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Objective: A major limitation of current suicide research is the lack of power to identify robust correlates of suicidal thoughts or behavior. Variation in suicide risk assessment instruments used across cohorts may represent a limitation to pooling data in international consortia. Method: Here, we examine this issue through two approaches: (a) an extensive literature search on the reliability and concurrent validity of the most commonly used instruments and (b) by pooling data (N ∼ 6,000 participants) from cohorts from the Enhancing NeuroImaging Genetics Through Meta-Analysis (ENIGMA) Major Depressive Disorder and ENIGMA–Suicidal Thoughts and Behaviour working groups, to assess the concurrent validity of instruments currently used for assessing suicidal thoughts or behavior. Results: We observed moderate-to-high correlations between measures, consistent with the wide range (κ range: 0.15–0.97; r range: 0.21–0.94) reported in the literature. Two common multi-item instruments, the Columbia Suicide Severity Rating Scale and the Beck Scale for Suicidal Ideation were highly correlated with each other (r = 0.83). Sensitivity analyses identified sources of heterogeneity such as the time frame of the instrument and whether it relies on self-report or a clinical interview. Finally, construct-specific analyses suggest that suicide ideation items from common psychiatric questionnaires are most concordant with the suicide ideation construct of multi-item instruments. Conclusions: Our findings suggest that multi-item instruments provide valuable information on different aspects of suicidal thoughts or behavior but share a modest core factor with single suicidal ideation items. Retrospective, multisite collaborations including distinct instruments should be feasible provided they harmonize across instruments or focus on specific constructs of suicidality.

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

Introduction - Cortical thickness (CT) and surface area (SA) are established biomarkers of brain pathology in posttraumatic stress disorder (PTSD). Structural covariance networks (SCN) are represented as graphs with brain regions as nodes and correlations between nodes as edges. Methods - We built SCNs for PTSD and control groups using 148 CT and SA measures that were harmonized for site in n=3439 subjects from ENIGMA-PGC PTSD. We compared centrality between PTSD and controls as well as interactions of diagnostic group with age, sex, and comorbid major depressive disorder (MDD) status. We investigated associations between network modularity and diagnostic grouping. Results - Nodes with higher CT-based centrality in PTSD compared to controls included the left inferior frontal sulcus, left fusiform gyrus, left superior temporal gyrus, and right inferior temporal gyrus. Children (10 years) and adolescents (10-21) with PTSD showed greater centrality in frontotemporal areas compared to young (22-39) and middle-aged adults (40-59) with PTSD, who showed higher centrality in occipital areas. PTSD diagnostic group interactions with sex and comorbid MDD showed altered centrality in occipital regions, along with greater visual network modularity in PTSD subjects compared to controls. Conclusion - Structural covariance in PTSD is associated with centrality differences in occipital areas and visual network modularity differences in a large well powered sample. In context of extensive structural covariance remodeling taking place prior to and during adolescence, the present findings suggest a process of cortical remodeling that commences with trauma and/or the onset of PTSD but may also predate these events.

Genetic contributions to human cortical structure manifest pervasive pleiotropy. This pleiotropy may be harnessed to identify unique genetically-informed parcellations of the cortex that are neurobiologically distinct from anatomical, functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to model the genetic architecture of cortical surface area (SA) and cortical thickness (CT) of 34 brain regions recently reported in the ENIGMA cortical GWAS. Genomic SEM uses the empirical genetic covariance estimated from GWAS summary statistics with LD score regression (LDSC) to discover factors underlying genetic covariance. Genomic SEM can fit a multivariate GWAS from summary statistics, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA was explained by 6 latent factors and CT was explained by 4 latent factors. The multivariate GWAS of these latent factors identified 74 genome-wide significant (GWS) loci (p−8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of latent factor GWAS results found that SA-derived factors had a positive genetic correlation with bipolar disorder (BPD), and major depressive disorder (MDD), and a negative genetic correlation with attention deficit hyperactivity disorder (ADHD), MDD, and insomnia, while CT factors displayed a negative genetic correlation with alcohol dependence. Jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across phenotypes offers a new vantage point for mapping genetically informed cortical networks.HIGHLIGHTSGenomic SEM can examine genetic correlation across cortical regions.We inferred regional genetic networks of cortical thickness and surface area.Network-associated variants have been implicated in multiple traits.These networks are genetically correlated with several psychiatric disorders including MDD, bipolar, ADHD, and alcohol dependence.

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.

The reproducibility crisis in neuroimaging has led to an increased demand for standardized data processing workflows. Within the ENIGMA consortium, we developed HALFpipe (Harmonized Analysis of Functional MRI pipeline), an open-source, containerized, user-friendly tool that facilitates reproducible analysis of task-based and resting-state fMRI data through uniform application of preprocessing, quality assessment, single-subject feature extraction, and group-level statistics. It provides state-of-the-art preprocessing using fMRIPrep without the requirement for input data in Brain Imaging Data Structure (BIDS) format. HALFpipe extends the functionality of fMRIPrep with additional preprocessing steps, which include spatial smoothing, grand mean scaling, temporal filtering, and confound regression. HALFpipe generates an interactive quality assessment (QA) webpage to rate the quality of key preprocessing outputs and raw data in general. HALFpipe features myriad post-processing functions at the individual subject level, including calculation of task-based activation, seed-based connectivity, network-template (or dual) regression, atlas-based functional connectivity matrices, regional homogeneity (ReHo), and fractional amplitude of low-frequency fluctuations (fALFF), offering support to evaluate a combinatorial number of features or preprocessing settings in one run. Finally, flexible factorial models can be defined for mixed-effects regression analysis at the group level, including multiple comparison correction. Here, we introduce the theoretical framework in which HALFpipe was developed, and present an overview of the main functions of the pipeline. HALFpipe offers the scientific community a major advance toward addressing the reproducibility crisis in neuroimaging, providing a workflow that encompasses preprocessing, post-processing, and QA of fMRI data, while broadening core principles of data analysis for producing reproducible results. Instructions and code can be found at https://github.com/HALFpipe/HALFpipe.

Posttraumatic stress disorder (PTSD) may develop when mechanisms for making accurate distinctions about threat relevance have gone awry. Generalization across conceptually related objects has been hypothesized based on clinical observation in PTSD, but the neural mechanisms remain unexplored. Recent trauma-exposed military veterans (n = 46) were grouped into PTSD (n = 23) and non-PTSD (n = 23). Participants learned to generalize fear across conceptual categories (animals or tools) of semantically related items that were partially reinforced by shock during functional magnetic resonance imaging. Conditioned fear learning was quantified by shock expectancy and skin conductance response (SCR). Relative to veteran controls, PTSD subjects exhibited a stronger neural response associated with fear generalization to the reinforced object category in the striatum, anterior cingulate cortex, amygdala, occipitotemporal cortex, and insula (Z > 2.3; p

BACKGROUND: The amygdala is a subcortical structure involved in socioemotional and associative fear learning processes relevant for understanding the mechanisms of posttraumatic stress disorder (PTSD). Research in animals indicates that the amygdala is a heterogeneous structure in which the basolateral and centromedial divisions are susceptible to stress. While the amygdala complex is implicated in the pathophysiology of PTSD, little is known about the specific contributions of the individual nuclei that constitute the amygdala complex. METHODS: Military veterans (n = 355), including military veterans with PTSD (n = 149) and trauma-exposed control subjects without PTSD (n = 206), underwent high-resolution T1-weighted anatomical scans. Automated FreeSurfer segmentation of the amygdala yielded 9 structures: basal, lateral, accessory basal, anterior amygdaloid, and central, medial, cortical, and paralaminar nuclei, along with the corticoamygdaloid transition zone. Subregional volumes were compared between groups using ordinary-least-squares regression with relevant demographic and clinical regressors followed by 3-dimensional shape analysis of whole amygdala. RESULTS: PTSD was associated with smaller left and right lateral and paralaminar nuclei, but with larger left and right central, medial, and cortical nuclei (p < .05, false discovery rate corrected). Shape analyses revealed lower radial distance in anterior bilateral amygdala and lower Jacobian determinant in posterior bilateral amygdala in PTSD compared with control subjects. CONCLUSIONS: Alterations in select amygdala subnuclear volumes and regional shape distortions are associated with PTSD in military veterans. Volume differences of the lateral nucleus and the centromedial complex associated with PTSD demonstrate a subregion-specific pattern that is consistent with their functional roles in fear learning and fear expression behaviors.

Combat-exposed Veterans are at increased risk for developing psychological distress, mood disorders, and trauma and stressor-related disorders. Trauma and mood disorders have been linked to alterations in brain volume, function, and connectivity. However, far less is known about the effects of combat exposure on brain health. The present study examined the relationship between severity of combat exposure and cortical thickness. Post-9/11 Veterans (N = 337; 80% male) were assessed with structural neuroimaging and clinically for combat exposure, depressive symptoms, prior head injury, and posttraumatic stress disorder (PTSD). Vertex-wide cortical thickness was estimated using FreeSurfer autosegmentation. FreeSurfer’s Qdec was used to examine relationship between combat exposure, PTSD, and prior head injuries on cortical thickness (Monte Carlo corrected for multiple comparisons, vertex-wise cluster threshold of 1.3, p

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.

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 3,047 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 1,426 individuals with PTSD and 1,621 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, structures for which structure and function have been consistently implicated in PTSD. Results remained significant/similar 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.

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.

The reproducibility crisis in neuroimaging has led to an increased demand for standardized data processing workflows. Within the ENIGMA consortium, we developed HALFpipe (Harmonized AnaLysis of Functional MRI pipeline), an open-source, containerized, user-friendly tool that facilitates reproducible analysis of task-based and resting-state fMRI data through uniform application of preprocessing, quality assessment, single-subject feature extraction, and group-level statistics. It provides state-of-the-art preprocessing using fMRIPrep without the requirement for input data in Brain Imaging Data Structure (BIDS) format. HALFpipe extends the functionality of fMRIPrep with additional preprocessing steps, which include spatial smoothing, grand mean scaling, temporal filtering, and confound regression. HALFpipe generates an interactive quality assessment (QA) webpage to assess the quality of key preprocessing outputs and raw data in general. HALFpipe features myriad post-processing functions at the individual subject level, including calculation of task-based activation, seed-based connectivity, network-template (or dual) regression, atlas-based functional connectivity matrices, regional homogeneity (ReHo), and fractional amplitude of low frequency fluctuations (fALFF), offering support to evaluate a combinatorial number of features or preprocessing settings in one run. Finally, flexible factorial models can be defined for mixed-effects regression analysis at the group level, including multiple comparison correction. Here, we introduce the theoretical framework in which HALFpipe was developed, and present an overview of the main functions of the pipeline. HALFpipe offers the scientific community a major advance toward addressing the reproducibility crisis in neuroimaging, providing a workflow that encompasses preprocessing, post-processing, and QA of fMRI data, while broadening core principles of data analysis for producing reproducible results. Instructions and code can be found at https://github.com/HALFpipe/HALFpipe.

Neurocognitive and brain structural differences are associated with adolescent onset alcohol use disorders (AUDs). Maltreatment histories may contribute to current results. To examine these issues, healthy adolescents ( n = 31), adolescents without maltreatment and AUD (AUD − MAL, n = 28), and adolescents with AUDs with maltreatment (AUD + MAL, n = 17) underwent comprehensive neurocognitive assessments and MRI structural scans. Controls performed significantly better than the two AUD groups in math and language. The AUD + MAL group performed significantly lower in sustained attention compared to the AUD − MAL and control groups and lower in reading compared to controls. The AUD + MAL group had larger left pars triangularis, a region of the inferior frontal gyrus, compared to the AUD-MAL and control groups, and smaller anterior corpus callosum volumes versus the AUD − MAL group. There were no group differences in other prefrontal cortex, amygdala, hippocampus, and parahippocampal volumes. The AUD + MAL group showed an inverse correlation between hippocampal volumes and age. AUD variables were associated with lower performance in fine-motor and executive function. Cannabis use variables were associated with lower performance in fine-motor, language, visual-spatial, memory, and executive function. Parahippocampal volumes positively correlated with abstinence. The preliminary results suggest adolescent AUD studies should consider examinations of maltreatment history, comorbid substance use disorders, and recovery during abstinence in their analyses.

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.

Alcohol use and exposure to psychological trauma frequently co-occur in adolescence and share many risk factors. Both exposures have deleterious effects on the brain during this sensitive developmental period, particularly on the hippocampus and amygdala. However, very little is known about the individual and interactive effects of trauma and alcohol exposure and their specific effects on functionally distinct substructures within the adolescent hippocampus and amygdala. Adolescents from a large longitudinal sample (N = 803, 2684 scans, 51% female, and 75% White/Caucasian) ranging in age from 12 to 21 years were interviewed about exposure to traumatic events at their baseline evaluation. Assessments for alcohol use and structural magnetic resonance imaging scans were completed at baseline and repeated annually to examine neurodevelopmental trajectories. Hippocampal and amygdala subregions were segmented using Freesurfer v6.0 tools, followed by volumetric analysis with generalized additive mixed models. Longitudinal statistical models examined the effects of cumulative lifetime trauma measured at baseline and alcohol use measured annually on trajectories of hippocampal and amygdala subregions, while controlling for covariates known to impact brain development. Greater alcohol use, quantified using the Cahalan scale and measured annually, was associated with smaller whole hippocampus (β = −12.0, pFDR = 0.009) and left hippocampus tail volumes (β = −1.2, pFDR = 0.048), and larger right CA3 head (β = 0.4, pFDR = 0.027) and left subiculum (β = 0.7, pFDR = 0.046) volumes of the hippocampus. In the amygdala, greater alcohol use was associated with larger right basal nucleus volume (β = 1.3, pFDR = 0.040). The effect of traumatic life events measured at baseline was associated with larger right CA3 head volume (β = 1.3, pFDR = 0.041) in the hippocampus. We observed an interaction between baseline trauma and within-person age change where younger adolescents with greater trauma exposure at baseline had smaller left hippocampal subfield volumes in the subiculum (β = 0.3, pFDR = 0.029) and molecular layer HP head (β = 0.3, pFDR = 0.041). The interaction also revealed that older adolescents with greater trauma exposure at baseline had larger right amygdala nucleus volume in the paralaminar nucleus (β = 0.1, pFDR = 0.045), yet smaller whole amygdala volume overall (β = −3.7, pFDR = 0.003). Lastly, we observed an interaction between alcohol use and baseline trauma such that adolescents who reported greater alcohol use with greater baseline trauma showed smaller right hippocampal subfield volumes in the CA1 head (β = −1.1, pFDR = 0.011) and hippocampal head (β = −2.6, pFDR = 0.025), yet larger whole hippocampus volume overall (β = 10.0, pFDR = 0.032). Cumulative lifetime trauma measured at baseline and alcohol use measured annually interact to affect the volume and trajectory of hippocampal and amygdala substructures (measured via structural MRI annually), regions that are essential for emotion regulation and memory. Our findings demonstrate the value of examining these substructures and support the hypothesis that the amygdala and hippocampus are not homogeneous brain regions.

Behavioural, structural, and functional neuroimaging have implicated the hippocampus as a critical brain region in posttraumatic stress disorder (PTSD) pathogenesis. Recent work in a normative, primarily European, sample identified 15 unique genetic loci contributing to structural variability in six hippocampal subfield volumes. We explored the relevance of these loci in two samples (Mental Illness Research Education and Clinical Centre [MIRECC] and Grady; n = 290) of trauma-exposed individuals enriched for PTSD and of diverse ancestry. Four of the previous loci demonstrated nominal evidence of replication in the MIRECC dataset, primarily within non-Hispanic whites (NHW). One locus replicated in the Grady cohort, which was composed exclusively of non-Hispanic blacks (NHB). Our data supported genetic interactions with diagnosis of lifetime PTSD and genetic interactions with childhood trauma in the MIRECC sample, but not the Grady sample. Given the racial, diagnostic, and trauma-exposure differences with the original genome-wide association study (GWAS) report, we conducted a full GWAS in the MIRECC and Grady datasets. Interactions between genetic variants and lifetime PTSD or childhood trauma were interrogated for single nucleotide polymorphisms (SNPs) with evidence of main effects. Genetic associations surpassed false discovery rate (FDR)-correction within hippocampal subfields in fimbria, subiculum, cornu ammonis-1 (CA1), and hippocampal amygdala transition area (HATA). One association was replicated in the Grady cohort (rs12880795 in TUNAR with left (L)-HATA volume). The most significant association in the MIRECC dataset was between rs6906714 in LINC02571 and right (R)-fimbria volume (p = 5.99×10−8, q = 0.0056). Interestingly, the effect of rs6906714 on R-fimbria volume increased with exposure to childhood trauma (gene*environment [G*E] interaction p = 0.022). These preliminary results argue for G*E interactions between genetic loci with PTSD and childhood trauma on hippocampal phenotypes. Our results underscore the need for larger neuroimaging-genetic studies in PTSD, trauma, and ancestrally diverse populations.

Child maltreatment is a major cause of pediatric posttraumatic stress disorder (PTSD). Previous studies have not investigated potential differences in network architecture in maltreated youth with PTSD and those resilient to PTSD. High-resolution magnetic resonance imaging brain scans at 3 T were completed in maltreated youth with PTSD (n= 31), without PTSD (n= 32), and nonmaltreated controls (n= 57). Structural covariance network architecture was derived from between-subject intraregional correlations in measures of cortical thickness in 148 cortical regions (nodes). Interregional positive partial correlations controlling for demographic variables were assessed, and those correlations that exceeded specified thresholds constituted connections in cortical brain networks. Four measures of network centrality characterized topology, and the importance of cortical regions (nodes) within the network architecture were calculated for each group. Permutation testing and principle component analysis method were employed to calculate between-group differences. Principle component analysis is a methodological improvement to methods used in previous brain structural covariance network studies. Differences in centrality were observed between groups. Larger centrality was found in maltreated youth with PTSD in the right posterior cingulate cortex; smaller centrality was detected in the right inferior frontal cortex compared to youth resilient to PTSD and controls, demonstrating network characteristics unique to pediatric maltreatment-related PTSD. Larger centrality was detected in right frontal pole in maltreated youth resilient to PTSD compared to youth with PTSD and controls, demonstrating structural covariance network differences in youth resilience to PTSD following maltreatment. Smaller centrality was found in the left posterior cingulate cortex and in the right inferior frontal cortex in maltreated youth compared to controls, demonstrating attributes of structural covariance network topology that is unique to experiencing maltreatment. This work is the first to identify cortical thickness-based structural covariance network differences between maltreated youth with and without PTSD. We demonstrated network differences in both networks unique to maltreated youth with PTSD and those resilient to PTSD. The networks identified are important for the successful attainment of age-appropriate social cognition, attention, emotional processing, and inhibitory control. Our findings in maltreated youth with PTSD versus those without PTSD suggest vulnerability mechanisms for developing PTSD.

Background Many studies report smaller hippocampal and amygdala volumes in posttraumatic stress disorder (PTSD), but findings have not always been consistent. Here, we present the results of a large-scale neuroimaging consortium study on PTSD conducted by the Psychiatric Genomics Consortium (PGC)–Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) PTSD Working Group. Methods We analyzed neuroimaging and clinical data from 1868 subjects (794 PTSD patients) contributed by 16 cohorts, representing the largest neuroimaging study of PTSD to date. We assessed the volumes of eight subcortical structures (nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus, and lateral ventricle). We used a standardized image-analysis and quality-control pipeline established by the ENIGMA consortium. Results In a meta-analysis of all samples, we found significantly smaller hippocampi in subjects with current PTSD compared with trauma-exposed control subjects (Cohen’s d = −0.17, p = .00054), and smaller amygdalae (d = −0.11, p = .025), although the amygdala finding did not survive a significance level that was Bonferroni corrected for multiple subcortical region comparisons (p Conclusions Our study is not subject to the biases of meta-analyses of published data, and it represents an important milestone in an ongoing collaborative effort to examine the neurobiological underpinnings of PTSD and the brain’s response to trauma.

BackgroundPTSD and depression commonly co-occur and have been associated with smaller hippocampal volumes compared to healthy and trauma-exposed controls. However, the hippocampus is heterogeneous, with subregions that may be uniquely affected in individuals with PTSD and depression.MethodsWe used random effects regressions and a harmonized neuroimaging protocol based on FreeSurfer (v6.0) to identify sub-structural hippocampal markers of current PTSD (C-PTSD), depression, and the interaction of these conditions across 31 cohorts worldwide (N=3,115;Mage=38.9±13.9 years). Secondary analyses tested these associations by sex and after modeling the simultaneous effects of remitted PTSD, childhood trauma, mild traumatic brain injury, and alcohol use disorder.ResultsA significant negative main effect of depression (n=800, vs. no depression, n=1456) was observed in the hippocampal tail (ß=−0.13) and CA1 (ß=−0.09) after adjusting for covariates and multiple testing (adjusted p’s (q)=0.028). A main effect of C-PTSD (n=1042 vs. control, n=1359) was not significant, but an interaction between C-PTSD and depression was significant in the CA1 (ß=−0.24, q=0.044). Pairwise comparisons revealed significantly smaller CA1 volumes in individuals with C-PTSD+Depression than controls (ß=−0.12, q=0.012), C-PTSD-only (ß=−0.17, q=0.001), and Depression-only (ß=−0.18, q=0.023). Follow-up analyses revealed sex effects in the hippocampal tail of depressed females, and an interaction effect of C-PTSD and depression in the fimbria of males.ConclusionsCollectively our results suggest that depression is a stronger predictor of hippocampal volumetry than PTSD, particularly in the CA1, and provide compelling evidence of more pronounced hippocampal phenotypes in comorbid PTSD and depression compared to either condition alone.

Behavioral, structural, and functional neuroimaging have implicated the hippocampus as a critical brain region in PTSD pathogenesis. We conducted a GWAS of hippocampal subfield volumes in a sample of recent military veteran trauma survivors (n=157), including some with PTSD (n=66). Covariates in our analysis included lifetime PTSD diagnosis, sex, intracranial volume, genomic estimates of ancestry, and childhood trauma. Interactions between genetic variants and lifetime PTSD or childhood trauma were interrogated for SNPs with significant main effects. Several genetic associations surpassed correction for multiple testing for several hippocampal subfields, including fimbria, subiculum, cornu ammonis-1(CA1), and hippocampal amygdala transition area (HATA). One association replicated in an independent cohort of civilians with PTSD (rs12880795 inTUNARwith L-HATA volume,p=3.43 × 10-7in the discovery andp=0.0004 in the replication cohort). However, the most significant association in the discovery data set was between rs6906714 inLINC02571and R-fimbria volume (p=5.99 ×10-8,q=0.0056). Interestingly, the effect of rs6906714 on R-fimbria volume increased with childhood trauma (G*E interactionp=0.022). In addition to variants in long intergenic non-coding RNAs (lincRNAs), we identified SNPs associated with hippocampal subfield volume, which are also quantitative trait loci (QTLs) for genes involved in RNA editing of glutamate receptor subunits (GluRs), oxidative stress, and autoimmune disorders. Genomic regions, some with putative regulatory roles, influence the volume of hippocampal subfields. Neuroimaging phenotypes may offer important insight into the genetic architecture and neurobiological pathways relevant to PTSD, as well as in the identification of potential biomarkers and drug targets for PTSD.

BackgroundSmaller hippocampal volume in patients with PTSD represents the most consistently reported structural alteration in the brain. Subfields of the hippocampus play distinct roles in encoding and processing of memories, which are disrupted in PTSD. We examined PTSD-associated alterations in 12 hippocampal subfields in relation to global hippocampal shape, and clinical features.MethodsCase-control cross-sectional study of US military veterans (n=282) from the Iraq and Afghanistan era were grouped into PTSD (n=142) and trauma-exposed controls (n=140). Participants underwent clinical evaluation for PTSD and associated clinical parameters followed by MRI at 3-Tesla. Segmentation with Free Surfer v6.0 produced hippocampal subfield volumes for the left and right CA1, CA3, CA4, DG, fimbria, fissure, hippocampus-amygdala transition area, molecular layer, parasubiculum, presubiculum, subiculum, and tail, as well as hippocampal meshes. Covariates included age, gender, trauma exposure, alcohol use, depressive symptoms, antidepressant medication use, total hippocampal volume, and MRI scanner model.ResultsSignificantly lower subfield volumes were associated with PTSD in left CA1 (p=.01; d=.21; uncorrected), CA3 (p=.04; d=.08; uncorrected), and right CA3 (p=.02; d=.07; uncorrected) only if ipsilateral whole hippocampal volume was included as a covariate. A trend level association of L-CA1 with PTSD [F4,221=3.32, p = 0.07] is present and the other subfield findings are non-significant if ipsilateral whole hippocampal volume is not included as a covariate. PTSD associated differences in global hippocampal shape were non-significant.ConclusionsThe present finding of smaller hippocampal CA1 in PTSD is consistent with model systems in rodents that exhibit increased anxiety-like behavior from repeated exposure to acute stress. Behavioral correlations with hippocampal subfield volume differences in PTSD will elucidate their relevance to PTSD, particularly behaviors of associative fear learning, extinction training, and formation of false memories.

Traumatic brain injury (TBI) is a significant cause of morbidity in military Veterans and Service Members. While most individuals recover fully from mild injuries within weeks, some continue to experience symptoms including headaches, disrupted sleep, and other cognitive, behavioral or physical symptoms. Diffusion magnetic resonance imaging (dMRI) shows promise in identifying areas of structural disruption and predicting outcomes. Although some studies suggest widespread structural disruption after brain injury, dMRI studies of military brain injury have yielded mixed results so far, perhaps due to the subtlety of mild injury, individual differences in injury location, severity and mechanism, and co-morbidity with other disorders such as post-traumatic stress disorder (PTSD), depression, and substance abuse. We present preliminary dMRI results from the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) military brain injury working group. We found higher fractional anisotropy (FA) in participants with a history of TBI. Understanding the injury and recovery process, along with factors that influence these, will lead to improved diagnosis and treatment.

Depending on the traumatic event, a significant fraction of trauma survivors subsequently develop PTSD. The additional variability in PTSD risk is expected to arise from genetic susceptibility. Unfortunately, several genome-wide association studies (GWAS) have failed to identify a consistent genetic marker for PTSD. The heritability of intermediate phenotypes such as regional brain volumes is often 80% or higher. We conducted a GWAS of subcortical brain volumes in a sample of recent military veteran trauma survivors (n = 157), grouped into PTSD (n = 66) and non-PTSD controls (n = 91). Covariates included PTSD diagnosis, sex, intracranial volume, ancestry, childhood trauma, SNP×PTSD diagnosis, and SNP×childhood trauma. We identified several genetic markers in high linkage disequilibrium (LD) with rs9373240 (p = 2.0 × 10−7, FDR q = 0.0375) that were associated with caudate volume. We also observed a significant interaction between rs9373240 and childhood trauma (p-values = 0.0007–0.002), whereby increased trauma exposure produced a stronger association between SNPs and increased caudate volume. We identified several SNPs in high LD with rs34043524, which is downstream of the TRAM1L1 gene that were associated with right lateral ventricular volume (p = 1.73 × 10−7; FDR q = 0.032) and were also associated with lifetime alcohol abuse or dependence (p = 2.49 × 10−7; FDR q = 0.0375). Finally, we identified several SNPs in high LD with rs13140180 (p = 2.58 × 10−7; FDR q = .0016), an intergenic region on chromosome 4, and several SNPs in the TMPRSS15 associated with right nucleus accumbens volume (p = 2.58 × 10−7; FDR q = 0.017). Both TRAM1L1 and TMPRSS15 have been previously implicated in neuronal function. Key results survived genome-wide multiple-testing correction in our sample. Leveraging neuroimaging phenotypes may offer a shortcut, relative to clinical phenotypes, in mapping the genetic architecture and neurobiological pathways of PTSD.

Background and purposeThe effect of exposing the developing brain of a high school football player to subconcussive impacts during a single season is unknown. The purpose of this pilot study was to use diffusion tensor imaging to assess white matter changes during a single high school football season, and to correlate these changes with impacts measured by helmet accelerometer data and neurocognitive test scores collected during the same period.Materials and methodsSeventeen male athletes (mean age, 16 ± 0.73 years) underwent MR imaging before and after the season. Changes in fractional anisotropy across the white matter skeleton were assessed with Tract-Based Spatial Statistics and ROI analysis.ResultsThe mean number of impacts over a 10-g threshold sustained was 414 ± 291. Voxelwise analysis failed to show significant changes in fractional anisotropy across the season or a correlation with impact frequency, after correcting for multiple comparisons. ROI analysis showed significant (P < .05, corrected) decreases in fractional anisotropy in the fornix-stria terminalis and cingulum hippocampus, which were related to impact frequency. The effects were strongest in the fornix-stria terminalis, where decreases in fractional anisotropy correlated with worsening visual memory.ConclusionsOur findings suggest that subclinical neurotrauma related to participation in American football may result in white matter injury and that alterations in white matter tracts within the limbic system may be detectable after only 1 season of play at the high school level.

The article by Spielberg and colleagues (1) in this issue explores the large-scale topology of intrinsic functional networks in military veterans with posttraumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI). The emerging approach, already applied to other neuropsychiatric disorders, begins to account for the sobering reality that neural representations of symptoms are distributed across widespread, dynamically interacting brain regions and subnetworks, which are further modulated by affective and cognitive states. Thus, the large scale of the search space under exploration requires unbiased approaches to gain the greatest flexibility and the widest possible aperture (2). By contrast, much of the prior research using resting-state functional connectivity in PTSD, while making valuable contributions to the field, has focused on the functional neural connections to specific a priori anatomical regions or seeds. The large repertoire of spatiotemporal patterns of activity in the brain is the basis for adaptive behavior, and uncovering disruptions in these patterns will be essential for improving our understanding of PTSD and mTBI. As such, the work of Spielberg et al. covers new ground in PTSD by using an agnostic (whole brain) approach to characterizing large-scale neural connectivity that will generate new hypotheses and spawn the discovery of the precise role that various circuits play in PTSD symptomatology.

The amygdala is a major structure that orchestrates defensive reactions to environmental threats and is implicated in hypervigilance and symptoms of heightened arousal in posttraumatic stress disorder (PTSD). The basolateral and centromedial amygdala (CMA) complexes are functionally heterogeneous, with distinct roles in learning and expressing fear behaviors. PTSD differences in amygdala-complex function and functional connectivity with cortical and subcortical structures remain unclear. Recent military veterans with PTSD (n=20) and matched trauma-exposed controls (n=22) underwent a resting-state fMRI scan to measure task-free synchronous blood-oxygen level dependent activity. Whole-brain voxel-wise functional connectivity of basolateral and CMA seeds was compared between groups. The PTSD group had stronger functional connectivity of the basolateral amygdala (BLA) complex with the pregenual anterior cingulate cortex (ACC), dorsomedial prefrontal cortex, and dorsal ACC than the trauma-exposed control group (p

Mild traumatic brain injury (TBI) is a common source of morbidity from the wars in Iraq and Afghanistan. With no overt lesions on structural MRI, diagnosis of chronic mild TBI in military veterans relies on obtaining an accurate history and assessment of behavioral symptoms that are also associated with frequent comorbid disorders, particularly posttraumatic stress disorder (PTSD) and depression. Military veterans from Iraq and Afghanistan with mild TBI (n = 30) with comorbid PTSD and depression and non-TBI participants from primary (n = 42) and confirmatory (n = 28) control groups were assessed with high angular resolution diffusion imaging (HARDI). White matter-specific registration followed by whole-brain voxelwise analysis of crossing fibers provided separate partial volume fractions reflecting the integrity of primary fibers and secondary (crossing) fibers. Loss of white matter integrity in primary fibers (P Language: en

The brain builds an association between action and sensory feedback to predict the sensory consequence of self-generated motor commands. This internal model of action is central to our ability to adapt movements and may also play a role in our ability to learn from observing others. Recently, we reported that the spatial generalization patterns that accompany adaptation of reaching movements were distinct in children with autism spectrum disorder (ASD) as compared with typically developing (TD) children. To test whether the generalization patterns are specific to ASD, here, we compared the patterns of adaptation with those in children with attention deficit hyperactivity disorder (ADHD). Consistent with our previous observations, we found that in ASD, the motor memory showed greater than normal generalization in proprioceptive coordinates compared with both TD children and children with ADHD; children with ASD also showed slower rates of adaptation compared with both control groups. Children with ADHD did not show this excessive generalization to the proprioceptive target, but they did show excessive variability in the speed of movements with an increase in the exponential distribution of responses (τ) as compared with both TD children and children with ASD. The results suggest that slower rate of adaptation and anomalous bias towards proprioceptive feedback during motor learning are characteristics of autism, whereas increased variability in execution is a characteristic of ADHD.

Fear conditioning is an established model for investigating posttraumatic stress disorder (PTSD). However, symptom triggers may vaguely resemble the initial traumatic event, differing on a variety of sensory and affective dimensions. We extended the fear-conditioning model to assess generalization of conditioned fear on fear processing neurocircuitry in PTSD. Military veterans (n=67) consisting of PTSD (n=32) and trauma-exposed comparison (n=35) groups underwent functional magnetic resonance imaging during fear conditioning to a low fear-expressing face while a neutral face was explicitly unreinforced. Stimuli that varied along a neutral-to-fearful continuum were presented before conditioning to assess baseline responses, and after conditioning to assess experience-dependent changes in neural activity. Compared with trauma-exposed controls, PTSD patients exhibited greater post-study memory distortion of the fear-conditioned stimulus toward the stimulus expressing the highest fear intensity. PTSD patients exhibited biased neural activation toward high-intensity stimuli in fusiform gyrus (PPPPPP=0.07). All regions except fusiform were moderated by childhood trauma. Amygdala–calcarine (P=0.01) and amygdala–thalamus (P=0.06) functional connectivity selectively increased in PTSD patients for high-intensity stimuli after conditioning. In contrast, amygdala–ventromedial prefrontal cortex (P=0.04) connectivity selectively increased in trauma-exposed controls compared with PTSD patients for low-intensity stimuli after conditioning, representing safety learning. In summary, fear generalization in PTSD is biased toward stimuli with higher emotional intensity than the original conditioned-fear stimulus. Functional brain differences provide a putative neurobiological model for fear generalization whereby PTSD symptoms are triggered by threat cues that merely resemble the index trauma.

Posttraumatic stress disorder (PTSD) is considered a disorder of recovery where individuals fail to learn and retain extinction of the traumatic fear response. In maltreated youth, PTSD is common, chronic, and associated with comorbidity. Studies of extinction-related structural volumes (amygdala, hippocampus, anterior cingulate cortex (ACC), and ventral medial prefrontal cortex (vmPFC)) and this stress diathesis, in maltreated youth were not previously investigated. In this cross-sectional study, neuroanatomical volumes associated with extinction in maltreated youth with PTSD (N=31), without PTSD (N=32), and in non-maltreated healthy volunteers (n=57) were examined using magnetic resonance imaging. Groups were sociodemographically similar. Participants underwent extensive assessments for strict inclusion/exclusion criteria and DSM-IV disorders. Maltreated youth with PTSD demonstrated decreased right vmPFC volumes compared with both maltreated youth without PTSD and non-maltreated controls. Maltreated youth without PTSD demonstrated larger left amygdala and right hippocampal volumes compared with maltreated youth with PTSD and non-maltreated control youth. PTSD symptoms inversely correlated with right and left hippocampal and left amygdala volumes. Confirmatory masked voxel base morphometry analyses demonstrated greater medial orbitofrontal cortex gray matter intensity in controls than maltreated youth with PTSD. Volumetric results were not influenced by psychopathology or maltreatment variables. We identified volumetric differences in extinction-related structures between maltreated youth with PTSD from those without PTSD. Alterations of the vmPFC may be one mechanism that mediates the pathway from PTSD to comorbidity. Further longitudinal work is needed to determine neurobiological factors related to chronic and persistent PTSD, and to PTSD resilience despite maltreatment.

OBJECTIVE:: Use diffusion tensor imaging to investigate white matter alterations associated with blast exposure with or without acute symptoms of traumatic brain injury (TBI). PARTICIPANTS:: Forty-five veterans of the recent military conflicts included 23 exposed to primary blast without TBI symptoms, 6 having primary blast with mild TBI, and 16 unexposed to blast. DESIGN:: Cross-sectional case-control study. MAIN MEASURES:: Neuropsychological testing and diffusion tensor imaging metrics that quantified the number of voxel clusters with altered fractional anisotropy (FA) radial diffusivity, and axial diffusivity, regardless of their spatial location. RESULTS:: Significantly lower FA and higher radial diffusivity were observed in veterans exposed to primary blast with and without mild TBI relative to blast-unexposed veterans. Voxel clusters of lower FA were spatially dispersed and heterogeneous across affected individuals. CONCLUSION:: These results suggest that lack of clear TBI symptoms following primary blast exposure may not accurately reflect the extent of brain injury. If confirmed, our findings would argue for supplementing the established approach of making diagnoses based purely on clinical history and observable acute symptoms with novel neuroimaging-based diagnostic criteria that "look below the surface" for pathology. Language: en

Children with autism spectrum disorder (ASD) exhibit deficits in motor control, imitation, and social function. Does a dysfunction in the neural basis of representing internal models of action contribute to these problems? We measured patterns of generalization as children learned to control a novel tool and found that the autistic brain built a stronger than normal association between self generated motor commands and proprioceptive feedback; furthermore, the greater the reliance on proprioception, the greater the child’s impairments in social function and imitation.