BACKGROUND:Patients with posttraumatic stress disorder (PTSD) exhibit smaller regional brain volumes in commonly reported regions including the amygdala and hippocampus, regions associated with fear and memory processing. In the current study, we have conducted a voxel-based morphometry (VBM) meta-analysis using whole-brain statistical maps with neuroimaging data from the ENIGMA-PGC PTSD working group. METHODS:T1-weighted structural neuroimaging scans from 36 cohorts (PTSD n = 1309; controls n = 2198) were processed using a standardized VBM pipeline (ENIGMA-VBM tool). We meta-analyzed the resulting statistical maps for voxel-wise differences in gray matter (GM) and white matter (WM) volumes between PTSD patients and controls, performed subgroup analyses considering the trauma exposure of the controls, and examined associations between regional brain volumes and clinical variables including PTSD (CAPS-4/5, PCL-5) and depression severity (BDI-II, PHQ-9). RESULTS:PTSD patients exhibited smaller GM volumes across the frontal and temporal lobes, and cerebellum, with the most significant effect in the left cerebellum (Hedges' g = 0.22, pcorrected = .001), and smaller cerebellar WM volume (peak Hedges' g = 0.14, pcorrected = .008). We observed similar regional differences when comparing patients to trauma-exposed controls, suggesting these structural abnormalities may be specific to PTSD. Regression analyses revealed PTSD severity was negatively associated with GM volumes within the cerebellum (p corrected  = .003), while depression severity was negatively associated with GM volumes within the cerebellum and superior frontal gyrus in patients (p corrected  = .001). CONCLUSIONS:PTSD patients exhibited widespread, regional differences in brain volumes where greater regional deficits appeared to reflect more severe symptoms. Our findings add to the growing literature implicating the cerebellum in PTSD psychopathology.

Background: Experiencing repeated childhood traumatisation impacts brain structure and function in individuals with dissociative identity disorder (DID) and post-traumatic stress disorder (PTSD). Quantitative grey matter neuroimaging research has shown aberrant volumes in traumatised individuals, however studies examining white matter are sparse, particularly for DID. The present study aims to examine white matter alterations of people with trauma-related disorders. Methods: Sixty-five female participants were included in this study: 33 diagnosed with a trauma-related disorder, namely 17 with DID and 16 with PTSD, and 32 healthy control (HC) participants. All participants underwent diffusion tensor imaging (DTI) and completed dissociation and traumatisation self-report measures. White matter integrity was characterised using voxel-based analysis (VBA), with network lesion mapping used to identify the implicated grey matter end points of the VBA findings. Results: Between-group VBA comparisons showed reduced fractional anisotropy (FA) for participants with DID compared to HCs in bilateral pallidum (implicating striatal projections to pre/post central gyri), midbrain, and pontocerebellar white matter. Compared to those with PTSD, DID subjects showed increased FA in the right internal capsule and right temporal areas (predominantly implicating the inferior longitudinal fasciculus). Across DID and PTSD subjects, FA values within the aforementioned findings negatively correlated with depersonalisation, psychoform and somatoform dissociation, and/or traumatisation scores. Conclusions: Our DTI findings indicate markedly differential white matter integrity in DID compared to PTSD and HCs. This provides valuable mechanistic insights regarding a role for aberrant white matter structural integrity in traumatised female individuals with DID.

The increasing scale and complexity of neuroimaging datasets aggregated from multiple study sites present substantial analytic challenges, as existing statistical analysis tools struggle to handle missing voxel-data, suffer from limited computational speed and inefficient memory allocation, and are restricted in the types of statistical designs they are able to model. We introduce Image-Based Meta- & Mega-Analysis (IBMMA), a novel software package implemented in R and Python that provides a unified framework for analyzing diverse neuroimaging features, efficiently handles large-scale datasets through parallel processing, offers flexible statistical modeling options, and properly manages missing voxel-data commonly encountered in multi-site studies. IBMMA successfully analyzed a large-n dataset of several thousand participants and revealed findings in brain regions that some traditional software overlooked due to missing voxel-data resulting in gaps in brain coverage. IBMMA has the potential to accelerate discoveries in neuroscience and enhance the clinical utility of neuroimaging findings.

Importance:Major depressive disorder (MDD) is highly heterogeneous, with marked individual differences in clinical presentation and neurobiology, which may obscure identification of structural brain abnormalities in MDD. To explore this, we used normative modeling to index regional patterns of variability in cortical thickness (CT) across individual patients. Objective:To use normative modeling in a large dataset from the ENIGMA MDD consortium to obtain individualised CT deviations from the norm (relative to age, sex and site) and examine the relationship between these deviations and clinical characteristics. Design setting and participants:A normative model adjusting for age, sex and site effects was trained on 35 CT measures from FreeSurfer parcellation of 3,181 healthy controls (HC) from 34 sites (40 scanners). Individualised z-score deviations from this norm for each CT measure were calculated for a test set of 2,119 HC and 3,645 individuals with MDD. For each individual, each CT z-score was classified as being within the normal range (95% of individuals) or within the extreme range (2.5% of individuals with the thinnest or thickest cortices). Main outcome measures:Z-score deviations of CT measures of MDD individuals as estimated from a normative model based on HC. Results:Z-score distributions of CT measures were largely overlapping between MDD and HC (minimum 92%, range 92-98%), with overall thinner cortices in MDD. 34.5% of MDD individuals, and 30% of HC individuals, showed an extreme deviation in at least one region, and these deviations were widely distributed across the brain. There was high heterogeneity in the spatial location of CT deviations across individuals with MDD: a maximum of 12% of individuals with MDD showed an extreme deviation in the same location. Extreme negative CT deviations were associated with having an earlier onset of depression and more severe depressive symptoms in the MDD group, and with higher BMI across MDD and HC groups. Extreme positive deviations were associated with being remitted, of not taking antidepressants and less severe symptoms. Conclusions and relevance:Our study illustrates a large heterogeneity in the spatial location of CT abnormalities across patients with MDD and confirms a substantial overlap of CT measures with HC. We also demonstrate that individualised extreme deviations can identify protective factors and individuals with a more severe clinical picture. Key points: Question:Can z-scores derived from normative modelling shed light on the heterogeneous group-level findings of cortical thickness abnormalities in major depression and what characterises individuals at the extreme ends of cortical thickness abnormalities? Finding:We confirmed a large overlap in z-score distributions between depressed individuals and healthy controls and a heterogeneous spatial distribution of extreme z-deviations across brain regions across individual patients. Lower z-scores for cortical thickness were related to more severe clinical characteristics. Meaning:Our findings confirm the heterogeneity in individual variation in the location and extent of CT abnormalities across patients with MDD and stress the importance of individualised predictions when examining cortical thickness abnormalities.

BACKGROUND:Although it has been suggested that the hippocampus and amygdala (HA) are involved in the neurobiology of obsessive-compulsive disorder (OCD), volumetric findings have been inconsistent, and little work has been undertaken on the volumetry of the heterogeneous anatomic units of HA, with their specific functions and cytoarchitecture, in OCD. We sought to explore potential sources of heterogeneity in brain volumes by performing a separate analysis for people with and without psychotropic medication use, as well as the association of subfield volumes with OCD symptom severity. METHODS:We segmented T 1-weighted images from people with OCD and healthy controls in the OCD Brain Imaging Consortium to produce 12 hippocampal subfields and 9 amygdala subfields using Free-Surfer 6.0. We assessed between-group differences in subfield volume using a mixed-effects model adjusted for age and quadratic effects of age, sex, site, and whole HA volume. We also performed subgroup analyses to examine subfield volume in relation to comorbid anxiety and depression, medication status, and symptom severity. We corrected all analyses for multiple comparisons using the false discovery rate (FDR). RESULTS:We included images from 381 people with OCD and 338 healthy controls. These groups did not significantly differ in HA subfield volumes. However, medicated people with OCD had significantly smaller volumes in the hippocampal dentate gyrus (p FDR = 0.04, d = -0.26) and molecular layer (p FDR = 0.04, d = -0.29), and larger volumes in the lateral (p FDR = 0.049, d = 0.23) and basal (p FDR = 0.049, d = 0.25) amygdala subfields, than healthy controls. Unmedicated people with OCD had significantly smaller volumes in the hippocampal cornu ammonis sector 1 (p FDR = 0.02, d = -0.28) than controls. We did not detect associations between any subfield volume and OCD severity. LIMITATIONS:We used cross-sectional data, which limits the interpretation of our analysis. CONCLUSION:Differences in HA subfields between people with OCD and healthy controls are dependent on medication status, in line with previous work on other brain volumetric alterations in OCD. This emphasizes the importance of considering psychotropic medication in neuroimaging studies of OCD.

Objective: Mental disorders are a challenge among Internally Displaced People (IDPs). Training lay mental health workers (LMHWs) might offer an effective solution. Method: The study was conducted in a camp for IDPs in Malakal, South Sudan. LMHWs were trained based on WHO Mental Health Gap Action Programme (mh-GAP) guidelines to provide psychosocial interventions. Participants (n = 988) were randomised to either the intervention arm, receiving help from the trained lay mental health workers (LMHWs), or a waitlist control arm. A structured clinical interview, the Composite International Diagnostic Interview (CIDI), as well as the Brief Disability Questionnaire (BDQ), Impact Event Scale (IES) and the Alcohol Use Disorders Identification Test (AUDIT) were used for baseline and follow-up assessment. Results: A statistically significant intervention effect on PTSD severity was established (Cohen's d = -0.20, 95% CI = (-0.23, -0.17), p < 0.001). Additionally, significant effects on the prevalence of several CIDI diagnoses, including anxiety and depression (OR = 0.46, 95% CI = (0.33, 0.63) and OR = 0.62, 95% CI = (0.45, 0.84), respectively) were found. Disability and alcohol abuse were also reduced (Cohen's d = -0.30, 95% CI = (-0.38, -0.21) and d = -0.08, 95% CI = (-0.13, -0.02), respectively). The positive impact of treatment was found to be lower in the control group. Discussion: The lessons learned could potentially be used future programs.

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing 7012 participants from 31 sites (N = 2772 MDD and N = 4240 HC), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible. Future studies are needed to determine whether more sophisticated integration of information from other MRI modalities such as fMRI and DWI will lead to a higher performance in this diagnostic task.

BACKGROUND:Identifying robust neural signatures of posttraumatic stress disorder (PTSD) symptoms is important to facilitate precision psychiatry and help in understanding and treatment of the disorder. Emergent research suggests that the structural covariance of early visual regions is associated with later PTSD development. However, large-scale analyses are needed in heterogeneous samples of trauma-exposed and trauma-naïve individuals to determine whether such a neural signature is a robust marker of vulnerability. METHODS:We analyzed data from the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)-PTSD dataset (N = 2814) and the HCP-YA (Human Connectome Project-Young Adult) dataset (N = 890) to investigate whether the structural covariance of the early visual cortex is associated with either PTSD symptoms or perceived stress. Structural covariance was derived from a multimodal pattern previously identified in recent trauma survivors, and participant loadings on the profile were included in linear mixed effects models to evaluate associations with stress. RESULTS:Early visual cortex covariance loadings were negatively associated with PTSD symptoms in the ENIGMA-PTSD dataset. The relationship persisted when accounting for prior childhood maltreatment; supporting PTSD symptom specificity, no relationship was observed with depressive symptoms, and no association was observed between loadings and perceived stress measures in the HCP-YA dataset. CONCLUSIONS:The structural covariance of early visual cortex was robustly associated with PTSD symptoms across an international, heterogeneous sample of trauma survivors. Future studies should aim to identify specific mechanisms that underlie structural alterations in the visual cortex to better understand posttrauma psychopathology.

Major Depression (MD) is a prevalent, disabling and life-limiting condition. The neurobiological associations of genetic risk for MD remain under-explored in large samples, with no comprehensive mega-analysis conducted to date. Our study analysed data from 11 separate studies, encompassing 50,975 participants from the ENIGMA Major Depressive Disorder Working Group. We developed highly consistent genetic and neuroimaging protocols and applied these throughout all participating studies, together with rigorous genetic methods to remove overlap between the polygenic risk scores (PRS) training and testing samples. Elevated PRS for MD correlated with lower intracranial volume and lower global measure of cortical surface area (βICV = −0.017, pICV = 1.97 × 10−6; βSurf = −0.013, pSurf = 4.5 × 10−4; pFDR < 3.62 × 10−4). The most significant cortical association was observed in the surface area of the frontal lobe (β = −0.011, p = 2.85 × 10−6, pFDR = 1.42 × 10−5), particularly in the left medial orbito-frontal gyrus (β = −0.021, p = 9.48 × 10−8, pFDR = 1.25 × 10−5). In subcortical regions, lower volumes of the thalamus, hippocampus, and pallidum correlated with higher PRS of MD (β ranged from −0.011 to −0.015, p ranged from 0.002–1.73 × 10−5, pFDR < 0.006). In a subsample of young individuals only (<25 years old, N = 5570), although there were no FDR-significant findings, directions of effects were highly consistent between the analyses of cortical surface areas in youth and the full sample (71.2% in the same direction, exact binomial test p-value = 7.56 × 10−4). Subsequent Mendelian randomisation analysis revealed potentially causal effects of smaller left hippocampal volume on higher liability for MD (Inverse variance weighted analysis β = −0.064, p = 8.04 × 10−3, pFDR = 0.04). Our findings represent an example of how extensive international collaborations can significantly advance our neurogenetic understanding of MD and give insights to avenues for early interventions in those at high risk for developing MD.

Accurately predicting individual antidepressant treatment response could expedite the lengthy trial-and-error process of finding an effective treatment for major depressive disorder (MDD). We tested and compared machine learning-based methods that predict individual-level pharmacotherapeutic treatment response using cortical morphometry from multisite longitudinal cohorts. We conducted an international analysis of pooled data from six sites of the ENIGMA-MDD consortium (n = 262 MDD patients; age = 36.5 ± 15.3 years; 154 (59%) female; mean response rate = 57%). Treatment response was defined as a ≥ 50% reduction in symptom severity score after 4-12 weeks post-initiation of antidepressant treatment. Structural MRI was acquired before, or < 14 days after, treatment initiation. The cortex was parcellated using FreeSurfer, from which cortical thickness and surface area were measured. We tested several machine learning pipeline configurations, which varied in (i) the way we presented the cortical data (i.e., average values per region of interest, as a vector containing voxel-wise cortical thickness and surface area measures, and as cortical thickness and surface area projections), (ii) whether we included clinical data, and the (iii) machine learning model (i.e., gradient boosting, support vector machine, and neural network classifiers) and (iv) cross-validation methods (i.e., k-fold and leave-one-site-out) we used. First, we tested if the overall predictive performance of the pipelines was better than chance, with a corrected 10-fold cross-validation permutation test. Second, we compared if some machine learning pipeline configurations outperformed others. In an exploratory analysis, we repeated our first analysis in three subpopulations, namely patients (i) from a single site, (ii) with comparable response rates, and (iii) showing the least (first quartile) and the most (fourth quartile) treatment response, which we call the extreme (non-)responders subpopulation. Finally, we explored the effect of including subcortical volumetric data on model performance. Overall, performance predicting antidepressant treatment response was not significantly better than chance (balanced accuracy = 50.5%; p = 0.66) and did not vary with alternative pipeline configurations. Exploratory analyses revealed that performance across models was only significantly better than chance in the extreme (non-)responders subpopulation (balanced accuracy = 63.9%, p = 0.001). Including subcortical data did not alter the observed model performance. Cortical structural MRI alone could not reliably predict individual pharmacotherapeutic treatment response in MDD. None of the used machine learning pipeline configurations outperformed the others. In exploratory analyses, we found that predicting response in the extreme (non-)responders subpopulation was feasible on both cortical data alone and combined with subcortical data, which suggests that specific MDD subpopulations may exhibit response-related patterns in structural data. Future work may use multimodal data to predict treatment response in MDD.

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.

IntroductionDissociative identity disorder (DID) is characterised by, among others, subjectively reported inter-identity amnesia, reflecting compromised information transfer between dissociative identity states. Studies have found conflicting results regarding memory transfer between dissociative identity states. Here, we investigated inter-identity amnesia in individuals with DID using self-relevant, subject specific stimuli, and behavioural and neural measures.MethodsData of 46 matched participants were included; 14 individuals with DID in a trauma-avoidant state, 16 trauma-avoiding DID simulators, and 16 healthy controls. Reaction times and neural activation patterns related to three types of subject specific words were acquired and statistically analysed, namely non-self-relevant trauma-related words (NSt), self-relevant trauma-related words from a trauma-avoidant identity state (St), and trauma-related words from a trauma-related identity state (XSt).ResultsWe found no differences in reaction times between XSt and St words and faster reaction times for XSt over NSt. Reaction times of the diagnosed DID group were the longest. Increased brain activation to XSt words was found in the frontal and parietal regions, while decreased brain activity was found in the anterior cingulate cortex in the diagnosed DID group.DiscussionThe current study reproduces and amalgamates previous behavioural reports as well as brain activation patterns. Our finding of increased cognitive control over self-relevant trauma-related knowledge processing has important clinical implications and calls for the redefinition of “inter-identity amnesia” to “inter-identity avoidance”.

OBJECTIVE:Specific phobia is a common anxiety disorder, but the literature on associated brain structure alterations exhibits substantial gaps. The ENIGMA Anxiety Working Group examined brain structure differences between individuals with specific phobias and healthy control subjects as well as between the animal and blood-injection-injury (BII) subtypes of specific phobia. Additionally, the authors investigated associations of brain structure with symptom severity and age (youths vs. adults).METHODS:Data sets from 31 original studies were combined to create a final sample with 1,452 participants with phobia and 2,991 healthy participants (62.7% female; ages 5-90). Imaging processing and quality control were performed using established ENIGMA protocols. Subcortical volumes as well as cortical surface area and thickness were examined in a preregistered analysis.RESULTS:Compared with the healthy control group, the phobia group showed mostly smaller subcortical volumes, mixed surface differences, and larger cortical thickness across a substantial number of regions. The phobia subgroups also showed differences, including, as hypothesized, larger medial orbitofrontal cortex thickness in BII phobia (N=182) compared with animal phobia (N=739). All findings were driven by adult participants; no significant results were observed in children and adolescents.CONCLUSIONS:Brain alterations associated with specific phobia exceeded those of other anxiety disorders in comparable analyses in extent and effect size and were not limited to reductions in brain structure. Moreover, phenomenological differences between phobia subgroups were reflected in diverging neural underpinnings, including brain areas related to fear processing and higher cognitive processes. The findings implicate brain structure alterations in specific phobia, although subcortical alterations in particular may also relate to broader internalizing psychopathology.

Morphological changes in the hippocampal, thalamic, and amygdala subfields have been suggested to form part of the pathophysiology of major depressive disorder (MDD). However, the use of conventional MRI scanners and acquisition techniques has prevented in-depth examinations at the subfield level, precluding a fine-grained understanding of these subfields and their involvement in MDD pathophysiology. We uniquely employed ultra-high field MRI at 7.0 Tesla to map hippocampal, thalamic, and amygdala subfields in MDD. Fifty-six MDD patients and 14 healthy controls (HCs) were enrolled in the final analysis. FreeSurfer protocols were used to segment hippocampal, thalamic, and amygdala subfields. Bayesian analysis was then implemented to assess differences between groups and relations with clinical features. While no effect was found for MDD diagnosis (i.e., case–control comparison), clinical characteristics of MDD patients were associated with subfield volumes of the hippocampus, thalamus, and amygdala. Specifically, the severity of depressive symptoms, insomnia, and childhood trauma in MDD patients related to lower thalamic subfield volumes. In addition, MDD patients with typical MDD versus those with atypical MDD showed lower hippocampal, thalamic, and amygdala subfield volumes. MDD patients with recurrent MDD versus those with first-episode MDD also showed lower thalamic subfield volumes. These findings allow uniquely fine-grained insights into hippocampal, thalamic, and amygdala subfield morphology in MDD, linking some of them to the clinical manifestation of MDD.

Whereas meta-analytical data highlight abnormal frontocortical macrostructure (thickness/surface area/volume) in Major Depressive Disorder (MDD), the underlying microstructural processes remain uncharted, due to the use of conventional MRI scanners and acquisition techniques. We uniquely combined Ultra-High Field MRI at 7.0 Tesla with Quantitative Imaging to map intracortical myelin (proxied by longitudinal relaxation time T1) and iron concentration (proxied by transverse relaxation time T2*), microstructural processes deemed particularly germane to cortical macrostructure. Informed by meta-analytical evidence, we focused specifically on orbitofrontal and rostral anterior cingulate cortices among adult MDD patients (N = 48) and matched healthy controls (HC; N = 10). Analyses probed the association of MDD diagnosis and clinical profile (severity, medication use, comorbid anxiety disorders, childhood trauma) with aforementioned microstructural properties. MDD diagnosis (p’s < 0.05, Cohen’s D = 0.55–0.66) and symptom severity (p’s < 0.01, r = 0.271–0.267) both related to decreased intracortical myelination (higher T1 values) within the lateral orbitofrontal cortex, a region tightly coupled to processing negative affect and feelings of sadness in MDD. No relations were found with local iron concentrations. These findings allow uniquely fine-grained insights on frontocortical microstructure in MDD, and cautiously point to intracortical demyelination as a possible driver of macroscale cortical disintegrity in MDD.

Neuroimaging studies point to neurostructural abnormalities in youth with anxiety disorders. Yet, findings are based on small-scale studies, often with small effect sizes, and have limited generalizability and clinical relevance. These issues have prompted a paradigm shift in the field towards highly powered (i.e., big data) individual-level inferences, which are data-driven, transdiagnostic, and neurobiologically informed. Here, we built and validated neurostructural machine learning (ML) models for individual-level inferences based on the largest-ever multi-site neuroimaging sample of youth with anxiety disorders (age: 10-25 years, N=3,343 individuals from 32 global sites), as compiled by three ENIGMA Anxiety Working Groups: Panic Disorder (PD), Generalized Anxiety Disorder (GAD), and Social Anxiety Disorder (SAD). ML classifiers were trained on MRI-derived regional measures of cortical thickness, surface area, and subcortical volumes to classify patients and healthy controls (HC) for each anxiety disorder separately and across disorders (transdiagnostic classification). Modest, yet robust, classification performance was achieved for PD vs. HC (AUC=0.62), but other disorder-specific and transdiagnostic classifications were not significantly different from chance. However, above chance-level transdiagnostic classifications were obtained in exploratory subgroup analyses of male patients vs. male HC, unmedicated patients vs. HC, and patients with low anxiety severity vs. HC (AUC 0.59-0.63). The above chance-level classifications were based on plausible and specific neuroanatomical features in fronto-striato-limbic and temporo-parietal regions. This study provides a realistic estimate of classification performance in a large, ecologically valid, multi-site sample of youth with anxiety disorders, and may as such serve as a benchmark.

Background:Intrusive traumatic re-experiencing domain (ITRED) was recently introduced as a novel perspective on posttraumatic psychopathology, proposing to focus research of posttraumatic stress disorder (PTSD) on the unique symptoms of intrusive and involuntary re-experiencing of the trauma, namely, intrusive memories, nightmares, and flashbacks. The aim of the present study was to explore ITRED from a neural network connectivity perspective. Methods:Data were collected from 9 sites taking part in the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) PTSD Consortium (n= 584) and included itemized PTSD symptom scores and resting-state functional connectivity (rsFC) data. We assessed the utility of rsFC in classifying PTSD, ITRED-only (no PTSD diagnosis), and trauma-exposed (TE)-only (no PTSD or ITRED) groups using a machine learning approach, examining well-known networks implicated in PTSD. A random forest classification model was built on a training set using cross-validation, and the averaged cross-validation model performance for classification was evaluated using the area under the curve. The model was tested using a fully independent portion of the data (test dataset), and the test area under the curve was evaluated. Results:rsFC signatures differentiated TE-only participants from PTSD and ITRED-only participants at about 60% accuracy. Conversely, rsFC signatures did not differentiate PTSD from ITRED-only individuals (45% accuracy). Common features differentiating TE-only participants from PTSD and ITRED-only participants mainly involved default mode network-related pathways. Some unique features, such as connectivity within the frontoparietal network, differentiated TE-only participants from one group (PTSD or ITRED-only) but to a lesser extent from the other group. Conclusions:Neural network connectivity supports ITRED as a novel neurobiologically based approach to classifying posttrauma psychopathology.

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.

BACKGROUND:The thalamus serves as a central relay station within the brain, and thalamic connectional anomalies are increasingly thought to be present in major depressive disorder (MDD). However, the use of conventional MRI scanners and acquisition techniques has prevented a thorough examination of the thalamus and its subnuclear connectional profile. We combined ultra-high field diffusion MRI acquired at 7.0 Tesla to map the white matter connectivity of thalamic subnuclei. METHODS:Fifty-three MDD patients and 12 healthy controls (HCs) were involved in the final analysis. FreeSurfer was used to segment the thalamic subnuclei, and MRtrix was used to perform the preprocessing and tractography. Fractional anisotropy, axial diffusivity, mean diffusivity, radial diffusivity, and streamline count of thalamic subnuclear tracts were measured as proxies of white matter microstructure. Bayesian multilevel model was used to assess group differences in white matter metrics for each thalamic subnuclear tract and the association between these white matter metrics and clinical features in MDD. RESULTS:Evidence was found for reduced whiter matter metrics of the tracts spanning from all thalamic subnuclei among MDD versus HC participants. Moreover, evidence was found that white matter in various thalamic subnuclear tracts is related to medication status, age of onset and recurrence in MDD. CONCLUSIONS:Structural connectivity was generally reduced in thalamic subnuclei in MDD participants. Several clinical characteristics are related to perturbed subnuclear thalamic connectivity with cortical and subcortical circuits that govern sensory processing, emotional function, and goal-directed behavior.