A deep learning method for autism spectrum disorder identification based on interactions of hierarchical brain networks.

Journal: Behavioural brain research
Published Date: Jul 27, 2023

Abstract

BACKGROUND: It has been recently shown that deep learning models exhibited remarkable performance of representing functional Magnetic Resonance Imaging (fMRI) data for the understanding of brain functional activities. With hierarchical structure, deep learning models can infer hierarchical functional brain networks (FBN) from fMRI. However, the applications of the hierarchical FBNs have been rarely studied.

Authors

  • Ning Qiang
    School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China.
  • Jie Gao
    Department of Nephrology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
  • Qinglin Dong
  • Jin Li
    Mental Health Center, West China Hospital, Sichuan University, Chengdu, China.
  • Shu Zhang
    State University of New York, Department of Radiology, Stony Brook, New York, United States.
  • Hongtao Liang
    School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China.
  • Yifei Sun
    School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China.
  • Bao Ge
  • Zhengliang Liu
    School of Computing, University of Georgia, Athens, GA, United States.
  • Zihao Wu
    School of Computing, University of Georgia, Athens, GA, United States.
  • Tianming Liu
    School of Computing, University of Georgia, Athens, GA, United States.
  • Huiji Yue
    School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China. Electronic address: yhj2004@snnu.edu.cn.
  • Shijie Zhao

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