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Background Genome-Wide Association Studies (GWASs) have identified several genes associated with Schizophrenia (SCZ) and exponentially increased knowledge on the genetic basis of the disease. In addition, products of GWAS genes interact with neuronal factors coded by genes lacking association, such that this interaction may confer risk for specific phenotypes of this brain disorder. In this regard, fragile X mental retardation syndrome-related 1 (FXR1) gene has been GWAS associated with SCZ. FXR1 protein is regulated by glycogen synthase kinase-3β (GSK3β), which has been implicated in pathophysiology of SCZ and response to antipsychotics (APs). rs496250 and rs12630592, two eQTLs (Expression Quantitative Trait Loci) of FXR1 and GSK3β, respectively, interact on emotion stability and amygdala/prefrontal cortex activity during emotion processing. These two phenotypes are associated with Negative Symptoms (NSs) of SCZ suggesting that the interaction between these SNPs may also affect NS severity and responsiveness to medication. Methods To test this hypothesis, in two independent samples of patients with SCZ, we investigated rs496250 by rs12630592 interaction on NS severity and response to APs. We also tested a putative link between APs administration and FXR1 expression, as already reported for GSK3β expression. Results We found that rs496250 and rs12630592 interact on NS severity. We also found evidence suggesting interaction of these polymorphisms also on response to APs. This interaction was not present when looking at positive and general psychopathology scores. Furthermore, chronic olanzapine administration led to a reduction of FXR1 expression in mouse frontal cortex. Discussion Our findings suggest that, like GSK3β, FXR1 is affected by APs while shedding new light on the role of the FXR1/GSK3β pathway for NSs of SCZ.

Background Patients with schizophrenia (SCZ) show lower volumetric estimates of gray matter (GM) than healthy controls (HC). Similar results have been reported in healthy siblings of patients (SIB). However, it is unclear whether this phenotype is also present in individuals at clinical high-risk (CHR), characterized by sub-threshold symptoms and loss of functioning. We hypothesized that GM volumetric differences are associated with both familial and clinical risk for schizophrenia Methods We processed the T1-weighted MRI scans acquired at 3 Tesla of 544 HC, 63 SIB, 20 CHR and 120 SCZ using CAT12. We used ANCOVA to assess group differences (HC vs. CHR vs. SIB vs. SCZ), with linear and quadratic age, gender and total intracranial volume as nuisance covariates. We assessed the reproducibility of our case/control findings in an independent sample of 127 HC and 36 SCZ. Group differences were tested post hoc through Fisher’s test. Results We found significant group effects in the bilateral thalamus, bilateral hippocampus and anterior cingulate (FWE Discussion Individuals with familial or clinical risk for schizophrenia have lower GM estimates in the same brain regions. These findings, suggest that these structural features are not only associated with familial risk for schizophrenia but that they are also associated with its sub-threshold symptoms.

BackgroundThe GluN2B subunit ofN-methyl-d-aspartate receptors is crucially involved in the physiology of the prefrontal cortex during working memory (WM). Consistently, genetic variants in the GluN2B coding gene (GRIN2B) have been associated with cognitive phenotypes. However, it is unclear howGRIN2Bgenetic variation affects gene expression and prefrontal cognitive processing. Using a composite score, we tested the combined effect ofGRIN2Bvariants on prefrontal activity during WM performance in healthy subjects.MethodWe computed a composite score to combine the effects of single nucleotide polymorphisms on post-mortem prefrontalGRIN2BmRNA expression. We then computed the composite score in independent samples of healthy participants in a peripheral blood expression study (n= 46), in a WM behavioural study (n= 116) and in a WM functional magnetic resonance imaging study (n= 122).ResultsFive polymorphisms were associated withGRIN2Bexpression: rs2160517, rs219931, rs11055792, rs17833967 and rs12814951 (all correctedp< 0.05). The score computed to account for their combined effect reliably indexed gene expression.GRIN2Bcomposite score correlated negatively with intelligence quotient, WM behavioural efficiency and dorsolateral prefrontal cortex activity. Moreover, there was a non-linear association betweenGRIN2Bgenetic score and prefrontal activity, i.e. both high and low putative genetic score levels were associated with high blood oxygen level-dependent signals in the prefrontal cortex.ConclusionsMultiple genetic variants inGRIN2Bare jointly associated with gene expression, prefrontal function and behaviour during WM. These results support the role ofGRIN2Bgenetic variants in WM prefrontal activity in human adults.

Rasd2 is a thyroid hormone target gene, which encodes for a GTP-binding protein enriched in the striatum where, among other functions, it modulates dopaminergic neurotransmission. Here we report that human RASD2 mRNA is abundant in putamen, but it also occurs in the cerebral cortex, with a distinctive expression pattern that differs from that present in rodents. Consistent with its localization, we found that a genetic variation in RASD2 (rs6518956) affects postmortem prefrontal mRNA expression in healthy humans and is associated with phenotypes of relevance to schizophrenia, including prefrontal and striatal grey matter volume and physiology during working memory, as measured with magnetic resonance imaging. Interestingly, quantitative real-time PCR analysis indicated that RASD2 mRNA is slightly reduced in postmortem prefrontal cortex of patients with schizophrenia. In the attempt to uncover the neurobiological substrates associated with Rasd2 activity, we used knockout mice to analyze the in vivo influence of this G-protein on the prepulse inhibition of the startle response and psychotomimetic drug-related behavioral response. Data showed that Rasd2 mutants display deficits in basal prepulse inhibition that, in turn, exacerbate gating disruption under psychotomimetic drug challenge. Furthermore, we documented that lack of Rasd2 strikingly enhances the behavioral sensitivity to motor stimulation elicited by amphetamine and phencyclidine. Based on animal model data, along with the finding that RASD2 influences prefronto-striatal phenotypes in healthy humans, we suggest that genetic mutation or reduced levels of this G-protein might have a role in cerebral circuitry dysfunction underpinning exaggerated psychotomimetic drugs responses and development of specific biological phenotypes linked to schizophrenia.

Background Diagnosis of schizophrenia is based on a collection of symptoms which are heterogeneous from one patient to the other. Therefore, improving the reliability of this diagnosis is a currently unmet need. Schizophrenia risk is associated with genetic variation and with environmental factors potentially affecting neurodevelopment. Moreover, among the symptoms, cognitive abnormalities are heritable and predate its clinical onset. Multivariate techniques can leverage the high dimensionality of data in order to study the combined effect of multiple risk factors and symptoms on clinical predictions. The aim of the current study is therefore to assess the predictability of schizophrenia diagnosis applying machine learning techniques to an ensemble of genetic, early environmental and cognitive deficits variables. Methods 442 subjects (339 healthy controls – HC – and 103 patients with schizophrenia – SCZ) were recruited for the study. Participants underwent a full neuropsychological evaluation (Modality 1, assessment of working memory, verbal fluency, intelligence quotient, attention, speed of processing and cognitive control), a broad environmental assessment (Modality 2, investigation of urbanicity, obstetric complications, developmental anomalies, socio-economic parental status and age of parents at birth) and genome-wide genotyping (Modality 3). Following published procedures, we computed individual risk scores for each of the single nucleotide polymorphisms (SNPs) associated with risk for schizophrenia in the Psychiatric Genomics Consortium (PGC) study. Data from Modalities 1, 2 and 3 entered NeuroMiner v0.998 and underwent preprocessing procedures through scaling, pruning of non-informative variables and imputation of missing values through Euclidean distance-based nearest-neighbor search. Then, these three modalities were included in a Support Vector Machine HC vs. SCZ classification algorithm, which applied decision-based data fusion strategies to integrate the individual predictions of the three modalities in a nested cross-validation framework. Results Our cross-validated results revealed that Modality 1 (cognition) predicted schizophrenia diagnosis with the highest Balanced Accuracy (BAC, 87.3%) and that the most selected cognitive indices were intelligence quotient scores and attentional abilities. Modality 2 (environment) classified HC and SCZ with a BAC of 67.2%, and the most predictive environmental features were the parental socio-economic status, the presence of developmental anomalies during the first year of life and the age of father at birth. On the other hand, Modality 3 (genetics) predicted schizophrenia diagnosis with BAC=54,1%. The most informative SNPs were FUT9 rs117074560, TCF4 rs72934570 and STAG1 rs7432375. Decision-based fusion combining individual cognitive, environmental and genetic decision scores predicted the classification of SCZ from HC with a 78.9% BAC. Discussion Our results using a novel machine learning approach suggest that an ensemble of cognitive, early environmental and genetic features can predict schizophrenia with significant accuracy. Our results also give key information on cognitive and environmental factors that can be targeted in early identification programs and offer novel insights about genetic loci that may be prioritized in future investigations of the pathophysiology of the disease. However, the near chance-level predictive ability of the genetic modality alone calls for the implementation and testing of more complex models of interaction between multiple risk factors.

Genetic risk for schizophrenia (SCZ) is determined by many genetic loci whose compound biological effects are difficult to determine. We hypothesized that co-expression pathways of SCZ risk genes are associated with system-level brain function and clinical phenotypes of SCZ. We examined genetic variants related to the dopamine D2 receptor gene DRD2 co-expression pathway and associated them with working memory (WM) behavior, the related brain activity and treatment response. Using two independent post-mortem prefrontal messenger RNA (mRNA) data sets (total N=249), we identified a DRD2 co-expression pathway enriched for SCZ risk genes. Next, we identified non-coding single-nucleotide polymorphisms (SNPs) associated with co-expression of this pathway. These SNPs were associated with regulatory genetic loci in the dorsolateral prefrontal cortex (PDRD2 pathway co-expression in both mRNA data sets (all PN=368) and 29 patients with SCZ who performed the n-back task. Greater predicted DRD2 pathway prefrontal co-expression was associated with greater prefrontal activity and longer WM reaction times (all corrected PN=87; PDRD2 co-expression pathway are a proof of concept that gene co-expression can parse SCZ risk genes into biological pathways associated with intermediate phenotypes as well as with clinically meaningful information.

Earlier neuroimaging studies of motor function in schizophrenia have demonstrated reduced functional lateralization in the motor network during motor tasks. Here, we used event-related functional magnetic resonance imaging during a visually guided motor task in 18 clinically unaffected siblings of patients with schizophrenia and 24 matched controls to investigate if abnormal functional lateralization is related to genetic risk for this brain disorder. Whereas activity associated with motor task performance was mainly contralateral with only a marginal ipsilateral component in healthy participants, unaffected siblings had strong bilateral activity with significantly greater response in ipsilateral and contralateral premotor areas as well as in contralateral subcortical motor regions relative to controls. Reduced lateralization in siblings was also identified with a measure of laterality quotient. These findings suggest that abnormal functional lateralization of motor circuitry is related to genetic risk of schizophrenia.

IntroductionNeuroimaging studies have identified several candidate biomarkers of schizophrenia. However, it is unclear whether the considerable variability in these neurobiological correlates between patients can be translated into the clinical setting.ObjectivesWe aimed to identify neuroimaging predictors of clinical course in patients with schizophrenia. Combined with the identification of genetically determined markers of schizophrenia risk, our studies aimed to elucidate the biological basis and the clinical relevance of inter-individual variability between patients.MethodsWe included over 150 patients with schizophrenia and 279 healthy volunteers across five neuroimaging centers in the framework of the IMAGEMEND project [1]. We performed multiple studies on MRI scans using random forests and ROC curves to predict clinical course. Data from healthy controls served to normalize the data from the clinical population and to provide a benchmark for the findings.ResultsWe identified ensembles of neuroimaging markers and of genetic variants predictive of clinical course. Results highlight that (i) brain imaging carries significant clinical information, (ii) clinical information at baseline can considerably increase prediction accuracy.ConclusionThe methodological challenges and the results will be discussed in the context of recent findings from other multi-site studies. We conclude that brain imaging data on their own right are relevant to stratify patients in terms of clinical course; however, complementing these data with other modalities such as genetics and clinical information is necessary to further develop the field towards clinical application of the predictions.Disclosure of interestGiulio Pergola is the academic supervisor of a Hoffmann-La Roche Collaboration grant that partially funds his salary.

Anomalies in behavioral correlates of attentional processing and related brain activity are crucial correlates of schizophrenia and associated with familial risk for this brain disorder. However, it is not clear how brain functional connectivity during attentional processes is key for schizophrenia and linked with trait vs. state related variables. To address this issue, we investigated patterns of functional connections during attentional control in healthy siblings of patients with schizophrenia, who share with probands genetic features but not variables related to the state of the disorder. 356 controls, 55 patients with schizophrenia on stable treatment with antipsychotics and 40 healthy siblings of patients with this brain disorder underwent the Variable Attentional Control (VAC) task during fMRI. Independent Component Analysis (ICA) is allowed to identify independent components (IC) of BOLD signal recorded during task performance. Results indicated reduced connectivity strength in patients with schizophrenia as well as in their healthy siblings in left thalamus within an attentional control component and greater connectivity in right medial prefrontal cortex (PFC) within the so-called Default Mode Network (DMN) compared to healthy individuals. These results suggest a relationship between familial risk for schizophrenia and brain functional networks during attentional control, such that this biological phenotype may be considered a useful intermediate phenotype in order to link genes effects to aspects of the pathophysiology of this brain disorder.

Background: Dopamine D2 receptors (D2R) contribute to the inverted-U shaped relationship between dopamine dorsolateral prefrontal cortex (DLPFC) and working memory (WM). Genetic variation in DRD2 coding for D2Rs modulates D2 signaling, but other genes in its pathway may be involved. In a previous work, using gene co-expression networks we identified 84 partner genes coregulated with DRD2 and eight single nucleotide polymorphisms (SNPs) predicting coexpression of the whole gene set in the human DLPFC [1]. These SNPs combined into a polygenic coexpression index (PCI) predicted WM performance and DLPFC activity in two independent samples of living healthy humans [1]. Here, we asked whether response to D2R targeting drugs is associated with this PCI. Thus, we investigated the interaction between WM behavioral/brain response to the D2R agonist Bromocriptine (BRO) and the PCI.

Importance Serotonin (5-hydroxytryptamine) receptor 2a (5-HT2AR) signaling is important for modulation of corticostriatal pathways and prefrontal activity during cognition. Furthermore, newer antipsychotic drugs target 5-HT2AR. A single-nucleotide polymorphism in the 5-HT2AR gene ( HTR2A rs6314, C>T; OMIM 182135) has been weakly associated with differential 5-HT2AR signaling and with physiologic as well as behavioral effects. Objective To use a hierarchical approach to determine the functional effects of this single-nucleotide polymorphism on 5-HT2AR messenger RNA and protein expression, on prefrontal phenotypes linked with genetic risk for schizophrenia, and on treatment with olanzapine. Design In silico predictions, in vitro, and case-control investigations. Setting Academic and clinical facilities. Participants The postmortem study included 112 brains from healthy individuals; the in vivo investigation included a total sample of 371 healthy individuals and patients with schizophrenia. Exposures Patients received olanzapine monotherapy for 8 weeks. Main Outcomes and Measures In silico predictions, messenger RNA, and protein expression in postmortem human prefrontal cortex and HeLa cells, functional magnetic resonance imaging prefrontal activity and behavior during working memory and attention in healthy individuals, and response to an 8-week trial of olanzapine treatment in patients with schizophrenia. Results Bioinformatic analysis predicted that rs6314 alters patterns of splicing, with possible effects on HTR2A expression. Moreover, the T allele was associated with reduced prefrontal messenger RNA expression in postmortem prefrontal cortex, with reduced protein expression in vitro, inefficient prefrontal blood oxygen level–dependent functional magnetic resonance imaging response during working memory and attentional control processing, and impaired working memory and attention behavior, as well as with attenuated improvement in negative symptoms after olanzapine treatment. Conclusions and Relevance Our results suggest that HTR2A rs6314 affects 5-HT2AR expression and functionally contributes to genetic modulation of known endophenotypes of schizophrenia-like higher-level cognitive behaviors and related prefrontal activity, as well as response to treatment with olanzapine.

Objective: Glycogen synthase kinase 3 beta (GSK-3 beta) is an enzyme implicated in neurodevelopmental processes with a broad range of substrates mediating several canonical signaling pathways in the brain. The authors investigated the association of variation in the GSK-3 beta gene with a series of progressively more complex phenotypes of relevance to schizophrenia, a neurodevelopmental disorder with strong genetic risk.Method: Based on computer predictions, the authors investigated in humans the association of GSK-3 beta functional variation with 1) GSK-3 beta mRNA expression from postmortem prefrontal cortex, 2) GSK-3 beta and beta-catenin protein expression from peripheral blood mononuclear cells (PBMCs), 3) prefrontal imaging phenotypes, and 4) diagnosis of schizophrenia.Results: Consistent with predictions, the TT genotype of a single-nucleotide polymorphism in GSK-3 beta (rs12630592) was associated with reduced GSK-3 beta mRNA from postmortem prefrontal cortex. Furthermore, this genotype was associated with GSK-3 beta protein expression and kinase activity, as well as with downstream effects on beta-catenin expression in PBMCs. Finally, the TT genotype was associated with attenuated functional MRI prefrontal activity, reduced prefrontal cortical thickness, and diagnosis of schizophrenia.Conclusions: These results suggest that GSK-3 beta variation is implicated in multiple phenotypes relevant to schizophrenia.

BackgroundAbnormalities in hippocampal–parahippocampal (H-PH) function are prominent features of schizophrenia and have been associated with deficits in episodic memory. However, it remains unclear whether these abnormalities represent a phenotype related to genetic risk for schizophrenia or whether they are related to disease state.MethodWe investigated H-PH-mediated behavior and physiology, using blood oxygenation level-dependent functional magnetic resonance imaging (BOLD fMRI), during episodic memory in a sample of patients with schizophrenia, clinically unaffected siblings and healthy subjects.ResultsPatients with schizophrenia and unaffected siblings displayed abnormalities in episodic memory performance. During an fMRI memory encoding task, both patients and siblings demonstrated a similar pattern of reduced H-PH engagement compared with healthy subjects.ConclusionsOur findings suggest that the pathophysiological mechanism underlying the inability of patients with schizophrenia to properly engage the H-PH during episodic memory is related to genetic risk for the disorder. Therefore, H-PH dysfunction can be assumed as a schizophrenia susceptibility-related phenotype.

The default mode network (DMN) comprises a set of brain regions with "increased" activity during rest relative to cognitive processing. Activity in the DMN is associated with functional connections with the striatum and dopamine (DA) levels in this brain region. A functional single-nucleotide polymorphism within the dopamine D2 receptor gene (DRD2, rs1076560 G > T) shifts splicing of the 2 D2 isoforms, D2 short and D2 long, and has been associated with striatal DA signaling as well as with cognitive processing. However, the effects of this polymorphism on DMN have not been explored. The aim of this study was to evaluate the effects of rs1076560 on DMN and striatal connectivity and on their relationship with striatal DA signaling. Twenty-eight subjects genotyped for rs1076560 underwent functional magnetic resonance imaging during a working memory task and 123 55 I-Fluoropropyl-2-beta-carbomethoxy-3-beta(4-iodophenyl) nortropan Single Photon Emission Computed Tomography ([(123)I]-FP-CIT SPECT) imaging (a measure of dopamine transporter [DAT] binding). Spatial group-independent component (IC) analysis was used to identify DMN and striatal ICs. Within the anterior DMN IC, GG subjects had relatively greater connectivity in medial prefrontal cortex (MPFC), which was directly correlated with striatal DAT binding. Within the posterior DMN IC, GG subjects had reduced connectivity in posterior cingulate relative to T carriers. Additionally, rs1076560 genotype predicted connectivity differences within a striatal network, and these changes were correlated with connectivity in MPFC and posterior cingulate within the DMN. These results suggest that genetically determined D2 receptor signaling is associated with DMN connectivity and that these changes are correlated with striatal function and presynaptic DA signaling.

BackgroundCatechol-O-methyltransferase (COMT) Val158Met has been associated with activity of the mesial temporal lobe during episodic memory and it may weakly increase risk for schizophrenia. However, how this variant affects parahippocampal and hippocampal physiology when dopamine transmission is perturbed is unclear. The aim of the present study was to compare the effects of the COMT Val158Met genotype on parahippocampal and hippocampal physiology during encoding of recognition memory in patients with schizophrenia and in healthy subjects.MethodUsing blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI), we studied 28 patients with schizophrenia and 33 healthy subjects matched for a series of sociodemographic and genetic variables while they performed a recognition memory task.ResultsWe found that healthy subjects had greater parahippocampal and hippocampal activity during memory encoding compared to patients with schizophrenia. We also found different activity of the parahippocampal region between healthy subjects and patients with schizophrenia as a function of the COMT genotype, in that the predicted COMT Met allele dose effect had an opposite direction in controls and patients.ConclusionsOur results demonstrate a COMT Val158Met genotype by diagnosis interaction in parahippocampal activity during memory encoding and may suggest that modulation of dopamine signaling interacts with other disease-related processes in determining the phenotype of parahippocampal physiology in schizophrenia.

Earlier studies have shown widespread alterations of functional connectivity of various brain networks in schizophrenia, including the default mode network (DMN). The DMN has also an important role in the performance of cognitive tasks. Furthermore, treatment with second-generation antipsychotic drugs may ameliorate to some degree working memory (WM) deficits and related brain activity. The aim of this study was to evaluate the effects of treatment with olanzapine monotherapy on functional connectivity among brain regions of the DMN during WM. Seventeen patients underwent an 8-week prospective study and completed two functional magnetic resonance imaging (fMRI) scans at 4 and 8 weeks of treatment during the performance of the N-back WM task. To control for potential repetition effects, 19 healthy controls also underwent two fMRI scans at a similar time interval. We used spatial group-independent component analysis (ICA) to analyze fMRI data. Relative to controls, patients with schizophrenia had reduced connectivity strength within the DMN in posterior cingulate, whereas it was greater in precuneus and inferior parietal lobule. Treatment with olanzapine was associated with increases in DMN connectivity with ventromedial prefrontal cortex, but not in posterior regions of DMN. These results suggest that treatment with olanzapine is associated with the modulation of DMN connectivity in schizophrenia. In addition, our findings suggest critical functional differences in the regions of DMN.

Background Rasd2 is a striatal GTP-binding protein that modulates Akt and mTOR signaling cascades, well known to be highly vulnerable pathways in psychiatric disorders. Aims We investigated the association of Rasd2 and its genetic variation with a series of prefronto-striatal phenotypes related to psychosis in rodents and humans. Objectives We want to provide evidence that Rasd2 controls the vulnerability to schizophrenia-related behavior induced by psychothomimetic drugs in mice. Moreover, we aim to find genetic variations within the Rasd2 gene that influence a series of brain schizophrenia-related phenotypes in human. Methods Rasd2 knockout mice were employed to evaluate schizophrenia-like behaviors induced by psychotomimetic drugs like amphetamine and phencyclidine. Furthermore, we investigated if RASD2 genetic variations in humans are associated with mRNA expression in post-mortem prefrontal cortex, as well as prefrontal and striatal grey matter volume and physiology during working memory as measured with MRI in healthy subjects. Finally, we assessed RASD2 mRNA expression levels in post-mortem brains of patients with schizophrenia and bipolar disorder. Results We found that both psychotomimetics triggered greater vulnerability to motor stimulation and to prepulse inhibition deficits in Rasd2 mutants. In humans, we found that a genetic variation (rs6518956) within RASD2 predicts prefrontal mRNA expression as well as prefrontal grey matter volume and prefronto-striatal activity during working memory. Finally, we reported that RASD2 mRNA expression is slightly reduced in post-mortem prefrontal cortex of patients with schizophrenia. Conclusions Collectively, our data suggests that RASD2 represents a gene of potential interest in psychiatric disorders for its ability to modulate prefronto-striatal phenotypes related to schizophrenia.

Adolescents with high genetic risk for schizophrenia are more prone than others to bullying episodes by their peers. Such episodes contribute to mental distress 2-4 years later, suggesting a novel potential risk mechanism involving genes and the environment. Data from the Dutch consortium "TRAILS" confirmed this innovative hypothesis, which has been published in "World Psychiatry". This research underscores the importance of studying adolescence for prevention in the field of mental health.