# Combined Use of MRI, fMRIand Cognitive Data for Alzheimer's Disease: Preliminary Results
###### Abstract  
MRI can favor clinical diagnosis providing morphological and functional information of several neurological disorders. This paper deals with the problem of exploiting both data, in a combined way, to develop a tool able to support clinicians in the study and diagnosis of Alzheimer's Disease (AD). In this work, 69 subjects from the ADNI open database, 33 AD patients and 36 healthy controls, were analyzed. The possible existence of a relationship between brain structure modifications and altered functions between patients and healthy controls was investigated performing a correlation analysis on brain volume, calculated from the MRI image, the clustering coefficient, derived from fRMI acquisitions, and the Mini Mental Score Examination (MMSE). A statistically-significant correlation was found only in four ROIs after [PERSON]'s correction. The correlation analysis alone was still not sufficient to provide a reliable and powerful clinical tool in AD diagnosis however. Therefore, a machine learning strategy was studied by training a set of support vector machine classifiers comparing different features. The use of a unimodal approach led to unsatisfactory results, whereas the multimodal approach, i.e., the synergistic combination of MRI, fMRI, and MMSE features, resulted in an accuracy of 95.65%, a specificity of 97.22%, and a sensibility of 93.93%. Alzheimer's disease; biomedical image processing; biomedical signal processing; cognitive science; correlation analysis; graph theory; magnetic resonance imaging; medical diagnostic imaging; SVM +
Footnote †: journal: Medical Imaging  
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features to investigate, such as the Grey Matter Volume (GMV). Typically, in clinical practice, the GMV is quantified to assess the degree of cerebral atrophy and to monitor the neuropathologies' state [9]. One of the most prevalent neurodegenerative disorders is Alzheimer's Disease (AD). This pathology is characterized by a severe derangement of cognitive functions and structural changes within the brain. The pathological hallmarks are the accumulation of \(\beta\)-amyloid (A\(\beta\)1-42) plaques and tau tangles, which cause cognitive and structural damage [10]. Several studies exploited fMRI to diagnose neurological pathology, such as schizophrenia [11], AD, and Mild Cognitive Impairment (MDI) [12]. In functional imaging, the BOLD signal reflects the activity degree of brain areas and can be used to analyze maps of functional connectivity or network measures [3]. The most important issue of fMRI is the physiological noise, which derives from cardiac, respiratory and head movements. In fact, BOLD signal can be corrupted by this kind of noise since it fluctuates within the same frequency band [13; 14]. In commercial software for brain imaging, there are several automatic functionalities to remove these artifacts, which can alter the BOLD signal and its relevance, such as the Component-based method (CompCor) [15]. New approaches founded on graph theory [16; 17] have been proposed to investigate in vivo alterations in functional and structural cerebral networks by means of fMRI. Several studies have shown that graph theory is sensitive in providing network measurements of psychiatric and neurological disease, as in AD diagnosis [18; 19]. In the literature, several research groups have focused on the use of MRI and fMRI to identify and stress the differences between AD and healthy subjects. Some work from the literature studied exclusively the anatomical structure, to highlight volume brain modifications [5; 20], whereas others analyzed only functional aspect to assess the cognitive status of the patients [21; 22]. Other studies showed the feasible correlation between these two aspects [23; 24], but using other types of measurements. To the best of the author's knowledge, none of the cited work ever carried out an investigation about the possible correlation between the ROI volumes, retrieved from MRI, and the graph connectivity measurements, derived from fMRI acquisitions.