Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, Southeast University, China; Department of Psychiatry and Translational Imaging, Columbia University & NYSPI, USA.
Changzhou Key Laboratory of Robots & Intelligent Technology, Hohai University, China.
Artif Intell Med. 2020 Mar;103:101786. doi: 10.1016/j.artmed.2019.101786. Epub 2020 Jan 13.
As one of the most common neurobehavioral diseases in school-age children, Attention Deficit Hyperactivity Disorder (ADHD) has been increasingly studied in recent years. But it is still a challenge problem to accurately identify ADHD patients from healthy persons. To address this issue, we propose a dual subspace classification algorithm by using individual resting-state Functional Connectivity (FC). In detail, two subspaces respectively containing ADHD and healthy control features, called as dual subspaces, are learned with several subspace measures, wherein a modified graph embedding measure is employed to enhance the intra-class relationship of these features. Therefore, given a subject (used as test data) with its FCs, the basic classification principle is to compare its projected component energy of FCs on each subspace and then predict the ADHD or control label according to the subspace with larger energy. However, this principle in practice works with low efficiency, since the dual subspaces are unstably obtained from ADHD databases of small size. Thereby, we present an ADHD classification framework by a binary hypothesis testing of test data. Here, the FCs of test data with its ADHD or control label hypothesis are employed in the discriminative FC selection of training data to promote the stability of dual subspaces. For each hypothesis, the dual subspaces are learned from the selected FCs of training data. The total projected energy of these FCs is also calculated on the subspaces. Sequentially, the energy comparison is carried out under the binary hypotheses. The ADHD or control label is finally predicted for test data with the hypothesis of larger total energy. In the experiments on ADHD-200 dataset, our method achieves a significant classification performance compared with several state-of-the-art machine learning and deep learning methods, where our accuracy is about 90 % for most of ADHD databases in the leave-one-out cross-validation test.
作为学龄儿童中最常见的神经行为疾病之一，注意力缺陷多动障碍（ADHD）近年来越来越受到研究关注。但要从健康人群中准确识别 ADHD 患者仍然是一个挑战。为了解决这个问题，我们提出了一种基于个体静息态功能连接（FC）的双子空间分类算法。具体来说，我们使用了几个子空间度量方法分别学习包含 ADHD 和健康对照组特征的两个子空间，称为双子空间，其中采用了一种改进的图嵌入度量方法来增强这些特征的类内关系。因此，对于一个具有其 FC 的个体（作为测试数据），基本的分类原理是比较其 FC 在每个子空间上的投影分量能量，然后根据具有较大能量的子空间预测 ADHD 或对照标签。然而，在实践中，这个原理的效率较低，因为双子空间是从小规模的 ADHD 数据库中不稳定地获得的。因此，我们提出了一种通过对测试数据进行二项假设检验的 ADHD 分类框架。在这里，测试数据的 FC 及其 ADHD 或对照标签假设被用于训练数据的判别 FC 选择中，以促进双子空间的稳定性。对于每个假设，双子空间都是从训练数据的所选 FC 中学习得到的。这些 FC 在子空间上的总投影能量也被计算出来。然后，在二进制假设下进行能量比较。最后，根据总能量较大的假设预测测试数据的 ADHD 或对照标签。在 ADHD-200 数据集上的实验中，我们的方法与几种最先进的机器学习和深度学习方法相比，表现出了显著的分类性能，在大多数 ADHD 数据库的留一交叉验证测试中，我们的准确率约为 90%。
