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Abstract Several multivariate prognostic models have been published to predict outcomes in patients with first episode psychosis (FEP), but it remains unclear whether those predictions generalize to independent populations. Using a subset of demographic and clinical baseline predictors, we aimed to develop and externally validate different models predicting functional outcome after a FEP in the context of a schizophrenia-spectrum disorder (FES), based on a previously published cross-validation and machine learning pipeline. A crossover validation approach was adopted in two large, international cohorts (EUFEST, n = 338, and the PSYSCAN FES cohort, n = 226). Scores on the Global Assessment of Functioning scale (GAF) at 12 month follow-up were dichotomized to differentiate between poor (GAF current

Food insecurity is a complex societal problem that disproportionately impacts households with children and those led by minoritized populations, with negative impacts on health across the life course. System to Achieve Food Equity adapted the learning systems model, used to address similarly complex problems, to tackle food insecurity at a neighborhood level. SAFE, born out of the COVID-19 pandemic, leverages a family-centered, community-based, cross-sector network fundamentally aimed at changing the food system so that all children in Cincinnati have the food they need to thrive. Through the following principles, Community-Led Network, Co-Production with Community, Equitable Sustainability, Learning to Learn Together, Distributing Leadership and Power, and Shared Data and Governance, SAFE has grown to over 300 individuals and 100 organizations, funded 9 novel interventions, distributed over 270,000 meals, and created a collaborative of motivated like-minded stakeholders. Future work includes improved data collection and sharing, support for increased stakeholder engagement and greater distribution of leadership and power, advocacy for policy change, refining measurement tools of network maturity for community settings, and collaboration with other efforts that contribute to food security indirectly.

Abstract Background The study of biological age acceleration may help identify at-risk individuals and reduce the rising global burden of age-related diseases. Using DNA methylation (DNAm) clocks, we investigated biological aging in schizophrenia (SCZ), a mental illness that is associated with an increased prevalence of age-related disabilities and morbidities. In a whole blood DNAm sample of 1090 SCZ cases and 1206 controls across four European cohorts, we performed a meta-analysis of differential aging using three DNAm clocks (i.e., Hannum, Horvath, and Levine). To dissect how DNAm aging contributes to SCZ, we integrated information on duration of illness and SCZ polygenic risk, as well as stratified our analyses by chronological age and biological sex. Results We found that blood-based DNAm aging is significantly altered in SCZ independent from duration of the illness since onset. We observed sex-specific and nonlinear age effects that differed between clocks and point to possible distinct age windows of altered aging in SCZ. Most notably, intrinsic cellular age (Horvath clock) is decelerated in SCZ cases in young adulthood, while phenotypic age (Levine clock) is accelerated in later adulthood compared to controls. Accelerated phenotypic aging was most pronounced in women with SCZ carrying a high polygenic burden with an age acceleration of + 3.82 years (CI 2.02–5.61, P = 1.1E−03). Phenotypic aging and SCZ polygenic risk contributed additively to the illness and together explained up to 14.38% of the variance in disease status. Conclusions Our study contributes to the growing body of evidence of altered DNAm aging in SCZ and points to intrinsic age deceleration in younger adulthood and phenotypic age acceleration in later adulthood in SCZ. Since increased phenotypic age is associated with increased risk of all-cause mortality, our findings indicate that specific and identifiable patient groups are at increased mortality risk as measured by the Levine clock. Our study did not find that DNAm aging could be explained by the duration of illness of patients, but we did observe age- and sex-specific effects that warrant further investigation. Finally, our results show that combining genetic and epigenetic predictors can improve predictions of disease outcomes and may help with disease management in schizophrenia.

Abstract There is currently no quantifiable method to predict long-term clinical outcomes in patients presenting with a first episode of psychosis. A major barrier to developing useful markers for this is biological heterogeneity, where many different pathological mechanisms may underly the same set of symptoms in different individuals. Normative modelling has been used to quantify this heterogeneity in established psychotic disorders by identifying regions of the cortex which are thinner than expected based on a normative healthy population range. These brain atypicalities are measured at the individual level and therefore potentially useful in a clinical setting. However, it is still unclear whether alterations in individual brain structure can be detected at the time of the first psychotic episode, and whether they are associated with subsequent clinical outcomes. We applied normative modelling of cortical thickness to a sample of first-episode psychosis patients, with the aim of quantifying heterogeneity and to use any pattern of cortical atypicality to predict symptoms and response to antipsychotic medication at timepoints from baseline up to 95 weeks (median follow-ups = 4). T1-weighted brain magnetic resonance images from the GAP and OPTiMiSE samples were processed with Freesurfer V6.0.0 yielding 148 cortical thickness features. An existing normative model of cortical thickness (n = 37,126) was adapted to integrate data from each clinical site and account for effects of gender and site. Our test sample consisted of control participants (n = 149, mean age = 26, SD = 6.7) and patient data (n = 295, mean age = 26, SD = 6.7), this sample was used for estimating deviations from the normative model and subsequent statistical analysis. For each individual, the 148 cortical thickness features were mapped to centiles of the normative distribution and converted to z-scores reflecting the distance from the population mean. Individual cortical thickness metrics of +/– 2.6 standard deviations from the mean were considered extreme deviations from the norm. We found that no more than 6.4% of psychosis patients had extreme deviations in a single brain region (regional overlap) demonstrating a high degree of heterogeneity. Mann-Whitney U tests were run on z-scores for each region and significantly lower z-scores were observed in FEP patients in the frontal, temporal, parietal and occipital lobes. Finally, linear mixed-effects modelling showed that negative deviations in cortical thickness in parietal and temporal regions at baseline are related to more severe negative symptoms over the medium-term. This study shows that even at the early stage of symptom onset normative modelling provides a framework to identify individualised cortical markers which can be used for early personalised intervention and stratification.

There have been active efforts in the U.S., Europe, and Japan on the design of a neutrino factory. This type of facility produces intense beams of neutrinos from the decay of muons in a high-energy storage ring. In the U.S., a second detailed feasibility study (FS2) for a neutrino factory was completed in 2001. Since that report was published, new ideas in bunching, cooling, and acceleration of muon beams have been developed. We have incorporated these ideas into a new facility design, which we designate as study 2B (ST2B), that should lead to significant cost savings over the FS2 design.

IntroductionPsychiatric comorbidities have a significant impact on the course of illness in patients with schizophrenia spectrum disorders. To accurately predict outcomes for individual patients using computerized prognostic models, it is essential to consider these comorbidities and their influence.MethodsIn our study, we utilized a multi-modal deep learning architecture to forecast symptomatic remission, focusing on a multicenter sample of patients with first-episode psychosis from the OPTiMiSE study. Additionally, we introduced a counterfactual model explanation technique to examine how scores on the Mini International Neuropsychiatric Interview (MINI) affected the likelihood of remission, both at the group level and for individual patients.ResultsOur findings at the group level revealed that most comorbidities had a negative association with remission. Among them, current and recurrent depressive disorders consistently exerted the greatest negative impact on the probability of remission across patients. However, we made an interesting observation: current suicidality within the past month and substance abuse within the past 12 months were associated with an increased chance of remission in patients. We found a high degree of variability among patients at the individual level. Through hierarchical clustering analysis, we identified two subgroups: one in which comorbidities had a relatively limited effect on remission (approximately 45% of patients), and another in which comorbidities more strongly influenced remission. By incorporating comorbidities into individualized prognostic prediction models, we determined which specific comorbidities had the greatest impact on remission at both the group level and for individual patients.DiscussionThese results highlight the importance of identifying and including relevant comorbidities in prediction models, providing valuable insights for improving the treatment and prognosis of patients with psychotic disorders. Furthermore, they open avenues for further research into the efficacy of treating these comorbidities to enhance overall patient outcomes.

Abstract A significant proportion of the personal and economic burden of schizophrenia can be attributed to the late diagnosis or misdiagnosis of the disorder. A novel, objective diagnostic approaches could facilitate the early detection and treatment of schizophrenia and improve patient outcomes. In the present study, we aimed to identify robust schizophrenia-specific blood biomarkers, with the goal of developing an accurate diagnostic model. The levels of selected serum and peripheral blood mononuclear cell (PBMC) markers relevant to metabolic and immune function were measured in healthy controls (n = 26) and recent-onset schizophrenia patients (n = 36) using multiplexed immunoassays and flow cytometry. Analysis of covariance revealed significant upregulation of insulin receptor (IR) and fatty acid translocase (CD36) levels in T helper cells (F = 10.75, P = 0.002, Q = 0.024 and F = 21.58, P = 2.8 × 10−5, Q = 0.0004, respectively), as well as downregulation of glucose transporter 1 (GLUT1) expression in monocytes (F = 21.46, P = 2.9 × 10−5, Q = 0.0004). The most robust predictors, monocyte GLUT1 and T helper cell CD36, were used to develop a diagnostic model, which showed a leave-one-out cross-validated area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI: 0.66–0.92). The diagnostic model was validated in two independent datasets. The model was able to distinguish first-onset, drug-naïve schizophrenia patients (n = 34) from healthy controls (n = 39) with an AUC of 0.75 (95% CI: 0.64–0.86), and also differentiated schizophrenia patients (n = 22) from patients with other neuropsychiatric conditions, including bipolar disorder, major depressive disorder and autism spectrum disorder (n = 68), with an AUC of 0.83 (95% CI: 0.75–0.92). These findings indicate that PBMC-derived biomarkers have the potential to support an accurate and objective differential diagnosis of schizophrenia.

Summary: Genetics have nominated many schizophrenia risk genes and identified convergent signals between schizophrenia and neurodevelopmental disorders. However, functional interpretation of the nominated genes in the relevant brain cell types is often lacking. We executed interaction proteomics for six schizophrenia risk genes that have also been implicated in neurodevelopment in human induced cortical neurons. The resulting protein network is enriched for common variant risk of schizophrenia in Europeans and East Asians, is down-regulated in layer 5/6 cortical neurons of individuals affected by schizophrenia, and can complement fine-mapping and eQTL data to prioritize additional genes in GWAS loci. A sub-network centered on HCN1 is enriched for common variant risk and contains proteins (HCN4 and AKAP11) enriched for rare protein-truncating mutations in individuals with schizophrenia and bipolar disorder. Our findings showcase brain cell-type-specific interactomes as an organizing framework to facilitate interpretation of genetic and transcriptomic data in schizophrenia and its related disorders.

Background: Structural magnetic resonance imaging studies in individuals at clinical high risk (CHR) for psychosis have yielded conflicting results. Methods: The aims of this study were to compare intracranial and structural brain volumes and variability of CHR individuals with those of healthy control (HC) subjects and to investigate brain volume differences and variability in CHR subjects with and without transition to psychosis. The PubMed and Embase databases were searched for relevant studies published before June 1, 2020. Results: A total of 34 studies were deemed eligible, which included baseline data of 2111 CHR and 1472 HC participants. In addition, data were included for 401 CHR subjects who subsequently transitioned to psychosis and 1023 nontransitioned CHR participants. Whole-brain and left, right, and bilateral hippocampal volume were significantly smaller in CHR subjects than in HC subjects. Cerebrospinal fluid and lateral ventricle volumes were significantly larger in CHR subjects than in HC subjects. Variability was not significantly different in CHR subjects compared with HC subjects. CHR individuals with and without subsequent transition to psychosis did not show significant differences in any of the volumetric assessments or in variability. Conclusions: This meta-analysis demonstrates reduced whole-brain and hippocampal volumes and increased cerebrospinal fluid and lateral ventricle volumes in CHR individuals. However, no significant differences were observed in any of the volumetric assessments between CHR individuals with and without subsequent transition to psychosis. These findings suggest that although structural brain alterations are present before the onset of the disorder, they may not significantly contribute to the identification of CHR individuals at the highest risk for the development of psychosis.

Digital immunoassays are powerful platforms but are complex and require multiple steps to perform. Here the authors show that digital plasmonic nanobubble detection allows rapid and ultrasensitive diagnostics via a one-step homogeneous immunoassay.

Abstract Multiple lines of evidence indicate that immunological and inflammatory alterations contribute at least in a subgroup to the pathophysiology of schizophrenia. In this retrospective chart review, we investigated whether clinical factors contribute to altered cerebrospinal fluid (CSF) findings in schizophrenia-spectrum disorders. Clinical data from electronic medical records of patients with psychotic disorders (ICD-10: F20-F29) who received routine CSF diagnostics at the Department of Psychiatry and Psychotherapy, LMU Munich, Germany, were included. Chi² tests for dichotomous outcomes and independent t tests for continuous outcomes were used to compare differences between groups. A total of 331 patients were included in the analyses (43.2% female and 56.8% male). The mean age was 37.67 years (±15.58). The mean duration of illness was 71.96 months (±102.59). In all, 40% (128/320) were first-episode psychosis (FEP) patients and 60% (192/320) were multi-episode psychosis (MEP) patients. Elevated CSF protein levels were found in 19.8% and elevated CSF/serum albumin ratios (QAlb) in 29.4% of the cases. Pleocytosis was found in 6.1% of patients. MEP patients showed significantly higher mean QAlb compared with FEP patients (t (304.57) = −2.75, p = 0.006), which did not remain significant after correcting for age. QAlb elevation occurred more frequently in men (X 2 (1) = 14.76, p =

Abstract Therapeutic interventions targeting viral infections remain a significant challenge for both the medical and scientific communities. While specific antiviral agents have shown success as therapeutics, viral resistance inevitably develops, making many of these approaches ineffective. This inescapable obstacle warrants alternative approaches, such as the targeting of host cellular factors. Respiratory syncytial virus (RSV), the major respiratory pathogen of infants and children worldwide, causes respiratory tract infection ranging from mild upper respiratory tract symptoms to severe life-threatening lower respiratory tract disease. Despite the fact that the molecular biology of the virus, which was originally discovered in 1956, is well described, there is no vaccine or effective antiviral treatment against RSV infection. Here, we demonstrate that targeting host factors, specifically, mTOR signaling, reduces RSV protein production and generation of infectious progeny virus. Further, we show that this approach can be generalizable as inhibition of mTOR kinases reduces coronavirus gene expression, mRNA transcription and protein production. Overall, defining virus replication-dependent host functions may be an effective means to combat viral infections, particularly in the absence of antiviral drugs.

Immune dysregulation has been reported in schizophrenia spectrum disorders (SSD). In the past decade, several trials using anti-inflammatory agents for treatment of SSD have been completed, with so far limited success. One such anti-inflammatory agent used is simvastatin. A recent, large-scale, randomized controlled trial with simvastatin augmentation failed to show improvement in the predefined primary outcome. However, baseline inflammatory profiles were not taken into account. Here we employed a data-driven clustering approach to investigate whether patients with an inflammatory monocyte gene signature respond better to add-on simvastatin treatment than those without such a signature, over a treatment period of 2 years. In 61 patients (60 randomized, 1:1 placebo:simvastatin) and healthy controls, a previously validated monocyte gene expression signature was assessed using quantitative polymerase chain reaction. Resulting delta cycle threshold values were used to identify patient clusters. Two major patient clusters with either up- or downregulated pro-inflammatory factors were detected. Linear mixed models showed a significant three-way interaction between the inflammatory cluster, treatment, and time for psychotic symptoms. Only patients treated with simvastatin who were in the inflammatory group, showed a consistent improvement: symptom severity gradually decreased after 3 months and reached significance after 12 and 24 months compared to baseline (p.adj

Objective: Social dysfunction is a major feature of clinical-high-risk states for psychosis (CHR-P). Prior research has identified a neuroanatomical pattern associated with impaired social function outcome in CHR-P. The aim of the current study was to test whether social dysfunction in CHR-P is neurobiologically distinct or in a continuum with the lower end of the normal distribution of individual differences in social functioning. Methods: We used a machine learning classifier to test for the presence of a previously validated brain structural pattern associated with impaired social outcome in CHR-P (CHR-outcome-neurosignature) in the neuroimaging profiles of individuals from two non-clinical samples (total n = 1763) and examined its association with social function, psychopathology and cognition. Results: Although the CHR-outcome-neurosignature could be detected in a subset of the non-clinical samples, it was not associated was adverse social outcomes or higher psychopathology levels. However, participants whose neuroanatomical profiles were highly aligned with the CHR-outcome-neurosignature manifested subtle disadvantage in fluid (PFDR = 0.004) and crystallized intelligence (PFDR = 0.01), cognitive flexibility (PFDR = 0.02), inhibitory control (PFDR = 0.01), working memory (PFDR = 0.0005), and processing speed (PFDR = 0.04). Conclusions: We provide evidence of divergence in brain structural underpinnings of social dysfunction derived from a psychosis-risk enriched population when applied to non-clinical samples. This approach appears promising in identifying brain mechanisms bound to psychosis through comparisons of patient populations to non-clinical samples with the same neuroanatomical profiles.

Introduction: Medications for opioid use disorder (MOUD), including buprenorphine, represent an evidence-based treatment that supports long-term recovery and reduces risk of overdose death. Patients in crisis from opioid use disorder (OUD) often seek care from emergency departments (ED). The New York Medication for Addiction Treatment and Electronic Referrals (MATTERS) network is designed to support ED-initiated buprenorphine and urgent referrals to long-term care for patients suffering from OUD. Methods: Using the PRECEDE-PROCEED implementation science framework, we provide an overview of the creation of the MATTERS network in Western New York. We also include an explanation of how the network was designed and launched as a response to the opioid epidemic. Finally, we analyzed the program’s outputs and outcomes, thus far, as it continues to grow across the state. Results: The New York MATTERS network was created and implemented in 2019 with a single hospital referring patients with OUD to three local clinics. In the social assessment and situational analysis phase, we describe the opioid epidemic and available resources in the region at the outset of the program. In the epidemiological assessment phase, we quantify the epidemic on the state and regional levels. In the educational and ecological assessment, we review local ED practices and resources. In the administrative and policy assessment and intervention alignment phase, the program’s unique framework is reviewed. In the piloting phase, we describe the initial deployment of New York MATTERS. Finally, in the process evaluation phase, we depict the early lessons we learned. By the beginning of 2021, the New York MATTERS network included 35 hospitals that refer to 47 clinics throughout New York State. Conclusion: The New York MATTERS network provides a structured approach to reduce barriers to ED-initiated buprenorphine and urgent referral to long-term care. An implementation framework provides a structured means of evaluating this best practice model.

Summary: Mapping genetic variants that regulate gene expression (eQTL mapping) in large-scale RNA sequencing (RNA-seq) studies is often employed to understand functional consequences of regulatory variants. However, the high cost of RNA-seq limits sample size, sequencing depth, and, therefore, discovery power in eQTL studies. In this work, we demonstrate that, given a fixed budget, eQTL discovery power can be increased by lowering the sequencing depth per sample and increasing the number of individuals sequenced in the assay. We perform RNA-seq of whole-blood tissue across 1,490 individuals at low coverage (5.9 million reads/sample) and show that the effective power is higher than that of an RNA-seq study of 570 individuals at moderate coverage (13.9 million reads/sample). Next, we leverage synthetic datasets derived from real RNA-seq data (50 million reads/sample) to explore the interplay of coverage and number individuals in eQTL studies, and show that a 10-fold reduction in coverage leads to only a 2.5-fold reduction in statistical power to identify eQTLs. Our work suggests that lowering coverage while increasing the number of individuals in RNA-seq is an effective approach to increase discovery power in eQTL studies.

BackgroundAccelerated aging has been proposed as a mechanism underlying the clinical and cognitive presentation of schizophrenia. The current study extends the field by examining both global and regional patterns of brain aging in schizophrenia, as inferred from brain structural data, and their association with cognitive and psychotic symptoms.MethodsGlobal and local brain-age-gap-estimates (G-brainAGE and L-brainAGE) were computed using a U-Net Model from T1-weighted structural neuroimaging data from 84 patients (aged 16–35 years) with early-stage schizophrenia (illness duration

Interleukin-2 can induce an anti-tumour response, but is associated with toxicity. Here, the authors demonstrate that an engineered interleukin-2 promotes intratumoral T regulatory cell depletion while enhancing effective anti-tumour CD8+ T cell responses that result in potent tumor suppression.

Abstract Background DNA methylation has recently been identified as a mediator between in utero famine exposure and a range of metabolic and psychiatric traits. However, genome-wide analyses are scarce and cross-sectional analyses are hampered by many potential confounding factors. Moreover, causal relations are hard to identify due to the lack of controlled experimental designs. In the current study, we therefore combined a comprehensive assessment of genome-wide DNA methylation differences in people exposed to the great Chinese famine in utero with an in vitro study in which we deprived fibroblasts of nutrition. Methods We compared whole blood DNA methylation differences between 25 individuals in utero exposed to famine and 54 healthy control individuals using the HumanMethylation450 platform. In vitro, we analyzed DNA methylation changes in 10 fibroblast cultures that were nutritionally deprived for 72 h by withholding fetal bovine serum. Results We identified three differentially methylated regions (DMRs) in four genes (ENO2, ZNF226, CCDC51, and TMA7) that were related to famine exposure in both analyses. Pathway analysis with data from both Chinese famine samples and fibroblasts highlighted the nervous system and neurogenesis pathways as the most affected by nutritional deprivation. Conclusions The combination of cross-sectional and experimental data provides indications that biological adaptation to famine leads to DNA methylation changes in genes involved in the central nervous system.

We performed a systematic analysis of blood DNA methylation profiles from 4483 participants from seven independent cohorts identifying differentially methylated positions (DMPs) associated with psychosis, schizophrenia, and treatment-resistant schizophrenia. Psychosis cases were characterized by significant differences in measures of blood cell proportions and elevated smoking exposure derived from the DNA methylation data, with the largest differences seen in treatment-resistant schizophrenia patients. We implemented a stringent pipeline to meta-analyze epigenome-wide association study (EWAS) results across datasets, identifying 95 DMPs associated with psychosis and 1048 DMPs associated with schizophrenia, with evidence of colocalization to regions nominated by genetic association studies of disease. Many schizophrenia-associated DNA methylation differences were only present in patients with treatment-resistant schizophrenia, potentially reflecting exposure to the atypical antipsychotic clozapine. Our results highlight how DNA methylation data can be leveraged to identify physiological (e.g., differential cell counts) and environmental (e.g., smoking) factors associated with psychosis and molecular biomarkers of treatment-resistant schizophrenia.

Rapid inhibition or modification of actions is a crucial cognitive ability, which is impaired in persons with schizophrenia (SZP). Primate neurophysiology studies have identified a network of brain regions that subserves control over gaze. Here, we examine effective connectivity within this oculomotor control network in SZP and healthy controls (HC). During fMRI, participants performed a stop-signal task variant in which they were instructed to saccade to a visual target (no-step trials) unless a second target appeared (redirect trials); on redirect trials, participants were instructed to inhibit the planned saccade and redirect to the new target. We compared functional responses on redirect trials to no-step trials and used dynamic causal modelling (DCM) to examine group differences in network effective connectivity. Behaviorally, SZP were less efficient at inhibiting, which was related to their employment status. Compared to HC, they showed a smaller difference in activity between redirect trials and no-step trials in frontal eye fields (FEF), supplementary eye fields (SEF), inferior frontal cortex (IFC), thalamus, and caudate. DCM analyses revealed widespread group differences in effective connectivity across the task, including different patterns of self-inhibition in many nodes in SZP. Group differences in how effective connectivity was modulated on redirect trials revealed differences between the FEF and SEF, between the SEF and IFC, between the superior colliculus and the thalamus, and self-inhibition within the FEF and caudate. These results provide insight into the neural mechanisms of inefficient inhibitory control in individuals with schizophrenia.

AbstractBackground.Studying offspring of schizophrenia (SZo) and bipolar disorder patients (BDo) provides important information on the putative neurodevelopmental trajectories underlying development toward severe mental illnesses. We compared intracranial volume (ICV), as a marker for neurodevelopment, and global and local brain measures between SZo or BDo and control offspring (Co) in relation to IQ and psychopathology.Methods.T1-weighted magnetic resonance imaging (MRI) brain scans were obtained from 146 participants (8–19 years; 40 SZo, 66 BDo, 40 Co). Linear mixed models were applied to compare ICV, global, and local brain measures between groups. To investigate the effect of ICV, IQ (four subtests Wechsler Intelligence Scale for Children/Wechsler Adult Intelligence Scale-III) or presence of psychopathology these variables were each added to the model.Results.SZo and BDo had significantly lower IQ and more often met criteria for a lifetime psychiatric disorder than Co. ICV was significantly smaller in SZo than in BDo (d = −0.56) and Co (d = −0.59), which was largely independent of IQ (respectively, d = −0.54 and d = −0.35). After ICV correction, the cortex was significantly thinner in SZo than in BDo (d = −0.42) and Co (d = −0.75) and lateral ventricles were larger in BDo than in Co (d = 0.55). Correction for IQ or lifetime psychiatric diagnosis did not change these findings.Conclusions.Despite sharing a lower IQ and a higher prevalence of psychiatric disorders, brain abnormalities in BDo appear less pronounced (but are not absent) than in SZo. Lower ICV in SZo implies that familial risk for schizophrenia has a stronger association with stunted early brain development than familial risk for bipolar disorder.

Schizophrenia has long been framed as a disorder of altered brain connectivity, with dysfunction in thalamocortical circuity potentially playing a key role in the development of the illness phenotype, including psychotic symptomatology and cognitive impairments. There is emerging evidence for functional and structural hypoconnectivity between thalamus and prefrontal cortex in persons with schizophrenia spectrum disorders, as well as hyperconnectivity between thalamus and sensory and motor cortices. However, it is unclear whether thalamocortical dysconnectivity is a general marker of vulnerability to schizophrenia or a specific mechanism of schizophrenia pathophysiology. This study aimed to answer this question by using diffusion-weighted imaging to examine thalamocortical structural connectivity in 22 persons with schizophrenia or schizoaffective disorder (SZ), 20 siblings of individuals with a schizophrenia spectrum disorder (SIB), and 44 healthy controls (HC) of either sex. Probabilistic tractography was used to quantify structural connectivity between thalamus and six cortical regions of interest. Thalamocortical structural connectivity was compared among the three groups using cross-thalamic and voxel-wise approaches. Thalamo-prefrontal structural connectivity was reduced in both SZ and SIB relative to HC, while SZ and SIB did not differ from each other. Thalamo-motor structural connectivity was increased in SZ relative to SIB and HC, while SIB and HC did not differ from each other. Hemispheric differences also emerged in thalamic connectivity with motor, posterior parietal, and temporal cortices across all groups. The results support the hypothesis that altered thalamo-prefrontal structural connectivity is a general marker of vulnerability to schizophrenia, whereas altered connectivity between thalamus and motor cortex is related to illness expression or illness-related secondary factors.

Brain organoid models reported to date lack cells of mesodermal origin, such as microglia. Here, the authors demonstrate that mature microglia-like cells are generated within their cerebral organoid model, providing new avenues for studying human microglia in a three-dimensional brain environment.

Abstract The role of the human microbiome in health and disease is increasingly appreciated. We studied the composition of microbial communities present in blood across 192 individuals, including healthy controls and patients with three disorders affecting the brain: schizophrenia, amyotrophic lateral sclerosis, and bipolar disorder. By using high-quality unmapped RNA sequencing reads as candidate microbial reads, we performed profiling of microbial transcripts detected in whole blood. We were able to detect a wide range of bacterial and archaeal phyla in blood. Interestingly, we observed an increased microbial diversity in schizophrenia patients compared to the three other groups. We replicated this finding in an independent schizophrenia case–control cohort. This increased diversity is inversely correlated with estimated cell abundance of a subpopulation of CD8+ memory T cells in healthy controls, supporting a link between microbial products found in blood, immunity and schizophrenia.

The ability to rapidly stop or change a planned action is a critical cognitive process that is impaired in schizophrenia. The current study aimed to examine whether this impairment reflects familial vulnerability to schizophrenia across two experiments comparing unaffected first-degree relatives to healthy controls. First, we examined performance on a saccadic stop-signal task that required rapid inhibition of an eye movement. Then, in a different sample, we investigated behavioral and neural responses (using fMRI) during a stop-signal task variant that required rapid modification of a prepared eye movement. Here, we examined differences between relatives and healthy controls in terms of activation and effective connectivity within an oculomotor control network during task performance. Like individuals with schizophrenia, the unaffected relatives showed behavioral evidence for more inefficient inhibitory processes. Unlike previous findings in individuals with schizophrenia, however, the relatives showed evidence for a compensatory waiting strategy. Behavioral differences were accompanied by more activation among the relatives in task-relevant regions across conditions and group differences in effective connectivity across the task that were modulated differently by the instruction to exert control over a planned saccade. Effective connectivity parameters were related to behavioral measures of inhibition efficiency. The results suggest that individuals at familial risk for schizophrenia were engaging an oculomotor control network differently than controls and in a way that compromises inhibition efficiency.

Background: The lack of SARS-CoV-2 antigen surveillance testing in the pediatric population has inhibited accurate infection and hospitalization prevalence estimates. We aim to report the estimated prevalence of and risk factors for COVID-19 infection, hospitalization, and intensive care unit (ICU) admission across the three United States (US) waves in one of the largest pediatric healthcare systems in the nation. Methods: Retrospective electronic health record (EHR) review of all COVID-19 surveillance data among children aged 0–19 years seeking healthcare at one pediatric healthcare system that serves predominantly Medicaid-dependent families from 1 March 2020 to 31 March 2021. COVID-19 infection status (Y/N), hospital admission (Y/N), and ICU admission (Y/N) are the main outcomes. Results: Of 22,377 children aged ≤ 19 years tested for SARS-CoV-2 infection from March 2020–March 2021, 3126 were positive (14.0%), and out of those positive, 53.7% were hospitalized and 2.9% were admitted to the ICU. Compared to Wave 1 (1 March 2020–31 May 2020), the risk of a positive test increased from 16% (RR 1.16, 95% CI, 1.07–1.26) in Wave 2 (1 June 2020–31 October 2020) to 33% (RR 1.33, 95% CI, 1.23–1.44) in Wave 3 (1 November 2020–31 March 2021). Similarly, compared to Wave 1, the risk for hospitalization increased 86% (RR 1.86, 95% CI, 1.86–2.06) in Wave 2 and 89% in Wave 3 (RR 1.89, 95% CI, 1.70–2.08), and the risk for ICU admission increased from 10% in Wave 2 (RR 1.10, 95% CI, 0.39–3.01) to 310% in Wave 3 (RR 3.10, 95% CI, 1.21–7.80). Children with asthma, depressive disorders, type 1 or 2 diabetes, and anemia were more likely to be hospitalized while children with diabetes, obesity, cardiac malformations, and hypertension were more likely to be admitted to the ICU versus children without these conditions. Conclusions: Children were cumulatively impacted by the COVID-19 pandemic through the three US waves with more than a third hospitalized in Wave 3. Children with underlying health conditions were particularly at risk for severe illness and should be monitored for any long-term impacts.

Relatives of patients with amyotrophic lateral sclerosis have an unexpectedly high incidence of schizophrenia. Here, the authors show a genetic link between the two conditions, suggesting shared neurobiological mechanisms.

The hippocampus in mammalian brain varies in size across individuals. Here, Hibar and colleagues perform a genome-wide association meta-analysis to find six genetic loci with significant association to hippocampus volume.