Predicting healthy older adult's brain age based on structural connectivity networks using artificial neural networks
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- 作者：Lan Lin ; lanlin@bjut.edu.cn" class="auth_mail" title="E-mail the corresponding author ; Cong Jin ; Zhenrong Fu ; Baiwen Zhang ; Guangyu Bin ; Shuicai Wu
- 关键词：MRI ; Healthy brain ageing ; Connectome ; DTI ; White matter
- 刊名：Computer Methods and Programs in Biomedicine
- 出版年：2016
- 出版时间：March 2016
- 年：2016
- 卷：125
- 期：Complete
- 页码：8-17
- 全文大小：2056 K
文摘
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We proposed a novel computational approach for modeling the normal elderly subjects's brain age by connectivity analyses of networks of the brain.
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Principal component analysis (PCA) is applied to reduce the redundancy in network topological parameters.
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BP artificial neural network (BPANN) is improved by hybrid genetic algorithm (GA) and Levenberg–Marquardt (LM) algorithm to model the relation among principal components (PCs) and brain age.
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The method has shown good performance for old cohort with limited samples.