## 4 Conclusions and Discussion
Certainly, the proposed approach of classification with an ad-DNN could represent a promising tool to employ in a dataset of great dimensions. A crucial issue of this work is in fact the small amount of data acquired at each site, which compose the large dataset. To determine the consistency of the brain areas found in our analysis with some similar studies, we compared the results with the ones obtained by [PERSON] et al. [14] from a structural analysis of the ABIDE dataset. The areas of temporal gyri are among the most discriminant regions: they belong to the temporal lobe, which is involved in processes such as language comprehension, emotion recognition and social cognition, and whose dysfunction has been consistently reported in ASD [27; 28]. Left angular and left supermarginal gyri are part of the inferior parietal lobule, a region implicated in impaired social-communicative and motor functions [29], including the processing of pragmatic information [30] and sentence comprehension [31] in individuals with ASD. Regions such as the right orbitofrontal and right accumbens are both involved in reward and emotion. Previous investigations detected an impaired function of these reward pathways when individuals with ASD viewed smiling faces [32] and heard human voices [33], supporting the impaired salience and reward value attributed to social stimuli in autism [34]. The left lingual gyrus, together with other brain regions--lateral occipital cortex, fusiform gyrus and posterior superior temporal sulcus--is part of a complex network implicated in atypical object/face recognition and processing biological motion in ASD [35]. As highlighted in this latter analysis, both the procedures, i.e., the deep learning classification of analytically harmonized data and the deep adversarial learning, lead to a common set of important features. This similarity became even more evident when we converted functional connectivity measures into the brain areas mainly involved. We observe that a great number of areas that we found as relevant in our discrimination problem based on brain functional data were actually identified as also important in a recent study [14] carried out on the structural MRI data, and that they are related to ASD symptoms. It is important to note that, while the AUC performance achieved indicates an acceptable discrimination capability that can reveal information on the neural correlates of ASD, the method presented in this work is not suitable for diagnostic purposes. In order to improve the prediction accuracy, other approaches should be considered, including the combination of genetic and imaging information as well as the application of approaches based of the deviation from the normative model, as already performed in other psychiatric disorders [36]. As a final consideration, we highlight that, despite the case study reported in this work being focused on the investigation of the ASD condition through fMRI data, the data analysis pipeline developed here constitutes a general procedure that can be extended to other disease and other patients' data acquisition modalities. In particular, the adversarial deep neural network approach presented here can not only be easily extended to the investigation of different conditions where an altered functional connectivity is suspected, but can be exploited in all classification or regression problems where confounding effects such as those related to acquisition instrumentation (including other MRI modalities, X-ray-based techniques, EEG, MEG, etc.) can affect the results. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13116486/s1](https://www.mdpi.com/article/10.3390/app13116486/s1)]([https://www.mdpi.com/article/10.3390/app13116486/s1](https://www.mdpi.com/article/10.3390/app13116486/s1)), Table S1: containing the list of subjects of the ABIDE dataset analyzed; Table S2: containing the look-up table for converting the connectivity feature numbers into the involved pairs of brain regions. Conceptualization, A.R.