The AURORA Study: a longitudinal, multimodal library of brain biology and function after traumatic stress exposure

Adverse posttraumatic neuropsychiatric sequelae (APNS) are common among civilian trauma survivors and military veterans. These APNS, as traditionally classified, include posttraumatic stress, postconcussion syndrome, depression, and regional or widespread pain. Traditional classifications have come to hamper scientific progress because they artificially fragment APNS into siloed, syndromic diagnoses unmoored to discrete components of brain functioning and studied in isolation. These limitations in classification and ontology slow the discovery of pathophysiologic mechanisms, biobehavioral markers, risk prediction tools, and preventive/treatment interventions. Progress in overcoming these limitations has been challenging because such progress would require studies that both evaluate a broad spectrum of posttraumatic sequelae (to overcome fragmentation) and also perform in-depth biobehavioral evaluation (to index sequelae to domains of brain function). This article summarizes the methods of the Advancing Understanding of RecOvery afteR traumA (AURORA) Study. AURORA conducts a large-scale (n = 5000 target sample) in-depth assessment of APNS development using a state-of-the-art battery of self-report, neurocognitive, physiologic, digital phenotyping, psychophysical, neuroimaging, and genomic assessments, beginning in the early aftermath of trauma and continuing for 1 year. The goals of AURORA are to achieve improved phenotypes, prediction tools, and understanding of molecular mechanisms to inform the future development and testing of preventive and treatment interventions

Altered white matter microstructural organization in posttraumatic stress disorder across 3047 adults: results from the PGC-ENIGMA PTSD consortium

A growing number of studies have examined alterations in white matter organization in people with posttraumatic stress disorder (PTSD) using diffusion MRI (dMRI), but the results have been mixed which may be partially due to relatively small sample sizes among studies. Altered structural connectivity may be both a neurobiological vulnerability for, and a result of, PTSD. In an effort to find reliable effects, we present a multi-cohort analysis of dMRI metrics across 3047 individuals from 28 cohorts currently participating in the PGC-ENIGMA PTSD working group (a joint partnership between the Psychiatric Genomics Consortium and the Enhancing NeuroImaging Genetics through Meta-Analysis consortium). Comparing regional white matter metrics across the full brain in 1426 individuals with PTSD and 1621 controls (2174 males/873 females) between ages 18-83, 92% of whom were trauma-exposed, we report associations between PTSD and disrupted white matter organization measured by lower fractional anisotropy (FA) in the tapetum region of the corpus callosum (Cohen's d = -0.11, p = 0.0055). The tapetum connects the left and right hippocampus, for which structure and function have been consistently implicated in PTSD. Results were consistent even after accounting for the effects of multiple potentially confounding variables: childhood trauma exposure, comorbid depression, history of traumatic brain injury, current alcohol abuse or dependence, and current use of psychotropic medications. Our results show that PTSD may be associated with alterations in the broader hippocampal network.New methods for child psychiatric diagnosis and treatment outcome evaluatio

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.Stress-related psychiatric disorders across the life spa

Getting Better. Neurobiological mechanisms of recovery from combat-related PTSD

Military personnel often experience traumatic events during deployment. In the aftermath of a traumatic event, a subgroup of trauma survivors develops posttraumatic stress disorder (PTSD).Most (neurobiological) studies aim at understanding why some trauma survivors develop PTSD whereas others do not. However, far fewer studies have focused on the question why some PTSD patients recover and why about thirty to fifty percent do not respond to treatment. This latter question is particularly relevant, because those who do not respond often develop a persistent state of PTSD in which the disorder places a large burden on the individual, their families and society in general. The aim of this dissertation was to investigate neurobiological mechanisms of recovery from combat-related PTSD. Two magnetic resonance imaging (MRI) scans were acquired from 58 war veterans with PTSD with a six to eight month interval. To control for the effect of time, habituation and learning, 29 healthy veterans (combat controls) were also assessed twice. In addition, a non-military healthy control group (N=26) was included in the first assessment to control for the effects of deployment.In between the scans, patients were treated with trauma-focused therapy, the preferred treatment according to international guidelines. Trauma-focused therapy is based on extinction learning for which attention, emotional and contextual cue processing, as well as memory and learning are vital processes. Therefore, these processes and related brain regions were analyzed with structural and functional MRI. Already prior to treatment, PTSD patients who are likely to recover were comparable to combat controls, but could be distinguished from patients who do not respond to treatment. This latter group is characterized by a smaller left hippocampal volume, a brain region involved in learning and memory. Moreover, increased pre-treatment activation of regions involved in attention and emotion processing (i.e. the dorsal anterior cingulate cortex, insula and amygdala) predicted persistence of symptoms. Finally, it was observed that more brain activation during contextual cue processing predicted a better treatment response. This activation was observed in the left inferior parietal lobe, a brain region involved in working memory updating. In this dissertation, it is demonstrated for the first time that brain regions involved in trauma-focused therapy constitute predictive biomarkers for treatment response in PTSD. Also, whereas neurobiological alterations were thought to characterize all patients, here it is shown that PTSD patients who were likely to recover did not differ from healthy veterans. This study has valuable implications for the prognosis of PTSD patients, and provides relevant information for psychoeducation during military training as well as during treatment. Moreover, this dissertation has suggestions for development of new treatment options for PTSD. Finally, it is an important step towards providing customized treatment for patients with PTSD

The predictive value of dorsal cingulate activity and fractional anisotropy on long-term PTSD symptom severity

BACKGROUND: Posttraumatic stress disorder (PTSD) can be treated with trauma-focused therapy, although only about 50% of the patients recover on the short-term. In order to improve response rates it is important to identify who will and will not recover from trauma-focused therapy. Although previous studies reported dorsal anterior cingulate cortex (ACC) activity, as well as dorsal cingulum bundle white matter microstructure integrity as markers for the persistence of PTSD symptoms on the short-term, it remains unclear whether these markers also predict long-term PTSD symptom severity. METHODS: PTSD patients (n = 57) were investigated with clinical interviews and an MRI protocol before the start of treatment. Clinical interviews were repeated after 6-8 months of treatment (short-term follow-up), and on average 4 years later (long-term follow-up). Twenty-eight PTSD patients returned for the long-term follow-up. Dorsal ACC activity in response to negative images, and fractional anisotropy (FA) of the dorsal cingulum were the neural markers investigated. RESULTS: In this long-term follow-up sample (n = 28), dorsal ACC activity and dorsal cingulum FA values significantly predicted CAPS scores on short- and long-term follow-up. The results remained significant after controlling for baseline CAPS score, early trauma, and comorbidity. CONCLUSION: This study confirms the importance of the cingulate cortex activation and white matter integrity not only for short-term treatment outcome, but also for PTSD long-term symptom severity. Future treatments should target ACC function in particular during treatment in order to improve response rates

Pre-treatment cortisol awakening response predicts symptom reduction in posttraumatic stress disorder after treatment

Dysfunction of the HPA-axis has frequently been found in the aftermath of trauma exposure with or without PTSD. Decreasing HPA-axis reactivity to different stress cues has been reported during PTSD treatment. The cortisol awakening response (CARi) is a well-validated, standardized measure of HPA-axis reactivity which can be easily acquired in the clinical setting. Whether CARi changes over time in traumatized individuals are specific to PTSD treatment is unknown. Furthermore, a possible role for the baseline CARi in predicting symptom reduction after treatment in PTSD has not been examined before. To answer these questions, a cohort study was conducted in which the awakening cortisol was measured in both PTSD (N=41) and non-PTSD (N=25) combat-exposed male subjects. Measurements took place at inclusion and 6-8 months after inclusion for both the PTSD and the non-PTSD group. During the 6-8 months interval, PTSD patients received trauma-focused focused psychotherapy, whereas non-PTSD patients received no treatment. We found a decrease in the CARi over time in both groups, suggesting it was not specific to PTSD or the effect of treatment. Therefore, caution is warranted when attributing diminished HPA-axis reactivity over time to effects of PTSD treatment. Second, CARi prior to treatment predicted PTSD symptom reduction (CAPS score change) after treatment, and accounted for 10% of the variance, even when adjusted for changes in depressive symptoms and medication use during the study period. A putative role emerges for CARi as a predictive biomarker of symptom reduction in male individuals with combat-related PTSD

Choosing the polarity of the phase-encoding direction in diffusion MRI: Does it matter for group analysis?

Notorious for degrading diffusion MRI data quality are so-called susceptibility-induced off-resonance fields, which cause non-linear geometric image deformations. While acquiring additional data to correct for these distortions alleviates the adverse effects of this artifact drastically – e.g., by reversing the polarity of the phase-encoding (PE) direction – this strategy is often not an option due to scan time constraints. Especially in a clinical context, where patient comfort and safety are of paramount importance, acquisition specifications are preferred that minimize scan time, typically resulting in data obtained with only one PE direction. In this work, we investigated whether choosing a different polarity of the PE direction would affect the outcome of a specific clinical research study. To address this methodological question, fractional anisotropy (FA) estimates of FreeSurfer brain regions were obtained in civilian and combat controls, remitted posttraumatic stress disorder (PTSD) patients, and persistent PTSD patients before and after trauma-focused therapy and were compared between diffusion MRI data sets acquired with different polarities of the PE direction (posterior-to-anterior, PA and anterior-to-posterior, AP). Our results demonstrate that regional FA estimates differ on average in the order of 5% between AP and PA PE data. In addition, when comparing FA estimates between different subject groups for specific cingulum subdivisions, the conclusions for AP and PA PE data were not in agreement. These findings increase our understanding of how one of the most pronounced data artifacts in diffusion MRI can impact group analyses and should encourage users to be more cautious when interpreting and reporting study outcomes derived from data acquired along a single PE direction

The effect of aging on fronto-striatal reactive and proactive inhibitory control

Inhibitory control, like most cognitive processes, is subject to an age-related decline. The effect of age on neurofunctional inhibition processing remains uncertain, with age-related increases as well as decreases in activation being reported. This is possibly because reactive (i.e., outright stopping) and proactive inhibition (i.e., anticipation of stopping) have not been evaluated separately. Here, we investigate the effects of aging on reactive as well as proactive inhibition, using functional MRI in 73 healthy subjects aged 30–70 years. We found reactive inhibition to slow down with advancing age, which was paralleled by increased activation in the motor cortex. Behaviorally, older adults did not exercise increased proactive inhibition strategies compared to younger adults. However, the pattern of activation in the right inferior frontal gyrus (rIFG) showed a clear age-effect on proactive inhibition: rather than flexibly engaging the rIFG in response to varying stop-signal probabilities, older subjects showed an overall hyperactivation. Whole-brain analyses revealed similar hyperactivations in various other frontal and parietal brain regions. These results are in line with the neural compensation hypothesis of aging: processing becomes less flexible and efficient with advancing age, which is compensated for by overall enhanced activation. Moreover, by disentangling reactive and proactive inhibition, we can show for the first time that the age-related increase in activation during inhibition that is reported generally by prior studies may be the result of compensation for reduced neural flexibility related to proactive control strategies

Predicting Trauma-Focused Therapy Outcome From Resting-State Functional Magnetic Resonance Imaging in Veterans With Posttraumatic Stress Disorder

Background Trauma-focused psychotherapy is the first-line treatment for posttraumatic stress disorder (PTSD) but 30-50% of patients do not benefit sufficiently. Here, we tested whether resting-state functional magnetic imaging (rs-fMRI) can predict treatment response for individual patients. Methods 44 male veterans with PTSD underwent baseline rs-fMRI scanning followed by trauma-focused therapy (EMDR or TF-CBT). Resting-state networks (RSN) were obtained using independent component analysis with 70 components on the basis of 28 trauma-exposed healthy controls, matched for age and gender. Dual regression was used to obtain subject-specific RSNs for the PTSD patients. All RSNs were individually included in a machine learning classification analysis using Gaussian process classifiers. Classifier performance was assessed using 10 times repeated 10-fold cross-validation. Results Patients were grouped into treatment responders (n = 24) and non-responders (n = 20), based on a 30% decrease in total clinician-administered PTSD scale for the DSM-IV (CAPS) score from pre- to post-treatment assessment. A network centered around the pre-supplementary motor area achieved an average accuracy of 81% (p < 0.001, based on a permutation test, corrected for multiple comparisons across 44 signal components), with a sensitivity of 84.5%, specificity of 77.5%, and area under receiver-operator curve (AUC) of 0.93. Conclusions Rs-fMRI recordings are capable of providing personalized predictions of treatment response in a sample of veterans with PTSD. It therefore has the potential to be useful as a biomarker of treatment response and should be validated in larger independent studies. Supported By ZonMw; AMC; Dutch Ministry of Defens

Predicting Trauma-Focused Therapy Outcome From Resting-State Functional Magnetic Resonance Imaging in Veterans With Posttraumatic Stress Disorder

Background Trauma-focused psychotherapy is the first-line treatment for posttraumatic stress disorder (PTSD) but 30-50% of patients do not benefit sufficiently. Here, we tested whether resting-state functional magnetic imaging (rs-fMRI) can predict treatment response for individual patients. Methods 44 male veterans with PTSD underwent baseline rs-fMRI scanning followed by trauma-focused therapy (EMDR or TF-CBT). Resting-state networks (RSN) were obtained using independent component analysis with 70 components on the basis of 28 trauma-exposed healthy controls, matched for age and gender. Dual regression was used to obtain subject-specific RSNs for the PTSD patients. All RSNs were individually included in a machine learning classification analysis using Gaussian process classifiers. Classifier performance was assessed using 10 times repeated 10-fold cross-validation. Results Patients were grouped into treatment responders (n = 24) and non-responders (n = 20), based on a 30% decrease in total clinician-administered PTSD scale for the DSM-IV (CAPS) score from pre- to post-treatment assessment. A network centered around the pre-supplementary motor area achieved an average accuracy of 81% (p < 0.001, based on a permutation test, corrected for multiple comparisons across 44 signal components), with a sensitivity of 84.5%, specificity of 77.5%, and area under receiver-operator curve (AUC) of 0.93. Conclusions Rs-fMRI recordings are capable of providing personalized predictions of treatment response in a sample of veterans with PTSD. It therefore has the potential to be useful as a biomarker of treatment response and should be validated in larger independent studies. Supported By ZonMw; AMC; Dutch Ministry of Defens