Accurate diagnosis of autism spectrum disorder (ASD), which is a neurodevelopmental disorder and often accompanied by abnormal social skills, communication skills, interests and behavior patterns, has always been a challenging task in clinical practice. Recent studies have shown great potential for using fMRI data to distinguish ASD from typical control (TC). However, it has always been a challenging problem to extract which features from fMRI data and how to combine these different types of features to achieve improved ASD/TC classification performance. To address this problem, in this study we propose an improved ASD/TC classification framework based on dynamic functional connectivity (DFC) and multi-task feature selection. Our proposed ASD/TC classification framework is evaluated on 871 subjects with fMRI data from the Autism Brain Imaging Data Exchange I (ABIDE I) via a 10-fold cross validation strategy. Experimental results show that our proposed method achieves an accuracy of 76.8% and an area under the receiver operating characteristic curve (AUC) of 0.81 for ASD/TC classification. In addition, compared with some existing state-of-the-art methods, our proposed method achieves better accuracy and AUC for ASD/TC classification. Overall, our proposed ASD/TC classification framework is effective and promising for automatic diagnosis of ASD in clinical practice.

自闭症谱系障碍（ASD）是一种神经发育障碍，通常伴有异常的社交技巧，沟通技巧，兴趣和行为模式，因此，准确诊断自闭症谱系障碍一直是临床实践中的一项艰巨任务。最近的研究表明，使用功能磁共振成像数据将ASD与典型对照（TC）区分的巨大潜力。但是，从fMRI数据中提取哪些特征以及如何组合这些不同类型的特征以实现改进的ASD / TC分类性能一直是一个具有挑战性的问题。为了解决这个问题，在这项研究中，我们提出了一种基于动态功能连接（DFC）和多任务特征选择的改进的ASD / TC分类框架。我们提出的ASD / TC分类框架通过10倍交叉验证策略，对来自自闭症脑成像数据交换I（ABIDE I）的fMRI数据进行了评估，评估了871位受试者。实验结果表明，对于ASD / TC分类，我们提出的方法达到了76.8％的精度，并且接收器工作特性曲线（AUC）下的面积为0.81。此外，与一些现有的最新技术相比，我们提出的方法在ASD / TC分类中实现了更高的准确性和AUC。总体而言，我们提出的ASD / TC分类框架对于在临床实践中自动诊断ASD是有效且有希望的。8％，对于ASD / TC分类，接收器工作特性曲线（AUC）下方的面积为0.81。此外，与一些现有的最新技术相比，我们提出的方法在ASD / TC分类中实现了更高的准确性和AUC。总体而言，我们提出的ASD / TC分类框架对于在临床实践中自动诊断ASD是有效且有希望的。8％，对于ASD / TC分类，接收机工作特性曲线（AUC）下方的面积为0.81。此外，与一些现有的最新技术相比，我们提出的方法在ASD / TC分类中实现了更高的准确性和AUC。总体而言，我们提出的ASD / TC分类框架对于在临床实践中自动诊断ASD是有效且有前途的。