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

BMC Psychiatry

卷:21

Walter H. L. Pinaya¹  Du Lei²  Kun Qin³  Xueling Suo³  Qiyong Gong³  Jing Yang³  Wenbin Li³  Graham J. Kemp⁴  Lingjiang Li⁵ 

[1] Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London;

[2] Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati;

[3] Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University;

[4] Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool;

[5] Mental Health Institute, the Second Xiangya Hospital of Central South University;

关键词: Deep learning;    Posttraumatic stress disorder;    Graph measure;    Topological properties;    Classification Psychoradiology;    Psychoradiology;   

DOI  :  10.1186/s12888-021-03503-9

来源: DOAJ

【 摘 要 】

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. Methods We studied 33 pediatric PTSD and 53 matched HC. Functional connectivity between 90 brain regions from the automated anatomical labeling atlas was established using partial correlation coefficients, and the whole-brain functional connectome was constructed by applying a threshold to the resultant 90 * 90 partial correlation matrix. Graph theory analysis was used to examine the topological properties of the functional connectome. A DL algorithm then used this measure to classify pediatric PTSD vs HC. Results Graphic topological measures using DL provide a potentially clinically useful classifier for differentiating pediatric PTSD and HC (overall accuracy 71.2%). Frontoparietal areas (central executive network), cingulate cortex, and amygdala contributed the most to the DL model’s performance. Conclusions Graphic topological measures based on fMRI data could contribute to imaging models of clinical utility in distinguishing pediatric PTSD from HC. DL model may be a useful tool in the identification of brain mechanisms PTSD participants.

【 授权许可】

Unknown