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Patients with major depressive disorder (MDD) exhibit concurrent deficits in both sensory and higher-order cognitive processing. Connectome studies have suggested a principal primary-to-transmodal gradient in functional brain networks, supporting the spectrum from sensation to cognition. However, whether this gradient structure is disrupted in patients with MDD and how this disruption associates with gene expression profiles and treatment outcome remain unknown. Using a large cohort of resting-state fMRI data from 2227 participants (1148 MDD patients and 1079 healthy controls) recruited at nine sites, we investigated MDD-related alterations in the principal connectome gradient. We further used Neurosynth, postmortem gene expression, and an 8-week antidepressant treatment (20 MDD patients) data to assess the meta-analytic cognitive functions, transcriptional profiles, and treatment outcomes related to MDD gradient alterations, respectively. Relative to the controls, MDD patients exhibited global topographic alterations in the principal primary-to-transmodal gradient, including reduced explanation ratio, gradient range, and gradient variation (Cohen’s d = 0.16–0.21), and focal alterations mainly in the primary and transmodal systems (d = 0.18–0.25). These gradient alterations were significantly correlated with meta-analytic terms involving sensory processing and higher-order cognition. The transcriptional profiles explained 53.9% variance of the altered gradient pattern, with the most correlated genes enriched in transsynaptic signaling and calcium ion binding. The baseline gradient maps of patients significantly predicted symptomatic improvement after treatment. These results highlight the connectome gradient dysfunction in MDD and its linkage with gene expression profiles and clinical management, providing insight into the neurobiological underpinnings and potential biomarkers for treatment evaluation in this disorder.

The global pandemic of COVID-19 is colliding with the epidemic of opioid use disorders (OUD) and other substance use disorders (SUD) in the United States (US). Currently, there is limited data on risks, disparity, and outcomes for COVID-19 in individuals suffering from SUD. This is a retrospective case-control study of electronic health records (EHRs) data of 73,099,850 unique patients, of whom 12,030 had a diagnosis of COVID-19. Patients with a recent diagnosis of SUD (within past year) were at significantly increased risk for COVID-19 (adjusted odds ratio or AOR = 8.699 [8.411–8.997], P < 10−30), an effect that was strongest for individuals with OUD (AOR = 10.244 [9.107–11.524], P < 10−30), followed by individuals with tobacco use disorder (TUD) (AOR = 8.222 ([7.925–8.530], P < 10−30). Compared to patients without SUD, patients with SUD had significantly higher prevalence of chronic kidney, liver, lung diseases, cardiovascular diseases, type 2 diabetes, obesity and cancer. Among patients with recent diagnosis of SUD, African Americans had significantly higher risk of COVID-19 than Caucasians (AOR = 2.173 [2.01–2.349], P < 10−30), with strongest effect for OUD (AOR = 4.162 [3.13–5.533], P < 10−25). COVID-19 patients with SUD had significantly worse outcomes (death: 9.6%, hospitalization: 41.0%) than general COVID-19 patients (death: 6.6%, hospitalization: 30.1%) and African Americans with COVID-19 and SUD had worse outcomes (death: 13.0%, hospitalization: 50.7%) than Caucasians (death: 8.6%, hospitalization: 35.2%). These findings identify individuals with SUD, especially individuals with OUD and African Americans, as having increased risk for COVID-19 and its adverse outcomes, highlighting the need to screen and treat individuals with SUD as part of the strategy to control the pandemic while ensuring no disparities in access to healthcare support.

Whilst cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers for amyloid-β (Aβ) and tau pathologies are accurate for the diagnosis of Alzheimer’s disease (AD), their broad implementation in clinical and trial settings are restricted by high cost and limited accessibility. Plasma phosphorylated-tau181 (p-tau181) is a promising blood-based biomarker that is specific for AD, correlates with cerebral Aβ and tau pathology, and predicts future cognitive decline. In this study, we report the performance of p-tau181 in >1000 individuals from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including cognitively unimpaired (CU), mild cognitive impairment (MCI) and AD dementia patients characterized by Aβ PET. We confirmed that plasma p-tau181 is increased at the preclinical stage of Alzheimer and further increases in MCI and AD dementia. Individuals clinically classified as AD dementia but having negative Aβ PET scans show little increase but plasma p-tau181 is increased if CSF Aβ has already changed prior to Aβ PET changes. Despite being a multicenter study, plasma p-tau181 demonstrated high diagnostic accuracy to identify AD dementia (AUC = 85.3%; 95% CI, 81.4–89.2%), as well as to distinguish between Aβ− and Aβ+ individuals along the Alzheimer’s continuum (AUC = 76.9%; 95% CI, 74.0–79.8%). Higher baseline concentrations of plasma p-tau181 accurately predicted future dementia and performed comparably to the baseline prediction of CSF p-tau181. Longitudinal measurements of plasma p-tau181 revealed low intra-individual variability, which could be of potential benefit in disease-modifying trials seeking a measurable response to a therapeutic target. This study adds significant weight to the growing body of evidence in the use of plasma p-tau181 as a non-invasive diagnostic and prognostic tool for AD, regardless of clinical stage, which would be of great benefit in clinical practice and a large cost-saving in clinical trial recruitment.

Neuropsychiatric manifestations are common in both the acute and post-acute phase of SARS-CoV-2 infection, but the mechanisms of these effects are unknown. In a newly established brain organoid model with innately developing microglia, we demonstrate that SARS-CoV-2 infection initiate neuronal cell death and cause a loss of post-synaptic termini. Despite limited neurotropism and a decelerating viral replication, we observe a threefold increase in microglial engulfment of postsynaptic termini after SARS-CoV-2 exposure. We define the microglial responses to SARS-CoV-2 infection by single cell transcriptomic profiling and observe an upregulation of interferon-responsive genes as well as genes promoting migration and synapse engulfment. To a large extent, SARS-CoV-2 exposed microglia adopt a transcriptomic profile overlapping with neurodegenerative disorders that display an early synapse loss as well as an increased incident risk after a SARS-CoV-2 infection. Our results reveal that brain organoids infected with SARS-CoV-2 display disruption in circuit integrity via microglia-mediated synapse elimination and identifies a potential novel mechanism contributing to cognitive impairments in patients recovering from COVID-19.

Coronavirus disease-2019 (COVID-19) is primarily a respiratory disease, however, an increasing number of reports indicate that SARS-CoV-2 infection can also cause severe neurological manifestations, including precipitating cases of probable Parkinson’s disease. As microglial NLRP3 inflammasome activation is a major driver of neurodegeneration, here we interrogated whether SARS-CoV-2 can promote microglial NLRP3 inflammasome activation. Using SARS-CoV-2 infection of transgenic mice expressing human angiotensin-converting enzyme 2 (hACE2) as a COVID-19 pre-clinical model, we established the presence of virus in the brain together with microglial activation and NLRP3 inflammasome upregulation in comparison to uninfected mice. Next, utilising a model of human monocyte-derived microglia, we identified that SARS-CoV-2 isolates can bind and enter human microglia in the absence of viral replication. This interaction of virus and microglia directly induced robust inflammasome activation, even in the absence of another priming signal. Mechanistically, we demonstrated that purified SARS-CoV-2 spike glycoprotein activated the NLRP3 inflammasome in LPS-primed microglia, in a ACE2-dependent manner. Spike protein also could prime the inflammasome in microglia through NF-κB signalling, allowing for activation through either ATP, nigericin or α-synuclein. Notably, SARS-CoV-2 and spike protein-mediated microglial inflammasome activation was significantly enhanced in the presence of α-synuclein fibrils and was entirely ablated by NLRP3-inhibition. Finally, we demonstrate SARS-CoV-2 infected hACE2 mice treated orally post-infection with the NLRP3 inhibitory drug MCC950, have significantly reduced microglial inflammasome activation, and increased survival in comparison with untreated SARS-CoV-2 infected mice. These results support a possible mechanism of microglial innate immune activation by SARS-CoV-2, which could explain the increased vulnerability to developing neurological symptoms akin to Parkinson’s disease in COVID-19 infected individuals, and a potential therapeutic avenue for intervention.

Intelligence, or general cognitive function, is phenotypically and genetically correlated with many traits, including a wide range of physical, and mental health variables. Education is strongly genetically correlated with intelligence (rg = 0.70). We used these findings as foundations for our use of a novel approach—multi-trait analysis of genome-wide association studies (MTAG; Turley et al. 2017)—to combine two large genome-wide association studies (GWASs) of education and intelligence, increasing statistical power and resulting in the largest GWAS of intelligence yet reported. Our study had four goals: first, to facilitate the discovery of new genetic loci associated with intelligence; second, to add to our understanding of the biology of intelligence differences; third, to examine whether combining genetically correlated traits in this way produces results consistent with the primary phenotype of intelligence; and, finally, to test how well this new meta-analytic data sample on intelligence predicts phenotypic intelligence in an independent sample. By combining datasets using MTAG, our functional sample size increased from 199,242 participants to 248,482. We found 187 independent loci associated with intelligence, implicating 538 genes, using both SNP-based and gene-based GWAS. We found evidence that neurogenesis and myelination—as well as genes expressed in the synapse, and those involved in the regulation of the nervous system—may explain some of the biological differences in intelligence. The results of our combined analysis demonstrated the same pattern of genetic correlations as those from previous GWASs of intelligence, providing support for the meta-analysis of these genetically-related phenotypes.

People’s differences in cognitive functions are partly heritable and are associated with important life outcomes. Previous genome-wide association (GWA) studies of cognitive functions have found evidence for polygenic effects yet, to date, there are few replicated genetic associations. Here we use data from the UK Biobank sample to investigate the genetic contributions to variation in tests of three cognitive functions and in educational attainment. GWA analyses were performed for verbal–numerical reasoning ( N =36 035), memory ( N =112 067), reaction time ( N =111 483) and for the attainment of a college or a university degree ( N =111 114). We report genome-wide significant single-nucleotide polymorphism (SNP)-based associations in 20 genomic regions, and significant gene-based findings in 46 regions. These include findings in the ATXN2 , CYP2DG , APBA1 and CADM2 genes. We report replication of these hits in published GWA studies of cognitive function, educational attainment and childhood intelligence. There is also replication, in UK Biobank, of SNP hits reported previously in GWA studies of educational attainment and cognitive function. GCTA-GREML analyses, using common SNPs (minor allele frequency>0.01), indicated significant SNP-based heritabilities of 31% (s.e.m.=1.8%) for verbal–numerical reasoning, 5% (s.e.m.=0.6%) for memory, 11% (s.e.m.=0.6%) for reaction time and 21% (s.e.m.=0.6%) for educational attainment. Polygenic score analyses indicate that up to 5% of the variance in cognitive test scores can be predicted in an independent cohort. The genomic regions identified include several novel loci, some of which have been associated with intracranial volume, neurodegeneration, Alzheimer’s disease and schizophrenia.

The current study investigated the mechanism and changes in psychopathology symptoms throughout the COVID-19 outbreak and after peak. Two studies were conducted separately in China during outbreak and the after peak stages, with 2540 participants were recruited from February 6 to 16, 2020, and 2543 participants were recruited from April 25 to May 5, 2020. The network models were created to explore the relationship between psychopathology symptoms both within and across anxiety and depression, with anxiety measured by the Generalized Anxiety Disorder-7 and depression measured by the Patient Health Questionnaire-9. Symptom network analysis was conducted to evaluate network and bridge centrality, and the network properties were compared between the outbreak and after peak. Noticeably, psychomotor symptoms such as impaired motor skills, restlessness, and inability to relax exhibited high centrality during the outbreak, which still relatively high but showed substantial remission during after peak stage (in terms of strength, betweenness, or bridge centrality). Meanwhile, symptoms of irritability (strength, betweenness, or bridge centrality) and loss of energy (bridge centrality) played an important role in the network after the peak of the pandemic. This study provides novel insights into the changes in central features during the different COVID-19 stages and highlights motor-related symptoms as bridge symptoms, which could activate the connection between anxiety and depression. The results revealed that restrictions on movement were associated with worsen in psychomotor symptoms, indicating that future psychological interventions should target motor-related symptoms as priority.

Due to an inability to ethically access developing human brain tissue as well as identify prospective cases, early-arising neurodevelopmental and cell-specific signatures of Schizophrenia (Scz) have remained unknown and thus undefined. To overcome these challenges, we utilized patient-derived induced pluripotent stem cells (iPSCs) to generate 3D cerebral organoids to model neuropathology of Scz during this critical period. We discovered that Scz organoids exhibited ventricular neuropathology resulting in altered progenitor survival and disrupted neurogenesis. This ultimately yielded fewer neurons within developing cortical fields of Scz organoids. Single-cell sequencing revealed that Scz progenitors were specifically depleted of neuronal programming factors leading to a remodeling of cell-lineages, altered differentiation trajectories, and distorted cortical cell-type diversity. While Scz organoids were similar in their macromolecular diversity to organoids generated from healthy controls (Ctrls), four GWAS factors (PTN, COMT, PLCL1, and PODXL) and peptide fragments belonging to the POU-domain transcription factor family (e.g., POU3F2/BRN2) were altered. This revealed that Scz organoids principally differed not in their proteomic diversity, but specifically in their total quantity of disease and neurodevelopmental factors at the molecular level. Single-cell sequencing subsequently identified cell-type specific alterations in neuronal programming factors as well as a developmental switch in neurotrophic growth factor expression, indicating that Scz neuropathology can be encoded on a cell-type-by-cell-type basis. Furthermore, single-cell sequencing also specifically replicated the depletion of BRN2 (POU3F2) and PTN in both Scz progenitors and neurons. Subsequently, in two mechanistic rescue experiments we identified that the transcription factor BRN2 and growth factor PTN operate as mechanistic substrates of neurogenesis and cellular survival, respectively, in Scz organoids. Collectively, our work suggests that multiple mechanisms of Scz exist in patient-derived organoids, and that these disparate mechanisms converge upon primordial brain developmental pathways such as neuronal differentiation, survival, and growth factor support, which may amalgamate to elevate intrinsic risk of Scz.

Despite the large toll of opioid use disorder (OUD), genome-wide association studies (GWAS) of OUD to date have yielded few susceptibility loci. We performed a large-scale GWAS of OUD in individuals of European (EUR) and African (AFR) ancestry, optimizing genetic informativeness by performing MTAG (Multi-trait analysis of GWAS) with genetically correlated substance use disorders (SUDs). Meta-analysis included seven cohorts: the Million Veteran Program, Psychiatric Genomics Consortium, iPSYCH, FinnGen, Partners Biobank, BioVU, and Yale-Penn 3, resulting in a total N = 639,063 (Ncases = 20,686;Neffective = 77,026) across ancestries. OUD cases were defined as having a lifetime OUD diagnosis, and controls as anyone not known to meet OUD criteria. We estimated SNP-heritability (h2SNP) and genetic correlations (rg). Based on genetic correlation, we performed MTAG on OUD, alcohol use disorder (AUD), and cannabis use disorder (CanUD). A leave-one-out polygenic risk score (PRS) analysis was performed to compare OUD and OUD-MTAG PRS as predictors of OUD case status in Yale-Penn 3. The EUR meta-analysis identified three genome-wide significant (GWS; p ≤ 5 × 10−8) lead SNPs—one at FURIN (rs11372849; p = 9.54 × 10−10) and two OPRM1 variants (rs1799971, p = 4.92 × 10−09; rs79704991, p = 1.11 × 10−08; r2 = 0.02). Rs1799971 (p = 4.91 × 10−08) and another OPRM1 variant (rs9478500; p = 1.95 × 10−08; r2 = 0.03) were identified in the cross-ancestry meta-analysis. Estimated h2SNP was 12.75%, with strong rg with CanUD (rg = 0.82; p = 1.14 × 10−47) and AUD (rg = 0.77; p = 6.36 × 10−78). The OUD-MTAG resulted in a GWAS Nequivalent = 128,748 and 18 independent GWS loci, some mapping to genes or gene regions that have previously been associated with psychiatric or addiction phenotypes. The OUD-MTAG PRS accounted for 3.81% of OUD variance (beta = 0.61;s.e. = 0.066; p = 2.00 × 10−16) compared to 2.41% (beta = 0.45; s.e. = 0.058; p = 2.90 × 10−13) explained by the OUD PRS. The current study identified OUD variant associations at OPRM1, single variant associations with FURIN, and 18 GWS associations in the OUD-MTAG. The genetic architecture of OUD is likely influenced by both OUD-specific loci and loci shared across SUDs.