Multi-View Feature Enhancement Based on Self-Attention Mechanism Graph Convolutional Network for Autism Spectrum Disorder Diagnosis

Feng Zhao; Na Li; Hongxin Pan; Xiaobo Chen; Yuan Li; Haicheng Zhang; Ning Mao; Dapeng Cheng

doi:10.3389/fnhum.2022.918969

Multi-View Feature Enhancement Based on Self-Attention Mechanism Graph Convolutional Network for Autism Spectrum Disorder Diagnosis

Front Hum Neurosci. 2022 Jul 15:16:918969. doi: 10.3389/fnhum.2022.918969. eCollection 2022.

Authors

Feng Zhao¹, Na Li¹, Hongxin Pan¹, Xiaobo Chen¹, Yuan Li², Haicheng Zhang³, Ning Mao³, Dapeng Cheng¹

Affiliations

¹ School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China.
² School of Management Science and Engineering, Shandong Technology and Business University, Yantai, China.
³ Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China.

Abstract

Functional connectivity (FC) network based on resting-state functional magnetic resonance imaging (rs-fMRI) has become an important tool to explore and understand the brain, which can provide objective basis for the diagnosis of neurodegenerative diseases, such as autism spectrum disorder (ASD). However, most functional connectivity (FC) networks only consider the unilateral features of nodes or edges, and the interaction between them is ignored. In fact, their integration can provide more comprehensive and crucial information in the diagnosis. To address this issue, a new multi-view brain network feature enhancement method based on self-attention mechanism graph convolutional network (SA-GCN) is proposed in this article, which can enhance node features through the connection relationship among different nodes, and then extract deep-seated and more discriminative features. Specifically, we first plug the pooling operation of self-attention mechanism into graph convolutional network (GCN), which can consider the node features and topology of graph network at the same time and then capture more discriminative features. In addition, the sample size is augmented by a "sliding window" strategy, which is beneficial to avoid overfitting and enhance the generalization ability. Furthermore, to fully explore the complex connection relationship among brain regions, we constructed the low-order functional graph network (Lo-FGN) and the high-order functional graph network (Ho-FGN) and enhance the features of the two functional graph networks (FGNs) based on SA-GCN. The experimental results on benchmark datasets show that: (1) SA-GCN can play a role in feature enhancement and can effectively extract more discriminative features, and (2) the integration of Lo-FGN and Ho-FGN can achieve the best ASD classification accuracy (79.9%), which reveals the information complementarity between them.

Keywords: autism spectrum disorder (ASD); feature enhancement; graph convolutional network (GCN); pooling operation; resting-state functional magnetic resonance imaging (rs-fMRI).