Identifying suicide attempts, ideation, and non-ideation in major depressive disorder from structural MRI data using deep learning.

Journal: Asian journal of psychiatry
PMID: 36791609

Abstract

The present study aims to identify suicide risks in major depressive disorders (MDD) patients from structural MRI (sMRI) data using deep learning. In this paper, we collected the sMRI data of 288 MDD patients, including 110 patients with suicide ideation (SI), 93 patients with suicide attempts (SA), and 85 patients without suicidal ideation or attempts (NS). And we developed interpretable deep neural network models to classify patients in three tasks including SA-versus-SI, SA-versus-NS, and SI-versus-NS, respectively. Furthermore, we interpreted the models by extracting the important features that contributed most to the classification, and further discussed these features or ROI/brain regions.

Authors

  • Jinlong Hu
    School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.
  • Yangmin Huang
    Guangdong Key Lab of Communication and Computer Network, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.
  • Xiaojing Zhang
    Department of Radiology, First Medical Center, Chinese PLA General Hospital, No. 28, Fuxing Road, Beijing, P. R. China.
  • Bin Liao
    College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China.
  • Gangqiang Hou
    Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, China. Electronic address: nihaohgq@163.com.
  • Ziyun Xu
    Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, China.
  • Shoubin Dong
    School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.
  • Ping Li
    Department of Gastroenterology, Beijing Ditan Hospital, Capital Medical University, Beijing, China.

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