Understanding trauma-related psychopathology increasingly requires tools capable of capturing how symptoms and underlying mechanisms unfold across real-world contexts. Traditional assessments, which depend on retrospective recall and intermittent clinical contact, are limited in their ability to reflect the dynamic and context-sensitive processes that shape trauma-related mental health outcomes. Emerging digital health technologies and computational approaches address this gap by enabling the continuous measurement of behavior and physiology, whereas biological markers provide mechanistic insights into processes such as stress-related neuroendocrine, autonomic, immune, and sleep-wake functioning that reflect the biological embedding of trauma over time. This review integrates recent advances in digital and biological measurement in posttraumatic stress disorder (PTSD) and outlines principles for combining these approaches within computational frameworks. We emphasize the importance of grounding digital proxies in established biological theory; modeling intraindividual dynamics rather than relying solely on cross-sectional group differences; and integrating multimodal behavioral, physiological, and contextual data. We further highlight essential considerations for ethical, inclusive, and methodologically rigorous implementation, including privacy-by-design, data quality, and clinical interpretability. Together, these approaches support a more mechanistic, dynamic, and personalized understanding of trauma-related risk and recovery, with the potential to enhance early risk stratification and guide preventive and individualized models of care.

Posttraumatic stress disorder (PTSD) is a psychiatric condition that may develop after trauma exposure. PTSD is characterized by considerable clinical heterogeneity. The amygdala's key role in fear conditioning makes it an important focus for investigating the neurobiology of PTSD. However, associations between amygdala volume and PTSD have been inconsistent. The amygdala consists of functionally distinct nuclei. Specific associations between amygdala nuclei volumes and PTSD may account for previous discrepancies between PTSD and whole amygdala volume. This study investigates the associations between amygdala nuclei volumes, PTSD diagnosis, severity, symptom cluster scores, age of onset and childhood trauma. Individuals with a PTSD diagnosis (n = 771) and controls (n = 1 081, 72% trauma-exposed) were sourced from the Enhancing Neuro-Imaging Genetics through Meta-Analysis and Psychiatric Genomics Consortium (mean age = 32.4 years, (SD = 13 years), 60% male). Nine amygdala nuclei volumes were compared to PTSD diagnosis, age of onset, overall severity, symptom cluster scores (re-experiencing, arousal, and avoidance/emotional numbing), and childhood trauma subscales. Analyses were performed using ordinary least-squares regression, corrected for age, sex, intracranial volume, and whole amygdala volume. PTSD diagnosis was not significantly associated with amygdala nuclei volumes. PTSD severity scores were associated with smaller right lateral nucleus volume (β = -0.26, pBON = 0.01). Smaller right lateral nucleus volume was also associated with re-experiencing (β = -1.01, pBON = 0.04) and arousal (β = -0.9, pBON = 0.04), smaller left paralaminar nucleus volume was associated with re-experiencing (β = -0.1, pBON = 0.04), smaller left corticoamygdaloid transition area volume was associated with avoidance (β = -0.31, pBON = 0.02). Larger left and right central nucleus volumes were significantly associated with childhood physical abuse (β = 0.24, pBON = 9 × 10-3) and neglect (β = 0.29, pBON = 0.04), respectively. Differences in select amygdala nuclei volumes among adults are associated with PTSD severity, symptom cluster scores, and childhood physical abuse and neglect. These findings demonstrate nuclei-specific patterns consistent with their functional roles in fear learning and expression.

The thalamus is a heterogeneous structure crucial for corticocortical communication, affective-perceptual integration, motor preparation, and memory-related functions. Posttraumatic stress disorder (PTSD) is characterized by various symptoms that likely relate to thalamic functions. Group-level and individual differential structural covariance (SC) analyses were conducted on intrathalamic, thalamocortical, and thalamosubcortical volumetric networks by segmenting structural MRI data from 2,784 subjects (PTSD n = 1,306; controls n = 1,478) into 25 thalamic nuclei per hemisphere. We found that PTSD was associated with stronger intrathalamic and thalamocortical network strength and stronger SC between the limbic thalamus and the somatomotor and auditory thalamus. PTSD severity was related to specific regional alterations in the intrathalamic network involving the lateral pulvinar. Comorbid depression severity positively correlated with global intrathalamic alterations, while avoidance symptoms positively correlated with global thalamosubcortical alterations. Hyperarousal symptoms related to altered SC in the thalamocortical network between the reuniens, central medial, paratenial, centromedian, and limitans-suprageniculate nuclei and lateral cortical regions spanning the occipital, temporal, and orbitofrontal cortices. Differential associations between avoidance, hyperarousal, and comorbid depression symptoms and thalamic SC in PTSD suggest that specific thalamic covariance patterns may be involved in unique facets of PTSD symptomatology.

Over 40 years ago, post-traumatic stress disorder was recognized as a formal psychiatric diagnosis by the American Psychiatric Association and was defined in the Diagnostic and Statistical Manual of Mental Disorders by characteristic signs and symptoms following exposure to an objectively defined traumatic event. This recognition led not only to a surge in research but also to debate about what constitutes psychological trauma. In this Review, we present the ongoing problems with the concept of ‘traumatic event’ as currently defined and link this discussion to empirical findings and theoretical models of trauma, specifically regarding direct and indirect causality, cognitive-behavioural approaches and active inference. These findings and theories have become particularly relevant in the current geopolitical and sociocultural context characterized by the impact of the COVID-19 pandemic, ongoing wars, racism-related stressors and the ubiquity of distressing stimuli on social media. We discuss the clinical utility of a more encompassing definition of psychological trauma, and we reflect on the positive and negative clinical and societal implications of this revised conceptualization. A scientific consensus about what the term ‘trauma’ means has so far proven elusive. In this Review, Engelhard et al. revisit clinical psychology theories and findings to advance a common understanding of the emotional, contextual and cognitive aspects of psychological trauma.

Objective: Studies investigating resting-state functional connectivity of the amygdala and hippocampus have produced inconsistent findings. The authors' objective was to conduct the largest systematic comparison of alterations in functional connectivity of the amygdala and hippocampus in individuals with posttraumatic stress disorder (PTSD) using a multicohort mega-analysis with uniform processing steps and parameters across all cohorts. Methods: Resting-state functional MRI data from 1,017 PTSD patients and 1,702 control participants from 32 international sites were centrally preprocessed with HALFpipe and analyzed using the Image-Based Meta- and Mega-Analysis (IBMMA) package for neuroimaging processing. Group-level seed-based whole-brain analyses were completed for the right and left amygdala and hippocampus. Additional correlation analyses were conducted between PTSD norm-severity scores and resting-state functional connectivity (rs-FC). Results: Compared to control participants, individuals with PTSD showed stronger rs-FC between the left amygdala seed and right hippocampus and amygdala and the left and right lingual gyri. Greater PTSD total norm-severity scores were significantly associated with rs-FC between the left amygdala and right hippocampus/amygdala and rs-FC between the right amygdala and left hippocampus/amygdala. Conclusions: Greater connectivity between subcortical threat centers involved in fear processing, memory, and extinction learning characterizes the resting state in PTSD. Future directions include investigating how different interventions, such as brain stimulation, neurofeedback, and psychotherapy, might modulate the aberrant neural networks in PTSD.

Background: Sex and gender impact trauma exposure, as well as trauma-related outcomes and treatment efficacy. Still, knowledge about the extent of these sex/gender differences, as well as the distinct mechanisms that contribute towards these differences in psychotraumatology, is limited.Objective: This article outlines how the use of Findable, Accessible, Interoperable, and Reusable (FAIR) research data can further enhance our insights into sex and gender aspects in trauma research.Method and Results: We present and discuss four ways in which FAIR trauma data can facilitate sex/gender-sensitive trauma research: (1) FAIR trauma data may overcome the current lack of sex/gender-sensitive reporting in our field by enabling secondary sex-stratified analyses; (2) FAIR trauma data may facilitate data pooling across studies and thereby increase the statistical power of sex/gender-related analyses into trauma-related outcomes and its underlying mechanisms; (3) FAIR trauma data may promote the reliability and robustness of sex/gender-related trauma research; and (4) FAIR trauma data may aid in creating an evidence base for currently understudied sex and/or gender minorities by means of data pooling across studies. Additional benefits, potential challenges, and further considerations are discussed.Conclusions: FAIR data provide an important and feasible avenue to address pressing and timely questions on sex/gender-related aspects in trauma research. Such knowledge will contribute towards refined sex/gender-sensitive interventions and advance health equity in the psychotraumatology field.

BACKGROUND:Patients with posttraumatic stress disorder (PTSD) exhibit smaller regional brain volumes in commonly reported regions including the amygdala and hippocampus, regions associated with fear and memory processing. In the current study, we have conducted a voxel-based morphometry (VBM) meta-analysis using whole-brain statistical maps with neuroimaging data from the ENIGMA-PGC PTSD working group. METHODS:T1-weighted structural neuroimaging scans from 36 cohorts (PTSD n = 1309; controls n = 2198) were processed using a standardized VBM pipeline (ENIGMA-VBM tool). We meta-analyzed the resulting statistical maps for voxel-wise differences in gray matter (GM) and white matter (WM) volumes between PTSD patients and controls, performed subgroup analyses considering the trauma exposure of the controls, and examined associations between regional brain volumes and clinical variables including PTSD (CAPS-4/5, PCL-5) and depression severity (BDI-II, PHQ-9). RESULTS:PTSD patients exhibited smaller GM volumes across the frontal and temporal lobes, and cerebellum, with the most significant effect in the left cerebellum (Hedges' g = 0.22, pcorrected = .001), and smaller cerebellar WM volume (peak Hedges' g = 0.14, pcorrected = .008). We observed similar regional differences when comparing patients to trauma-exposed controls, suggesting these structural abnormalities may be specific to PTSD. Regression analyses revealed PTSD severity was negatively associated with GM volumes within the cerebellum (p corrected  = .003), while depression severity was negatively associated with GM volumes within the cerebellum and superior frontal gyrus in patients (p corrected  = .001). CONCLUSIONS:PTSD patients exhibited widespread, regional differences in brain volumes where greater regional deficits appeared to reflect more severe symptoms. Our findings add to the growing literature implicating the cerebellum in PTSD psychopathology.

The increasing scale and complexity of neuroimaging datasets aggregated from multiple study sites present substantial analytic challenges, as existing statistical analysis tools struggle to handle missing voxel-data, suffer from limited computational speed and inefficient memory allocation, and are restricted in the types of statistical designs they are able to model. We introduce Image-Based Meta- & Mega-Analysis (IBMMA), a novel software package implemented in R and Python that provides a unified framework for analyzing diverse neuroimaging features, efficiently handles large-scale datasets through parallel processing, offers flexible statistical modeling options, and properly manages missing voxel-data commonly encountered in multi-site studies. IBMMA successfully analyzed a large-n dataset of several thousand participants and revealed findings in brain regions that some traditional software overlooked due to missing voxel-data resulting in gaps in brain coverage. IBMMA has the potential to accelerate discoveries in neuroscience and enhance the clinical utility of neuroimaging findings.

BACKGROUND:Previous investigations of whole thalamus and thalamic nuclei volumes in posttrauma psychopathology have been sparse and limited in scope and have yielded inconsistent results. To address this, volumetric estimates of whole thalamus and thalamic nuclei were obtained from structural brain magnetic resonance imaging scans from 2058 participants across 20 worldwide sites in the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Posttraumatic Stress Disorder (PTSD) working group. METHODS:Thalamic volumes were compared in trauma-exposed participants with PTSD (n = 238), major depressive disorder (MDD) (n = 184), and comorbid PTSD+MDD (n = 618) and in trauma-exposed control participants (n = 1018). PTSD and MDD symptom severity, PTSD symptom clusters, and childhood trauma were similarly examined for associations with thalamic volume. RESULTS:Participants with PTSD had smaller sensorimotor thalamic nuclei, while participants with MDD or comorbid PTSD+MDD had smaller mediodorsal (MD) thalamus volumes relative to control participants. Severity of PTSD and MDD symptoms negatively correlated with MD volume. A significant interaction between PTSD and MDD severity was found, such that MDD severity was positively associated with whole thalamus volume only among individuals with high PTSD severity. We observed both positive and negative volumetric associations for specific PTSD symptom clusters and childhood trauma subtypes. CONCLUSIONS:Whole thalamus volume and volumes of the sensorimotor and limbic thalamus may play an important role in the development of PTSD and MDD in the aftermath of trauma exposure. The interaction between PTSD and MDD symptoms and contrasting effects across PTSD symptom clusters and types of childhood adversity suggest that multiple neurobiological mechanisms are involved in shaping thalamic volume posttrauma.

Investigating long-term posttraumatic stress disorder (PTSD) course and its predictors may guide prevention and early intervention strategies following trauma exposure, potentially reducing the long-lasting impact of trauma. N = 155 emergency-admitted adults with (suspected) serious injury were repeatedly assessed until one-year post-trauma and completed a 12–15 year follow-up including a clinical PTSD interview. Adverse one-year PTSD trajectories; more exposure to additional potentially traumatic events and recent life stressors; and early post-trauma predictors (younger age, greater perceived impact of prior potentially traumatic events, higher heart rate) were significantly associated with higher PTSD symptom severity 12–15 years post-trauma. This study showed high consistency between one-year PTSD and its early post-trauma predictors with long-term PTSD outcomes. Early post-trauma predictors had predictive value up to 12–15 years. This suggests that early risk identification of one-year PTSD and subsequent effective early interventions also hold long-term beneficial effects for PTSD outcome.

Background: Despite known gender/sex differences in the prevalence of posttraumatic stress disorder (PTSD), potential differences in the associations among PTSD symptoms between men and women in the early post-trauma period are not well-characterized.Objective: This study utilized network analysis to assess potential differences in the associations among PTSD symptom clusters between men and women during the early post-trauma period.Method: We included n = 475 participants (57.5% self-identified women) who recently (≤2 months) experienced an interpersonal or motor vehicle potential traumatic event in the Netherlands. Past month PTSD symptoms were measured with the PTSD Checklist for DSM-5 (PCL-5) and composited according to the five-node PTSD symptom cluster dysphoric arousal model. We estimated the network as well as indices of centrality (strength and predictability) and assessed the stability of the modelled networks in subsamples of men (n = 202) and women (n = 273). We compared network structures using the Network Comparison Test (NCT).Results: Results largely demonstrated adequate correlation stability for the estimated network structures for women and men. For both men and women, avoidance symptoms were among the strongest nodes with greatest predictability in the networks. In men, anxious arousal additionally showed high strength whereas re-experiencing showed high predictability. In women, re-experiencing symptoms demonstrated both high strength and predictability. The NCT demonstrated nonsignificant differences in global network structure (M = 0.08, p = .054) and strength (S = .073, p = .067). Post hoc comparisons showed an association of re-experiencing symptoms with negative alterations in cognitions and mood in men but not women (E = .038, p = .005).Conclusion: Results demonstrated possible modest gender differences in aspects of network structure although most elements of the network structure were similar across genders. These results help to characterize gender differences in associations among PTSD symptom clusters during the early post-trauma period, which may inform the potential relevance of future gender-sensitive early intervention strategies to ameliorate the risk for long-term PTSD.

Identifying robust neural signatures of posttraumatic stress disorder (PTSD) symptoms is important to facilitate precision psychiatry and help in understanding and treatment of the disorder. Emergent research suggests structural covariance of early visual regions is associated with later PTSD development. However, large-scale analyses are needed – in heterogeneous samples of trauma-exposed and trauma naive individuals – to determine if such a neural signature is a robust – and potentially a pretrauma – marker of vulnerability. We analyzed data from the ENIGMA-PTSD dataset (n = 2,814) and the Human Connectome Project – Young Adult (HCP-YA) dataset (n = 890) to investigate whether structural covariance of early visual cortex is associated with either PTSD symptoms or perceived stress. Structural covariance was derived from a multimodal pattern previously identified in recent trauma survivors, and participant loadings on the profile were included in linear mixed effects models to evaluate associations with stress. Early visual cortex covariance loadings were negatively associated with PTSD symptoms in the ENIGMA-PTSD dataset. The relationship persisted when accounting for prior childhood maltreatment; supporting PTSD symptom specificity, no relationship was observed with depressive symptoms and no association was observed between loadings and perceived stress measures in the HCP-YA dataset. Structural covariance of early visual cortex was robustly associated with PTSD symptoms across an international, heterogeneous sample of trauma survivors. Future studies should aim to identify specific mechanisms that underlie structural alterations in the visual cortex to better understand posttrauma psychopathology.

BACKGROUND:Identifying robust neural signatures of posttraumatic stress disorder (PTSD) symptoms is important to facilitate precision psychiatry and help in understanding and treatment of the disorder. Emergent research suggests that the structural covariance of early visual regions is associated with later PTSD development. However, large-scale analyses are needed in heterogeneous samples of trauma-exposed and trauma-naïve individuals to determine whether such a neural signature is a robust marker of vulnerability. METHODS:We analyzed data from the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)-PTSD dataset (N = 2814) and the HCP-YA (Human Connectome Project-Young Adult) dataset (N = 890) to investigate whether the structural covariance of the early visual cortex is associated with either PTSD symptoms or perceived stress. Structural covariance was derived from a multimodal pattern previously identified in recent trauma survivors, and participant loadings on the profile were included in linear mixed effects models to evaluate associations with stress. RESULTS:Early visual cortex covariance loadings were negatively associated with PTSD symptoms in the ENIGMA-PTSD dataset. The relationship persisted when accounting for prior childhood maltreatment; supporting PTSD symptom specificity, no relationship was observed with depressive symptoms, and no association was observed between loadings and perceived stress measures in the HCP-YA dataset. CONCLUSIONS:The structural covariance of early visual cortex was robustly associated with PTSD symptoms across an international, heterogeneous sample of trauma survivors. Future studies should aim to identify specific mechanisms that underlie structural alterations in the visual cortex to better understand posttrauma psychopathology.

By now it is well known that trauma-related psychiatric disorders are associated with a broad range of biological dysregulations. This special issue focused on research aiming to advance our understanding of the biological mechanisms underlying adverse mental health outcome after trauma, in particular the development of trauma-related disorders and differences in individual susceptibility (i.e. risk or resilience) to their development. We included 51 papers published in EJPT between 2021 and 2023, either directly submitted to the special issue or whose focus clearly fitted the aim of the special issue. Included papers consisted of 43 original research papers and 8 review articles, addressing a wide range of biological processes, ranging from brain and central nervous system functioning, to cardiovascular and autonomic nervous system functioning; endocrinology; (epi)genetics; metabolism; inflammation; sleep and pain. Collectively, the included articles showcase that significant steps are taken in advancing our understanding of the biological mechanisms underlying development of and susceptibility to trauma-related disorders, as well their recovery. Recommendations for future research are provided with regard to study design; assessment of trauma exposure characteristics; sex and/or gender (minority)-related factors; geographic, developmental/lifespan and transgenerational aspects; and the temporal dynamics of biological processes in relation to the stage of the disorder under investigation. Together, these articles will hopefully further facilitate the development of more effective preventive, early and curative intervention strategies, as well as more effective timely and personalised allocation towards these interventions.

BACKGROUND: Posttraumatic stress disorder (PTSD) is accompanied by disrupted cortical neuroanatomy. We investigated alteration in covariance of structural networks associated with PTSD in regions that demonstrate the case-control differences in cortical thickness (CT) and surface area (SA). METHODS: Neuroimaging and clinical data were aggregated from 29 research sites in >1300 PTSD cases and >2000 trauma-exposed control subjects (ages 6.2-85.2 years) by the ENIGMA-PGC (Enhancing Neuro Imaging Genetics through Meta Analysis-Psychiatric Genomics Consortium) PTSD working group. Cortical regions in the network were rank ordered by the effect size of PTSD-related cortical differences in CT and SA. The top-n (n = 2-148) regions with the largest effect size for PTSD > non-PTSD formed hypertrophic networks, the largest effect size for PTSD < non-PTSD formed atrophic networks, and the smallest effect size of between-group differences formed stable networks. The mean structural covariance (SC) of a given n-region network was the average of all positive pairwise correlations and was compared with the mean SC of 5000 randomly generated n-region networks. RESULTS: Patients with PTSD, relative to non-PTSD control subjects, exhibited lower mean SC in CT-based and SA-based atrophic networks. Comorbid depression, sex, and age modulated covariance differences of PTSD-related structural networks. CONCLUSIONS: Covariance of structural networks based on CT and cortical SA are affected by PTSD and further modulated by comorbid depression, sex, and age. The SC networks that are perturbed in PTSD comport with converging evidence from resting-state functional connectivity networks and networks affected by inflammatory processes and stress hormones in PTSD.

Background: Timely identification of individuals at risk for developing PTSD following trauma is crucial for providing targeted preventive interventions. Machine learning techniques show promise for deriving accurate prognostic screening instruments. However, accurate externally validated prognostic screening instruments for broad application in trauma-exposed civilians are not yet available. Moreover, it remains unknown whether prognostic screening instrument accuracy may be improved if developed in a sex-stratified manner.Objective: We aimed to develop an externally validated prognostic PTSD screening instrument based on self-report information obtained within 2 months post-trauma in two independent cohorts of recently trauma-exposed civilians, using machine learning techniques allowing for extraction of a short screener. We examined whether separate models for males and females improved prognostic accuracy compared to sex-combined models.Methods: Prognostic machine learning models (CART and XGBoost) were developed in a longitudinal cohort of N = 327 adults (38% females) requiring evaluation of (suspected) serious injury by an emergency department. External validation was performed in another longitudinal cohort of N = 466 adults (57% females) referred for emotional, practical or legal victim support following crime or traffic accidents. PTSD status at 1 year post-trauma was based on CAPS-IV for internal and PCL-5 for external validation.Results: During internal validation, all models achieved excellent accuracy (AUC/sensitivity/specificity > 0.90). During external validation, sufficient accuracy was only achieved for the sex-combined XGBoost model (AUC = 0.73, sensitivity = 0.69, specificity = 0.68), including 22 items of demographic and health characteristics, trauma characteristics, peri-traumatic distress or dissociation, post-traumatic cognitions, PTSD symptoms and social support.Conclusion: We developed an accurate externally validated short prognostic screening instrument for PTSD based on self-report questions that is applicable to a broad population of recently trauma-exposed civilians. This novel instrument enables timely identification of individuals at risk for PTSD following trauma, and research into early targeted interventions to prevent long-term PTSD for civilians following trauma.

Background:Cortical gyrification involves the formation of folds in the cerebral cortex, coinciding with key neurodevelopmental processes. Its strong correlation with increased cortical surface area and decreased cortical thickness may improve cortical signaling efficiency by decreasing cortico-cortical distance. Differences in brain structure have been found in posttraumatic stress disorder (PTSD), yet few small studies have examined cortical gyrification. Methods:Gyrification was quantified using FreeSurfer's Local Gyrification Index (lGI), derived from 3-dimensional T1-weighted volumetric brain magnetic resonance imaging in 1876 participants (PTSD n = 789, control n = 1087) across 24 sites from the ENIGMA-PGC (Enhancing Neuro Imaging Genetics through Meta-Analysis and Psychiatric Genomics Consortium) PTSD working group. Using a region of interest-based approach, we fitted a linear mixed-effects model with age (mean = 35.6, SD = 9.23, range = 8-95), sex (female = 967 [52%], male = 909 [48%]), pial surface area, PTSD, and random site effects to test associations between PTSD diagnosis/severity and regional lGI. We examined moderating effects of depression, childhood trauma, age, and sex. Results:PTSD diagnosis and severity were both associated with lower lGI for the right medial orbitofrontal and right rostral anterior cingulate cortices. The interaction of PTSD and age was associated with lower lGI for the rostral middle frontal cortex bilaterally. Contrasting comorbid PTSD and major depressive disorder with the PTSD-only group showed that comorbidity was associated with lower lGI in the left inferior and medial temporal cortices. Conclusions:Lower lGI, which is associated with impaired signaling efficiency, was observed in the PTSD group compared with the control group for the ventromedial prefrontal cortex, a region that has been strongly implicated in associative fear learning and extinction. It is possible that PTSD accelerates the typical age-associated decline in lGI of the rostral middle frontal cortices.

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.