Fig. 1: Studying Resting-State Network (RSN) input processing strategies and the role of recurrent feedback with computational brain network models.

Shown here is a schematic of the hypotheses, methodology, and general conceptual framework of the present work. A Intracerebral electrical stimulation (iES) applied to an intracortical target region generates an early (~20-30 ms) response (evoked-related potential (ERP) waveform component) at high-density scalp electroencephalography (hd-EEG) channels sensitive to that region and its immediate neighbors (red arrows). This also appears in more distal connected regions after a short delay due to axonal conduction and polysynaptic transmission. Subsequent second (~60–80 ms) and third (~140–200 ms) late evoked components are frequently observed (blue arrows). After identifying the stimulated network in this way, we aim to determine the extent to which this second component relies on intrinsic network activity versus recurrent whole-brain feedback. B Schematic of the hierarchical spatial layout of canonical RSNs as demonstrated in Margulies and colleagues¹², spanning low-order networks showing greater functional segregation to high-order networks showing greater functional integration¹⁵. Networks are distributed based on their position along the first principal gradient. The stimulation sites are distributed across different levels of this gradient. C Schematic of virtual dissection methodology and key hypotheses tested. We first fit personalized connectome-based computational models of iES-evoked responses to the hd-EEG time series, for each patient and stimulation location. Then, precisely timed communication interruptions (virtual dissections) were introduced to the fitted models, and the resulting changes in the iES-evoked propagation pattern were evaluated. We hypothesized that lesioning would lead to activity suppression (C, right side) in high-order but not low-order networks.