Tomas Carissa W, Fitzgerald Jacklynn M, Baird C Lexi, Haswell Courtney C, Abdallah Chadi G, Angstadt Michael, Baker Justin T, Berg Hannah, Blackford Jennifer U, Cisler Josh, Cotton Andrew S, Daniels Judith K, Davenport Nicholas D, Davidson Richard J, deRoon-Cassini Terri A, Disner Seth G, El Hage Wissam, Fani Negar, Frijling Jessie L, Gordon Evan M, Grupe Daniel W, He Xiaofu, Herringa Ryan, Hofmann David, Huggins Ashley A, Hussain Ahmed, Ipser Jonathan, Jahanshad Neda, Jovanovic Tanja, Kaufman Milissa L, Kim Yoojean, King Anthony, Koch Saskia B J, Koopowitz Sheri, Lazarov Amit, Lebois Lauren A M, Liberzon Isreal, Lissek Shmuel, Manthey Antje, May Geoffrey, McLaughlin Katie A, Nawijn Laura, Nelson Steven M, Neria Yuval, Nitschke Jack B, Olatunji Bunmi O, Olff Miranda, Peverill Matthew, Quidé Yann, Ravid Orren, Ressler Kerry, Ross Marisa, Salminen Lauren E, Sambrook Kelly, Shih Chiahao, Sierk Anika, Sponheim Scott R, Stein Dan J, Stevens Jennifer, Straube Thomas, Suarez-Jimenez Benjamin, Thompson Paul M, van der Wee Nic J A, van der Werff Steven J A, van Rooij Sanne J H, van Zuiden Mirjam, Veltman Dick J, Vermeiren Robert R J M, Walter Henrik, Wang Xin, Xie Hong, Zhu Xi, Zilcha-Mano Sigal, Larson Christine L, Morey Rajendra
Division of Epidemiology and Social Sciences, Institute of Health and Equity, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
Comprehensive Injury Center, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
Hum Brain Mapp. 2025 Aug 1;46(11):e70116. doi: 10.1002/hbm.70116.
Using functional magnetic resonance imaging (fMRI), symptoms of posttraumatic stress disorder (PTSD) have been associated with aberrations in brain networks in the absence of a given cognitive demand or task, called resting-state networks. Prior work has focused on disruption in the static functional connectivity (FC) among specific regions constrained by a priori hypotheses. However, dynamic FC, an approach that examines brain network characteristics over time, may provide a more sensitive measure to understand the network properties underlying dysfunction in PTSD. Further, using a data-driven analytic approach may reveal the contribution of other larger network disturbances beyond those revealed by hypothesis-driven examinations of ROIs or canonical networks. Therefore, the current study used group independent components analysis (ICA) and graph theory principles to identify, characterize, and subsequently compare brain network dynamics and recurrent connectivity states in a large sample of trauma exposed individuals (N = 1035) with and without PTSD from the ENIGMA-PGC PTSD workgroup. Neither static FC nor dynamic FC results showed robust differences between groups. There were also no group differences in dwell time or number of transitions of recurrent connectivity states. This multi-cohort sample with heterogenous trauma types and demographic features offers a significantly larger scale approach than prior literature with smaller homogenous trauma cohorts. Heterogeneity of PTSD, especially within diffuse brain networks, may not be captured by evaluating only diagnostic groups, further work should be done to evaluate brain network dynamics with respect to specific symptom profiles and trauma types.
使用功能磁共振成像（fMRI）技术，创伤后应激障碍（PTSD）的症状已被发现与静息态网络（即在没有特定认知需求或任务的情况下大脑网络的异常）有关。先前的研究主要集中在先验假设所限定的特定区域之间静态功能连接（FC）的破坏上。然而，动态功能连接（一种随时间检查大脑网络特征的方法）可能为理解PTSD功能障碍背后的网络特性提供更敏感的测量手段。此外，使用数据驱动的分析方法可能会揭示除假设驱动的感兴趣区域（ROI）或典型网络检查所揭示的之外的其他更大网络干扰的作用。因此，本研究使用组独立成分分析（ICA）和图论原理，对来自ENIGMA-PGC PTSD工作组的一大群有创伤暴露经历的个体（N = 1035）进行研究，这些个体中有或没有PTSD，以识别、表征并随后比较大脑网络动力学和反复连接状态。静态FC和动态FC结果均未显示出组间的显著差异。反复连接状态的停留时间或转换次数在组间也没有差异。与之前较小的同质创伤队列研究相比，这个具有异质创伤类型和人口统计学特征的多队列样本提供了一个规模显著更大的研究方法。PTSD的异质性，尤其是在弥漫性脑网络中，仅通过评估诊断组可能无法捕捉到，应进一步开展工作以评估针对特定症状特征和创伤类型的脑网络动力学。
