2Swiss Federal Institute of Technology (ETH Zurich), Zürich, Switzerland
*Email: neufeld@itis.swiss
Introduction
Temporal Interference (TI) stimulation, an innovative formof transcranial electrical stimulation [1], uses multiple kHz currents with frequency offsets in the brain’s physiological range to steerably and selectivelystimulate deep targets. However, the complex and heterogeneous head environment, along with important inter-subject variability, make it challenging toidentify suitable stimulation parameters. An easy-to-use TI Planning (TIP)application was published [2] to facilitate study design and stimulation personalization. However, due to computational limitations, brute-force explorationof the full parameter space was not feasible, requiring users to impose pre-constrains. This oftenleads to suboptimal settings and making the tool lessaccessible for beginners.
Methods TIP generates detailed head models from T1-weighted MRI data [3], co-registers the ICBM152 atlas [4] for target region identification, assigns DTI-based anisotropic conductivity maps [5], places electrodes according to the 10-10 system, and performs EM simulations to establish a full E-field basis. Surrogate based optimization (SBO) [6] combines an iteratively-refined Gaussian-process (GP) surrogate and a multi-objective genetic algorithm (MOGA) [7] to identify the front of Pareto-optimal conditions (electrode locations and currents), with regard to the goals of 1) maximizing stimulation strength and 2) selectivity, while 3) avoiding collateral stimulation.
Results Based on the identified Pareto front, users can interactively weightthe three conflicting goals and compare configurations with comparable perfor-mances based on quantified quality metrics and visualized distributions. Theiterative SBO approachdramatically minimizes the number of full evaluationsrequired to predict the performance metrics (<100 instead of millions), enablingcomprehensive exploration of high-dimensional parameter spaces(5n − 1, n ≥ 2: number of channels). Discussion A fully automatic, online accessible tool for personalized TI stimulation planning has been established that leverages AI and image-based simulations. By introducing hybridized, iterative surrogate modeling and MOGA, systematic, comprehensive, and computationally tractable optimization in high-dimensional parameter spaces is achieved and interactive weighting of conflicting objectives becomes possible. The comprehensive search reduces the level of required user expertise, removes arbitrariness, and ensures identification of optimal conditions. The method readily generalizes to non-classic forms of multi-channel TI.
