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Using deep learning to classify pediatric posttraumatic stress disorder at the individual level
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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8555083/ https://www.ncbi.nlm.nih.gov/pubmed/34711200 http://dx.doi.org/10.1186/s12888-021-03503-9 |
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author | Yang, Jing Lei, Du Qin, Kun Pinaya, Walter H. L. Suo, Xueling Li, Wenbin Li, Lingjiang Kemp, Graham J. Gong, Qiyong |
author_facet | Yang, Jing Lei, Du Qin, Kun Pinaya, Walter H. L. Suo, Xueling Li, Wenbin Li, Lingjiang Kemp, Graham J. Gong, Qiyong |
author_sort | Yang, Jing |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12888-021-03503-9. |
format | Online Article Text |
id | pubmed-8555083 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-85550832021-10-29 Using deep learning to classify pediatric posttraumatic stress disorder at the individual level Yang, Jing Lei, Du Qin, Kun Pinaya, Walter H. L. Suo, Xueling Li, Wenbin Li, Lingjiang Kemp, Graham J. Gong, Qiyong BMC Psychiatry Research Article 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12888-021-03503-9. BioMed Central 2021-10-28 /pmc/articles/PMC8555083/ /pubmed/34711200 http://dx.doi.org/10.1186/s12888-021-03503-9 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Yang, Jing Lei, Du Qin, Kun Pinaya, Walter H. L. Suo, Xueling Li, Wenbin Li, Lingjiang Kemp, Graham J. Gong, Qiyong Using deep learning to classify pediatric posttraumatic stress disorder at the individual level |
title | Using deep learning to classify pediatric posttraumatic stress disorder at the individual level |
title_full | Using deep learning to classify pediatric posttraumatic stress disorder at the individual level |
title_fullStr | Using deep learning to classify pediatric posttraumatic stress disorder at the individual level |
title_full_unstemmed | Using deep learning to classify pediatric posttraumatic stress disorder at the individual level |
title_short | Using deep learning to classify pediatric posttraumatic stress disorder at the individual level |
title_sort | using deep learning to classify pediatric posttraumatic stress disorder at the individual level |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8555083/ https://www.ncbi.nlm.nih.gov/pubmed/34711200 http://dx.doi.org/10.1186/s12888-021-03503-9 |
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