# Combined Use of MRI, fMRIand Cognitive Data for Alzheimer's Disease: Preliminary Results
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**ROI—Number and Description**} & \multicolumn{2}{c}{**HC + AD**} & \multicolumn{2}{c}{**HC**} & \multicolumn{2}{c}{**AD**} \\  & \(\rho\) & \(p\)**-Value** & \(\rho\) & \(p\)**-Value** & \(\rho\) & \(p\)**-Value** \\ \hline
1 Precentral L & **0.2417** & **0.0457** & 0.2981 & 0.0778 & 0.1340 & 0.4556 \\
7 Frontal Mid L & 0.2054 & 0.0904 & 0.0996 & 0.5619 & **0.3663** & **0.0367** \\
12 Frontal Inf Oper R & 0.2327 & 0.0545 & 0.0965 & 0.5742 & **0.3767** & **0.0314** \\
19 Supp Motor Area L & **0.2435** & **0.0440** & **0.3629** & **0.0175** & 0.0775 & 0.6669 \\
20 Supp Motor Area R & **0.3107** & **0.0096** & **0.3959** & **0.0175** & 0.1942 & 0.2777 \\
37 L Hippocampus & 0.4014 & 0.0213 & \(-\)0.0337 & 0.8450 & **0.4014** & **0.0213** \\
43 Calcarine L & 0.1828 & 0.1326 & **0.4597** & **0.0052** & \(-\)0.1334 & 0.4578 \\
45 Cuneus L & **0.2578** & **0.0327** & **0.4301** & **0.0094** & 0.0869 & 0.6294 \\
46 Cuneus R & 0.2178 & 0.0723 & **0.4461** & **0.0069** & \(-\)0.0338 & 0.8519 \\
49 Occipital Sup L & 0.2102 & 0.0831 & **0.4417** & **0.0075** & \(-\)0.0097 & 0.9578 \\
50 Occipital Sup R & 0.2337 & 0.0535 & **0.4571** & **0.0055** & \(-\)0.0267 & 0.8826 \\
51 Occipital Mid L & 0.2162 & 0.0754 & **0.4309** & **0.0092** & \(-\)0.0331 & 0.8548 \\
65 Angular L & 0.1127 & 0.3558 & **0.3514** & **0.0362** & \(-\)0.0575 & -0.7500 \\
66 Angular R & **0.1859** & **0.0464** & 0.2762 & 0.1031 & 0.1390 & 0.4388 \\
69 Paracentral Lobule L & **0.2887** & **0.0164** & 0.2046 & 0.2303 & 0.3225 & 0.0676 \\
74 Putamen R & **0.2575** & **0.0330** & 0.3040 & 0.0718 & 0.1922 & 0.2827 \\
79 Heschl L & 0.2288 & 0.0588 & 0.1292 & 0.4511 & **0.3606** & **0.0399** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Regions with significant correlation between Grey Matter Volume (GMV) and the Clustering Coefficient (CC) across and with AD and HC groups. The area that survived [PERSON]’s correction is highlighted in bold font. Figure 6: Correlation between the Clustering Coefficient (CC) and GMV (**a**) within groups in the right supplementary motor area (\(\rho\) = 0.3107, \(\sigma\) = \(\pm\) 1.998), (**b**) in the HC group in left calcarine (\(\rho\) = 0.4597, \(\sigma\) = \(\pm\)0.4787), and (**c**) in the AD group in left hippocampus (\(\rho\) = 0.4014, \(\sigma\) = \(\pm\)0.7299). The three regions with the highest correlation values are presented. when relying on MRI only, the classification of AD subjects from HC for the mono-modal method showed a classification accuracy of 50.72%, a sensitivity of 30%, and a specificity of 69.44%. As a comparison with the previous two classification methods, the combined measurements of MRI, fMRI, and MMSE were used to train the SVM classifier. For each subject, the set of 90 features coming from MRI, GMV from 90 ROIs, 90 features derived from fMRI, clustering coefficient from 90 ROIs, and one feature from MMSE, was concatenated. As can be noticed from Table 5 and Figure 7, the combination of MRI, fMRI, and MMSE measurements achieved a more accurate classification and discrimination between AD and HC. In this case, the method could achieve a classification accuracy of 95.65%, a sensitivity of 93.93%, and a specificity of 97.22%. While other studies [25; 82] applied the SVM unimodal classification method or exploited images obtained from different imaging techniques [27; 28], our study implemented a multimodal classification from the MRI and fMRI method combined with MMSE. The results derived from training the SVM with MRI, fMRI, and MMSE data allowed us to infer that the approach proposed in this paper is valuable and can be of potential interest to clinicians and researchers. The presented method can be adapted to carry out a similar analysis to evaluate if differences between AD and MCI patients exist. To this aim, the use of the MMSE as additional information and the investigation of its relationship with MRI- and fMRI-derived data can support AD research.