A Computational Pipeline for Personalized Modeling of Neuromodulation in Brain Networks

We present an advanced computational pipeline designed to assess the physiological effects of non-invasive brain stimulation (NIBS) using highly personalized, large-scale models of brain network dynamics. Starting from structural magnetic resonance imaging (MRI) data, the pipeline constructs detailed, subject-specific 3D finite element models of the head and brain, and performs personalized dosimetric NIBS exposure assessments using these models. Finally, an embedded, mean field representation of brain network dynamics is used to capture the dynamic response to stimulation. This approach supports multiple neurostimulation modalities, and promises to accelerate the optimization of personalized neurostimulation therapies and the development of advanced, closed-loop stimulation protocols.