Individual differences in cognitive reappraisal use and emotion regulatory brain function in combat‐exposed veterans with and without PTSD

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135971/1/da22551.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135971/2/da22551_am.pd

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

Acute posterior cingulum integrity post-trauma prospectively predicts depression but not PTSD 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

DACC Resting State Functional Connectivity as a Predictor of Pain Symptoms Following Motor Vehicle Crash: A Preliminary Investigation

There is significant heterogeneity in pain outcomes following motor vehicle crashes (MVCs), such that a sizeable portion of individuals develop symptoms of chronic pain months after injury while others recover. Despite variable outcomes, the pathogenesis of chronic pain is currently unclear. Previous neuroimaging work implicates the dorsal anterior cingulate cortex (dACC) in adaptive control of pain, while prior resting state functional magnetic resonance imaging studies find increased functional connectivity (FC) between the dACC and regions involved in pain processing in those with chronic pain. Hyper-connectivity of the dACC to regions that mediate pain response may therefore relate to pain severity. The present study completed rsfMRI scans on N=22 survivors of MVCs collected within two weeks of the incident to test whole-brain dACC-FC as a predictor of pain severity six months later. At two weeks, pain symptoms were predicted by positive connectivity between the dACC and the premotor cortex. Controlling for pain symptoms at two weeks, pain symptoms at six months were predicted by negative connectivity between the dACC and the precuneus. Previous research implicates the precuneus in the individual subjective awareness of pain. Given a relatively small sample size, approximately half of which did not experience chronic pain at six months, findings warrant replication. Nevertheless, this study provides preliminary evidence of enhanced dACC connectivity with motor regions and decreased connectivity with pain processing regions as immediate and prospective predictors of pain following MVC. Perspective: This article presents evidence of distinct neural vulnerabilities that predict chronic pain in motor vehicle crash survivors based on whole-brain connectivity with the dorsal anterior cingulate cortex

Assessment of brain age in posttraumatic stress disorder: Findings from the ENIGMA PTSD and brain age working groups

Background: Posttraumatic stress disorder (PTSD) is associated with markers of accelerated aging. Estimates of brain age, compared to chronological age, may clarify the effects of PTSD on the brain and may inform treatment approaches targeting the neurobiology of aging in the context of PTSD. Method: Adult subjects (N = 2229; 56.2% male) aged 18–69 years (mean = 35.6, SD = 11.0) from 21 ENIGMA-PGC PTSD sites underwent T1-weighted brain structural magnetic resonance imaging, and PTSD assessment (PTSD+, n = 884). Previously trained voxel-wise (brainageR) and region-of-interest (BARACUS and PHOTON) machine learning pipelines were compared in a subset of control subjects (n = 386). Linear mixed effects models were conducted in the full sample (those with and without PTSD) to examine the effect of PTSD on brain predicted age difference (brain PAD; brain age − chronological age) controlling for chronological age, sex, and scan site. Results: BrainageR most accurately predicted brain age in a subset (n = 386) of controls (brainageR: ICC = 0.71, R = 0.72, MAE = 5.68; PHOTON: ICC = 0.61, R = 0.62, MAE = 6.37; BARACUS: ICC = 0.47, R = 0.64, MAE = 8.80). Using brainageR, a three-way interaction revealed that young males with PTSD exhibited higher brain PAD relative to male controls in young and old age groups; old males with PTSD exhibited lower brain PAD compared to male controls of all ages. Discussion: Differential impact of PTSD on brain PAD in younger versus older males may indicate a critical window when PTSD impacts brain aging, followed by age-related brain changes that are consonant with individuals without PTSD. Future longitudinal research is warranted to understand how PTSD impacts brain aging across the lifespan

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.</p

Assessment of Brain Age in Posttraumatic Stress Disorder: Findings from the ENIGMA PTSD and Brain Age Working Groups

Background Posttraumatic stress disorder (PTSD) is associated with markers of accelerated aging. Estimates of brain age, compared to chronological age, may clarify the effects of PTSD on the brain and may inform treatment approaches targeting the neurobiology of aging in the context of PTSD. Method Adult subjects (N = 2229; 56.2% male) aged 18–69 years (mean = 35.6, SD = 11.0) from 21 ENIGMA-PGC PTSD sites underwent T1-weighted brain structural magnetic resonance imaging, and PTSD assessment (PTSD+, n = 884). Previously trained voxel-wise (brainageR) and region-of-interest (BARACUS and PHOTON) machine learning pipelines were compared in a subset of control subjects (n = 386). Linear mixed effects models were conducted in the full sample (those with and without PTSD) to examine the effect of PTSD on brain predicted age difference (brain PAD; brain age − chronological age) controlling for chronological age, sex, and scan site. Results BrainageR most accurately predicted brain age in a subset (n = 386) of controls (brainageR: ICC = 0.71, R = 0.72, MAE = 5.68; PHOTON: ICC = 0.61, R = 0.62, MAE = 6.37; BARACUS: ICC = 0.47, R = 0.64, MAE = 8.80). Using brainageR, a three-way interaction revealed that young males with PTSD exhibited higher brain PAD relative to male controls in young and old age groups; old males with PTSD exhibited lower brain PAD compared to male controls of all ages. Discussion Differential impact of PTSD on brain PAD in younger versus older males may indicate a critical window when PTSD impacts brain aging, followed by age-related brain changes that are consonant with individuals without PTSD. Future longitudinal research is warranted to understand how PTSD impacts brain aging across the lifespan