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Utilization of machine learning to test the impact of cognitive processing and emotion recognition on the development of PTSD following trauma exposure
BACKGROUND: Though lifetime exposure to traumatic events is significant, only a minority of individuals develops symptoms of posttraumatic stress disorder (PTSD). Post-trauma alterations in neurocognitive and affective functioning are likely to reflect changes in underlying brain networks that are p...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7310383/ https://www.ncbi.nlm.nih.gov/pubmed/32576245 http://dx.doi.org/10.1186/s12888-020-02728-4 |
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author | Augsburger, Mareike Galatzer-Levy, Isaac R. |
author_facet | Augsburger, Mareike Galatzer-Levy, Isaac R. |
author_sort | Augsburger, Mareike |
collection | PubMed |
description | BACKGROUND: Though lifetime exposure to traumatic events is significant, only a minority of individuals develops symptoms of posttraumatic stress disorder (PTSD). Post-trauma alterations in neurocognitive and affective functioning are likely to reflect changes in underlying brain networks that are predictive of PTSD. These constructs are assumed to interact in a highly complex way. The aim of this exploratory study was to apply machine learning models to investigate the contribution of these interactions on PTSD symptom development and identify measures indicative of circuit related dysfunction. METHODS: N = 94 participants admitted to the emergency room of an inner-city hospital after trauma exposure completed a battery of neurocognitive and emotional tests 1 month after the incident. Different machine learning algorithms were applied to predict PTSD symptom severity and clusters after 3 months based. RESULTS: Overall, model accuracy did not differ between PTSD clusters, though the importance of cognitive and emotional domains demonstrated both key differences and overlap. Alterations in higher-order executive functioning, speed of information processing, and processing of emotionally incongruent cues were the most important predictors. CONCLUSIONS: Data-driven approaches are a powerful tool to investigate complex interactions and can enhance the mechanistic understanding of PTSD. The study identifies important relationships between cognitive processing and emotion recognition that may be valuable to predict and understand mechanisms of risk and resilience responses to trauma prospectively. |
format | Online Article Text |
id | pubmed-7310383 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-73103832020-06-23 Utilization of machine learning to test the impact of cognitive processing and emotion recognition on the development of PTSD following trauma exposure Augsburger, Mareike Galatzer-Levy, Isaac R. BMC Psychiatry Research Article BACKGROUND: Though lifetime exposure to traumatic events is significant, only a minority of individuals develops symptoms of posttraumatic stress disorder (PTSD). Post-trauma alterations in neurocognitive and affective functioning are likely to reflect changes in underlying brain networks that are predictive of PTSD. These constructs are assumed to interact in a highly complex way. The aim of this exploratory study was to apply machine learning models to investigate the contribution of these interactions on PTSD symptom development and identify measures indicative of circuit related dysfunction. METHODS: N = 94 participants admitted to the emergency room of an inner-city hospital after trauma exposure completed a battery of neurocognitive and emotional tests 1 month after the incident. Different machine learning algorithms were applied to predict PTSD symptom severity and clusters after 3 months based. RESULTS: Overall, model accuracy did not differ between PTSD clusters, though the importance of cognitive and emotional domains demonstrated both key differences and overlap. Alterations in higher-order executive functioning, speed of information processing, and processing of emotionally incongruent cues were the most important predictors. CONCLUSIONS: Data-driven approaches are a powerful tool to investigate complex interactions and can enhance the mechanistic understanding of PTSD. The study identifies important relationships between cognitive processing and emotion recognition that may be valuable to predict and understand mechanisms of risk and resilience responses to trauma prospectively. BioMed Central 2020-06-23 /pmc/articles/PMC7310383/ /pubmed/32576245 http://dx.doi.org/10.1186/s12888-020-02728-4 Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver (http://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 Augsburger, Mareike Galatzer-Levy, Isaac R. Utilization of machine learning to test the impact of cognitive processing and emotion recognition on the development of PTSD following trauma exposure |
title | Utilization of machine learning to test the impact of cognitive processing and emotion recognition on the development of PTSD following trauma exposure |
title_full | Utilization of machine learning to test the impact of cognitive processing and emotion recognition on the development of PTSD following trauma exposure |
title_fullStr | Utilization of machine learning to test the impact of cognitive processing and emotion recognition on the development of PTSD following trauma exposure |
title_full_unstemmed | Utilization of machine learning to test the impact of cognitive processing and emotion recognition on the development of PTSD following trauma exposure |
title_short | Utilization of machine learning to test the impact of cognitive processing and emotion recognition on the development of PTSD following trauma exposure |
title_sort | utilization of machine learning to test the impact of cognitive processing and emotion recognition on the development of ptsd following trauma exposure |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7310383/ https://www.ncbi.nlm.nih.gov/pubmed/32576245 http://dx.doi.org/10.1186/s12888-020-02728-4 |
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