Transcranial Direct Current Stimulation Optimization – From Physics-Based Computer Simulations to High-Fidelity Head Phantom Fabrication and Measurements

BackgroundTranscranial direct current stimulation (tDCS) modulates neural networks. Computer simulations, while used to identify how currents behave within tissues of different conductivity properties, still need to be complemented by physical models.Objective/HypothesisTo better understand tDCS eff...

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Bibliographic Details
Main Authors: Leon Morales-Quezada, Mirret M. El-Hagrassy, Beatriz Costa, R. Andy McKinley, Pengcheng Lv, Felipe Fregni
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-10-01
Series:Frontiers in Human Neuroscience
Subjects:
EEG
Online Access:https://www.frontiersin.org/article/10.3389/fnhum.2019.00388/full
Description
Summary:BackgroundTranscranial direct current stimulation (tDCS) modulates neural networks. Computer simulations, while used to identify how currents behave within tissues of different conductivity properties, still need to be complemented by physical models.Objective/HypothesisTo better understand tDCS effects on biology-mimicking tissues by developing and testing the feasibility of a high-fidelity 3D head phantom model that has sensing capabilities at different compartmental levels.MethodsModels obtained from MRI images generated 3D printed molds. Agar phantoms were fabricated, and 18 monitoring electrodes were placed on specific phantom brain areas.ResultsWhen using rectangular electrodes, the measured and simulated voltages at the monitoring electrodes agreed reasonably well, except at excitation locations. The electric field distribution in different phantom layers appeared better confined with circular electrodes compared to rectangular electrodes.ConclusionThe high-fidelity 3D head model was found to be feasible and comparable with computer-based electrical simulations, with high correlation between simulated and measured brain voltages. This feasibility study supports testing to further assess the reliability of this model.
ISSN:1662-5161