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Autism spectrum disorder diagnosis based on deep unrolling-based spatial constraint representation

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A Correction to this article was published on 04 January 2024

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Abstract

Accurate diagnosis of autism spectrum disorder (ASD) is crucial for effective treatment and prognosis. Functional brain networks (FBNs) constructed from functional magnetic resonance imaging (fMRI) have become a popular tool for ASD diagnosis. However, existing model-driven approaches used to construct FBNs lack the ability to capture potential non-linear relationships between data and labels. Moreover, most existing studies treat the FBNs construction and disease classification as separate steps, leading to large inter-subject variability in the estimated FBNs and reducing the statistical power of subsequent group comparison. To address these limitations, we propose a new approach to FBNs construction called the deep unrolling-based spatial constraint representation (DUSCR) model and integrate it with a convolutional classifier to create an end-to-end framework for ASD recognition. Specifically, the model spatial constraint representation (SCR) is solved using a proximal gradient descent algorithm, and we unroll it into deep networks using the deep unrolling algorithm. Classification is then performed using a convolutional prototype learning model. We evaluated the effectiveness of the proposed method on the ABIDE I dataset and observed a significant improvement in model performance and classification accuracy.

Graphical abstract

The resting state fMRI images are preprocessed into time series data and 3D coordinates of each region of interest. The data are fed into the DUSCR model, a model for building functional brain networks using deep learning instead of traditional models, that we propose, and then the outputs are fed into the convolutional classifier with prototype learning to determine whether the patient has ASD disease.

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Funding

This work was supported in part by the National Key Research and Development Program of China under Grant 2021YFF0704100; in part by the National Natural Science Foundation of China under Grant 62106032 and 62027827; in part by the China Postdoctoral Science Foundation under Grant 2022MD713691; in part by the Chongqing Postdoctoral Science Special Foundation under Grant 2021XM3028; in part by the Key Cooperation Projects of Chongqing Municipal Education Commission under Grant HZ2021008; and in part by the Natural Science Foundation of Chongqing under Grant cstc2020jcyjzdxmX0025 and Grant cstc2019jcyj-cxttX0002; in part by the Doctoral Research Fund of Chongqing University of Posts and Telecommunications under Grant A2023-01.

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Authors and Affiliations

  1. School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China

    Dajiang Lei, Tao Zhang, Yue Wu & Weisheng Li

  2. School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China

    Xinwei Li

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Contributions

Dajiang Lei: conceptualization, methodology, validation. Tao Zhang: data curation, software, writing-original draft preparation. Yue Wu: visualization, investigation. Weisheng Li: writing-reviewing and editing. Xinwei Li: writing-reviewing and editing, supervision.

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Correspondence to Xinwei Li.

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Lei, D., Zhang, T., Wu, Y. et al. Autism spectrum disorder diagnosis based on deep unrolling-based spatial constraint representation. Med Biol Eng Comput 61, 2829–2842 (2023). https://doi.org/10.1007/s11517-023-02859-2

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