Data_Sheet_1_Transcranial Direct Current Stimulation Optimization – From Physics-Based Computer Simulations to High-Fidelity Head Phantom Fabrication and Measurements.docx
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https://figshare.com/articles/dataset/Data_Sheet_1_Transcranial_Direct_Current_Stimulation_Optimization_From_Physics-Based_Computer_Simulations_to_High-Fidelity_Head_Phantom_Fabrication_and_Measurements_docx/10110854
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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.
背景:经颅直流电刺激(Transcranial direct current stimulation, tDCS)可调节神经网络。计算机模拟虽可用于探究不同导电特性组织内的电流行为,但仍需物理模型予以补充完善。
目标与假设:本研究旨在通过开发并验证一款具备多分区层面传感能力的高保真3D头部体模的可行性,以更好地阐明经颅直流电刺激(tDCS)对仿生组织的作用效果。
方法:基于磁共振成像(Magnetic Resonance Imaging, MRI)图像构建的模型生成了3D打印模具;随后制备琼脂体模,并在体模的特定脑部区域安置18个监测电极。
结果:当使用矩形电极时,除刺激位点外,监测电极处的实测电压与仿真电压吻合度较好;相较于矩形电极,圆形电极在体模各层中的电场分布更为局限。
结论:本研究证实该高保真3D头部体模具备可行性,且与计算机辅助电学仿真结果具有良好一致性,仿真与实测的脑部电压间存在高度相关性;此项可行性研究为进一步评估该模型的可靠性提供了支持。
创建时间:
2019-10-31



