TY - JOUR
T1 - Identifying suicide attempts, ideation, and non-ideation in major depressive disorder from structural MRI data using deep learning
AU - Hu, Jinlong
AU - Huang, Yangmin
AU - Zhang, Xiaojing
AU - Liao, Bin
AU - Hou, Gangqiang
AU - Xu, Ziyun
AU - Dong, Shoubin
AU - Li, Ping
N1 - Funding Information:
This research was supported by the Natural Science Foundation of Guangdong Province of China [2021A1515011942]; the Innovation Fund of Introduced High-end Scientific Research Institutions of Zhongshan [2019AG031]; the Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties [SZGSP013]; and the Guangdong Pearl River Talents Plan Innovative and Entrepreneurial Team [2016ZT06S220].
Funding Information:
The authors wish to thank Shenzhen Kangning Hospital for providing the raw data. Supplementary material associated with this article can be found in the online version.
Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2023/4
Y1 - 2023/4
N2 - 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.
AB - 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.
KW - Major depressive disorder
KW - Suicide behavior
KW - Deep learning
KW - Structural MRI
UR - http://www.scopus.com/inward/record.url?scp=85147972087&partnerID=8YFLogxK
U2 - 10.1016/j.ajp.2023.103511
DO - 10.1016/j.ajp.2023.103511
M3 - Journal article
SN - 1876-2018
VL - 82
JO - Asian Journal of Psychiatry
JF - Asian Journal of Psychiatry
M1 - 103511
ER -