Speaker
Description
Understanding the brain’s response to transcranial magnetic stimulation (TMS) is crucial for advanc-
ing both basic and clinical neuroscience. This study applies neural mass modelling to analyze TMS-
evoked potentials (TEPs) through electroencephalography (EEG). Building on Momi et al. (2023)
[1], who used source-localized TMS-EEG analyses to disentangle local from network dynamics, we
aim to replicate and extend these findings by modelling the response from the resting state.
We adopt the whole-brain Hopf model proposed by Ponce-Alvarez and Deco (2024) [2], adapting
it to EEG data [3]. Our results show that this model can reproduce resting state activity in the Fourier
domain, while the TMS-evoked dynamics are accurately captured in the EEG trajectory space. This
highlights the model’s potential in bridging spontaneous and perturbation-driven brain activity.
Current efforts focus on integrating site-specific effective connectivity into the model, estimated
for different TMS stimulation targets. This approach aims to capture how local connectivity profiles
shape both spontaneous and evoked activity. By combining resting state dynamics with region-
dependent effective connectivity, we aim to predict perturbation responses more accurately across
stimulation sites. This direction holds promise for informing stimulation strategies and improving
individualized neuromodulation protocols.
REFERENCES
[1] Momi, D., et al. TMS-evoked responses are driven by recurrent large-scale network dynamics, eLife 12
e83232 (2023).
[2] Ponce-Alvarez, A., Deco, G. The Hopf whole-brain model and its linear approximation, Sci Rep 14 2615
(2024).
[3] Fecchio, M., Pigorini, A., et al. The spectral features of EEG responses to transcranial magnetic stimula-
tion of the primary motor cortex depend on the amplitude of the motor evoked potentials, PLOS One 12
e0184910 (2017)
