Network-based classification of ADHD patients using discriminative subnetwork selection and graph kernel PCA
Network-based classification of ADHD patients using discriminative subnetwork selection and graph kernel PCA
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DOI：
10.1016/j.compmedimag.2016.04.004
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发表时间：
2016-09-01
影响因子：
5.7
通讯作者：
Zhang, Daoqiang
中科院分区：
文献类型：
--
作者：
Du, Junqiang;Wang, Lipeng;Zhang, Daoqiang
背景：注意缺陷多动障碍(ADHD)是儿童和青春期最常见的行为障碍之一。由于ADHD疾病不仅与单个脑区有关，而且与它们之间的联系有关，因此基于网络的ADHD诊断引起了人们的极大关注，而现有的方法很难发现与多个脑区相关的紊乱模式。新方法：为了克服这一缺陷，提出了一种判别性子网络选择方法，直接从ADHD和正常对照(NC)组的整个脑网络中挖掘频繁和有区别的子网络。然后，应用图核主成分(PCA)从这些区分子网络中提取特征。结果：我们使用包含118名ADHD患者和98名正常对照的ADHD200数据集对我们提出的方法的性能进行了评估。实验结果表明，该方法对ADHD和NC的分类准确率高达94.91%。与已有的方法(S)相比，该方法的准确率提高了9.20%。结论：在ADHD200数据集上的大量实验表明，与现有的方法相比，该方法的性能有了显著的提高。(C)2016爱思唯尔有限公司。保留所有权利。
Background: Attention Deficit Hyperactivity Disorder (ADHD) is one of the most prevalent behavioral disorders in childhood and adolescence. Recently, network-based diagnosis of ADHD has attracted great attentions due to the fact that ADHD disease is related to not only individual brain regions but also the connections among them, while existing methods are hard to discover disorder patterns related with several brain regions.New method: To overcome this drawback, a discriminative subnetwork selection method is proposed to directly mine those frequent and discriminative subnetworks from the whole brain networks of ADHD and normal control (NC) groups. Then, the graph kernel principal component (PCA) is applied to extract features from those discriminative subnetworks. Finally, support vector machine (SVM) is adopted for classification of ADHD and NC subjects.Results: We evaluate the performances of our proposed method using the ADHD200 dataset, which contains 118 ADHD patients and 98 normal controls. The experimental results show that our proposed method can achieve a very high accuracy of 94.91% for ADHD vs. NC classification. Moreover, our proposed method can also discover the discriminative subnetworks as well as the discriminative brain regions, which are helpful for enhancing our understanding of ADHD disease.Comparison with existing method(s): The accuracy of our proposed method is 9.20% higher than those of the state-of-the-art methods.Conclusions: A lot of experiments in ADHD200 dataset show that, our proposed method can improve the performance significantly comparing to the state-of-the-art methods. (C) 2016 Elsevier Ltd. All rights reserved.