Background

Improvements in electroconvulsive therapy (ECT) outcomes have followed refinement in device electrical output and electrode montage. The physical properties of the ECT stimulus, together with those of the patient's head, determine the impedances measured by the device and govern current delivery to the brain and ECT outcomes.

Objective

However, the precise relations among physical properties of the stimulus, patient head anatomy, and patient-specific impedance to the passage of current are long-standing questions in ECT research and practice. To this end, we develop a computational framework based on diverse clinical data sets.

Methods

We developed anatomical MRI-derived models of transcranial electrical stimulation (tES) that included changes in tissue conductivity due to local electrical current flow. These “adaptive” models simulate ECT both during therapeutic stimulation using high current (∼1 A) and when dynamic impedance is measured, as well as prior to stimulation when low current (∼1 mA) is used to measure static impedance. We modeled two scalp layers: a superficial scalp layer with adaptive conductivity that increases with electric field up to a subject-specific maximum (${\mathrm{\sigma }}_{\overline{\text{SS}}}$), and a deep scalp layer with a subject-specific fixed conductivity (${\mathrm{\sigma }}_{\text{DS}}$).

Results

We demonstrated that variation in these scalp parameters may explain clinical data on subject-specific static impedance and dynamic impedance, their imperfect correlation across subjects, their relationships to seizure threshold, and the role of head anatomy. Adaptive tES models demonstrated that current flow changes local tissue conductivity which in turn shapes current delivery to the brain in a manner not accounted for in fixed tissue conductivity models.

Conclusions

Our predictions that variation in individual skin properties, rather than other aspects of anatomy, largely govern the relationship between static impedance, dynamic impedance, and ECT current delivery to the brain, themselves depend on assumptions about tissue properties. Broadly, our novel modeling pipeline opens the door to explore how adaptive-scalp conductivity may impact transcutaneous electrical stimulation (tES).

中文翻译：

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ECT 的自适应电流模型：解释个体静态阻抗、动态阻抗和脑电流密度

背景

随着设备电输出和电极蒙太奇的改进，电休克疗法 (ECT) 的结果得到了改善。ECT 刺激的物理特性以及患者头部的物理特性决定了设备测量的阻抗，并控制向大脑和 ECT 结果的电流传输。

客观的

然而，刺激的物理特性、患者头部解剖结构和患者特定的电流通过阻抗之间的精确关系是 ECT 研究和实践中长期存在的问题。为此，我们开发了一个基于不同临床数据集的计算框架。

方法

我们开发了经颅电刺激 (tES) 的解剖 MRI 衍生模型，其中包括由于局部电流引起的组织电导率变化。这些“自适应”模型在使用高电流 (~1 A) 的治疗刺激期间和测量动态阻抗时以及在使用低电流 (~1 mA) 测量静态阻抗时在刺激之前模拟 ECT。我们模拟了两个头皮层：具有自适应电导率的表层头皮层，其随电场增加到特定对象的最大值（${\mathrm{\sigma }}_{\overline{\text{SS}}}$），以及具有特定对象固定电导率的深层头皮层（${\mathrm{\sigma }}_{\text{DS}}$）。

结果

我们证明了这些头皮参数的变化可以解释关于受试者特定静态阻抗和动态阻抗的临床数据、它们在受试者之间的不完美相关性、它们与癫痫发作阈值的关系以及头部解剖结构的作用。自适应 tES 模型表明，电流会改变局部组织的电导率，从而以固定组织电导率模型中未考虑的方式塑造向大脑的电流输送。

结论

我们的预测是个体皮肤特性的变化，而不是解剖结构的其他方面，在很大程度上决定了静态阻抗、动态阻抗和 ECT 电流传递到大脑之间的关系，它们本身取决于对组织特性的假设。从广义上讲，我们新颖的建模管道为探索适应性头皮电导率如何影响经皮电刺激 (tES) 打开了大门。