Search

This study aimed to characterize the prevalence of irritability among U.S. adults, and the extent to which it co-occurs with major depressive and anxious symptoms. A non-probability internet survey of individuals 18 and older in 50 U.S. states and the District of Columbia was conducted between November 2, 2023, and January 8, 2024. Regression models with survey weighting were used to examine associations between the Brief Irritability Test (BITe5) and sociodemographic and clinical features. The survey cohort included 42,739 individuals, mean age 46.0 (SD 17.0) years; 25,001 (58.5%) identified as women, 17,281 (40.4%) as men, and 457 (1.1%) as nonbinary. A total of 1218(2.8%) identified as Asian American, 5971 (14.0%) as Black, 5348 (12.5%) as Hispanic, 1775 (4.2%) as another race, and 28,427 (66.5%) as white. Mean irritability score was 13.6 (SD 5.6) on a scale from 5 to 30. In linear regression models, irritability was greater among respondents who were female, younger, had lower levels of education, and lower household income. Greater irritability was associated with likelihood of thoughts of suicide in logistic regression models adjusted for sociodemographic features (OR 1.23, 95% CI 1.22–1.24). Among 1979 individuals without thoughts of suicide on the initial survey assessed for such thoughts on a subsequent survey, greater irritability was also associated with greater likelihood of thoughts of suicide being present (adjusted OR 1.17, 95% CI 1.12–1.23). The prevalence of irritability and its association with thoughts of suicide suggests the need to better understand its implications among adults outside of acute mood episodes.

Objective: We sought to develop a prediction model using prenatal diagnosis codes that could help clinicians objectively stratify a women’s risk for delivery-related morbidity. Study design: We performed a prospective cohort study of women delivering at a single academic medical center between 2016 and 2019. Diagnosis codes from outpatient encounters were extracted from the electronic health record. Standard and common machine-learning methods for variable selection were compared. The performance characteristics from the selected model in the training data set—a LASSO model with a lambda that minimized the Bayes information criteria—were compared in a testing and external validation set. Results: The model identified a group of women, those in the highest decile of predicted risk, who were at a two to threefold increased risk of maternal morbidity. Conclusion: As EHR data becomes more ubiquitous, other data types generated from the prenatal period may improve the model’s performance.

Neurodevelopmental changes and impaired stress resistance have been implicated in the pathogenesis of bipolar disorder (BD), but the underlying regulatory mechanisms are unresolved. Here we describe a human cerebral organoid model of BD that exhibits altered neural development, elevated neural network activity, and a major shift in the transcriptome. These phenotypic changes were reproduced in cerebral organoids generated from iPS cell lines derived in different laboratories. The BD cerebral organoid transcriptome showed highly significant enrichment for gene targets of the transcriptional repressor REST. This was associated with reduced nuclear REST and REST binding to target gene recognition sites. Reducing the oxygen concentration in organoid cultures to a physiological range ameliorated the developmental phenotype and restored REST expression. These effects were mimicked by treatment with lithium. Reduced nuclear REST and derepression of REST targets genes were also observed in the prefrontal cortex of BD patients. Thus, an impaired cellular stress response in BD cerebral organoids leads to altered neural development and transcriptional dysregulation associated with downregulation of REST. These findings provide a new model and conceptual framework for exploring the molecular basis of BD.

Management of depressive episodes in bipolar disorder remains challenging for clinicians despite the availability of treatment guidelines. In other contexts, large language models have yielded promising results for supporting clinical decisionmaking. We developed 50 sets of clinical vignettes reflecting bipolar depression and presented them to experts in bipolar disorder, who were asked to identify 5 optimal next-step pharmacotherapies and 5 poor or contraindicated choices. The same vignettes were then presented to a large language model (GPT4-turbo; gpt-4-1106-preview), with or without augmentation by prompting with recent bipolar treatment guidelines, and asked to identify the optimal next-step pharmacotherapy. Overlap between model output and gold standard was estimated. The augmented model prioritized the expert-designated optimal choice for 508/1000 vignettes (50.8%, 95% CI 47.7–53.9%; Cohen’s kappa = 0.31, 95% CI 0.28–0.35). For 120 vignettes (12.0%), at least one model choice was among the poor or contraindicated treatments. Results were not meaningfully different when gender or race of the vignette was permuted to examine risk for bias. By comparison, an un-augmented model identified the optimal treatment for 234 (23.0%, 95% CI 20.8–26.0%; McNemar’s p< 0.001 versus augmented model) of the vignettes. A sample of community clinicians scoring the same vignettes identified the optimal choice for 23.1% (95% CI 15.7–30.5%) of vignettes, on average; McNemar’s p< 0.001 versus augmented model. Large language models prompted with evidence-based guidelines represent a promising, scalable strategy for clinical decision support. In addition to prospective studies of efficacy, strategies to avoid clinician overreliance on such models, and address the possibility of bias, will be needed.

While most of the efforts to uncover mechanisms contributing to bipolar disorder (BD) focused on phenotypes at the mature neuron stage, little research has considered events that may occur during earlier timepoints of neurodevelopment. Further, although aberrant calcium (Ca2+) signaling has been implicated in the etiology of this condition, the possible contribution of store-operated Ca2+entry (SOCE) is not well understood. Here, we report Ca2+and developmental dysregulations related to SOCE in BD patient induced pluripotent stem cell (iPSC)-derived neural progenitor cells (BD-NPCs) and cortical-like glutamatergic neurons. First, using a Ca2+re-addition assay we found that BD-NPCs and neurons had attenuated SOCE. Intrigued by this finding, we then performed RNA-sequencing and uncovered a unique transcriptome profile in BD-NPCs suggesting accelerated neurodifferentiation. Consistent with these results, we measured a slower rate of proliferation, increased neurite outgrowth, and decreased size in neurosphere formations with BD-NPCs. Also, we observed decreased subventricular areas in developing BD cerebral organoids. Finally, BD NPCs demonstrated high expression of the let-7family while BD neurons had increased miR-34a, both being microRNAs previously implicated in neurodevelopmental deviations and BD etiology. In summary, we present evidence supporting an accelerated transition towards the neuronal stage in BD-NPCs that may be indicative of early pathophysiological features of the disorder.

Pregnancy is a risk factor for increased severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other respiratory infections, but the mechanisms underlying this risk are poorly understood. To gain insight into the role of pregnancy in modulating immune responses at baseline and upon SARS-CoV-2 infection, we collected peripheral blood mononuclear cells and plasma from 226 women, including 152 pregnant individuals and 74 non-pregnant women. We find that SARS-CoV-2 infection is associated with altered T cell responses in pregnant women, including a clonal expansion of CD4-expressing CD8+T cells, diminished interferon responses, and profound suppression of monocyte function. We also identify shifts in cytokine and chemokine levels in the sera of pregnant individuals, including a robust increase of interleukin-27, known to drive T cell exhaustion. Our findings reveal nuanced pregnancy-associated immune responses, which may contribute to the increased susceptibility of pregnant individuals to viral respiratory infection.

Maternal immune activation is associated with adverse offspring neurodevelopmental outcomes, many mediated by in uteromicroglial programming. As microglia remain inaccessible throughout development, identification of noninvasive biomarkers reflecting fetal brain microglial programming could permit screening and intervention. We used lineage tracing to demonstrate the shared ontogeny between fetal brain macrophages (microglia) and fetal placental macrophages (Hofbauer cells) in a mouse model of maternal diet-induced obesity, and single-cell RNA-seq to demonstrate shared transcriptional programs. Comparison with human datasets demonstrated conservation of placental resident macrophage signatures between mice and humans. Single-cell RNA-seq identified common alterations in fetal microglial and Hofbauer cell gene expression induced by maternal obesity, as well as sex differences in these alterations. We propose that Hofbauer cells, which are easily accessible at birth, provide insights into fetal brain microglial programs and may facilitate the early identification of offspring vulnerable to neurodevelopmental disorders.

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.

Supplemental Digital Content is available in the text

Neuropsychiatric symptoms may persist following acute COVID-19 illness, but the extent to which these symptoms are specific to COVID-19 has not been established. We utilized electronic health records across 6 hospitals in Massachusetts to characterize cohorts of individuals discharged following admission for COVID-19 between March 2020 and May 2021, and compared them to individuals hospitalized for other indications during this period. Natural language processing was applied to narrative clinical notes to identify neuropsychiatric symptom domains up to 150 days following hospitalization, in addition to those reflected in diagnostic codes as measured in prior studies. Among 6619 individuals hospitalized for COVID-19 drawn from a total of 42,961 hospital discharges, the most commonly-documented symptom domains between 31 and 90 days after initial positive test were fatigue (13.4%), mood and anxiety symptoms (11.2%), and impaired cognition (8.0%). In regression models adjusted for sociodemographic features and hospital course, none of these were significantly more common among COVID-19 patients; indeed, mood and anxiety symptoms were less frequent (adjusted OR 0.72 95% CI 0.64–0.92). Between 91 and 150 days after positivity, most commonly-detected symptoms were fatigue (10.9%), mood and anxiety symptoms (8.2%), and sleep disruption (6.8%), with impaired cognition in 5.8%. Frequency was again similar among non-COVID-19 post-hospital patients, with mood and anxiety symptoms less common (aOR 0.63, 95% CI 0.52–0.75). Propensity-score matched analyses yielded similar results. Overall, neuropsychiatric symptoms were common up to 150 days after initial hospitalization, but occurred at generally similar rates among individuals hospitalized for other indications during the same period. Post-acute sequelae of COVID-19 may benefit from standard if less-specific treatments developed for rehabilitation after hospitalization.

Early reports and case series suggest cognitive deficits occurs in some patients with COVID-19. We evaluated the frequency, severity, and profile of cognitive dysfunction in patients recovering from prolonged COVID-19 hospitalization who required acute inpatient rehabilitation prior to discharge. We analyzed cross-sectional scores from the Brief Memory and Executive Test (BMET) in a cohort of N= 57 COVID-19 patients undergoing inpatient rehabilitation, calculating the frequency of impairment based on neuropsychologist diagnosis and by age-normed BMET subtests. In total, 43 patients (75%) were male, 35 (61%) were non-white, and mean age was 64.5 (SD = 13.9) years. In total, 48 (84%) were previously living at home independently. Two patients had documented preexisting cognitive dysfunction; none had known dementia. Patients were evaluated at a mean of 43.2 (SD = 19.2) days after initial admission. In total, 50 patients (88%) had documented hypoxemic respiratory failure and 44 (77%) required intubation.  Forty-six patients (81%) had cognitive impairment, ranging from mild to severe. Deficits were common in working memory (26/47 [55%] of patients), set-shifting (21/44 [47%]), divided attention (18/39 [46%]), and processing speed (14/35 [40%]). Executive dysfunction was not significantly associated with intubation length or the time from extubation to assessment, psychiatric diagnosis, or preexisting cardiovascular/metabolic disease. Attention and executive functions are frequently impaired in COVID-19 patients who require acute rehabilitation prior to discharge. Though interpretation is limited by lack of a comparator group, these results provide an early benchmark for identifying and characterizing cognitive difficulties after COVID-19. Given the frequency and pattern of impairment, easy-to-disseminate interventions that target attention and executive dysfunctions may be beneficial to this population.

Evidence from epidemiological and laboratory studies, as well as randomized placebo-controlled trials, suggests supplementation with n-3 polyunsaturated fatty acids (PUFAs) may be efficacious for treatment of major depressive disorder (MDD). The mechanisms underlying n-3 PUFAs potential therapeutic properties remain unknown. There are suggestions in the literature that glial hypofunction is associated with depressive symptoms and that antidepressants may normalize glial function. In this study, induced pluripotent stem cells (iPSC)-derived neuronal stem cell lines were generated from individuals with MDD. Astrocytes differentiated from patient-derived neuronal stem cells (iNSCs) were verified by GFAP. Cells were treated with eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA) or stearic acid (SA). During astrocyte differentiation, we found that n-3 PUFAs increased GFAP expression and GFAP positive cell formation. BDNF and GDNF production were increased in the astrocytes derived from patients subsequent to n-3 PUFA treatment. Stearic Acid (SA) treatment did not have this effect. CREB activity (phosphorylated CREB) was also increased by DHA and EPA but not by SA. Furthermore, when these astrocytes were treated with n-3 PUFAs, the cAMP antagonist, RP-cAMPs did not block n-3 PUFA CREB activation. However, the CREB specific inhibitor (666-15) diminished BDNF and GDNF production induced by n-3 PUFA, suggesting CREB dependence. Together, these results suggested that n-3 PUFAs facilitate astrocyte differentiation, and may mimic effects of some antidepressants by increasing production of neurotrophic factors. The CREB-dependence and cAMP independence of this process suggests a manner in which n-3 PUFA could augment antidepressant effects. These data also suggest a role for astrocytes in both MDD and antidepressant action.

Multidimensional transdiagnostic phenotyping systems are increasingly important to neuropsychiatric phenotyping, particularly in translational research settings. The relationship the National Institute of Mental Health's Research Domain Criteria multidimensional approach to psychopathology and nonpsychiatric diagnoses has not been studied at scale but is relevant to those caring for neuropsychiatric illness in medical and surgical settings.

Around 5% of the population is affected by a rare genetic disease, yet most endure years of uncertainty before receiving a genetic test. A common feature of genetic diseases is the presence of multiple rare phenotypes that often span organ systems. Here, we use diagnostic billing information from longitudinal clinical data in the electronic health records (EHRs) of 2,286 patients who received a chromosomal microarray test, and 9,144 matched controls, to build a model to predict who should receive a genetic test. The model achieved high prediction accuracies in a held-out test sample (area under the receiver operating characteristic curve (AUROC), 0.97; area under the precision–recall curve (AUPRC), 0.92), in an independent hospital system (AUROC, 0.95; AUPRC, 0.62), and in an independent set of 172,265 patients in which cases were broadly defined as having an interaction with a genetics provider (AUROC, 0.9; AUPRC, 0.63). Patients carrying a putative pathogenic copy number variant were also accurately identified by the model. Compared with current approaches for genetic test determination, our model could identify more patients for testing while also increasing the proportion of those tested who have a genetic disease. We demonstrate that phenotypic patterns representative of a wide range of genetic diseases can be captured from EHRs to systematize decision-making for genetic testing, with the potential to speed up diagnosis, improve care and reduce costs.

Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies.

Studies in post-mortem human brain tissue have associated major depressive disorder (MDD) with cortical transcriptomic changes, whose potential in vivo impact remains unexplored. To address this translational gap, we recently developed a transcriptome-based polygenic risk score (T-PRS) based on common functional variants capturing ‘depression-like’ shifts in cortical gene expression. Here, we used a non-clinical sample of young adults (n= 482, Duke Neurogenetics Study: 53% women; aged 19.8 ± 1.2 years) to map T-PRS onto brain morphology measures, including Freesurfer-derived subcortical volume, cortical thickness, surface area, and local gyrification index, as well as broad MDD risk, indexed by self-reported family history of depression. We conducted side-by-side comparisons with a PRS independently derived from a Psychiatric Genomics Consortium (PGC) MDD GWAS (PGC-PRS), and sought to link T-PRS with diagnosis and symptom severity directly in PGC-MDD participants (n= 29,340, 59% women; 12,923 MDD cases, 16,417 controls). T-PRS was associated with smaller amygdala volume in women (t= −3.478, p= 0.001) and lower prefrontal gyrification across sexes. In men, T-PRS was associated with hypergyrification in temporal and occipital regions. Prefrontal hypogyrification mediated a male-specific indirect link between T-PRS and familial depression (b= 0.005, p= 0.029). PGC-PRS was similarly associated with lower amygdala volume and cortical gyrification; however, both effects were male-specific and hypogyrification emerged in distinct parietal and temporo-occipital regions, unassociated with familial depression. In PGC-MDD, T-PRS did not predict diagnosis (OR = 1.007, 95% CI = [0.997–1.018]) but correlated with symptom severity in men (rho = 0.175, p= 7.957 × 10−4) in one cohort (N= 762, 48% men). Depression-like shifts in cortical gene expression have sex-specific effects on brain morphology and may contribute to broad depression vulnerability in men.

We aimed to develop and validate classification models able to identify individuals at high risk for transition from a diagnosis of depressive disorder to one of bipolar disorder. This retrospective health records cohort study applied outpatient clinical data from psychiatry and nonpsychiatry practice networks affiliated with two large academic medical centers between March 2008 and December 2017. Participants included 67,807 individuals with a diagnosis of major depressive disorder or depressive disorder not otherwise specified and no prior diagnosis of bipolar disorder, who received at least one of the nine antidepressant medications. The main outcome was at least one diagnostic code reflective of a bipolar disorder diagnosis within 3 months of index antidepressant prescription. Logistic regression and random forests using diagnostic and procedure codes as well as sociodemographic features were used to predict this outcome, with discrimination and calibration assessed in a held-out test set and then a second academic medical center. Among 67,807 individuals who received at least one antidepressant medication, 925 (1.36%) subsequently received a diagnosis of bipolar disorder within 3 months. Models incorporating coded diagnoses and procedures yielded a mean area under the receiver operating characteristic curve of 0.76 (ranging from 0.73 to 0.80). Standard supervised machine learning methods enabled development of discriminative and transferable models to predict transition to bipolar disorder. With further validation, these scores may enable physicians to more precisely calibrate follow-up intensity for high-risk patients after antidepressant initiation.

Given its chronicity, contribution to disability and morbidity, and prevalence of more than 2%, the effective treatment, and prevention of bipolar disorder represents an area of significant unmet medical need. While more than half a century has passed since the introduction of lithium into widespread use at the birth of modern psychopharmacology, that medication remains a mainstay for the acute treatment and prevention of recurrent mania/hypomania and depression that characterize bipolar disorder. However, the continued limited understanding of how lithium modulates affective behavior and lack of validated cellular and animal models have resulted in obstacles to discovering more effective mood stabilizers with fewer adverse side effects. In particular, while there has been progress in developing new pharmacotherapy for mania, developing effective treatments for acute bipolar depression remain inadequate. Recent large-scale human genetic studies have confirmed the complex, polygenic nature of the risk architecture of bipolar disorder, and its overlap with other major neuropsychiatric disorders. Such discoveries have begun to shed light on the pathophysiology of bipolar disorder. Coupled with broader advances in human neurobiology, neuropharmacology, noninvasive neuromodulation, and clinical trial design, we can envision novel therapeutic strategies informed by defined molecular mechanisms and neural circuits and targeted to the root cause of the pathophysiology. Here, we review recent advances toward the goal of better treatments for bipolar disorder, and we outline major challenges for the field of translational neuroscience that necessitate continued focus on fundamental research and discovery.

Although circular RNAs (circRNAs) are enriched in the mammalian brain, very little is known about their potential involvement in brain function and psychiatric disease. Here, we show that circHomer1a, a neuronal-enriched circRNA abundantly expressed in the frontal cortex, derived from Homer protein homolog 1 (HOMER1), is significantly reduced in both the prefrontal cortex (PFC) and induced pluripotent stem cell-derived neuronal cultures from patients with schizophrenia (SCZ) and bipolar disorder (BD). Moreover, alterations in circHomer1awere positively associated with the age of onset of SCZ in both the dorsolateral prefrontal cortex (DLPFC) and orbitofrontal cortex (OFC). No correlations between the age of onset of SCZ and linear HOMER1mRNA were observed, whose expression was mostly unaltered in BD and SCZ postmortem brain. Using in vivo circRNA-specific knockdown of circHomer1ain mouse PFC, we show that it modulates the expression of numerous alternative mRNA transcripts from genes involved in synaptic plasticity and psychiatric disease. Intriguingly, in vivo circHomer1aknockdown in mouse OFC resulted in specific deficits in OFC-mediated cognitive flexibility. Lastly, we demonstrate that the neuronal RNA-binding protein HuD binds to circHomer1aand can influence its synaptic expression in the frontal cortex. Collectively, our data uncover a novel psychiatric disease-associated circRNA that regulates synaptic gene expression and cognitive flexibility.

Minimal phenotyping refers to the reliance on the use of a small number of self-reported items for disease case identification, increasingly used in genome-wide association studies (GWAS). Here we report differences in genetic architecture between depression defined by minimal phenotyping and strictly defined major depressive disorder (MDD): the former has a lower genotype-derived heritability that cannot be explained by inclusion of milder cases and a higher proportion of the genome contributing to this shared genetic liability with other conditions than for strictly defined MDD. GWAS based on minimal phenotyping definitions preferentially identifies loci that are not specific to MDD, and, although it generates highly predictive polygenic risk scores, the predictive power can be explained entirely by large sample sizes rather than by specificity for MDD. Our results show that reliance on results from minimal phenotyping may bias views of the genetic architecture of MDD and impede the ability to identify pathways specific to MDD.

Supplemental digital content is available in the text.

Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P< 1 × 10−4in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P< 5 × 10−8) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder.

Synapse density is reduced in postmortem cortical tissue from schizophrenia patients, which is suggestive of increased synapse elimination. Using a reprogrammed in vitro model of microglia-mediated synapse engulfment, we demonstrate increased synapse elimination in patient-derived neural cultures and isolated synaptosomes. This excessive synaptic pruning reflects abnormalities in both microglia-like cells and synaptic structures. Further, we find that schizophrenia risk-associated variants within the human complement component 4 locus are associated with increased neuronal complement deposition and synapse uptake; however, they do not fully explain the observed increase in synapse uptake. Finally, we demonstrate that the antibiotic minocycline reduces microglia-mediated synapse uptake in vitro and its use is associated with a modest decrease in incident schizophrenia risk compared to other antibiotics in a cohort of young adults drawn from electronic health records. These findings point to excessive pruning as a potential target for delaying or preventing the onset of schizophrenia in high-risk individuals.

We generated cortical interneurons (cINs) from induced pluripotent stem cells derived from 14 healthy controls and 14 subjects with schizophrenia. Both healthy control cINs and schizophrenia cINs were authentic, fired spontaneously, received functional excitatory inputs from host neurons, and induced GABA-mediated inhibition in host neurons in vivo. However, schizophrenia cINs had dysregulated expression of protocadherin genes, which lie within documented schizophrenia loci. Mice lacking protocadherin-α showed defective arborization and synaptic density of prefrontal cortex cINs and behavioral abnormalities. Schizophrenia cINs similarly showed defects in synaptic density and arborization that were reversed by inhibitors of protein kinase C, a downstream kinase in the protocadherin pathway. These findings reveal an intrinsic abnormality in schizophrenia cINs in the absence of any circuit-driven pathology. They also demonstrate the utility of homogenous and functional populations of a relevant neuronal subtype for probing pathogenesis mechanisms during development.

Major depressive disorder is a clinically heterogeneous psychiatric disorder with a polygenic architecture. Genome-wide association studies have identified a number of risk-associated variants across the genome and have reported growing evidence of NETRIN1 pathway involvement. Stratifying disease risk by genetic variation within the NETRIN1 pathway may provide important routes for identification of disease mechanisms by focusing on a specific process, excluding heterogeneous risk-associated variation in other pathways. Here, we sought to investigate whether major depressive disorder polygenic risk scores derived from the NETRIN1 signaling pathway (NETRIN1-PRSs) and the whole genome, excluding NETRIN1 pathway genes (genomic-PRSs), were associated with white matter microstructure.

Brain bioenergetic abnormalities have been observed frequently in adults with major depressive disorder (MDD); however, results have been inconsistent regarding whether decreased or increased metabolism was observed. Phosphorus-31 magnetic resonance spectroscopy (31P MRS) allows for the quantification of bioenergetic molecules, containing high-energy phosphates, over the whole brain as well as measuring the differences between gray matter and white matter. We recruited 50 subjects with a current diagnosis of MDD, not currently treated with psychotropic medication, between ages of 18 and 65 (mean±SD age: 43.4±13.6; 46% female) and 30 healthy volunteers, matched for age and gender (39.0±12.5 years of age; 36.6% female). All subjects received a T1 MP-FLASH scan for tissue segmentation followed by 31P MRS, chemical shift imaging scan with 84 voxels of data collected over the entire brain utilizing a dual-tuned, proton-phosphorus coil to minimize subject movement. Phosphocreatine and inorganic phosphate (Pi) varied in opposite directions across gray matter and white matter when MDD subjects were compared with controls. This finding suggests alterations in high-energy phosphate metabolism and regulation of oxidative phosphorylation in MDD patients. In addition, within the MDD group, gray matter Pi, a regulator of oxidative phosphorylation, correlated positively with severity of depression. These data support a model that includes changes in brain bioenergetic function in subjects with major depression.

Delirium is an acute confusional state, associated with morbidity and mortality in diverse medically ill populations. Delirium is preventable and treatable when diagnosed but the diagnosis is often missed. This important and difficult diagnosis is an attractive candidate for computer-aided decision support if it can be reliably identified at scale.