  Alzheimer's disease is the most common dementia leading to an irreversible
neurodegenerative process. To date, subject revealed advanced brain structural
alterations when the diagnosis is established. Therefore, an earlier diagnosis
of this dementia is crucial although it is a challenging task. Recently, many
studies have proposed biomarkers to perform early detection of Alzheimer's
disease. Some of them have proposed methods based on inter-subject similarity
while other approaches have investigated framework using intra-subject
variability. In this work, we propose a novel framework combining both
approaches within an efficient graph of brain structures grading. Subsequently,
we demonstrate the competitive performance of the proposed method compared to
state-of-the-art methods.
