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



Yang, Jing, Lei, Du, Qin, Kun, Pinaya, Walter HL, Suo, Xueling, Li, Wenbin, Li, Lingjiang, Kemp, Graham J ORCID: 0000-0002-8324-9666 and Gong, Qiyong
(2021) Using deep learning to classify pediatric posttraumatic stress disorder at the individual level. BMC PSYCHIATRY, 21 (1). 535-.

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Abstract

<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.

Item Type: Article
Uncontrolled Keywords: Deep learning, Posttraumatic stress disorder, Graph measure, Topological properties, Classification Psychoradiology, Psychoradiology
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User: Symplectic Admin
Date Deposited: 17 Nov 2021 09:17
Last Modified: 18 Jan 2023 21:24
DOI: 10.1186/s12888-021-03503-9
Open Access URL: https://bmcpsychiatry.biomedcentral.com/articles/1...
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3143312