Biomedical Engineering Department, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China.
Biomedical Engineering Department, College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China.
Comput Methods Programs Biomed. 2016 Mar;125:8-17. doi: 10.1016/j.cmpb.2015.11.012. Epub 2015 Dec 8.
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.
脑老化伴随着白质（WM）连通性的变化和灰质（GM）浓度的变化。神经退行性疾病更容易受到加速脑老化的影响，这与前瞻性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老化的准确检测对于旨在阻碍异常脑变化的早期干预具有很大的潜力。为了捕捉脑老化，我们提出了一种新的计算方法，通过对脑网络的连接分析来模拟 112 名正常老年受试者（年龄在 50-79 岁之间）的脑年龄。我们提出的方法应用主成分分析（PCA）来减少网络拓扑参数的冗余。通过混合遗传算法（GA）和 Levenberg-Marquardt（LM）算法改进的反向传播人工神经网络（BPANN）被建立来模拟主成分（PCs）和脑年龄之间的关系。预测的脑龄与实际年龄高度相关（r=0.8）。该模型的平均绝对误差（MAE）为 4.29 岁。因此，我们相信该方法可以为定量描述人类大脑的典型和非典型网络组织提供一种可能的方法，并作为未来神经退行性疾病的无症状检测的生物标志物。
