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首页> 外文期刊>BMC Medical Genomics >A network clustering based feature selection strategy for classifying autism spectrum disorder
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A network clustering based feature selection strategy for classifying autism spectrum disorder

机译：基于网络聚类的特征选择策略，用于分类自闭症谱系障碍

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摘要

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. 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. The computational experiments show that our proposed method achieves better performances than those trained with features extracted from the whole brain network. It is a good strategy to train the classifiers for ASD based on features from the default mode subnetwork.
机译：先进的无侵入性神经影像学技术提供了研究人类脑的功能和结构的新方法。从静息状态函数磁共振成像获得的全脑功能网络已被广泛用于研究自闭症谱系（ASD）等脑疾病。 ASD的自动分类已成为一个重要问题。 ASD的现有分类方法基于从整个脑功能网络中提取的特征，这可能不会判别足以实现良好的性能。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，用于对ASD进行分类。在我们提出的方法中，我们首先应用对称的非负矩阵分解，将脑网络分成四个模块。然后，我们从名为默认模式网络（DMN）中的四个模块中的一个提取特征，并使用它们培训多个分类器进行ASD分类。计算实验表明，我们的提出方法比从整个脑网络中提取的特征训练得更好的表现。基于默认模式子网的功能培训ASD的分类器是一种很好的策略。

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