Fast and Accurate Parameter Optimization of Transcranial Temporal Interference Stimulation by Selectively Employing...

Transcranial temporal interference (TI) stimulation offers non-invasive modulation of deep brain regions with limited neocortical activation. Achieving focal TI stimulation requires individualized optimization of the stimulation parameters, including electrode configurations and injection currents. However, conventional exhaustive search (ES) approach is computationally expensive, hindering the clinical applicability of subject-specific parameter optimization. We propose a target-specific restricted exhaustive search (RES) strategy that substantially reduces the computational burden while preserving reliability of ES-based optimization. ES was conducted to identify the optimal two-pair TI stimulation parameters for three deep brain targets (the nucleus accumbens, hippocampal head, and caudate body) using 40 individual finite element head models. Target-specific restricted candidate electrode subsets were constructed based on the electrode selection patterns observed across multiple subjects. RES was then performed by constraining the search space only for these selected subsets. A 4-fold cross-validation approach was employed to quantify the average changes in the objective function and computational time, which were then compared with those of the conventional ES approach. A parametric analysis was also conducted to identify the size of the minimal subset that maintained a robust performance for each target. RES maintained an optimization accuracy comparable to that of conventional ES across all three targets, while reducing the computation time approximately 30- to 100-fold, enabling optimization within minutes or seconds. Specifically, targeting the nucleus accumbens allowed for the most extensive search space reduction without compromising stimulation efficacy. This is the first study to identify target-specific electrode selection patterns across individuals to optimize TI stimulation. The RES strategy leverages these patterns to preserve the deterministic nature of ES while reducing the optimization time from hours to minutes, thereby facilitating the clinical application of personalized TI stimulation.