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

Authors :
Suarez-Jimenez B
Lazarov A
Zhu X
Zilcha-Mano S
Kim Y
Marino CE
Rjabtsenkov P
Bavdekar SY
Pine DS
Bar-Haim Y
Larson CL
Huggins AA
Terri deRoon-Cassini
Tomas C
Fitzgerald J
Kennis M
Varkevisser T
Geuze E
Quidé Y
El Hage W
Wang X
O'Leary EN
Cotton AS
Xie H
Shih C
Disner SG
Davenport ND
Sponheim SR
Koch SBJ
Frijling JL
Nawijn L
van Zuiden M
Olff M
Veltman DJ
Gordon EM
May G
Nelson SM
Jia-Richards M
Neria Y
Morey RA
Source :
Biological psychiatry global open science [Biol Psychiatry Glob Open Sci] 2023 Jun 08; Vol. 4 (1), pp. 299-307. Date of Electronic Publication: 2023 Jun 08 (Print Publication: 2024).
Publication Year :
2023

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.<br />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.<br />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.<br />Conclusions: Neural network connectivity supports ITRED as a novel neurobiologically based approach to classifying posttrauma psychopathology.<br /> (© 2023 The Authors.)

Details

Language :
English
ISSN :
2667-1743
Volume :
4
Issue :
1
Database :
MEDLINE
Journal :
Biological psychiatry global open science
Publication Type :
Academic Journal
Accession number :
38298781
Full Text :
https://doi.org/10.1016/j.bpsgos.2023.05.006