There is a great interest in reliable biomarkers of early dementia diagnosis and disease progression. In this context, magnetic resonance imaging (MRI) is a very promising tool due to its sensitivity to brain abnormalities and its non-invasiveness. Diffusion tensor (DT) MRI allows assessing the structural integrity of the white matter (WM) tracts belonging to a specific network, while the use of functional magnetic resonance imaging (fMRI) at rest allows exploring functional organization of the principal brain functional networks. Attempts to characterize data derived from these two techniques have led to the emergence of a new, multidisciplinary approach to the study of complex systems, known as "graph theory". Using "graph theory" approaches, several recent studies have described the effects of diseases such as schizophrenia, attention deficit hyperactivity disorder, and Alzheimer's disease (AD) on fMRI brain networks, while others have characterized aberrant topology of the structural infrastructure of the brain network in these patients. In this project, we will determine whether graph theory properties derived from DT MRI and RS fMRI might provide sensitive and specific markers to differentiate early AD patients from healthy controls and patients with other early neurodegenerative dementia. Furthermore, this study is likely to allow the identification of useful biomarkers to monitor the efficacy of new experimental treatment in these conditions.