General psychopathology factor and unresolved-disorganized attachment uniquely correlated to white matter integrity using diffusion tensor imaging

Multivariate analysis of psychological dat

Cortical thickness in Dutch police officers: an examination of factors associated with resilience

Previous neuroimaging studies on resilience have generally compared resilience and psychopathology after stress exposure, which does not allow for conclusions regarding correlates specific to resilience. The aim of the present study was to investigate resilience-specific correlates in cortical thickness and/or cortical surface area and their correlations with psychometric measurements, using a three-group design that included a non-trauma-exposed control group in order to disentangle effects related to resilience from those related to psychopathology. Structural magnetic resonance imaging scans were acquired from 82 Dutch police officers. Participants were categorized into resilient (n = 31; trauma exposure, no psychopathology), vulnerable (n = 32; trauma exposure, psychopathology), and control groups (n = 19; no trauma exposure, no psychopathology). Specific regions of interest (ROIs) were identified based on previous studies that found the rostral and caudal anterior cingulate cortex (ACC) to be implicated in trauma-related psychopathology. Cortical thickness and surface area of the ROIs-the rostral and caudal ACC-and of the whole brain were examined. No significant differences in cortical thickness or surface area were found between the resilient group and other groups in the ROI and whole-brain analyses. Thus, the results of the present study provide no evidence of an association between resilience to traumatic stress and measures of thickness and surface area in cortical regions of the brain in a sample of Dutch police officers.Stress-related psychiatric disorders across the life spa

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.Stress-related psychiatric disorders across the life spa

Subcortical volumes across the lifespan: data from 18,605 healthy individuals aged 3-90 years

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.Education and Child Studie

Use of health belief model–based deep learning classifiers for COVID-19 social media content to examine public perceptions of physical distancing: Model development and case study

10.2196/20493JMIR Public Health and Surveillance63e2049

Measuring the Outreach Efforts of Public Health Authorities and the Public Response on Facebook during the COVID-19 Pandemic in Early 2020: Cross-Country Comparison

10.2196/19334Journal of Medical Internet Research225e1933

Phosphoprotein network analysis of white adipose tissues unveils deregulated pathways in response to high-fat diet

10.1038/srep25844Scientific Reports62584

Structural brain correlates of resilience to traumatic stress in Dutch police officers

Stress-related psychiatric disorders across the life spa

Resting-State Functional Connectivity Characteristics of Resilience to Traumatic Stress in Dutch Police Officers

BackgroundInsights into the neurobiological basis of resilience can have important implications for the prevention and treatment of stress-related disorders, especially in populations that are subjected to high-stress environments. Evaluating large-scale resting-state networks (RSNs) can provide information regarding resilient specific brain function which may be useful in understanding resilience. This study aimed to explore functional connectivity patterns specific for (high) resilience in Dutch policemen after exposure to multiple work-related traumatic events. We investigated resting-state functional connectivity (RSFC) of the salience network (SN), limbic network, and the default-mode network (DMN). MethodsResting-state functional MRI scans were obtained from trauma-exposed executive personnel of the Dutch police force and non-trauma-exposed recruits from the police academy. Participants were divided into three groups: a resilient group (n = 31; trauma exposure; no psychopathology), a vulnerable group (n = 32; trauma exposure, psychopathology), and a control group (n = 19; no trauma exposure, no psychopathology). RSFC of the three networks of interest was compared between these groups, using an independent component analysis and a dual regression approach. ResultsWe found decreased resilience-specific positive RSFC of the salience network with several prefrontal regions. The DMN and limbic network RFSC did not show resilience-specific patterns. ConclusionThis study shows a differential RSFC specific for resilient police officers. This differential RSFC may be related to a greater capacity for internal-focused thought and interoceptive awareness, allowing more effective higher-order responses to stress in highly resilient individuals.Stress-related psychiatric disorders across the life spa