scholarly journals Optimization of closed-loop electrical stimulation enables robust cerebellar-directed seizure control

2021 ◽  
Author(s):  
Bethany Stieve ◽  
Thomas Richner ◽  
Chris Krook-Magnuson ◽  
Theoden Netoff ◽  
Esther Krook-Magnuson
Keyword(s):  
Electrical Stimulation ◽  
Temporal Lobe Epilepsy ◽  
Gaussian Process ◽  
Parameter Space ◽  
Seizure Control ◽  
Gaussian Process Regression ◽  
Bayesian Optimization ◽  
On Demand ◽  
Stimulation Parameters ◽  
Stimulation Of

Additional treatment options for temporal lobe epilepsy are needed, and potential interventions targeting the cerebellum are of interest. Previous animal work has shown strong inhibition of hippocampal seizures through on-demand optogenetic manipulation of the cerebellum. However, decades of work examining electrical stimulation - a more immediately translatable approach - targeting the cerebellum has produced very mixed results. We were therefore interested in exploring the impact that stimulation parameters may have on seizure outcomes. Using a mouse model of temporal lobe epilepsy, we conducted on-demand electrical stimulation of the cerebellar cortex, and varied stimulation charge, frequency, and pulse width, resulting in over a thousand different potential combinations of settings. To explore this parameter space in an efficient, data-driven, manner, we utilized Bayesian optimization with Gaussian process regression, implemented in Matlab with an Expected Improvement Plus acquisition function. We examined two different fitting conditions and two different electrode orientations. Following the optimization process, we conducted additional on-demand experiments to test the effectiveness of selected settings. Across all animals, we found that Bayesian optimization allowed identification of effective intervention settings. Additionally, generally similar optimal settings were identified across animals, suggesting that personalized optimization may not always be necessary. While optimal settings were consistently effective, stimulation with settings predicted from the Gaussian process regression to be ineffective failed to provide seizure control. Taken together, our results provide a blueprint for exploration of a large parameter space for seizure control, and illustrate that robust inhibition of seizures can be achieved with electrical stimulation of the cerebellum, but only if the correct stimulation parameters are used.

Electrical Stimulation Using Kilohertz-Frequency Alternating Current

2009 ◽  
Vol 89 (2) ◽  
pp. 181-190 ◽  
Author(s):  
Alex R Ward
Keyword(s):  
Electrical Stimulation ◽  
Mechanism Of Action ◽  
Alternating Current ◽  
Transcutaneous Electrical Stimulation ◽  
Muscle Strengthening ◽  
Stimulation Parameters ◽  
Stimulus Parameters ◽  
Stimulation Of Nerve ◽  
Insight Into ◽  
Stimulation Of

Transcutaneous electrical stimulation using kilohertz-frequency alternating current (AC) became popular in the 1950s with the introduction of “interferential currents,” promoted as a means of producing depth-efficient stimulation of nerve and muscle. Later, “Russian current” was adopted as a means of muscle strengthening. This article reviews some clinically relevant, laboratory-based studies that offer an insight into the mechanism of action of kilohertz-frequency AC. It provides some answers to the question: “What are the optimal stimulus parameters for eliciting forceful, yet comfortable, electrically induced muscle contractions?” It is concluded that the stimulation parameters commonly used clinically (Russian and interferential currents) are suboptimal for achieving their stated goals and that greater benefit would be obtained using short-duration (2–4 millisecond), rectangular bursts of kilohertz-frequency AC with a frequency chosen to maximize the desired outcome.

scholarly journals ASCENT (Automated Simulations to Characterize Electrical Nerve Thresholds): A pipeline for sample-specific computational modeling of electrical stimulation of peripheral nerves

2021 ◽  
Vol 17 (9) ◽  
pp. e1009285
Author(s):  
Eric D. Musselman ◽  
Jake E. Cariello ◽  
Warren M. Grill ◽  
Nicole A. Pelot
Keyword(s):  
Electrical Stimulation ◽  
Computational Modeling ◽  
Peripheral Nerves ◽  
Nerve Fibers ◽  
Great Promise ◽  
Simulation Data ◽  
Stimulation Parameters ◽  
Neural Anatomy ◽  
Disease And Disorders ◽  
Stimulation Of

Electrical stimulation and block of peripheral nerves hold great promise for treatment of a range of disease and disorders, but promising results from preclinical studies often fail to translate to successful clinical therapies. Differences in neural anatomy across species require different electrodes and stimulation parameters to achieve equivalent nerve responses, and accounting for the consequences of these factors is difficult. We describe the implementation, validation, and application of a standardized, modular, and scalable computational modeling pipeline for biophysical simulations of electrical activation and block of nerve fibers within peripheral nerves. The ASCENT (Automated Simulations to Characterize Electrical Nerve Thresholds) pipeline provides a suite of built-in capabilities for user control over the entire workflow, including libraries for parts to assemble electrodes, electrical properties of biological materials, previously published fiber models, and common stimulation waveforms. We validated the accuracy of ASCENT calculations, verified usability in beta release, and provide several compelling examples of ASCENT-implemented models. ASCENT will enable the reproducibility of simulation data, and it will be used as a component of integrated simulations with other models (e.g., organ system models), to interpret experimental results, and to design experimental and clinical interventions for the advancement of peripheral nerve stimulation therapies.

scholarly journals Designing staggered platelet composite structure with Gaussian process regression based Bayesian optimization

2021 ◽  
Author(s):  
Kundo Park ◽  
Youngsoo Kim ◽  
Minki Kim ◽  
Chihyeon Song ◽  
Jinkyoo Park ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Composite Structure ◽  
Optimization Problems ◽  
Gaussian Process Regression ◽  
Monte Carlo Algorithm ◽  
Bayesian Optimization ◽  
Expected Improvement ◽  
Uniaxial Tensile ◽  
Initial Training ◽  
Optimization Framework

The staggered platelet composite structure, one of the most well-known examples of biomimetics, is inspired by the microstructure of nacre, where stiff mineral platelets are stacked with a small fraction of soft polymer in a brick-and-mortar style. Significant efforts have been made to establish a framework for designing a staggered platelet pattern that achieves an excellent balance of toughness and stiffness. However, because no analytical formula for accurately predicting its toughness is available because of the complexity of the failure mechanism of realistic composites, existing studies have investigated either idealized composites with simplified material properties or realistic composites designed by heuristics. In the present study, we propose a Bayesian optimization framework to design a staggered platelet structure that renders high toughness. Gaussian process regression (GPR) was adopted to model statistically the complex relationship between the shape of the staggered platelet array and the resultant toughness. The Markov chain Monte Carlo algorithm was used to determine the optimal kernel hyperparameter set for the GPR. Starting with 14 initial training data collected with uniaxial tensile tests, a GPR-based Bayesian optimization using the expected improvement (EI) acquisition function was carried out. As a result, it was possible to design a staggered platelet pattern with a toughness 11% higher than that of the best sample in the initial training set, and this improvement was achieved after only three iterations of our optimization cycle. As this optimization framework does not require any material theories and models, this process can be easily adapted and applied to various other material optimization problems based on a limited set of experiments or computational simulations.

Pain suppression induced by electrical stimulation of the pontine parabrachial region

1985 ◽  
Vol 62 (3) ◽  
pp. 397-407 ◽  
Author(s):  
Antonio A. F. DeSalles ◽  
Yoichi Katayama ◽  
Donald P. Becker ◽  
Ronald L. Hayes
Keyword(s):  
Electrical Stimulation ◽  
Time Course ◽  
Low Frequency ◽  
Cholinergic Stimulation ◽  
Technical Problems ◽  
Physiological Functions ◽  
Content Type ◽  
Analgesic Effects ◽  
Stimulation Parameters ◽  
Stimulation Of

✓ Cholinergic stimulation by microinjection of drugs into a region surrounding the lateral half of the brachium conjunctivum selectively produces a non-opiate form of pain suppression in the cat. Since this suppression does not appear to involve neural systems that mediate morphine analgesia, stimulation of this pontine parabrachial region (PBR) may potentially be useful for control of human pain resistant or tolerant to opiate treatment. Because of technical problems associated with the clinical use of microinjection techniques in the human brain, we investigated whether electrical stimulation of the PBR can produce pain suppression similar to pain suppression produced by cholinergic stimulation. The results indicate that electrical stimulation of an area generally corresponding to the PBR can also produce significant pain suppression. Although the PBR is a region previously implicated in a variety of behavioral and physiological functions, the stimulation parameters that produce maximal pain suppressive effects (namely, low frequency and relatively low intensity) were not associated with noticeable changes in such functions. The prolonged onset period and persistent analgesic effects outlasting the period of stimulation — features that have been reported in other studies of brain stimulation-produced pain suppression — were observed in the present study. The time course of pain suppression did not parallel other changes in behavioral and physiological functions. These data indicate that electrical stimulation of the PBR, under certain stimulation parameters, can activate previously demonstrated neural populations related to pain suppression without affecting neural elements contributing to other behavioral or physiological functions. The authors suggest that electrical stimulation of the PBR may be clinically applicable for treatment of human pain.

Estimation of municipal waste generation of Turkey using socio-economic indicators by Bayesian optimization tuned Gaussian process regression

2020 ◽  
Vol 38 (8) ◽  
pp. 840-850 ◽  
Author(s):  
Zeynep Ceylan
Keyword(s):  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Kernel Functions ◽  
Machine Learning Algorithms ◽  
Economic Indicators ◽  
Bayesian Optimization ◽  
Superior Performance ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Support Vector

Accurate estimation of municipal solid waste (MSW) generation has become a crucial task in decision-making processes for the MSW planning and management systems. In this study, the Gaussian process regression (GPR) model tuned by Bayesian optimization was used to forecast the MSW generation of Turkey. The Bayesian optimization method, which can efficiently optimize the hyperparameters of kernel functions in the machine learning algorithms, was applied to reduce the computation redundancy and enhance the estimation performance of the models. Four socio-economic indicators such as population, gross domestic product per capita, inflation rate, and the unemployment rate were used as input variables. The performance of the Bayesian GPR (BGPR) model was compared with the multiple linear regression (MLR) and Bayesian support vector regression (BSVR) models. Different performance measures such as mean absolute deviation (MAD), root mean square error (RMSE), and coefficient of determination (R2) values were used to evaluate the performance of the models. The exponential-GPR model tuned by Bayesian optimization showed superior performance with minimum MAD (0.0182), RMSE (0.0203), and high R2 (0.9914) values in the training phase and minimum MAD (0.0342), RMSE (0.0463), and high R2 (0.9841) values in the testing phase. The results of this study can help decision-makers to be aware of social-economic factors associated with waste management and ensure optimal usage of their resources in future planning.

scholarly journals Gaussian Process Regression Tuned by Bayesian Optimization for Seawater Intrusion Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
George Kopsiaftis ◽  
Eftychios Protopapadakis ◽  
Athanasios Voulodimos ◽  
Nikolaos Doulamis ◽  
Aristotelis Mantoglou
Keyword(s):  
Gaussian Process ◽  
Seawater Intrusion ◽  
Surrogate Model ◽  
Gaussian Process Regression ◽  
Variable Density ◽  
Regression Method ◽  
Bayesian Optimization ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Pumping Rate

Accurate prediction of the seawater intrusion extent is necessary for many applications, such as groundwater management or protection of coastal aquifers from water quality deterioration. However, most applications require a large number of simulations usually at the expense of prediction accuracy. In this study, the Gaussian process regression method is investigated as a potential surrogate model for the computationally expensive variable density model. Gaussian process regression is a nonparametric kernel-based probabilistic model able to handle complex relations between input and output. In this study, the extent of seawater intrusion is represented by the location of the 0.5 kg/m3 iso-chlore at the bottom of the aquifer (seawater intrusion toe). The initial position of the toe, expressed as the distance of the specific line from a number of observation points across the coastline, along with the pumping rates are the surrogate model inputs, whereas the final position of the toe constitutes the output variable set. The training sample of the surrogate model consists of 4000 variable density simulations, which differ not only in the pumping rate pattern but also in the initial concentration distribution. The Latin hypercube sampling method is used to obtain the pumping rate patterns. For comparison purposes, a number of widely used regression methods are employed, specifically regression trees and Support Vector Machine regression (linear and nonlinear). A Bayesian optimization method is applied to all the regressors, to maximize their efficiency in the prediction of seawater intrusion. The final results indicate that the Gaussian process regression method, albeit more time consuming, proved to be more efficient in terms of the mean absolute error (MAE), the root mean square error (RMSE), and the coefficient of determination (R2).

Electrical Stimulation of Subiculum for the Treatment of Refractory Mesial Temporal Lobe Epilepsy with Hippocampal Sclerosis: A 2-Year Follow-Up Study

2020 ◽  
pp. 1-8
Author(s):  
Daruni Vázquez-Barrón ◽  
Manola Cuéllar-Herrera ◽  
Francisco Velasco ◽  
Ana Luisa Velasco
Keyword(s):  
Electrical Stimulation ◽  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Hippocampal Sclerosis ◽  
Mesial Temporal Lobe Epilepsy ◽  
Open Label ◽  
Anticonvulsive Effect ◽  
Mesial Temporal Lobe ◽  
Stimulation Of

<b><i>Introduction:</i></b> Evidence has been provided that the subiculum may play an important role in the generation of seizures. Electrical stimulation at this target has been reported to have anticonvulsive effects in kindling and pilocarpine rat models, while in a clinical study of hippocampal deep brain stimulation (DBS), contacts closest to the subiculum were associated with a better anticonvulsive effect. <b><i>Objectives:</i></b> To evaluate the effect of electrical stimulation of the subiculum in patients with refractory mesial temporal lobe epilepsy (MTLE) who have hippocampal sclerosis (HS). <b><i>Methods:</i></b> Six patients with refractory MTLE and HS, who had focal impaired awareness seizures (FIAS) and focal to bilateral tonic-clonic seizures (FBTCS), had DBS electrodes implanted in the subiculum. During the first month after implantation, all patients were OFF stimulation, then they all completed an open-label follow-up of 24 months ON stimulation. DBS parameters were set at 3 V, 450 µs, 130 Hz, cycling stimulation 1 min ON, 4 min OFF. <b><i>Results:</i></b> There was a mean reduction of 49.16% (±SD 41.65) in total seizure number (FIAS + FBTCS) and a mean reduction of 67.93% (±SD 33.33) in FBTCS at 24 months. FBTCS decreased significantly with respect to baseline, starting from month 2 ON stimulation. <b><i>Conclusions:</i></b> Subiculum stimulation is effective for FBTCS reduction in patients with MTLE and HS, suggesting that the subiculum mediates the generalization rather than the genesis of mesial temporal lobe seizures. Better results are observed at longer follow-up times.

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