Non-invasive stimulation with Temporal Interference: Optimization of the electric field deep in the brain with the use of a genetic algorithm
ObjectiveSince 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 aforementioned stimulation settings were optimized using a genetic algorithm. MethodsSimulations 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. ResultsGreater focality was achieved with optimization, specifically for the right hippocampus (region of interest), with a significant decrease in the surrounding electric field intensity. In the non-optimized case, the brain volumes stimulated above 0.2V/m were 99.9% in the region of interest (ROI), and 76.4% in the rest of the gray matter. In contrast, the stimulated volumes were 91.4% and 29.6% for the best optimization case. Additionally, the maximum electric field intensity inside the region of interest was consistently higher than that outside the ROI for all optimized cases. SignificanceGiven 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 and provide usable results. Although the method is promising and the indications are becoming more robust, such that optimal solutions can be achieved at the individual level, there are limitations on what can be achieved using only two electrode pairs.