Cui Xiaohong, Xiang Jie, Wang Bin, Xiao Jihai, Niu Yan, Chen Junjie
College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
Center of Information Management and Development, Taiyuan University of Technology, Taiyuan, China.
Front Neurosci. 2018 Oct 8;12:701. doi: 10.3389/fnins.2018.00701. eCollection 2018.
Abnormalities in the brain connectivity in patients with neurodegenerative diseases, such as early mild cognitive impairment (EMCI), have been widely reported. Current research shows that the combination of multiple features of the threshold connectivity network can improve the classification accuracy of diseases. However, in the construction of the threshold connectivity network, the selection of the threshold is very important, and an unreasonable setting can seriously affect the final classification results. Recent neuroscience research suggests that the minimum spanning tree (MST) brain functional network is helpful, as it avoids the methodological biases while comparing networks. In this paper, by employing the multikernel method, we propose a framework to integrate the multiple properties of the MST brain functional network for improving the classification performance. Initially, the Kruskal algorithm was used to construct an unbiased MST brain functional network. Subsequently, the vector kernel and graph kernel were used to quantify the two different complementary properties of the network, such as the local connectivity property and the topological property. Finally, the multikernel support vector machine (SVM) was adopted to combine the two different kernels for EMCI classification. We tested the performance of our proposed method for Alzheimer's Disease Neuroimaging Initiative (ANDI) datasets. The results showed that our method achieved a significant performance improvement, with the classification accuracy of 85%. The abnormal brain regions included the right hippocampus, left parahippocampal gyrus, left posterior cingulate gyrus, middle temporal gyrus, and other regions that are known to be important in the EMCI. Our results suggested that, combining the multiple features of the MST brain functional connectivity offered a better classification performance in the EMCI.
神经退行性疾病患者，如早期轻度认知障碍（EMCI）患者的大脑连接异常已被广泛报道。当前研究表明，阈值连接网络的多个特征相结合可以提高疾病的分类准确率。然而，在阈值连接网络的构建中，阈值的选择非常重要，不合理的设置会严重影响最终的分类结果。最近的神经科学研究表明，最小生成树（MST）脑功能网络是有帮助的，因为它在比较网络时避免了方法上的偏差。在本文中，我们采用多核方法，提出了一个整合MST脑功能网络多种属性的框架，以提高分类性能。首先，使用克鲁斯卡尔算法构建一个无偏差的MST脑功能网络。随后，使用向量核和图核来量化网络的两种不同的互补属性，如局部连接属性和拓扑属性。最后，采用多核支持向量机（SVM）将两种不同的核结合起来用于EMCI分类。我们在阿尔茨海默病神经影像学计划（ANDI）数据集上测试了我们提出的方法的性能。结果表明，我们的方法取得了显著的性能提升，分类准确率达到了85%。异常脑区包括右侧海马体、左侧海马旁回、左侧后扣带回、颞中回以及其他在EMCI中已知重要的区域。我们的结果表明，结合MST脑功能连接的多个特征在EMCI中提供了更好的分类性能。
