Poster No:
36
Submission Type:
Abstract Submission
Authors:
Andrea Veronese1, Davide Momi2, Simone Sarasso3, Michele Allegra1, Maurizio Corbetta1, Samir Suweis1
Institutions:
1University of Padova, Padova, Italy, 2Stanford University, Stanford, CA, 3State University of Milan, Milan, Italy
First Author:
Co-Author(s):
Introduction:
The complexity of the stimulation response observed in TMS-EEG has been shown to be a very effective predictor of brain state (including consciousness level) and clinical status. However, a mechanistic understanding of how the response shape relates to the underlying physiology is lacking. Furthermore, the dependence of the stimulation response on the site and timing of stimulation is not fully explained. Fundamental insight into both questions may come from computational models, but current modeling attempts are not fully satisfactory in this regard. While models can accurately reproduce the observed response, they achieve this by a direct fit of the model parameters on data acquired during stimulation. As a result, the models are valid only for a specific stimulation site, which impairs their generality in predicting the effects of stimulating other sites and their interpretability in terms of the underlying biophysics. Our work makes a significant step forward by showing that a computational model fitted on the spontaneous EEG response can successfully predict the response to TMS stimulation of several sites.
Methods:
We considered data from N=3 healthy participants who underwent three sessions of TMS-EEG, with stimulation sites targeting different brain areas (frontal, motor and occipital).
The same participants underwent separate sessions of spontaneous EEG activity recording.
EEG measurements were recorded by using a TMS-compatible 64-channel device. Each TMS-EEG recording session consisted of 200 TMS pulses per stimulation site at 2000–2500 ms intervals (a duration of 7 min each). EEG data were source localized and resampled in a standard space. We considered two whole-brain computational models: a simplified model ('Hopf model') based on coupled Stuart-Landau oscillators, frequently used in whole-brain effective connectivity studies, and a biophysically realistic model based on Jansen-Rit neural masses, previously used in models of the TMS-EEG response. Both models feature local neural oscillators coupled via structural connectivity and delays.
Results:
The simplified Hopf model was first fitted to the stimulation response data acquired under TMS-EEG (model 1), where it could reliably reproduce the observed response, achieving comparable performance to the (much more complex) Jansen-Rit model (model2). In a subsequent step, the Hopf model was fitted on resting data of each participant, trying to optimally reproduce the observed cross-covariances in spontaneous activity (model 3). This step was only possible because of the relative simplicity of the model, which would make a similar endeavor impossible in the case of the Janset-Rit model. The fitted model was then used to predict the effect of stimulation of different sites, and model predictions were compared to the observed TMS-EEG responses. Despite no use of the observed TMS-EEG responses in model3, we could correctly reproduce the observed responses, with a precision that was only slightly inferior to that given by model1.
Conclusions:
Being able to predict the effects of external perturbation from measurements of unperturbed activity is one of the key motivations behind computational modeling of brain activity. However, this goal had remained elusive in the case of the response to TMS stimulation.
By predicting TMS-EEG responses solely on the basis of the resting EEG activity of individual participants, we demonstrated that stimulation effects are strongly linked to effective connectivity between brain areas, as well as local excitability, both of which are ground biophysical properties that can be clearly recovered from from spontaneous activity. Moreover, our modeling approach holds great potential in clinical settings, where TMS is used in the treatment of several brain disorders, as it could be used to tailor stimulation therapies to individual patients by only acquiring EEG at rest, without the need of trial-and-error practice.
Brain Stimulation:
Non-invasive Magnetic/TMS 1
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 2
Keywords:
Electroencephaolography (EEG)
Modeling
Transcranial Magnetic Stimulation (TMS)
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Not applicable
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
EEG/ERP
TMS
Provide references using APA citation style.
Momi, D., Wang, Z., Parmigiani, S., Mikulan, E., Bastiaens, S., Oveisi, M., ... & Griffiths, J. D. (2024). Stimulation mapping and whole-brain modeling reveal gradients of excitability and recurrence in cortical networks. bioRxiv, 2024-02.
Ponce-Alvarez, A., & Deco, G. (2024). The Hopf whole-brain model and its linear approximation. Scientific reports, 14(1), 2615.
No