Improving early detection, diagnosis and intervention for children with autism spectrum disorder: A cross-sectional survey in China

Dai, Yushen; Deng, Tao; Chen, Miaoying; Huang, Baoqin; Ji, Yan; Feng, Yongshen; Liu, Shaofei; Zhong, Dongmei; Zhang, Tao; Zhang, Lifeng

doi:10.1016/j.ridd.2023.104616

Cited by 3 publications

(1 citation statement)

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“…Artificial intelligence-assisted diagnosis of ASD can alleviate the contradiction between supply and demand between limited psychiatrists and more ASD patients (Abbas et al, 2020;Mertz, 2021). Early detection and intervention for ASD can enhance language, social, and learning skills in affected children, concurrently fostering optimal brain development (Bejarano-Martin et al, 2020;Dai et al, 2023). Wang et al 10.3389/fncom.2024.1388083 Frontiers in Computational Neuroscience 02 frontiersin.org…”

Section: Introductionmentioning

confidence: 99%

A novel approach for ASD recognition based on graph attention networks

Wang,

Xiao,

et al. 2024

Front. Comput. Neurosci.

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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.

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