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Computational Intelligence and Neuroscience - Volume 2016 2016, Article ID 2429691, 15 pages -

Research Article

College of Automation, Huazhong University of Science and Technology, Wuhan 430074, China

Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Wuhan 430074, China

School of Electrical and Electronic Engineering, East China Jiaotong University, Nanchang 330013, China

Received 12 October 2015; Revised 12 January 2016; Accepted 31 January 2016

Academic Editor: Reinoud Maex

Copyright © 2016 Wei Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The human brain undergoes complex reorganization and changes during aging. Using graph theory, scientists can find differences in topological properties of functional brain networks between young and elderly adults. However, these differences are sometimes significant and sometimes not. Several studies have even identified disparate differences in topological properties during normal aging or in age-related diseases. One possible reason for this issue is that existing brain network construction methods cannot fully extract the -intrinsic edges- to prevent useful signals from being buried into noises. This paper proposes a new subnetwork voting SNV method with sliding window to construct functional brain networks for young and elderly adults. Differences in the topological properties of brain networks constructed from the classic and SNV methods were consistent. Statistical analysis showed that the SNV method can identify much more statistically significant differences between groups than the classic method. Moreover, support vector machine was utilized to classify young and elderly adults; its accuracy, based on the SNV method, reached 89.3%, significantly higher than that with classic method. Therefore, the SNV method can improve consistency within a group and highlight differences between groups, which can be valuable for the exploration and auxiliary diagnosis of aging and age-related diseases.

Autor: Wei Li, Miao Wang, Yapeng Li, Yue Huang, and Xi Chen

Fuente: https://www.hindawi.com/


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