Abstract
To explore the changes in network characteristics of brain functional network in Alzheimer’s disease (AD), the analyses of graph theory were performed. A total of 15 subjects (5 AD patients, 5 mild cognitive impairment (MCI) patients, and 5 normal controls (NC)) were recruited from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The brain functional networks were constructed, and the network characteristics of clustering coefficient (Cp), characteristic path length (Lp), global efficiency (Eg), local efficiency (Eloc) and small-worldness (σ) were calculated. The statistical methods of Chi-square test and analysis of covariance (ANOVA) were used to compare the differences in demographic, neuropsychological, and network characteristic parameters among AD, MCI, and NC. The results showed that three groups all have small-worldness properties. The σ, Lp and Eg of AD were smaller than NC (p < 0.05). There were no significant differences in σ, Eg and Eloc between AD and MCI (p > 0.05). Our findings suggested that functional network characteristics can be used as biological markers for AD research.
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This project was funded by the National Natural Science Foundation of China (Nos. 61971275 and 81830052).
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Xu, S., Yao, X., Bu, X., Lv, Y., Huang, G. (2021). Evaluation of Functional Magnetic Resonance Imaging Brain Network for Alzheimer’s Disease. In: Meng, H., Lei, T., Li, M., Li, K., Xiong, N., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 88. Springer, Cham. https://doi.org/10.1007/978-3-030-70665-4_174
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DOI: https://doi.org/10.1007/978-3-030-70665-4_174
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