Project Summary Title: Real-time visualization and precision targeting in transcranial magnetic stimulation Transcranial magnetic stimulation (TMS) is a non-invasive device-based neuromodulation technique for probing neuronal networks and treating mental disorders such as major depressive disorder (MDD) and Obsessive- Compulsive Disorder (OCD). The treatment efficacy of TMS relies on placing TMS coils to accurately stimulate the underlying disease-related brain target. Since TMS-evoked electric field (E-field) is affected by complex tissue structures and brain geometry, it relies on using computational algorithms, such as boundary element modeling (BEM) and finite element modeling (FEM), to estimate the stimulation site. But the relative long computation time of these methods is a limitation for real-time visualization of the stimulation target during brain mapping and for computing the optimal coil position for treatment planning. In this grant, we propose to develop a deep-neural-network based method to accelerate E-field prediction. We will develop a novel deep-neural- network architecture to predict E-field by using training data computed using the FEM algorithm with anisotropic tissue conductivity. Then, we will integrate the trained neural network into a 3DSlicer software module for real- time E-field visualization. Moreover, we will develop a computational algorithm to search for the optimal coil position to maximize the stimulation of a selected brain target within a clinically feasible time. The outcome of this grant will transform deep-learning techniques into a useful tool to enhance the application of TMS in clinical research.