Combined Use of MRI, fMRIand Cognitive Data for Alzheimer’s Disease: Preliminary Results

• Published in: Applied sciences Vol. 9; no. 15; p. 3156
• Main Authors: Dachena, Chiara, Casu, Sergio, Fanti, Alessandro, Lodi, Matteo Bruno, Mazzarella, Giuseppe
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2019
• Subjects: Alzheimer's disease; biomedical image processing; biomedical signal processing; Brain research; Cognitive ability; cognitive science; Correlation analysis; Dementia; graph theory; magnetic resonance imaging; medical diagnostic imaging; Medical imaging; NMR; Noise; Nuclear magnetic resonance; Pathology; Software; Studies; SVM; Italy
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app9153156

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 Bonferroni’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%.