Smaller total and subregional cerebellar volumes in posttraumatic stress disorder: a mega-analysis by the ENIGMA-PGC PTSD workgroup.

Although the cerebellum contributes to higher-order cognitive and emotional functions relevant to posttraumatic stress disorder (PTSD), prior research on cerebellar volume in PTSD is scant, particularly when considering subregions that differentially map on to motor, cognitive, and affective functions. In a sample of 4215 adults (PTSD n = 1642; Control n = 2573) across 40 sites from the ENIGMA-PGC PTSD working group, we employed a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation to obtain 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 volumes in PTSD compared to healthy controls (88% trauma-exposed). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume, as well as reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), vermis (VI, VIII), flocculonodular lobe (lobule X), and corpus medullare (all p-FDR < 0.05). Effects of PTSD on volume were consistent, and generally more robust, when examining symptom severity rather than diagnostic status. These findings implicate regionally specific cerebellar volumetric differences in the pathophysiology of PTSD. The cerebellum appears to play an important role in higher-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.

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.

Altered lateralization of the cingulum in deployment-related traumatic brain injury: An ENIGMA military-relevant brain injury study.

Traumatic brain injury (TBI) in military populations can cause disruptions in brain structure and function, along with cognitive and psychological dysfunction. Diffusion magnetic resonance imaging (dMRI) can detect alterations in white matter (WM) microstructure, but few studies have examined brain asymmetry. Examining asymmetry in large samples may increase sensitivity to detect heterogeneous areas of WM alteration in mild TBI. Through the Enhancing Neuroimaging Genetics Through Meta-Analysis Military-Relevant Brain Injury working group, we conducted a mega-analysis of neuroimaging and clinical data from 16 cohorts of Active Duty Service Members and Veterans (n = 2598). dMRI data were processed together along with harmonized demographic, injury, psychiatric, and cognitive measures. Fractional anisotropy in the cingulum showed greater asymmetry in individuals with deployment-related TBI, driven by greater left lateralization in TBI. Results remained significant after accounting for potentially confounding variables including posttraumatic stress disorder, depression, and handedness, and were driven primarily by individuals whose worst TBI occurred before age 40. Alterations in the cingulum were also associated with slower processing speed and poorer set shifting. The results indicate an enhancement of the natural left laterality of the cingulum, possibly due to vulnerability of the nondominant hemisphere or compensatory mechanisms in the dominant hemisphere. The cingulum is one of the last WM tracts to mature, reaching peak FA around 42 years old. This effect was primarily detected in individuals whose worst injury occurred before age 40, suggesting that the protracted development of the cingulum may lead to increased vulnerability to insults, such as TBI.

A comparison of methods to harmonize cortical thickness measurements across scanners and sites.

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 1,340 cases with posttraumatic stress disorder (PTSD) (6.2-81.8 years old) and 2,057 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 was more sensitive to the detection of significant case-control differences (Χ2(3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ2(3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ2(3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects.

ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data.

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.

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.

Cortical volume abnormalities in posttraumatic stress disorder: an ENIGMA-psychiatric genomics consortium PTSD workgroup mega-analysis.

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.

Brain structural covariance network centrality in maltreated youth with PTSD and in maltreated youth resilient to PTSD.

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.

Smaller hippocampal CA1 subfield volume in posttraumatic stress disorder.

BACKGROUND: Smaller hippocampal volume in patients with posttraumatic stress disorder (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. METHODS: Case-control cross-sectional studies of U.S. 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 T. Segmentation with FreeSurfer 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. RESULTS: Significantly lower subfield volumes were associated with PTSD in left CA1 (P = 0.01; d = 0.21; uncorrected), CA3 (P = 0.04; d = 0.08; uncorrected), and right CA3 (P = 0.02; d = 0.07; 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 nonsignificant if ipsilateral whole hippocampal volume is not included as a covariate. PTSD-associated differences in global hippocampal shape were nonsignificant. CONCLUSIONS: The 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.

White matter compromise in veterans exposed to primary blast forces.

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.

ENIGMA's simple seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury.

Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.

Examining the association between posttraumatic stress disorder and disruptions in cortical networks identified using data-driven methods.

Posttraumatic stress disorder (PTSD) is associated with lower cortical thickness (CT) in prefrontal, cingulate, and insular cortices in diverse trauma-affected samples. However, some studies have failed to detect differences between PTSD patients and healthy controls or reported that PTSD is associated with greater CT. Using data-driven dimensionality reduction, we sought to conduct a well-powered study to identify vulnerable networks without regard to neuroanatomic boundaries. Moreover, this approach enabled us to avoid the excessive burden of multiple comparison correction that plagues vertex-wise methods. We derived structural covariance networks (SCNs) by applying non-negative matrix factorization (NMF) to CT data from 961 PTSD patients and 1124 trauma-exposed controls without PTSD. We used regression analyses to investigate associations between CT within SCNs and PTSD diagnosis (with and without accounting for the potential confounding effect of trauma type) and symptom severity in the full sample. We performed additional regression analyses in subsets of the data to examine associations between SCNs and comorbid depression, childhood trauma severity, and alcohol abuse. NMF identified 20 unbiased SCNs, which aligned closely with functionally defined brain networks. PTSD diagnosis was most strongly associated with diminished CT in SCNs that encompassed the bilateral superior frontal cortex, motor cortex, insular cortex, orbitofrontal cortex, medial occipital cortex, anterior cingulate cortex, and posterior cingulate cortex. CT in these networks was significantly negatively correlated with PTSD symptom severity. Collectively, these findings suggest that PTSD diagnosis is associated with widespread reductions in CT, particularly within prefrontal regulatory regions and broader emotion and sensory processing cortical regions.

Effects of chronic mild traumatic brain injury on white matter integrity in Iraq and Afghanistan war veterans.

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 < 0.05; corrected) was associated with chronic mild TBI in a widely distributed pattern of major fiber bundles and smaller peripheral tracts including the corpus callosum (genu, body, and splenium), forceps minor, forceps major, superior and posterior corona radiata, internal capsule, superior longitudinal fasciculus, and others. Distributed loss of white matter integrity correlated with duration of loss of consciousness and most notably with "feeling dazed or confused," but not diagnosis of PTSD or depressive symptoms. This widespread spatial extent of white matter damage has typically been reported in moderate to severe TBI. The diffuse loss of white matter integrity appears consistent with systemic mechanisms of damage shared by blast- and impact-related mild TBI that involves a cascade of inflammatory and neurochemical events.

Network Centrality and Modularity of Structural Covariance Networks in Posttraumatic Stress Disorder: A Multisite ENIGMA-PGC Study.

Introduction: Cortical thickness (CT) and surface area (SA) are established biomarkers of brain pathology in posttraumatic stress disorder (PTSD). Structural covariance networks (SCNs) 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 Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA)-Psychiatric Genomics Consortium (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 with 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 with young (22-39) and middle-aged adults (40-59) with PTSD, who showed higher centrality in occipital areas. The PTSD diagnostic group interactions with sex and comorbid MDD showed altered centrality in occipital regions, along with greater visual network (VN) modularity in PTSD subjects compared with controls. Conclusion: Structural covariance in PTSD is associated with centrality differences in occipital areas and VN modularity differences in a large well-powered sample. In the context of extensive structural covariance remodeling taking place before 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. Impact statement Centrality is a graph theory measure that offers insights into a node's relationship with all other nodes in the brain. Centrality pinpoints the drivers of brain communication within networks and nodes and may be a promising target for treatments such as neuromodulation. Modularity can pinpoint modules that exist within larger networks and quantify the connections between these modules. Centrality and modularity complement functional and structural connectivity measurements within specific brain networks.

Genomic Structural Equation Modeling Reveals Latent Phenotypes in the Human Cortex with Distinct Genetic Architecture.

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 functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to map the genetic architecture of cortical surface area (SA) and cortical thickness (CT) for the 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, which we are denoting genetically informed brain networks (GIBNs). Genomic SEM can fit a multivariate GWAS from summary statistics for each of the GIBNs, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA identified 6 GIBNs and CT identified 4 GIBNs. The multivariate GWASs of these GIBNs identified 74 genome-wide significant (GWS) loci (p<5×10-8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of GIBN GWASs found that SA-derived GIBNs had a positive genetic correlation with bipolar disorder (BPD), and cannabis use disorder, indicating genetic predisposition to a larger SA in the specific GIBN is associated with greater genetic risk of these disorders. A negative genetic correlation was observed with attention deficit hyperactivity disorder (ADHD), major depressive disorder (MDD), and insomnia, indicating genetic predisposition to a larger SA in the specific GIBN is associated with lower genetic risk of these disorders. CT GIBNs 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 the cortex into genetically informed networks.

Concurrent validity and reliability of suicide risk assessment instruments: A meta-analysis of 20 instruments across 27 international cohorts.

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. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Remodeling of the Cortical Structural Connectome in Posttraumatic Stress Disorder: Results From the ENIGMA-PGC Posttraumatic Stress Disorder Consortium.

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.

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

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

Volumetric trajectories of hippocampal subfields and amygdala nuclei influenced by adolescent alcohol use and lifetime trauma.

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 (ß = -12.0, pFDR = 0.009) and left hippocampus tail volumes (ß = -1.2, pFDR = 0.048), and larger right CA3 head (ß = 0.4, pFDR = 0.027) and left subiculum (ß = 0.7, pFDR = 0.046) volumes of the hippocampus. In the amygdala, greater alcohol use was associated with larger right basal nucleus volume (ß = 1.3, pFDR = 0.040). The effect of traumatic life events measured at baseline was associated with larger right CA3 head volume (ß = 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 (ß = 0.3, pFDR = 0.029) and molecular layer HP head (ß = 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 (ß = 0.1, pFDR = 0.045), yet smaller whole amygdala volume overall (ß = -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 (ß = -1.1, pFDR = 0.011) and hippocampal head (ß = -2.6, pFDR = 0.025), yet larger whole hippocampus volume overall (ß = 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.

Trauma and posttraumatic stress disorder modulate polygenic predictors of hippocampal and amygdala volume.

The volume of subcortical structures represents a reliable, quantitative, and objective phenotype that captures genetic effects, environmental effects such as trauma, and disease effects such as posttraumatic stress disorder (PTSD). Trauma and PTSD represent potent exposures that may interact with genetic markers to influence brain structure and function. Genetic variants, associated with subcortical volumes in two large normative discovery samples, were used to compute polygenic scores (PGS) for the volume of seven subcortical structures. These were applied to a target sample enriched for childhood trauma and PTSD. Subcortical volume PGS from the discovery sample were strongly associated in our trauma/PTSD enriched sample (n = 7580) with respective subcortical volumes of the hippocampus (p = 1.10 × 10-20), thalamus (p = 7.46 × 10-10), caudate (p = 1.97 × 10-18), putamen (p = 1.7 × 10-12), and nucleus accumbens (p = 1.99 × 10-7). We found a significant association between the hippocampal volume PGS and hippocampal volume in control subjects from our sample, but was absent in individuals with PTSD (GxE; (beta = -0.10, p = 0.027)). This significant GxE (PGS × PTSD) relationship persisted (p < 1 × 10-19) in four out of five threshold peaks (0.024, 0.133, 0.487, 0.730, and 0.889) used to calculate hippocampal volume PGSs. We detected similar GxE (G × ChildTrauma) relationships in the amygdala for exposure to childhood trauma (rs4702973; p = 2.16 × 10-7) or PTSD (rs10861272; p = 1.78 × 10-6) in the CHST11 gene. The hippocampus and amygdala are pivotal brain structures in mediating PTSD symptomatology. Trauma exposure and PTSD modulate the effect of polygenic markers on hippocampal volume (GxE) and the amygdala volume PGS is associated with PTSD risk, which supports the role of amygdala volume as a risk factor for PTSD.