The coronavirus disease 2019 (COVID-19) pandemic is having a profound effect on all aspects of society, including mental health and physical health. We explore the psychological, social, and neuroscientific effects of COVID-19 and set out the immediate priorities and longer-term strategies for mental health science research. These priorities were informed by surveys of the public and an expert panel convened by the UK Academy of Medical Sciences and the mental health research charity, MQ: Transforming Mental Health, in the first weeks of the pandemic in the UK in March, 2020. We urge UK research funding agencies to work with researchers, people with lived experience, and others to establish a high level coordination group to ensure that these research priorities are addressed, and to allow new ones to be identified over time. The need to maintain high-quality research standards is imperative. International collaboration and a global perspective will be beneficial. An immediate priority is collecting high-quality data on the mental health effects of the COVID-19 pandemic across the whole population and vulnerable groups, and on brain function, cognition, and mental health of patients with COVID-19. There is an urgent need for research to address how mental health consequences for vulnerable groups can be mitigated under pandemic conditions, and on the impact of repeated media consumption and health messaging around COVID-19. Discovery, evaluation, and refinement of mechanistically driven interventions to address the psychological, social, and neuroscientific aspects of the pandemic are required. Rising to this challenge will require integration across disciplines and sectors, and should be done together with people with lived experience. New funding will be required to meet these priorities, and it can be efficiently leveraged by the UK's world-leading infrastructure. This Position Paper provides a strategy that may be both adapted for, and integrated with, research efforts in other countries.

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These findings suggest that there is reduced frontal cortical glial cell density and neuronal size in major depressive disorder.

Reductions in glial cell density and neuronal size have been described recently in major depressive disorder (MDD). Considering the important trophic influence of glia on neurons, we hypothesized that this glial cell deficit is more prominent close to neurons. In this investigation we have characterized neuronal and glia cytoarchitecture in prefrontal area 9 using spatial point pattern techniques and two-dimensional measures of cell size and density. In post-mortem brain tissue of subjects with MDD, schizophrenia, bipolar disorder (BPD), and normal controls (15 subjects per group), we examined the laminar location and size of all neurons and glial nuclei in a 500 microm wide strip of cortex extending from the pia to the grey-white matter border. In MDD, we observed reductions in glial cell density (30%; P = 0.007) in layer 5 and neuronal size (20%; P = 0.003) in layer 6. We also found that glial cell density (34%; P = 0.003) was reduced in layer 5 in schizophrenia, while neuronal size was reduced in layers 5 (14%) (P = 0.006) and 6 (18%; P = 0.007) in BPD. The spatial pattern investigation of neurons and glia demonstrated no alteration in the clustering of glia about neurons between control and patient groups. These findings confirm that glial cell loss and neuronal size reductions occur in the deeper cortical layers in MDD, but provide no support for the hypothesis that an altered spatial distribution of glia about neurons plays a role in the development of these changes.

Microarray techniques hold great promise for identifying risk factors for schizophrenia (SZ) buthave not yet generated widely reproducible results due to methodological differences between studies and the high risk of type I inferential errors. Here we established a protocol for conservative analysis and interpretation of gene expression data from the dorsolateral prefrontal cortex of SZ patients using statistical and bioinformatic methods that limit false positives. We also compared brain gene expression profiles with those from peripheral blood cells of a separate sample of SZ patients to identify disease-associated genes that generalize across tissues and populations and further substantiate the use of gene expression profiling of blood for detecting valid SZ biomarkers. Implementing this systematic approach, we: (i) discovered 177 putative SZ risk genes in brain, 28 of which map to linked chromosomal loci; (ii) delineated six biological processes and 12 molecular functions that may be particularly disrupted in the illness; (iii) identified 123 putative SZ biomarkers in blood, 6 of which (BTG1, GSK3A, HLA-DRB1, HNRPA3, SELENBP1, and SFRS1) had corresponding differential expression in brain; (iv) verified the differential expression of the strongest candidate SZ biomarker (SELENBP1) in blood; and (v) demonstrated neuronal and glial expression of SELENBP1 protein in brain. The continued application of this approach in other brain regions and populations should facilitate the discovery of highly reliable and reproducible candidate risk genes and biomarkers for SZ. The identification of valid peripheral biomarkers for SZ may ultimately facilitate early identification, intervention, and prevention efforts as well.microarray ͉ ontology S chizophrenia (SZ) has a substantial genetic basis (1), but its biological underpinnings remain largely unknown. Early attempts to profile the expression of specific neurochemicals in blood and postmortem brain detected several promising candidate risk factors for SZ (2, 3) that ultimately could not be substantiated (4, 5). Subsequent progress in mapping the human genome increased the viability of candidate gene association studies, which have since proliferated (6). Most candidate genes have been targeted based on their expression within systems widely implicated in the disorder (e.g., dopamine and glutamate neurotransmitter systems), and this approach is essential for clarifying the nature of dysfunction within these recognized candidate pathways; however, it may not be optimal for identifying additional novel risk factors outside of these systems.The advent of microarrays that can survey the entire expressed human genome has made it possible to simultaneously investigate the roles of several thousand genes in a disorder. Relative to traditional candidate gene studies predicated on existing disease models, microarray analysis is a less-constrained strategy that could foster the discovery of novel risk genes that otherwise would not come under study. Because gene expression can ref...

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