Dreaming in Urbach-Wiethe patients the effect of amygdala damage on dreaming

Includes bibliographical references.As it stands, there is a paucity of literature looking at the effect of damaged amygdalae on dreaming and dream content. Of the many functions, the amygdala is heavily involved in processing emotional stimuli and fear conditioning. In Revonsuo’s threat simulation theory (TST), the amygdala plays an important role in the threat simulation mechanism. This mechanism evaluates the threatening situation, then chooses and executes the avoidant type behaviour to successfully avoid the potential threat. All of this is done in the dream world to ensure that humans have a safe virtual environment in which to practice these responses. To test this theory, a sample of people without a functioning amygdala was needed. Unfortunately, bilateral amygdala lesions are extremely rare in the human population. Urbach-Wiethe disease (UWD) is a rare, autosomal recessive disorder that presents with characteristic amygdala calcifications. A sample of 8 UWD patients and 8 matched controls (all females) from the Northern Cape in South Africa were used

Efficacy of a dialogic book-sharing intervention in a South African birth cohort: a randomized controlled trial

Evidence shows that dialogic book-sharing improves language development in young children in low-middle income countries (LMICs), particularly receptive and expressive language. It is unclear whether this intervention also boosts development of other neurocognitive and socio-emotional domains in children. Using a randomized controlled trial (RCT) nested in the Drakenstein Child Health Study (DCHS), a book-sharing intervention was implemented in caregivers of 3.5-year-old preschool children living in low-income South African communities. 122 Caregivers and their children (mean age 3.5 years) were randomly assigned to an intervention group (n = 61) or waitlist control group (n = 61). A neurocognitive battery determined baseline receptive and expressive language, executive function, theory of mind, and behavior scores. No differences were observed between intervention and control groups on receptive and expressive language, or any of the neurocognitive or socio-emotional measures from baseline (3.5 years) to 4 months post-intervention administration (4 years). The benefits noted in prior literature of book-sharing in infants did not appear to be demonstrated at 4 months post-intervention, in children from 3.5 to 4 years of age. This suggests the importance of early intervention and emphasizes the need for further research on adaptation of book-sharing for older participants in a South African context. retrospectively registered on 03/04/2022 PACTR202204697674974

DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

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 an extensive sample of people with MDD (N=2,772) and HC (N=4,240), 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

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

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

Psychological trauma and posttraumatic stress disorder: risk factors and associations with birth outcomes in the Drakenstein Child Health Study

Background: Prenatal and peripartum trauma may be associated with poor maternal–fetal outcomes. However, relatively few data on these associations exist from low-middle income countries, and populations in transition. Objective: We investigated the prevalence and risk factors for maternal trauma and posttraumatic stress disorder (PTSD), and their association with adverse birth outcomes in the Drakenstein Child Health Study, a South African birth cohort study. Methods: Pregnant women were recruited from two clinics in a peri-urban community outside Cape Town. Trauma exposure and PTSD were assessed using diagnostic interviews; validated self-report questionnaires measured other psychosocial characteristics. Gestational age at delivery was calculated and birth outcomes were assessed by trained staff. Multiple logistic regression explored risk factors for trauma and PTSD; associations with birth outcomes were investigated using linear regression. Potential confounders included study site, socioeconomic status (SES), and depression. Results: A total of 544 mother–infant dyads were included. Lifetime trauma was reported in approximately two-thirds of mothers, with about a third exposed to past-year intimate partner violence (IPV). The prevalence of current/lifetime PTSD was 19%. In multiple logistic regression, recent life stressors were significantly associated with lifetime trauma, when controlling for SES, study site, and recent IPV. Childhood trauma and recent stressors were significantly associated with PTSD, controlling for SES and study site. While no association was observed between maternal PTSD and birth outcomes, maternal trauma was significantly associated with a 0.3 unit reduction (95% CI: 0.1; 0.5) in infant head-circumference-for-age z-scores (HCAZ scores) at birth in crude analysis, which remained significant when adjusted for study site and recent stressors in a multivariate regression model. Conclusions: In this exploratory study, maternal trauma and PTSD were found to be highly prevalent, and preliminary evidence suggested that trauma may adversely affect fetal growth, as measured by birth head circumference. However, these findings are limited by a number of methodological weaknesses, and further studies are required to extend findings and delineate causal links and mechanisms of association

A large-scale ENIGMA multisite replication study of brain age in depression

International audienceBackgroundSeveral studies have evaluated whether depressed persons have older appearing brains than their nondepressed peers. However, the estimated neuroimaging-derived “brain age gap” has varied from study to study, likely driven by differences in training and testing sample (size), age range, and used modality/features. To validate our previously developed ENIGMA brain age model and the identified brain age gap, we aim to replicate the presence and effect size estimate previously found in the largest study in depression to date (N = 2126 controls & N = 2675 cases; +1.08 years [SE 0.22], Cohen's d = 0.14, 95% CI: 0.08–0.20), in independent cohorts that were not part of the original study.MethodsA previously trained brain age model (www.photon-ai.com/enigma_brainage) based on 77 FreeSurfer brain regions of interest was used to obtain unbiased brain age predictions in 751 controls and 766 persons with depression (18–75 years) from 13 new cohorts collected from 20 different scanners. Meta-regressions were used to examine potential moderating effects of basic cohort characteristics (e.g., clinical and scan technical) on the brain age gap.ResultsOur ENIGMA MDD brain age model generalized reasonably well to controls from the new cohorts (predicted age vs. age: r = 0.73, R2 = 0.47, MAE = 7.50 years), although the performance varied from cohort to cohort. In these new cohorts, on average, depressed persons showed a significantly higher brain age gap of +1 year (SE 0.35) (Cohen's d = 0.15, 95% CI: 0.05–0.25) compared with controls, highly similar to our previous finding. Significant moderating effects of FreeSurfer version 6.0 (d = 0.41, p = 0.007) and Philips scanner vendor (d = 0.50, p 3400 patients and >2800 controls worldwide show reliable but subtle effects of brain aging in adult depression. Future studies are needed to identify factors that may further explain the brain age gap variance between cohorts

Smaller Hippocampal Volume in Posttraumatic Stress Disorder : A Multisite ENIGMA-PGC Study: Subcortical Volumetry Results From Posttraumatic Stress Disorder Consortia

Background Many studies report smaller hippocampal and amygdala volumes in posttraumatic stress disorder (PTSD), but findings have not always been consistent. Here, we present the results of a large-scale neuroimaging consortium study on PTSD conducted by the Psychiatric Genomics Consortium (PGC)–Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) PTSD Working Group. Methods We analyzed neuroimaging and clinical data from 1868 subjects (794 PTSD patients) contributed by 16 cohorts, representing the largest neuroimaging study of PTSD to date. We assessed the volumes of eight subcortical structures (nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus, and lateral ventricle). We used a standardized image-analysis and quality-control pipeline established by the ENIGMA consortium. Results In a meta-analysis of all samples, we found significantly smaller hippocampi in subjects with current PTSD compared with trauma-exposed control subjects (Cohen's d = −0.17, p =.00054), and smaller amygdalae (d = −0.11, p =.025), although the amygdala finding did not survive a significance level that was Bonferroni corrected for multiple subcortical region comparisons (p <.0063). Conclusions Our study is not subject to the biases of meta-analyses of published data, and it represents an important milestone in an ongoing collaborative effort to examine the neurobiological underpinnings of PTSD and the brain's response to trauma

Smaller Hippocampal Volume in Posttraumatic Stress Disorder: A Multisite ENIGMA-PGC Study: Subcortical Volumetry Results From Posttraumatic Stress Disorder Consortia

Background Many studies report smaller hippocampal and amygdala volumes in posttraumatic stress disorder (PTSD), but findings have not always been consistent. Here, we present the results of a large-scale neuroimaging consortium study on PTSD conducted by the Psychiatric Genomics Consortium (PGC)–Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) PTSD Working Group. Methods We analyzed neuroimaging and clinical data from 1868 subjects (794 PTSD patients) contributed by 16 cohorts, representing the largest neuroimaging study of PTSD to date. We assessed the volumes of eight subcortical structures (nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus, and lateral ventricle). We used a standardized image-analysis and quality-control pipeline established by the ENIGMA consortium. Results In a meta-analysis of all samples, we found significantly smaller hippocampi in subjects with current PTSD compared with trauma-exposed control subjects (Cohen's d = −0.17, p =.00054), and smaller amygdalae (d = −0.11, p =.025), although the amygdala finding did not survive a significance level that was Bonferroni corrected for multiple subcortical region comparisons (p <.0063). Conclusions Our study is not subject to the biases of meta-analyses of published data, and it represents an important milestone in an ongoing collaborative effort to examine the neurobiological underpinnings of PTSD and the brain's response to trauma