Mortality associated with clozapine: what is the evidence?

While clozapine has risks, relative risk of fatality is overestimated. The UK pharmacovigilance programme is efficient, but comparisons with other drugs can mislead because of reporting variations. Clozapine actually lowers mortality, partly by reducing schizophrenia-related suicides, but preventable deaths still occur. Clozapine should be used earlier and more widely, but there should be better monitoring and better management of toxicity.

Multivariable prediction of functional outcome after first-episode psychosis: a crossover validation approach in EUFEST and PSYSCAN.

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 < 65) and good outcome (GAF current ≥ 65). Pooled non-linear support vector machine (SVM) classifiers trained on the separate cohorts identified patients with a poor outcome with cross-validated balanced accuracies (BAC) of 65-66%, but BAC dropped substantially when the models were applied to patients from a different FES cohort (BAC = 50-56%). A leave-site-out analysis on the merged sample yielded better performance (BAC = 72%), highlighting the effect of combining data from different study designs to overcome calibration issues and improve model transportability. In conclusion, our results indicate that validation of prediction models in an independent sample is essential in assessing the true value of the model. Future external validation studies, as well as attempts to harmonize data collection across studies, are recommended.

A Bayesian analysis of heart rate variability changes over acute episodes of bipolar disorder.

Bipolar disorder (BD) involves autonomic nervous system dysfunction, detectable through heart rate variability (HRV). HRV is a promising biomarker, but its dynamics during acute mania or depression episodes are poorly understood. Using a Bayesian approach, we developed a probabilistic model of HRV changes in BD, measured by the natural logarithm of the Root Mean Square of Successive RR interval Differences (lnRMSSD). Patients were assessed three to four times from episode onset to euthymia. Unlike previous studies, which used only two assessments, our model allowed for more accurate tracking of changes. Results showed strong evidence for a positive lnRMSSD change during symptom resolution (95.175% probability of positive direction), though the sample size limited the precision of this effect (95% Highest Density Interval [-0.0366, 0.4706], with a Region of Practical Equivalence: [-0.05; 0.05]). Episode polarity did not significantly influence lnRMSSD changes.

Effectiveness of Omega-3 Fatty Acids Versus Placebo in Subjects at Ultra-High Risk for Psychosis: The PURPOSE Randomized Clinical Trial.

BACKGROUND AND HYPOTHESES: In the past 2 decades, substantial effort has been put into research on therapeutic options for people at ultra-high risk (UHR) for developing a first episode of psychosis (FEP), focusing on omega-3 polyunsaturated fatty acids (PUFAs) in preventing transition to psychosis. Despite an initial positive finding, subsequent studies failed to find a beneficial effect. The current study aimed to further investigate the effect of omega-3 PUFAs in UHR, to determine whether this line of research is worth pursuing. STUDY DESIGN: A double-blind, randomized, placebo-controlled study testing the efficacy of 6-month treatment with omega-3 PUFAs in 135 subjects at UHR for FEP, aged 13 to 20 years on the prevention of a transition to psychosis, followed up for 18 months post-treatment. The trial was conducted at 16 general hospitals and psychiatric specialty centers located in 8 European countries and Israel. STUDY RESULTS: There was no beneficial effect of treatment with omega-3 PUFAs compared to placebo; the rate of transition over 2 years did not differ between treatment arms nor was there a difference in change in symptom severity after 6-month treatment. Dropout rates and serious adverse events were similar across the groups. CONCLUSIONS: This is the third study that fails to replicate the original finding on the protective effect of omega-3 PUFAs in UHR subjects for transition to psychosis. The accumulating evidence therefore suggests that omega-3 PUFAs do not reduce transition rates to psychosis in those at increased risk at 2 years follow-up. CLINICAL TRIALS: This trial is registered with ClinicalTrials.gov (NCT02597439; Study Details | Placebo-controlled Trial in Subjects at Ultra-high Risk for Psychosis With Omega-3 Fatty Acids in Europe | ClinicalTrials.gov).

Academic psychiatry is everyone's business.

This editorial considers the value and nature of academic psychiatry by asking what defines the specialty and psychiatrists as academics. We frame academic psychiatry as a way of thinking that benefits clinical services and discuss how to inspire the next generation of academics.

Wearable Data From Subjects Playing Super Mario, Taking University Exams, or Performing Physical Exercise Help Detect Acute Mood Disorder Episodes via Self-Supervised Learning: Prospective, Exploratory, Observational Study.

BACKGROUND: Personal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, is a promising paradigm to monitor mood disorders (MDs), a major determinant of the worldwide disease burden. However, collecting and annotating wearable data is resource intensive. Studies of this kind can thus typically afford to recruit only a few dozen patients. This constitutes one of the major obstacles to applying modern supervised machine learning techniques to MD detection. OBJECTIVE: In this paper, we overcame this data bottleneck and advanced the detection of acute MD episodes from wearables' data on the back of recent advances in self-supervised learning (SSL). This approach leverages unlabeled data to learn representations during pretraining, subsequently exploited for a supervised task. METHODS: We collected open access data sets recording with the Empatica E4 wristband spanning different, unrelated to MD monitoring, personal sensing tasks-from emotion recognition in Super Mario players to stress detection in undergraduates-and devised a preprocessing pipeline performing on-/off-body detection, sleep/wake detection, segmentation, and (optionally) feature extraction. With 161 E4-recorded subjects, we introduced E4SelfLearning, the largest-to-date open access collection, and its preprocessing pipeline. We developed a novel E4-tailored transformer (E4mer) architecture, serving as the blueprint for both SSL and fully supervised learning; we assessed whether and under which conditions self-supervised pretraining led to an improvement over fully supervised baselines (ie, the fully supervised E4mer and pre-deep learning algorithms) in detecting acute MD episodes from recording segments taken in 64 (n=32, 50%, acute, n=32, 50%, stable) patients. RESULTS: SSL significantly outperformed fully supervised pipelines using either our novel E4mer or extreme gradient boosting (XGBoost): n=3353 (81.23%) against n=3110 (75.35%; E4mer) and n=2973 (72.02%; XGBoost) correctly classified recording segments from a total of 4128 segments. SSL performance was strongly associated with the specific surrogate task used for pretraining, as well as with unlabeled data availability. CONCLUSIONS: We showed that SSL, a paradigm where a model is pretrained on unlabeled data with no need for human annotations before deployment on the supervised target task of interest, helps overcome the annotation bottleneck; the choice of the pretraining surrogate task and the size of unlabeled data for pretraining are key determinants of SSL success. We introduced E4mer, which can be used for SSL, and shared the E4SelfLearning collection, along with its preprocessing pipeline, which can foster and expedite future research into SSL for personal sensing.

PsyCog: A computerised mini battery for assessing cognition in psychosis.

Despite the functional impact of cognitive deficit in people with psychosis, objective cognitive assessment is not typically part of routine clinical care. This is partly due to the length of traditional assessments and the need for a highly trained administrator. Brief, automated computerised assessments could help to address this issue. We present data from an evaluation of PsyCog, a computerised, non-verbal, mini battery of cognitive tests. Healthy Control (HC) (N = 135), Clinical High Risk (CHR) (N = 233), and First Episode Psychosis (FEP) (N = 301) participants from a multi-centre prospective study were assessed at baseline, 6 months, and 12 months. PsyCog was used to assess cognitive performance at baseline and at up to two follow-up timepoints. Mean total testing time was 35.95 min (SD = 2.87). Relative to HCs, effect sizes of performance impairments were medium to large in FEP patients (composite score G = 1.21, subtest range = 0.52-0.88) and small to medium in CHR patients (composite score G = 0.59, subtest range = 0.18-0.49). Site effects were minimal, and test-retest reliability of the PsyCog composite was good (ICC = 0.82-0.89), though some practice effects and differences in data completion between groups were found. The present implementation of PsyCog shows it to be a useful tool for assessing cognitive function in people with psychosis. Computerised cognitive assessments have the potential to facilitate the evaluation of cognition in psychosis in both research and in clinical care, though caution should still be taken in terms of implementation and study design.

A comprehensive hierarchical comparison of structural connectomes in Major Depressive Disorder cases v. controls in two large population samples.

BACKGROUND: The brain can be represented as a network, with nodes as brain regions and edges as region-to-region connections. Nodes with the most connections (hubs) are central to efficient brain function. Current findings on structural differences in Major Depressive Disorder (MDD) identified using network approaches remain inconsistent, potentially due to small sample sizes. It is still uncertain at what level of the connectome hierarchy differences may exist, and whether they are concentrated in hubs, disrupting fundamental brain connectivity. METHODS: We utilized two large cohorts, UK Biobank (UKB, N = 5104) and Generation Scotland (GS, N = 725), to investigate MDD case-control differences in brain network properties. Network analysis was done across four hierarchical levels: (1) global, (2) tier (nodes grouped into four tiers based on degree) and rich club (between-hub connections), (3) nodal, and (4) connection. RESULTS: In UKB, reductions in network efficiency were observed in MDD cases globally (d = -0.076, pFDR = 0.033), across all tiers (d = -0.069 to -0.079, pFDR = 0.020), and in hubs (d = -0.080 to -0.113, pFDR = 0.013-0.035). No differences in rich club organization and region-to-region connections were identified. The effect sizes and direction for these associations were generally consistent in GS, albeit not significant in our lower-N replication sample. CONCLUSION: Our results suggest that the brain's fundamental rich club structure is similar in MDD cases and controls, but subtle topological differences exist across the brain. Consistent with recent large-scale neuroimaging findings, our findings offer a connectomic perspective on a similar scale and support the idea that minimal differences exist between MDD cases and controls.

Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number.

Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable in ecological physiological recordings thanks to recent advances in wearable technology. Therefore, near-continuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning (ML), could mitigate this problem, bringing MDs monitoring outside the clinician's office. Previous works predict a single label, either the disease state or a psychometric scale total score. However, clinical practice suggests that the same label may underlie different symptom profiles, requiring specific treatments. Here we bridge this gap by proposing a new task: inferring all items in HDRS and YMRS, the two most widely used standardized scales for assessing MDs symptoms, using physiological data from wearables. To that end, we develop a deep learning pipeline to score the symptoms of a large cohort of MD patients and show that agreement between predictions and assessments by an expert clinician is clinically significant (quadratic Cohen's κ and macro-average F1 score both of 0.609). While doing so, we investigate several solutions to the ML challenges associated with this task, including multi-task learning, class imbalance, ordinal target variables, and subject-invariant representations. Lastly, we illustrate the importance of testing on out-of-distribution samples.

A naturalistic cohort study of first-episode schizophrenia spectrum disorder: A description of the early phase of illness in the PSYSCAN cohort.

BACKGROUND: We examined the course of illness over a 12-month period in a large, international multi-center cohort of people with a first-episode schizophrenia spectrum disorder (FES) in a naturalistic, prospective study (PSYSCAN). METHOD: Patients with a first episode of schizophrenia, schizoaffective disorder (depressive type) or schizophreniform disorder were recruited at 16 institutions in Europe, Israel and Australia. Participants (N = 304) received clinical treatment as usual throughout the study. RESULTS: The mean age of the cohort was 24.3 years (SD = 5.6), and 67 % were male. At baseline, participants presented with a range of intensities of psychotic symptoms, 80 % were taking antipsychotic medication, 68 % were receiving psychological treatment, with 46.5 % in symptomatic remission. The mean duration of untreated psychosis was 6.2 months (SD = 17.0). After one year, 67 % were in symptomatic remission and 61 % were in functional remission, but 31 % had been readmitted to hospital at some time after baseline. In the cohort as a whole, depressive symptoms remained stable over the follow-up period. In patients with a current depressive episode at baseline, depressive symptoms slightly improved. Alcohol, tobacco and cannabis were the most commonly used substances, with daily users of cannabis ranging between 9 and 11 % throughout the follow-up period. CONCLUSIONS: This study provides valuable insight into the early course of a broad range of clinical and functional aspects of illness in FES patients in routine clinical practice.

Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk.

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.

Disrupted limbic-prefrontal effective connectivity in response to fearful faces in lifetime depression.

BACKGROUND: Multiple brain imaging studies of negative emotional bias in major depressive disorder (MDD) have used images of fearful facial expressions and focused on the amygdala and the prefrontal cortex. The results have, however, been inconsistent, potentially due to small sample sizes (typically N<50). It remains unclear if any alterations are a characteristic of current depression or of past experience of depression, and whether there are MDD-related changes in effective connectivity between the two brain regions. METHODS: Activations and effective connectivity between the amygdala and dorsolateral prefrontal cortex (DLPFC) in response to fearful face stimuli were studied in a large population-based sample from Generation Scotland. Participants either had no history of MDD (N=664 in activation analyses, N=474 in connectivity analyses) or had a diagnosis of MDD during their lifetime (LMDD, N=290 in activation analyses, N=214 in connectivity analyses). The within-scanner task involved implicit facial emotion processing of neutral and fearful faces. RESULTS: Compared to controls, LMDD was associated with increased activations in left amygdala (PFWE=0.031,kE=4) and left DLPFC (PFWE=0.002,kE=33), increased mean bilateral amygdala activation (ß=0.0715,P=0.0314), and increased inhibition from left amygdala to left DLPFC, all in response to fearful faces contrasted to baseline. Results did not appear to be attributable to depressive illness severity or antidepressant medication status at scan time. LIMITATIONS: Most studied participants had past rather than current depression, average severity of ongoing depression symptoms was low, and a substantial proportion of participants were receiving medication. The study was not longitudinal and the participants were only assessed a single time. CONCLUSIONS: LMDD is associated with hyperactivity of the amygdala and DLPFC, and with stronger amygdala to DLPFC inhibitory connectivity, all in response to fearful faces, unrelated to depression severity at scan time. These results help reduce inconsistency in past literature and suggest disruption of 'bottom-up' limbic-prefrontal effective connectivity in depression.

Positive thinking about negative studies.

The non-reporting of negative studies results in a scientific record that is incomplete, one-sided and misleading. The consequences of this range from inappropriate initiation of further studies that might put participants at unnecessary risk to treatment guidelines that may be in error, thus compromising day-to-day clinical practice.

Normative Modeling of Brain Morphometry in Clinical High Risk for Psychosis.

Importance: The lack of robust neuroanatomical markers of psychosis risk has been traditionally attributed to heterogeneity. A complementary hypothesis is that variation in neuroanatomical measures in individuals at psychosis risk may be nested within the range observed in healthy individuals. Objective: To quantify deviations from the normative range of neuroanatomical variation in individuals at clinical high risk for psychosis (CHR-P) and evaluate their overlap with healthy variation and their association with positive symptoms, cognition, and conversion to a psychotic disorder. Design, Setting, and Participants: This case-control study used clinical-, IQ-, and neuroimaging software (FreeSurfer)-derived regional measures of cortical thickness (CT), cortical surface area (SA), and subcortical volume (SV) from 1340 individuals with CHR-P and 1237 healthy individuals pooled from 29 international sites participating in the Enhancing Neuroimaging Genetics Through Meta-analysis (ENIGMA) Clinical High Risk for Psychosis Working Group. Healthy individuals and individuals with CHR-P were matched on age and sex within each recruitment site. Data were analyzed between September 1, 2021, and November 30, 2022. Main Outcomes and Measures: For each regional morphometric measure, deviation scores were computed as z scores indexing the degree of deviation from their normative means from a healthy reference population. Average deviation scores (ADS) were also calculated for regional CT, SA, and SV measures and globally across all measures. Regression analyses quantified the association of deviation scores with clinical severity and cognition, and 2-proportion z tests identified case-control differences in the proportion of individuals with infranormal (z < -1.96) or supranormal (z > 1.96) scores. Results: Among 1340 individuals with CHR-P, 709 (52.91%) were male, and the mean (SD) age was 20.75 (4.74) years. Among 1237 healthy individuals, 684 (55.30%) were male, and the mean (SD) age was 22.32 (4.95) years. Individuals with CHR-P and healthy individuals overlapped in the distributions of the observed values, regional z scores, and all ADS values. For any given region, the proportion of individuals with CHR-P who had infranormal or supranormal values was low (up to 153 individuals [<11.42%]) and similar to that of healthy individuals (<115 individuals [<9.30%]). Individuals with CHR-P who converted to a psychotic disorder had a higher percentage of infranormal values in temporal regions compared with those who did not convert (7.01% vs 1.38%) and healthy individuals (5.10% vs 0.89%). In the CHR-P group, only the ADS SA was associated with positive symptoms (ß = -0.08; 95% CI, -0.13 to -0.02; P = .02 for false discovery rate) and IQ (ß = 0.09; 95% CI, 0.02-0.15; P = .02 for false discovery rate). Conclusions and Relevance: In this case-control study, findings suggest that macroscale neuromorphometric measures may not provide an adequate explanation of psychosis risk.

Intact Mismatch Negativity Responses in Clinical High Risk for Psychosis and First-Episode Psychosis: Evidence From Source-Reconstructed Event-Related Fields and Time-Frequency Data.

BACKGROUND: This study examined whether mismatch negativity (MMN) responses are impaired in participants at clinical high risk for psychosis (CHR-P) and patients with first-episode psychosis (FEP) and whether MMN deficits predict clinical outcomes in CHR-Ps. METHODS: Magnetoencephalography data were collected during a duration-deviant MMN paradigm for a group of 116 CHR-P participants, 33 FEP patients (15 antipsychotic-naïve), clinical high risk negative group (n = 38) with substance abuse and affective disorder, and 49 healthy control participants. Analysis of group differences of source-reconstructed event-related fields as well as time-frequency and intertrial phase coherence focused on the bilateral Heschl's gyri and bilateral superior temporal gyri. RESULTS: Significant magnetic MMN responses were found across participants in the bilateral Heschl's gyri and bilateral superior temporal gyri. However, MMN amplitude as well as time-frequency and intertrial phase coherence responses were intact in CHR-P participants and FEP patients compared with healthy control participants. Furthermore, MMN deficits were not related to persistent attenuated psychotic symptoms or transitions to psychosis in CHR-P participants. CONCLUSIONS: Our data suggest that magnetic MMN responses in magnetoencephalography data are not impaired in early-stage psychosis and may not predict clinical outcomes in CHR-P participants.

Striatal correlates of Bayesian beliefs in self-efficacy in adolescents and their relation to mood and autonomy: a pilot study.

Major depressive disorder often originates in adolescence and is associated with long-term functional impairment. Mechanistically characterizing this heterogeneous illness could provide important leads for optimizing treatment. Importantly, reward learning is known to be disrupted in depression. In this pilot fMRI study of 21 adolescents (16-20 years), we assessed how reward network disruption impacts specifically on Bayesian belief representations of self-efficacy (SE-B) and their associated uncertainty (SE-U), using a modified instrumental learning task probing activation induced by the opportunity to choose, and an optimal Hierarchical Gaussian Filter computational model. SE-U engaged caudate, nucleus accumbens (NAcc), precuneus, posterior parietal and dorsolateral prefrontal cortex (PFWE < 0.005). Sparse partial least squares analysis identified SE-U striatal activation as associating with one's sense of perceived choice and depressive symptoms, particularly anhedonia and negative feelings about oneself. As Bayesian uncertainty modulates belief flexibility and their capacity to steer future actions, this suggests that these striatal signals may be informative developmentally, longitudinally and in assessing response to treatment.

Pathways to care for psychosis in Malawi.

People with psychosis in Malawi have very limited access to timely assessment and evidence-based care, leading to a long duration of untreated psychosis and persistent disability. Most people with psychosis in the country consult traditional or religious healers. Stigmatising attitudes are common and services have limited capacity, particularly in rural areas. This paper, focusing on pathways to care for psychosis in Malawi, is based on the Wellcome Trust Psychosis Flagship Report on the Landscape of Mental Health Services for Psychosis in Malawi. Its purpose is to inform Psychosis Recovery Orientation in Malawi by Improving Services and Engagement (PROMISE), a longitudinal study that aims to build on existing services to develop sustainable psychosis detection systems and management pathways to promote recovery.

Pathway-Based Polygenic Risk Scores for Schizophrenia and Associations With Reported Psychotic-like Experiences and Neuroimaging Phenotypes in the UK Biobank.

Background: Schizophrenia is a heritable psychiatric disorder with a polygenic architecture. Genome-wide association studies have reported that an increasing number of risk-associated variants and polygenic risk scores (PRSs) explain 17% of the variance in the disorder. Substantial heterogeneity exists in the effect of these variants, and aggregating them based on biologically relevant functions may provide mechanistic insight into the disorder. Methods: Using the largest schizophrenia genome-wide association study conducted to date, we associated PRSs based on 5 gene sets previously found to contribute to schizophrenia pathophysiology-postsynaptic density of excitatory synapses, postsynaptic membrane, dendritic spine, axon, and histone H3-K4 methylation-along with respective whole-genome PRSs, with neuroimaging (n > 29,000) and reported psychotic-like experiences (n > 119,000) variables in healthy UK Biobank subjects. Results: Several variables were significantly associated with the axon gene-set (psychotic-like communications, parahippocampal gyrus volume, fractional anisotropy thalamic radiations, and fractional anisotropy posterior thalamic radiations (ß range -0.016 to 0.0916, false discovery rate-corrected p [pFDR] ≤ .05), postsynaptic density gene-set (psychotic-like experiences distress, global surface area, and cingulate lobe surface area [ß range -0.014 to 0.0588, pFDR ≤ .05]), and histone gene set (entorhinal surface area: ß = -0.016, pFDR = .035). From these, whole-genome PRSs were significantly associated with psychotic-like communications (ß = 0.2218, pFDR = 1.34 × 10-7), distress (ß = 0.1943, pFDR = 7.28 × 10-16), and fractional anisotropy thalamic radiations (ß = -0.0143, pFDR = .036). Permutation analysis revealed that these associations were not due to chance. Conclusions: Our results indicate that genetic variation in 3 gene sets relevant to schizophrenia may confer risk for the disorder through effects on previously implicated neuroimaging variables. Because associations were stronger overall for whole-genome PRSs, findings here highlight that selection of biologically relevant variants is not yet sufficient to address the heterogeneity of the disorder.

Treatment resistance NMDA receptor pathway polygenic score is associated with brain glutamate in schizophrenia.

Dysfunction of glutamate neurotransmission has been implicated in the pathophysiology of schizophrenia and may be particularly relevant in severe, treatment-resistant symptoms. The underlying mechanism may involve hypofunction of the NMDA receptor. We investigated whether schizophrenia-related pathway polygenic scores, composed of genetic variants within NMDA receptor encoding genes, are associated with cortical glutamate in schizophrenia. Anterior cingulate cortex (ACC) glutamate was measured in 70 participants across 4 research sites using Proton Magnetic Resonance Spectroscopy (1H-MRS). Two NMDA receptor gene sets were sourced from the Molecular Signatories Database and NMDA receptor pathway polygenic scores were constructed using PRSet. The NMDA receptor pathway polygenic scores were weighted by single nucleotide polymorphism (SNP) associations with treatment-resistant schizophrenia, and associations with ACC glutamate were tested. We then tested whether NMDA receptor pathway polygenic scores with SNPs weighted by associations with non-treatment-resistant schizophrenia were associated with ACC glutamate. A higher NMDA receptor complex pathway polygenic score was significantly associated with lower ACC glutamate (ß = -0.25, 95 % CI = -0.49, -0.02, competitive p = 0.03). When SNPs were weighted by associations with non-treatment-resistant schizophrenia, there was no association between the NMDA receptor complex pathway polygenic score and ACC glutamate (ß = 0.05, 95 % CI = -0.18, 0.27, competitive p = 0.79). These results provide initial evidence of an association between common genetic variation implicated in NMDA receptor function and ACC glutamate levels in schizophrenia. This association was specific to when the NMDA receptor complex pathway polygenic score was weighted by SNP associations with treatment-resistant schizophrenia.

Network analysis of inflammation and symptoms in recent onset schizophrenia and the influence of minocycline during a clinical trial.

Attempts to delineate an immune subtype of schizophrenia have not yet led to the clear identification of potential treatment targets. An unbiased informatic approach at the level of individual immune cytokines and symptoms may reveal organisational structures underlying heterogeneity in schizophrenia, and potential for future therapies. The aim was to determine the network and relative influence of pro- and anti-inflammatory cytokines on depressive, positive, and negative symptoms. We further aimed to determine the effect of exposure to minocycline or placebo for 6 months on cytokine-symptom network connectivity and structure. Network analysis was applied to baseline and 6-month data from the large multi-center BeneMin trial of minocycline (N = 207) in schizophrenia. Pro-inflammatory cytokines IL-6, TNF-α, and IFN-γ had the greatest influence in the inflammatory network and were associated with depressive symptoms and suspiciousness at baseline. At 6 months, the placebo group network connectivity was 57% stronger than the minocycline group, due to significantly greater influence of TNF-α, early wakening, and pathological guilt. IL-6 and its downstream impact on TNF-α, and IFN-γ, could offer novel targets for treatment if offered at the relevant phenotypic profile including those with depression. Future targeted experimental studies of immune-based therapies are now needed.