Jiao Zhuqing, Ji Yixin, Zhang Jiahao, Shi Haifeng, Wang Chuang
School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China.
School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China.
Front Cell Dev Biol. 2021 Jan 11;8:610569. doi: 10.3389/fcell.2020.610569. eCollection 2020.
Brain functional networks constructed via regularization has been widely used in early mild cognitive impairment (eMCI) classification. However, few methods can properly reflect the similarities and differences of functional connections among different people. Most methods ignore some topological attributes, such as connection strength, which may delete strong functional connections in brain functional networks. To overcome these limitations, we propose a novel method to construct dynamic functional networks (DFN) based on weighted regularization (WR) and tensor low-rank approximation (TLA), and apply it to identify eMCI subjects from normal subjects. First, we introduce the WR term into the DFN construction and obtain WR-based DFNs (WRDFN). Then, we combine the WRDFNs of all subjects into a third-order tensor for TLA processing, and obtain the DFN based on WR and TLA (WRTDFN) of each subject in the tensor. We calculate the weighted-graph local clustering coefficient of each region in each WRTDFN as the effective feature, and use the -test for feature selection. Finally, we train a linear support vector machine (SVM) classifier to classify the WRTDFNs of all subjects. Experimental results demonstrate that the proposed method can obtain DFNs with the scale-free property, and that the classification accuracy (ACC), the sensitivity (SEN), the specificity (SPE), and the area under curve (AUC) reach 87.0662% ± 0.3202%, 83.4363% ± 0.5076%, 90.6961% ± 0.3250% and 0.9431 ± 0.0023, respectively. We also achieve the best classification results compared with other comparable methods. This work can effectively improve the classification performance of DFNs constructed by existing methods for eMCI and has certain reference value for the early diagnosis of Alzheimer's disease (AD).
通过正则化构建的脑功能网络已广泛应用于早期轻度认知障碍（eMCI）分类。然而，很少有方法能够恰当地反映不同人之间功能连接的异同。大多数方法忽略了一些拓扑属性，如连接强度，这可能会删除脑功能网络中的强功能连接。为克服这些限制，我们提出了一种基于加权正则化（WR）和张量低秩近似（TLA）构建动态功能网络（DFN）的新方法，并将其应用于从正常受试者中识别eMCI受试者。首先，我们将WR项引入DFN构建中，得到基于WR的DFN（WRDFN）。然后，我们将所有受试者的WRDFN组合成一个三阶张量进行TLA处理，并在张量中得到每个受试者基于WR和TLA的DFN（WRTDFN）。我们计算每个WRTDFN中每个区域的加权图局部聚类系数作为有效特征，并使用t检验进行特征选择。最后，我们训练一个线性支持向量机（SVM）分类器对所有受试者的WRTDFN进行分类。实验结果表明，所提方法能够获得具有无标度特性的DFN，分类准确率（ACC）、灵敏度（SEN）、特异性（SPE）和曲线下面积（AUC）分别达到87.0662%±0.3202%、83.4363%±0.5076%、90.6961%±0.3250%和0.9431±0.0023。与其他可比方法相比，我们也取得了最佳分类结果。这项工作能够有效提高现有方法构建的DFN对eMCI的分类性能，对阿尔茨海默病（AD）的早期诊断具有一定的参考价值。
