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Background: Clinical forecasting models have potential to optimize treatment and improve outcomes in psychosis, but predicting long-term outcomes is challenging and long-term follow-up data are scarce. In this 10-year longitudinal study, we aimed to characterize the temporal evolution of cortical correlates of psychosis and their associations with symptoms., Design: Structural magnetic resonance imaging (MRI) from people with first-episode psychosis and controls (n = 79 and 218) were obtained at enrollment, after 12 months (n = 67 and 197), and 10 years (n = 23 and 77), within the Thematically Organized Psychosis (TOP) study. Normative models for cortical thickness estimated on public MRI datasets (n = 42 983) were applied to TOP data to obtain deviation scores for each region and timepoint. Positive and Negative Syndrome Scale (PANSS) scores were acquired at each timepoint along with registry data. Linear mixed effects models assessed effects of diagnosis, time, and their interactions on cortical deviations plus associations with symptoms., Results: LMEs revealed conditional main effects of diagnosis and time × diagnosis interactions in a distributed cortical network, where negative deviations in patients attenuate over time. In patients, symptoms also attenuate over time. LMEs revealed effects of anterior cingulate on PANSS total, and insular and orbitofrontal regions on PANSS negative scores., Conclusions: This long-term longitudinal study revealed a distributed pattern of cortical differences which attenuated over time together with a reduction in symptoms. These findings are not in line with a simple neurodegenerative account of schizophrenia, and deviations from normative models offer a promising avenue to develop biomarkers to track clinical trajectories over time., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.)

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., (© 2024. The Author(s).)

Objective: Auditory mismatch negativity (MMN) impairment is a candidate endophenotype in psychotic disorders, yet the genetic underpinnings remain to be clarified. Here, we examined the relationships between auditory MMN and polygenic risk scores (PRS) for individuals with psychotic disorders, including schizophrenia spectrum disorders (SSD) and bipolar disorder (BD) and in healthy controls (HC)., Methods: Genotyped and clinically well-characterized individuals with psychotic disorders (n = 102), including SSD (n = 43) and BD (n = 59), and HC (n = 397) underwent a roving MMN paradigm. In addition MMN, we measured the memory traces of the repetition positivity (RP) and the deviant negativity (DN), which is believed to reflect prediction encoding and prediction error signals, respectively. SCZ and BD PRS were computed using summary statistics from the latest genome-wide association studies. The relationships between the MMN, RP, and DN and the PRSs were assessed with linear regressions., Results: We found no significant association between the SCZ or BD PRS and grand average MMN in the psychotic disorders group or in the HCs group (all p > 0.05). SCZ PRS and BD PRS were negatively associated with RP in the psychotic disorders group (β = -0.46, t = -2.86, p = 0.005 and β = -0.29, t = -0.21, p = 0.034, respectively). No significant associations were found between DN and PRS., Conclusion: These findings suggest that genetic variants associated with SCZ and BD may be associated with MMN subcomponents linked to predictive coding among patients with psychotic disorders. Larger studies are needed to confirm these findings and further elucidate the genetic underpinnings of MMN impairment in psychotic disorders., Competing Interests: Declaration of competing interest Torbjørn Elvsåshagen. is a consultant to BrainWaveBank and Synovion and received speaker's honoraria from Lundbeck and Janssen Cilag. Ole Andreassen. is a consultant to HealthLytix and received speaker's honoraria from Lundbeck. The other authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

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 (β = -0.08; 95% CI, -0.13 to -0.02; P = .02 for false discovery rate) and IQ (β = 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.

Individuals at Clinical High Risk for Psychosis (CHR-P) demonstrate heterogeneity in clinical profiles and outcome features. However, the extent of neuroanatomical heterogeneity in the CHR-P state is largely undetermined. We aimed to quantify the neuroanatomical heterogeneity in structural magnetic resonance imaging measures of cortical surface area (SA), cortical thickness (CT), subcortical volume (SV), and intracranial volume (ICV) in CHR-P individuals compared with healthy controls (HC), and in relation to subsequent transition to a first episode of psychosis. The ENIGMA CHR-P consortium applied a harmonised analysis to neuroimaging data across 29 international sites, including 1579 CHR-P individuals and 1243 HC, offering the largest pooled CHR-P neuroimaging dataset to date. Regional heterogeneity was indexed with the Variability Ratio (VR) and Coefficient of Variation (CV) ratio applied at the group level. Personalised estimates of heterogeneity of SA, CT and SV brain profiles were indexed with the novel Person-Based Similarity Index (PBSI), with two complementary applications. First, to assess the extent of within-diagnosis similarity or divergence of neuroanatomical profiles between individuals. Second, using a normative modelling approach, to assess the 'normativeness' of neuroanatomical profiles in individuals at CHR-P. CHR-P individuals demonstrated no greater regional heterogeneity after applying FDR corrections. However, PBSI scores indicated significantly greater neuroanatomical divergence in global SA, CT and SV profiles in CHR-P individuals compared with HC. Normative PBSI analysis identified 11 CHR-P individuals (0.70%) with marked deviation (>1.5 SD) in SA, 118 (7.47%) in CT and 161 (10.20%) in SV. Psychosis transition was not significantly associated with any measure of heterogeneity. Overall, our examination of neuroanatomical heterogeneity within the CHR-P state indicated greater divergence in neuroanatomical profiles at an individual level, irrespective of psychosis conversion. Further large-scale investigations are required of those who demonstrate marked deviation., (© 2022. The Author(s).)

Young chronological age is one of the strongest predictors for antisocial behaviour in the general population and for violent offending in individuals with psychotic disorders. An individual's age can be predicted with high accuracy using neuroimaging and machine-learning. The deviation between predicted and chronological age, i.e., brain age gap (BAG) has been suggested to reflect brain health, likely relating partly to neurodevelopmental and aging-related processes and specific disease mechanisms. Higher BAG has been demonstrated in patients with psychotic disorders. However, little is known about the brain-age in violent offenders with psychosis and the possible associations with psychopathy traits. We estimated brain-age in 782 male individuals using T1-weighted MRI scans. Three machine learning models (random forest, extreme gradient boosting with and without hyper parameter tuning) were first trained and tested on healthy controls (HC, n = 586). The obtained BAGs were compared between HC and age matched violent offenders with psychosis (PSY-V, n = 38), violent offenders without psychosis (NPV, n = 20) and non-violent psychosis patients (PSY-NV, n = 138). We ran additional comparisons between BAG of PSY-V and PSY-NV and associations with Positive and Negative Syndrome Scale (PANSS) total score as a measure of psychosis symptoms. Psychopathy traits in the violence groups were assessed with Psychopathy Checklist-revised (PCL-R) and investigated for associations with BAG. We found significantly higher BAG in PSY-V compared with HC (4.9 years, Cohen'sd = 0.87) and in PSY-NV compared with HC (2.7 years, d = 0.41). Total PCL-R scores were negatively associated with BAG in the violence groups (d = 1.17, p < 0.05). Additionally, there was a positive association between psychosis symptoms and BAG in the psychosis groups (d = 1.12, p < 0.05). While the significant BAG differences related to psychosis and not violence suggest larger BAG for psychosis, the negative associations between BAG and psychopathy suggest a complex interplay with psychopathy traits. This proof-of-concept application of brain age prediction in severe mental disorders with a history of violence and psychopathy traits should be tested and replicated in larger samples., Competing Interests: Declaration of Competing Interest The authors report no financial relationships with commercial interest, other than dr. Andreassen who received speakeŕs honorarium from Lundbeck and Sunovion, and is a consultant for HealthLytix., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Abnormal default mode network (DMN) connectivity has been found in schizophrenia and other psychotic disorders. However, there are limited studies on early onset psychosis (EOP), and their results show lack of agreement. Here, we investigated within-network DMN connectivity in EOP compared to healthy controls (HC), and its relationship to clinical characteristics. A sample of 68 adolescent patients with EOP (mean age 16.53 ± 1.12 [SD] years, females 66%) and 95 HC (mean age 16.24 ± 1.50 [SD], females 60%) from two Scandinavian cohorts underwent resting state functional magnetic resonance imaging (rsfMRI). A group independent component analysis (ICA) was performed to identify the DMN across all participants. Dual regression was used to estimate spatial maps reflecting each participant's DMN network, which were compared between EOP and HC using voxel-wise general linear models and permutation-based analyses. Subgroup analyses were performed within the patient group, to explore associations between diagnostic subcategories and current use of psychotropic medication in relation to connectivity strength. The analysis revealed significantly reduced DMN connectivity in EOP compared to HC in the posterior cingulate cortex, precuneus, fusiform cortex, putamen, pallidum, amygdala, and insula. The subgroup analysis in the EOP group showed strongest deviations for affective psychosis, followed by other psychotic disorders and schizophrenia. There was no association between DMN connectivity strength and the current use of psychotropic medication. In conclusion, the findings demonstrate weaker DMN connectivity in adolescent patients with EOP compared to healthy peers, and differential effects across diagnostic subcategories, which may inform our understanding of underlying disease mechanisms in EOP., (Copyright © 2021. Published by Elsevier Inc.)

Background: Schizophrenia (SCZ) and bipolar disorder (BD) are severe mental illnesses (SMI) that are part of a psychosis continuum, and dysregulated innate immune responses have been suggested to be involved in their pathophysiology. However, disease-specific immune mechanisms in SMI are not known yet. Recently, dyslipidemia has been linked to systemic inflammasome activation, and elevated atherogenic lipid ratios have been shown to correlate with circulating levels of inflammatory biomarkers in SMI. It is, however, not yet known if increased systemic cholesterol load leads to inflammasome activation in these patients., Methods: We tested the hypothesis that patients with SCZ and BD display higher circulating levels compared to healthy individuals of key members of the IL-18 system using a large patient cohort (n = 1632; including 737 SCZ and 895 BD), and healthy controls (CTRL; n = 1070). In addition, we assessed associations with coronary artery disease risk factors in SMI, focusing on relevant inflammasome-related, neuroendocrine, and lipid markers., Results: We report higher baseline levels of circulating IL-18 system components (IL-18, IL-18BPA, IL-18R1), and increased expression of inflammasome-related genes (NLRP3 and NLRC4) in the blood of patients relative to CTRL. We demonstrate a cholesterol dyslipidemia pattern in psychotic disorders, and report correlations between levels of blood cholesterol types and the expression of inflammasome system elements in SMI., Conclusions: Based on these results, we suggest a role for inflammasome activation/dysregulation in SMI. Our findings further the understanding of possible underlying inflammatory mechanisms and may expose important therapeutic targets in SMI., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

While a growing literature links cardiac autonomic dysregulation to a variety of psychiatric disorders, the relationship between cardiac autonomic functioning and specific symptoms in schizophrenia (SZ) and bipolar disorder (BD) remains elusive. Thus, we investigated heart rate variability (HRV), a proxy for vagal activity, as a biological marker for symptom severity in patients with SZ and BD. HRV was calculated in 35 patients with SZ and 52 patients with BD, as well as in 149 healthy controls. In the patient groups, symptom severity and function were measured by the Positive and Negative Syndrome Scale (PANSS) and the Global Assessment of Functioning (GAF) scale. Results showed that HRV was significantly lower in both clinical groups compared to the healthy controls, with no significant HRV differences between patient groups. PANSS general psychopathology scores, GAF symptom scores, and GAF function scores showed statistically significant associations with HRV across groups. These results suggest that disease severity is associated with autonomic dysfunction and that HRV may provide a potential biomarker of disease severity in SZ and BD., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Several lines of research suggest that impairments in long-term potentiation (LTP)-like synaptic plasticity might be a key pathophysiological mechanism in schizophrenia (SZ) and bipolar disorder type I (BDI) and II (BDII). Using modulations of visually evoked potentials (VEP) of the electroencephalogram, impaired LTP-like visual cortical plasticity has been implicated in patients with BDII, while there has been conflicting evidence in SZ, a lack of research in BDI, and mixed results regarding associations with symptom severity, mood states, and medication. We measured the VEP of patients with SZ spectrum disorders (n = 31), BDI (n = 34), BDII (n = 33), and other BD spectrum disorders (n = 2), and age-matched healthy control (HC) participants (n = 200) before and after prolonged visual stimulation. Compared to HCs, modulation of VEP component N1b, but not C1 or P1, was impaired both in patients within the SZ spectrum (χ 2 = 35.1, P = 3.1 × 10-9) and BD spectrum (χ 2 = 7.0, P = 8.2 × 10-3), including BDI (χ 2 = 6.4, P = .012), but not BDII (χ 2 = 2.2, P = .14). N1b modulation was also more severely impaired in SZ spectrum than BD spectrum patients (χ 2 = 14.2, P = 1.7 × 10-4). N1b modulation was not significantly associated with Positive and Negative Syndrome Scale (PANSS) negative or positive symptoms scores, number of psychotic episodes, Montgomery and Åsberg Depression Rating Scale (MADRS) scores, or Young Mania Rating Scale (YMRS) scores after multiple comparison correction, although a nominal association was observed between N1b modulation and PANSS negative symptoms scores among SZ spectrum patients. These results suggest that LTP-like plasticity is impaired in SZ and BD. Adding to previous genetic, pharmacological, and electrophysiological evidence, these results implicate aberrant synaptic plasticity as a mechanism underlying SZ and BD., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.)

Importance: The ENIGMA clinical high risk (CHR) for psychosis initiative, the largest pooled neuroimaging sample of individuals at CHR to date, aims to discover robust neurobiological markers of psychosis risk., Objective: To investigate baseline structural neuroimaging differences between individuals at CHR and healthy controls as well as between participants at CHR who later developed a psychotic disorder (CHR-PS+) and those who did not (CHR-PS-)., Design, Setting, and Participants: In this case-control study, baseline T1-weighted magnetic resonance imaging (MRI) data were pooled from 31 international sites participating in the ENIGMA Clinical High Risk for Psychosis Working Group. CHR status was assessed using the Comprehensive Assessment of At-Risk Mental States or Structured Interview for Prodromal Syndromes. MRI scans were processed using harmonized protocols and analyzed within a mega-analysis and meta-analysis framework from January to October 2020., Main Outcomes and Measures: Measures of regional cortical thickness (CT), surface area, and subcortical volumes were extracted from T1-weighted MRI scans. Independent variables were group (CHR group vs control group) and conversion status (CHR-PS+ group vs CHR-PS- group vs control group)., Results: Of the 3169 included participants, 1428 (45.1%) were female, and the mean (SD; range) age was 21.1 (4.9; 9.5-39.9) years. This study included 1792 individuals at CHR and 1377 healthy controls. Using longitudinal clinical information, 253 in the CHR-PS+ group, 1234 in the CHR-PS- group, and 305 at CHR without follow-up data were identified. Compared with healthy controls, individuals at CHR exhibited widespread lower CT measures (mean [range] Cohen d = -0.13 [-0.17 to -0.09]), but not surface area or subcortical volume. Lower CT measures in the fusiform, superior temporal, and paracentral regions were associated with psychosis conversion (mean Cohen d = -0.22; 95% CI, -0.35 to 0.10). Among healthy controls, compared with those in the CHR-PS+ group, age showed a stronger negative association with left fusiform CT measures (F = 9.8; P < .001; q < .001) and left paracentral CT measures (F = 5.9; P = .005; q = .02). Effect sizes representing lower CT associated with psychosis conversion resembled patterns of CT differences observed in ENIGMA studies of schizophrenia (ρ = 0.35; 95% CI, 0.12 to 0.55; P = .004) and individuals with 22q11.2 microdeletion syndrome and a psychotic disorder diagnosis (ρ = 0.43; 95% CI, 0.20 to 0.61; P = .001)., Conclusions and Relevance: This study provides evidence for widespread subtle, lower CT measures in individuals at CHR. The pattern of CT measure differences in those in the CHR-PS+ group was similar to those reported in other large-scale investigations of psychosis. Additionally, a subset of these regions displayed abnormal age associations. Widespread disruptions in CT coupled with abnormal age associations in those at CHR may point to disruptions in postnatal brain developmental processes.

Genome-wide association studies (GWAS) and family-based studies have revealed partly overlapping genetic architectures between various psychiatric disorders. Given clinical overlap between disorders, our knowledge of the genetic architectures underlying specific symptom profiles and risk factors is limited. Here, we aimed to derive distinct profiles relevant to mental health in healthy individuals and to study how these genetically relate to each other and to common psychiatric disorders. Using independent component analysis, we decomposed self-report mental health questionnaires from 136,678 healthy individuals of the UK Biobank, excluding data from individuals with a diagnosed neurological or psychiatric disorder, into 13 distinct profiles relevant to mental health, capturing different symptoms as well as social and risk factors underlying reduced mental health. Utilizing genotypes from 117,611 of those individuals with White British ancestry, we performed GWAS for each mental health profile and assessed genetic correlations between these profiles, and between the profiles and common psychiatric disorders and cognitive traits. We found that mental health profiles were genetically correlated with a wide range of psychiatric disorders and cognitive traits, with strongest effects typically observed between a given mental health profile and a disorder for which the profile is common (e.g. depression symptoms and major depressive disorder, or psychosis and schizophrenia). Strikingly, although the profiles were phenotypically uncorrelated, many of them were genetically correlated with each other. This study provides evidence that statistically independent mental health profiles partly share genetic underpinnings and show genetic overlap with psychiatric disorders, suggesting that shared genetics across psychiatric disorders cannot be exclusively attributed to the known overlapping symptomatology between the disorders.

Background: Cerebral myeloarchitecture shows substantial development across childhood and adolescence, and aberrations in these trajectories are relevant for a range of mental disorders. Differential myelination between intracortical and subjacent white matter can be approximated using signal intensities in T1-weighted magnetic resonance imaging., Methods: To test the sensitivity of gray/white matter contrast (GWC) to age and individual differences in psychopathology and general cognitive ability in youths (8-23 years), we formed data-driven psychopathology and cognitive components using a large population-based sample, the Philadelphia Neurodevelopmental Cohort (N = 6487, 52% female). We then tested for associations with regional GWC defined by an independent component analysis in a subsample with available magnetic resonance imaging data (n = 1467, 53% female)., Results: The analyses revealed a global GWC component, which showed an age-related decrease from late childhood and across adolescence. In addition, we found regional anatomically meaningful components with differential age associations explaining variance beyond the global component. When accounting for age and sex, both higher symptom levels of anxiety or prodromal psychosis and lower cognitive ability were associated with higher GWC in insula and cingulate cortices and with lower GWC in pre- and postcentral cortices. We also found several additional regional associations with anxiety, prodromal psychosis, and cognitive ability., Conclusions: Independent modes of GWC variation are sensitive to global and regional brain developmental processes, possibly related to differences between intracortical and subjacent white matter myelination, and individual differences in regional GWC are associated with both mental health and general cognitive functioning., (Copyright © 2018 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Genetic factors are known to influence both risk for schizophrenia (SZ) and variation in brain structure. A pressing question is whether the genetic underpinnings of brain phenotype and the disorder overlap. Using multivariate analytic methods and focusing on 1,402 common single-nucleotide polymorphisms (SNPs) mapped from the Psychiatric Genomics Consortium (PGC) 108 regions, in 777 discovery samples, we identified 39 SNPs to be significantly associated with SZ-discriminating gray matter volume (GMV) reduction in inferior parietal and superior temporal regions. The findings were replicated in 609 independent samples. These 39 SNPs in chr6:28308034-28684183 (6p22.1), the most significant SZ-risk region reported by PGC, showed regulatory effects on both DNA methylation and gene expression of postmortem brain tissue and saliva. Furthermore, the regulated methylation site and gene showed significantly different levels of methylation and expression in the prefrontal cortex between cases and controls. In addition, for one regulated methylation site we observed a significant in vivo methylation-GMV association in saliva, suggesting a potential SNP-methylation-GMV pathway. Notably, the risk alleles inferred for GMV reduction from in vivo imaging are all consistent with the risk alleles for SZ inferred from postmortem data. Collectively, we provide evidence for shared genetic risk of SZ and regional GMV reduction in 6p22.1 and demonstrate potential molecular mechanisms that may drive the observed in vivo associations. This study motivates dissecting SZ-risk variants to better understand their associations with focal brain phenotypes and the complex pathophysiology of the illness.

There is growing interest in using intranasal oxytocin (OT) to treat social dysfunction in schizophrenia and bipolar disorders (i.e., psychotic disorders). While OT treatment results have been mixed, emerging evidence suggests that OT system dysfunction may also play a role in the etiology of metabolic syndrome (MetS), which appears in one-third of individuals with psychotic disorders and associated with increased mortality. Here we examine the evidence for a potential role of the OT system in the shared risk for MetS and psychotic disorders, and its prospects for ameliorating MetS. Using several studies to demonstrate the overlapping neurobiological profiles of metabolic risk factors and psychiatric symptoms, we show that OT system dysfunction may be one common mechanism underlying MetS and psychotic disorders. Given the critical need to better understand metabolic dysregulation in these disorders, future OT trials assessing behavioural and cognitive outcomes should additionally include metabolic risk factor parameters., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Background: Childhood trauma increases risk of a range of mental disorders including psychosis. Whereas the mechanisms are unclear, previous evidence has implicated atypical processing of emotions among the core cognitive models, in particular suggesting altered attentional allocation towards negative stimuli and increased negativity bias. Here, we tested the association between childhood trauma and brain activation during emotional face processing in patients diagnosed with psychosis continuum disorders. In particular, we tested if childhood trauma was associated with the differentiation in brain responses between negative and positive face stimuli. We also tested if trauma was associated with emotional ratings of negative and positive faces., Method: We included 101 patients with a Diagnostic and Statistical Manual of Mental Disorders (DSM) schizophrenia spectrum or bipolar spectrum diagnosis. History of childhood trauma was obtained using the Childhood Trauma Questionnaire. Brain activation was measured with functional magnetic resonance imaging during presentation of faces with negative or positive emotional expressions. After the scanner session, patients performed emotional ratings of the same faces., Results: Higher levels of total childhood trauma were associated with stronger differentiation in brain responses to negative compared with positive faces in clusters comprising the right angular gyrus, supramarginal gyrus, middle temporal gyrus and the lateral occipital cortex (Cohen's d = 0.72-0.77). In patients with schizophrenia, childhood trauma was associated with reporting negative faces as more negative, and positive faces as less positive (Cohen's d > 0.8)., Conclusions: Along with the observed negativity bias in the assessment of emotional valence of faces, our data suggest stronger differentiation in brain responses between negative and positive faces with higher levels of trauma.

Objective: The effect of antipsychotic medication on brain structure remains unclear. Given the prevalence of weight gain as a side-effect, body mass index (BMI) change could be a confounder., Method: Patients with first-episode psychosis (n = 78) and healthy controls (n = 119) underwent two 1.5T MRI scans with a 1-year follow-up interval. siena (fsl 5.0) was used to measure whole-brain volume change. Weight and height were measured at both time points. Antipsychotic medication use at baseline and follow-up was converted into chlorpromazine equivalent dose and averaged., Results: Patients did not show significantly larger brain volume loss compared with healthy controls. In the whole sample (n = 197), BMI change was negatively associated with brain volume change (β = -0.19, P = 0.008); there was no interaction effect of group. Among patients, higher antipsychotic medication dosage was associated with greater brain volume loss (β = -0.45, P < 0.001). This association was not affected by adjusting for BMI change., Conclusion: Weight gain was related to brain volume reductions to a similar degree among patients and controls. Antipsychotic dosage-related reductions of brain volume were not confounded by BMI change. Generalizability to contexts involving severe weight gain needs to be established. Furthermore, disentangling effects of medication from illness severity remains a challenge., (© 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Background: The human brain is organized into functionally distinct modules of which interactions constitute the human functional connectome. Accumulating evidence has implicated perturbations in the patterns of brain connectivity across a range of neurologic and neuropsychiatric disorders, but little is known about diagnostic specificity. Schizophrenia and bipolar disorders are severe mental disorders with partly overlapping symptomatology. Neuroimaging has demonstrated brain network disintegration in the pathophysiologies; however, to which degree the 2 diagnoses present with overlapping abnormalities remains unclear., Methods: We collected resting-state fMRI data from patients with schizophrenia or bipolar disorder and from healthy controls. Aiming to characterize connectivity differences across 2 severe mental disorders, we derived global functional connectivity using eigenvector centrality mapping, which allows for regional inference of centrality or importance in the brain network., Results: Seventy-one patients with schizophrenia, 43 with bipolar disorder and 196 healthy controls participated in our study. We found significant effects of diagnosis in 12 clusters, where pairwise comparisons showed decreased global connectivity in high-centrality clusters: sensory regions in patients with schizophrenia and subcortical regions in both patient groups. Increased connectivity occurred in frontal and parietal clusters in patients with schizophrenia, with intermediate effects in those with bipolar disorder. Patient groups differed in most cortical clusters, with the strongest effects in sensory regions., Limitations: Methodological concerns of in-scanner motion and the use of full correlation measures may make analyses more vulnerable to noise., Conclusion: Our results show decreased eigenvector centrality of limbic structures in both patient groups and in sensory regions in patients with schizophrenia as well as increased centrality in frontal and parietal regions in both groups, with stronger effects in patients with schizophrenia.

Objective: We investigated whether elevated plasma levels of immune markers were associated with verbal memory and hippocampal subfield volumes in patients with severe mental illnesses and in healthy controls., Method: In total, 230 patients with a broad DSM-IV schizophrenia spectrum illness or bipolar disorder and 236 healthy controls were recruited. Memory was assessed using the Wechsler Memory Scale-Third Edition (WMS-III) Logical Memory immediate and delayed recall, and the California Verbal Learning Test summed recall over learning list (CVLT learning) and delayed free recall. We measured plasma levels of soluble tumor necrosis factor receptor 1 (sTNF-R1), interleukin-1 receptor antagonist, interleukin-6, von Willebrand factor, osteoprotegerin, high-sensitivity C-reactive protein and sCD40 ligand. Hippocampal subfield estimates were obtained using FreeSurfer., Results: We found a moderate negative association between sTNF-R1 and performance on verbal memory learning and recall tests as measured by the WMS-III Logical Memory after controlling for age, sex and diagnosis. We observed no interaction effect of diagnosis and sTNF-R1 on memory scores. We also found a nominally significant positive association between CVLT learning and hippocampal volumes., Conclusion: The findings suggest a role for immune involvement in memory independent of severe mental disorders and may support the 'bigger is better' hypothesis of hippocampal subfield volumes., (© 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Schizophrenia (SZ) is characterized by cognitive dysfunction and disorganized thought, in addition to hallucinations and delusions, and is regarded a disorder of brain connectivity. Recent efforts have been made to characterize the underlying brain network organization and interactions. However, to which degree connectivity alterations in SZ vary across different levels of cognitive effort is unknown. Utilizing independent component analysis (ICA) and methods for delineating functional connectivity measures from functional magnetic resonance imaging (fMRI) data, we investigated the effects of cognitive effort, SZ and their interactions on between-network functional connectivity during 2 levels of cognitive load in a large and well-characterized sample of SZ patients (n = 99) and healthy individuals (n = 143). Cognitive load influenced a majority of the functional connections, including but not limited to fronto-parietal and default-mode networks, reflecting both decreases and increases in between-network synchronization. Reduced connectivity in SZ was identified in 2 large-scale functional connections across load conditions, with a particular involvement of an insular network. The results document an important role of interactions between insular, default-mode, and visual networks in SZ pathophysiology. The interplay between brain networks was robustly modulated by cognitive effort, but the reduced functional connectivity in SZ, primarily related to an insular network, was independent of cognitive load, indicating a relatively general brain network-level dysfunction., (© The Author 2015. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Hippocampal dysfunction and volume reductions have been reported in patients with schizophrenia and bipolar disorder. The hippocampus consists of anatomically distinct subfields. We investigated to determine whether in vivo volumes of hippocampal subfields differ between clinical groups and healthy control subjects., Methods: Clinical examination and magnetic resonance imaging were performed in 702 subjects (patients with schizophrenia spectrum [n = 210; mean age, 32.0 ± 9.3 (SD) years; 59% male], patients with bipolar spectrum [n = 192; mean age, 35.5 ± 11.5 years; 40% male] and healthy control subjects [n = 300; mean age, 35.3 ± 9.9 years; 53% male]). Hippocampal subfield volumes were estimated with FreeSurfer. General linear models were used to explore diagnostic differences in hippocampal subfield volumes, covarying for age, intracranial volume, and medication. Post hoc analyses of associations to psychosis symptoms (Positive and Negative Syndrome Scale) and cognitive function (verbal memory [California Verbal Learning Test, second edition] and IQ [Wechsler Abbreviated Scale of Intelligence]) were performed., Results: Patient groups had smaller cornu ammonis (CA) subfields CA2/3 (left, p = 7.2 × 10(-6); right, p = 2.3 × 10(-6)), CA4/dentate gyrus (left, p = 1.4 × 10(-5); right, p = 2.3 × 10(-6)), subiculum (left, p = 3.7 × 10(-6); right, p = 2.8 × 10(-8)), and right CA1 (p = .006) volumes than healthy control subjects, but smaller presubiculum volumes were found only in patients with schizophrenia (left, p = 6.7 × 10(-5); right, p = 1.6 × 10(-7)). Patients with schizophrenia had smaller subiculum (left, p = .035; right, p = .031) and right presubiculum (p = .002) volumes than patients with bipolar disorder. Smaller subiculum volumes were related to poorer verbal memory in patients with bipolar disorder and healthy control subjects and to negative symptoms in patients with schizophrenia., Conclusions: Hippocampal subfield volume reductions are found in patients with schizophrenia and bipolar disorder. The magnitude of reduction is greater in patients with schizophrenia, particularly in the hippocampal outflow regions presubiculum and subiculum., (Copyright © 2015 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)