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）近年来已得到越来越多的研究。但是，从健康人中准确识别多动症患者仍然是一个挑战性问题。为了解决这个问题，我们提出了通过使用单个静止状态功能连接（FC）的双重子空间分类算法。详细地，通过几个子空间量度来学习分别包含ADHD和健康控制特征的两个子空间，称为双重子空间，其中采用改进的图形嵌入量度来增强这些特征的类内关系。因此，给定一个具有FC的对象（用作测试数据），基本的分类原理是比较每个子空间上FC的预测组件能量，然后根据具有更大能量的子空间预测ADHD或控制标签。但是，由于从较小尺寸的ADHD数据库中不稳定地获得了两个子空间，因此该原理在实践中效率很低。因此，我们通过对测试数据进行二元假设检验来提出ADHD分类框架。在此，在训练数据的判别式FC选择中采用具有ADHD或控制标签假设的测试数据FC，以促进对偶子空间的稳定性。对于每个假设，从选定的训练数据FC中学习对偶子空间。这些FC的总预计能量也在子空间上计算。依序，能量比较是在二元假设下进行的。最终，以总能量较大的假设为测试数据预测了ADHD或对照标签。在ADHD-200数据集上进行的实验中，与几种最新的机器学习和深度学习方法相比，我们的方法实现了显着的分类性能，对于大多数ADHD数据库而言，我们的方法的准确性约为90％。交叉验证测试。