1Foundation for Research on Information Technologies in Society (IT'IS), Zurich, Switzerland 2Universita La Sapienza, Rome, Italy
*Email: cassara@itis.swiss Introduction
To facilitate the design and interpretation of human studies involving Transcranial Magnetic Stimulation (TMS), we developed a cloud-based and web-accessible computational framework that enables the execution of subject-specific virtual TMS experiments towards assessing and optimizing safety and efficacy. It also facilitates the formulation of testable hypotheses regarding stimulation mechanisms across various temporal and spatial scales, including mechanisms by which induced electric fields (E-fields) interact with individual neurons and high level brain network dynamics.
Methods The framework extends a previously established pipeline [1] for non-invasive brain stimulation modeling, which combined image-based generation of detailed head models (personalized anatomy and tissue properties), personalized electromagnetic simulations (exposure and lead-fields for virtual EEG), image-based brain network model construction (mean field), and dynamic functional connectivity assessment. The current work extends this pipeline with a) TMS coil modeling and positioning, b) neuron polarization and stimulation probability mapping based on statistical sub- and supra-threshold responses of morphologically-detailed cortical neuron populations, and c) derived coupling terms for the assessment of TMS impact on network dynamics.
Results The pipeline was employed to investigate TMS stimulation mechanisms at the single-cell and the network dynamics level. Key findings include: the dielectric contrast between gray and white matter is insufficient to directly induce spiking; mapping functions for population- and orientation-dependent threshold E-fields probabilities have been established for various pulse shapes (Figure 1), offering insights into the stimulability of different neuronal populations; electrophysiology-based activation maps have been generated for simplified models of commercial TMS coils under relevant stimulation conditions. Model validation is ongoing.
Discussion Our pipeline extends prior modeling work [1-3] to provide a customizable framework for investigating TMS mechanisms and designing virtual clinical trials. Probability maps link dosimetric exposure predictions with electrophysiological responses that in turn modulate brain network dynamics. The pipeline serves to shed light on interaction mechanisms on to help design superior stimulation paradigms, tuned towards optimization electrophysiological response, with improve selectivity, effectivity, and safety.
Figure 1. Figure 1. (a) Illustration of the segmented head model, with 40 tissues; (b) example of user-defined TMS coils; (c) final model, featuring the optimally placed TMS coil; (d) neuronal population, orientation and pulse-specific threshold E-fields; (d) spiking threshold maps for several neuronal populations. Acknowledgements “This research is supported by the NIH Common Fund’s SPARC program under award3OT3OD025348-01S8” References [1] Karimi, F., et al. (2025). Precision non-invasive brain stimulation: an in silico pipeline for personalized control of brain dynamics. J. Neural Eng., 10.1088/1741-2552/adb88f. [2] Aberra, A.S., et al. (2020). Simulation of TMS in head model with morphologically-realistic cortical neurons. Brain Stimul., 13(1):175-189. [3] Jansen, B.H., Rit, V.G. (1995). EEG and VEP generation in a model of coupled cortical columns. Biol. Cybern., 73:357–366.
