Benjamin Suarez-Jimenez , Amit Lazarov , Xi Zhu , Sigal Zilcha-Mano , Yoojean Kim , Claire E. Marino , Pavel Rjabtsenkov , Shreya Y. Bavdekar , Daniel S. Pine , Yair Bar-Haim , Christine L. Larson , Ashley A. Huggins , Terri deRoon-Cassini , Carissa Tomas , Jacklynn Fitzgerald , Mitzy Kennis , Tim Varkevisser , Elbert Geuze , Yann Quidé , Wissam El Hage , Rajendra A. Morey
{"title":"Intrusive Traumatic Re-Experiencing Domain: Functional Connectivity Feature Classification by the ENIGMA PTSD Consortium","authors":"Benjamin Suarez-Jimenez , Amit Lazarov , Xi Zhu , Sigal Zilcha-Mano , Yoojean Kim , Claire E. Marino , Pavel Rjabtsenkov , Shreya Y. Bavdekar , Daniel S. Pine , Yair Bar-Haim , Christine L. Larson , Ashley A. Huggins , Terri deRoon-Cassini , Carissa Tomas , Jacklynn Fitzgerald , Mitzy Kennis , Tim Varkevisser , Elbert Geuze , Yann Quidé , Wissam El Hage , Rajendra A. Morey","doi":"10.1016/j.bpsgos.2023.05.006","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Intrusive traumatic re-experiencing domain (ITRED) was recently introduced as a novel perspective on posttraumatic psychopathology, proposing to focus research of posttraumatic stress disorder (PTSD) on the unique symptoms of intrusive and involuntary re-experiencing of the trauma, namely, intrusive memories, nightmares, and flashbacks. The aim of the present study was to explore ITRED from a neural network connectivity perspective.</p></div><div><h3>Methods</h3><p>Data were collected from 9 sites taking part in the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) PTSD Consortium (<em>n</em> <em>=</em> 584) and included itemized PTSD symptom scores and resting-state functional connectivity (rsFC) data. We assessed the utility of rsFC in classifying PTSD, ITRED-only (no PTSD diagnosis), and trauma-exposed (TE)–only (no PTSD or ITRED) groups using a machine learning approach, examining well-known networks implicated in PTSD. A random forest classification model was built on a training set using cross-validation, and the averaged cross-validation model performance for classification was evaluated using the area under the curve. The model was tested using a fully independent portion of the data (test dataset), and the test area under the curve was evaluated.</p></div><div><h3>Results</h3><p>rsFC signatures differentiated TE-only participants from PTSD and ITRED-only participants at about 60% accuracy. Conversely, rsFC signatures did not differentiate PTSD from ITRED-only individuals (45% accuracy). Common features differentiating TE-only participants from PTSD and ITRED-only participants mainly involved default mode network–related pathways. Some unique features, such as connectivity within the frontoparietal network, differentiated TE-only participants from one group (PTSD or ITRED-only) but to a lesser extent from the other group.</p></div><div><h3>Conclusions</h3><p>Neural network connectivity supports ITRED as a novel neurobiologically based approach to classifying posttrauma psychopathology.</p></div>","PeriodicalId":72373,"journal":{"name":"Biological psychiatry global open science","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266717432300054X/pdfft?md5=7768ff356bdada4cbe512998b7b05bac&pid=1-s2.0-S266717432300054X-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biological psychiatry global open science","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S266717432300054X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Background
Intrusive traumatic re-experiencing domain (ITRED) was recently introduced as a novel perspective on posttraumatic psychopathology, proposing to focus research of posttraumatic stress disorder (PTSD) on the unique symptoms of intrusive and involuntary re-experiencing of the trauma, namely, intrusive memories, nightmares, and flashbacks. The aim of the present study was to explore ITRED from a neural network connectivity perspective.
Methods
Data were collected from 9 sites taking part in the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) PTSD Consortium (n= 584) and included itemized PTSD symptom scores and resting-state functional connectivity (rsFC) data. We assessed the utility of rsFC in classifying PTSD, ITRED-only (no PTSD diagnosis), and trauma-exposed (TE)–only (no PTSD or ITRED) groups using a machine learning approach, examining well-known networks implicated in PTSD. A random forest classification model was built on a training set using cross-validation, and the averaged cross-validation model performance for classification was evaluated using the area under the curve. The model was tested using a fully independent portion of the data (test dataset), and the test area under the curve was evaluated.
Results
rsFC signatures differentiated TE-only participants from PTSD and ITRED-only participants at about 60% accuracy. Conversely, rsFC signatures did not differentiate PTSD from ITRED-only individuals (45% accuracy). Common features differentiating TE-only participants from PTSD and ITRED-only participants mainly involved default mode network–related pathways. Some unique features, such as connectivity within the frontoparietal network, differentiated TE-only participants from one group (PTSD or ITRED-only) but to a lesser extent from the other group.
Conclusions
Neural network connectivity supports ITRED as a novel neurobiologically based approach to classifying posttrauma psychopathology.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
