No abstract

BACKGROUND The amygdala plays a central role in detecting and responding to fear related stimuli. A number of recent studies have reported decreased amygdala activation in schizophrenia to emotional stimuli (such as fearful faces) compared to matched neutral stimuli (such as neutral faces). Here we have investigated whether the apparent decrease in amygdala activation in schizophrenia could actually derive from increased amygdala activation to the neutral comparator stimuli.METHODS Nineteen patients with schizophrenia and 24 matched control participants viewed pictures of faces with either fearful or neutral facial expressions, and a baseline condition, during functional magnetic resonance imaging scanning.RESULTS Patients with schizophrenia showed a relative decrease in amygdala activation to fearful faces when compared to neutral faces. However this difference resulted from an increase in amygdala activation to the neutral faces in patients with schizophrenia, not from a decreased response to the fearful faces. 2. The relevance of the findings to symptoms that characterize the disorder would strengthen the paper (i.e., relate the findings to symptoms in individuals with schizophrenia in the context of theories in the literature on amygdala function). Otherwise, the findings seem overly data driven.Response: We have sought to relate the current data to two of the main theories of the pathogenesis of schizophrenia in the discussion (2, 3). The former of these theories (2) argues for inappropriate amygdala activation in schizophrenia, a view which is directly supported by our data. The second hypothesis (3) argues that individual with schizophrenia attribute increased affective salience to otherwise neutral events, providing the setting for the formation of symptoms such as delusional beliefs. We believe that the present finding of increased amygdala activation to neutral faces in schizophrenia provides a potential biological basis for such a liability to psychosis. We have attempted to re-word part of the discussion to make these links more explicit, although fuller coverage is precluded by the word limit. Fear of faces in schizophreniaHall et al looked at the response of the amygdala, a brain region mediating fear, to faces in control subjects and participants with schizophrenia. They found that control subjects show amygdala activation to fearful faces, but not neutral faces. However patients with schizophrenia activated the amygdala fear system to both neutral and fearful faces. These results suggest that people with schizophrenia may perceive neutral faces as fearful, potentially contributing to the development of psychotic symptoms. IN THIS ISSUE StatementHall J et al AbstractBackground The amygdala plays a central role in detecting and responding to fear

Inappropriate activation of the midbrain in response to neutral stimuli during conditioning is associated with the severity of delusional symptoms in patients with schizophrenia.

To examine the differences in hospital emergency psychiatric presentations for self-harm of children and adolescents during the covid-19 lockdown in March and April 2020 compared with the same period in 2019. Retrospective cohort study. We used electronic patient records from 23 hospital emergency departments in ten countries grouped into 14 areas. We examined data on 2073 acute hospital presentations by 1795 unique children and adolescents through age 18. We examined the total number of emergency psychiatric hospital presentations and the proportion of children and adolescents presenting with severe self-harm as our two main outcome measures. In addition, we examined sociodemographic and clinical characteristics and clinical management variables for those presenting with self-harm. To compare the number of hospital presentations between 2020 and 2019 a negative binomial model was used. For other variables, individual participant data (IPD) meta-analyses were carried out. Emergency psychiatric hospital presentations decreased from 1239 in 2019 to 834 in 2020, incident rate ratio 0.67, 95% CI 0.62–0.73; p < 0.001. The proportion of children and adolescents presenting with self-harm increased from 50% in 2019 to 57% in 2020, odds ratio 1.33, 1.07–1.64; p = 0.009 but there was no difference in the proportion presenting with severe self-harm. Within the subpopulation presenting with self-harm the proportion of children and adolescents presenting with emotional disorders increased from 58 to 66%, odds ratio 1.58, 1.06–2.36; p = 0.025. The proportion of children and adolescents admitted to an observation ward also decreased from 13 to 9% in 2020, odds ratio 0.52, 0.28–0.96; p = 0.036. Service planners should consider that, during a lockdown, there are likely to be fewer emergency psychiatric presentations. Many children and adolescents with psychiatric emergencies might not receive any service. A focus on developing intensive community care services with outreach capabilities should be prioritised.

Computational modelling has been used to address: (1) the variety of symptoms observed in schizophrenia using abstract models of behavior (e.g. Bayesian models - top-down descriptive models of psychopathology); (2) the causes of these symptoms using biologically realistic models involving abnormal neuromodulation and/or receptor imbalance (e.g. connectionist and neural networks - bottom-up realistic models of neural processes). These different levels of analysis have been used to answer different questions (i.e. understanding behavioral vs. neurobiological anomalies) about the nature of the disorder. As such, these computational studies have mostly supported diverging hypotheses of schizophrenia's pathophysiology, resulting in a literature that is not always expanding coherently. Some of these hypotheses are however ripe for revision using novel empirical evidence. Here we present a review that first synthesizes the literature of computational modelling for schizophrenia and psychotic symptoms into categories supporting the dopamine, glutamate, GABA, dysconnection and Bayesian inference hypotheses respectively. Secondly, we compare model predictions against the accumulated empirical evidence and finally we identify specific hypotheses that have been left relatively under-investigated.