## 4 Discussion
### Limitations and General Considerations  
Variability in brain data can be extremely large and can pose several problems when seeking general or average patterns across both healthy subjects and neurological patients. However, it can also be very informative as it emphasizes that each brain is unique and subjects may present their own particularities in responding to an intervention, even when common patterns can indeed be found for a group of participants; in other words, brain variability can also provide relevant data, and should not be solely considered as noise [57]. With this under consideration, our analyses explored alteration patterns occurring for all subjects, as well as individually.  
Since we are comparing the moments immediately prior, and post, the AO+MI-VR intervention, and amongst the outlined changes we also identify variations that occurred for all subjects in motor related areas, compatible with MI-related changes found in other works [32; 58], our study assumes that detectable changes in RS-FC patterns are at least partially due to the AO+MI VR task that the patients performed. Additionally, fronto-parietal or occipital changes might be due to the attention requirements or visual stimuli implied in task. However, the determination of the specific contributions would need additional research.  
Based on our results, we are currently unable to attribute any differences between subjects to the available demographic factors, as no sub-grouping was evident with the accessible quantities. Nonetheless, general hypotheses that could be drawn from this initial study include the larger consistency in FC patterns, at rest, across subjects prior to the MI-VR intervention and the distinct short-term modifications mechanisms at play in these patterns according to different EEG frequency bands.  
In any case, even these more general hypotheses still require further investigation in larger datasets, as our small number of participants and, as a result, the lack of statistical power, impairs the extrapolation of our conclusions. In addition, the neurological damage is patient-specific, which can further hinder generalized observations. Regardless, our methodology can be easily replicated in future studies and contributes in providing quantitative basis for the investigation of FC patterns in CP patients submitted to MI-based interventions, a research topic still lacking on this sort of analyses procedures.  
Finally, despite our FC analysis being performed at the sensor level, instead of the source domain, a 2018 study showed that, at least regarding the global connectivity (i.e., the overall indices in the adjacency matrices), estimates of FC in both domains (sensor and source) were closely correlated [59]. They generally concluded that FC can be reliably estimated, too, from the sensor space. However, they do stress that analyses should be carefully considered, and that metrics that account for volume conduction effects when estimating FC should be preferred [59]. Regarding these points, the CAR filtering in our preprocessing steps should, at least partially, minimize volume conduction effects. Moreover,the correntropy, the measure we employed in our study for estimating the FC patterns, has been suggested to be robust to noise and able to detect non-linear relationships between EEG signals [44]. It has also suggested to be more robust than other more traditionally employed metrics for EEG-FC studies [44]. Besides, studies from our group have also evidenced correntropy as a solid alternative to other well-established correlation metrics, being able to provide additional information the others might miss [43].