Using deep learning to classify pediatric posttraumatic stress disorder at the individual level.

Journal: BMC psychiatry
Published Date: Oct 28, 2021

Abstract

BACKGROUND: Children exposed to natural disasters are vulnerable to developing posttraumatic stress disorder (PTSD). Previous studies using resting-state functional neuroimaging have revealed alterations in graph-based brain topological network metrics in pediatric PTSD patients relative to healthy controls (HC). Here we aimed to apply deep learning (DL) models to neuroimaging markers of classification which may be of assistance in diagnosis of pediatric PTSD.

Authors

  • Jing Yang
    Beijing Novartis Pharma Co. Ltd., Beijing, China.
  • Du Lei
    Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
  • Kun Qin
    Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, College of Medicine, Cincinnati, OH 45219, USA.
  • Walter H L Pinaya
    Center of Mathematics, Computation, and Cognition. Universidade Federal do ABC, Santo André, Brazil.
  • Xueling Suo
    Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, 610041, China.
  • Wenbin Li
    Key Laboratory of the plateau of environmental damage control, Lanzhou General Hospital of Lanzhou Military Command, Lanzhou, China.
  • Lingjiang Li
    Mental Health Institute, the Second Xiangya Hospital of Central South University, Changsha, 410008, Hunan, China.
  • Graham J Kemp
    Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, L9 7AL, UK.
  • Qiyong Gong
    Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.

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