## 3 Results
### Feature Importance  
Identifying the key features that distinguish ASD from TD subjects is essential for understanding ASD. Given that these features assess the correlation between the temporal signals of ROIs, they provide valuable insight into which aspects most significantly impact the distinction between ASD and TD subjects.  
As we only obtained 59 features with a positive score (greater than zero) using the XGBoost classifier, we selected the top 50 features with the highest scores for each classifier to compare which regions were most significant in discriminating ASD/TD across all the analyzed classifiers. Subsequently, we checked for common features among all the top 50 features. From this analysis, we did not find any common feature for all the classifiers but only some features that were in common between two or three of them.  
However, despite the lack of global common features, we looked for brain region occurrences in the 50 most relevant correlations by counting the number of times these regions were present in all classifiers. This allowed us to observe which regions had the most significant effect on ASD/TD classification based on their connectivity to other regions. The results are shown in Table 3. Consistent regions can be identified in all classifiers. These regions are those whose correlation with other regions was most significant  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **AUC** & **\# of PCs** \\ \hline MLP & \(0.71\pm 0.02\) & no PCA \\  & \(0.71\pm 0.05\) & 200 PCs \\ \hline TabNet & \(0.65\pm 0.02\) & no PCA \\ \hline XGBoost & \(0.67\pm 0.02\) & no PCA \\ \hline L-SVM & \(0.74\pm 0.02\) & 50 PCs \\  & \(0.74\pm 0.05\) & 100 PCs \\ \hline SVM-RBF & \(0.75\pm 0.03\) & 100 PCs \\ \hline \hline \end{tabular}
\end{table}
Table 2: Best classification performances for each classifier.  
Figure 3: The ASD and TD classification results are reported for each classifier considered and for different values of PCs. The blue, orange, green, red and purple colors represent the different classifiers (TabNet, MLP, XGBoost, L-SVM annd SVM-RBF, respectively). Dots and error bars represent the average AUC score and standard deviation, respectively. The average and standard deviation are calculated across the 5 folds and 10 repetitions of the repeated stratified k-fold cross-validation scheme.  
in discriminating between ASD and TD. We also examined the location of these regions in the functional networks of the Mesulam [45] catalog. In this way, it was possible to highlight that the most significant areas to distinguish between ASD and TD belong to the heteromodal, unimodal, primary and paralimbic networks. The importance of these networks has also been found in the literature [46; 47; 48]. These highlighted areas are crucial for sensory perception, processing visual and auditory signals, spatial perception, and attention modulation. They are fundamental for understanding social signals that require the integration of complex sensory information such as facial expression, tone of voice, and gesture [49]. Therefore, they are important in understanding the mechanisms underlying autism spectrum disorder [50]. A heteromodal network, involving various cortical areas, is crucial for integrating complex sensory information and processing multisensory knowledge. In contrast, a unimodal network is specialized in a specific sensory modality. Neuroimaging studies have shown alterations in these areas in autism spectrum disorder, suggesting dysfunction in sensory integration and the processing of complex information in this disorder [51].