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Identifying Visual Attention Features Accurately Discerning Between Autism and Typically Developing: a Deep Learning Framework

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Abstract

Atypical visual attention is a hallmark of autism spectrum disorder (ASD). Identifying the attention features accurately discerning between people with ASD and typically developing (TD) at the individual level remains a challenge. In this study, we developed a new systematic framework combining high accuracy deep learning classification, deep learning segmentation, image ablation and a direct measurement of classification ability to identify the discriminative features for autism identification. Our two-stream model achieved the state-of-the-art performance with a classification accuracy of 0.95. Using this framework, two new categories of features, Food & drink and Outdoor-objects, were identified as discriminative attention features, in addition to the previously reported features including Center-object and Human-faces, etc. Altered attention to the new categories helps to understand related atypical behaviors in ASD. Importantly, the area under curve (AUC) based on the combined top-9 features identified in this study was 0.92, allowing an accurate classification at the individual level. We also obtained a small but informative dataset of 12 images with an AUC of 0.86, suggesting a potentially efficient approach for the clinical diagnosis of ASD. Together, our deep learning framework based on VGG-16 provides a novel and powerful tool to recognize and understand abnormal visual attention in ASD, which will, in turn, facilitate the identification of biomarkers for ASD.

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Acknowledgements

We are grateful for the financial support from the National Key R&D Program of China (2017YFC1307500), the National Natural Science Foundation of China (62027804 and 31671108) as well as the Scientific Instrument Innovation Team of the Chinese Academy of Sciences (GJJSTD20180002).

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Authors and Affiliations

  1. The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055, China

    Jin Xie, Longfei Wang, Yang Yao, Jiayao Sun & Huihui Zhou

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Jin Xie & Jiayao Sun

  3. The Research Center for Artificial Intelligence, Peng Cheng Laboratory, No. 2 Xingke First Street, Nanshan District, Shenzhen, 518000, China

    Huihui Zhou

  4. Department of Radiology, Washington University in St. Louis, St. Louis, MO, 63130, USA

    Shuo Wang

  5. Department of Chemical and Biomedical Engineering and Rockefeller Neuroscience Institute, West Virginia University, Morgantown, WV, 26506, USA

    Paula Webster

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  1. Jin Xie
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Correspondence to Shuo Wang or Huihui Zhou.

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Xie, J., Wang, L., Webster, P. et al. Identifying Visual Attention Features Accurately Discerning Between Autism and Typically Developing: a Deep Learning Framework. Interdiscip Sci Comput Life Sci 14, 639–651 (2022). https://doi.org/10.1007/s12539-022-00510-6

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  • Issue Date:

  • DOI: https://doi.org/10.1007/s12539-022-00510-6

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