Sadegh Sulaimany - Department of Computer Engineering, ShahabDanesh University, Qom, Iran
Marziyeh Karimiyan - Department of Computer Engineering, University of Kurdistan, Sananadaj

IntroductionGraph theory provides popular tools for studying complex brain networks. Alzheimer's disease (AD) is the most common form of wits, which as an incurable, progressive and neurological disease, reduces cognition and memory. Several papers are investigating the topological properties of the brain network for Alzheimer's disease. But, to the best of our knowledge, no one covers the topological changes of the Alzheimer's disease during its different stages. In this research, we examine the weighted graphs of ۲۰۲ participants who were pioneers in Alzheimer's disease brain imaging (ADNI), including ۵۰ normal, ۷۲ individuals with early mild cognitive impairment (eMCI), and ۳۸ individuals with late mild cognitive impairment (lMCI) and ۴۲ Alzheimer's (AD) patients.MethodThe data in this paper are obtained from reference [۱], which is a ۷۰ × ۷۰ weighted adjacency matrix for ۴ brain networks in the Normal, eMCI, lMCI, and AD stages. In this study, several important graph topological parameters [۲] for each of the networks of the four stages of the disease are calculated using the MATLAB toolbox (www.brain-connectivity-toolbox.net). ResultsGraphical views of numbers of topological changes have showed in figure ۱.Topological feature changes of the brain network during Alzheimer's briefly include: the link number, average network weighted degrees, cost, clustering coefficient, average path length, measure of small_world network, closeness centrality and assortativity coefficient. Interpretation of changes in any of the above parameters can provide a better understanding of Alzheimer's disease as it progresses.ConclusionsIt is useful to study the topological changes of the brain network to describe its connections. But this study is better to be during the whole period of the disease so that we can gain a better understanding of the pattern of changes in the structure of the physical communications of the brain over time.