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Abstract #2386

Application of Graph Theory in the study of functional and structural connectivity in Mild Cognitive Impairment (MCI) disease

Emilio Cipriano1,2, Laura Biagi1, Paolo Bosco1, Giovanni Cioni1, Alessandro Sale3, Nicoletta Berardi3, Michela Matteoli3, Michela Tosetti1, and the Train the Brain Consortium4
1FiRMLAB, IRCCS Stella Maris, Pisa, Italy, 2Department of Physics, University of Pisa, Pisa, Italy, 3Institute of Neuroscience of the CNR, Pisa, Italy, 4the Train the Brain Consortium, Pisa, Italy


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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