侵入性创伤再体验域(ITRED) - ENIGMA PTSD联盟的功能连接特征分类

IF 4 Q2 NEUROSCIENCES Biological psychiatry global open science Pub Date : 2024-01-01 DOI:10.1016/j.bpsgos.2023.05.006

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 deRoon-Cassini , Carissa Tomas , Jacklynn Fitzgerald , Mitzy Kennis , Tim Varkevisser , Elbert Geuze , Yann Quidé , Wissam El Hage , Rajendra A. Morey

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引用次数: 0

摘要

背景侵入性创伤再体验域（ITRED）是最近作为创伤后精神病理学的一个新视角而提出的，它建议将创伤后应激障碍（PTSD）的研究重点放在侵入性和不自主地再体验创伤的独特症状上，即侵入性回忆、噩梦和闪回。本研究旨在从神经网络连通性的角度探讨ITRED。方法数据收集自参加ENIGMA（通过元分析增强神经成像遗传学）创伤后应激障碍联合会的9个研究机构（n = 584），包括逐项的创伤后应激障碍症状评分和静息态功能连通性（rsFC）数据。我们采用机器学习方法评估了 rsFC 在划分创伤后应激障碍组、纯创伤后应激障碍组（无创伤后应激障碍诊断）和纯创伤暴露组（无创伤后应激障碍或纯创伤后应激障碍）时的实用性，并研究了与创伤后应激障碍有关的知名网络。利用交叉验证在训练集上建立了随机森林分类模型，并利用曲线下面积评估了分类模型的平均交叉验证性能。使用完全独立的部分数据（测试数据集）对模型进行了测试，并评估了测试的曲线下面积。结果rsFC特征将纯 TE参与者与纯创伤后应激障碍和纯 ITRED参与者区分开来的准确率约为60%。相反，rsFC特征并不能将创伤后应激障碍患者与纯 ITRED患者区分开来（准确率为 45%）。区分创伤后应激障碍患者和纯创伤后应激障碍患者的共同特征主要涉及默认模式网络相关通路。一些独特的特征，如额顶叶网络内的连通性，将纯 TE 参与者与其中一组（创伤后应激障碍或纯 ITRED）区分开来，但与另一组的区分程度较低。结论神经网络连通性支持 ITRED 作为一种基于神经生物学的新方法来对创伤后精神病理学进行分类。

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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.