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Mapping PTSD symptoms to brain networks: a machine learning study

Posttraumatic Stress Disorder (PTSD) is a prevalent and debilitating condition with complex and variable presentation. While PTSD symptom domains (intrusion, avoidance, cognition/mood, and arousal/reactivity) correlate highly, the relative importance of these symptom subsets often differs across pat...

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Autores principales: Zandvakili, Amin, Barredo, Jennifer, Swearingen, Hannah R., Aiken, Emily M., Berlow, Yosef A., Greenberg, Benjamin D., Carpenter, Linda L., Philip, Noah S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303205/
https://www.ncbi.nlm.nih.gov/pubmed/32555146
http://dx.doi.org/10.1038/s41398-020-00879-2
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author Zandvakili, Amin
Barredo, Jennifer
Swearingen, Hannah R.
Aiken, Emily M.
Berlow, Yosef A.
Greenberg, Benjamin D.
Carpenter, Linda L.
Philip, Noah S.
author_facet Zandvakili, Amin
Barredo, Jennifer
Swearingen, Hannah R.
Aiken, Emily M.
Berlow, Yosef A.
Greenberg, Benjamin D.
Carpenter, Linda L.
Philip, Noah S.
author_sort Zandvakili, Amin
collection PubMed
description Posttraumatic Stress Disorder (PTSD) is a prevalent and debilitating condition with complex and variable presentation. While PTSD symptom domains (intrusion, avoidance, cognition/mood, and arousal/reactivity) correlate highly, the relative importance of these symptom subsets often differs across patients. In this study, we used machine learning to derive how PTSD symptom subsets differ based upon brain functional connectivity. We acquired resting-state magnetic resonance imaging in a sample (N = 50) of PTSD patients and characterized clinical features using the PTSD Checklist for DSM-5 (PCL-5). We compared connectivity among 100 cortical and subcortical regions within the default mode, salience, executive, and affective networks. We then used principal component analysis and least-angle regression (LARS) to identify relationships between symptom domain severity and brain networks. We found connectivity predicted PTSD symptom profiles. The goodness of fit (R(2)) for total PCL-5 score was 0.29 and the R(2) for intrusion, avoidance, cognition/mood, and arousal/reactivity symptoms was 0.33, 0.23, −0.01, and 0.06, respectively. The model performed significantly better than chance in predicting total PCL-5 score (p = 0.030) as well as intrusion and avoidance scores (p = 0.002 and p = 0.034). It was not able to predict cognition and arousal scores (p = 0.412 and p = 0.164). While this work requires replication, these findings demonstrate that this computational approach can directly link PTSD symptom domains with neural network connectivity patterns. This line of research provides an important step toward data-driven diagnostic assessments in PTSD, and the use of computational methods to identify individual patterns of network pathology that can be leveraged toward individualized treatment.
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spelling pubmed-73032052020-06-22 Mapping PTSD symptoms to brain networks: a machine learning study Zandvakili, Amin Barredo, Jennifer Swearingen, Hannah R. Aiken, Emily M. Berlow, Yosef A. Greenberg, Benjamin D. Carpenter, Linda L. Philip, Noah S. Transl Psychiatry Article Posttraumatic Stress Disorder (PTSD) is a prevalent and debilitating condition with complex and variable presentation. While PTSD symptom domains (intrusion, avoidance, cognition/mood, and arousal/reactivity) correlate highly, the relative importance of these symptom subsets often differs across patients. In this study, we used machine learning to derive how PTSD symptom subsets differ based upon brain functional connectivity. We acquired resting-state magnetic resonance imaging in a sample (N = 50) of PTSD patients and characterized clinical features using the PTSD Checklist for DSM-5 (PCL-5). We compared connectivity among 100 cortical and subcortical regions within the default mode, salience, executive, and affective networks. We then used principal component analysis and least-angle regression (LARS) to identify relationships between symptom domain severity and brain networks. We found connectivity predicted PTSD symptom profiles. The goodness of fit (R(2)) for total PCL-5 score was 0.29 and the R(2) for intrusion, avoidance, cognition/mood, and arousal/reactivity symptoms was 0.33, 0.23, −0.01, and 0.06, respectively. The model performed significantly better than chance in predicting total PCL-5 score (p = 0.030) as well as intrusion and avoidance scores (p = 0.002 and p = 0.034). It was not able to predict cognition and arousal scores (p = 0.412 and p = 0.164). While this work requires replication, these findings demonstrate that this computational approach can directly link PTSD symptom domains with neural network connectivity patterns. This line of research provides an important step toward data-driven diagnostic assessments in PTSD, and the use of computational methods to identify individual patterns of network pathology that can be leveraged toward individualized treatment. Nature Publishing Group UK 2020-06-18 /pmc/articles/PMC7303205/ /pubmed/32555146 http://dx.doi.org/10.1038/s41398-020-00879-2 Text en © The Author(s) 2020 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Zandvakili, Amin
Barredo, Jennifer
Swearingen, Hannah R.
Aiken, Emily M.
Berlow, Yosef A.
Greenberg, Benjamin D.
Carpenter, Linda L.
Philip, Noah S.
Mapping PTSD symptoms to brain networks: a machine learning study
title Mapping PTSD symptoms to brain networks: a machine learning study
title_full Mapping PTSD symptoms to brain networks: a machine learning study
title_fullStr Mapping PTSD symptoms to brain networks: a machine learning study
title_full_unstemmed Mapping PTSD symptoms to brain networks: a machine learning study
title_short Mapping PTSD symptoms to brain networks: a machine learning study
title_sort mapping ptsd symptoms to brain networks: a machine learning study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303205/
https://www.ncbi.nlm.nih.gov/pubmed/32555146
http://dx.doi.org/10.1038/s41398-020-00879-2
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