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Classification of ASD based on fMRI data with deep learning

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Abstract

Autism spectrum disorder (ASD) is a neuro-developmental disorder that affects the social abilities of patients. Studies have shown that a small number of abnormal functional connections (FCs) exist in the cerebral hemisphere of ASD patients. The identification of these abnormal FCs provides a biological ground for the diagnosis of ASD. In this paper, we propose a combined deep feature selection (DFS) and graph convolutional network method to classify ASD. Firstly, in the DFS process, a sparse one-to-one layer is added between the input and the first hidden layer of a multilayer perceptron, thus each functional connection (FC) feature can be weighted and a subset of FC features can be selected accordingly. Then based on the selected FCs and the phenotypic information of subjects, a graph convolutional network is constructed to classify ASD and typically developed controls. Finally, we test our proposed method on the ABIDE database and compare it with some other methods in the literature. Experimental results indicate that the DFS can effectively select critical FC features for classification according to the weights of input FC features. With DFS, the performance of GCN classifier can be improved dramatically. The proposed method achieves state-of-the-art performance with an accuracy of 79.5% and an area under the receiver operating characteristic curve (AUC) of 0.85 on the preprocessed ABIDE dataset; it is superior to the other methods. Further studies on the top-ranked thirty FCs obtained by DFS show that these FCs are widespread over the cerebral hemisphere, and the ASD group appears a significantly higher number of weak connections compared to the typically developed group.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 12071025), and the Scientific and Technological Innovation Foundation of Shunde Graduate School of University of Science and Technology Beijing (Nos. BK19CE017 and BK20AE004).

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Authors and Affiliations

  1. Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Lizhen Shao, Cong Fu, Yang You & Dongmei Fu

  2. Shunde Graduate School of University of Science and Technology Beijing, Foshan, 528399, China

    Lizhen Shao & Dongmei Fu

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  1. Lizhen Shao
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Correspondence to Lizhen Shao.

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The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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The datasets generated during and/or analysed during the current study are available in the Preprocessed Connectomes Project repository, http://preprocessed-connectomes-project.org/.

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Shao, L., Fu, C., You, Y. et al. Classification of ASD based on fMRI data with deep learning. Cogn Neurodyn 15, 961–974 (2021). https://doi.org/10.1007/s11571-021-09683-0

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