Non-invasive stimulation with temporal interference: optimization of the electric field deep in the brain with the use of a genetic algorithm

Abstract

Objective. Since the introduction of transcranial temporal interference stimulation (tTIS), 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). Methods. Simulations were performed on head models from the Population Head Model (PHM) 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. Results. 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/m were 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. Significance. 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.

Population-level insights into temporal interference for focused deep brain neuromodulation

“…Finally, we explored montage location based on an initial reference montage used for effective TI modulation in the deep brain. Future studies may adopt optimization studies to investigate optimized focality considering all scalp positions (Fernández-Corazza et al, 2020;Stoupis and Samaras, 2022). Also, TI and 2-tACS used the same montage location for fair comparison.…”

A novel non-invasive brain stimulation technique: “Temporally interfering electrical stimulation”

“…This study revealed that TI electrical stimulation permitted more steerable and deeper stimulation than traditional tACS, suggesting that it might be used as an alternative or improved stimulation approach for tACS. Besides, a few studies also attempted to use more advanced algorithmic methods, such as artificial neural network (ANN) or genetic algorithm, to estimate the stimulation parameters of TI electrical stimulation, leading to more targeted stimulation on individual models (Karimi et al, 2019;Stoupis and Samaras, 2022).…”

A mini-review: recent advancements in temporal interference stimulation in modulating brain function and behavior

“… Bahn et al (2023) developed an unsupervised neural network for simulating TI stimulation on the human brain that could rapidly and precisely optimize stimuli at deep brain targets. Moreover, Stoupis and Samaras (2022) discovered that using a genetic algorithm could drastically reduce the optimization time, making it possible to create personal stimulation plans rapidly. Research has indicated that unsupervised neural networks and genetic algorithms can be effective TI simulation algorithms.…”