Intrusive Traumatic Re-Experiencing Domain : Functional Connectivity Feature Classification by the ENIGMA PTSD Consortium

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.

Reference: 
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 de Roon-Cassini, Carissa Tomas, Jacklynn Fitzgerald, Mitzy Kennis, Tim Varkevisser, Elbert Geuze, Yann Quidé, Wissam El Hage, Xin Wang, Erin N. O’Leary, Andrew S. Cotton, Hong Xie, Chiahao Shih, Seth G. Disner, Nicholas D. Davenport, Scott R. Sponheim, Saskia B.J. Koch, Jessie L. Frijling, Laura Nawijn, Mirjam van Zuiden, Miranda Olff, Dick J. Veltman, Evan M. Gordon, Geoffery May, Steven M. Nelson, Meilin Jia-Richards, Yuval Neria, Rajendra A. Morey | 2023
In: Biological Psychiatry: Global Open Science ; ISSN: 2667-1743
https://doi.org/10.1016/j.bpsgos.2023.05.006
Online ahead of print doi: 10.1016/j.bpsgos.2023.05.006
Keywords: 
Exposure, Machine learning, Posttraumatic Stress Disorder, Psychotrauma, PTSD (en), Treatment
Affiliation author(s): 
ARQ Centre of Expertise Impact, ARQ National Psychotrauma Centre