Search

Introduction Mental health-related stigma occurs not only within the public community but is also an issue among healthcare professionals. The relationship between national culture and provider stigma remains yet to be empirically attested. Objectives We performed a cross-sectional multicentre study across 32 European countries to investigate the attitudes of psychiatrists towards patients with mental health problems. We aimed to examine the relationship of attitude with country-specific indicators. Methods We measured stigmatizing attitudes using the Opening Minds Stigma Scale for Health Care Providers (OMS-HC) within an online survey among specialists and trainees in general adult, child and adolescent psychiatry. Its total score was correlated with the Human Development Index (HDI), the Democracy Index (DI), the Social Progress Index (SPI), the number of psychiatrists per 100,000 people, and the Hofstede dimensions. Latent class analysis was done to find subgroups of countries according to the stigmatizing attitudes of psychiatrists and the six Hofstede dimensions. Results Altogether, n=4245 participants completed the survey. The total score of the OMS-HC significantly correlated with the long-term orientation (r=0.453, p=0.015) and indulgence dimensions (r=-0.629, p

Introduction Autobiographical memory is known to be disturbed in schizophrenia. In addition, a leading theory of auditory hallucinations (AVH) is that they are intrusive – typically negative – autobiographical memories that are misinterpreted as perceptions. Objectives The aim of this study was to examine the brain functional correlates of recall of negatively emotionally valanced autobiographical memories in patients with schizophrenia, with a longer term aim of comparing patients with and without AVH. Methods 11 patients meeting DSM-5 criteria for schizophrenia or schizoaffective disorder and 10 age, sex and estimated premorbid IQ-matched healthy controls have so far taken part. Participants underwent functional MRI in a 3T scanner while performing a task requiring them to recall autobiographical memories in response to individually tailored pairs of cue words. The cue words were based on autobiographical memories previously elicited in an interview with each patient and were designed to evoke the same memory. The cue words were presented in 10 20-second blocks interspersed with blocks where the subjects viewed cue words that did not evoke autobiographical memories. Brain activations were examined in three contrasts of interest: memory evoking words vs baseline, neutral words vs baseline and memory evoking vs neutral words. Pre-processing and analysis were carried out with the FEAT module included in the FSL software. Statistical analysis was performed by means of a General Linear Model (GLM) approach. Results In the memory evoking vs baseline contrast the patients showed hypoactivation in the medial frontal cortex compared to the healthy controls (Figure 1). There were no differences in activation between the patients and the controls comparing the memory evoking and neutral cues. Image: Conclusions The finding of hypoactivation in the medial frontal cortex compared to low level baseline in patients with schizophrenia suggests dysfunction in the default mode network, which is known to activate during recall of autobiographical memories. These preliminary results suggest that recall of negative autobiographical memories in patients with schizophrenia is associated with reduced activity in the default mode network. A planned larger sample of patients and controls will be used to examine activations in patients with and without AVH. Disclosure of Interest None Declared

Introduction Since the literature investigating the stigmatising attitudes of psychiatrists is scarce, this is the first study which examines the phenomena across Europe. The Opening Minds Stigma Scale for Health Care Providers (OMS-HC) is a widely used questionnaire to measure stigma in healthcare providers towards people with mental illness, although it has not been validated in many European countries. Objectives A cross-sectional, observational, multi-centre study was conducted in 32 European countries to investigate the attitudes towards patients among specialists and trainees in general adult and child psychiatry. In order to be able to compare stigma scores across cultures, we aimed to calculate measurement invariance. Methods An internet-based, anonymous survey was distributed in the participating countries, which was completed by n=4245 psychiatrists. The factor structure of the scale was investigated by using separate confirmatory factor analyses for each country. The cross-cultural validation was based on multigroup confirmatory factor analyses. Results When country data were analysed separately, the three dimensions of the OMS-HC were confirmed, and the bifactor model showed the best model fit. However, in some countries, a few items were found to be weak. The attitudes towards patients seemed favourable since stigma scores were less than half of the reachable maximum. Results allowed comparison to be made between stigma scores in different countries and subgroups. Conclusions This international cooperation has led to the cross-cultural validation of the OMS-HC on a large sample of practicing psychiatrists. The results will be useful in the evaluation of future anti-stigma interventions and will contribute to the knowledge of stigma. Disclosure No significant relationships.

Background: Schizophrenia (SCZ) is characterized by a disconnect from reality that manifests as various clinical and cognitive symptoms, and persistent neurobiological abnormalities. Sex-related differences in clinical presentation imply separate brain substrates. The present study characterized deep brain morphology using shape features to understand the independent effects of diagnosis and sex on the brain, and to determine whether the neurobiology of schizophrenia varies as a function of sex., Methods: This study analyzed multi-site archival data from 1,871 male (M) and 955 female (F) participants with SCZ, and 2,158 male and 1,877 female healthy controls (CON) from twenty-three cross-sectional samples from the ENIGMA Schizophrenia Workgroup. Harmonized shape analysis protocols were applied to each site's data for seven deep brain regions obtained from T1-weighted structural MRI scans. Effect sizes were calculated for the following main contrasts: 1) Sex effects;2) Diagnosis-by-Sex interaction; 3) within sex tests of diagnosis; 4) within diagnosis tests of sex differences. Meta-regression models between brain structure and clinical variables were also computed separately in men and women with schizophrenia., Results: Mass univariate meta-analyses revealed more concave-than-convex shape differences in all regions for women relative to men, across diagnostic groups ( d = -0.35 to 0.20, SE = 0.02 to 0.07); there were no significant diagnosis-by-sex interaction effects. Within men and women separately, we identified more-concave-than-convex shape differences for the hippocampus, amygdala, accumbens, and thalamus, with more-convex-than-concave differences in the putamen and pallidum in SCZ ( d = -0.30 to 0.30, SE = 0.03 to 0.10). Within CON and SZ separately, we found more-concave-than-convex shape differences in the thalamus, pallidum, putamen, and amygdala among females compared to males, with mixed findings in the hippocampus and caudate ( d = -0.30 to 0.20, SE = 0.03 to 0.09). Meta-regression models revealed similarly small, but significant relationships, with medication and positive symptoms in both SCZ-M and SCZ-F., Conclusions: Sex-specific variation is an overriding feature of deep brain shape regardless of disease status, underscoring persistent patterns of sex differences observed both within and across diagnostic categories, and highlighting the importance of including it as a critical variable in studies of neurobiology. Future work should continue to explore these dimensions independently to determine whether these patterns of brain morphology extend to other aspects of neurobiology in schizophrenia, potentially uncovering broader implications for diagnosis and treatment., Key Points: Statistical analyses revealed significant main effects for diagnosis and sex in deep brain shape morphology. Among patients with schizophrenia, there was a pattern of thinning and surface contraction in the bilateral hippocampus, amygdala, accumbens, and thalamus, and a pattern of significant thickening and surface expansion in the bilateral putamen and pallidum compared to healthy control participants. Between males and females, there was a pattern of significant thinning and surface contraction in the bilateral thalamus, pallidum, putamen, and amygdala in females compared to males.There was no significant interaction between diagnosis and biological sex, suggesting that sex differences in deep brain shape and asymmetry among patients with schizophrenia reflect those observed in healthy individuals.Small but statistically significant relationships exist between brain structure and clinical correlates of schizophrenia were similar for both men and women with the disease, such that higher CPZ was associated with shape-derived thinning and surface contraction in the caudate, accumbens, hippocampus, amygdala, and thalamus, and elevated positive symptoms were associated with shape-derived thinning and surface contraction in the bilateral caudate, right hippocampus, and right amygdala.

Background and Hypothesis: This umbrella review aims to comprehensively synthesize the evidence of association between peripheral, electrophysiological, neuroimaging, neuropathological, and other biomarkers and diagnosis of psychotic disorders., Study Design: We selected systematic reviews and meta-analyses of observational studies on diagnostic biomarkers for psychotic disorders, published until February 1, 2018. Data extraction was conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Evidence of association between biomarkers and psychotic disorders was classified as convincing, highly suggestive, suggestive, weak, or non-significant, using a standardized classification. Quality analyses used the Assessment of Multiple Systematic Reviews (AMSTAR) tool., Study Results: The umbrella review included 110 meta-analyses or systematic reviews corresponding to 3892 individual studies, 1478 biomarkers, and 392 210 participants. No factor showed a convincing level of evidence. Highly suggestive evidence was observed for transglutaminase autoantibodies levels (odds ratio [OR] = 7.32; 95% CI: 3.36, 15.94), mismatch negativity in auditory event-related potentials (standardized mean difference [SMD] = 0.73; 95% CI: 0.5, 0.96), P300 component latency (SMD = -0.6; 95% CI: -0.83, -0.38), ventricle-brain ratio (SMD = 0.61; 95% CI: 0.5, 0.71), and minor physical anomalies (SMD = 0.99; 95% CI: 0.64, 1.34). Suggestive evidence was observed for folate, malondialdehyde, brain-derived neurotrophic factor, homocysteine, P50 sensory gating (P50 S2/S1 ratio), frontal N -acetyl-aspartate, and high-frequency heart rate variability. Among the remaining biomarkers, weak evidence was found for 626 and a non-significant association for 833 factors., Conclusions: While several biomarkers present highly suggestive or suggestive evidence of association with psychotic disorders, methodological biases, and underpowered studies call for future higher-quality research., (© The Author(s) 2024. Published by Oxford University Press on behalf of the University of Maryland's school of medicine, Maryland Psychiatric Research Center.)

Introduction: Research data show the impact of the endocannabinoid system on psychosis through its neurotransmission homeostatic functions. However, the effect of the endocannabinoid system genetic variability on the relationship between cannabis use and psychosis has been unexplored, even less in first-episode patients. Here, through a case-only design, we investigated the effect of cannabis use and the genetic variability of endocannabinoid receptors on clinical and cognitive outcomes in first-episode psychosis (FEP) patients., Methods: The sample comprised 50 FEP patients of European ancestry (mean age (sd) = 26.14 (6.55) years, 76% males), classified as cannabis users (58%) or cannabis non-users. Two Single Nucleotide Polymorphisms (SNP) were genotyped at the cannabinoid receptor type 1 gene ( CNR1 rs1049353) and cannabinoid receptor type 2 gene ( CNR2 rs2501431). Clinical (PANSS, GAF) and neuropsychological (WAIS, WMS, BADS) assessments were conducted. By means of linear regression models, we tested the main effect of cannabis use and its interaction with the polymorphic variants on the clinical and cognitive outcomes., Results: First, as regards cannabis effects, our data showed a trend towards more severe positive symptoms (PANSS, p  = 0.05) and better performance in manipulative abilities (matrix test-WAIS, p  = 0.041) among cannabis users compared to non-users. Second, concerning the genotypic effects, the T allele carriers of the CNR1 rs1049353 presented higher PANSS disorganization scores than CC homozygotes ( p  = 0.014). Third, we detected that the observed association between cannabis and manipulative abilities is modified by the CNR2 polymorphism ( p  = 0.022): cannabis users carrying the G allele displayed better manipulative abilities than AA genotype carriers, while the cannabis non-users presented the opposite genotype-performance pattern. Such gene-environment interaction significantly improved the overall fit of the cannabis-only model (Δ-R 2  = 8.4%, p  = 0.019)., Discussion: Despite the preliminary nature of the sample, our findings point towards the role of genetic variants at CNR1 and CNR2 genes in the severity of the disorganized symptoms of first-episode psychosis and modulating cognitive performance conditional to cannabis use. This highlights the need for further characterization of the combined role of endocannabinoid system genetic variability and cannabis use in the understanding of the pathophysiology of psychosis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Oscoz-Irurozqui, Guardiola-Ripoll, Almodóvar-Payá, Guerrero-Pedraza, Hostalet, Carrion, Sarró, Gomar, Pomarol-Clotet and Fatjó-Vilas.)

Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal 'trajectory' of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors., (© 2024. The Author(s).)

Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15-90. The effects of dementia, mild cognitive impairment, Parkinson's disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia ( p < 0.001), while neither depression nor ADHD showed consistent associations with VLM scores ( p > 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders.

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables., (© 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects., (© 2024. The Author(s).)

Background: Mental health-related stigma occurs among the public and professionals alike. The lived experience of mental illness has been linked to less stigmatising attitudes. However, data on psychiatrists and the relationship between stigmatising attitudes and psychotherapeutic activity or case discussion groups remains scarce., Methods: A cross-sectional multicentre study was performed in 32 European countries to investigate the lived experiences and attitudes of psychiatrists toward patients with mental illness as well as the relationship between stigma, psychosocial and professional factors. The self-reported, anonymous, internet-based Opening Minds Stigma Scale for Health Care Providers was used to measure the stigmatising attitudes. The survey was translated into the local language of each participating country. All participants were practising specialists and trainees in general adult or child and adolescent psychiatry. The study took place between 2nd October, 2019 and 9th July, 2021 and was preregistered at ClinicalTrial.gov (NCT04644978)., Findings: A total of 4245 psychiatrists completed the survey. The majority, 2797 (66%), had completed training in psychiatry, and 3320 (78%) worked in adult psychiatry. The final regression model showed that across European countries more favourable attitudes toward people with mental illness were statistically significantly associated with the lived experience of participants (including seeking help for their own mental health conditions (d = -0.92, 95% confidence interval (CI) = -1.68 to -0.15, p = 0.019), receiving medical treatment for a mental illness (d = -0.88, 95% CI = -1.71 to -0.04, p = 0.040), as well as having a friend or a family member similarly affected (d = -0.68, 95% CI = -1.14 to -0.22, p = 0.004)), being surrounded by colleagues who are less stigmatising (d = -0.98, 95% CI = -1.26 to -0.70, p < 0.001), providing psychotherapy to patients (d = -1.14, 95% CI = -1.63 to -0.65 p < 0.001), and being open to (d = -1.69, 95% CI = -2.53 to -0.85, p < 0.001) and actively participating in (d = -0.94, 95% CI = -1.45 to -0.42, p < 0.001) case discussion, supervision, or Balint groups., Interpretation: Our study highlights the importance of psychotherapy training, supervision, case discussions and Balint groups in reducing the stigmatising attitudes of psychiatrists toward patients. As the findings represent cross-national predictors, Europe-wide policy interventions, national psychiatric education systems and the management of psychiatric institutions should take these findings into consideration., Funding: National Youth Talent Award (Ministry of Human Resources, Hungary, (NTP-NFTÖ-20-B-0134). All authors received no funding for their contribution., Competing Interests: DŐ received the National Youth Talent Award in 2020 and 2021 (Ministry of Human Resources, Hungary, (NTP-NFTÖ-20-B-0134 and NTP-NFTÖ-21-B-0280) that covered the membership fee for one year of the online survey platform, and the expenses of the dissemination of the results at international congresses. She received grants from the Fulbright Program in 2022, the Semmelweis 250+ Excellence PhD Scholarship in 2023 and the New National Excellence Program in 2023 (ÚNKP-23-III-2). She received support for travel from Kerpel-Fronius Talent Support Program of Semmelweis University in 2022 and 2023 (EFOP-3.6.3-VEKOP-16-2017-00009). She is a medical Secretariat at the Hungarian Psychiatric Association. The article processing charge is covered by the Semmelweis University. SMa received a grant to attend the World Congress of Psychiatry (WPA) in 2022 and 2023 from the World Psychiatric Association. She is the Vice-chair of Psychiatric trainees committee (2020–2021) of the Royal College of Psychiatrists (UK). MW is the past president of the European Federation of Psychaitric Trainees. He is a Co-president of the Verband der Schweizerischen Assistenz—und Oberarzte (Association of Swiss Assistant and Senior Physicians) Section Thuragu. SRó received support from The Károli Gáspár University of the Reformed Church for the statistical analyses carried out (Grant No. 20754B800). Other authors declare no competing financial interests., (© 2023 The Author(s).)

Schizophrenia may represent a trade-off in the evolution of human-specific ontogenetic mechanisms that guide neurodevelopment. Human Accelerated Regions (HARs) are evolutionary markers functioning as neurodevelopmental transcription enhancers that have been associated with brain configuration, neural information processing, and schizophrenia risk. Here, we have investigated the influence of HARs' polygenic load on neuroanatomical measures through a case-control approach (128 patients with schizophrenia and 115 controls). To this end, we have calculated the global schizophrenia Polygenic Risk Score (Global PRS SZ ) and that specific to HARs (HARs PRS SZ ). We have also estimated the polygenic burden restricted to the HARs linked to transcriptional regulatory elements active in the foetal brain (FB-HARs PRS SZ ) and the adult brain (AB-HARs PRS SZ ). We have explored the main effects of the PRSs and the PRSs x diagnosis interactions on brain regional cortical thickness (CT) and surface area (SA). The results indicate that a higher FB-HARs PRS SZ is associated with patients' lower SA in the lateral orbitofrontal cortex, the superior temporal cortex, the pars triangularis and the paracentral lobule. While noHARs-derived PRSs show an effect on the risk, our neuroanatomical findings suggest that the human-specific transcriptional regulation during the prenatal period underlies SA variability, highlighting the role of these evolutionary markers in the schizophrenia genomic architecture., (© 2023. Springer Nature Limited.)

Despite diffusion tensor imaging (DTI) evidence for widespread fractional anisotropy (FA) reductions in the brain white matter of patients with bipolar disorder, questions remain regarding the specificity and sensitivity of FA abnormalities as opposed to other diffusion metrics in the disorder. We conducted a whole-brain voxel-based multicompartment diffusion MRI study on 316 participants (i.e., 158 patients and 158 matched healthy controls) employing four diffusion metrics: the mean diffusivity (MD) and FA estimated from DTI, and the intra-axonal signal fraction (IASF) and microscopic axonal parallel diffusivity (Dpar) derived from the spherical mean technique. Our findings provide novel evidence about widespread abnormalities in other diffusion metrics in BD. An extensive overlap between the FA and IASF results suggests that the lower FA in patients may be caused by a reduced intra-axonal volume fraction or a higher macromolecular content in the intra-axonal water. We also found a diffuse alteration in MD involving white and grey matter tissue and more localised changes in Dpar. A Machine Learning analysis revealed that FA, followed by IASF, were the most helpful metric for the automatic diagnosis of BD patients, reaching an accuracy of 72%. Number of mood episodes, age of onset/duration of illness, psychotic symptoms, and current treatment with lithium, antipsychotics, antidepressants, and antiepileptics were all significantly associated with microstructure abnormalities. Lithium treatment was associated with less microstructure abnormality., (© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Aims: To measure the stigma of healthcare providers toward people suffering from mental illness, the Opening Minds Stigma Scale for Health Care Providers (OMS-HC) is a commonly applied instrument. However, this scale has not been thoroughly validated in many European countries, its psychometric properties are still unknown and data on practicing psychiatrists is lacking. Therefore, this multicenter study aimed to assess the psychometric characteristics of the 15-item OMS-HC in trainees and specialists in adult and child psychiatry in 32 countries across Europe., Materials and Methods: The OMS-HC was conducted as an anonymous online survey and sent via Email to European adult and child psychiatrists. Parallel analysis was used to estimate the number of OMS-HC dimensions. Separate for each country, the bifactor ESEM, a bifactor exploratory structural equation modeling approach, was applied to investigate the factor structure of the scale. Cross-cultural validation was done based on multigroup confirmatory factor analyses and reliability measures., Results: A total of 4,245 practitioners were included, 2,826 (67%) female, 1,389 (33%) male. The majority (66%) of participants were specialists, with 78% working in adult psychiatry. When country data were analyzed separately, the bifactor model (higher-order factor solution with a general factor and three specific factors) showed the best model fit (for the total sample χ 2 /df = 9.760, RMSEA = 0.045 (0.042-0.049), CFI = 0.981; TLI = 0.960, WRMR = 1.200). The average proportion of variance explained by the general factor was high (ECV = 0.682). This suggests that the aspects of 'attitude,' 'disclosure and help-seeking,' and 'social distance' could be treated as a single dimension of stigma. Among the specific factors, the 'disclosure and help-seeking' factor explained a considerable unique proportion of variance in the observed scores., Conclusion: This international study has led to cross-cultural analysis of the OMS-HC on a large sample of practicing psychiatrists. The bifactor structure displayed the best overall model fit in each country. Rather than using the subscales, we recommend the total score to quantify the overall stigmatizing attitudes. Further studies are required to strengthen our findings in countries where the proposed model was found to be weak., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Őri, Szocsics, Molnár, Bankovska Motlova, Kazakova, Mörkl, Wallies, Abdulhakim, Boivin, Bruna, Cabacos, Carbone, Dashi, Grech, Greguras, Ivanovic, Guevara, Kakar, Kotsis, Klinkby, Maslak, Matheiken, Mirkovic, Nechepurenko, Panayi, Pereira, Pomarol-Clotet, Raaj, Rus Prelog, Soler-Vidal, Strumila, Schuster, Kisand, Hargi, Ahmadova, Vircik, Yilmaz Kafali, Grinko, Győrffy and Rózsa.)

Background and Hypothesis: The existing developmental bond between fingerprint generation and growth of the central nervous system points to a potential use of fingerprints as risk markers in schizophrenia. However, the high complexity of fingerprints geometrical patterns may require flexible algorithms capable of characterizing such complexity., Study Design: Based on an initial sample of scanned fingerprints from 612 patients with a diagnosis of non-affective psychosis and 844 healthy subjects, we have built deep learning classification algorithms based on convolutional neural networks. Previously, the general architecture of the network was chosen from exploratory fittings carried out with an independent fingerprint dataset from the National Institute of Standards and Technology. The network architecture was then applied for building classification algorithms (patients vs controls) based on single fingers and multi-input models. Unbiased estimates of classification accuracy were obtained by applying a 5-fold cross-validation scheme., Study Results: The highest level of accuracy from networks based on single fingers was achieved by the right thumb network (weighted validation accuracy = 68%), while the highest accuracy from the multi-input models was attained by the model that simultaneously used images from the left thumb, index and middle fingers (weighted validation accuracy = 70%)., Conclusion: Although fitted models were based on data from patients with a well established diagnosis, since fingerprints remain lifelong stable after birth, our results imply that fingerprints may be applied as early predictors of psychosis. Specially, if they are used in high prevalence subpopulations such as those of individuals at high risk for psychosis., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.)

The role of both cannabis use and genetic background has been shown in the risk for psychosis. However, the effect of the interplay between cannabis and variability at the endocannabinoid receptor genes on the neurobiological underpinnings of psychosis remains inconclusive. Through a case-only design, including patients with a first-episode of psychosis (n = 40) classified as cannabis users (50%) and non-users (50%), we aimed to evaluate the interaction between cannabis use and common genetic variants at the endocannabinoid receptor genes on brain activity. Genetic variability was assessed by genotyping two Single Nucleotide Polymorphisms (SNP) at the cannabinoid receptor type 1 gene ( CNR1 ; rs1049353) and cannabinoid receptor type 2 gene (CNR2 ; rs2501431). Functional Magnetic Resonance Imaging (fMRI) data were obtained while performing the n-back task. Gene × cannabis interaction models evidenced a combined effect of CNR1 and CNR2 genotypes and cannabis use on brain activity in different brain areas, such as the caudate nucleus, the cingulate cortex and the orbitofrontal cortex. These findings suggest a joint role of cannabis use and cannabinoid receptor genetic background on brain function in first-episode psychosis, possibly through the impact on brain areas relevant to the reward circuit.

Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case-control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case-control status. Subtle case-control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia.

Multisite machine-learning neuroimaging studies, such as those conducted by the ENIGMA Consortium, need to remove the differences between sites to avoid effects of the site (EoS) that may prevent or fraudulently help the creation of prediction models, leading to impoverished or inflated prediction accuracy. Unfortunately, we have shown earlier that current Methods Aiming to Remove the EoS (MAREoS, e.g., ComBat) cannot remove complex EoS (e.g., including interactions between regions). And complex EoS may bias the accuracy. To overcome this hurdle, groups worldwide are developing novel MAREoS. However, we cannot assess their effectiveness because EoS may either inflate or shrink the accuracy, and MAREoS may both remove the EoS and degrade the data. In this work, we propose a strategy to measure the effectiveness of a MAREoS in removing different types of EoS. FOR MAREOS DEVELOPERS, we provide two multisite MRI datasets with only simple true effects (i.e., detectable by most machine-learning algorithms) and two with only simple EoS (i.e., removable by most MAREoS). First, they should use these datasets to fit machine-learning algorithms after applying the MAREoS. Second, they should use the formulas we provide to calculate the relative accuracy change associated with the MAREoS in each dataset and derive an EoS-removal effectiveness statistic. We also offer similar datasets and formulas for complex true effects and EoS that include first-order interactions. FOR MACHINE-LEARNING RESEARCHERS, we provide an extendable benchmark website to show: a) the types of EoS they should remove for each given machine-learning algorithm and b) the effectiveness of each MAREoS for removing each type of EoS. Relevantly, a MAREoS only able to remove the simple EoS may suffice for simple machine-learning algorithms, whereas more complex algorithms need a MAREoS that can remove more complex EoS. For instance, ComBat removes all simple EoS as needed for predictions based on simple lasso algorithms, but it leaves residual complex EoS that may bias the predictions based on standard support vector machine algorithms., (Copyright © 2022. Published by Elsevier Inc.)

The experience of auditory verbal hallucinations (AVH, "hearing voices") in schizophrenia has been found to be associated with reduced auditory cortex activation during perception of real auditory stimuli like tones and speech. We re-examined this finding using 46 patients with schizophrenia (23 with frequent AVH and 23 hallucination-free), who underwent fMRI scanning while they heard words, sentences and reversed speech. Twenty-five matched healthy controls were also examined. Perception of words, sentences and reversed speech all elicited activation of the bilateral superior temporal cortex, the inferior and lateral prefrontal cortex, the inferior parietal cortex and the supplementary motor area in the patients and the healthy controls. During the sentence and reversed speech conditions, the schizophrenia patients as a group showed reduced activation in the left primary auditory cortex (Heschl's gyrus) relative to the healthy controls. No differences were found between the patients with and without hallucinations in any condition. This study therefore fails to support previous findings that experience of AVH attenuates speech-perception-related brain activations in the auditory cortex. At the same time, it suggests that schizophrenia patients, regardless of presence of AVH, show reduced activation in the primary auditory cortex during speech perception, a finding which could reflect an early information processing deficit in the disorder., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Soler-Vidal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Detecting patients at high relapse risk after the first episode of psychosis (HRR-FEP) could help the clinician adjust the preventive treatment. To develop a tool to detect patients at HRR using their baseline clinical and structural MRI, we followed 227 patients with FEP for 18-24 months and applied MRIPredict. We previously optimized the MRI-based machine-learning parameters (combining unmodulated and modulated gray and white matter and using voxel-based ensemble) in two independent datasets. Patients estimated to be at HRR-FEP showed a substantially increased risk of relapse (hazard ratio = 4.58, P < 0.05). Accuracy was poorer when we only used clinical or MRI data. We thus show the potential of combining clinical and MRI data to detect which individuals are more likely to relapse, who may benefit from increased frequency of visits, and which are unlikely, who may be currently receiving unnecessary prophylactic treatments. We also provide an updated version of the MRIPredict software., (© 2022. The Author(s).)

The main objective of the present study was to investigate the association between several epigenetic clocks, covering different aspects of aging, with schizophrenia relapse evaluated over a 3-year follow-up period in a cohort of ninety-one first-episode schizophrenia patients. Genome-wide DNA methylation was profiled and four epigenetic clocks, including epigenetic clocks of chronological age, mortality and telomere length were calculated. Patients that relapsed during the follow-up showed epigenetic acceleration of the telomere length clock (p = 0.030). Shorter telomere length was associated with cognitive performance (working memory, r = 0.31 p = 0.015; verbal fluency, r = 0.28 p = 0.028), but no direct effect of cognitive function or symptom severity on relapse was detected. The results of the present study suggest that epigenetic age acceleration could be involved in the clinical course of schizophrenia and could be a useful marker of relapse when measured in remission stages., (© 2022. The Author(s).)

Included in the neurotrophins family, the Neuritin 1 gene ( NRN1 ) has emerged as an attractive candidate gene for schizophrenia (SZ) since it has been associated with the risk for the disorder and general cognitive performance. In this work, we aimed to further investigate the association of NRN1 with SZ by exploring its role on age at onset and its brain activity correlates. First, we developed two genetic association analyses using a family-based sample (80 early-onset (EO) trios (offspring onset ≤ 18 years) and 71 adult-onset (AO) trios) and an independent case-control sample (120 healthy subjects (HS), 87 EO and 138 AO patients). Second, we explored the effect of NRN1 on brain activity during a working memory task (N-back task; 39 HS, 39 EO and 39 AO; matched by age, sex and estimated IQ). Different haplotypes encompassing the same three Single Nucleotide Polymorphisms(SNPs, rs3763180-rs10484320-rs4960155) were associated with EO in the two samples (GCT, TCC and GTT). Besides, the GTT haplotype was associated with worse N-back task performance in EO and was linked to an inefficient dorsolateral prefrontal cortex activity in subjects with EO compared to HS. Our results show convergent evidence on the NRN1 association with EO both from genetic and neuroimaging approaches, highlighting the role of neurotrophins in the pathophysiology of SZ.

Regularization may be used as an alternative to dimensionality reduction when the number of variables in a model is much larger than the number of available observations. In a recent study from our group regularized regression was employed to quantify brain functional connectivity in a sample of healthy controls using a brain parcellation and resting state fMRI images. Here regularization is applied to evaluate resting state connectivity abnormalities at the voxel level in a sample of patients with schizophrenia. Specifically, ridge regression is implemented with different degrees of regularization. Results are compared to those delivered by the weighted global brain connectivity method (GBC), which is based on averaged bivariate correlations and from the non-redundant connectivity method (NRC), a dimensionality reduction approach that applies supervised principal component regressions. Ridge regression is able to detect a larger set of abnormally connected regions than both GBC and NRC methods, including schizophrenia related connectivity reductions in fronto-medial, somatosensory and occipital structures. Due to its multivariate nature, the proposed method is much more sensitive to group abnormalities than the GBC, but it also outperforms the NRC, which is multivariate too. Voxel based regularized regression is a simple and sensitive alternative for quantifying brain functional connectivity., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Salvador, Fuentes-Claramonte, García-León, Ramiro, Soler-Vidal, Torres, Salgado-Pineda, Munuera, Voineskos and Pomarol-Clotet.)

The DISC1 gene is one of the most relevant susceptibility genes for psychosis. However, the complex genetic landscape of this locus, which includes protective and risk variants in interaction, may have hindered consistent conclusions on how DISC1 contributes to schizophrenia (SZ) liability. Analysis from haplotype approaches and brain-based phenotypes can contribute to understanding DISC1 role in the neurobiology of this disorder. We assessed the brain correlates of DISC1 haplotypes associated with SZ through a functional neuroimaging genetics approach. First, we tested the association of two DISC1 haplotypes, the HEP1 (rs6675281-1000731-rs999710) and the HEP3 (rs151229-rs3738401), with the risk for SZ in a sample of 138 healthy subjects (HS) and 238 patients. This approach allowed the identification of three haplotypes associated with SZ (HEP1-CTG, HEP3-GA and HEP3-AA). Second, we explored whether these haplotypes exerted differential effects on n-back associated brain activity in a subsample of 70 HS compared to 70 patients (diagnosis × haplotype interaction effect). These analyses evidenced that HEP3-GA and HEP3-AA modulated working memory functional response conditional to the health/disease status in the cuneus, precuneus, middle cingulate cortex and the ventrolateral and dorsolateral prefrontal cortices. Our results are the first to show a diagnosis-based effect of DISC1 haplotypes on working memory-related brain activity, emphasising its role in SZ., (© 2022. The Author(s).)

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta-Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1-weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed-effects models and mega-analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = -0.20), cornu ammonis (CA)1 (d = -0.18), CA2/3 (d = -0.11), CA4 (d = -0.19), molecular layer (d = -0.21), granule cell layer of dentate gyrus (d = -0.21), hippocampal tail (d = -0.10), subiculum (d = -0.15), presubiculum (d = -0.18), and hippocampal amygdala transition area (d = -0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non-users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Background: The negative symptoms of schizophrenia have been proposed to reflect prefrontal cortex dysfunction. However, this proposal has not been consistently supported in functional imaging studies, which have also used executive tasks that may not capture key aspects of negative symptoms such as lack of volition., Method: Twenty-four DSM-5 schizophrenic patients with high negative symptoms (HNS), 25 with absent negative symptoms (ANS) and 30 healthy controls underwent fMRI during performance of the Computerized Multiple Elements Test (CMET), a task designed to measure poor organization of goal directed behaviour or 'goal neglect'. Negative symptoms were rated using the PANSS and the Clinical Assessment Interview for Negative Symptoms (CAINS)., Results: On whole brain analysis, the ANS patients showed no significant clusters of reduced activation compared to the healthy controls. In contrast, the HNS patients showed hypoactivation compared to the healthy controls in the left anterior frontal cortex, the right dorsolateral prefrontal cortex (DLPFC), the anterior insula bilaterally and the bilateral inferior parietal cortex. When compared to the ANS patients, the HNS patients showed reduced activation in the left anterior frontal cortex, the left DLPFC and the left inferior parietal cortex. After controlling for disorganization scores, differences remained in clusters in the left anterior frontal cortex and the bilateral inferior parietal cortex., Conclusions: This study provides evidence that reduced prefrontal activation, perhaps especially in the left anterior frontal cortex, is a brain functional correlate of negative symptoms in schizophrenia. The simultaneous finding of reduced inferior parietal cortex activation was unexpected, but could reflect this region's involvement in cognitive control, particularly the 'regulative' component of this., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d = -0.42, p = 3 × 10 -5 ), with weak evidence of IQ reductions among BD-FDRs (d = -0.23, p = .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. In contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Auditory verbal hallucinations (AVH) are a key symptom of schizophrenia (SZ) defined by anomalous perception of speech. Anomalies of processing external speech stimuli have also been reported in people with AVH, but it is unexplored which specific dimensions of language are processed differently. Using a speech perception task (passive listening), we here targeted the processing of deixis, a key dimension of language governing the contextual anchoring of speech in interpersonal context. We designed naturalistic speech stimuli that were either non-personal and fact-reporting ('low-deixis' condition), or else involved rich deictic devices such as the grammatical first and second persons, direct questions, and vocatives ('high-deixis'). We asked whether neural correlates of deixis obtained with fMRI would distinguish patients with and without frequent hallucinations (AVH + vs AVH-) from controls and each other. Results showed that high-deixis relative to low-deixis was associated with clusters of increased activation in the bilateral middle temporal gyri extending into the temporal poles and the inferior parietal cortex, in all groups. The AVH + and AVH- groups did not differ. When unifying them, the SZ group as a whole showed altered activity in the precuneus, midline regions and inferior parietal cortex. These results fail to confirm deictic processing anomalies specific to patients with AVH, but reveal such anomalies across SZ. Hypoactivation of this network may relate to a cognitive mechanism for attributing and anchoring thought and referential speech content in context., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Individuals with schizophrenia exhibit greater inter-patient variability in functional brain activity during neurocognitive task performance. Some studies have shown associations of age and illness duration with brain function; however, the association of these variables with variability in brain function activity is not known. In order to better understand the progressive effects of age and illness duration across disorders, we examined the relationship with individual variability in brain activity., Methods: Neuroimaging and behavioural data were extracted from harmonized datasets collectively including 212 control participants, 107 individuals with bipolar disorder, and 232 individuals with schizophrenia (total n = 551). Functional activity in response to an N-back working memory task (2-back vs 1-back) was examined. Individual variability was quantified via the correlational distance of fMRI activity between participants; mean correlational distance of one participant in relation to all others was defined as a 'variability score'., Results: Greater individual variability was found in the schizophrenia group compared to the bipolar disorder and control groups (p = 1.52e-09). Individual variability was significantly associated with aging (p = 0.027), however, this relationship was not different across diagnostic groups. In contrast, in the schizophrenia sample only, a longer illness duration was associated with increased variability (p = 0.027)., Conclusion: An increase in variability was observed in the schizophrenia group related to illness duration, beyond the effects of normal aging, implying illness-related deterioration of cognitive networks. This has clinical implications for considering long-term trajectories in schizophrenia and progressive neural and cognitive decline which may be amiable to novel treatments., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Inc.)

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Schizophrenia is associated with widespread alterations in subcortical brain structure. While analytic methods have enabled more detailed morphometric characterization, findings are often equivocal. In this meta-analysis, we employed the harmonized ENIGMA shape analysis protocols to collaboratively investigate subcortical brain structure shape differences between individuals with schizophrenia and healthy control participants. The study analyzed data from 2,833 individuals with schizophrenia and 3,929 healthy control participants contributed by 21 worldwide research groups participating in the ENIGMA Schizophrenia Working Group. Harmonized shape analysis protocols were applied to each site's data independently for bilateral hippocampus, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained from T1-weighted structural MRI scans. Mass univariate meta-analyses revealed more-concave-than-convex shape differences in the hippocampus, amygdala, accumbens, and thalamus in individuals with schizophrenia compared with control participants, more-convex-than-concave shape differences in the putamen and pallidum, and both concave and convex shape differences in the caudate. Patterns of exaggerated asymmetry were observed across the hippocampus, amygdala, and thalamus in individuals with schizophrenia compared to control participants, while diminished asymmetry encompassed ventral striatum and ventral and dorsal thalamus. Our analyses also revealed that higher chlorpromazine dose equivalents and increased positive symptom levels were associated with patterns of contiguous convex shape differences across multiple subcortical structures. Findings from our shape meta-analysis suggest that common neurobiological mechanisms may contribute to gray matter reduction across multiple subcortical regions, thus enhancing our understanding of the nature of network disorganization in schizophrenia., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Auditory verbal hallucinations (AVH, 'hearing voices') are an important symptom of schizophrenia but their biological basis is not well understood. One longstanding approach proposes that they are perceptual in nature, specifically that they reflect spontaneous abnormal neuronal activity in the auditory cortex, perhaps with additional 'top down' cognitive influences. Functional imaging studies employing the symptom capture technique-where activity when patients experience AVH is compared to times when they do not-have had mixed findings as to whether the auditory cortex is activated. Here, using a novel variant of the symptom capture technique, we show that the experience of AVH does not induce auditory cortex activation, even while real speech does, something that effectively rules out all theories that propose a perceptual component to AVH. Instead, we find that the experience of AVH activates language regions and/or regions that are engaged during verbal short-term memory., (© 2021. The Author(s).)