Devices for non-invasive brain stimulation (NIBS) are increasingly used for treatment of mental health indications. Transcranial magnetic stimulation (TMS) is FDA-cleared for the treatment of depression, obsessive compulsive disorder, and smoking addiction. Two forms of transcranial electric stimulation (TES) are approved for psychiatric treatment, as well: electroconvulsive therapy (ECT) for depression and catatonia and cranial electrotherapy stimulation (CES) for anxiety and insomnia. Further, there are ongoing clinical trials of other TES paradigms, as well as new indications for TMS. However, these interventions have limitations including the variable response to TMS, low efficacy of subthreshold TES, and cognitive side-effects of ECT. A contributing factor is the lack of understanding of the neural elements and populations engaged by the stimulation, both in general and within an individual patient. This precludes rational selection of the stimulation dose to target specific neural elements or to account for individual differences in anatomy. For example, TMS intensity is individualized based on motor cortex stimulation, which has limited relevance to typical targets in prefrontal cortex. Moreover, the current amplitude in ECT and other forms of TES is not individualized at all, and anatomical differences thus result in variable stimulation strengths within the brain. This is in contrast to invasive approaches, such as deep brain stimulation, where modeling of neural target engagement is an established part of surgical planning and dose selection. The goal of this project is to develop computational tools to simulate, quantify, and visualize the direct effects of TMS and TES on neurons in the brains of individual patients. The modeled effects will include both subthreshold polarization and suprathreshold activation of neural elements by the TMS or TES electric field (E-field), which comprise the critical mechanistic link to subsequent brain circuit modulation. Aim 1 is to implement high-fidelity models of cortical neurons as well as cortical and subcortical myelinated axons and place them in individual head models. The neural models will have morphologies and membrane dynamics optimized to represent layer- and brain-region-specific neurons, and will be validated with existing experimental data. Since the computational demands to calculate the response of a large population of neurons are prohibitive, Aim 2 is to develop and validate computationally efficient estimators of the neural responses to make the simulations accessible for the average user with limited computational resources. Finally, Aim 3 is to make these simulation and estimation tools widely available to researchers and clinicians by integrating the neural response quantifications into the SimNIBS software package for E-field simulation to create an integrated tool termed NeuroSimNIBS. This user-friendly software will enable researchers and clinicians to develop a better underst...