Graph theory approach was used to analyze structural and functional connectivity networks to identify candidate biomarkers of Mild Cognitive Impairment (MCI), a prodromal stage of Alzheimer’s disease (AD), for an early diagnosis and intervention. We proposed a method of brain network analysis to combine structural and functional connectivity networks information. Through graph measures (Segregation, Centrality and Global Efficiency), we evaluated the differences between MCI and healthy control (HC) subjects as well the possible effects of physical/cognitive training, in terms of structural and functional connectivity, in MCI population.
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