Enhanced Temporal Interference Stimulation Modeling through Magnetic Resonance Conductivity Tensor Imaging

Temporal interference stimulation (TIS) optimization relies on electric field inside brain, which could be calculated via electromagnetic simulation. Recently, isotropic conductivity models were used, which neglect brain anisotropy and individual variability, potentially deviating from actual conditions. In this study, we integrate magnetic resonance conductivity imaging and diffusion tensor imaging with NODDI model to develop an individualized, anisotropic conductivity model for TIS. To compare the performance of proposed model, three models-namely, isotropic, individualized isotropic, and individualized anisotropic-are employed in finite element simulation with five montages of electrode placements. Electric field and current distributions within target regions (e.g., STN and GPi) are compared to assess their feasibility in TIS simulations. The results demonstrate that the personalized anisotropic model achieves higher accuracy in TIS simulation compared to the other two models.