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BackgroundCurrent clinical assessments of Posttraumatic stress disorder (PTSD) rely solely on subjective symptoms and experiences reported by the patient, rather than objective biomarkers of the illness. Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. Here we aimed to classify individuals with PTSD versus controls using heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group.MethodsWe analyzed brain MRI data from 3,527 structural-MRI; 2,502 resting state-fMRI; and 1,953 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls (TEHC and HC) 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.ResultsWe 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 across all three modalities (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.ConclusionOur findings highlight the promise offered by machine learning methods for the diagnosis of patients with PTSD. The utility of brain biomarkers across three MRI modalities and the contribution of DVAE models for improving generalizability offers new insights into neural mechanisms involved in PTSD.Significance⍰Classifying PTSD from trauma-unexposed healthy controls (HC) using three imaging modalities performed well (∼75% AUC), but performance suffered markedly when classifying PTSD from trauma-exposed healthy controls (TEHC) using three imaging modalities (∼60% AUC).⍰Using deep learning for feature reduction (denoising variational auto-encoder; DVAE) dramatically reduced the number of features with no concomitant performance degradation.⍰Utilizing denoising variational autoencoder (DVAE) models improves generalizability across heterogeneous multi-site data compared with the traditional machine learning frameworks

BackgroundThe cerebellum critically contributes to higher-order cognitive and emotional functions such fear learning and memory. Prior research on cerebellar volume in PTSD is scant and has neglected neuroanatomical subdivisions of the cerebellum that differentially map on to motor, cognitive, and affective functions.MethodsWe quantified cerebellar lobule volumes using structural magnetic resonance imaging in 4,215 adults (PTSD n= 1640; Control n=2575) across 40 sites from the from the ENIGMA-PGC PTSD working group. Using a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation, we obtained volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum total and subregional volume in PTSD compared to healthy controls. The Benjamini-Hochberg procedure was used to control the false discovery rate (p-FDR< .05).ResultsPTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume. In addition, people with PTSD showed reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), but also the vermis (VI, VIII), flocculonodular lobe (lobule X), and cerebellar white matter (allp-FDR< 0.05). Effects of PTSD on volume were consistent, and generally more robust, when examining symptom severity rather than diagnostic status.ConclusionsThese findings implicate regionally specific cerebellar volumetric differences in the pathophysiology of PTSD. The cerebellum appears to play an important role in high-order cognitive and emotional processes, far beyond its historical association with vestibulomotor function. Further examination of the cerebellum in trauma-related psychopathology will help to clarify how cerebellar structure and function may disrupt cognitive and affective processes at the center of translational models for PTSD.

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.

Studies of posttraumatic stress disorder (PTSD) report volume abnormalities in multiple regions of the cerebral cortex. However, findings for many regions, particularly regions outside commonly studied emotion-related prefrontal, insular, and limbic regions, are inconsistent and tentative. Also, few studies address the possibility that PTSD abnormalities may be confounded by comorbid depression. A mega-analysis investigating all cortical regions in a large sample of PTSD and control subjects can potentially provide new insight into these issues. Given this perspective, our group aggregated regional volumes data of 68 cortical regions across both hemispheres from 1379 PTSD patients to 2192 controls without PTSD after data were processed by 32 international laboratories using ENIGMA standardized procedures. We examined whether regional cortical volumes were different in PTSD vs. controls, were associated with posttraumatic stress symptom (PTSS) severity, or were affected by comorbid depression. Volumes of left and right lateral orbitofrontal gyri (LOFG), left superior temporal gyrus, and right insular, lingual and superior parietal gyri were significantly smaller, on average, in PTSD patients than controls (standardized coefficients = −0.111 to −0.068, FDR corrected P values < 0.039) and were significantly negatively correlated with PTSS severity. After adjusting for depression symptoms, the PTSD findings in left and right LOFG remained significant. These findings indicate that cortical volumes in PTSD patients are smaller in prefrontal regulatory regions, as well as in broader emotion and sensory processing cortical regions.

Results of neuroimaging datasets aggregated from multiple sites may be biased by site- specific profiles in participants’ demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LMEINT), (2) LME that models both site-specific random intercepts and age-related random slopes (LMEINT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,343 cases with posttraumatic stress disorder (PTSD) (6.2-81.8 years old) and 2,067 trauma-exposed controls without PTSD (6.3-85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM were more sensitive to the detection of significant case-control differences in regional cortical thickness (X2(3) = 34.339, p < 0.001), and case-control differences in age-related cortical thinning (X2(3) = 15.128, p = 0.002). Specifically, ComBat-GAM led to larger effect size estimates of cortical thickness reductions (corrected p-values < 0.001), smaller age-appropriate declines (corrected p-values < 0.001), and lower female to male contrast (corrected p-values < 0.001) in cases compared to controls relative to other harmonization methods. Harmonization with ComBat-GAM also led to greater estimates of age-related declines in cortical thickness (corrected p-values < 0.001) in both cases and controls compared to other harmonization methods. Our results support the use of ComBat-GAM for harmonizing cortical thickness data aggregated from multiple sites and scanners to minimize confounds and increase statistical power.

Posttraumatic stress disorder (PTSD) is a complex psychiatric condition that has generated much attention in the neuroimaging literature. A neurocircuitry model supporting fronto-limbic dysfunction as a major player in facilitating clinical symptoms of PTSD is well-characterized; however, recent literature suggests that network-based approaches may provide additional insight into neural dysfunction in PTSD. Our analysis uses resting-state neuroimaging scans of 1063 adults from the PGC-ENIGMA PTSD Consortium to investigate a network-based model of functional connectivity in PTSD. With a novel, resolution limit-free community detection approach, 16 communities corresponding to functionally meaningful networks were detected with high quality. After group-level community detection, participants were classified into three groups (PTSD, n=418, trauma-exposed controls without PTSD, n=434, and non-trauma exposed healthy controls, n=211). Individual network connectivity metrics were calculated, including whole-brain, default mode network, and central executive network participation coefficient and connectivity strength. Linear mixed effects models revealed group differences in the whole-brain, default mode, and central executive network participation coefficient and connectivity strength such that individuals with PTSD demonstrated overall greater values. We also described sex differences such that males demonstrate greater whole-brain participation coefficient vs. females and females demonstrate greater default mode network connectivity strength vs. males. Our results suggest that PTSD in adults is associated with reduced specialization and enhanced inter-module communication throughout the brain network, which may contribute to inefficient information processing and poor emotional regulation. This study presents a novel use of resolution limit-free community detection in a large PTSD sample, revealing robust differences in resting-state network topology.

IntroductionCortical thickness (CT) and surface area (SA) are established biomarkers of brain pathology in posttraumatic stress disorder (PTSD). Structural covariance networks (SCN) constructed from CT and SA may represent developmental associations, or unique interactions between brain regions, possibly influenced by a common causal antecedent. The ENIGMA-PGC PTSD Working Group aggregated PTSD and control subjects’ data from 29 cohorts in five countries (n=3439).MethodsUsing Destrieux Atlas, we built SCNs and compared centrality measures between PTSD subjects and controls. Centrality is a graph theory measure derived using SCN.ResultsNotable nodes with higher CT-based centrality in PTSD compared to controls were left fusiform gyrus, left superior temporal gyrus, and right inferior temporal gyrus. We found sex-based centrality differences in bilateral frontal lobe regions, left anterior cingulate, left superior occipital cortex and right ventromedial prefrontal cortex (vmPFC). Comorbid PTSD and MDD showed higher CT-based centrality in the right anterior cingulate gyrus, right parahippocampal gyrus and lower SA-based centrality in left insular gyrus.ConclusionUnlike previous studies with smaller sample sizes (≤318), our study found differences in centrality measures using a sample size of 3439 subjects. This is the first cross-sectional study to examine SCN interactions with age, sex, and comorbid MDD. Although limited to group level inferences, centrality measures offer insights into a node’s relationship to the entire functional connectome unlike approaches like seed-based connectivity or independent component analysis. Nodes having higher centrality have greater structural or functional connections, lending them invaluable for translational treatments like neuromodulation.

Classification and prediction of posttraumatic stress disorder (PTSD) based on brain imaging measures is important because it could aid in PTSD diagnosis and clinical management of PTSD. The goal of the present study was to test the effectiveness of using cortical morphological measures (i.e. volume, thickness, and surface area) to classify PTSD cases and controls on 3571 individuals from the ENIGMA-Psychiatric Genomics Consortium PTSD Workgroup, the largest PTSD neuroimaging dataset to date. We constructed 6 feature sets from different demographic variables (age and sex) and cortical morphological measures and used four machine learning algorithms for classification: logistic regression, random forest, support vector machine, and multi-layer perceptron. We found that classifiers trained using only cortical morphological measures (any one of volume, thickness, or surface area) performed better than classifiers trained using only demographic variables. Among all 6 feature sets, combining demographic variables and all three cortical morphological measures yielded the best prediction accuracy, with area under the receiver operating characteristic curve (ROC AUC) scores ranging from 0.615 for logistic regression to 0.648 for random forest. These findings suggest that using cortical morphological measures only has modest prediction power for PTSD classification. Future studies that wish to produce clinically and practically significant findings should consider using whole brain morphological measures, as well as incorporating other neuroimaging modalities and relevant clinical and behavioral symptoms.

Adverse childhood experiences (ACEs) potentially contribute to posttraumatic stress disorder (PTSD) after adult trauma exposure, but underlying brain changes remain unclear. The present study tested relationships between ACEs, whole thalamus and thalamic nuclei volumes, and post-trauma stress symptoms (PTSS) after adult trauma. Trauma survivors (n = 101) completed the Childhood Trauma Questionnaire (CTQ), the PTSD checklist-special stressor version 5 (PCL), and a structural magnetic resonance imaging (sMRI) scan within post-trauma 2 weeks. At post-trauma 3 months, survivors completed a second PCL survey and a PTSD diagnosis interview using the Clinician-Administered PTSD Scale (CAPS). CTQ scores significantly positively correlated with PCL scores at post-trauma 2 weeks and 3 months (respective p's0.01 and0.001). CTQ scores significantly negatively correlated with whole thalamus and 7 thalamic nuclei volumes at post-trauma 2 weeks in the PTSD (N = 50), but not the non-PTSD (N = 51) group. Whole thalamus and 22 nuclei volumes significantly negatively correlated with PCL scores at post-trauma 3 months in the PTSD, but not the non-PTSD group. These results suggest ACEs negatively influence early post-trauma thalamic volumes which, in turn, are negatively associated with PTSS in survivors who develop PTSD.

Mild traumatic brain injury (mTBI) patients frequently experience emotion dysregulation symptoms, including post-traumatic stress. Although mTBI likely affects cortical activation and structure, resulting in cognitive symptoms after mTBI, early effects of mTBI on cortical emotion processing circuits have rarely been examined. To assess early mTBI effects on cortical functional and structural components of emotion processing, we assessed cortical activation to fearful faces within the first 2 weeks after motor vehicle collision (MVC) in survivors who did and did not experience mTBI. We also examined the thicknesses of cortical regions with altered activation. MVC survivors with mTBI (n = 21) had significantly less activation in left superior parietal gyrus (SPG) (−5.9, −81.8, 33.8; p = 10−3.623), left medial orbitofrontal gyrus (mOFG) (−4.7, 36.1, −19.3; p = 10−3.231), and left and right lateral orbitofrontal gyri (lOFG) (left: −16.0, 41.4, −16.6; p = 10−2.573; right: 18.7, 22.7, −17.7; p = 10−2.764) than MVC survivors without mTBI (n = 23). SPG activation in mTBI survivors within 2 weeks after MVC was negatively correlated with subsequent post-traumatic stress symptom severity at 3 months (r = −0.68, p = 0.03). Finally, the SPG region was thinner in the mTBI survivors than in the non-mTBI survivors (F = 11.07, p = 0.002). These results suggest that early differences in activation and structure in cortical emotion processing circuits in trauma survivors who sustain mTBI may contribute to the development of emotion-related symptoms.

In a motor vehicle collision (MVC), survivors often receive mild traumatic brain injuries (mTBI). Although there have been some reports of early white matter changes after an mTBI, much less is known about early cortical structural changes. To investigate early cortical changes within a few days after an MVC, we compared cortical thickness of mTBI survivors with non-mTBI survivors, then reexamined cortical thickness in the same survivors 3 months later. MVC survivors were categorized as mTBI or non-mTBI based on concussive symptoms documented in emergency departments (EDs). Cortical thickness was measured from MRI images using FreeSurfer within a few days and again at 3 months after MVC. Post-traumatic stress symptoms and physical conditions were also assessed. Compared with the non-mTBI group (n = 23), the mTBI group (n = 21) had thicker cortex in the left rostral middle frontal (rMFG) and right precuneus gyri, but thinner cortex in the left posterior middle temporal gyrus at 7.2 ± 3.1 days after MVC. After 3 months, cortical thickness had decreased in left rMFG in the mTBI group but not in the non-mTBI group. The cortical thickness of the right precuneus region in the initial scans was positively correlated with acute traumatic stress symptoms for all survivors and with the number of reduced activity days for mTBI survivors who completed the follow-up. The preliminary results suggest that alterations in cortical thickness may occur at an early stage of mTBI and that frontal cortex structure may change dynamically over the initial 3 months after mTBI.

Accumulating evidence suggests traumatic experience can rapidly alter brain activation associated with emotion processing. However, little is known about acute changes in emotion neurocircuits that underlie PTSD symptom development. To examine acute alterations in emotion circuit activation and structure that may be linked to PTSD symptoms, thirty-eight subjects performed a task of appraisal of emotional faces as their brains were functionally and structurally studied with MRI at both two weeks and three months after motor vehicle collision (MVC). As determined by symptoms reported in the PTSD Checklist at three months, sixteen survivors developed probable PTSD, whereas the remaining 22 did not meet criteria for PTSD diagnosis (non-PTSD). The probable PTSD group had greater activation than the non-PTSD group in dorsal and ventral medial prefrontal cortex (dmPFC and vmPFC) while appraising fearful faces within two weeks after MVC and in left insular cortex (IC) three months after MVC. dmPFC activation at two weeks significantly positively correlated with PTSD symptom severity at two weeks (R = 0.462, P = 0.006) and three months (R = 0.418, p = 0.012). Changes over time in dmPFC activation and in PTSD symptom severity were also significantly positively correlated in the probable PTSD group (R = 0.641, P = 0.018). A significant time by group interaction was found for volume changes in left superior frontal gyrus (SFG, F = 6.048, p = 0.019) that partially overlapped dmPFC active region. Between two weeks and three months, left SFG volume decreased in probable PTSD survivors. These findings identify alterations in frontal cortical activity and structure during the early post-trauma period that appear to be associated with development of PTSD symptoms.