BACKGROUND Advanced non-invasive neuroimaging techniques offer new approaches to study functions and structures of human brains. Whole-brain functional networks obtained from resting state functional magnetic resonance imaging has been widely used to study brain diseases like autism spectrum disorder (ASD). Auto-classification of ASD has become an important issue. Existing classification methods for ASD are based on features extracted from the whole-brain functional networks, which may be not discriminant enough for good performance. METHODS In this study, we propose a network clustering based feature selection strategy for classifying ASD. In our proposed method, we first apply symmetric non-negative matrix factorization to divide brain networks into four modules. Then we extract features from one of four modules called default mode network (DMN) and use them to train several classifiers for ASD classification. RESULTS The computational experiments show that our proposed method achieves better performances than those trained with features extracted from the whole brain network. CONCLUSION It is a good strategy to train the classifiers for ASD based on features from the default mode subnetwork.

中文翻译：

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一种基于网络聚类的特征选择策略，用于对自闭症谱系障碍进行分类。

背景技术先进的非侵入性神经成像技术提供了研究人脑功能和结构的新方法。从静息态功能磁共振成像获得的全脑功能网络已广泛用于研究自闭症谱系障碍（ASD）等脑部疾病。ASD的自动分类已成为一个重要问题。现有的 ASD 分类方法基于从全脑功能网络中提取的特征，这可能不足以实现良好的性能。方法在本研究中，我们提出了一种基于网络聚类的特征选择策略来对 ASD 进行分类。在我们提出的方法中，我们首先应用对称非负矩阵分解将大脑网络划分为四个模块。然后，我们从称为默认模式网络 (DMN) 的四个模块之一中提取特征，并使用它们来训练几个用于 ASD 分类的分类器。结果计算实验表明，我们提出的方法比使用从全脑网络提取的特征进行训练的方法取得了更好的性能。结论 基于默认模式子网络的特征来训练 ASD 分类器是一个很好的策略。