## 2 Materials and Methods
### Model Simulations
#### 2.1.3 Spontaneous Activity and Stimulation Protocol  
For the analysis of spontaneous activity, each condition was run for 4000 s, and the time-series were split into 10 segments of 400 s.  
On the other hand, to examine the model's evoked response, a square wave pulse of 5 ms and amplitude 1 was applied to a single connectome node, specifically targeting the excitatory pyramidal cells (as an additive term to Equation (1)). In our study, we individually stimulated a total of 12 nodes located in the right hemisphere (see Section 2.1.4 below for details), homogeneously sampling all cortical lobes (Table 1). In all conditions (control and lesions) a stimulus was delivered to the same node (i.e., same stimulus) 500 times (500 trials) by reinitializing each simulation. We also jittered the onset of the stimulus within a range of 2 s. Note that in the Larter-Breakspear model, all quantities are dimensionless except for time [56], which allows a straight comparison with the experimental time courses. Figure 1: **Whole-brain model for investigating brain complexity in silico. ** (**aI**) Each node of the whole-brain network is modelled by the Larter and Breakspear (LB) neural mass model (NMM), describing the interaction between excitatory (E) and inhibitory (I) neurons. The weights connecting the nodes are reported in the structural connectivity (SC) matrix. (**aII**) The output of the model of a given node is the mean membrane potential of the excitatory population (green trace), and the timing of the spikes (red dots), detected with a hard threshold on the voltage, are then converted to an instantaneous firing rate (IFR) (black overlay trace). (**b**) The working point of the model (i.e., set of model parameters) was obtained based on the spontaneous and evoked activity following three steps. First (**bI**), the correlations between the spontaneous IFR traces were computed to estimate a functional connectivity (FC) matrix. Then, the similarity between the FC and SC matrices was assessed through the Pearson correlation coefficient. Second (**bII**), by stimulating a node of the connectome (here rPCI), the propagation of activity in the network was quantified in terms of \(\Delta\)IFR\({}_{\text{SH}}\) (\(\Delta\)IFR averaged over the nodes of the stimulated hemisphere), and some representative trials of \(\Delta\)IFR\({}_{\text{SH}}\) are reported. The evoked area is obtained as the integral after stimulation (red line, between 0 and 500 ms) of \(\Delta\)IFR\({}_{\text{SH}}\) averaged across trials. Third (**bIII**), the SC-FC correlation and the evoked area are reported for different global couplings (i.e., a scaling factor of the SC), and both metrics reached a peak at G = 4 (working point). The dashed blue lines and arrows illustrate the matches between the FC and the SC maps for different G values. The dashed green lines and arrows illustrate the evoked area by the stimulus. (**cI**) We simulated control and local manipulation conditions. Local manipulations were implemented by silencing selected nodes. (**cII**) To assess the impact of local node silencing on spontaneous complexity, we calculated Lempel–Ziv complexity (LZc) on ongoing network activity. Furthermore, we separately stimulated different connectome nodes under different local manipulations and calculated the Perturbational Complexity Index (PCI) to gauge the impact of local silencing on the evoked activity.