A review of neuroimaging studies of race-related prejudice: does amygdala response reflect threat?

Prejudice is an enduring and pervasive aspect of human cognition. An emergent trend in modern psychology has focused on understanding how cognition is linked to neural function, leading researchers to investigate the neural correlates of prejudice. Research in this area using racial group memberships has quickly highlighted the amygdala as a neural structure of importance. In this article, we offer a critical review of social neuroscientific studies of the amygdala in race-related prejudice. Rather than the dominant interpretation that amygdala activity reflects a racial or outgroup bias per se, we argue that the observed pattern of sensitivity in this literature is best considered in terms of potential threat. More specifically, we argue that negative culturally-learned associations between black males and potential threat better explain the observed pattern of amygdala activity. Finally, we consider future directions for the field and offer specific experiments and predictions to directly address unanswered questions

Cerebellar and cortical abnormalities in paediatric opsoclonus-myoclonus syndrome.

AIM: Paediatric opsoclonus-myoclonus syndrome (OMS) is a poorly understood condition with long-term cognitive, behavioural, and motor sequelae. Neuroimaging has indicated cerebellar atrophy in the chronic phase, but this alone may not explain the cognitive sequelae seen in many children with OMS. This study aimed to determine the extent of structural change throughout the brain that may underpin the range of clinical outcomes. METHOD: Nine participants with OMS (one male, eight females; mean age [SD] 14y, [6y 5mo], range 12-30y) and 10 comparison individuals (three males, seven females; mean age 12y 6mo, [4y 9mo], range 10-23y) underwent magnetic resonance imaging to acquire T1-weighted structural images, diffusion-weighted images, and magnetic resonance spectroscopy scans. Neuroblastoma had been present in four participants with OMS. Voxel-based morphometry was used to determine changes in grey matter volume, tract-based spatial statistics to analyze white matter integrity, and Freesurfer to analyze cortical thickness across visual and motor cortices. RESULTS: Whole-brain analysis indicated that cerebellar grey matter was significantly reduced in the patients with OMS, particularly in the vermis and flocculonodular lobe. A region-of-interest analysis indicated significantly lower cerebellar grey matter volume, particularly in patients with the greatest OMS scores. Diffusion-weighted images did not show effects at a whole brain level, but all major cerebellar tracts showed increased mean diffusivity when analysis was restricted to the cerebellum. Cortical thickness was reduced across the motor and visual areas in the OMS group, indicating involvement beyond the cerebellum. INTERPRETATION: Across individuals with OMS, there is considerable cerebellar atrophy, particularly in the vermis and flocculonodular lobes with atrophy severity associated with persistent symptomatology. Differences in cerebral cortical thickness indicate disease effects beyond the cerebellum

Trends in inpatient care for psychiatric disorders in NHS hospitals across England, 1998/99–2019/20: an observational time series analysis

Purpose It is unclear how hospitals are responding to the mental health needs of the population in England, against a backdrop of diminishing resources. We aimed to document patterns in hospital activity by psychiatric disorder and how these have changed over the last 22 years. Methods In this observational time series analysis, we used routinely collected data on all NHS hospitals in England from 1998/99 to 2019/20. Trends in hospital admissions and bed days for psychiatric disorders were smoothed using negative binomial regression models with year as the exposure and rates (per 1000 person-years) as the outcome. When linear trends were not appropriate, we fitted segmented negative binomial regression models with one change-point. We stratified by gender and age group [children (0–14 years); adults (15 years +)]. Results Hospital admission rates and bed days for all psychiatric disorders decreased by 28.4 and 38.3%, respectively. Trends were not uniform across psychiatric disorders or age groups. Admission rates mainly decreased over time, except for anxiety and eating disorders which doubled over the 22-year period, significantly increasing by 2.9% (AAPC = 2.88; 95% CI: 2.61–3.16; p < 0.001) and 3.4% (AAPC = 3.44; 95% CI: 3.04–3.85; p < 0.001) each year. Inpatient hospital activity among children showed more increasing and pronounced trends than adults, including an increase of 212.9% for depression, despite a 63.8% reduction for adults with depression during the same period. Conclusion In the last 22 years, there have been overall reductions in hospital activity for psychiatric disorders. However, some disorders showed pronounced increases, pointing to areas of growing need for inpatient psychiatric care, especially among children

Predicting barriers to treatment for depression in a U.S. national sample: A cross-sectional, proof-of-concept study

Objective: Even though safe and effective treatments for depression are available, many individuals with a diagnosis of depression do not obtain treatment. This study aimed to develop a tool to identify persons who might not initiate treatment among those who acknowledge a need.Methods: Data were aggregated from the 2008–2014 U.S. National Survey on Drug Use and Health (N=391,753), including 20,785 adults given a diagnosis of depression by a health care provider in the 12 months before the survey. Machine learning was applied to self-report survey items to develop strategies for identifying individuals who might not get needed treatment.Results: A derivation cohort aggregated between 2008 and 2013 was used to develop a model that identified the 30.6% of individuals with depression who reported needing but not getting treatment. When applied to independent responses from the 2014 cohort, the model identified 72% of those who did not initiate treatment (p&lt;.01), with a balanced accuracy that was also significantly above chance (71%, p&lt;.01). For individuals who did not get treatment, the model predicted 10 (out of 15) reasons that they endorsed as barriers to treatment, with balanced accuracies between 53% and 65% (p&lt;.05 for all).Conclusions: Considerable work is needed to improve follow-up and retention rates after the critical initial meeting in which a patient is given a diagnosis of depression. Routinely collected information about patients with depression could identify those at risk of not obtaining needed treatment, which may inform the development and implementation of interventions to reduce the prevalence of untreated depression.</br

Recent advances in the application of predictive coding and active inference models within clinical neuroscience

Research in clinical neuroscience is founded on the idea that a better understanding of brain (dys)function will improve our ability to diagnose and treat neurological and psychiatric disorders. In recent years, neuroscience has converged on the notion that the brain is a 'prediction machine'-in that it actively predicts the sensory input that it will receive if one or another course of action is chosen. These predictions are used to select actions that will (most often, and in the long-run) maintain the body within the narrow range of physiological states consistent with survival. This insight has given rise to an area of clinical computational neuroscience research that focuses on characterizing neural circuit architectures that can accomplish these predictive functions, and on how the associated processes may break down or become aberrant within clinical conditions. Here, we provide a brief review of examples of recent work on the application of predictive processing models of brain function to study clinical (psychiatric) disorders, with the aim of highlighting current directions and their potential clinical utility. We offer examples of recent conceptual models, formal mathematical models, and applications of such models in empirical research in clinical populations, with a focus on making this material accessible to clinicians without expertise in computational neuroscience. In doing so, we aim to highlight the potential insights and opportunities that understanding the brain as a prediction machine may offer to clinical research and practice