Fig. 4: Methodological workflow for characterizing the stimulated network and performing subject-specific connectome-based neurophysiological modeling of evoked potentials.

A Simultaneous stereotactic electroencephalography (sEEG) and scalp high-density electroencephalography (hd-EEG) signals were recorded. The black triangle and dashed vertical line indicate the time at which intracerebral electrical stimulation (iES) was delivered. For further details on the methodology and data preprocessing please refer to refs. ^45,85. B To pinpoint the brain network where the stimulus was delivered, we employed the Schaefer atlas⁸⁷, which divides the brain into 1000 regions across seven distinct Resting-State Networks (RSNs): Visual Network, Somatomotor Network, Limbic Network, Dorsal attention network, Ventral Attention Network, Frontoparietal Network and Default Mode Network. Subsequently, we identified the parcellation region that overlapped with the intracerebral electrode responsible for delivering the stimulus, ultimately enabling us to determine the stimulated network. C To model individual stimulus-evoked time series, the Jansen-Rit model⁹⁰, a neural mass model comprising pyramidal, excitatory interneuron, and inhibitory interneuron populations, was embedded in every parcel of the lower-resolution 200-region Schaefer atlas⁸⁷ for simulating and fitting neural activity time series. The connectivity between regions was modeled using diffusion-weighted magnetic resonance imaging (MRI) tractography computed from a sample of healthy young individuals from the Human Connectome Project (HCP) Dataset⁹⁴, and then averaged to give a grand-mean anatomical connectome. The iES-induced depolarization of the resting membrane potential was modeled by a perturbing voltage offset to the mean membrane potential of the excitatory interneuron population. Next, a lead field matrix was employed to project the time series from the cortical surface parcels into EEG channel space, resulting in the generation of simulated scalp hd-EEG measurements. The quality of fit (loss) was quantified by calculating the cosine similarity between the simulated and empirical stimulus-evoked time series. Optimization of model parameters was accomplished by leveraging the autodiff-computed gradient⁹⁵ between the objective function and the model parameters, employing the ADAM algorithm⁹⁶. Ultimately, the optimized model parameters were utilized to generate the fitted, simulated (optimized) stimulus-evoked hd-EEG activity.