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Background Childhood trauma (CT) is a major yet elusive psychiatric risk factor, whose multidimensional conceptualization and heterogeneous effects on brain morphology might demand advanced mathematical modeling. Therefore, we present an unsupervised machine learning approach to characterize the clinical and neuroanatomical complexity of CT in a larger, transdiagnostic context. Methods We used a multicenter European cohort of 1076 female and male individuals (discovery: n = 649; replication: n = 427) comprising young, minimally medicated patients with clinical high-risk states for psychosis; patients with recent-onset depression or psychosis; and healthy volunteers. We employed multivariate sparse partial least squares analysis to detect parsimonious associations between combinations of items from the Childhood Trauma Questionnaire and gray matter volume and tested their generalizability via nested cross-validation as well as via external validation. We investigated the associations of these CT signatures with state (functioning, depressivity, quality of life), trait (personality), and sociodemographic levels. Results We discovered signatures of age-dependent sexual abuse and sex-dependent physical and sexual abuse, as well as emotional trauma, which projected onto gray matter volume patterns in prefronto-cerebellar, limbic, and sensory networks. These signatures were associated with predominantly impaired clinical state- and trait-level phenotypes, while pointing toward an interaction between sexual abuse, age, urbanicity, and education. We validated the clinical profiles for all three CT signatures in the replication sample. Conclusions Our results suggest distinct multilayered associations between partially age- and sex-dependent patterns of CT, distributed neuroanatomical networks, and clinical profiles. Hence, our study highlights how machine learning approaches can shape future, more fine-grained CT research.

In schizophrenic psychoses, the normal sexual dimorphism of the brain has been shown to be disrupted or even reversed. Little is known, however, at what time point in emerging psychosis this occurs. We have therefore examined, if these alterations are already present in the at-risk mental state (ARMS) for psychosis and in first episode psychosis (FEP) patients.; Data from 65 ARMS (48 (73.8%) male; age=25.1±6.32) and 50 FEP (37 (74%) male; age=27±6.56) patients were compared to those of 70 healthy controls (HC; 27 (38.6%) male; age=26±4.97). Structural T1-weighted images were acquired using a 3 Tesla magnetic resonance imaging (MRI) scanner. Linear mixed effects models were used to investigate whether subcortical brain volumes are dependent on sex.; We found men to have larger total brain volumes (p

Background Bullying as a specific subtype of adverse life events is a major risk factor for poor mental health. Although many questionnaires on bullying are available, so far none covers bullying retrospectively throughout school and working life. To close this gap, the Bullying Scale for Adults (BSA) was designed. Methods Based on data of 622 participants from five European countries collected in the prospective multicenter Personalized Prognostic Tools for Early Psychosis Management (PRONIA) study, we investigated whether the BSA is a reliable and valid measurement for bullying and whether there is a difference across different diagnostic groups of early mental disorders (recent onset depressive/ psychotic patients, patients at clinical high-risk of psychosis) and healthy controls. Results Bullying experiences were significantly less frequent in healthy controls than in patient groups, with no significant differences between the three clinical groups. The BSA exhibited a high item scale discrimination (r > .3) and very good internal consistency (Cronbach's α = .93). Four factors were identified: 1. Sexual harassment, 2. Emotional Abuse, 3. Physical Abuse, 4. Problems at school. The highly significant correlation between bullying, and childhood adversities and trauma (r = .645, p Discussion The BSA is the first validated questionnaire that, in retrospective, reliably records various aspects of bullying (incl. its consequences) not only throughout childhood but also working life. It can be used to assess bullying as a transdiagnostic risk factor of mental disorders in different mental disorders, esp. psychosis and depression.

Depressive symptoms in subjects at Clinical High Risk for Psychosis (CHR-P) or at first-episode psychosis (FEP) are often treated with antidepressants. Our cross-sectional study investigated whether brain morphology is altered by antidepressant medication. High-resolution T1-weighted structural MRI scans of 33 CHR-P and FEP subjects treated with antidepressants, 102 CHR-P and FEP individuals without antidepressant treatment and 55 controls, were automatically segmented using Freesurfer 6.0. Linear mixed-effects modelling was applied to assess the differences in subcortical volume, surface area and cortical thickness in treated, non-treated and healthy subjects, taking into account converted dosages of antidepressants. Increasing antidepressant dose was associated with larger volume of the pallidum and the putamen, and larger surface of the left inferior temporal gyrus. In a pilot subsample of separately studied subjects of known genomic risk loci, we found that in the right postcentral gyrus, the left paracentral lobule and the precentral gyrus antidepressant dose-associated surface increase depended on polygenic schizophrenia-related-risk score. As the reported regions are linked to the symptoms of psychosis, our findings reflect the possible beneficial effects of antidepressant treatment on an emerging psychosis.

Several magnetic resonance imaging studies have reported reductions in hippocampal volume in patients with psychosis. It is unclear whether structural abnormalities predate illness onset. We conducted a detailed, systematic literature search for studies reporting hippocampal volume in subjects with clinical high-risk, compared to healthy controls. The overall sample size comprised 1429 subjects. Meta-analysis revealed no difference for left, but a small, albeit significant, difference for right hippocampal volume, such that clinical high-risk patients had slightly smaller hippocampal volume than healthy controls (g=0.24, p=0.0418). Meta-regression indicated a moderating effect of manual tracing approach, due to one outlying site. The small difference on the right side did not remain significant (g=0.14, 95%CI=[-0.03-0.32], p=0.11) after removal of this outlier. This meta-analysis suggests that there is no reduction in hippocampal volume before transition to psychosis and hippocampal volume cannot be used as a biomarker in clinical high-risk individuals.

Background Machine learning classifications of first-episode psychosis (FEP) using neuroimaging have predominantly analyzed brain volumes. Some studies examined cortical thickness data, but most of them have used parcellation approaches with data from single sites, which limits claims of generalizability. To address these limitations, we conducted a large-scale, multi-site analysis of cortical thickness comparing parcellations and vertex-wise approaches. By leveraging the multi-site nature of the study, we further investigated how different demographical and site-dependent variables affected predictions. Finally, we assessed relationships between the predictions and clinical variables. Methods 428 subjects (147 females, mean age 27.14) with FEP and 448 (230 females, mean age 27.06) healthy controls were enrolled in 8 centers by the ClassiFEP group. All subjects underwent a structural MRI (sMRI) session and were clinically assessed. Cortical thickness parcellation (68 areas) and full cortical maps (20484 vertices) were extracted. Supervised (linear Support Vector Machine) classification was used to differentiate FEP from HC, within a repeated nested Cross-Validation (CV) framework through the NeuroMiner software. In both inner and outer CVs, a 10-fold CV cycle was employed. We performed repeated nested CV at the outer cross-validation cycle by randomly permuting the participants within their groups (10 permutations) and repeating the CV cycle for each of these permutations. As feature preprocessing, regression of covariates (age, sex, and site), Principal Component Analysis and Scaling were applied. All preprocessing steps were implemented within the CV. Further analyses were conducted by stratifying the sample for MRI scanner, sex and by performing random resampling with increasingly reduced sample sizes. Results Vertex-wise thickness maps outperformed parcellation-based methods with a balanced accuracy (BAC) of 66.2% and an Area Under the Curve of 72%, compared to a BAC of 59% and an Area Under the Curve of 61% obtained with the ROI-based approach. The two BACs were significantly different based on the McNemar’s Test. By stratifying our sample for MRI scanner, we increased the overall BAC to more than 70% and we also increased generalizability across sites. Temporal areas resulted the most influential regions in the classification. The predictive decision scores presented significant correlations with age at onset, duration of treatment and the presence of affective vs non-affective psychosis. Discussion Cortical thickness could represent a valid measure to classify FEP subjects, showing temporal areas as potential markers in the early stages of psychosis. The assessment of site-dependent variables allowed us to increase the across-site generalizability of the model, thus attempting to address an important machine learning limitation, especially in the framework of large multi-site cohort and big data analysis.

We present several deep learning models for assessing the morphometric fidelity of deep grey matter region models extracted from brain MRI. We test three different convolutional neural net architectures (VGGNet, ResNet and Inception) over 2D maps of geometric features. Further, we present a novel geometry feature augmentation technique based on parametric spherical mapping. Finally, we present an approach for model decision visualization, allowing human raters to see the areas of subcortical shapes most likely to be deemed of failing quality by the machine. Our training data is comprised of 5200 subjects from the ENIGMA Schizophrenia MRI cohorts, and our test dataset contains 1500 subjects from the ENIGMA Major Depressive Disorder cohorts. Our final models reduce human rater time by 46-70%. ResNet outperforms VGGNet and Inception for all of our predictive tasks.

Grey matter (GM) volume alterations have been repeatedly demonstrated in patients with first episode psychosis (FEP). Some of these neuroanatomical abnormalities are already evident in the at-risk mental state (ARMS) for psychosis. Not only GM alterations but also neurocognitive impairments predate the onset of frank psychosis with verbal learning and memory (VLM) being among the most impaired domains. Yet, their interconnection with alterations in GM volumes remains ambiguous. Thus, we evaluated associations of different subcortical GM volumes in the medial temporal lobe with VLM performance in antipsychotic-naive ARMS and FEP patients. Data from 59 ARMS and 31 FEP patients, collected within the prospective Fruherkennung von Psychosen study, were analysed. Structural T1-weighted images were acquired using a 3 Tesla magnetic resonance imaging scanner. VLM was assessed using the California Verbal Learning Test and its factors Attention Span, Learning Efficiency, Delayed Memory and Inaccurate Memory. FEP patients showed significantly enlarged volumes of hippocampus, pallidum, putamen and thalamus compared to ARMS patients. A significant negative association between amygdala and pallidum volume and Attention Span was found in ARMS and FEP patients combined, which however did not withstand correction for multiple testing. Although we found significant between-group differences in subcortical volumes and VLM is among the most impaired cognitive domains in emerging psychosis, we could not demonstrate an association between low performance and subcortical GM volumes alterations in antipsychotic-naive patients. Hence, deficits in this domain do not appear to stem from alterations in subcortical structures.

Objectives Impairment in working memory (WM) is a core symptom in schizophrenia. However, little is known about how clinical features influence functional brain activity specific to WM processing during the development of first-episode psychosis (FEP) to schizophrenia (SZ). We compared functional WM-specific brain activity in FEP and SZ patients, including the effects of the duration of illness, psychopathological factors and antipsychotic medication. Methods Cross-sectional study of male FEP (n = 22) and SZ (n = 20) patients performing an n-back task when undergoing functional magnetic resonance imaging (fMRI). Clinical features were collected by semi-structured interviews and medical records. Results The SZ group performed significantly worse than the FEP group in the 2-back condition. The SZ group also showed significantly higher activation in the left superior frontal gyrus in the 2-back versus 0-back condition (2-back > 0-back). This frontal activation correlated positively with negative symptoms and with cumulative antipsychotic medication during the year before the fMRI examination. There were no significant correlations between activation and duration of illness. Conclusion There was greater frontal neural activation in SZ than in FEP. This indicated differences in WM processing, and was significantly related to cumulative antipsychotic exposure and negative symptoms, but not to the duration of illness.

Background Deficits in motivational salience processing have been related to psychotic symptoms and disturbances in dopaminergic neurotransmission. We aimed at exploring changes in salience processing and brain activity during different stages of psychosis and antipsychotic medication effect. Methods We used fMRI during the Salience Attribution Task to investigate hemodynamic differences between 19 healthy controls (HCs), 34 at-risk mental state (ARMS) individuals and 29 individuals with first-episode psychosis (FEP), including a subgroup of 17 FEP without antipsychotic medication (FEP-UM) and 12 FEP with antipsychotic medication (FEP-M). Motivational salience processing was operationalized by brain activity in response to high-probability rewarding cues (adaptive salience) and in response to low-probability rewarding cues (aberrant salience). Results Behaviorally, adaptive salience response was not accelerated in FEP, although they correctly distinguished between trials with low and high reward probability. In comparison to HC, ARMS exhibited a lower hemodynamic response during adaptive salience in the right inferior parietal lobule and FEP-UM in the left dorsal cingulate gyrus. The FEP-M group exhibited a lower adaptive salience response than HC in the right insula and than ARMS in the anterior cingulate gyrus. In unmedicated individuals, the severity of hallucinations and delusions correlated negatively with the insular- and anterior cingulate hemodynamic response during adaptive salience. We found no differences in aberrant salience processing associated with behavior or medication. Conclusion The changes in adaptive motivational salience processing during psychosis development reveal neurofunctional abnormalities in the somatosensory and premotor cortex. Antipsychotic medication seems to modify hemodynamic responses in the anterior cingulate and insula.

Background Brain-derived neurotrophic factor (BDNF) is a neurotrophin involved in neurogenesis and synaptic plasticity in the central nervous system, especially in the hippocampus, and has been implicated in the pathophysiology of several neuropsychiatric disorders. Its Val66Met polymorphism (refSNP Cluster Report: rs6265) is a functionally relevant single nucleotide polymorphism affecting the secretion of BDNF and is implicated in differences in hippocampal volumes. Methods This is a systematic meta-analytical review of findings from imaging genetic studies on the impact of the rs6265 SNP on hippocampal volumes in neuropsychiatric patients with major depressive disorder, anxiety, bipolar disorder or schizophrenia. Results The overall sample size of 18 independent clinical cohorts comprised 1695 patients. Our results indicated no significant association of left (Hedge's g = 0.08, p = 0.12), right (g = 0.07, p = 0.22) or bilateral (g = 0.07, p = 0.16) hippocampal volumes with BDNF rs6265 in neuropsychiatric patients. There was no evidence for a publication bias or any demographic, clinical, or methodological moderating effects. Both Val/Val homozygotes (g = 0.32, p = 0.004) and Met-carriers (g = 0.20, p = 0.004) from the patient sample had significantly smaller hippocampal volumes than the healthy control sample with the same allele. The magnitude of these effects did not differ between the two genotypes. Conclusion This meta-analysis suggests that there is no association between this BDNF polymorphism and hippocampal volumes. For each BDNF genotype, the hippocampal volumes were significantly lower in neuropsychiatric patients than in healthy controls.

Background: There is only limited agreement with respect to location, directionality and functional implications of brain structural alterations observed in patients with schizophrenia. Additionally, their link to occurrence of psychotic symptoms remains unclear. A viable way of addressing these questions is to examine populations in an at-risk mental state (ARMS) before the transition to psychosis. Methods: We tested for structural brain alterations in individuals in an ARMS compared with healthy controls and patients with first-episode psychosis (FEP) using voxel-based morphometry and measures of cortical thickness. Furthermore, we evaluated if these alterations were modified by age and whether they were linked to the observed clinical symptoms. Results: Our sample included 59 individuals with ARMS, 26 healthy controls and 59 patients with FEP. We found increased grey matter volume and cortical thickness in individuals with ARMS and a similar pattern of structural alterations in patients with FEP. We further found stronger age-related reductions in grey matter volume and cortical thickness in both patients with FEP and individuals with ARMS, linking these alterations to observed clinical symptoms. Limitations: The ARMS group comprised subgroups with heterogeneous levels of psychosis risk and medication status. Furthermore, the cross-sectional nature of our study and the reduced number of older patients limit conclusions with respect to observed interactions with age. Conclusion: Our findings on consistent structural alterations in individuals with ARMS and patients with FEP and their link to clinical symptoms have major implications for understanding their time of occurrence and relevance to psychotic symptoms. Interactions with age found for these alterations may explain the heterogeneity of findings reported in the literature.

As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.

Background There is urgent need to improve the limited prognostic accuracy of psychopathology-based classifications to predict the onset of psychosis in clinical high-risk (CHR) subjects for psychosis. However, as yet no reliable biological marker has been established to differentiate CHR subjects who will develop psychosis from those who will not. This study investigated abnormalities in graph-based gyrification connectome in CHR subjects and patients with first-episode psychosis (FEP) and tested the accuracy of this systems-based approach to predict the transition to psychosis among CHR individuals. Methods 44 healthy controls (HC), 63 at-risk mental state (ARMS) subjects without later transition to psychosis (ARMS-NT), 16 ARMS subjects with later transition (ARMS-T), and 38 antipsychotic-free patients with FEP were recruited from the specialized clinic for the early detection of psychosis at the Department of Psychiatry, University of Basel, Basel, Switzerland. Gyrification-based structural covariance networks (connectomes) were constructed to quantify global integration, segregation and small-worldness. Extremely randomized trees with repeated, nested cross-validation was performed to differentiate ARMS-T from ARMS-NT individuals. Permutation testing was used to assess the significance of classification performance measures. Results Small-worldness is reduced in both ARMS-T and FEP patients, secondary to reduced integration and increased segregation in both groups. In addition, we also found that transitivity (segregation) was significantly higher in ARMS-T and FEP groups compared to both ARMS-NT and healthy controls. Using the connectome properties as features, we obtained a high classification accuracy of 90% (balanced accuracy: 81%, positive predictive value: 85%, negative predictive value: 92%.) All performance measures were highly significant as indicated by permutation tests (all p < 0.01). Discussion Our findings suggest that there is poor integration in the coordinated development of cortical folding in patients who develop psychosis. This study further indicates that gyrification-based connectomes might be a promising means to generate systems-based measures from anatomical data that improves individual prediction of psychosis transition in CHR subjects.

Reduction in hippocampal volume is a hallmark of schizophrenia and already present in the clinical high-risk state. Nevertheless, other subcortical structures, such as the thalamus, amygdala and pallidum can differentiate schizophrenia patients from controls. We studied the role of hippocampal and subcortical structures in clinical high-risk individuals from two cohorts. High-resolution T1-weighted structural MRI brain scans of a total of 91 clinical high-risk individuals and 64 healthy controls were collected in two centers. The bilateral volume of the hippocampus, the thalamus, the caudate, the putamen, the pallidum, the amygdala, and the accumbens were automatically segmented using FSL-FIRST. A linear mixed-effects model and a prospective meta-analysis were applied to assess group-related volumetric differences. We report reduced hippocampal and thalamic volumes in clinical high-risk individuals compared to healthy controls. No volumetric alterations were detected for the caudate, the putamen, the pallidum, the amygdala, or the accumbens. Moreover, we found comparable medium effect sizes for group-related comparison of the thalamus in the two analytical methods. These findings underline the relevance of specific alterations in the hippocampal and subcortical volumes in the high-risk state. Further analyses may allow hippocampal and thalamic volumes to be used as biomarkers to predict psychosis.

Importance There is urgent need to improve the limited prognostic accuracy of clinical instruments to predict psychosis onset in individuals at clinical high risk (CHR) for psychosis. As yet, no reliable biological marker has been established to delineate CHR individuals who will develop psychosis from those who will not. Objectives To investigate abnormalities in a graph-based gyrification connectome in the early stages of psychosis and to test the accuracy of this systems-based approach to predict a transition to psychosis among CHR individuals. Design, Setting, and Participants This investigation was a cross-sectional magnetic resonance imaging (MRI) study with follow-up assessment to determine the transition status of CHR individuals. Participants were recruited from a specialized clinic for the early detection of psychosis at the Department of Psychiatry (Universitare Psychiatrische Kliniken [UPK]), University of Basel, Basel, Switzerland. Participants included individuals in the following 4 study groups: 44 healthy controls (HC group), 63 at-risk mental state (ARMS) individuals without later transition to psychosis (ARMS-NT group), 16 ARMS individuals with later transition to psychosis (ARMS-T group), and 38 antipsychotic-free patients with first-episode psychosis (FEP group). The study dates were November 2008 to November 2014. The dates of analysis were March to November 2017. Main Outcomes and Measures Gyrification-based structural covariance networks (connectomes) were constructed to quantify global integration, segregation, and small-worldness. Group differences in network measures were assessed using functional data analysis across a range of network densities. The extremely randomized trees algorithm with repeated 5-fold cross-validation was used to delineate ARMS-T individuals from ARMS-NT individuals. Permutation tests were conducted to assess the significance of classification performance measures. Results The 4 study groups comprised 161 participants with mean (SD) ages ranging from 24.0 (4.7) to 25.9 (5.7) years. Small-worldness was reduced in the ARMS-T and FEP groups and was associated with decreased integration and increased segregation in both groups (Hedgesgrange, 0.666-1.050). Using the connectome properties as features, a good classification performance was obtained (accuracy, 90.49%; balanced accuracy, 81.34%; positive predictive value, 84.47%; negative predictive value, 92.18%; sensitivity, 66.11%; specificity, 96.58%; and area under the curve, 88.30%). Conclusions and Relevance These findings suggest that there is poor integration in the coordinated development of cortical folding in patients who develop psychosis. These results further suggest that gyrification-based connectomes might be a promising means to generate systems-based measures from anatomical data to improve individual prediction of a transition to psychosis in CHR individuals.

Background In psychiatric practice, when symptoms “come together” we call the resulting construct as a diagnosis. We believe that there is a disease process that binds together, enabling co-occurrence of varied symptoms. We use either diagnostic or syndromic labels to describe this construct (e.g. positive syndrome, negative syndrome, schizophrenia, at-risk mental state). An emerging idea, promoted by network theorists, is that symptoms may relate by their own intrinsic nature, with no external constructs bringing them together e.g. paranoia leads to social withdrawal, loss of appetite leads to loss of weight etc. This intrinsic organisation of symptom relationships can be studied using network models by applying graph theory to symptom data. Methods We recruited 63 subjects with at-risk mental state [on the basis of Melbourne PACE criteria] but no transition (ARMS-NT), 16 that later developed psychosis (ARMS-T) and 38 drug-naïve patients with first-episode psychosis (FEP) from Basel, Switzerland. Symptoms were measured using Brief Psychiatric Rating Scale. Clinical symptoms can be construed as a system of individual elements (24 nodes) and their relationship (24x23 possible edges) within a group. We estimate each individual’s contribution to the intrinsic organisation of symptoms using a jack-knife bias estimation procedure. Bias values for each pair of symptoms in an individual subject quantified the contribution of that subject to the overall within-group relationship for that symptom pair. Higher values meant greater relationship between the two given nodes in that subject, relative to the rest of the group. We then used Graph Analysis Toolbox, with a range of binarization thresholds based on cost-density of connectivity to extract adjacency matrices. Results None of the 24 individual symptoms of BPRS significantly differentiated ARMS-NT from ARMS-T, though a number of symptoms (suspiciousness, hallucinations, disorganisation, motor retardation, hostility and suicidality) showed a gradient of FEP>ARMS-T>ARMS-NT (F test, FDR corrected pARMS-T>ARMS-NT (except for modularity where FEP=ARMS-T). Post-hoc tests revealed significantly high clustering (Hedges’s g = 0.60, p

Previous network studies in chronic schizophrenia patients revealed impaired structural organization of the brain’s rich-club members, a set of highly interconnected hub regions that play an important integrative role for global brain communication. Moreover, impaired rich-club connectivity has also been found in unaffected siblings of schizophrenia patients, suggesting that abnormal rich-club connectivity is related to familiar, possibly reflecting genetic, vulnerability for schizophrenia. However, no study has yet investigated whether structural rich-club organization is also impaired in individuals with a clinical risk syndrome for psychosis. Diffusion tensor imaging and probabilistic tractography was used to construct structural whole-brain networks in 24 healthy controls and 24 subjects with an at-risk mental state (ARMS). Graph theory was applied to quantify the structural rich-club organization and global network properties. ARMS subjects revealed a significantly altered structural rich-club organization compared with the control group. The disruption of rich-club organization was associated with the severity of negative psychotic symptoms and led to an elevated level of modularity in ARMS subjects. This study shows that abnormal structural rich-club organization is already evident in clinical high-risk subjects for psychosis and further demonstrates the impact of rich-club disorganization on global network communication. Together with previous evidence in chronic schizophrenia patients and unaffected siblings, our findings suggest that abnormal structural rich-club organization may reflect an endophenotypic marker of psychosis.

Brain changes in schizophrenia evolve along a dynamic trajectory, emerging before disease onset and proceeding with ongoing illness. Recent investigations have focused attention on functional brain interactions, with experimental imaging studies supporting the disconnection hypothesis of schizophrenia. These studies have revealed a broad spectrum of abnormalities in brain connectivity in patients, particularly for connections integrating the frontal cortex. A critical point is that brain connectivity abnormalities, including altered resting state connectivity within the fronto-parietal (FP) network, are already observed in non-help-seeking individuals with psychotic-like experiences. If we consider psychosis as a continuum, with individuals with psychotic-like experiences at the lower and psychotic patients at the upper ends, individuals with psychotic-like experiences represent a key population for investigating the validity of putative biomarkers underlying the onset of psychosis. This paper selectively addresses the role played by FP connectivity in the psychosis continuum, which includes patients with chronic psychosis, early psychosis, clinical high risk, genetic high risk, as well as the general population with psychotic experiences. We first discuss structural connectivity changes among the FP pathway in each domain in the psychosis continuum. This may provide a basis for us to gain an understanding of the subsequent changes in functional FP connectivity. We further indicate that abnormal FP connectivity may arise from glutamatergic disturbances of this pathway, in particular from abnormal NMDA receptor-mediated plasticity. In the second part of this paper we propose some concepts for further research on the use of network connectivity in the classification of the psychosis continuum. These concepts are consistent with recent efforts to enhance the role of data in driving the diagnosis of psychiatric spectrum diseases.

IntroductionPatients with a first episode psychosis (FEP) have repeatedly been shown to have gray matter (GM) volume alterations. Some of these neuroanatomical abnormalities are already evident in the at-risk mental state (ARMS) for psychosis. Not only GM alterations but also neurocognitive impairments predate the onset of frank psychosis with verbal learning and memory (VLM) being among the most impaired domains. Yet, their interconnection with alterations in GM volumes remains ambiguous.ObjectiveTo evaluate associations of different subcortical GM volumes in the medial temporal lobe with VLM performance in ARMS and FEP patients.MethodsData were collected within the prospective Früherkennung von Psychosen (FePsy) study, which aims to improve the early detection of psychosis. VLM was assessed using the California Verbal Learning Test (CVLT) and its latent variables Attention Span (AS), Learning Efficiency (LE), Delayed Memory (DM) and Inaccurate Memory (IM). Structural images were acquired using a 3 Tesla magnetic resonance imaging scanner.ResultsData from 59 ARMS and 47 FEP patients were analysed. Structural equation models revealed significant associations between the amygdala and AS, LE and IM; thalamus and LE and IM; and the caudate, hippocampus and putamen with IM. However, none of these significant results withstood correction for multiple testing.ConclusionsAlthough VLM is among the most impaired cognitive domains in emerging psychosis, we could not find an association between low performance in this domain and reductions in subcortical GM volumes. Our results suggest that deficits in this domain may not stem from alterations in subcortical structures.Disclosure of interestThe authors have not supplied their declaration of competing interest.

European Community Recent years have seen considerable progress in epidemiological and molecular genetic research into environmental and genetic factors in schizophrenia, but methodological uncertainties remain with regard to validating environmental exposures, and the population risk conferred by individual molecular genetic variants is small. There are now also a limited number of studies that have investigated molecular genetic candidate gene-environment interactions (G x E), however, so far, thorough replication of findings is rare and G x E research still faces several conceptual and methodological challenges. in this article, we aim to review these recent developments and illustrate how integrated, large-scale investigations may overcome contemporary challenges in G x E research, drawing on the example of a large, international, multi-center study into the identification and translational application of G x E in schizophrenia. While such investigations are now well underway, new challenges emerge for G x E research from late-breaking evidence that genetic variation and environmental exposures are, to a significant degree, shared across a range of psychiatric disorders, with potential overlap in phenotype. Maastricht Univ, Med Ctr, Dept Psychiat & Neuropsychol, Sch Mental Hlth & Neurosci,South Limburg Mental H, NL-6200 MD Maastricht, Netherlands Kings Coll London, Inst Psychiat, Dept Psychosis Studies, London WC2R 2LS, England Mondriaan Mental Hlth Trust, Maastricht, Heerlen, Netherlands Univ Groningen, Univ Med Ctr Groningen, Rob Giel Clin Res, Univ Ctr Psychiat, Groningen, Netherlands Kings Coll London, Inst Psychiat, Dept Hlth Serv & Populat Res, London WC2R 2LS, England Kings Coll London, Inst Psychiat, Dept Psychol, London WC2R 2LS, England Cardiff Univ, MRC, Ctr Neuropsychiat Genet, Cardiff CF10 3AX, S Glam, Wales Cent Inst Mental Hlth, Dept Psychiat & Psychotherapy, Mannheim, Germany Dokuz Eylul Univ, Sch Med, Dept Psychiat, Konak, Turkey Istanbul Univ, Istanbul Fac Med, Dept Psychiat, Psychot Disorders Res Unit, Istanbul, Turkey Ankara Univ, Sch Med, Dept Psychiat, Cebeci Hosp, TR-06100 Ankara, Turkey Ankara Univ, Brain Res Ctr, TR-06100 Ankara, Turkey Yale Univ, Sch Med, Dept Psychiat, New Haven, CT USA Middle E Tech Univ, Dept Psychol, TR-06531 Ankara, Turkey Diskapi YB Res & Training Hosp, Ankara, Turkey Turkish Federat Schizophrenia Assoc, Ankara, Turkey Ataturk Training & Res Hosp, Psychiat Clin, Ankara, Turkey Manisa Mental Hlth Hosp, Manisa, Turkey Istanbul Univ, Expt Med Res Inst, Dept Adv Neurol Sci, Istanbul Fac Med, Istanbul, Turkey Univ Complutense, IiSGM CIBERSAM, Dept Child & Adolescent Psychiat, Hosp Gen Univ Gregorio Maranon,Sch Med, E-28040 Madrid, Spain Univ Barcelona, Dept Psychiat, Hosp Clin, IDIBAPS,Ctr Invest Biomed Red Salud Mental CIBERS, Barcelona, Spain Univ Valencia, Sch Med, Dept Psychiat, Ctr Invest Biomed Red Salud Mental CIBERSAM, Valencia, Spain Univ Oviedo, Sch Med, Dept Med,Psychiat Area, Ctr Invest Biomed Red Salud Mental CIBERSAM, Oviedo, Spain Univ Santiago de Compostela, Dept Mental Hlth & Drug Addit Assistance, Hlth Serv Galicia,Psychiat Genet Grp IDIS, Hosp Clin,Ctr Invest Biomedica Red Salud Mental C, Santiago de Compostela 15706, Spain Hosp Virgen de la Luz, Serv Psiquiat, Dept Psychiat, Cuenca, Spain Hosp Univ Infanta Leonor Hosp Virgen Torre, Villa de Vallecas Mental Hlth Ctr, Villa de Vallecas Mental Hlth Dept, Madrid, Spain Hosp Univ Infanta Leonor Hosp Virgen Torre, Puente de Vallecas Mental Hlth Dept, Ctr Salud Mental Puente Vallecas, Madrid, Spain Hosp Clin Univ, Fdn Publ Galega Med Xenomica, Santiago de Compostela, Spain Univ Complutense, Sch Med, Hosp Gen Univ Gregorio Maranon, Dept Psychiat, E-28040 Madrid, Spain Hosp Psiquiatr Conxo, Santiago de Compostela, Spain Univ Amsterdam, Acad Med Ctr, Early Psychosis Sect, Dept Psychiat, NL-1105 AZ Amsterdam, Netherlands Vrije Univ Amsterdam, Dept Clin Psychol, Amsterdam, Netherlands EMGO Inst Hlth & Care Res, Amsterdam, Netherlands Parnassia Psychiat Inst, Dept Psychosis Res, the Hague, Netherlands Rivierduinen Psychiat Inst, Leiden, Netherlands Grp Hosp Mondor, AP HP, Creteil, France Hop Henri Mondor, INSERM, U955, Equipe 15, F-94010 Creteil, France Univ Paris Est, Fac Med, Creteil, France Fdn Fondamental, Creteil, France CMP B CHU, F-63003 Clermont Ferrand 1, France Univ Auvergne, EA 7280, Clermont Ferrand, France EPS Maison Blanche, Paris, France UPC KU Leuven, Dept Neurosci, UPC, Kortenberg, Belgium UPC, Dept Neurosci, Res Grp Psychiat, Leuven, Belgium Katholieke Univ Leuven, Univ Hosp Gasthuisberg, Dept Human Genet, Leuven, Belgium Assoc Sci Res Multiple Births, Ghent, Belgium Univ Ghent, Dept Neurol, Ghent Univ Hosp, B-9000 Ghent, Belgium Univ Athens, Sch Med, Eginit Hosp, Athens 11528, Greece Med Univ Vienna, Dept Psychiat & Psychotherapy, Vienna, Austria Psychiat Univ Clin Basel, Ctr Gender Res & Early Detect, Basel, Switzerland Psychiat Univ Clin Basel, Diagnost & Crisis Intervent Ctr, Basel, Switzerland Univ Cologne, Dept Psychiat & Psychotherapy, D-50931 Cologne, Germany Univ Hong Kong, Li Ka Shing Fac Med, Ctr Genom Sci, State Key Lab Brain & Cognit Sci, Hong Kong, Hong Kong, Peoples R China Univ Hong Kong, Li Ka Shing Fac Med, Dept Psychiat, Hong Kong, Hong Kong, Peoples R China Univ Hong Kong, Queen Mary Hosp, Li Ka Shing Fac Med, State Key Lab Brain & Cognit Sci, Hong Kong, Hong Kong, Peoples R China Univ Hong Kong, Queen Mary Hosp, Li Ka Shing Fac Med, Dept Psychiat, Hong Kong, Hong Kong, Peoples R China Univ Basque Country, Univ Deusto, Interuniv Chair Law & Human Genome Prov Govt Bisk, Bilbao, Bizkaia, Spain Univ Zaragoza, Zaragoza, Spain Univ Cambridge, Dept Psychiat, Cambridge, England UCL, Div Psychiat, London, England Royal Coll Surgeons Ireland, Beaumont Hosp, Educ & Res Ctr, Dept Psychiat, Dublin 9, Ireland Univ Munich, Dept Psychiat, Div Mol & Clin Neurobiol, Munich, Germany Univ Bologna, Alma Mater Studiorium, Psychiat Unit, Dept Med & Surg Sci, Bologna, Italy Univ Bologna, Alma Mater Studiorium, Genet Unit, Dept Med & Surg Sci, Bologna, Italy Local Hlth Trust, Dept Mental Hlth & Pathol Addict, Bologna, Italy Univ Palermo, Sect Psychiat, Dept Expt Biomed & Clin Neurosci, Palermo, Italy P Giaccone Gen Hosp, Unit Psychiat, Palermo, Italy Univ Melbourne, Ctr Youth Mental Hlth, Parkville, Vic 3052, Australia Univ Melbourne, Melbourne Neuropsychiat Ctr, Carlton, Vic, Australia Univ São Paulo, Fac Med, Dept Med Prevent, BR-01246903 São Paulo, Brazil Univ São Paulo, Nucleo Pesquina Saude Mental Populac, São Paulo, Brazil Univ São Paulo, Fac Med Ribeirao Preto, Dept Neurociencias & Ciencias Comportamento, BR-14049 Ribeirao Preto, Brazil Univ Verona, Sect Psychiat, Dept Publ Hlth & Community Med, I-37100 Verona, Italy Copenhagen Univ Hosp, Res Unit, Mental Hlth Ctr Copenhagen, Copenhagen, Denmark Univ Paris 05, Fac Med, Serv Hosp Univ, Hop St Anne, Paris, France Univ Autonoma Barcelona, Dept Psicol Clin & Salut, E-08193 Barcelona, Spain St Pere Claver Fundacio Sanitaria, Dept Salut Mental, Barcelona, Spain Univ N Carolina, Dept Psychol, Greensboro, NC 27412 USA CIBERSAM, Spanish Mental Hlth Res Network, Barcelona, Spain Universidade Federal de São Paulo, Dept Psychiat, PRISMA Early Intervent Program, São Paulo, Brazil Univ Belgrade, Sch Med, Beograd, Serbia Universidade Federal de São Paulo, Dept Psychiat, PRISMA Early Intervent Program, São Paulo, Brazil European Community: HEALTH-F2-2009-241909 Web of Science

article i nfo Background: Aggression and suicidality prior to the initiation of treatment are frequent phenomena in psychosis patients.IncreasedscoresintheBriefPsychiatric Rating Scale ExcitedComponent(BPRS-EC)have beenshownto predict involuntary treatment, aggression, and suicide in first-episode psychosis (FEP) patients. However, it is unclear if an agitated-aggressive syndrome as measured with the BPRS-EC is already present in at-risk mental state (ARMS). Methods: BPRS-EC scores from 43 ARMS patients, 50 FEP patients, and 25 healthy controls (HC) were analyzed. Multivariate analyses were performed to review if group differences were mediated by potential confounders. In addition, the association of BPRS-EC scores with clinical variables was examined. Results: BPRS-EC scores were significantly different across diagnostic groups (H(2) = 22.1; p b .001), and post- hoc analyses showed significantly higher BPRS-EC scores for ARMS (p = .001) and for FEP patients (p b .001) compared to HC. Differences remained significant after controlling for gender, years of education, and intelli- gence. No significant differences emerged between ARMS and FEP patients. BPRS-EC was significantly correlated with lower intelligence (r = −.27; p = .008), reduced level of functioning (r = −.44; p b .001), and with smoking behavior (r =. 22;p =. 019). Conclusions: ARMS and FEP patients in our sample had significantly higher BPRS-EC scores compared to HC. This may constitute a correlate of an agitated-aggressive syndrome and an increased risk for aggression and suicidality.

BackgroundThe brain-derived neurotrophic factor (BDNF) Val66Met polymorphism (refSNP Cluster Report: rs6265) is a common and functionally relevant single nucleotide polymorphism (SNP). The gene itself, as well as the SNP rs6265, have been implicated in hippocampal learning and memory. However, imaging genetic studies have produced controversial results about the impact of this SNP on hippocampal volumes in healthy subjects.MethodsWe examined the association between the rs6265 polymorphism and hippocampal volume in 643 healthy young subjects using automatic segmentation and subsequently included these data in a meta-analysis based on published studies with 5298 healthy subjects in total.ResultsWe found no significant association between SNP rs6265 and hippocampal volumes in our sample (g=0.05, p=0.58). The meta-analysis revealed a small, albeit significant difference in hippocampal volumes between genotype groups, such that Met-carriers had slightly smaller hippocampal volumes than Val/Val homozygotes (g=0.09, p=0.04), an association that was only evident when manual (g=0.22, p=0.01) but not automatic tracing approaches (g=0.04, p=0.38) were used. Studies using manual tracing showed evidence for publication bias and a significant decrease in effect size over the years with increasing sample sizes.ConclusionsThis study does not support the association between SNP rs6265 and hippocampal volume in healthy individuals. The weakly significant effect observed in the meta-analysis is mainly driven by studies with small sample sizes. In contrast, our original data and the meta-analysis of automatically segmented hippocampal volumes, which was based on studies with large samples sizes, revealed no significant genotype effect. Thus, meta-analyses of the association between rs6265 and hippocampal volumes should consider possible biases related to measuring technique and sample size.

Alterations in hippocampal volume are a known marker for first-episode psychosis (FEP) as well as for the clinical high-risk state. The Polygenic Schizophrenia-related Risk Score (PSRS), derived from a large case–control study, indicates the polygenic predisposition for schizophrenia in our clinical sample. A total of 65 at-risk mental state (ARMS) and FEP patients underwent structural magnetic resonance imaging. We used automatic segmentation of hippocampal volumes using the FSL-FIRST software and an odds-ratio-weighted PSRS based on the publicly available top single-nucleotide polymorphisms from the Psychiatric Genomics Consortium genome-wide association study (GWAS). We observed a negative association between the PSRS and hippocampal volumes (β=−0.42, P=0.01, 95% confidence interval (CI)=(−0.72 to −0.12)) across FEP and ARMS patients. Moreover, a higher PSRS was significantly associated with a higher probability of an individual being assigned to the FEP group relative to the ARMS group (β=0.64, P=0.03, 95% CI=(0.08–1.29)). These findings provide evidence that a subset of schizophrenia risk variants is negatively associated with hippocampal volumes, and higher values of this PSRS are significantly associated with FEP compared with the ARMS. This implies that FEP patients have a higher genetic risk for schizophrenia than the total cohort of ARMS patients. The identification of associations between genetic risk variants and structural brain alterations will increase our understanding of the neurobiology underlying the transition to psychosis.