# Combined Use of MRI, fMRIand Cognitive Data for Alzheimer's Disease: Preliminary Results
###### Abstract
Besides the pure statistical approach, from the meta-analysis of the literature, it is possible to identify the trend of employing machine learning to classify AD patients from HCs. On the one hand, many of them used only structural information obtained from MRI images [25; 26]; whilst, on the other hand, others used both structural and functional information to train the classifier [27; 28]. The cited works faced the problem of recognizing diseased subjects using a reduced number of features, which can be derived by a single clinical exam. It should be pointed out that the functional information was derived from PET exams. None of them used graph theory to extract functional information of fMRI, but pulled out other types of information from fluorodeoxyglucose positron emission tomography (FDG-PET). Moreover, relying on the assumption, shared by the imaging community, that given a high number of features, a classifier would perform better, this work also investigates the combined use of the MRI- and fMRI-derived features, together with the Mini Mental State Examination (MMSE), to investigate differences between AD and HC subjects. In this work, the MMSE is employed instead of other clinical indexes, such as the Neuropysciatric Inventor (NPI-Q) [29] or the Clinical Dementia Rating (CDR) [30]. The chosen cognitive screen is known to suffer from unstable inter-rater reliability and from a limited score range; however, it is free, widely available, quick to administer, and allows experts and non-experts to measure the general cognitive ability, while allowing the comparison between different studies [31]. In this paper, the data were extracted from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, consisting of 33 AD patients and 36 healthy controls. The MRI data were processed with the Statistical Parametric Map (SPM) segmentation software to extract the structural feature, while the BOLD signal from fMRI exams was exploited to evaluate connectivity measures through the graph theory. The synthesis of the findings derived from MRI and fMRI data processing, through Spearman correlation analysis between the Clustering Coefficient (CC) and GMV, can provide clinicians relevant clinical information about AD diagnosis. Moreover, for the first time, to the best of our knowledge, we evaluated the possibility of correlating the GMV with the CC. Finally, the measurements from three data, i.e., MRI, fMRI, and MMSE, were combined to discriminate between AD and HC, through a Support Vector Machine (SVM). The findings presented in this work demonstrate that, instead of exploiting one type of measurement, using the combined and synergistic information from different imaging techniques (i.e., fMRI and MRI) and the clinical data from MMSE enhanced the AD classification, with an accuracy of 95.65%, a sensitivity of 93.93%, and a specificity of 97.22%. In fact, with a unimodal classification method, it is possible to obtain an accuracy of 62.32%, a sensitivity of 66.66%, and a specificity of 58.33% for fMRI; and an accuracy of 50.72%, a sensitivity of 30%, and a specificity of 69.44% for MRI.