within each behavioral state. Across states, significant differences were present with respect to both the latency and amplitude of individual response components, with greater differences present for longer-latency components (all p < 0.05). Power spectral density analysis revealed a high-beta peak within the evoked response, with significant changes in power between disease states across multiple frequency bands. CONCLUSIONS Our findings underscore the spatiotemporal specificity and relative stability of the DBS-CEP associated with different disease states and with therapeutic benefit. DBS-CEP may be a viable biomarker for therapeutic programming.
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Affiliation(s)
- Brett A Campbell
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Department of Neurosciences, Cleveland Clinic, Cleveland, OH, USA
| | - Hanbin Cho
- Department of Neurosciences, Cleveland Clinic, Cleveland, OH, USA
| | | | - Olivia Hogue
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA
| | | | | | - Andre G Machado
- Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Kenneth B Baker
- Department of Neurosciences, Cleveland Clinic, Cleveland, OH, USA.
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21
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Malekmohammadi M, Mustakos R, Sheth S, Pouratian N, McIntyre CC, Bijanki KR, Tsolaki E, Chiu K, Robinson ME, Adkinson JA, Oswalt D, Carcieri S. Automated optimization of deep brain stimulation parameters for modulating neuroimaging-based targets. J Neural Eng 2022; 19:10.1088/1741-2552/ac7e6c. [PMID: 35790135 PMCID: PMC11090244 DOI: 10.1088/1741-2552/ac7e6c] [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: 02/25/2022] [Accepted: 07/05/2022] [Indexed: 11/12/2022]
Abstract
Objective.Therapeutic efficacy of deep brain stimulation (DBS) in both established and emerging indications, is highly dependent on accurate lead placement and optimized clinical programming. The latter relies on clinicians' experience to search among available sets of stimulation parameters and can be limited by the time constraints of clinical practice. Recent innovations in device technology have expanded the number of possible electrode configurations and parameter sets available to clinicians, amplifying the challenge of time constraints. We hypothesize that patient specific neuroimaging data can effectively assist the clinical programming using automated algorithms.Approach.This paper introduces the DBS Illumina 3D algorithm as a tool which uses patient-specific imaging to find stimulation settings that optimizes activating a target area while minimizing the stimulation of areas outside the target that could result in unknown or undesired side effects. This approach utilizes preoperative neuroimaging data paired with the postoperative reconstruction of the lead trajectory to search the available stimulation space and identify optimized stimulation parameters. We describe the application of this algorithm in three patients with treatment-resistant depression who underwent bilateral implantation of DBS in subcallosal cingulate cortex and ventral capsule/ventral striatum using tractography optimized targeting with an imaging defined target previously described.Main results.Compared to the stimulation settings selected by the clinicians (informed by anatomy), stimulation settings produced by the algorithm that achieved similar or greater target coverage, produced a significantly smaller stimulation area that spilled outside the target (P= 0.002).Significance. The DBS Illumina 3D algorithm is seamlessly integrated with the clinician programmer software and effectively and rapidly assists clinicians with the analysis of image based anatomy, and provides a starting point to search the highly complex stimulation parameter space and arrive at the stimulation settings that optimize activating a target area.
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Affiliation(s)
- Mahsa Malekmohammadi
- Boston Scientific Neuromodulation, 25155 Rye Canyon Loop, Valencia, CA 91355, USA
| | - Richard Mustakos
- Boston Scientific Neuromodulation, 25155 Rye Canyon Loop, Valencia, CA 91355, USA
| | - Sameer Sheth
- Department of Neurosurgery, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Nader Pouratian
- Department of Neurological Surgery, University of Texas Southwestern Medical Center, 8353 Harry Hines Blvd MC8855, Dallas, TX 75239, USA
| | - Cameron C. McIntyre
- Departments of Biomedical Engineering and Neurosurgery, Duke University, 100 Science Drive, Durham, NC 27708, USA
| | - Kelly R. Bijanki
- Department of Neurosurgery, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Evangelia Tsolaki
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, 300 Stein Plaza Suite 562, Los Angeles, CA 90095, USA
| | - Kevin Chiu
- Brainlab, Inc., 5 Westbrook Corporate Center, Suite 1000, Westchester IL 60154, USA
| | - Meghan E. Robinson
- Department of Neurosurgery, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Joshua A. Adkinson
- Department of Neurosurgery, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Denise Oswalt
- Department of Neurosurgery, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Stephen Carcieri
- Boston Scientific Neuromodulation, 25155 Rye Canyon Loop, Valencia, CA 91355, USA
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22
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Wårdell K, Nordin T, Vogel D, Zsigmond P, Westin CF, Hariz M, Hemm S. Deep Brain Stimulation: Emerging Tools for Simulation, Data Analysis, and Visualization. Front Neurosci 2022; 16:834026. [PMID: 35478842 PMCID: PMC9036439 DOI: 10.3389/fnins.2022.834026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 03/01/2022] [Indexed: 01/10/2023] Open
Abstract
Deep brain stimulation (DBS) is a well-established neurosurgical procedure for movement disorders that is also being explored for treatment-resistant psychiatric conditions. This review highlights important consideration for DBS simulation and data analysis. The literature on DBS has expanded considerably in recent years, and this article aims to identify important trends in the field. During DBS planning, surgery, and follow up sessions, several large data sets are created for each patient, and it becomes clear that any group analysis of such data is a big data analysis problem and has to be handled with care. The aim of this review is to provide an update and overview from a neuroengineering perspective of the current DBS techniques, technical aids, and emerging tools with the focus on patient-specific electric field (EF) simulations, group analysis, and visualization in the DBS domain. Examples are given from the state-of-the-art literature including our own research. This work reviews different analysis methods for EF simulations, tractography, deep brain anatomical templates, and group analysis. Our analysis highlights that group analysis in DBS is a complex multi-level problem and selected parameters will highly influence the result. DBS analysis can only provide clinically relevant information if the EF simulations, tractography results, and derived brain atlases are based on as much patient-specific data as possible. A trend in DBS research is creation of more advanced and intuitive visualization of the complex analysis results suitable for the clinical environment.
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Affiliation(s)
- Karin Wårdell
- Neuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Teresa Nordin
- Neuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Dorian Vogel
- Neuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Institute for Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
| | - Peter Zsigmond
- Department of Neurosurgery and Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Carl-Fredrik Westin
- Neuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Marwan Hariz
- Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, London, United Kingdom
- Department of Clinical Sciences, Neuroscience, Ume University, Umeå, Sweden
| | - Simone Hemm
- Neuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Institute for Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland
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23
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Frey J, Cagle J, Johnson KA, Wong JK, Hilliard JD, Butson CR, Okun MS, de Hemptinne C. Past, Present, and Future of Deep Brain Stimulation: Hardware, Software, Imaging, Physiology and Novel Approaches. Front Neurol 2022; 13:825178. [PMID: 35356461 PMCID: PMC8959612 DOI: 10.3389/fneur.2022.825178] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 02/04/2022] [Indexed: 11/13/2022] Open
Abstract
Deep brain stimulation (DBS) has advanced treatment options for a variety of neurologic and neuropsychiatric conditions. As the technology for DBS continues to progress, treatment efficacy will continue to improve and disease indications will expand. Hardware advances such as longer-lasting batteries will reduce the frequency of battery replacement and segmented leads will facilitate improvements in the effectiveness of stimulation and have the potential to minimize stimulation side effects. Targeting advances such as specialized imaging sequences and "connectomics" will facilitate improved accuracy for lead positioning and trajectory planning. Software advances such as closed-loop stimulation and remote programming will enable DBS to be a more personalized and accessible technology. The future of DBS continues to be promising and holds the potential to further improve quality of life. In this review we will address the past, present and future of DBS.
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Affiliation(s)
- Jessica Frey
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Jackson Cagle
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Kara A. Johnson
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Joshua K. Wong
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Justin D. Hilliard
- Department of Neurosurgery, University of Florida, Gainesville, FL, United States
| | - Christopher R. Butson
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
- Department of Neurosurgery, University of Florida, Gainesville, FL, United States
| | - Michael S. Okun
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Coralie de Hemptinne
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
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24
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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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25
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Noga BR, Guest JD. Combined neuromodulatory approaches in the central nervous system for treatment of spinal cord injury. Curr Opin Neurol 2021; 34:804-811. [PMID: 34593718 PMCID: PMC8595808 DOI: 10.1097/wco.0000000000000999] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
PURPOSE OF REVIEW To report progress in neuromodulation following spinal cord injury (SCI) using combined brain and spinal neuromodulation.Neuromodulation refers to alterations in neuronal activity for therapeutic purposes. Beneficial effects are established in disease states such as Parkinson's Disease (PD), chronic pain, epilepsy, and SCI. The repertoire of neuromodulation and bioelectric medicine is rapidly expanding. After SCI, cohort studies have reported the benefits of epidural stimulation (ES) combined with training. Recently, we have explored combining ES with deep brain stimulation (DBS) to increase activation of descending motor systems to address limitations of ES in severe SCI. In this review, we describe the types of applied neuromodulation that could be combined in SCI to amplify efficacy to enable movement. These include ES, mesencephalic locomotor region (MLR) - DBS, noninvasive transcutaneous stimulation, transcranial magnetic stimulation, paired-pulse paradigms, and neuromodulatory drugs. We examine immediate and longer-term effects and what is known about: (1) induced neuroplastic changes, (2) potential safety concerns; (3) relevant outcome measures; (4) optimization of stimulation; (5) therapeutic limitations and prospects to overcome these. RECENT FINDINGS DBS of the mesencephalic locomotor region is emerging as a potential clinical target to amplify supraspinal command circuits for locomotion. SUMMARY Combinations of neuromodulatory methods may have additive value for restoration of function after spinal cord injury.
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Affiliation(s)
- Brian R Noga
- The Miami Project to Cure Paralysis, Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, Florida, USA
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26
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Roediger J, Dembek TA, Wenzel G, Butenko K, Kühn AA, Horn A. StimFit-A Data-Driven Algorithm for Automated Deep Brain Stimulation Programming. Mov Disord 2021; 37:574-584. [PMID: 34837245 DOI: 10.1002/mds.28878] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 10/07/2021] [Accepted: 11/04/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Finding the optimal deep brain stimulation (DBS) parameters from a multitude of possible combinations by trial and error is time consuming and requires highly trained medical personnel. OBJECTIVE We developed an automated algorithm to identify optimal stimulation settings in Parkinson's disease (PD) patients treated with subthalamic nucleus (STN) DBS based on imaging-derived metrics. METHODS Electrode locations and monopolar review data of 612 stimulation settings acquired from 31 PD patients were used to train a predictive model for therapeutic and adverse stimulation effects. Model performance was then evaluated within the training cohort using cross-validation and on an independent cohort of 19 patients. We inverted the model by applying a brute-force approach to determine the optimal stimulation sites in the target region. Finally, an optimization algorithm was established to identify optimal stimulation parameters. Suggested stimulation parameters were compared to the ones applied in clinical practice. RESULTS Predicted motor outcome correlated with observed outcome (R = 0.57, P < 10-10 ) across patients within the training cohort. In the test cohort, the model explained 28% of the variance in motor outcome differences between settings. The stimulation site for maximum motor improvement was located at the dorsolateral border of the STN. When compared to two empirical settings, model-based suggestions more closely matched the setting with superior motor improvement. CONCLUSION We developed and validated a data-driven model that can suggest stimulation parameters leading to optimal motor improvement while minimizing the risk of stimulation-induced side effects. This approach might provide guidance for DBS programming in the future. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Jan Roediger
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité University Medicine Berlin, Charitéplatz 1, Berlin, 10117, Germany.,Einstein Center for Neurosciences Berlin, Charité University Medicine Berlin, Charitéplatz 1, Berlin, 10117, Germany
| | - Till A Dembek
- Department of Neurology, Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Gregor Wenzel
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité University Medicine Berlin, Charitéplatz 1, Berlin, 10117, Germany
| | - Konstantin Butenko
- Institute of General Electrical Engineering, University of Rostock, Rostock, Germany
| | - Andrea A Kühn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité University Medicine Berlin, Charitéplatz 1, Berlin, 10117, Germany.,Berlin School of Mind and Brain, Charité University Medicine, Berlin, Germany.,NeuroCure Clinical Research Centre, Charité University Medicine, Berlin, Germany.,DZNE, German Center for Degenerative Diseases, Berlin, Germany
| | - Andreas Horn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité University Medicine Berlin, Charitéplatz 1, Berlin, 10117, Germany
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27
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Bremm RP, Berthold C, Krüger R, Koch KP, Gonçalves J, Hertel F. Therapeutic maps for a sensor-based evaluation of deep brain stimulation programming. BIOMED ENG-BIOMED TE 2021; 66:603-611. [PMID: 34727584 DOI: 10.1515/bmt-2020-0210] [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: 08/06/2020] [Accepted: 10/01/2021] [Indexed: 11/15/2022]
Abstract
Programming in deep brain stimulation (DBS) is a labour-intensive process for treating advanced motor symptoms. Specifically for patients with medication-refractory tremor in multiple sclerosis (MS). Wearable sensors are able to detect some manifestations of pathological signs, such as intention tremor in MS. However, methods are needed to visualise the response of tremor to DBS parameter changes in a clinical setting while patients perform the motor task finger-to-nose. To this end, we attended DBS programming sessions of a MS patient and intention tremor was effectively quantified by acceleration amplitude and frequency. A new method is introduced which results in the generation of therapeutic maps for a systematic review of the programming procedure in DBS. The maps visualise the combination of tremor acceleration power, clinical rating scores, total electrical energy delivered to the brain and possible side effects. Therapeutic maps have not yet been employed and could lead to a certain degree of standardisation for more objective decisions about DBS settings. The maps provide a base for future research on visualisation tools to assist physicians who frequently encounter patients for DBS therapy.
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Affiliation(s)
- Rene Peter Bremm
- National Department of Neurosurgery, Centre Hospitalier de Luxembourg, Luxembourg (City), Luxembourg
- Interventional Neuroscience, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Christophe Berthold
- National Department of Neurosurgery, Centre Hospitalier de Luxembourg, Luxembourg (City), Luxembourg
| | - Rejko Krüger
- Translational Neuroscience, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Klaus Peter Koch
- Department of Electrical Engineering, Trier University of Applied Sciences, Trier, Germany
| | - Jorge Gonçalves
- Systems Control, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Frank Hertel
- National Department of Neurosurgery, Centre Hospitalier de Luxembourg, Luxembourg (City), Luxembourg
- Interventional Neuroscience, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
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28
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Jiang F, Elahi B, Saxena M, Telkes I, DiMarzio M, Pilitsis JG, Golestanirad L. Patient-specific modeling of the volume of tissue activated (VTA) is associated with clinical outcome of DBS in patients with an obsessive-compulsive disorder. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5889-5892. [PMID: 34892459 PMCID: PMC10829536 DOI: 10.1109/embc46164.2021.9630273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Deep brain stimulation (DBS) promises to treat an increasing number of neurological and psychiatric disorders. DBS outcome is directly a factor of optimal targeting of the relevant brain structures. Computational models can help to interpret a patient's outcome by predicting the volume of tissue activated (VTA) around DBS electrode contacts. Here we report results of a preliminary study of DBS in two patients with obsessive-compulsive disorder and show that VTA predictions, which are based on patient-specific volume conductor models, correlate with clinical outcome. Our results suggest that patient specific VTA calculation can help inform device programing to maximize therapeutic effects and minimize side effects.Clinical Relevance- Patient-specific modeling of the volume of activated tissue can predict clinical outcomes and thus, can help to optimize DBS device programing to maximize therapeutic effects.
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29
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Yoo J, Shoaran M. Neural interface systems with on-device computing: machine learning and neuromorphic architectures. Curr Opin Biotechnol 2021; 72:95-101. [PMID: 34735990 DOI: 10.1016/j.copbio.2021.10.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 10/18/2021] [Accepted: 10/19/2021] [Indexed: 11/26/2022]
Abstract
Development of neural interface and brain-machine interface (BMI) systems enables the treatment of neurological disorders including cognitive, sensory, and motor dysfunctions. While neural interfaces have steadily decreased in form factor, recent developments target pervasive implantables. Along with advances in electrodes, neural recording, and neurostimulation circuits, integration of disease biomarkers and machine learning algorithms enables real-time and on-site processing of neural activity with no need for power-demanding telemetry. This recent trend on combining artificial intelligence and machine learning with modern neural interfaces will lead to a new generation of low-power, smart, and miniaturized therapeutic devices for a wide range of neurological and psychiatric disorders. This paper reviews the recent development of the 'on-chip' machine learning and neuromorphic architectures, which is one of the key puzzles in devising next-generation clinically viable neural interface systems.
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Affiliation(s)
- Jerald Yoo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117585, Singapore; The N.1 Institute for Health, Singapore, Singapore, 117456, Singapore
| | - Mahsa Shoaran
- Institute of Electrical Engineering, Center for Neuroprosthetics, École polytechnique federal de Lausanne (EPFL), 1202, Geneva, Switzerland.
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30
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Masuda H, Shirozu H, Ito Y, Fukuda M, Fujii Y. Surgical Strategy for Directional Deep Brain Stimulation. Neurol Med Chir (Tokyo) 2021; 62:1-12. [PMID: 34719582 PMCID: PMC8754682 DOI: 10.2176/nmc.ra.2021-0214] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Deep brain stimulation (DBS) is a well-established treatment for drug-resistant involuntary movements. However, the conventional quadripole cylindrical lead creates electrical fields in all directions, and the resulting spread to adjacent eloquent structures may induce unintended effects. Novel directional leads have therefore been designed to allow directional stimulation (DS). Directional leads have the advantage of widening the therapeutic window (TW), compensating for slight misplacement of the lead and requiring less electrical power to provide the same effect as a cylindrical lead. Conversely, the increase in the number of contacts from four to eight and the addition of directional elements has made stimulation programming more complex. For these reasons, new treatment strategies are required to allow effective directional DBS. During lead implantation, the directional segment should be placed in a "sweet spot," and the orientation of the directional segment is important for programming. Trial-and-error testing of a large number of contacts is unnecessary, and efficient and systematic execution of the programmed procedure is desirable. Recent improvements in imaging technologies have enabled image-guided programming. In the future, optimal stimulations are expected to be programmed by directional recording of local field potentials.
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Affiliation(s)
- Hiroshi Masuda
- Division of Functional Neurosurgery, Nishiniigata National Hospital
| | - Hiroshi Shirozu
- Division of Functional Neurosurgery, Nishiniigata National Hospital
| | - Yosuke Ito
- Division of Functional Neurosurgery, Nishiniigata National Hospital
| | - Masafumi Fukuda
- Division of Functional Neurosurgery, Nishiniigata National Hospital
| | - Yukihiko Fujii
- Department of Neurosurgery, Brain Research Institute, Niigata University
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31
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Zhu B, Shin U, Shoaran M. Closed-Loop Neural Prostheses With On-Chip Intelligence: A Review and a Low-Latency Machine Learning Model for Brain State Detection. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021; 15:877-897. [PMID: 34529573 PMCID: PMC8733782 DOI: 10.1109/tbcas.2021.3112756] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
The application of closed-loop approaches in systems neuroscience and therapeutic stimulation holds great promise for revolutionizing our understanding of the brain and for developing novel neuromodulation therapies to restore lost functions. Neural prostheses capable of multi-channel neural recording, on-site signal processing, rapid symptom detection, and closed-loop stimulation are critical to enabling such novel treatments. However, the existing closed-loop neuromodulation devices are too simplistic and lack sufficient on-chip processing and intelligence. In this paper, we first discuss both commercial and investigational closed-loop neuromodulation devices for brain disorders. Next, we review state-of-the-art neural prostheses with on-chip machine learning, focusing on application-specific integrated circuits (ASIC). System requirements, performance and hardware comparisons, design trade-offs, and hardware optimization techniques are discussed. To facilitate a fair comparison and guide design choices among various on-chip classifiers, we propose a new energy-area (E-A) efficiency figure of merit that evaluates hardware efficiency and multi-channel scalability. Finally, we present several techniques to improve the key design metrics of tree-based on-chip classifiers, both in the context of ensemble methods and oblique structures. A novel Depth-Variant Tree Ensemble (DVTE) is proposed to reduce processing latency (e.g., by 2.5× on seizure detection task). We further develop a cost-aware learning approach to jointly optimize the power and latency metrics. We show that algorithm-hardware co-design enables the energy- and memory-optimized design of tree-based models, while preserving a high accuracy and low latency. Furthermore, we show that our proposed tree-based models feature a highly interpretable decision process that is essential for safety-critical applications such as closed-loop stimulation.
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32
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Cagle JN, Wong JK, Johnson KA, Foote KD, Okun MS, de Hemptinne C. Suppression and Rebound of Pallidal Beta Power: Observation Using a Chronic Sensing DBS Device. Front Hum Neurosci 2021; 15:749567. [PMID: 34566612 PMCID: PMC8458625 DOI: 10.3389/fnhum.2021.749567] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 08/23/2021] [Indexed: 11/14/2022] Open
Abstract
Pallidal deep brain stimulation (DBS) is an increasingly used therapy for Parkinson’s disease (PD). Here, we study the effect of DBS on pallidal oscillatory activity as well as on symptom severity in an individual with PD implanted with a new pulse generator (Medtronic Percept system) which facilitates chronic recording of local field potentials (LFP) through implanted DBS lead. Pallidal LFPs were recorded while delivering stimulation in a monopolar configuration using stepwise increments (0.5 mA, every 20 s). At each stimulation amplitude, the power spectral density (PSD) was computed, and beta power (13–30 Hz) was calculated and correlated with the degree of bradykinesia. Pallidal beta power was reduced when therapeutic stimulation was delivered. Beta power correlated to the severity of bradykinesia. Worsening of parkinsonism when excessive stimulation was applied was associated with a rebound in the beta band power. These preliminary results suggest that pallidal beta power might be used as an objective marker of the disease state in PD. The use of brain sensing from implanted neural interfaces may in the future facilitate clinical programming. Detection of rebound could help to optimize benefits and minimize worsening from overstimulation.
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Affiliation(s)
- Jackson N Cagle
- Department of Neurology, University of Florida, Gainesville, FL, United States.,Norman Fixel Institute for Neurological Diseases, Gainesville, FL, United States
| | - Joshua K Wong
- Department of Neurology, University of Florida, Gainesville, FL, United States.,Norman Fixel Institute for Neurological Diseases, Gainesville, FL, United States
| | - Kara A Johnson
- Department of Neurology, University of Florida, Gainesville, FL, United States.,Norman Fixel Institute for Neurological Diseases, Gainesville, FL, United States
| | - Kelly D Foote
- Norman Fixel Institute for Neurological Diseases, Gainesville, FL, United States.,Department of Neurosurgery, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Michael S Okun
- Department of Neurology, University of Florida, Gainesville, FL, United States.,Norman Fixel Institute for Neurological Diseases, Gainesville, FL, United States
| | - Coralie de Hemptinne
- Department of Neurology, University of Florida, Gainesville, FL, United States.,Norman Fixel Institute for Neurological Diseases, Gainesville, FL, United States
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33
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Patel B, Chiu S, Wong JK, Patterson A, Deeb W, Burns M, Zeilman P, Wagle-Shukla A, Almeida L, Okun MS, Ramirez-Zamora A. Deep brain stimulation programming strategies: segmented leads, independent current sources, and future technology. Expert Rev Med Devices 2021; 18:875-891. [PMID: 34329566 DOI: 10.1080/17434440.2021.1962286] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Introduction: Advances in neuromodulation and deep brain stimulation (DBS) technologies have facilitated opportunities for improved clinical benefit and side effect management. However, new technologies have added complexity to clinic-based DBS programming.Areas covered: In this article, we review basic basal ganglia physiology, proposed mechanisms of action and technical aspects of DBS. We discuss novel DBS technologies for movement disorders including the role of advanced imaging software, lead design, IPG design, novel programming techniques including directional stimulation and coordinated reset neuromodulation. Additional topics include the use of potential biomarkers, such as local field potentials, electrocorticography, and adaptive stimulation. We will also discuss future directions including optogenetically inspired DBS.Expert opinion: The introduction of DBS for the management of movement disorders has expanded treatment options. In parallel with our improved understanding of brain physiology and neuroanatomy, new technologies have emerged to address challenges associated with neuromodulation, including variable effectiveness, side-effects, and programming complexity. Advanced functional neuroanatomy, improved imaging, real-time neurophysiology, improved electrode designs, and novel programming techniques have collectively been driving improvements in DBS outcomes.
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Affiliation(s)
- Bhavana Patel
- Department of Neurology, University of Florida College of Medicine, Gainesville, FL, USA.,Norman Fixel Institute for Neurological Diseases, . Gainesville, FL, USA
| | - Shannon Chiu
- Department of Neurology, University of Florida College of Medicine, Gainesville, FL, USA.,Norman Fixel Institute for Neurological Diseases, . Gainesville, FL, USA
| | - Joshua K Wong
- Department of Neurology, University of Florida College of Medicine, Gainesville, FL, USA.,Norman Fixel Institute for Neurological Diseases, . Gainesville, FL, USA
| | - Addie Patterson
- Department of Neurology, University of Florida College of Medicine, Gainesville, FL, USA.,Norman Fixel Institute for Neurological Diseases, . Gainesville, FL, USA
| | - Wissam Deeb
- Department of Neurology, University of Massachusetts College of Medicine, Worcester, MA, USA
| | - Matthew Burns
- Department of Neurology, University of Florida College of Medicine, Gainesville, FL, USA.,Norman Fixel Institute for Neurological Diseases, . Gainesville, FL, USA
| | - Pamela Zeilman
- Department of Neurology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Aparna Wagle-Shukla
- Department of Neurology, University of Florida College of Medicine, Gainesville, FL, USA.,Norman Fixel Institute for Neurological Diseases, . Gainesville, FL, USA
| | - Leonardo Almeida
- Department of Neurology, University of Florida College of Medicine, Gainesville, FL, USA.,Norman Fixel Institute for Neurological Diseases, . Gainesville, FL, USA
| | - Michael S Okun
- Department of Neurology, University of Florida College of Medicine, Gainesville, FL, USA.,Norman Fixel Institute for Neurological Diseases, . Gainesville, FL, USA
| | - Adolfo Ramirez-Zamora
- Department of Neurology, University of Florida College of Medicine, Gainesville, FL, USA.,Norman Fixel Institute for Neurological Diseases, . Gainesville, FL, USA
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34
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Makarov SN, Golestanirad L, Wartman WA, Nguyen BT, Noetscher GM, Ahveninen JP, Fujimoto K, Weise K, Nummenmaa AR. Boundary element fast multipole method for modeling electrical brain stimulation with voltage and current electrodes. J Neural Eng 2021; 18. [PMID: 34311449 DOI: 10.1088/1741-2552/ac17d7] [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: 02/23/2021] [Accepted: 07/26/2021] [Indexed: 01/03/2023]
Abstract
Objective. To formulate, validate, and apply an alternative to the finite element method (FEM) high-resolution modeling technique for electrical brain stimulation-the boundary element fast multipole method (BEM-FMM). To include practical electrode models for both surface and embedded electrodes.Approach. Integral equations of the boundary element method in terms of surface charge density are combined with a general-purpose fast multipole method and are expanded for voltage, shunt, current, and floating electrodes. The solution of coupled and properly weighted/preconditioned integral equations is accompanied by enforcing global conservation laws: charge conservation law and Kirchhoff's current law.Main results.A sub-percent accuracy is reported as compared to the analytical solutions and simple validation geometries. Comparison to FEM considering realistic head models resulted in relative differences of the electric field magnitude in the range of 3%-6% or less. Quantities that contain higher order spatial derivatives, such as the activating function, are determined with a higher accuracy and a faster speed as compared to the FEM. The method can be easily combined with existing head modeling pipelines such as headreco or mri2mesh.Significance.The BEM-FMM does not rely on a volumetric mesh and is therefore particularly suitable for modeling some mesoscale problems with submillimeter (and possibly finer) resolution with high accuracy at moderate computational cost. Utilizing Helmholtz reciprocity principle makes it possible to expand the method to a solution of EEG forward problems with a very large number of cortical dipoles.
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Affiliation(s)
- Sergey N Makarov
- Electrical & Computer Engineering Department, Worcester Polytechnic Institute, Worcester, MA 01609, United States of America.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, United States of America
| | - Laleh Golestanirad
- Biomedical Engineering and Radiology Depts., Northwestern University, Chicago, IL 60611, United States of America
| | - William A Wartman
- Electrical & Computer Engineering Department, Worcester Polytechnic Institute, Worcester, MA 01609, United States of America
| | - Bach Thanh Nguyen
- Biomedical Engineering and Radiology Depts., Northwestern University, Chicago, IL 60611, United States of America
| | - Gregory M Noetscher
- Electrical & Computer Engineering Department, Worcester Polytechnic Institute, Worcester, MA 01609, United States of America
| | - Jyrki P Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, United States of America
| | - Kyoko Fujimoto
- Center for Devices and Radiological Health (CDRH), FDA, Silver Spring, MD 20993, United States of America
| | - Konstantin Weise
- Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstr. 1a, 04103 Leipzig, Germany
| | - Aapo R Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, United States of America
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Weerasinghe G, Duchet B, Bick C, Bogacz R. Optimal closed-loop deep brain stimulation using multiple independently controlled contacts. PLoS Comput Biol 2021; 17:e1009281. [PMID: 34358224 PMCID: PMC8405008 DOI: 10.1371/journal.pcbi.1009281] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 08/30/2021] [Accepted: 07/15/2021] [Indexed: 11/18/2022] Open
Abstract
Deep brain stimulation (DBS) is a well-established treatment option for a variety of neurological disorders, including Parkinson’s disease and essential tremor. The symptoms of these disorders are known to be associated with pathological synchronous neural activity in the basal ganglia and thalamus. It is hypothesised that DBS acts to desynchronise this activity, leading to an overall reduction in symptoms. Electrodes with multiple independently controllable contacts are a recent development in DBS technology which have the potential to target one or more pathological regions with greater precision, reducing side effects and potentially increasing both the efficacy and efficiency of the treatment. The increased complexity of these systems, however, motivates the need to understand the effects of DBS when applied to multiple regions or neural populations within the brain. On the basis of a theoretical model, our paper addresses the question of how to best apply DBS to multiple neural populations to maximally desynchronise brain activity. Central to this are analytical expressions, which we derive, that predict how the symptom severity should change when stimulation is applied. Using these expressions, we construct a closed-loop DBS strategy describing how stimulation should be delivered to individual contacts using the phases and amplitudes of feedback signals. We simulate our method and compare it against two others found in the literature: coordinated reset and phase-locked stimulation. We also investigate the conditions for which our strategy is expected to yield the most benefit. In this paper we use computer models of brain tissue to derive an optimal control algorithm for a recently developed new generation of deep brain stimulation (DBS) devices. DBS is a treatment for a variety of neurological disorders including Parkinson’s disease, essential tremor, depression and pain. There is a growing amount of evidence to suggest that delivering stimulation according to feedback from patients, or closed-loop, has the potential to improve the efficacy, efficiency and side effects of the treatment. An important recent development in DBS technology are electrodes with multiple independently controllable contacts and this paper is a theoretical study into the effects of using this new technology. On the basis of a theoretical model, we devise a closed-loop strategy and address the question of how to best apply DBS across multiple contacts to maximally desynchronise neural populations. We demonstrate using numerical simulation that, for the systems we consider, our methods are more effective than two well-known alternatives, namely phase-locked stimulation and coordinated reset. We also predict that the benefits of using multiple contacts should depend strongly on the intrinsic neuronal response. The insights from this work should lead to a better understanding of how to implement and optimise closed-loop multi-contact DBS systems which in turn should lead to more effective and efficient DBS treatments.
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Affiliation(s)
- Gihan Weerasinghe
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- * E-mail:
| | - Benoit Duchet
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Christian Bick
- Department of Mathematics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Systems and Network Neuroscience, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
- Department of Mathematics, University of Exeter, Exeter, United Kingdom
| | - Rafal Bogacz
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
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Duffley G, Lutz BJ, Szabo A, Wright A, Hess CW, Ramirez-Zamora A, Zeilman P, Chiu S, Foote KD, Okun MS, Butson CR. Home Health Management of Parkinson Disease Deep Brain Stimulation: A Randomized Clinical Trial. JAMA Neurol 2021; 78:972-981. [PMID: 34180949 DOI: 10.1001/jamaneurol.2021.1910] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Importance The travel required to receive deep brain stimulation (DBS) programming causes substantial burden on patients and limits who can access DBS therapy. Objective To evaluate the efficacy of home health DBS postoperative management in an effort to reduce travel burden and improve access. Design, Settings, and Participants This open-label randomized clinical trial was conducted at University of Florida Health from November 2017 to April 2020. Eligible participants had a diagnosis of Parkinson disease (PD) and were scheduled to receive DBS independently of the study. Consenting participants were randomized 1:1 to receive either standard of care or home health postoperative DBS management for 6 months after surgery. Primary caregivers, usually spouses, were also enrolled to assess caregiver strain. Interventions The home health postoperative management was conducted by a home health nurse who chose DBS settings with the aid of the iPad-based Mobile Application for PD DBS system. Prior to the study, the home health nurse had no experience providing DBS care. Main Outcomes and Measures The primary outcome was the number of times each patient traveled to the movement disorders clinic during the study period. Secondary outcomes included changes from baseline on the Unified Parkinson's Disease Rating Scale part III. Results Approximately 75 patients per year were scheduled for DBS. Of the patients who met inclusion criteria over the entire study duration, 45 either declined or were excluded for various reasons. Of the 44 patients enrolled, 19 of 21 randomized patients receiving the standard of care (mean [SD] age, 64.1 [10.0] years; 11 men) and 23 of 23 randomized patients receiving home health who underwent a minimum of 1 postoperative management visit (mean [SD] age, 65.0 [10.9] years; 13 men) were included in analysis. The primary outcome revealed that patients randomized to home health had significantly fewer clinic visits than the patients in the standard of care arm (mean [SD], 0.4 [0.8] visits vs 4.8 [0.4] visits; P < .001). We found no significant differences between the groups in the secondary outcomes measuring the efficacy of DBS. No adverse events occurred in association with the study procedure or devices. Conclusions and Relevance This study provides evidence supporting the safety and feasibility of postoperative home health DBS management. Trial Registration ClinicalTrials.gov Identifier: NCT02474459.
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Affiliation(s)
- Gordon Duffley
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City.,Department of Biomedical Engineering, University of Utah, Salt Lake City
| | - Barbara J Lutz
- School of Nursing, University of North Carolina-Wilmington, Wilmington
| | - Aniko Szabo
- Division of Biostatistics, Institute for Health & Equity, Medical College of Wisconsin, Milwaukee
| | - Adrienne Wright
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville
| | - Christopher W Hess
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville
| | - Adolfo Ramirez-Zamora
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville
| | - Pamela Zeilman
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville
| | - Shannon Chiu
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville
| | - Kelly D Foote
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville
| | - Michael S Okun
- Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville
| | - Christopher R Butson
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City.,Department of Biomedical Engineering, University of Utah, Salt Lake City.,Norman Fixel Institute for Neurological Diseases, Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville.,Departments of Neurology, Neurosurgery, and Psychiatry, University of Utah, Salt Lake City
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37
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Therapies to Restore Consciousness in Patients with Severe Brain Injuries: A Gap Analysis and Future Directions. Neurocrit Care 2021; 35:68-85. [PMID: 34236624 PMCID: PMC8266715 DOI: 10.1007/s12028-021-01227-y] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 03/04/2021] [Indexed: 02/06/2023]
Abstract
Background/Objective For patients with disorders of consciousness (DoC) and their families, the search for new therapies has been a source of hope and frustration. Almost all clinical trials in patients with DoC have been limited by small sample sizes, lack of placebo groups, and use of heterogeneous outcome measures. As a result, few therapies have strong evidence to support their use; amantadine is the only therapy recommended by current clinical guidelines, specifically for patients with DoC caused by severe traumatic brain injury. To foster and advance development of consciousness-promoting therapies for patients with DoC, the Curing Coma Campaign convened a Coma Science Work Group to perform a gap analysis. Methods We consider five classes of therapies: (1) pharmacologic; (2) electromagnetic; (3) mechanical; (4) sensory; and (5) regenerative. For each class of therapy, we summarize the state of the science, identify gaps in knowledge, and suggest future directions for therapy development. Results Knowledge gaps in all five therapeutic classes can be attributed to the lack of: (1) a unifying conceptual framework for evaluating therapeutic mechanisms of action; (2) large-scale randomized controlled trials; and (3) pharmacodynamic biomarkers that measure subclinical therapeutic effects in early-phase trials. To address these gaps, we propose a precision medicine approach in which clinical trials selectively enroll patients based upon their physiological receptivity to targeted therapies, and therapeutic effects are measured by complementary behavioral, neuroimaging, and electrophysiologic endpoints. Conclusions This personalized approach can be realized through rigorous clinical trial design and international collaboration, both of which will be essential for advancing the development of new therapies and ultimately improving the lives of patients with DoC. Supplementary Information The online version contains supplementary material available at 10.1007/s12028-021-01227-y.
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Wu YC, Liao YS, Yeh WH, Liang SF, Shaw FZ. Directions of Deep Brain Stimulation for Epilepsy and Parkinson's Disease. Front Neurosci 2021; 15:680938. [PMID: 34194295 PMCID: PMC8236576 DOI: 10.3389/fnins.2021.680938] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 05/12/2021] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Deep brain stimulation (DBS) is an effective treatment for movement disorders and neurological/psychiatric disorders. DBS has been approved for the control of Parkinson disease (PD) and epilepsy. OBJECTIVES A systematic review and possible future direction of DBS system studies is performed in the open loop and closed-loop configuration on PD and epilepsy. METHODS We searched Google Scholar database for DBS system and development. DBS search results were categorized into clinical device and research system from the open-loop and closed-loop perspectives. RESULTS We performed literature review for DBS on PD and epilepsy in terms of system development by the open loop and closed-loop configuration. This study described development and trends for DBS in terms of electrode, recording, stimulation, and signal processing. The closed-loop DBS system raised a more attention in recent researches. CONCLUSION We overviewed development and progress of DBS. Our results suggest that the closed-loop DBS is important for PD and epilepsy.
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Affiliation(s)
- Ying-Chang Wu
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan
| | - Ying-Siou Liao
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan
| | - Wen-Hsiu Yeh
- Institute of Basic Medical Science, National Cheng Kung University, Tainan, Taiwan
| | - Sheng-Fu Liang
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan
- Institute of Medical Informatics, National Cheng Kung University, Tainan, Taiwan
| | - Fu-Zen Shaw
- Institute of Basic Medical Science, National Cheng Kung University, Tainan, Taiwan
- Department of Psychology, National Cheng Kung University, Tainan, Taiwan
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Louie KH, Petrucci MN, Grado LL, Lu C, Tuite PJ, Lamperski AG, MacKinnon CD, Cooper SE, Netoff TI. Semi-automated approaches to optimize deep brain stimulation parameters in Parkinson's disease. J Neuroeng Rehabil 2021; 18:83. [PMID: 34020662 PMCID: PMC8147513 DOI: 10.1186/s12984-021-00873-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 04/27/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Deep brain stimulation (DBS) is a treatment option for Parkinson's disease patients when medication does not sufficiently manage their symptoms. DBS can be a highly effect therapy, but only after a time-consuming trial-and-error stimulation parameter adjustment process that is susceptible to clinician bias. This trial-and-error process will be further prolonged with the introduction of segmented electrodes that are now commercially available. New approaches to optimizing a patient's stimulation parameters, that can also handle the increasing complexity of new electrode and stimulator designs, is needed. METHODS To improve DBS parameter programming, we explored two semi-automated optimization approaches: a Bayesian optimization (BayesOpt) algorithm to efficiently determine a patient's optimal stimulation parameter for minimizing rigidity, and a probit Gaussian process (pGP) to assess patient's preference. Quantified rigidity measurements were obtained using a robotic manipulandum in two participants over two visits. Rigidity was measured, in 5Hz increments, between 10-185Hz (total 30-36 frequencies) on the first visit and at eight BayesOpt algorithm-selected frequencies on the second visit. The participant was also asked their preference between the current and previous stimulation frequency. First, we compared the optimal frequency between visits with the participant's preferred frequency. Next, we evaluated the efficiency of the BayesOpt algorithm, comparing it to random and equal interval selection of frequency. RESULTS The BayesOpt algorithm estimated the optimal frequency to be the highest tolerable frequency, matching the optimal frequency found during the first visit. However, the participants' pGP models indicate a preference at frequencies between 70-110 Hz. Here the stimulation frequency is lowest that achieves nearly maximal suppression of rigidity. BayesOpt was efficient, estimating the rigidity response curve to stimulation that was almost indistinguishable when compared to the longer brute force method. CONCLUSIONS These results provide preliminary evidence of the feasibility to use BayesOpt for determining the optimal frequency, while pGP patient's preferences include more difficult to measure outcomes. Both novel approaches can shorten DBS programming and can be expanded to include multiple symptoms and parameters.
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Affiliation(s)
- Kenneth H. Louie
- Department of Biomedical Engineering, University of Minnesota, 312 Church St. SE, Minneapolis, MN 55455 US
| | - Matthew N. Petrucci
- Department of Neurology, University of Minnesota, 516 Delaware St. SE, 55455 Minneapoli, MN US
| | - Logan L. Grado
- Department of Biomedical Engineering, University of Minnesota, 312 Church St. SE, Minneapolis, MN 55455 US
| | - Chiahao Lu
- Department of Neurology, University of Minnesota, 516 Delaware St. SE, 55455 Minneapoli, MN US
| | - Paul J. Tuite
- Department of Neurology, University of Minnesota, 516 Delaware St. SE, 55455 Minneapoli, MN US
| | - Andrew G. Lamperski
- Department of Electrical and Computer Engineering, University of Minnesota, 200 Union St. SE, Minneapolis, MN 55455 US
| | - Colum D. MacKinnon
- Department of Neurology, University of Minnesota, 516 Delaware St. SE, 55455 Minneapoli, MN US
| | - Scott E. Cooper
- Department of Neurology, University of Minnesota, 516 Delaware St. SE, 55455 Minneapoli, MN US
| | - Theoden I. Netoff
- Department of Biomedical Engineering, University of Minnesota, 312 Church St. SE, Minneapolis, MN 55455 US
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40
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Connolly MJ, Cole ER, Isbaine F, de Hemptinne C, Starr PA, Willie JT, Gross RE, Miocinovic S. Multi-objective data-driven optimization for improving deep brain stimulation in Parkinson's disease. J Neural Eng 2021; 18. [PMID: 33862604 DOI: 10.1088/1741-2552/abf8ca] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 04/16/2021] [Indexed: 11/12/2022]
Abstract
Objective.Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease (PD) but its success depends on a time-consuming process of trial-and-error to identify the optimal stimulation settings for each individual patient. Data-driven optimization algorithms have been proposed to efficiently find the stimulation setting that maximizes a quantitative biomarker of symptom relief. However, these algorithms cannot efficiently take into account stimulation settings that may control symptoms but also cause side effects. Here we demonstrate how multi-objective data-driven optimization can be used to find the optimal trade-off between maximizing symptom relief and minimizing side effects.Approach.Cortical and motor evoked potential data collected from PD patients during intraoperative stimulation of the subthalamic nucleus were used to construct a framework for designing and prototyping data-driven multi-objective optimization algorithms. Using this framework, we explored how these techniques can be applied clinically, and characterized the design features critical for solving this optimization problem. Our two optimization objectives were to maximize cortical evoked potentials, a putative biomarker of therapeutic benefit, and to minimize motor potentials, a biomarker of motor side effects.Main Results.Using thisin silicodesign framework, we demonstrated how the optimal trade-off between two objectives can substantially reduce the stimulation parameter space by 61 ± 19%. The best algorithm for identifying the optimal trade-off between the two objectives was a Bayesian optimization approach with an area under the receiver operating characteristic curve of up to 0.94 ± 0.02, which was possible with the use of a surrogate model and a well-tuned acquisition function to efficiently select which stimulation settings to sample.Significance.These findings show that multi-objective optimization is a promising approach for identifying the optimal trade-off between symptom relief and side effects in DBS. Moreover, these approaches can be readily extended to newly discovered biomarkers, adapted to DBS for disorders beyond PD, and can scale with the development of more complex DBS devices.
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Affiliation(s)
- Mark J Connolly
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, United States of America
| | - Eric R Cole
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, United States of America
| | - Faical Isbaine
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA 30322, United States of America
| | - Coralie de Hemptinne
- Department of Neurology, University of Florida, Gainesville, FL 32608, United States of America
| | - Phillip A Starr
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, United States of America
| | - Jon T Willie
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Robert E Gross
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, United States of America.,Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA 30322, United States of America.,Department of Neurology, Emory University School of Medicine, 12 Executive Park Drive North East, Atlanta, GA 30322, United States of America
| | - Svjetlana Miocinovic
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, United States of America.,Department of Neurology, Emory University School of Medicine, 12 Executive Park Drive North East, Atlanta, GA 30322, United States of America
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Vedam-Mai V, Deisseroth K, Giordano J, Lazaro-Munoz G, Chiong W, Suthana N, Langevin JP, Gill J, Goodman W, Provenza NR, Halpern CH, Shivacharan RS, Cunningham TN, Sheth SA, Pouratian N, Scangos KW, Mayberg HS, Horn A, Johnson KA, Butson CR, Gilron R, de Hemptinne C, Wilt R, Yaroshinsky M, Little S, Starr P, Worrell G, Shirvalkar P, Chang E, Volkmann J, Muthuraman M, Groppa S, Kühn AA, Li L, Johnson M, Otto KJ, Raike R, Goetz S, Wu C, Silburn P, Cheeran B, Pathak YJ, Malekmohammadi M, Gunduz A, Wong JK, Cernera S, Wagle Shukla A, Ramirez-Zamora A, Deeb W, Patterson A, Foote KD, Okun MS. Proceedings of the Eighth Annual Deep Brain Stimulation Think Tank: Advances in Optogenetics, Ethical Issues Affecting DBS Research, Neuromodulatory Approaches for Depression, Adaptive Neurostimulation, and Emerging DBS Technologies. Front Hum Neurosci 2021; 15:644593. [PMID: 33953663 PMCID: PMC8092047 DOI: 10.3389/fnhum.2021.644593] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 03/10/2021] [Indexed: 12/20/2022] Open
Abstract
We estimate that 208,000 deep brain stimulation (DBS) devices have been implanted to address neurological and neuropsychiatric disorders worldwide. DBS Think Tank presenters pooled data and determined that DBS expanded in its scope and has been applied to multiple brain disorders in an effort to modulate neural circuitry. The DBS Think Tank was founded in 2012 providing a space where clinicians, engineers, researchers from industry and academia discuss current and emerging DBS technologies and logistical and ethical issues facing the field. The emphasis is on cutting edge research and collaboration aimed to advance the DBS field. The Eighth Annual DBS Think Tank was held virtually on September 1 and 2, 2020 (Zoom Video Communications) due to restrictions related to the COVID-19 pandemic. The meeting focused on advances in: (1) optogenetics as a tool for comprehending neurobiology of diseases and on optogenetically-inspired DBS, (2) cutting edge of emerging DBS technologies, (3) ethical issues affecting DBS research and access to care, (4) neuromodulatory approaches for depression, (5) advancing novel hardware, software and imaging methodologies, (6) use of neurophysiological signals in adaptive neurostimulation, and (7) use of more advanced technologies to improve DBS clinical outcomes. There were 178 attendees who participated in a DBS Think Tank survey, which revealed the expansion of DBS into several indications such as obesity, post-traumatic stress disorder, addiction and Alzheimer’s disease. This proceedings summarizes the advances discussed at the Eighth Annual DBS Think Tank.
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Affiliation(s)
- Vinata Vedam-Mai
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States
| | - Karl Deisseroth
- Department of Bioengineering, Stanford University, Stanford, CA, United States.,Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, United States
| | - James Giordano
- Department of Neurology and Neuroethics Studies Program, Georgetown University Medical Center, Washington, DC, United States
| | - Gabriel Lazaro-Munoz
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| | - Winston Chiong
- Weill Institute for Neurosciences, Memory and Aging Center, University of California, San Francisco, San Francisco, CA, United States
| | - Nanthia Suthana
- Department of Neurosurgery, David Geffen School of Medicine and Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States
| | - Jean-Philippe Langevin
- Department of Neurosurgery, David Geffen School of Medicine and Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States.,Neurosurgery Service, Department of Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, United States
| | - Jay Gill
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States
| | - Wayne Goodman
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Nicole R Provenza
- School of Engineering, Brown University, Providence, RI, United States
| | - Casey H Halpern
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, United States
| | - Rajat S Shivacharan
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, United States
| | - Tricia N Cunningham
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, United States
| | - Sameer A Sheth
- Department of Neurological Surgery, Baylor College of Medicine, Houston, TX, United States
| | - Nader Pouratian
- Department of Neurosurgery, David Geffen School of Medicine and Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States
| | - Katherine W Scangos
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
| | - Helen S Mayberg
- Department of Neurology and Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Andreas Horn
- Movement Disorders & Neuromodulation Unit, Department for Neurology, Charité - University Medicine Berlin, Berlin, Germany
| | - Kara A Johnson
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.,Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States
| | - Christopher R Butson
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.,Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States
| | - Ro'ee Gilron
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Coralie de Hemptinne
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States.,Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Robert Wilt
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Maria Yaroshinsky
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Simon Little
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Philip Starr
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Greg Worrell
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Prasad Shirvalkar
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States.,Department of Anesthesiology (Pain Management) and Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Edward Chang
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Jens Volkmann
- Neurologischen Klinik Universitätsklinikum Würzburg, Würzburg, Germany
| | - Muthuraman Muthuraman
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Focus Program Translational Neuroscience, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Sergiu Groppa
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Focus Program Translational Neuroscience, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Andrea A Kühn
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Luming Li
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Matthew Johnson
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Kevin J Otto
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Robert Raike
- Restorative Therapies Group Implantables, Research and Core Technology, Medtronic, Minneapolis, MN, United States
| | - Steve Goetz
- Restorative Therapies Group Implantables, Research and Core Technology, Medtronic, Minneapolis, MN, United States
| | - Chengyuan Wu
- Department of Neurological Surgery, Thomas Jefferson University Hospitals, Philadelphia, PA, United States
| | - Peter Silburn
- Asia Pacific Centre for Neuromodulation, Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Binith Cheeran
- Neuromodulation Division, Abbott, Plano, TX, United States
| | - Yagna J Pathak
- Neuromodulation Division, Abbott, Plano, TX, United States
| | | | - Aysegul Gunduz
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States.,J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Joshua K Wong
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States
| | - Stephanie Cernera
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States.,J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Aparna Wagle Shukla
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States
| | - Adolfo Ramirez-Zamora
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States
| | - Wissam Deeb
- Department of Neurology, University of Massachusetts, Worchester, MA, United States
| | - Addie Patterson
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, 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, Department of Neurology, University of Florida, Gainesville, FL, United States
| | - Michael S Okun
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States
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Howell B, Isbaine F, Willie JT, Opri E, Gross RE, De Hemptinne C, Starr PA, McIntyre CC, Miocinovic S. Image-based biophysical modeling predicts cortical potentials evoked with subthalamic deep brain stimulation. Brain Stimul 2021; 14:549-563. [PMID: 33757931 DOI: 10.1016/j.brs.2021.03.009] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Revised: 02/19/2021] [Accepted: 03/14/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Subthalamic deep brain stimulation (DBS) is an effective surgical treatment for Parkinson's disease and continues to advance technologically with an enormous parameter space. As such, in-silico DBS modeling systems have become common tools for research and development, but their underlying methods have yet to be standardized and validated. OBJECTIVE Evaluate the accuracy of patient-specific estimates of neural pathway activations in the subthalamic region against intracranial, cortical evoked potential (EP) recordings. METHODS Pathway activations were modeled in eleven patients using the latest advances in connectomic modeling of subthalamic DBS, focusing on the hyperdirect pathway (HDP) and corticospinal/bulbar tract (CSBT) for their relevance in human research studies. Correlations between pathway activations and respective EP amplitudes were quantified. RESULTS Good model performance required accurate lead localization and image fusions, as well as appropriate selection of fiber diameter in the biophysical model. While optimal model parameters varied across patients, good performance could be achieved using a global set of parameters that explained 60% and 73% of electrophysiologic activations of CSBT and HDP, respectively. Moreover, restricted models fit to only EP amplitudes of eight standard (monopolar and bipolar) electrode configurations were able to extrapolate variation in EP amplitudes across other directional electrode configurations and stimulation parameters, with no significant reduction in model performance across the cohort. CONCLUSIONS Our findings demonstrate that connectomic models of DBS with sufficient anatomical and electrical details can predict recruitment dynamics of white matter. These results will help to define connectomic modeling standards for preoperative surgical targeting and postoperative patient programming applications.
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Affiliation(s)
- Bryan Howell
- Department of Biomedical Engineering, Case Western Reserve University, USA
| | | | - Jon T Willie
- Department of Neurosurgery, Emory University, USA
| | - Enrico Opri
- Department of Neurology, Emory University, USA
| | | | | | - Philip A Starr
- Department of Neurological Surgery, University of California San Francisco, USA
| | - Cameron C McIntyre
- Department of Biomedical Engineering, Case Western Reserve University, USA
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Luo M, Narasimhan S, Larson PS, Martin AJ, Konrad PE, Miga MI. Impact of brain shift on neural pathways in deep brain stimulation: a preliminary analysis via multi-physics finite element models. J Neural Eng 2021; 18. [PMID: 33740780 DOI: 10.1088/1741-2552/abf066] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 03/19/2021] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The effectiveness of deep brain stimulation (DBS) depends on electrode placement accuracy, which can be compromised by brain shift during surgery. While there have been efforts in assessing the impact of electrode misplacement due to brain shift using preop- and postop- imaging data, such analysis using preop- and intraop- imaging data via biophysical modeling has not been conducted. This work presents a preliminary study that applies a multi-physics analysis framework using finite element biomechanical and bioelectric models to examine the impact of realistic intraoperative shift on neural pathways determined by tractography. APPROACH The study examined six patients who had undergone interventional magnetic resonance (iMR)-guided DBS surgery. The modeling framework utilized a biomechanical approach to update preoperative MR to reflect shift-induced anatomical changes. Using this anatomically deformed image and its undeformed counterpart, bioelectric effects from shifting electrode leads could be simulated and neural activation differences were approximated. Specifically, for each configuration, volume of tissue activation (VTA) was computed and subsequently used for tractography estimation. Total tract volume and overlapping volume with motor regions as well as connectivity profile were compared. In addition, volumetric overlap between different fiber bundles among configurations was computed and correlated to estimated shift. MAIN RESULT The study found deformation-induced differences in tract volume, motor region overlap, and connectivity behavior, suggesting the impact of shift. There is a strong correlation (R=-0.83) between shift from intended target and intended neural pathway recruitment, where at threshold of ~2.94 mm, intended recruitment completely degrades. The determined threshold is consistent with and provides quantitative support to prior observations and literature that deviations of 2-3 mm are detrimental. SIGNIFICANCE The findings support and advance prior studies and understanding to illustrate the need to account for shift in DBS and the potentiality of computational modeling for estimating influence of shift on neural activation.
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Affiliation(s)
- Ma Luo
- Biomedical Engineering, Vanderbilt University, 5824 Stevenson Center, Nashville, Tennessee, 37232, UNITED STATES
| | - Saramati Narasimhan
- Department of Neurological Surgery, Vanderbilt University Medical Center, Village at Vanderbilt, 1500 21st Ave. South, Nashville, Tennessee, 37212, UNITED STATES
| | - Paul S Larson
- Department of Neurological Surgery, University of California San Francisco, Box 0112, 505 Parnassus Ave, Room M779, San Francisco, California, 94143, UNITED STATES
| | - Alastiar J Martin
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Avenue, San Francisco, California, 94143, UNITED STATES
| | - Peter E Konrad
- Department of Neurosurgery, West Virginia University, PO Box 9183, Morgantown, West Virginia, 26506, UNITED STATES
| | - Michael I Miga
- Department of Biomedical Engineering, Vanderbilt University, 5901 Stevenson Center, Nashville, Tennessee, 37235, UNITED STATES
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44
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Dembek TA, Baldermann JC, Petry-Schmelzer JN, Jergas H, Treuer H, Visser-Vandewalle V, Dafsari HS, Barbe MT. Sweetspot Mapping in Deep Brain Stimulation: Strengths and Limitations of Current Approaches. Neuromodulation 2021; 25:877-887. [PMID: 33476474 DOI: 10.1111/ner.13356] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 12/15/2020] [Accepted: 12/21/2020] [Indexed: 01/23/2023]
Abstract
OBJECTIVES Open questions remain regarding the optimal target, or sweetspot, for deep brain stimulation (DBS) in, for example, Parkinson's disease. Previous studies introduced different methods of mapping DBS effects to determine sweetspots. While having a direct impact on surgical targeting and postoperative programming in DBS, these methods so far have not been compared. MATERIALS AND METHODS This study investigated five previously published DBS mapping approaches regarding their potential to correctly identify a predefined target. Methods were investigated in silico in eight different use-case scenarios, which incorporated different types of clinical data, noise, and differences in underlying neuroanatomy. Dice coefficients were calculated to determine the overlap between identified sweetspots and the predefined target. Additionally, out-of-sample predictive capabilities were assessed using the amount of explained variance R2 . RESULTS The five investigated methods resulted in highly variable sweetspots. Methods based on voxel-wise statistics against average outcomes showed the best performance overall. While predictive capabilities were high, even in the best of cases Dice coefficients remained limited to values around 0.5, highlighting the overall limitations of sweetspot identification. CONCLUSIONS This study highlights the strengths and limitations of current approaches to DBS sweetspot mapping. Those limitations need to be taken into account when considering the clinical implications. All future approaches should be investigated in silico before being applied to clinical data.
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Affiliation(s)
- Till A Dembek
- Department of Neurology, Faculty of Medicine, University of Cologne, Cologne, Germany
| | | | | | - Hannah Jergas
- Department of Neurology, Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Harald Treuer
- Department of Stereotactic and Functional Neurosurgery, Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Veerle Visser-Vandewalle
- Department of Stereotactic and Functional Neurosurgery, Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Haidar S Dafsari
- Department of Neurology, Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Michael T Barbe
- Department of Neurology, Faculty of Medicine, University of Cologne, Cologne, Germany
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45
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Johnson KA, Duffley G, Anderson DN, Ostrem JL, Welter ML, Baldermann JC, Kuhn J, Huys D, Visser-Vandewalle V, Foltynie T, Zrinzo L, Hariz M, Leentjens AFG, Mogilner AY, Pourfar MH, Almeida L, Gunduz A, Foote KD, Okun MS, Butson CR. Structural connectivity predicts clinical outcomes of deep brain stimulation for Tourette syndrome. Brain 2020; 143:2607-2623. [PMID: 32653920 DOI: 10.1093/brain/awaa188] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 03/12/2020] [Accepted: 04/20/2020] [Indexed: 12/11/2022] Open
Abstract
Deep brain stimulation may be an effective therapy for select cases of severe, treatment-refractory Tourette syndrome; however, patient responses are variable, and there are no reliable methods to predict clinical outcomes. The objectives of this retrospective study were to identify the stimulation-dependent structural networks associated with improvements in tics and comorbid obsessive-compulsive behaviour, compare the networks across surgical targets, and determine if connectivity could be used to predict clinical outcomes. Volumes of tissue activated for a large multisite cohort of patients (n = 66) implanted bilaterally in globus pallidus internus (n = 34) or centromedial thalamus (n = 32) were used to generate probabilistic tractography to form a normative structural connectome. The tractography maps were used to identify networks that were correlated with improvement in tics or comorbid obsessive-compulsive behaviour and to predict clinical outcomes across the cohort. The correlated networks were then used to generate 'reverse' tractography to parcellate the total volume of stimulation across all patients to identify local regions to target or avoid. The results showed that for globus pallidus internus, connectivity to limbic networks, associative networks, caudate, thalamus, and cerebellum was positively correlated with improvement in tics; the model predicted clinical improvement scores (P = 0.003) and was robust to cross-validation. Regions near the anteromedial pallidum exhibited higher connectivity to the positively correlated networks than posteroventral pallidum, and volume of tissue activated overlap with this map was significantly correlated with tic improvement (P < 0.017). For centromedial thalamus, connectivity to sensorimotor networks, parietal-temporal-occipital networks, putamen, and cerebellum was positively correlated with tic improvement; the model predicted clinical improvement scores (P = 0.012) and was robust to cross-validation. Regions in the anterior/lateral centromedial thalamus exhibited higher connectivity to the positively correlated networks, but volume of tissue activated overlap with this map did not predict improvement (P > 0.23). For obsessive-compulsive behaviour, both targets showed that connectivity to the prefrontal cortex, orbitofrontal cortex, and cingulate cortex was positively correlated with improvement; however, only the centromedial thalamus maps predicted clinical outcomes across the cohort (P = 0.034), but the model was not robust to cross-validation. Collectively, the results demonstrate that the structural connectivity of the site of stimulation are likely important for mediating symptom improvement, and the networks involved in tic improvement may differ across surgical targets. These networks provide important insight on potential mechanisms and could be used to guide lead placement and stimulation parameter selection, as well as refine targets for neuromodulation therapies for Tourette syndrome.
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Affiliation(s)
- Kara A Johnson
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, USA.,Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
| | - Gordon Duffley
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, USA.,Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
| | - Daria Nesterovich Anderson
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, USA.,Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA.,Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
| | - Jill L Ostrem
- Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Marie-Laure Welter
- Institut du Cerveau et de la Moelle Epiniere, Sorbonne Universités, University of Pierre and Marie Curie University of Paris, the French National Institute of Health and Medical Research U 1127, the National Center for Scientific Research 7225, Paris, France
| | - Juan Carlos Baldermann
- Department of Psychiatry and Psychotherapy, University of Cologne, Cologne, Germany.,Department of Neurology, University of Cologne, Cologne, Germany
| | - Jens Kuhn
- Department of Psychiatry and Psychotherapy, University of Cologne, Cologne, Germany.,Department of Psychiatry, Psychotherapy, and Psychosomatic Medicine, Johanniter Hospital Oberhausen, EVKLN, Oberhausen, Germany
| | - Daniel Huys
- Department of Psychiatry and Psychotherapy, University of Cologne, Cologne, Germany
| | - Veerle Visser-Vandewalle
- Department of Stereotaxy and Functional Neurosurgery, University Hospital Cologne, Cologne, Germany
| | - Thomas Foltynie
- Functional Neurosurgery Unit, Department of Clinical and Movement Neurosciences, University College London, Queen Square Institute of Neurology, London, UK
| | - Ludvic Zrinzo
- Functional Neurosurgery Unit, Department of Clinical and Movement Neurosciences, University College London, Queen Square Institute of Neurology, London, UK
| | - Marwan Hariz
- Functional Neurosurgery Unit, Department of Clinical and Movement Neurosciences, University College London, Queen Square Institute of Neurology, London, UK.,Department of Clinical Neuroscience, Umea University, Umea, Sweden
| | - Albert F G Leentjens
- Department of Psychiatry and Neuropsychology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Alon Y Mogilner
- Center for Neuromodulation, New York University Langone Medical Center, New York, New York, USA
| | - Michael H Pourfar
- Center for Neuromodulation, New York University Langone Medical Center, New York, New York, USA
| | - Leonardo Almeida
- Norman Fixel Institute for Neurological Diseases , Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, Florida, USA
| | - Aysegul Gunduz
- Norman Fixel Institute for Neurological Diseases , Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, Florida, USA.,J Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA
| | - Kelly D Foote
- Norman Fixel Institute for Neurological Diseases , Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, Florida, USA
| | - Michael S Okun
- Norman Fixel Institute for Neurological Diseases , Program for Movement Disorders and Neurorestoration, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, Florida, USA
| | - Christopher R Butson
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, USA.,Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA.,Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA.,Departments of Neurology and Psychiatry, University of Utah, Salt Lake City, Utah, USA
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46
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Iwasa SN, Shi HH, Hong SH, Chen T, Marquez-Chin M, Iorio-Morin C, Kalia SK, Popovic MR, Naguib HE, Morshead CM. Novel Electrode Designs for Neurostimulation in Regenerative Medicine: Activation of Stem Cells. Bioelectricity 2020; 2:348-361. [PMID: 34471854 PMCID: PMC8370381 DOI: 10.1089/bioe.2020.0034] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Neural stem and progenitor cells (i.e., neural precursors) are found within specific regions in the central nervous system and have great regenerative capacity. These cells are electrosensitive and their behavior can be regulated by the presence of electric fields (EFs). Electrical stimulation is currently used to treat neurological disorders in a clinical setting. Herein we propose that electrical stimulation can be used to enhance neural repair by regulating neural precursor cell (NPC) kinetics and promoting their migration to sites of injury or disease. We discuss how intrinsic and extrinsic factors can affect NPC migration in the presence of an EF and how this impacts electrode design with the goal of enhancing tissue regeneration. We conclude with an outlook on future clinical applications of electrical stimulation and highlight technological advances that would greatly support these applications.
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Affiliation(s)
- Stephanie N Iwasa
- The KITE Research Institute, Toronto Rehabilitation Institute-University Health Network, Toronto, Canada
- CRANIA, University Health Network and University of Toronto, Toronto, Canada
| | - HaoTian H Shi
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada
| | - Sung Hwa Hong
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Canada
| | - Tianhao Chen
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Melissa Marquez-Chin
- The KITE Research Institute, Toronto Rehabilitation Institute-University Health Network, Toronto, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Christian Iorio-Morin
- Department of Neurosurgery, University Health Network, University of Toronto, Toronto, Canada
| | - Suneil K Kalia
- The KITE Research Institute, Toronto Rehabilitation Institute-University Health Network, Toronto, Canada
- CRANIA, University Health Network and University of Toronto, Toronto, Canada
- Department of Neurosurgery, University Health Network, University of Toronto, Toronto, Canada
- Krembil Research Institute, Toronto, Canada
| | - Milos R Popovic
- The KITE Research Institute, Toronto Rehabilitation Institute-University Health Network, Toronto, Canada
- CRANIA, University Health Network and University of Toronto, Toronto, Canada
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Hani E Naguib
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
- Department of Materials Science & Engineering, University of Toronto, Toronto, Canada
| | - Cindi M Morshead
- The KITE Research Institute, Toronto Rehabilitation Institute-University Health Network, Toronto, Canada
- CRANIA, University Health Network and University of Toronto, Toronto, Canada
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
- Department of Surgery, University of Toronto, Toronto, Canada
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47
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Baniasadi M, Proverbio D, Gonçalves J, Hertel F, Husch A. FastField: An open-source toolbox for efficient approximation of deep brain stimulation electric fields. Neuroimage 2020; 223:117330. [DOI: 10.1016/j.neuroimage.2020.117330] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 05/26/2020] [Accepted: 08/25/2020] [Indexed: 01/25/2023] Open
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48
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Aubignat M, Lefranc M, Tir M, Krystkowiak P. Deep brain stimulation programming in Parkinson's disease: Introduction of current issues and perspectives. Rev Neurol (Paris) 2020; 176:770-779. [DOI: 10.1016/j.neurol.2020.02.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2019] [Revised: 01/28/2020] [Accepted: 02/12/2020] [Indexed: 12/11/2022]
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49
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Kramme J, Dembek TA, Treuer H, Dafsari HS, Barbe MT, Wirths J, Visser-Vandewalle V. Potentials and Limitations of Directional Deep Brain Stimulation: A Simulation Approach. Stereotact Funct Neurosurg 2020; 99:65-74. [PMID: 33080600 DOI: 10.1159/000509781] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 06/22/2020] [Indexed: 11/19/2022]
Abstract
BACKGROUND Directional leads are increasingly used in deep brain stimulation. They allow shaping the electrical field in the axial plane. These new possibilities increase the complexity of programming. Thus, optimized programming approaches are needed to assist clinical testing and to obtain full clinical benefit. OBJECTIVES This simulation study investigates to what extent the electrical field can be shaped by directional steering to compensate for lead malposition. METHOD Binary volumes of tissue activated (VTA) were simulated, by using a finite element method approach, for different amplitude distributions on the three directional electrodes. VTAs were shifted from 0 to 2 mm at different shift angles with respect to the lead orientation, to determine the best compensation of a target volume. RESULTS Malpositions of 1 mm can be compensated with the highest gain of overlap with directional leads. For larger shifts, an improvement of overlap of 10-30% is possible, depending on the stimulation amplitude and shift angle of the lead. Lead orientation and shift determine the amplitude distribution of the electrodes. CONCLUSION To get full benefit from directional leads, both the shift angle as well as the shift to target volume are required to choose the correct amplitude distribution on the electrodes. Current directional leads have limitations when compensating malpositions >1 mm; however, they still outperform conventional leads in reducing overstimulation. Further, their main advantage probably lies in the reduction of side effects. Databases like the one from this simulation could serve for optimized lead programming algorithms in the future.
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Affiliation(s)
- Johanna Kramme
- Department of Stereotactic and Functional Neurosurgery, University of Cologne Faculty of Medicine and University Hospital Cologne, Cologne, Germany,
| | - Till A Dembek
- Department of Neurology, University of Cologne Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Harald Treuer
- Department of Stereotactic and Functional Neurosurgery, University of Cologne Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Haidar S Dafsari
- Department of Neurology, University of Cologne Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Michael T Barbe
- Department of Neurology, University of Cologne Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Jochen Wirths
- Department of Stereotactic and Functional Neurosurgery, University of Cologne Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Veerle Visser-Vandewalle
- Department of Stereotactic and Functional Neurosurgery, University of Cologne Faculty of Medicine and University Hospital Cologne, Cologne, Germany
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50
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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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