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

ENIGMA PTSD Consortium; M. Kennis; M. van Zuiden

doi:10.1016/j.bpsgos.2023.05.006

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

ENIGMA PTSD Consortium
, M. Kennis
, M. van Zuiden

Research output: Contribution to journal › Article › Academic › peer-review

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.

Original language	English
Pages (from-to)	299-307
Number of pages	9
Journal	Biological Psychiatry Global Open Science
Volume	4
Issue number	1
DOIs	https://doi.org/10.1016/j.bpsgos.2023.05.006
Publication status	Published - Jan 2024

Bibliographical note

Publisher Copyright:
© 2023 The Authors

Funding

This work was supported by National Institute of Mental Health Grant Nos. K01MH118428 (to BS-J), R01MH131532 (to BS-J), K01MH122774 (to XZ), R01MH105355 (to YN), 1R01MH106574 (to CLL, TdR-C), 1R01MH110483 (to XW), 1R21MH098198 (to XW), 1R21MH125277 (to XW), and R01MH111671 (to RAM); National Eye Institute Core Grant No. P30 EY001319 (to BS-J); Brain and Behavior Research Foundation NARSAD Young Investigator Grants (to BS-J, XZ); Israel Science Foundation Grant No. 374/20 (to AL); National Institute of Mental Health Intramural Research Program Project Grant No. ZIA-MH-002782 (to DSP); the Programme Hospitalier de Recherche Clinique (to WEH); Fondation Pierre Deniker (to WEH); SFR Grant No. FED4226 (to WEH); VA Office of Rehabilitation Research and Development Grant Nos. 1IK2RX000709 (to SGD), I01RX000622 (to NDD), 1K1RX002325 (to SRS), and 1K2RX002922 (to SRS); Congressionally Directed Medical Research Program Grant No. W81XWH-08-2-0038 (to NDD); Netherlands Organization for Health Research and Development Grant No. 40-00812-98-10041 [to SBJK, JLF, LN, MvZ, MO, DJV]); Academic Medical Center Research Council Grant No. 110614 (to SBJK, JLF, LN, MvZ, MO, DJV); VA Clinical Science Research and Development Grant No. 1IK2CX001680 [to EMG, GM, SMN]; and VISN17 Center of Excellence Pilot funding (to EMG, GM, SMN). The funding agencies had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the article for publication. WEH received support from Air Liquide, CHUGAI, EISAI, Jazz Pharmaceuticals, Janssen, Lundbeck, Otsuka, and UCB for work unrelated to the current study. All other authors report no biomedical financial interests or potential conflicts of interest. This work was supported by National Institute of Mental Health Grant Nos. K01MH118428 (to BS-J), R01MH131532 (to BS-J), K01MH122774 (to XZ), R01MH105355 (to YN), 1R01MH106574 (to CLL, TdR-C), 1R01MH110483 (to XW), 1R21MH098198 (to XW), and 1R21MH125277 (to XW); National Eye Institute Core Grant No. P30 EY001319 (to BS-J); Brain and Behavior Research Foundation NARSAD Young Investigator Grants (to BS-J, XZ); Israel Science Foundation Grant No. 374/20 (to AL); National Institute of Mental Health Intramural Research Program Project Grant No. ZIA-MH-002782 (to DSP); the Programme Hospitalier de Recherche Clinique (to WEH); Fondation Pierre Deniker (to WEH); SFR Grant No. FED4226 (to WEH); VA Office of Rehabilitation Research and Development Grant Nos. 1IK2RX000709 (to SGD), I01RX000622 (to NDD), 1K1RX002325 (to SRS), and 1K2RX002922 (to SRS); Congressionally Directed Medical Research Program Grant No. W81XWH-08-2-0038 (to NDD); Netherlands Organization for Health Research and Development Grant No. 40-00812-98-10041 [to SBJK, JLF, LN, MvZ, MO, DJV]) ; Academic Medical Center Research Council Grant No. 110614 (to SBJK, JLF, LN, MvZ, MO, DJV); VA Clinical Science Research and Development Grant No. 1IK2CX001680 [to EMG, GM, SMN]; and VISN17 Center of Excellence Pilot funding (to EMG, GM, SMN). The funding agencies had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the article for publication.

Funders	Funder number
Academic Medical Center Research Council	110614
Programme Hospitalier de Recherche Clinique
VA Office of Rehabilitation Research and Development
National Institute of Mental Health	K01MH122774, TdR-C, 1R21MH098198, 1R21MH125277, R01MH111671, K01MH118428, R01MH131532, 1R01MH106574, 1R01MH110483, R01MH105355
Kennedy Institute - National Eye Clinic	P30 EY001319
Congressionally Directed Medical Research Programs	W81XWH-08-2-0038
Brain and Behavior Research Foundation
Rehabilitation Research and Development Service	1IK2RX000709, 1K2RX002922, I01RX000622, 1K1RX002325
UCB Celltech
Utrecht University	1IK2CX001680
ZonMw	40-00812-98-10041
Israel Science Foundation	374/20, ZIA-MH-002782
Fondation Pierre Deniker pour la Recherche et la Prévention en Santé Mentale	FED4226

Keywords

ITRED
Machine learning
PTSD
Re-experiencing
Resting-state functional connectivity
Trauma exposure

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.bpsgos.2023.05.006Licence: CC BY-NC-ND

1-s2.0-S266717432300054X-mainFinal published version, 571 KBLicence: CC BY-NC-ND

Cite this

@article{5577617f62b341faa1829055e1b19d51,

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

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",

author = "\{ENIGMA PTSD Consortium\} and Benjamin Suarez-Jimenez and Amit Lazarov and Xi Zhu and Sigal Zilcha-Mano and Yoojean Kim and Marino, \{Claire E.\} and Pavel Rjabtsenkov and Bavdekar, \{Shreya Y.\} and Pine, \{Daniel S.\} and Yair Bar-Haim and Larson, \{Christine L.\} and Huggins, \{Ashley A.\} and \{Terri deRoon-Cassini\}, deRoon-Cassini and Carissa Tomas and Jacklynn Fitzgerald and M. Kennis and Tim Varkevisser and Elbert Geuze and Yann Quid{\'e} and \{El Hage\}, Wissam and Xin Wang and O'Leary, \{Erin N.\} and Cotton, \{Andrew S.\} and Hong Xie and Chiahao Shih and Disner, \{Seth G.\} and Davenport, \{Nicholas D.\} and Sponheim, \{Scott R.\} and Koch, \{Saskia B.J.\} and Frijling, \{Jessie L.\} and Laura Nawijn and \{van Zuiden\}, M. and Miranda Olff and Veltman, \{Dick J.\} and Gordon, \{Evan M.\} and Geoffery May and Nelson, \{Steven M.\} and Meilin Jia-Richards and Yuval Neria and Morey, \{Rajendra A.\}",

note = "Publisher Copyright: {\textcopyright} 2023 The Authors",

year = "2024",

month = jan,

doi = "10.1016/j.bpsgos.2023.05.006",

language = "English",

volume = "4",

pages = "299--307",

journal = "Biological Psychiatry Global Open Science",

issn = "2667-1743",

publisher = "Elsevier Inc.",

number = "1",

}

TY - JOUR

T1 - Intrusive Traumatic Re-Experiencing Domain

T2 - Functional Connectivity Feature Classification by the ENIGMA PTSD Consortium

AU - 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, M.

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, M.

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.

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 - https://www.scopus.com/pages/publications/85169476683

U2 - 10.1016/j.bpsgos.2023.05.006

DO - 10.1016/j.bpsgos.2023.05.006

M3 - Article

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 -

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

Abstract

Bibliographical note

Funding

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this