利用深度学习从结构磁共振成像数据中识别重度抑郁症中的自杀未遂、自杀意念和非自杀意念。
Identifying suicide attempts, ideation, and non-ideation in major depressive disorder from structural MRI data using deep learning.
作者信息
Hu Jinlong, Huang Yangmin, Zhang Xiaojing, Liao Bin, Hou Gangqiang, Xu Ziyun, Dong Shoubin, Li Ping
机构信息
Guangdong Key Lab of Communication and Computer Network, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.
Guangdong Provincial Key Laboratory of Genome Stability and Disease Prevention and Regional Immunity and Diseases, Department of Pathology, Shenzhen University Medical School, Shenzhen University, Shenzhen, China.
出版信息
Asian J Psychiatr. 2023 Apr;82:103511. doi: 10.1016/j.ajp.2023.103511. Epub 2023 Feb 10.
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.
本研究旨在利用深度学习从结构磁共振成像（sMRI）数据中识别重度抑郁症（MDD）患者的自杀风险。在本文中，我们收集了288例MDD患者的sMRI数据，其中包括110例有自杀意念（SI）的患者、93例有自杀未遂（SA）的患者以及85例无自杀意念或自杀未遂（NS）的患者。并且我们开发了可解释的深度神经网络模型，分别在SA与SI、SA与NS、SI与NS这三个任务中对患者进行分类。此外，我们通过提取对分类贡献最大的重要特征来解释模型，并进一步讨论这些特征或感兴趣区域/脑区。
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