Intrusive Traumatic Re-Experiencing Domain: Functional Connectivity Feature Classification by the ENIGMA PTSD Consortium
Publication date
2024-01
Authors
Suarez-Jimenez, Benjamin
Lazarov, Amit
Zhu, Xi
Zilcha-Mano, Sigal
Kim, Yoojean
Marino, Claire E.
Rjabtsenkov, Pavel
Bavdekar, Shreya Y.
Pine, Daniel S.
Bar-Haim, Yair
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Advisors
Supervisors
Document Type
Article
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cc_by_nc_nd
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.
Keywords
ITRED, Machine learning, PTSD, Re-experiencing, Resting-state functional connectivity, Trauma exposure, Phychiatric Mental Health, Clinical Neurology, Psychiatry and Mental health, Biological Psychiatry, Journal Article
Citation
Suarez-Jimenez, B, Lazarov, A, Zhu, X, Zilcha-Mano, S, Kim, Y, Marino, C E, Rjabtsenkov, P, Bavdekar, S Y, Pine, D S, Bar-Haim, Y, Larson, C L, Huggins, A A, Terri deRoon-Cassini, D-C, Tomas, C, Fitzgerald, J, Kennis, M, Varkevisser, T, Geuze, E, Quidé, Y, El Hage, W, Wang, X, O'Leary, E N, Cotton, A S, Xie, H, Shih, C, Disner, S G, Davenport, N D, Sponheim, S R, Koch, S B J, Frijling, J L, Nawijn, L, van Zuiden, M, Olff, M, Veltman, D J, Gordon, E M, May, G, Nelson, S M, Jia-Richards, M, Neria, Y & Morey, R A 2024, 'Intrusive Traumatic Re-Experiencing Domain : Functional Connectivity Feature Classification by the ENIGMA PTSD Consortium', Biological psychiatry global open science, vol. 4, no. 1, pp. 299-307. https://doi.org/10.1016/j.bpsgos.2023.05.006