Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation method that is used to study brain function and conduct neuropsychiatric therapy. During TMS, a magnetic coil driven by low-frequency current pulses is placed against the scalp to induce electric fields in the brain. These electric fields, in turn, stimulate targeted cortical regions. High-fidelity electric field (E-field) simulations have become commonplace in the analysis of TMS and its effects. The predicted cortical E-field magnitudes are highly sensitive to precise geometry and conductivity distribution of bone, cerebrospinal  fluid (CSF), and gray matter. As such, the accuracy and precision of TMS simulations are still largely uncertain because of possible geometric errors introduced in the generation of head models by segmenting medical images into tissue types. One the other hand, the tissue conductivities also reveal inhomogeneous characteristics and need to be treated as random field. The goal of this project is to ensure safe and effective use of electric field simulation results. In this work, a probabilistic simulation framework is developed to enable the integration of the geometric and conductivity-related errors as uncertainties in patient-specific brain geometries. Stochastic representations for random interfaces and conductivities are first constructed. An in-house finite element solver is subsequently used to perform a forward propagation analysis and quantify the impact of uncertainties on the induced electrical field.

Uncertainty quantification of TMS simulation considering MRI segmentation error

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