TY - JOUR
T1 - Intrusive Traumatic Re-Experiencing Domain
T2 - Functional Connectivity Feature Classification by the ENIGMA PTSD Consortium
AU - Suarez-Jimenez, Benjamin
AU - Lazarov, Amit
AU - Zhu, Xi
AU - Zilcha-Mano, Sigal
AU - Kim, Yoojean
AU - Marino, Claire E.
AU - Rjabtsenkov, Pavel
AU - Bavdekar, Shreya Y.
AU - Pine, Daniel S.
AU - Bar-Haim, Yair
AU - Larson, Christine L.
AU - Huggins, Ashley A.
AU - Terri deRoon-Cassini, deRoon-Cassini
AU - Tomas, Carissa
AU - Fitzgerald, Jacklynn
AU - Kennis, Mitzy
AU - Varkevisser, Tim
AU - Geuze, Elbert
AU - Quidé, Yann
AU - El Hage, Wissam
AU - Wang, Xin
AU - O'Leary, Erin N.
AU - Cotton, Andrew S.
AU - Xie, Hong
AU - Shih, Chiahao
AU - Disner, Seth G.
AU - Davenport, Nicholas D.
AU - Sponheim, Scott R.
AU - Koch, Saskia B.J.
AU - Frijling, Jessie L.
AU - Nawijn, Laura
AU - van Zuiden, Mirjam
AU - Olff, Miranda
AU - Veltman, Dick J.
AU - Gordon, Evan M.
AU - May, Geoffery
AU - Nelson, Steven M.
AU - Jia-Richards, Meilin
AU - Neria, Yuval
AU - Morey, Rajendra A.
N1 - Publisher Copyright:
© 2023 The Authors
PY - 2024/1
Y1 - 2024/1
N2 - 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.
AB - 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.
KW - ITRED
KW - Machine learning
KW - PTSD
KW - Re-experiencing
KW - Resting-state functional connectivity
KW - Trauma exposure
UR - http://www.scopus.com/inward/record.url?scp=85169476683&partnerID=8YFLogxK
U2 - 10.1016/j.bpsgos.2023.05.006
DO - 10.1016/j.bpsgos.2023.05.006
M3 - Article
C2 - 38298781
AN - SCOPUS:85169476683
SN - 2667-1743
VL - 4
SP - 299
EP - 307
JO - Biological Psychiatry Global Open Science
JF - Biological Psychiatry Global Open Science
IS - 1
ER -