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BACKGROUND: The Collaborative Outcome study on Health and Functioning during Infection Times (COH-FIT; www.coh-fit.com) is an anonymous and global online survey measuring health and functioning during the COVID-19 pandemic. The aim of this study was to test concurrently the validity of COH-FIT items and the internal validity of the co-primary outcome, a composite psychopathology "P-score". METHODS: The COH-FIT survey has been translated into 30 languages (two blind forward-translations, consensus, one independent English back-translation, final harmonization). To measure mental health, 1-4 items ("COH-FIT items") were extracted from validated questionnaires (e.g. Patient Health Questionnaire 9). COH-FIT items measured anxiety, depressive, post-traumatic, obsessive-compulsive, bipolar and psychotic symptoms, as well as stress, sleep and concentration. COH-FIT Items which correlated r ≥ 0.5 with validated companion questionnaires, were initially retained. A P-score factor structure was then identified from these items using exploratory factor analysis (EFA) and confirmatory factor analyses (CFA) on data split into training and validation sets. Consistency of results across languages, gender and age was assessed. RESULTS: From >150,000 adult responses by May 6th, 2022, a subset of 22,456 completed both COH-FIT items and validated questionnaires. Concurrent validity was consistently demonstrated across different languages for COH-FIT items. CFA confirmed EFA results of five first-order factors (anxiety, depression, post-traumatic, psychotic, psychophysiologic symptoms) and revealed a single second-order factor P-score, with high internal reliability (ω = 0.95). Factor structure was consistent across age and sex. CONCLUSIONS: COH-FIT is a valid instrument to globally measure mental health during infection times. The P-score is a valid measure of multidimensional mental health. ispartof: JOURNAL OF AFFECTIVE DISORDERS vol:326 pages:249-261 ispartof: location:Netherlands status: published

BACKGROUND Identifying neurobiologically based transdiagnostic categories of depression and psychosis may elucidate heterogeneity and provide better candidates for predictive modeling. We aimed to identify clusters across patients with recent-onset depression (ROD) and recent-onset psychosis (ROP) based on structural neuroimaging data. We hypothesized that these transdiagnostic clusters would identify patients with poor outcome and allow more accurate prediction of symptomatic remission than traditional diagnostic structures. METHODS HYDRA (Heterogeneity through Discriminant Analysis) was trained on whole-brain volumetric measures from 577 participants from the discovery sample of the multisite PRONIA study to identify neurobiologically driven clusters, which were then externally validated in the PRONIA replication sample (n = 404) and three datasets of chronic samples (Centre for Biomedical Research Excellence, n = 146; Mind Clinical Imaging Consortium, n = 202; Munich, n = 470). RESULTS The optimal clustering solution was two transdiagnostic clusters (cluster 1: n = 153, 67 ROP, 86 ROD; cluster 2: n = 149, 88 ROP, 61 ROD; adjusted Rand index = 0.618). The two clusters contained both patients with ROP and patients with ROD. One cluster had widespread gray matter volume deficits and more positive, negative, and functional deficits (impaired cluster), and one cluster revealed a more preserved neuroanatomical signature and more core depressive symptomatology (preserved cluster). The clustering solution was internally and externally validated and assessed for clinical utility in predicting 9-month symptomatic remission, outperforming traditional diagnostic structures. CONCLUSIONS We identified two transdiagnostic neuroanatomically informed clusters that are clinically and biologically distinct, challenging current diagnostic boundaries in recent-onset mental health disorders. These results may aid understanding of the etiology of poor outcome patients transdiagnostically and improve development of stratified treatments.

Promotion of good mental health in young people is important. Our aim was to evaluate the consistency and magnitude of the efficacy of universal/selective interventions to promote good mental health. A systematic PRISMA/RIGHT-compliant meta-analysis (PROSPERO: CRD42018088708) search of Web of Science until 04/31/2019 identified original studies comparing the efficacy of universal/selective interventions for good mental health vs a control group, in samples with a mean age < 35 years. Meta-analytical random-effects model, heterogeneity statistics, assessment of publication bias, study quality and sensitivity analyses investigated the efficacy (Hedges' g = effect size, ES) of universal/selective interventions to promote 14 good mental health outcomes defined a-priori. 276 studies were included (total participants: 159,508, 79,142 interventions and 80,366 controls), mean age = 15.0 (SD = 7.4); female = 56.0%. There was a significant overall improvement in 10/13 good mental health outcome categories that could be meta-analysed: compared to controls, interventions significantly improved (in descending order of magnitude) mental health literacy (ES = 0.685, p < 0.001), emotions (ES = 0.541, p < 0.001), self-perceptions and values (ES = 0.49, p < 0.001), quality of life (ES = 0.457, p = 0.001), cognitive skills (ES = 0.428, p < 0.001), social skills (ES = 0.371, p < 0.001), physical health (ES = 0.285, p < 0.001), sexual health (ES = 0.257, p = 0.017), academic/occupational performance (ES = 0.211, p < 0.001) and attitude towards mental disorders (ES = 0.177, p = 0.006). Psychoeducation was the most effective intervention for promoting mental health literacy (ES = 0.774, p < 0.001) and cognitive skills (ES = 1.153, p = 0.03). Physical therapy, exercise and relaxation were more effective than psychoeducation and psychotherapy for promoting physical health (ES= 0.498, p

Altres ajuts: SRB: The Australian Schizophrenia Research Bank (ASRB) was supported by the National Health and Medical Research Council of Australia (NHMRC) (Enabling Grant, ID 386500), the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation and the Schizophrenia Research Institute. Chief Investigators for ASRB were Carr, V., Schall, U., Scott, R., Jablensky, A., Mowry, B., Michie, P., Catts, S., Henskens, F., Pantelis, C. We thank Loughland, C., the ASRB Manager, and acknowledge the help of Jason Bridge for ASRB database queries. CP was supported by NHMRC Senior Principal Research Fellowships (IDs: 628386 & 1105825); GC was supported by the Schizophrenia Research Institute utilizing infrastructure funding from the New South Wales Ministry of Health and New South Wales Ministry of Trade and Investment (Australia); JMF was supported by NHMRC project grant (1063960) and the Janette Mary O'Neil Research Fellowship; MJG was supported by NHMRC as an R.D. Wright Biomedical Career Development Fellow (1061875). MJC was supported by NHMRC Senior Research Fellowship (1121474). CASSI: CSW is funded by the NSW Ministry of Health, Office of Health and Medical Research. CSW is a recipient of a National Health and Medical Research Council (Australia) Principal Research Fellowship (PRF) (#1117079). CIAM: The CIAM study (FMH - PI) was supported by the University Research Committee, University of Cape Town and South African funding bodies National Research Foundation and Medical Research Council. COBRE: The COBRE dataset and investigators were supported by NIH grants R01EB006841 & P20GM103472, as well as NSF grant 1539067. JT (senior author) and VDC are supported by 5R01MH094524. JMS is supported by R01 AA021771 and P50 AA022534. EONCKS: This work was supported by a New Partnership for Africa's Development (NEPAD) grant through the Department of Science and Technology of South Africa, the Medical Research Council of South Africa (grant number 65174). ESO: The ESO study was funded by NPU I - LO1611 and Ministry of Health, Czech Republic - Conceptual Development of Research Organization 00023001 (IKEM). FIDMAG/Project: This work was supported by the Catalan Government and several grants from the Instituto de Salud Carlos III and co-funded by European Union (ERDF/ESF, 'Investing in your future'): Miguel Servet Research Contracts and Research Project Grants. FOR2107 Marburg: The FOR2107 Marburg study was funded by the German Research Foundation (DFG), Tilo Kircher (speaker FOR2107; DFG grant numbers KI588/14-1, KI588/14-2), Axel Krug (KR 3822/5-1, KR 3822/7-2), Igor Nenadic (NE 2254/1-2), Carsten Konrad (KO 4291/3-1). FOR2107 Muenster: The FOR2107 Muenster study was funded by the German Research Foundation (DFG, grant FOR2107 DA1151/5-1 and DA1151/5-2 to UD) and the Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Münster (grant Dan3/012/17 to UD). TH was supported by grants from the German Research Foundation (DFG grants HA7070/2-2, HA7070/3, HA7070/4). Frankfurt: MRI was performed at the Frankfurt Brain Imaging Center, supported by the German Research Council (DFG) and the German Ministry for Education and Research (BMBF; Brain Imaging Center Frankfurt/Main, DLR 01GO0203). GIPSI: This study was supported by Colciencias PRISMA-U.T. Huilong1 & Huilong2: This study was funded by the National Natural Science Foundation of China (81761128021; 31671145; 81401115; 81401133), Beijing Municipal Science and Technology Commission grant (Z141107002514016) and Beijing Natural Science Foundation(7162087, Beijing Municipal Administration of Hospitals Clinical medicine Development of special funding (XMLX201609; zylx201409). IGP: This study was funded by Project Grants from the Australian National Health and Medical Research Council of Australia (NHMRC; APP630471 and APP1081603), the Macquarie University's Australian Research Council Centre of Excellence in Cognition and its Disorders (CE110001021). Johns Hopkins: Supported by National Institutes of Health Grant Nos. MH-092443, MH-094268 (Silvio O. Conte Center), MH-105660, and MH-107730; foundation grants from Stanley, RUSK/S-R, and NARSAD/Brain and Behavior Research Foundation. Madrid: Supported by the Spanish Ministry of Science, Innovation and Universities, Instituto de Salud Carlos III, co-financed by ERDF Funds from the European Commission, "A way of making Europe", CIBERSAM. Madrid Regional Government (B2017/BMD-3740 AGES-CM-2), European Union Structural Funds and European Union Seventh Framework Program and H2020 Program; Fundación Familia Alonso, Fundación Alicia Koplowitz and Fundación Mutua Madrileña. MPRC1 & MPRC2: Support was received from NIH grants U01MH108148, 2R01EB015611, R01MH112180, R01DA027680, R01MH085646, P50MH103222 and T32MH067533, a State of Maryland contract (M00B6400091) and NSF grant (1620457). OLIN: The Olin study was supported by NIH grants R37MH43375 and R01MH074797. Oxford: The Oxford study MRC G0500092. SLF Rome: Support from the Italian Ministry of Health grants RC-12-13-14-15-16-17-18-19/A. RSCZ: RSCZ data collection was supported by RFBR 15-06-05758 grant. SCORE: This study was supported in part by grant 3232BO_119382 from the Swiss National Science Foundation. We thank the FePsy (Frueherkennung von Psychosen; early detection of psychosis) Study Group from the University of Basel, Department of Psychiatry, Switzerland, for the recruitment of the study participants. The FePsy Study was supported in part by grant No. SNF 3200-057216/1, ext./2, ext./3. Singapore: This study was supported by research grants from the National Healthcare Group, Singapore (SIG/05004; SIG/11003), and the Singapore Bioimaging Consortium (RP C-009/2006) research grants awarded to KS. SNUH: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (Grant no. 2013R1A2A1A03071089 and 2017M3C7A1029610). UCISZ: The UCISZ study was supported by the National Institutes of Mental Health grant number R21MH097196 to TGMvE. UCISZ data were processed by the UCI High Performance Computing cluster supported by Joseph Farran, Harry Mangalam, and Adam Brenner and the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1 TR000153. UNIBA: The UNIBA study was supported by grant funding from the Italian Ministry of Health (PE-2011-02347951). UNIMAAS: The study was supported by Dutch Organization for Health Research and Development (ZonMw 91112002) and a personal grant to Thérèse van Amelsvoort (ZonMw-VIDI: 91712394). The data was collected in a clinical trial registered in the Dutch clinical trial registry under ID: NTR5094 (http://www.trialregister.nl). UPenn: This study was supported by the National Institute of Mental Health grants MH064045, MH 60722, MH019112, MH085096 (DHW), and R01MH112847 (RTS and TDS). Zurich: This study was supported by the Swiss National Science Foundation (105314_140351 to S.K.). Matthias Kirschner acknowledges support from the National Bank Fellowship (McGill University) and the Swiss National Foundation (P2SKP3_178175). Research reported in this publication was also supported by the following National Institutes of Health grants: U54 EB020403 to PMT, R01 MH116147, U24 RR21992, R21MH097196, and TR000153 to TGMvE, S10 OD023696 and R01EB015611 to PK, T32 AG058507and 5T32 MH073526 to CRKC, R01 MH117601 to NJ, ENIGMA's NIH Big Data to Knowledge (BD2K) initiative U54 EB020403, ENIGMA Sex Differences R01MH116147, and ENIGMA-COINSTAC: Advanced World-wide Transdiagnostic Analysis of Valence System Brain Circuits R01MH121246. A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).

In this meta-analysis, we aimed to assess the evidence from neuroimaging studies for chronic alterations in the brains of MDMA users. The databases PubMed, Embase, and Web of Science were searched for studies published from inception to August 24, 2018, without any language restriction. Sixteen independent studies comprising 356 MDMA users and 311 controls were included. Of these, five studies investigated frontal and occipital N-acetylaspartate/creatine and myo-inositol/creatine ratios, three studies assessed basal ganglia blood flow and ten studies investigated serotonin transporter (SERT) density in various regions. We found significantly decreased SERT density in eight of 13 investigated regions. Meta-regression indicated a positive association with abstinence, but none with lifetime episodes of use. Therefore, other variables (such as doses taken per occasion) might be more important determinants. Positive associations between time of abstinence and SERT density might indicate that these alterations are reversible to some extent. Furthermore, there were no significant differences between user and control groups in terms of neurochemical ratios in the frontal and occipital lobes and blood flow in the basal ganglia. Overall, MDMA user groups showed heavy use patterns and study quality was poor.

The effects of hallucinogenic drugs on the human brain have been studied since the earliest days of neuroimaging in the 1990s. However, approaches are often hard to compare and results are heterogeneous. In this chapter, we summarize studies investigating the effects of hallucinogens on the resting brain, with a special emphasis on replicability and limitations. In previous studies, similarities were observed between psilocybin, LSD, and ayahuasca, with respect to decreases in cerebral blood flow and increases in global functional connectivity in the precuneus and thalamus. Additionally, LSD consistently decreased functional connectivity within distinct resting state networks. Little convergence was observed for connectivity between networks and for blood flow in other brain regions. Although these studies are limited by small sample sizes and might be biased by unspecific drug effects on physiological parameters and the vascular system, current results indicate that neuroimaging could be a useful tool to elucidate the neuronal correlates of hallucinogenic effects.

The brain-gut-axis is an interdependent system affecting neural functions and controlling our eating behaviour. In recent decades, neuroimaging techniques have facilitated its investigation. We systematically looked into functional and neurochemical brain imaging studies investigating how key molecules such as ghrelin, glucagon-like peptide-1 (GLP-1), peptide tyrosine-tyrosine (PYY), cholecystokinin (CCK), leptin, glucose and insulin influence the function of brain regions regulating appetite and satiety. Of the 349 studies published before July 2016 identified in the database search, 40 were included (27 on healthy and 13 on obese subjects). Our systematic review suggests that the plasma level of ghrelin, the gut hormone promoting appetite, is positively correlated with activation in the pre-frontal cortex (PFC), amygdala and insula and negatively correlated with activation in subcortical areas such as the hypothalamus. In contrast, the plasma levels of glucose, insulin, leptin, PYY, GLP-1 affect the same brain regions conversely. Our study integrates previous investigations of the gut-brain matrix during food-intake and homeostatic regulation and may be of use for future meta-analyses of brain-gut interactions.

The time period during which patients manifest psychotic or unspecific symptoms prior to treatment (duration of untreated psychosis, DUP, and the duration of untreated illness, DUI) has been found to be moderately associated with poor clinical and social outcome. Equivocal evidence exists of an association between DUP/DUI and structural brain abnormalities, such as reduced hippocampus volume (HV), pituitary volume (PV) and grey matter volume (GMV). Thus, the goal of the present work was to examine if DUP and DUI are associated with abnormalities in HV, PV and GMV. Using a region of interest (ROI) based approach, we present data of 39 patients from the Basel FePsy (Früherkennung von Psychosen, early detection of psychosis) study for which information about DUP, DUI and HV, PV and GMV data could be obtained. Twenty-three of them were first episode psychosis (FEP) and 16 at-risk mental state (ARMS) patients who later made the transition to frank psychosis. In unadjusted analyses, we found a significant positive correlation between DUP and PV in FEP patients. However, when adjusted for covariates, we found no significant correlation between DUP or DUI and HV, PV or GMV anymore. There only was a trend for decreasing GMV with increasing DUI in FEP. Our results do not comprehensively support the hypothesis of a "toxic" effect of the pathogenic mechanism underlying untreated psychosis on brain structure. If there is any effect, it might rather occur very early in the disease process, during which patients experience only unspecific symptoms.

Major depression is a common, recurrent mental illness that affects millions of people worldwide. Recently, a unique fast neuroprotective and antidepressant treatment effect has been observed by ketamine, which acts via the glutamatergic system. Hence, a steady accumulation of evidence supporting a role for the excitatory amino acid neurotransmitter (EAA) glutamate in the treatment of depression has been observed in the last years. Emerging evidence indicates that N-methyl-D-aspartate (NMDA), group 1 metabotropic glutamate receptor antagonists and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) agonists have antidepressant properties. Indeed, treatment with NMDA receptor antagonists has shown the ability to sprout new synaptic connections and reverse stress-induced neuronal changes. Based on glutamatergic signaling, a number of therapeutic drugs might gain interest in the future. Several compounds such as ketamine, memantine, amantadine, tianeptine, pioglitazone, riluzole, lamotrigine, AZD6765, magnesium, zinc, guanosine, adenosine aniracetam, traxoprodil (CP-101,606), MK-0657, GLYX-13, NRX-1047, Ro25-6981, LY392098, LY341495, D-cycloserine, D-serine, dextromethorphan, sarcosine, scopolamine, pomaglumetad methionil, LY2140023, LY404039, MGS0039, MPEP, 1-aminocyclopropanecarboxylic acid, all of which target this system, have already been brought up, some of them recently. Drugs targeting the glutamatergic system might open up a promising new territory for the development of drugs to meet the needs of patients with major depression.

The inability to inhibit compulsive drug-driven behavior is a key hallmark of heroin addiction. This chapter provides behavioral and neural evidence of how cognitive control functions are affected in chronic and abstinent heroin-dependent individuals, as well as in patients actively enrolled in an opioid maintenance treatment. We first show that heroin addiction is accompanied by behavioral impairments across a wide range of cognitive control functions including deficits in attentional and inhibitory processes. The second part emphasizes that heroin-addicted subjects reveal structural and functional abnormalities in the right inferior frontal gyrus as measured with brain imaging techniques and indicates how they are related to cognitive performance, providing a putative relation between brain structure, function, and behavior. The reported findings suggest that heroin addiction is more related to a general rather than to a specific cognitive impairment. Longitudinal study designs are needed to understand how these behavioral and neural correlates of cognitive control deficits develop over both the course of addiction and different therapeutic scenarios.

Objective To discuss the diagnostic validity of unusual bodily perceptions along the spectrum from age-specific, often transitory and normal, to pathological phenomena in adolescence to hypochondriasis and finally to psychosis. Methods Critical literature review of the cornerstone diagnostic groups along the spectrum embracing anxiety and cenesthopathy in adolescence, hypochondriasis, and cenesthopathy and psychosis, followed by a discussion of the diagnostic overlaps along this spectrum. Results The review highlights significant overlaps between the diagnostic cornerstones. It is apparent that adolescents with unusual bodily perceptions may conceptually qualify for more than one diagnostic group along the spectrum. To determine whether cenesthopathies in adolescence mirror emerging psychosis, a number of issues need to be considered, i.e. age and mode of onset, gender, level of functioning and drug use. The role of overvalued ideas at the border between hypochondriasis and psychosis must be considered. Conclusion As unusual bodily symptoms may in some instances meet formal psychosis risk criteria, a narrow understanding of these symptoms may lead to both inappropriate application of the new DSM-5 attenuated psychosis syndrome and of treatment selection. On the other hand, the possibility of a psychotic dimension of unusual bodily symptoms in adolescents must always be considered as most severe expression of the cenesthopathy spectrum.

Background Studies examining the influence of duration of untreated psychosis (DUP) or duration of untreated illness (DUI) on cognition vary with regard to results and methods. This study is the first in this field to include an at risk mental state with later transition to psychosis (ARMS-T) sample and to analyse how the DUI relates to their cognitive functioning. Because methodological operationalization of cognitive functioning in previous studies is highly heterogeneous, we aimed to compare different approaches. Method 60 first episode psychosis (FEP) patients and 24 ARMS-T patients were examined. Associations between DUP, DUI and neurocognitive performance were tested by three different operationalizations of cognition: as the raw outcome measure of different neuropsychological tests, as outcome scores which were normed on a sample of 75 healthy participants, and as the deterioration index (DI). Results There were no significant correlations between DUP or DUI and outcome of neuropsychological tests in both normed and raw scores. When adjusted for covariates, DUP and DUI also did not significantly predict any cognitive performance. There was no significant relationship between DUP or DUI and the DI index. However, longer DUP and DUI were significantly associated with stronger negative symptoms. Conclusions This study could not confirm an association between duration of untreated psychosis or duration of untreated illness and neurocognitive performance in the ARMS-T and FEP samples. This could be because schizophrenic psychoses are neurodevelopmental disorders in which most cognitive deficits exist long before the onset of psychiatric symptoms.

Neurological soft signs (NSS) are more common in schizophrenic psychoses and in genetically high-risk individuals than in healthy controls. But nothing is known so far regarding individuals with a clinical at-risk mental state (ARMS). The goals of our study therefore were (a) to compare the NSS frequency in ARMS individuals to that of first-episode psychosis (FEP) patients and (b) to test whether NSS could predict the transition to psychosis. Neurological soft signs were assessed using a shortened version of the Neurological Evaluation Scale (NES). Fifty-three ARMS individuals (16 with later transition to psychosis=ARMS-T, and 37 without transition=ARMS-NT) and 27 FEP patients were recruited through the Basel Early Detection Clinic FePsy. Of the FEP patients 37% showed NSS. We found no significant differences between FEP and ARMS-T patients or between ARMS-NT and ARMS-T. Our findings of NSS being present already before transition to psychosis to the same extent as after transition provide further support to the neurodevelopmental hypothesis of schizophrenic psychoses. Furthermore, our findings might indicate that ARMS-NT individuals also suffer from some sort of neurodevelopmental abnormalities.

As cannabis use is more frequent in patients with psychosis than in the general population and is known to be a risk factor for psychosis, the question arises whether cannabis contributes to recently detected brain volume reductions in schizophrenic psychoses. This study is the first to investigate how cannabis use is related to the cingulum volume, a brain region involved in the pathogenesis of schizophrenia, in a sample of both at-risk mental state (ARMS) and first episode psychosis (FEP) subjects. A cross-sectional magnetic resonance imaging (MRI) study of manually traced cingulum in 23 FEP and 37 ARMS subjects was performed. Cannabis use was assessed with the Basel Interview for Psychosis. By using repeated measures analyses of covariance, we investigated whether current cannabis use is associated with the cingulum volume, correcting for age, gender, alcohol consumption, whole brain volume and antipsychotic medication. There was a significant three-way interaction between region (anterior/posterior cingulum), hemisphere (left/right cingulum) and cannabis use (yes/no). Post-hoc analyses revealed that this was due to a significant negative effect of cannabis use on the volume of the posterior cingulum which was independent of the hemisphere and diagnostic group and all other covariates we controlled for. In the anterior cingulum, we found a significant negative effect only for the left hemisphere, which was again independent of the diagnostic group. Overall, we found negative associations of current cannabis use with grey matter volume of the cingulate cortex, a region rich in cannabinoid CB1 receptors. As this finding has not been consistently found in healthy controls, it might suggest that both ARMS and FEP subjects are particularly sensitive to exogenous activation of these receptors.

Background: Hippocampal volume (HV) reduction is well documented in schizophrenia. However, it is still unclear whether this change is a pre-existing vulnerability factor, a sign of disease progression, a consequence of environmental factors, such as drug use, antipsychotic medication, or malnutrition. The timing of HV changes is not well established, but a lack of macrostructural hippocampal brain abnormalities before disease onset would rather support a neuroprogressive illness model. Aim: To investigate the timing of HV changes in emerging psychosis. Methods: A cross-sectional MRI study of manually traced HVs in 37 individuals with an At Risk Mental State (ARMS) for psychosis, 23 individuals with First-Episode Psychosis (FEP), and 22 Healthy Controls (HC) was performed. We compared left and right HVs corrected for whole brain volume across groups using analysis of covariance (ANCOVA) with gender as a covariate. Sixteen of 37 ARMS individuals developed a psychotic disorder during follow up (ARMS-T). The mean duration of follow up in ARMS was 25.1 months. Results: The overall ANCOVA model comparing left HVs across FEP, ARMS and HC indicated a significant general group effect (p < .05) with largest volumes in ARMS and smallest in FEP. ARMS-T subjects had significantly larger left HVs compared to FE but no HV differences compared to HC (p < 0.05). Over all groups, we found an asymmetry between the left and right mean HVs and a strong effect of sex. Discussion: The present study suggests that macrostructural hippocampal abnormalities probably occur in the context of the first psychotic breakdown.

OBJECTIVES: In early stage psychosis research the identification of neurobiological correlates of vulnerability to schizophrenia is an important hurdle. METHODS: We systematically reviewed the neuroimaging publications on high-risk subjects with subsequent transition to psychosis (HR-T) and conducted a meta-analysis calculating the effect size Cohen's d. RESULTS: Out of 30 identified studies 25 met the inclusion criteria. Structural (s)MRI studies showed small to medium effect sizes of decreased prefrontal, cingulate, insular and cerebellar gray matter volume in HR-T compared to high-risk subjects without transition (HR-NT). Meta-analysis revealed relatively larger whole brain volumes in HR-T compared to HR-NT subjects (mean Cohen's d 0.36, 95% CI 0.27-0. 46). Compared to HR-NT, HR-T subjects showed in functional imaging studies reduced brain activation in prefrontal cortex, reduced neuronal density, and increased membrane turnover in frontal and cingulate cortex with medium to large effect sizes. CONCLUSIONS: Despite methodological differences between studies, structural and neurochemical abnormalities in prefrontal, anterior cingulate, medial temporal and cerebellar cortex might be predictive for development of psychosis within HR subjects.