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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Predicting healthy older adult's brain age based on structural connectivity networks using artificial neural networks
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Predicting healthy older adult's brain age based on structural connectivity networks using artificial neural networks

机译：使用人工神经网络基于结构连接网络预测健康的老年人的大脑年龄

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Brain ageing is followed by changes of the connectivity of white matter (WM) and changes of the grey matter (GM) concentration. Neurodegenerative disease is more vulnerable to an accelerated brain ageing, which is associated with prospective cognitive decline and disease severity. Accurate detection of accelerated ageing based on brain network analysis has a great potential for early interventions designed to hinder atypical brain changes. To capture the brain ageing, we proposed a novel computational approach for modeling the 112 normal older subjects (aged 50-79 years) brain age by connectivity analyses of networks of the brain. Our proposed method applied principal component analysis (PCA) to reduce the redundancy in network topological parameters. Back propagation artificial neural network (BPANN) improved by hybrid genetic algorithm (GA) and Levenberg-Marquardt (LM) algorithm is established to model the relation among principal components (PCs) and brain age. The predicted brain age is strongly correlated with chronological age (r=0.8). The model has mean absolute error (MAE) of 4.29 years. Therefore, we believe the method can provide a possible way to quantitatively describe the typical and atypical network organization of human brain and serve as a biomarker for presymptomatic detection of neurodegenerative diseases in the future. (C) 2015 Elsevier Ireland Ltd. All rights reserved.
机译：脑老化后，白质（WM）的连通性发生变化，灰质（GM）浓度发生变化。神经退行性疾病更容易加速大脑老化，这与预期的认知能力下降和疾病严重程度有关。基于脑网络分析的加速衰老的准确检测具有很大的潜力，可以用于旨在阻止非典型脑部改变的早期干预措施。为了捕捉大脑的衰老，我们提出了一种新颖的计算方法，可以通过对大脑网络的连通性分析来对112名正常的老年受试者（年龄在50-79岁之间）进行建模。我们提出的方法应用主成分分析（PCA）来减少网络拓扑参数中的冗余。建立了由混合遗传算法（GA）和Levenberg-Marquardt（LM）算法改进的反向传播人工神经网络（BPANN），以建模主成分（PC）与大脑年龄之间的关系。预测的大脑年龄与时间年龄密切相关（r = 0.8）。该模型的平均绝对误差（MAE）为4.29年。因此，我们认为该方法可以为定量描述人脑的典型和非典型网络组织提供一种可能的方法，并且可以作为将来对神经退行性疾病进行症状前检测的生物标记。 （C）2015 Elsevier Ireland Ltd.保留所有权利。

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