A novel approach for ASD recognition based on graph attention networks

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Canhua Wang, Zhiyong Xiao, Yilu Xu, Qi Zhang, Jingfang Chen
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Abstract

Early detection and diagnosis of Autism Spectrum Disorder (ASD) can significantly improve the quality of life for affected individuals. Identifying ASD based on brain functional connectivity (FC) poses a challenge due to the high heterogeneity of subjects’ fMRI data in different sites. Meanwhile, deep learning algorithms show efficacy in ASD identification but lack interpretability. In this paper, a novel approach for ASD recognition is proposed based on graph attention networks. Specifically, we treat the region of interest (ROI) of the subjects as node, conduct wavelet decomposition of the BOLD signal in each ROI, extract wavelet features, and utilize them along with the mean and variance of the BOLD signal as node features, and the optimized FC matrix as the adjacency matrix, respectively. We then employ the self-attention mechanism to capture long-range dependencies among features. To enhance interpretability, the node-selection pooling layers are designed to determine the importance of ROI for prediction. The proposed framework are applied to fMRI data of children (younger than 12 years old) from the Autism Brain Imaging Data Exchange datasets. Promising results demonstrate superior performance compared to recent similar studies. The obtained ROI detection results exhibit high correspondence with previous studies and offer good interpretability.
基于图注意力网络的新型 ASD 识别方法
自闭症谱系障碍(ASD)的早期检测和诊断可大大改善患者的生活质量。由于受试者不同部位的 fMRI 数据具有高度异质性,因此基于大脑功能连接(FC)来识别 ASD 是一项挑战。与此同时,深度学习算法在 ASD 识别中显示出功效,但缺乏可解释性。本文提出了一种基于图注意力网络的新型 ASD 识别方法。具体来说,我们将受试者的兴趣区域(ROI)作为节点,对每个ROI中的BOLD信号进行小波分解,提取小波特征,并将其与BOLD信号的均值和方差分别作为节点特征和优化后的FC矩阵作为邻接矩阵。然后,我们利用自注意机制来捕捉特征之间的长程依赖关系。为了增强可解释性,我们设计了节点选择池层,以确定 ROI 对预测的重要性。我们将提出的框架应用于自闭症脑成像数据交换数据集中的儿童(12 岁以下)fMRI 数据。与最近的类似研究相比,有希望的结果显示出更优越的性能。所获得的 ROI 检测结果与之前的研究具有很高的对应性,并提供了良好的可解释性。
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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