## 3 Experimental Results
### Discussion and Error Analysis  
The results of this study indicate that healthy control can help to boost the functional network identification for unhealthy patient data by adding the healthy data during the training process. In this section, we discuss the observed errors and further analyze the origin of the transferability between healthy to unhealthy data. One may wonder \"where do the classification errors in this experiment can come from?\". We generated the confusion matrix (Figure 8) as well as the sensitivity (true positive rate) and specificity (true negative rate) of the classification individual functional brain networks to discover the most sensitive cases. Table 5 shows the model evaluation of each individual network for the classification of healthy subjects, unhealthy patients and transfer learning. The primary source of confusion between the different functional networks is the spatial overlap between the activated areas. We segmented the functional network identification into classification steps, identifying in each of them between the 55 ICs the best-fitted ICs for all 7 functional networks. We realized that the main sources of error came from the confusion between LANG and the VAN, as well as DAN and rFPCN as shown in Figure 8. The difficulty in differentiating between DAN and rFPCN may be explained by the spatial overlapping between the two networks [56]. In contrast, the relationship between VAN and LANG networks is more complex than in other networks. The distinction between the language and ventral attentional networks in rs-fMRI may be difficult, as they present similar activations in the ventrolateral prefrontal cortex, inferior frontal cortex and temporal gyrus in right-handed patients [57]. However, slight differences in the activation may allow for discrimination between these two networks in the inferior parietal lobule, in which the activation is more anterior, located in the temporoorietal junction and the supramarginal gyrus for the attentional network and more posterior in the angular gyrus for the language network [13; 57; 58]. The ventral attentional network is also located in the non-dominant hemisphere, almost symmetrical to the language network in the dominant hemisphere, which may also explain the difficulties of discriminating between these two networks. Considering the lateralization of these two networks, the handedness assessment using the Edinburgh handedness inventory has been considered as a supplement to discriminate between ventral attentional and language networks [13]. However, while this information may be useful in right-handed patients where left-hemisphere dominance exists in 96% of patients. Left-handed patients should be considered with caution since only 27% of left-handed patients have a dominant right hemisphere and, therefore, a left-lateralized ventral attentional network [59]. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Networks** & \multicolumn{2}{c}{**Healthy Subjects**} & \multicolumn{2}{c}{**Unhealthy Patients**} & \multicolumn{2}{c}{**Healthy-to-Unhealthy**} \\ \hline  & **Sensitivity** & **Specificity** & **Sensitivity** & **Specificity** & **Sensitivity** & **Specificity** \\ \hline DMN & 1.00 & 1.00 & 0.97 & 1.00 & 0.98 & 0.90 \\ \hline LANG & 0.98 & 0.70 & 0.97 & 0.80 & 0.97 & 0.60 \\ \hline LFPCN & 1.00 & 1.00 & 0.95 & 0.70 & 0.98 & 0.90 \\ \hline RFPCN & 0.98 & 0.90 & 0.95 & 0.80 & 0.98 & 0.70 \\ \hline VAN & 0.95 & 0.90 & 0.95 & 0.80 & 0.88 & 0.80 \\ \hline DAN & 1.00 & 0.90 & 1.00 & 0.60 & 0.92 & 0.80 \\ \hline SAL & 0.98 & 1.00 & 0.98 & 0.97 & 0.95 & 0.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Model Evaluation for individual networks of healthy subjects, unhealthy patients and the transfer learning (Healthy to unhealthy). We investigated the overlapping surface of thresholded functional networks and the lesion mask in unhealthy patients to understand better the possibility of transfer from healthy to unhealthy data.