The functional connectivity provides new insights into the mechanisms of the human brain at network-level, which has been proved to be an effective biomarker for brain disease classification. Recently, machine learning methods have played an important role in functional connectivity classification, among which convolutional neural network (CNN) based methods become a new hot topic since they can extract topological features in the brain network. However, the conventional CNN-based methods haven’t taken sparse connectivity patterns (SCPs) of the human brain into consideration, which may lead to redundancy of the topological features, and limit their performance and generalization. To solve it, we propose a novel CNN-based model with graphical Lasso (CNNGLasso) to extract sparse topological features for brain disease classification. First, we develop a novel graphical Lasso model for revealing the SCPs at group-level. Then, the SCPs are used to guide the topological feature extraction. Finally, the obtained sparse topological features are used to classify the patients from normal controls. The experiment results on the ABIDE dataset demonstrate that the CNNGLasso outperforms the others on various performances. Besides, the abnormal brain regions derived from the trained model are consistent with the previous investigations, which further proves the application prospect of the CNNGLasso.

中文翻译：

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具有图形套索的卷积神经网络提取稀疏拓扑特征用于脑疾病分类

功能连接为网络层面的人脑机制提供了新的见解，已被证明是脑疾病分类的有效生物标志物。最近，机器学习方法在功能连接分类中发挥了重要作用，其中基于卷积神经网络（CNN）的方法由于可以提取大脑网络中的拓扑特征而成为一个新的热门话题。然而，传统的基于 CNN 的方法没有考虑人脑的稀疏连接模式（SCP），这可能导致拓扑特征的冗余，并限制其性能和泛化性。为了解决这个问题，我们提出了一种基于 CNN 的新型图形 Lasso 模型 (CNNGLasso)，以提取稀疏拓扑特征用于脑疾病分类。第一的，我们开发了一种新颖的图形 Lasso 模型，用于在组级别揭示 SCP。然后，SCP用于指导拓扑特征提取。最后，获得的稀疏拓扑特征用于将患者与正常对照进行分类。ABIDE 数据集上的实验结果表明，CNNGLasso 在各种性能上都优于其他方法。此外，训练模型得出的异常脑区与之前的研究一致，进一步证明了CNNGLasso的应用前景。ABIDE 数据集上的实验结果表明，CNNGLasso 在各种性能上都优于其他方法。此外，训练模型得出的异常脑区与之前的研究一致，进一步证明了CNNGLasso的应用前景。ABIDE 数据集上的实验结果表明，CNNGLasso 在各种性能上都优于其他方法。此外，训练模型得出的异常脑区与之前的研究一致，进一步证明了CNNGLasso的应用前景。