Fig. 1: The workflow of the virtual brain twin for estimating the EZN using stimulation techniques.

a,b, A personalized high-resolution model (a) is based on individual brain geometry extracted from T1-weighted MRI and structural connectivity from tractography on diffusion-weighted MRI data (b). High-resolution virtual brain models simulate neural source activity with spatial resolutions of about 10 mm². The modeling parameters are inferred from the spontaneous SEEG recordings (b). c,d, We illustrate two types of stimulation: SEEG and TI, to induce seizure activity. c, SEEG stimulation uses bipolar stimulation in which two electrodes are used: one serves as the cathode and the other as the anode. The electric current flows between two electrodes, which is parameterized by current amplitude, pulse width and frequency. d, TI stimulation applies two current sources (I) simultaneously via electrically isolated pairs of scalp electrodes (green and pink) at kliohertz frequencies f and f + Δf. The currents generate oscillating electric fields, which results in an envelope amplitude that is modulated periodically at Δf. The electric field influences the brain activity that can be generated by the high-resolution personalized whole-brain model (a). The red and blue dots represent SEEG and scalp-EEG electrodes, respectively. e, The simulated source activity can be mapped onto the corresponding SEEG and scalp-EEG signals, through the gain matrices, which are constructed based on the locations of SEEG and scalp-EEG electrodes relative to the source vertices. The red curves on the scalp-EEG recordings are plotted using a different scale to visualize the signals following the high-amplitude signals induced by TI stimulation. f, By utilizing data features extracted from SEEG and scalp-EEG signals, Bayesian inference methods can estimate a posterior distribution of EVs, suggesting the potential EZN.