Connectivity analysis allows researchers to explore interregional correlations, and thus is well suited for analysis of complex networks such as the brain. We applied whole brain connectivity analysis to assess the progression of Alzheimer's disease (AD). To detect early AD progression, we focused on distinguishing between normal control (NC) subjects and subjects with mild cognitive impairment (MCI). Fludeoxyglucose (FDG) and Pittsburgh compound B (PiB)-positron emission tomography (PET) were acquired for 75 participants. A graph network was implemented using correlation matrices. Correlation matrices of FDG and PiB-PET were combined into one matrix using a novel method. Group-wise differences between NC and MCI patients were assessed using clustering coefficients, characteristic path lengths, and betweenness centrality using various correlation matrices. Using connectivity analysis, this study identified important regions differentially affected by AD progression.
Bibliographical noteFunding Information:
This study was supported in part by the Basic Science Research Program through grants of the National Research Foundation of Korea (grant numbers 2012R1A2A2A01005939 , 20100023233 , and 2013R1A2A2A04016262 ).
© 2015 Elsevier Ireland Ltd and the Japan Neuroscience Society.
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