White Matter Integrity in Individuals At-Risk for PTSD Development: a Longitudinal Investigation

Trauma exposure is prevalent and while most are resilient, some go on to develop post-traumatic stress disorder (PTSD)—an anxiety-related disorder that results from traumatic experience. The brain changes that result from traumatic experience and PTSD are not well understood. Further, little is known about what distinguishes those who are resilient after trauma from those at risk for developing PTSD. Previous work indicates white matter integrity may be a useful biomarker in predicting PTSD and researchers have found changes in the integrity of three white matter tracts—the cingulum bundle, corpus callosum (CC), and uncinate fasciculus (UF)—in the aftermath of trauma. However, few have examined the relationship between white matter integrity and PTSD symptoms longitudinally. Thus, the aims of the current study are 1) to investigate the predictive utility of white matter integrity in the acute stages of trauma to chronic PTSD symptoms and 2) to examine how white matter integrity varies with PTSD symptoms over time. Fifty-seven individuals being treated for traumatic injuries in the emergency department at Froedert Hospital (Milwaukee, WI) completed several self-report measures and underwent structural and diffusion-weighted magnetic resonance imaging at 2 weeks (T1) and 6 months (T2) post-trauma. At T1 greater UF integrity at T1 was related to greater T2 arousal symptoms. In addition, greater T1 anterior cingulum integrity was related to greater T2 re-experiencing symptoms. However, decreased anterior cingulum and CC integrity from T1 to T2 was related to increased symptoms over time. Therefore, acute white matter integrity post-trauma may be a useful predictor of chronic PTSD symptoms, and changes in white matter integrity may track changes in symptoms over time. Together these results suggest white matter integrity may be a potential biomarker for clinicians to help identify those at risk for PTSD development

Data-Driven Approach to Dynamic Resting State Functional Connectivity in Post-Traumatic Stress Disorder

Posttraumatic stress disorder (PTSD) is a heterogenous psychological disorder that may result from exposure to a traumatic event. Using functional magnetic resonance imaging (fMRI), symptoms of PTSD have been associated with aberrations in brain networks that emerge in the absence of a given cognitive demand or task, called resting state networks. Most previous research in resting state networks and PTSD has focused on aberrations in the static functional connectivity among specific regions of interest (ROI) in the brain and within canonical networks constrained by a priori hypotheses. However, dynamic fMRI, an approach that examines changes in brain network characteristics over time, may provide a more sensitive measure to understand the network properties underlying dysfunction in PTSD. In addition, 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 a data-driven approach to characterize and subsequently compare brain network dynamics and recurrent connectivity states in a large sample of trauma exposed individuals (1,000+) with and without PTSD from the ENIGMA-PGC-PTSD workgroup. Static functional connectivity results showed those with PTSD had lower network efficiencies than Controls within and between sensorimotor and visual subnetworks. Further, network dynamics showed increased network efficiencies through the course of the scan for both groups, except in the visual subnetwork where those with PTSD showed blunted efficiencies through time. Those with PTSD also had fewer individual-level connectivity states, especially in the second half of the scan, compared to Controls suggesting a degree of stochasticity in the network over time. Finally, there were no group differences in dwell time or number of transitions of group-level connectivity states. Together, results suggest aberrancies in large-scale brain networks related to PTSD diagnosis beyond the most common analyzed ROIs. Unsurprisingly, in a large and heterogenous trauma sample, larger scale group results were not as robust compared to similar analyses in smaller homogenous trauma samples. Heterogeneity of PTSD, especially within diffuse brain networks, cannot be captured by evaluating only diagnostic groups, further work should be done to evaluate brain network dynamics with respect to specific symptoms and trauma types

White Matter Integrity in Individuals At-Risk for PTSD Development: a Longitudinal Investigation

Trauma exposure is prevalent and while most are resilient, some go on to develop post-traumatic stress disorder (PTSD)—an anxiety-related disorder that results from traumatic experience. The brain changes that result from traumatic experience and PTSD are not well understood. Further, little is known about what distinguishes those who are resilient after trauma from those at risk for developing PTSD. Previous work indicates white matter integrity may be a useful biomarker in predicting PTSD and researchers have found changes in the integrity of three white matter tracts—the cingulum bundle, corpus callosum (CC), and uncinate fasciculus (UF)—in the aftermath of trauma. However, few have examined the relationship between white matter integrity and PTSD symptoms longitudinally. Thus, the aims of the current study are 1) to investigate the predictive utility of white matter integrity in the acute stages of trauma to chronic PTSD symptoms and 2) to examine how white matter integrity varies with PTSD symptoms over time. Fifty-seven individuals being treated for traumatic injuries in the emergency department at Froedert Hospital (Milwaukee, WI) completed several self-report measures and underwent structural and diffusion-weighted magnetic resonance imaging at 2 weeks (T1) and 6 months (T2) post-trauma. At T1 greater UF integrity at T1 was related to greater T2 arousal symptoms. In addition, greater T1 anterior cingulum integrity was related to greater T2 re-experiencing symptoms. However, decreased anterior cingulum and CC integrity from T1 to T2 was related to increased symptoms over time. Therefore, acute white matter integrity post-trauma may be a useful predictor of chronic PTSD symptoms, and changes in white matter integrity may track changes in symptoms over time. Together these results suggest white matter integrity may be a potential biomarker for clinicians to help identify those at risk for PTSD development

Acute White Matter Integrity Post-trauma and Prospective Posttraumatic Stress Disorder Symptoms

Background: Little is known about what distinguishes those who are resilient after trauma from those at risk for developing posttraumatic stress disorder (PTSD). Previous work indicates white matter integrity may be a useful biomarker in predicting PTSD. Research has shown changes in the integrity of three white matter tracts—the cingulum bundle, corpus callosum (CC), and uncinate fasciculus (UNC)—in the aftermath of trauma relate to PTSD symptoms. However, few have examined the predictive utility of white matter integrity in the acute aftermath of trauma to predict prospective PTSD symptom severity in a mixed traumatic injury sample. Method: Thus, the current study investigated acute brain structural integrity in 148 individuals being treated for traumatic injuries in the Emergency Department of a Level 1 trauma center. Participants underwent diffusion-weighted magnetic resonance imaging 2 weeks post-trauma and completed several self-report measures at 2-weeks (T1) and 6 months (T2), including the Clinician Administered PTSD Scale for DSM-V (CAPS-5), post-injury. Results: Consistent with previous work, T1 lesser anterior cingulum fractional anisotropy (FA) was marginally related to greater T2 total PTSD symptoms. No other white matter tracts were related to PTSD symptoms. Conclusions: Results demonstrate that in a traumatically injured sample with predominantly subclinical PTSD symptoms at T2, acute white matter integrity after trauma is not robustly related to the development of chronic PTSD symptoms. These findings suggest the timing of evaluating white matter integrity and PTSD is important as white matter differences may not be apparent in the acute period after injury

Neural Impact of Neighborhood Socioeconomic Disadvantage in Traumatically Injured Adults

Nearly 14 percent of Americans live in a socioeconomically disadvantaged neighborhood. Lower individual socioeconomic position (iSEP) has been linked to increased exposure to trauma and stress, as well as to alterations in brain structure and function; however, the neural effects of neighborhood SEP (nSEP) factors, such as neighborhood disadvantage, are unclear. Using a multi-modal approach with participants who recently experienced a traumatic injury (N = 185), we investigated the impact of neighborhood disadvantage, acute post-traumatic stress symptoms, and iSEP on brain structure and functional connectivity at rest. After controlling for iSEP, demographic variables, and acute PTSD symptoms, nSEP was associated with decreased volume and alterations of resting-state functional connectivity in structures implicated in affective processing, including the insula, ventromedial prefrontal cortex, amygdala, and hippocampus. Even in individuals who have recently experienced a traumatic injury, and after accounting for iSEP, the impact of living in a disadvantaged neighborhood is apparent, particularly in brain regions critical for experiencing and regulating emotion. These results should inform future research investigating how various levels of socioeconomic circumstances may impact recovery after a traumatic injury as well as policies and community-developed interventions aimed at reducing the impact of socioeconomic stressors

Racial Discrimination and Resting-State Functional Connectivity of Salience Network Nodes in Trauma-Exposed Black Adults in the United States

Importance For Black US residents, experiences of racial discrimination are still pervasive and frequent. Recent empirical work has amplified the lived experiences and narratives of Black people and further documented the detrimental effects of racial discrimination on both mental and physical health; however, there is still a need for further research to uncover the mechanisms connecting experiences of racial discrimination with adverse health outcomes. Objective To examine neurobiological mechanisms that may offer novel insight into the association of racial discrimination with adverse health outcomes. Design, Setting, and Participants This cross-sectional study included 102 Black adults who had recently experienced a traumatic injury. In the acute aftermath of the trauma, participants underwent a resting-state functional magnetic resonance imaging scan. Individuals were recruited from the emergency department at a Midwestern level 1 trauma center in the United States between March 2016 and July 2020. Data were analyzed from February to May 2021. Exposures Self-reported lifetime exposure to racial discrimination, lifetime trauma exposure, annual household income, and current posttraumatic stress disorder (PTSD) symptoms were evaluated. Main Outcomes and Measures Seed-to-voxel analyses were conducted to examine the association of racial discrimination with connectivity of salience network nodes (ie, amygdala and anterior insula). Results A total of 102 individuals were included, with a mean (SD) age of 33 (10) years and 58 (57%) women. After adjusting for acute PTSD symptoms, annual household income, and lifetime trauma exposure, greater connectivity between the amygdala and thalamus was associated with greater exposure to discrimination (t(97) = 6.05; false discovery rate (FDR)–corrected P = .03). Similarly, racial discrimination was associated with greater connectivity between the insula and precuneus (t(97) = 4.32; FDR-corrected P = .02). Conclusions and Relevance These results add to the mounting literature that racial discrimination is associated with neural correlates of vigilance and hyperarousal. The study findings extend this theory by showing that this association is apparent even when accounting for socioeconomic position, lifetime trauma, and symptoms of psychological distress related to an acute trauma

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable

Structural Connectivity of the Posterior Cingulum Is Related to Reexperiencing Symptoms in Posttraumatic Stress Disorder

Posttraumatic stress disorder is a heterogeneous disorder with disturbances in hyperarousal or avoidance behaviors and intrusive or reexperiencing thoughts. The uncinate fasciculus and cingulum bundle are white matter pathways implicated in stress and trauma pathophysiology, yet their structural integrity related to posttraumatic stress disorder symptom domains is yet to be understood. Forty-four trauma-exposed young adults underwent structural and diffusion-weighted magnetic resonance imaging. Stress and trauma exposure indices and severity of posttraumatic stress disorder symptoms were collected and used to predict current integrity of the uncinate fasciculus and cingulum bundle. Severity of reexperiencing posttraumatic stress disorder symptoms was significantly related to increased fractional anisotropy ( r  = .469 p   0.05) or avoidance ( p ’s > 0.2) posttraumatic stress disorder symptoms. The posterior cingulum connects medial temporal lobe structures with visual areas in the occipital lobe and has been implicated in visual memory and self-referential thought. Increased structural connectivity along this pathway may therefore explain the emergence of reexperiencing posttraumatic stress disorder symptoms. This along with the lack of results with respect to stress exposure suggests that structural aberrations in white matter pathways are more strongly linked with the actual experience of stress-related psychological symptoms than just exposure to stress

Hippocampal Resting-State Functional Connectivity Forecasts Individual Posttraumatic Stress Disorder Symptoms: A Data-Driven Approach

Background: Posttraumatic Stress Disorder (PTSD) is a debilitating disorder and there is no current accurate prediction of who develops it after trauma. Neurobiologically, individuals with chronic PTSD exhibit aberrant resting-state functional connectivity (rsFC) between the hippocampus and other brain regions (e.g., amygdala, prefrontal cortex, posterior cingulate), and these aberrations correlate with severity of illness. Prior small-scale research (n \u3c 25) has also shown that hippocampal-rsFC measured acutely after trauma is predictive of future severity using an ROI-based approach. While a promising biomarker, to-date no study has employed a data-driven approach to test whole-brain hippocampal-FC patterns in forecasting the development of PTSD symptoms. Methods: Ninety-eight adults at risk of PTSD were recruited from the emergency department following traumatic injury and completed resting functional magnetic resonance imaging (rsfMRI; 8min) within 1-month; 6-months later they completed the Clinician-Administered PTSD Scale (CAPS-5) for assessment of PTSD symptom severity. Whole-brain rsFC values with bilateral hippocampi were extracted (CONN) and used in a machine learning kernel ridge regression analysis (PRoNTo); both a k-folds (k=10) and 70/30 testing vs. training split approach were used for cross-validation (1,000 iterations to bootstrap confidence intervals for significance values). Results: Acute hippocampal-rsFC significantly predicted CAPS-5 scores at 6-months (r=0.30, p=0.006; MSE=120.58, p=0.006; R2=0.09, p=0.025). In post-hoc analyses, hippocampal-rsFC remained significant after controlling for demographics, PTSD symptoms at baseline, and depression, anxiety, and stress severity at 6-months (B=0.59, SE=0.20, p=0.003). Conclusions: Findings suggest functional connectivity of the hippocampus across the brain acutely after traumatic injury is associated with prospective PTSD symptom severit

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable