## 4 Discussion
### Intra-Subject Analysis  
**Degree:** According to our methodology, functional connections ultimately represent the extent of similarity between two electrodes' time series and, thus, an increase (decrease) in the degree of the \(i\)-_th_ node can be interpreted as an increased (decreased) overall similarity between time series recorded at \(i\) and the other neighbor nodes. Hence, even though subjects display specific sets of spatial alterations in FC patterns (analyzed here through the graph metrics variations), at least regarding the degree (i.e., the number of connections at each node), frontal and parietal sites do seem to play a critical role in the short-term plasticity (as also indicated by the group results). Nonetheless, a common pattern of synchronicity alteration could not be found for all subjects: for example, subjects S04 and S05 displayed degree increases on frontal sites in the \(\alpha\) range, whereas S02 showed pronounced decreases. This only emphasizes each patient's uniqueness. In addition, for all subjects, the graphs' topology alterations patterns are more distinct among the \(\alpha\) and \(\beta_{2}\) bands than between \(\alpha_{1}\) and \(\alpha_{2}\), or between \(\beta_{2}\) and \(\gamma\) bands; i.e., alteration patterns are more consistent both for within the \(\alpha\) range, as well as within \(\beta_{2}\) and \(\gamma\). Therefore, we hypothesize that the observed short-term plasticity mechanisms, besides being frequency band-specific (as already suggested at the group level by Figures 3 and 4), seem to behave as if the lower (\(\alpha_{1}\) and \(\alpha_{2}\)) and upper (\(\beta_{2}\) and \(\gamma\)) frequency bounds constituted distinct states, or phases, of the brain's functional reorganization, with results for \(\beta_{1}\) providing a \"transition\" frequency band between the two. In fact, the change in the degree can occur distinctly at the same electrodes for different frequency bands; that is: nodes that present increases in one band can show a decrease in another, and vice versa--such as P3 for S04 and FC2 for S05, supporting this hypothesis. **Clustering Coefficient:** The CC for a node \(i\) considers mainly the connections among its closest neighbors. Thus, the neighbors of a node with high CC are largely connected among themselves, and vice versa. Even though, in a sense, CC also accounts for the number of connections, this metric reflects more local interactions than the degree. The concomitant interpretation of both metrics can, then, provide complementary information. A few common trends found for the degree can be observed for the clustering coefficient as well (Figure 6), such as the frequency-dependent patterns and the highly subject-specificity of results. Notwithstanding, for the CC, none of the patients showed significant variations for the \(\alpha_{1}\) graph. Moreover, it is important to emphasize that, even though decreases in the total number of connections were mainly associated with a correspondent loss in the local number of links (i.e., concomitant decrease of degree and CC), this is not necessarily true for all instances, such as with FC2 for S04 at \(\gamma\). This then illustrates that specific nodes can display a decrease in their total number of connections, even if their clusterization (emphasized by the nearest-neighbour connections) increases. **Node path length:** Not finding any type of statistically significant result for the path length might be due to an ill-posed problem by isolated nodes in the network, possibly caused by thresholding, which theoretically sets their path length to mathematical infinity. We are still reporting it here, though, to stress the current existing need of research to address optimal threshold choices for graphs representing brain functional networks. **Betweenness Centrality:** The evident decreases (Figure 7) indicate a \"loss of importance\" in that specific node regarding information flow in the network.