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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., (© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Background: To prospectively chart pathways of risk and resiliency following childhood trauma studies need to address three limitations of prior work: 1) recruit beyond social service/ treatment settings; 2) include broad spectrum of trauma types and 3) cast a broad longitudinal measurement framework of both clinical diagnoses and traumatic exposures. The Texas-Childhood Trauma Research Network (TX-CTRN) is a multi-site collaboration that addresses these limitations. In this baseline-only report, we examined domains of trauma and evaluated the concurrent predictive validity of various traumatic event scoring schemas for clinical diagnoses., Methods: Broad-base recruitment of 8-20 year-olds (N = 1289) included trauma centers, emergency departments, pediatric and primary care clinics, and other community settings. Assessments were comprehensive and based on clinical interviews by trained research interviewers., Results: Factor analyses supported a five-factor solution of trauma domains including unintentional/acute, intentional/interpersonal, bullying, in-home versus community witnessed interpersonal harms. Trauma burden scoring schemas were examined for their predictive superiority. Domain-specific counts of traumas that met DSM-5 post-traumatic-stress disorder (PTSD) Criterion-A was the best overall schema in distinguishing among youth with no diagnosis, comorbidities, those with depression, suicidality, substance misuse, and PTSD., Limitations: There were no assessments of neglect., Conclusions: Findings largely aligned with earlier studies on the relative importance of intentional interpersonal traumas and showed bullying may be an important source of traumatic stress that independently adds to prediction of several diagnoses and should be considered in clinical practice., Competing Interests: Declaration of competing interest Drs. Aksan Clark, Dodd, Taylor, Shahidullah, Richmond, Wagner, Cisler, Cross, Garza, have reported no biomedical financial interests or potential conflicts of interest. PJR serves on a data safety monitoring board for Sunovion Pharmaceuticals. DJN has received research support from Eli Lilly, Glaxo SmithKline (GSK), Janssen, the National Alliance for Research on Schizophrenia and Depression (NARSAD), Navitor, Sage Therapeutics, Takeda Pharmaceuticals, the Texas Health & Human Services Commission, and Wyeth. He has served on speakers' bureaus and/or received honoraria from Astra-Zeneca, Eli Lilly, GSK, Pfizer and Wyeth. He has served on advisory boards for GSK, Janssen, Merck, and Sage Therapeutics. He has served as a consultant to Sage Therapeutics. Neither he nor family members have ever held equity positions in biomedical or pharmaceutical corporations. CBM, in the last three years, served as a consultant to AbbVie, ANeuroTech (division of Anima BV), Signant Health, Magstim, Inc., Intra-Cellular Therapies, Inc., EMA Wellness, Sage, Silo Pharma, Engrail Therapeutics, Pasithea Therapeutic Corp., EcoR1, GoodCap Pharmaceuticals, Inc., Senseye, Clexio, EmbarkBio, SynapseBio, BioXcel Therapeutics. He is a stockholder with Seattle Genetics, Antares, Inc., Corcept Therapeutics Pharmaceuticals Company, EMA Wellness, Precisement Health, Relmada Therapeutics. He has served on advisory boards for ANeuroTech (division of Anima BV), Brain and Behavior Research Foundation (BBRF), Anxiety and Depression Association of America (ADAA), Skyland Trail, Signant Health, Laureate Institute for Brain Research (LIBR), Inc., Heading Health, Pasithea Therapeutic Corp., Sage. He has served on the Board of Directors for Gratitude America, ADAA, Lucy Scientific Discovery, Inc. He holds the following patents: Method and devices for transdermal delivery of lithium (US 6375,990B1); Method of assessing antidepressant drug therapy via transport inhibition of monoamine neurotransmitters by ex vivo assay (US 7148,027B2)., (Copyright © 2023 Elsevier B.V. All rights reserved.)

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., Competing Interests: Declaration of Competing Interest Dr. Thompson received partial grant support from Biogen, Inc., and Amazon, Inc., for work unrelated to the current study; Dr. Lebois reports unpaid membership on the Scientific Committee for International Society for the Study of Trauma and Dissociation (ISSTD), grant support from the National Institute of Mental Health, K01 MH118467 and the Julia Kasparian Fund for Neuroscience Research, McLean Hospital. Dr. Lebois also reports spousal IP payments from Vanderbilt University for technology licensed to Acadia Pharmaceuticals unrelated to the present work. ISSTD and NIMH were not involved in the analysis or preparation of the manuscript; Dr. Etkin reports salary and equity from Alto Neuroscience, equity from Mindstrong Health and Akili Interactive. Other authors have no conflicts of interest to declare., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objective: Childhood abuse represents one of the most potent risk factors for developing psychopathology, especially in females. Evidence suggests that exposure to early-life adversity may be related to advanced maturation of emotion processing neural circuits. However, it remains unknown whether abuse is related to early circuit maturation and whether maturation patterns depend on the presence of psychopathology., Methods: A multisite sample of 234 girls (ages 8-18 years) completed clinical assessment, maltreatment histories, and high-resolution T 1 -weighted structural MRI. Girls were stratified by abuse history and internalizing disorder diagnosis into typically developing (no abuse/no diagnosis), resilient (abuse/no diagnosis), and susceptible (abuse/current diagnosis) groups. Machine learning models of normative brain development were aggregated in a stacked generalization framework trained to predict chronological age using gray matter volume in whole-brain, emotion, and language circuit parcellations. Brain age gap estimations (BrainAGEs; predicted age minus true chronological age) were calculated as indices of relative circuit maturation., Results: Childhood abuse was related to reduced BrainAGE (delayed maturation) specific to emotion circuits. Delayed emotion circuit BrainAGE was further related to increased hyperarousal symptoms. Childhood physical neglect was associated with increased whole-brain BrainAGE (advanced maturation). Neural contributors to emotion circuit BrainAGE differed in girls with and without an internalizing diagnosis, especially in the lateral prefrontal, parietal, and insular cortices and the hippocampus., Conclusions: Abuse exposure in girls is associated with a delayed structural maturation pattern specific to emotion circuitry, a potentially adaptive mechanism enhancing threat generalization. Physical neglect, on the other hand, is associated with a broader brain-wide pattern of advanced structural maturation. The differential influence of fronto-parietal cortices and the hippocampus on emotion circuit maturity in resilient girls may represent neurodevelopmental markers of reduced psychiatric risk following abuse.

Numerous data demonstrate that distracting emotional stimuli cause behavioral slowing (i.e. emotional conflict) and that behavior dynamically adapts to such distractors. However, the cognitive and neural mechanisms that mediate these behavioral findings are poorly understood. Several theoretical models have been developed that attempt to explain these phenomena, but these models have not been directly tested on human behavior nor compared. A potential tool to overcome this limitation is Hidden Markov Modeling (HMM), which is a computational approach to modeling indirectly observed systems. Here, we administered an emotional Stroop task to a sample of healthy adolescent girls (N = 24) during fMRI and used HMM to implement theoretical behavioral models. We then compared the model fits and tested for neural representations of the hidden states of the most supported model. We found that a modified variant of the model posited by Mathews et al. (1998) was most concordant with observed behavior and that brain activity was related to the model-based hidden states. Particularly, while the valences of the stimuli themselves were encoded primarily in the ventral visual cortex, the model-based detection of threatening targets was associated with increased activity in the bilateral anterior insula, while task effort (i.e. adaptation) was associated with reduction in the activity of these areas. These findings suggest that emotional target detection and adaptation are accomplished partly through increases and decreases, respectively, in the perceived immediate relevance of threatening cues and also demonstrate the efficacy of using HMM to apply theoretical models to human behavior.

To effectively address the needs of youth who perpetrate sexual violence and reduce rates of recidivism, a better understanding of the mechanisms of juvenile sexual offending is needed. Current literature identifies various factors that are believed to put youth at risk for sexual offending, two of which are empathy deficits and childhood sexual abuse (CSA). The extent to which empathy deficits contribute to juvenile sexual offending, however, is often debated, though studies have not yet explored a neurobehavioral model of this mechanism. This pilot study used functional magnetic resonance imaging (fMRI) to explore the neural correlates of empathy in juveniles who sexually offend (JSOs), and the possible role of CSA. A total of 38 males (ages 12-20) were enrolled, including 11 healthy control subjects and 27 JSOs, of which, 11 had a history of CSA. Participants underwent clinical assessment and completed an empathy task during fMRI. Using both whole-brain and region-of-interest analysis, results of the fMRI data showed no statistical differences in engagement of brain regions associated with empathy between controls and all JSOs. There were also no significant differences between JSOs with and without a history of CSA. These null findings pose implications for guiding future research studies with larger samples and more statistical power, and may support the need to further explore empathy-related explanatory models and interventions for JSOs. Neuroimaging may demonstrate to be a useful tool to identify individualized risk factors and aid in tailoring interventions for this population.

An important, open problem in neuroimaging analyses is developing analytical methods that ensure precise inferences about neural activity underlying fMRI BOLD signal despite the known presence of confounds. Here, we develop and test a new meta-algorithm for conducting semi-blind (i.e., no knowledge of stimulus timings) deconvolution of the BOLD signal that estimates, via bootstrapping, both the underlying neural events driving BOLD as well as the confidence of these estimates. Our approach includes two improvements over the current best performing deconvolution approach; 1) we optimize the parametric form of the deconvolution feature space; and, 2) we pre-classify neural event estimates into two subgroups, either known or unknown, based on the confidence of the estimates prior to conducting neural event classification. This knows-what-it-knows approach significantly improves neural event classification over the current best performing algorithm, as tested in a detailed computer simulation of highly-confounded fMRI BOLD signal. We then implemented a massively parallelized version of the bootstrapping-based deconvolution algorithm and executed it on a high-performance computer to conduct large scale (i.e., voxelwise) estimation of the neural events for a group of 17 human subjects. We show that by restricting the computation of inter-regional correlation to include only those neural events estimated with high-confidence the method appeared to have higher sensitivity for identifying the default mode network compared to a standard BOLD signal correlation analysis when compared across subjects., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Rapid, robust computation of effective connectivity between neural regions is an important next step in characterizing the brain's organization, particularly in the resting state. However, recent work has called into question the value of causal inference computed directly from BOLD, demonstrating that valid inferences require transformation of the BOLD signal into its underlying neural events as necessary for accurate causal inference. In this work we develop an approach for effective connectivity estimation directly from deconvolution-based features and estimates of inter-regional communication lag. We then test, in both simulation as well as whole-brain fMRI BOLD signal, the viability of this approach. Our results show that deconvolution precision and network size play outsized roles in effective connectivity estimation performance. Idealized simulation conditions allow for statistically significant effective connectivity estimation of networks of up to four hundred regions-of-interest (ROIs). Under simulation of realistic recording conditions and deconvolution performance, however, our result indicates that effective connectivity is viable in networks containing up to approximately sixty ROIs. We then validated the ability for the proposed method to reliably detect effective connectivity in whole-brain fMRI signal parcellated into networks of viable size., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Early-life interpersonal violence (IV) is a significant risk factor for a broad range of mental health disorders, increased rates of re-victimization, and psychosocial dysfunction. However, the cognitive mechanisms by which these risks are conferred are largely unknown. The current study attempted to address this empirical gap. Thirty-two adolescent girls, aged 12 to 16 (15 victims of IV), completed a social learning task. A computational learning model was fit to the behavioral data (ratings of trustworthiness during the learning task) to test for group differences in the cognitive mechanisms by which adolescent girls learn to differentially trust others. Specifically, we tested for differences in task performance and subject-level learning parameters: learning rate (the extent to which preferences are updated with new information) and preference stochasticity (the extent to which preferences seem random). Adolescent girls who were victims of IV demonstrated significantly worse performance than their control counterparts. Among IV victims, we observed a relationship between higher learning rates and greater preference stochasticity. Theoretical and clinical implications are discussed., (© The Author(s) 2015.)

Inferences made from analysis of BOLD data regarding neural processes are potentially confounded by multiple competing sources: cardiac and respiratory signals, thermal effects, scanner drift, and motion-induced signal intensity changes. To address this problem, we propose deconvolution filtering, a process of systematically deconvolving and reconvolving the BOLD signal via the hemodynamic response function such that the resultant signal is composed of maximally likely neural and neurovascular signals. To test the validity of this approach, we compared the accuracy of BOLD signal variants (i.e., unfiltered, deconvolution filtered, band-pass filtered, and optimized band-pass filtered BOLD signals) in identifying useful properties of highly confounded, simulated BOLD data: (1) reconstructing the true, unconfounded BOLD signal, (2) correlation with the true, unconfounded BOLD signal, and (3) reconstructing the true functional connectivity of a three-node neural system. We also tested this approach by detecting task activation in BOLD data recorded from healthy adolescent girls (control) during an emotion processing task. Results for the estimation of functional connectivity of simulated BOLD data demonstrated that analysis (via standard estimation methods) using deconvolution filtered BOLD data achieved superior performance to analysis performed using unfiltered BOLD data and was statistically similar to well-tuned band-pass filtered BOLD data. Contrary to band-pass filtering, however, deconvolution filtering is built upon physiological arguments and has the potential, at low TR, to match the performance of an optimal band-pass filter. The results from task estimation on real BOLD data suggest that deconvolution filtering provides superior or equivalent detection of task activations relative to comparable analyses on unfiltered signals and also provides decreased variance over the estimate. In turn, these results suggest that standard preprocessing of the BOLD signal ignores significant sources of noise that can be effectively removed without damaging the underlying signal., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Neuroimaging methodology predominantly relies on the blood oxygenation level dependent (BOLD) signal. While the BOLD signal is a valid measure of neuronal activity, variances in fluctuations of the BOLD signal are not only due to fluctuations in neural activity. Thus, a remaining problem in neuroimaging analyses is developing methods that ensure specific inferences about neural activity that are not confounded by unrelated sources of noise in the BOLD signal. Here, we develop and test a new algorithm for performing semiblind (i.e., no knowledge of stimulus timings) deconvolution of the BOLD signal that treats the neural event as an observable, but intermediate, probabilistic representation of the system's state. We test and compare this new algorithm against three other recent deconvolution algorithms under varied levels of autocorrelated and Gaussian noise, hemodynamic response function (HRF) misspecification and observation sampling rate. Further, we compare the algorithms' performance using two models to simulate BOLD data: a convolution of neural events with a known (or misspecified) HRF versus a biophysically accurate balloon model of hemodynamics. We also examine the algorithms' performance on real task data. The results demonstrated good performance of all algorithms, though the new algorithm generally outperformed the others (3.0% improvement) under simulated resting-state experimental conditions exhibiting multiple, realistic confounding factors (as well as 10.3% improvement on a real Stroop task). The simulations also demonstrate that the greatest negative influence on deconvolution accuracy is observation sampling rate. Practical and theoretical implications of these results for improving inferences about neural activity from fMRI BOLD signal are discussed., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Recent evidence indicates that the propensity towards experiencing disgust may contribute to the development and maintenance of some anxiety disorders. This article summarizes the empirical evidence with emphasis on illuminating potential mediators, moderators, and mechanisms of the disgust-anxiety disorder association that may inform the development of an integrative conceptual model. Early research using neuroimaging methods suggest that disgust processing is associated with activation of the insula. This research has the potential to facilitate progress in developing an empirically informed psychobiological theory on the causal role of disgust in the anxiety disorders.

Research has begun to implicate the role of disgust in the etiology of specific phobias and obsessive-compulsive disorder (OCD). However, it remains unclear if the association between disgust and specific anxiety disorder symptoms is an artifact of trait anxiety or a potential mechanism through which trait anxiety effects specific anxiety disorder symptoms. The present study employed structural equation modeling to differentiate disgust from trait anxiety in the prediction of four types of specific anxiety disorder symptoms in a non-clinical sample (N=352). Results indicate that disgust and trait anxiety latent factors were independently related to spider fears, blood-injection-injury (BII) fears, general OCD symptoms, and OCD washing concerns. However, when both variables were simultaneously modeled as predictors, latent disgust remained significantly associated with the anxiety disorder symptoms, whereas the association between latent trait anxiety and the anxiety disorder symptoms became non-significant or was substantially reduced. Statistical tests of intervening variable effects converged in support of disgust as a significant intervening variable between trait anxiety and spider fears, BII fears, and OCD symptoms (particularly washing concerns). The relevance of these findings for future research investigating the role of disgust in specific anxiety disorders is discussed.

The Marfan syndrome is an autosomal dominant heritable disorder of connective tissue that involves principally the skeletal, ocular, and cardiovascular systems. The most severe end of the phenotypic spectrum, the neonatal Marfan syndrome (nMFS), is characterized by pronounced atrioventricular valve dysfunction, and death often occurs within the first year of life due to congestive heart failure. Mutations in the gene coding for fibrillin-1, FBN1, are known to cause Marfan syndrome, and have been identified in almost all exons of FBN1. Here, we describe a novel mutation affecting the invariant + 1 position of the splice donor site in intron 31, associated with skipping of exon 31, in a patient with nMFS. Published reports of nMFS are reviewed and a strict definition for nMFS is suggested. If this definition is used, all nMFS mutations reported to date lie in one of two hot spots, comprising mainly missense mutations in FBN1 exons 24-27 and mutations causing skipping of exon 31 or 32.

The Marfan syndrome (MFS) is a connective tissue disorder manifested by variable and pleiotropic features in the skeletal, ocular, and cardiovascular systems. The average life span in MFS is about 35 years. A group with much more severe cardiovascular disease and a mean life span of approximately 1 year also exists. We refer to this latter group as "neonatal Marfan syndrome" (nMFS). Fibrillin defects are now known to be the cause of MFS and nMFS. Immunofluorescence studies were the first to demonstrate this association. Here we describe immunofluorescence studies in a series of 10 neonates and summarize their salient clinical features. In vitro accumulation of fibrillin reactive fibers was assayed using monoclonal antibodies to fibrillin in hyperconfluent fibroblast cultures. As was previously observed in MFS, fibroblast cultures from nMFS patients showed an apparent decrease in accumulation of immunostainable fibrillin. Significantly, however, the morphology of the immunostained fibrils in the nMFS cultures were abnormal and differed not only from control cultures, but also from those seen in cultures of MFS fibroblasts. The nMFS fibrils appeared short, fragmented, and frayed, characteristics that are not seen in MFS. Both the clinical and fibrillin morphology data provide evidence to suggest a useful subclassification of nMFS in the spectrum of MFS.

The Marfan syndrome (MFS) is an autosomal dominant heritable disorder of connective tissue. Variable and pleiotropic clinical features are observed in the skeletal, ocular, and cardiovascular systems. The most severe end of the phenotypic spectrum of this disorder comprises a group of patients usually diagnosed at birth, who have a life expectancy of little more than a year. To distinguish this group of patients from those with classical MFS, we refer to them as neonatal Marfan syndrome (nMFS). These infants usually die of congestive heart failure rather than aortic aneurysmal disease, the most frequent cause of morbidity and mortality in classical MFS. Defects in fibrillin, an elastin-associated microfibrillar glycoprotein, are now known to cause both the classical and neonatal forms of MFS. Here we report the recurrent mis-splicing of fibrillin (FBN1) exon 32, a precursor EGF-like calcium binding domain, in two unrelated infants with nMFS. The mis-splicing, in one patient, was due to an A-->T transversion at the -2 position of the consensus acceptor splice site; while that in the second patient was caused by a G-->A transition at the +1 position of the donor splice site. Characterization of FBN1 mutations in individuals at the most severe end of the Marfan syndrome spectrum should provide greater understanding of the multiple domains and regions of fibrillin.