Sexually dimorphic subcortical brain volumes in emerging psychosis

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<0.001), and smaller bilateral caudate (p=0.008) and hippocampus volume (p<0.001) than women across all three groups. Older subjects had more GM and WM volume than younger subjects. No significant sex×group interaction was found.; In emerging psychosis there still seem to exist patterns of normal sexual dimorphism in total brain and caudate volume. The only structure affected by reversed sexual dimorphism was the hippocampus, with women showing larger volumes than men even in HC. Thus, we conclude that subcortical volumes may not be primarily affected by disrupted sexual dimorphism in emerging psychosis

Reply to: New Meta- and Mega-analyses of Magnetic Resonance Imaging Findings in Schizophrenia: Do They Really Increase Our Knowledge About the Nature of the Disease Process?

This work was supported by National Institute of Biomedical Imaging and Bioengineering Grant No. U54EB020403 (to the ENIGMA consortium)

Cortical brain abnormalities in 4474 individuals with schizophrenia and 5098 control subjects via the enhancing neuro Imaging genetics through meta analysis (ENIGMA) Consortium

BACKGROUND: The profile of cortical neuroanatomical abnormalities in schizophrenia is not fully understood, despite hundreds of published structural brain imaging studies. This study presents the first meta-analysis of cortical thickness and surface area abnormalities in schizophrenia conducted by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Schizophrenia Working Group. METHODS: The study included data from 4474 individuals with schizophrenia (mean age, 32.3 years; range, 11-78 years; 66% male) and 5098 healthy volunteers (mean age, 32.8 years; range, 10-87 years; 53% male) assessed with standardized methods at 39 centers worldwide. RESULTS: Compared with healthy volunteers, individuals with schizophrenia have widespread thinner cortex (left/right hemisphere: Cohen's d = -0.530/-0.516) and smaller surface area (left/right hemisphere: Cohen's d = -0.251/-0.254), with the largest effect sizes for both in frontal and temporal lobe regions. Regional group differences in cortical thickness remained significant when statistically controlling for global cortical thickness, suggesting regional specificity. In contrast, effects for cortical surface area appear global. Case-control, negative, cortical thickness effect sizes were two to three times larger in individuals receiving antipsychotic medication relative to unmedicated individuals. Negative correlations between age and bilateral temporal pole thickness were stronger in individuals with schizophrenia than in healthy volunteers. Regional cortical thickness showed significant negative correlations with normalized medication dose, symptom severity, and duration of illness and positive correlations with age at onset. CONCLUSIONS: The findings indicate that the ENIGMA meta-analysis approach can achieve robust findings in clinical neuroscience studies; also, medication effects should be taken into account in future genetic association studies of cortical thickness in schizophrenia

Traces of trauma – a multivariate pattern analysis of childhood trauma, brain structure and clinical phenotypes

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

Brain structural alterations, genetic risk variants and the onset of psychosis

One of the central motivations behind research of the at-risk mental state is to prevent or delay potential transition to psychosis and further progression to schizophrenia, by studying the early signs and symptoms without potential confounding effects of disease progression and medication. And although the pathophysiological mechanism is still poorly understood, it is known that there is a large genetic heritability where a combination of different genetic variants sets a predisposition. Therefore, the identification of markers that characterise all states of the disease, namely schizophrenia, first-episode of psychosis and the at-risk mental state, are a main goal. A very robust marker is hippocampal volume reduction in schizophrenia, first- episode of psychosis and the at-risk mental state. In this thesis, I will present research for a deeper characterisation of the hippocampus in schizophrenia, first-episode of psychosis and the at-risk mental state and the association to genetic risk variants. First, we we found no association of the brain- derived neurotrophic factor rs6265 polymorphism with the hippocampal volumes neither in the original analysis of large cohort of young healthy individuals nor a meta-analysis with 5298 healthy subjects in total. Moreover, we detected differences between the applied hippocampal measuring techniques, i.e. manual or automated segmentation. Second, a meta-analysis of the same association but in 18 independent neuropsychiatric patient cohorts including schizophrenia revealed again no association. Also, we showed similar hippocampal reductions for Val/Val homozygote and Met-carrier patients compared to healthy controls. Third, group- related comparison of subcortical volumes revealed hippocampal and thalamic reductions in at-risk mental state individuals compared to healthy controls. Moreover, we found comparable medium effect sizes for both structures assessed with two different statistical methods. Fourth, in a cohort of at-risk mental state individuals and first-episode of psychosis patients we found a negative association between the hippocampal volumes and a polygenic schizophrenia-related risk score. Furthermore, a higher polygenic schizophrenia-related risk score was significantly associated with a higher probability of an individual being assigned to the first-episode of psychosis group compared to the total at-risk mental state group. These studies aid a better understanding of hippocampal volume reduction and genetic variants associated with schizophrenia, first-episode of psychosis and the at- risk mental state

O10.1. DISORGANIZED GYRIFICATION NETWORK PROPERTIES DURING THE TRANSITION TO PSYCHOSIS

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.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.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).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

Structural network disorganization in subjects at clinical high risk for psychosis

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

Disorganized gyrification network properties during the transition to 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 (Universitäre 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 (Hedges g range, 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

Impact on the Onset of Psychosis of a Polygenic Schizophrenia-Related Risk Score and Changes in White Matter Volume

Reductions in the volume of brain white matter are a common feature in schizophrenia and bipolar disorder while the association between white matter and polygenic schizophrenia-related risk is unclear. To look at the intermediate state between health and the full-blown disorder, we investigated this aspect in groups of patients before and after the onset of psychosis.; On a 3 Tesla scanner, total and regional white matter volumes were investigated by structural magnetic resonance imaging (MRI) in the following groups: 37 at-risk mental state patients (ARMS), including 30 with no transition to psychosis (ARMS-NT) and 7 with a transition to psychosis (ARMS-T) pooled with 25 first episode psychosis (FEP) patients. These T1-weighted images were automatically processed with the FreeSurfer software and compared with an odds-ratio-weighted polygenic schizophrenia-related risk score (PSRS) based on the publicly available top white matter single-nucleotide polymorphisms.; We found no association, only a trend, between PSRS and white matter volume over all groups (β = 0.24, p = 0.07, 95% confidence interval = [-0.02 - 0.49]). However, a higher PSRS was significantly associated with a higher probability of being assigned to the ARMS-T + FEP group rather than to the ARMS-NT group (β = 0.70, p = 0.02, 95% confidence interval = [0.14 - 1.33]); there was no such association with white matter volume. Additionally, a positive association was found between PSRS and the Brief Psychiatric Rating Scale (BPRS) total score for the pooled ARMS-NT/ARMS-T+FEP sample and for the ARMS-T + FEP group also, but none for the ARMS-NT group only.; These findings suggest that at-risk mental state patients with a transition and first-episode psychosis patients have a higher genetic risk for schizophrenia than at-risk mental state patients with no transition to psychosis; this risk was associated with psychopathological symptoms. Further analyses may allow polygenic schizophrenia-related risk scores to be used as biomarkers to predict psychosis

Association of antidepressants with brain morphology in early stages of psychosis:an imaging genomics approach

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 T; 1; -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