Application of MRI, fMRI and Cognitive Data for Alzheimer's Disease detection

Magnetic resonance imaging (MRI) has the clinical potential of helping diagnosis in providing to doctors structural and functional information of several neurological disorders. In this study, we proposed a new method based on the elaboration of MR-Images and functional Magnetic Resonace Images (fMRI), combined with the explotation of Mini Mental Score Examination (MMSE) to discriminate Alzheimer's Disease (AD) by control subjects using Support Vector Machine (SVM) classification. 69 subjects from the Alzheimer's disease Neuroimaging Initiative (ADNI) open database, 33 AD patients and 36 healthy controls (HC), were analyzed. The use of a unimodal approach led to unsatisfactory results, whereas the multimodal approach, i.e., the combination of MRI, fMRI, and MMSE features, provided an accuracy of 95.65%, a specificity of 97.22%, and a sensibility of 93.39%.