## 4 Discussion  
The obtained results in the classification task are in agreement with the current literature, where performance typically hovers around 70% in the multicenter dataset [52; 53; 54]. This AUC percentage is also achieved using advanced techniques like in [55], where they used a multi-site clustering and nested feature extraction technique. When the analysis is limited to homogeneous and small datasets, the identification of ASD has a high accuracy [56; 57; 58], while classification results for heterogeneous and multicenter dataset have shown lower accuracy, like in [PERSON] et al. [59]. With this type of analysis (multicenter dataset, classification task, and tabular data), we believe that traditional ML classifiers are \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Occurrences** & **ROI** & **Anatomical Part** & **Mesulam** \\ \hline
18 & 3102 & L-Precuneous Cortex & Heteromodal \\ \hline
15 & 1002 & L-Superior Temporal Gyrus; posterior division & Unimodal \\ \hline
15 & 501 & R-Inferior Frontal Gyrus; pars triangularis & Heteromodal \\ \hline
14 & 1302 & L-Middle Temporal Gyrus; temporo-occipital & Heteromodal \\ \hline
11 & 1101 & R-Middle Temporal Gyrus; anterior division & Heteromodal \\ \hline
10 & 1301 & R-Middle Temporal Gyrus; temporo-occipital & Heteromodal \\ \hline
8 & 4301 & R-Parietal Operculum Cortex & Unimodal \\ \hline
8 & 3301 & R-Frontal Orbital Cortex & Paralimbic \\ \hline
8 & 2702 & L-Subcallosal Cortex & Paralimbic \\ \hline
8 & 1102 & L-Middle Temporal Gyrus; anterior division & Heteromodal \\ \hline
7 & 3401 & R-Parahippocampal Gyrus; anterior division & Paralimbic \\ \hline
7 & 2801 & R-Paracingulate Gyrus & Heteromodal \\ \hline
7 & 2302 & L-Lateral Occipital Cortex; inferior division & Paralimbic \\ \hline
7 & 1702 & L-Postcentral Gyrus & Primary \\ \hline
6 & 2201 & R-Lateral Occipital Cortex; superior division & Unimodal \\ \hline
6 & 401 & R-Middle Frontal Gyrus & Heteromodal \\ \hline
5 & 4402 & L-Planum Polare & Unimodal \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ROIs whose connectivity with other regions had the most significant effect on ASD/TD classification. The _Occurrences_ column includes the number of times an ROI appears in the five classifiers, while the numbers in the _ROI_ column represent the identifiers of the ROIs in the HO atlas. The _Anatomical Part_ column lists the corresponding anatomical parts of the brain (according to HO parcellation), while the _Mesulam_ column identifies the associated functional networks. more suitable, but when dealing with multi-modal features, especially as the complexity increases with data, we believe that deep learning still has potential. The difference in the most important features between the studied classifiers can be attributed to the high number of features (5253) and the intrinsic multivariate nature of the problem. Hence, a large set of features appears to be relevant in the classification, while no small subset can be defined relevant in the classification. To provide an example, the most important features present a score that is in the range of 0.1-1% of AUC, depending on the classifier. This result could also be related to overfitting, highlighting the challenges posed by the dataset's complexity and the abundance of features in achieving robust classifier generalization and accurately identifying significant features. One of the main advantages of DL classifiers is that it is possible to avoid the feature extraction procedure. This is useful, for example, when directly analyzing raw images. However, this approach brings an intrinsic challenge due to the high dimensionality of MRI data and limited size of the datasets. Despite the fact that there are more advanced DL classifiers to study brain images, as a consequence of the improvements in AI research, functional connectivity still remains one of the most used and reliable method when studying rs-fMRI.