## 4 Experimental Results
### Feature Importance
No significant connections between subcortical regions were found, confirming our previous results about the hypothesis that AD connectivity alterations mostly regard the inter-connectivity between subcortical and cortical regions rather than the intra-subcortical connectivity. Among the most significant different connections we found the Temporal-Frontal and Temporal-Parietal which are known to be affected in AD [41; 42]. The anatomical areas most occurring in the most significant brain region pairs are Occipital, Parietal and Temporal Lobe, which are highly AD-related brain regions [43; 44; 45]. Indeed, the Occipital area is responsible for visual processing, the Parietal area has an important role in integrating senses while the Temporal area is essential for memory. The Cerebellum has also been found among the most occurring regions. The role of the Cerebellum in AD has been a topic of debate in the last years: only recently its closely association with cognitive deterioration has emerged, e.g., [46; 47]. Three subcortical regions were found among the most significantly different node pairs: right Caudate, left and right Hippocamp and Putamen. Also these regions play an important role in AD [48; 49; 50]. It is interesting to note that there are some significantly region pairs with greater communicability in AD compared to HC. Similar findings have been reported for example in [51] where some areas of greater communicability in stroke patients compared to controls were found also in the lesioned hemisphere, also in this case starting from DWI date for studying a disconnection syndrome. One possible interpretation of this pattern is the hypothesis that the increased communicability in AD could reflect adaptive changes in the white matter structure that have occurred secondary to the disease. We also compared the most occurring regions resulting from this study to our previous findings [15; 16]. In particular, we found a significant overlap with some cortical and sub-cortical anatomical regions: parietal lobe, paracentral gyrus and temporal areas are the most overlapping regions. In addition, we found a consistent overlap in both parahippocampal and caudal regions. Another important issue concerns the agreement between the three classification algorithms. Although the classification models are based on different algorithms, they showed very similar findings in Accuracy, AUC and Specificity and in the detection of the most significant features. The results attested the robustness of the framework based on whole brain graph communicability. Other main open issues should be addressed in future work. In this paper, only the binary discrimination HC/AD was taken into account: the classification task involving mild cognitive impairment (MCI) subjects should also be considered in order to support the disease diagnosis at earlier stages. However, it should be noted that the AD patients we have taken into account for the present study, from the ADNI database, are characterized by a MMSE score indicating a moderate and not severe cognitive impairment. In addition, a greater sample size should be used and other classification strategies should be explored to further improve the diagnostic accuracy. Novel insights could be obtained, for example, by using a _multi-expert_ approach or a set of fuzzy inference rules. Promising results from using these techniques for diagnostic purposes in other domains have been recently reported, e.g., [52; 53]. A multi-expert approach, which is based on ensembling different classifiers trained for the same task, may further improve prediction accuracy, as it can benefit from the different viewpoints from which these classifiers may look at the data. Concerning the use of fuzzy inference rules, they can be extracted from data with the aim to provide more explicit classification rules and thus to better support the decisions made by physicians. The use of Fuzzy Logic revealed beneficial in brain MRI.