Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
J Neural Eng. 2022 Sep 22;19(5). doi: 10.1088/1741-2552/ac89b3.
. Since the introduction of transcranial temporal interference stimulation, there has been an ever-growing interest in this novel method, as it theoretically allows non-invasive stimulation of deep brain target regions. To date, attempts have been made to optimize the electrode montages and injected current to achieve personalized area targeting using two electrode pairs. Most of these methods use exhaustive search to find the best match, but faster and, at the same time, reliable solutions are required. In this study, the electrode combinations as well as the injected current for a two-electrode pair stimulation were optimized using a genetic algorithm, considering the right hippocampus as the region of interest (ROI).. Simulations were performed on head models from the Population Head Model repository. First, each model was fitted with an electrode array based on the 10-10 international EEG electrode placement system. Following electrode placement, the models were meshed and solved for all single-pair electrode combinations, using an electrode on the left mastoid as a reference (ground). At the optimization stage, different electrode pairs and injection currents were tested using a genetic algorithm to obtain the optimal combination for each model, by setting three different maximum electric field thresholds (0.2, 0.5, and 0.8 V m) in the ROI. The combinations below the set threshold were given a high penalty.. Greater focality was achieved with our optimization, specifically in the ROI, with a significant decrease in the surrounding electric field intensity. In the non-optimized case, the mean brain volumes stimulated above 0.2 V mwere 99.9% in the ROI, and 76.4% in the rest of the gray matter. In contrast, the stimulated mean volumes were 91.4% and 29.6%, respectively, for the best optimization case with a threshold of 0.8 V m. Additionally, the maximum electric field intensity inside the ROI was consistently higher than that outside of the ROI for all optimized cases.. Given that the accomplishment of a globally optimal solution requires a brute-force approach, the use of a genetic algorithm can significantly decrease the optimization time, while achieving personalized deep brain stimulation. The results of this work can be used to facilitate further studies that are more clinically oriented; thus, enabling faster and at the same time accurate treatment planning for the stimulation sessions.
. 自从经颅颞部干扰刺激技术问世以来，人们对这种新方法的兴趣与日俱增，因为它理论上可以无创性地刺激深部脑目标区域。迄今为止，人们已经尝试通过使用两个电极对来优化电极组合和注入电流，以实现针对特定区域的个性化靶向刺激。这些方法中的大多数都使用穷举搜索来寻找最佳匹配，但需要更快且同时可靠的解决方案。在这项研究中，使用遗传算法优化了两个电极对刺激的电极组合和注入电流，将右侧海马体作为感兴趣区域（ROI）。......在Population Head Model 存储库中对头模型进行了模拟。首先，根据 10-10 国际脑电图电极放置系统，为每个模型配备一个电极阵列。放置电极后，对模型进行网格化，并为所有单对电极组合进行求解，其中左乳突上的一个电极用作参考（地）。在优化阶段，使用遗传算法测试不同的电极对和注入电流，为每个模型获得最佳组合，在 ROI 中设置三个不同的最大电场阈值（0.2、0.5 和 0.8 V/m）。低于设定阈值的组合将被给予较高的惩罚。......通过优化，我们获得了更高的聚焦性，特别是在 ROI 中，同时显著降低了周围电场强度。在未优化的情况下，在 ROI 中刺激超过 0.2 V/m 的平均脑体积为 99.9%，在灰质的其余部分为 76.4%。相比之下，在阈值为 0.8 V/m 的最佳优化情况下，刺激的平均体积分别为 91.4%和 29.6%。此外，对于所有优化情况，ROI 内的最大电场强度始终高于 ROI 外的最大电场强度。......由于实现全局最优解需要采用暴力搜索方法，因此遗传算法的使用可以大大减少优化时间，同时实现个性化的深部脑刺激。这项工作的结果可以用于促进更具临床导向的进一步研究，从而为刺激疗程提供更快且同时准确的治疗计划。
