Polygenic risk for five psychiatric disorders and cross-disorder and disorder-specific neural connectivity in two independent populations. Neuroimage Clin. 2017;14:441-449. [PMID: 28275544 PMCID: PMC5328751 DOI: 10.1016/j.nicl.2017.02.011]

Polygenic risk scores and the prediction of onset of mood and psychotic disorders in adolescents and young adults

AIM

To examine whether polygenic risk scores (PRS) for neuroticism, depression, bipolar disorder and schizophrenia are higher in individuals manifesting trans-diagnostic risk factors for the development of major mental disorders and whether PRS enhance prediction of early onset full-threshold disorders.

METHODS

Using data from the Brisbane Longitudinal Twin Study, we examined individual PRS for neuroticism, depression, bipolar disorder and schizophrenia, recorded evidence of subthreshold syndromes and family history of mood and/or psychotic disorders and noted progression to trans-diagnostic clinical caseness (onset of major mental disorders) at follow-up. We undertook multivariate, receiver operating curve and logistic regression analyses that were adjusted for known variables of influence (age, twin status, and so on).

RESULTS

Of 1473 eligible participants (female = 866, 59%; mean age 26.3 years), 28% (n = 409) met caseness criteria for a mood and/or psychotic disorder. All PRS were higher in cases versus non-cases but associations with different levels of risk were inconsistent. The prediction of caseness (reported as area under the curve with 95% confidence intervals [CI]) improved from 0.68 (95% CI: 0.65, 0.71) when estimated using clinical risk factors alone up to 0.71 (95% CI: 0.69, 0.73) when PRS were added to the model. Logistic regression identified five variables that optimally classified individuals according to caseness: age, sex, individual risk characteristics, PRS for depression and mental health case status of cotwins or siblings.

CONCLUSIONS

The findings need replication. However, this exploratory study suggests that combining PRS with other risk factors has the potential to improve outcome prediction in youth.

Hypergraph Structural Information Aggregation Generative Adversarial Networks for Diagnosis and Pathogenetic Factors Identification of Alzheimer's Disease With Imaging Genetic Data

Alzheimer's disease (AD) is a neurodegenerative disease with profound pathogenetic causes. Imaging genetic data analysis can provide comprehensive insights into its causes. To fully utilize the multi-level information in the data, this article proposes a hypergraph structural information aggregation model, and constructs a novel deep learning method named hypergraph structural information aggregation generative adversarial networks (HSIA-GANs) for the automatic sample classification and accurate feature extraction. Specifically, HSIA-GAN is composed of generator and discriminator. The generator has three main functions. First, vertex graph and edge graph are constructed based on the input hypergraph to present the low-order relations. Second, the low-order structural information of hypergraph is extracted by the designed vertex convolution layers and edge convolution layers. Finally, the synthetic hypergraph is generated as the input of the discriminator. The discriminator can extract the high-order structural information directly from hypergraph through vertex-edge convolution, fuse the high and low-order structural information, and finalize the results through the full connection (FC) layers. Based on the data acquired from AD neuroimaging initiative, HSIA-GAN shows significant advantages in three classification tasks, and extracts discriminant features conducive to better disease classification.

Jnk1 and downstream signalling hubs regulate anxiety-like behaviours in a zebrafish larvae phenotypic screen

Current treatments for anxiety and depression show limited efficacy in many patients, indicating the need for further research into the underlying mechanisms. JNK1 has been shown to regulate anxiety- and depressive-like behaviours in mice, however the effectors downstream of JNK1 are not known. Here we compare the phosphoproteomes from wild-type and Jnk1-/- mouse brains and identify JNK1-regulated signalling hubs. We next employ a zebrafish (Danio rerio) larvae behavioural assay to identify an antidepressant- and anxiolytic-like (AA) phenotype based on 2759 measured stereotypic responses to clinically proven antidepressant and anxiolytic (AA) drugs. Employing machine learning, we classify an AA phenotype from extracted features measured during and after a startle battery in fish exposed to AA drugs. Using this classifier, we demonstrate that structurally independent JNK inhibitors replicate the AA phenotype with high accuracy, consistent with findings in mice. Furthermore, pharmacological targeting of JNK1-regulated signalling hubs identifies AKT, GSK-3, 14-3-3 ζ/ε and PKCε as downstream hubs that phenocopy clinically proven AA drugs. This study identifies AKT and related signalling molecules as mediators of JNK1-regulated antidepressant- and anxiolytic-like behaviours. Moreover, the assay shows promise for early phase screening of compounds with anti-stress-axis properties and for mode of action analysis.

Identifying plasma metabolic characteristics of major depressive disorder, bipolar disorder, and schizophrenia in adolescents

Major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) are classified as major mental disorders and together account for the second-highest global disease burden, and half of these patients experience symptom onset in adolescence. Several studies have reported both similar and unique features regarding the risk factors and clinical symptoms of these three disorders. However, it is still unclear whether these disorders have similar or unique metabolic characteristics in adolescents. We conducted a metabolomics analysis of plasma samples from adolescent healthy controls (HCs) and patients with MDD, BD, and SCZ. We identified differentially expressed metabolites between patients and HCs. Based on the differentially expressed metabolites, correlation analysis, metabolic pathway analysis, and potential diagnostic biomarker identification were conducted for disorders and HCs. Our results showed significant changes in plasma metabolism between patients with these mental disorders and HCs; the most distinct changes were observed in SCZ patients. Moreover, the metabolic differences in BD patients shared features with those in both MDD and SCZ, although the BD metabolic profile was closer to that of MDD than to SCZ. Additionally, we identified the metabolites responsible for the similar and unique metabolic characteristics in multiple metabolic pathways. The similar significant differences among the three disorders were found in fatty acid, steroid-hormone, purine, nicotinate, glutamate, tryptophan, arginine, and proline metabolism. Interestingly, we found unique characteristics of significantly altered glycolysis, glycerophospholipid, and sphingolipid metabolism in SCZ; lysine, cysteine, and methionine metabolism in MDD and BD; and phenylalanine, tyrosine, and aspartate metabolism in SCZ and BD. Finally, we identified five panels of potential diagnostic biomarkers for MDD-HC, BD-HC, SCZ-HC, MDD-SCZ, and BD-SCZ comparisons. Our findings suggest that metabolic characteristics in plasma vary across psychiatric disorders and that critical metabolites provide new clues regarding molecular mechanisms in these three psychiatric disorders.

Polygenic effects on brain functional endophenotype for deficit and non-deficit schizophrenia

Deficit schizophrenia (DS) is a subtype of schizophrenia (SCZ). The polygenic effects on the neuroimaging alterations in DS still remain unknown. This study aims to calculate the polygenic risk scores for schizophrenia (PRS-SCZ) in DS, and further explores the potential associations with functional features of brain. PRS-SCZ was calculated according to the Whole Exome sequencing and Genome-wide association studies (GWAS). Resting-state fMRI, as well as biochemical features and neurocognitive data were obtained from 33 DS, 47 NDS and 41 HCs, and association studies of genetic risk with neuroimaging were performed in this sample. The analyses of amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo) and functional connectivity (FC) were performed to detect the functional alterations between DS and NDS. In addition, correlation analysis was used to investigate the relationships between functional features (ALFF, ReHo, FC) and PRS-SCZ. The PRS-SCZ of DS was significantly lower than that in NDS and HC. Compared to NDS, there was a significant increase in the ALFF of left inferior temporal gyrus (ITG.L) and left inferior frontal gyrus (IFG.L) and a significant decrease in the ALFF of right precuneus (PCUN.R) and ReHo of right middle frontal gyrus (MFG.R) in DS. FCs were widely changed between DS and NDS, mainly concentrated in default mode network, including ITG, PCUN and angular gyrus (ANG). Correlation analysis revealed that the ALFF of left ITG, the ReHo of right middle frontal gyrus, the FC value between insula and ANG, left ITG and right corpus callosum, left ITG and right PCUN, as well as the scores of Trail Making Test-B, were associated with PRS-SCZ in DS. The present study demonstrated the differential polygenic effects on functional changes of brain in DS and NDS, providing a potential neuroimaging-genetic perspective for the pathogenesis of schizophrenia.

Association of polygenic risk for bipolar disorder with resting-state network functional connectivity in youth with and without bipolar disorder

Little is known regarding the polygenic underpinnings of anomalous resting-state functional connectivity (rsFC) in youth bipolar disorder (BD). The current study examined the association of polygenic risk for BD (BD-PRS) with whole-brain rsFC at the large-scale network level in youth with and without BD. 99 youth of European ancestry (56 BD, 43 healthy controls [HC]), ages 13-20 years, completed resting-state fMRI scans. BD-PRS was calculated using summary statistics from the latest adult BD genome-wide association study. Data-driven independent component analyses of the resting-state fMRI data were implemented to examine the association of BD-PRS with rsFC in the overall sample, and separately in BD and HC. In the overall sample, higher BD-PRS was associated with lower rsFC of the salience network and higher rsFC of the frontoparietal network with frontal and parietal regions. Within the BD group, higher BD-PRS was associated with higher rsFC of the default mode network with orbitofrontal cortex, and altered rsFC of the visual network with frontal and occipital regions. Within the HC group, higher BD-PRS was associated with altered rsFC of the frontoparietal network with frontal, temporal and occipital regions. In conclusion, the current study found that BD-PRS generated based on adult genetic data was associated with altered rsFC patterns of brain networks in youth. Our findings support the usefulness of BD-PRS to investigate genetically influenced neuroimaging markers of vulnerability to BD, which can be observed in youth with BD early in their course of illness as well as in healthy youth.

The absurdity of the latent disease model in mental health: 10,130,814 ways to have a DSM-5-TR psychological disorder

BACKGROUND

Latent disease classification is currently the accepted approach to mental illness diagnosis. In the United States, this takes the form of the Diagnostic and Statistical Manual of Mental Disorders-5-Text Revision (DSM-5-TR). Latent disease classification has been criticized for reliability and validity problems, particularly regarding diagnostic heterogeneity. No authors have calculated the scope of the heterogeneity problem of the entire DSM-5-TR.

AIMS

We addressed this issue by calculating the unique diagnostic profiles that exist for every DSM-5-TR diagnosis.

METHODS

We did this by applying formulas previously used in smaller heterogeneity analyses to all diagnoses within the DSM-5-TR.

RESULTS

We found that there are 10,130,814 ways to be diagnosed with a mental illness using DSM-5-TR criteria. When specifiers are considered, this number balloons to over 161 septillion unique diagnostic presentations (driven mainly by bipolar II disorder). Additionally, there are 1,951,065 ways to present with psychiatric symptoms, yet not meet diagnostic criteria.

CONCLUSIONS

Latent disease classification leads to considerable heterogeneity in possible presentations. We provide examples of how latent disease classification harms research and treatment programs. We echo recommendations for the dismissal of latent disease classification as a mental illness diagnostic program.

Association of 11 variants of the dopaminergic and cognitive pathways genes with major depression, schizophrenia and bipolar disorder in the Pakistani population

Background: The dopaminergic pathways control neural signals that modulate mood and behaviour along and have a vital role in the aetiology of major depression (MDD), schizophrenia (SHZ) and bipolar disorder (BD). Genome-wide association studies (GWAS) have reported several dopaminergic and cognitive pathway genes association with these disorders however, no such comprehensive data was available regarding the Pakistani population.Objective: The present study was conducted to analyse the 11 genetic variants of dopaminergic and cognitive system genes in MDD, SHZ, and BD in the Pakistani population.Methods: A total of 1237 subjects [MDD n = 479; BD n = 222; SHZ n = 146; and controls n = 390], were screened for 11 genetic variants through polymerase chain reaction (PCR) techniques. Univariant followed by multivariant logistic regression analysis was applied to determine the genetic association.Results: Significant risk associations were observed for rs4532 and rs1799732 with MDD; and rs1006737 and rs2238056 with BD. However, after applying multiple test corrections rs4532 and rs1799732 association did not remain significant for MDD. Moreover, a protective association was found for three variants; DRD4-120bp, rs10033951 and rs2388334 in the current cohort.Conclusions: The present study revealed the risk association of single nucleotide polymorphisms (SNPs) rs1006737 and rs2238056 with BD and the protective effect of the DRD4-120bp variant in MDD and BD, of rs2388334 in BD and of rs10033951 in MDD, BD, and SHZ in the current Pakistani cohort. Thus, the study is valuable in understanding the genetic basis of MDD, BD and SHZ in the Pakistani population, which may pave the way for future functional studies.

Resting-state fMRI functional connectivity and clinical correlates in Afro-descendants with schizophrenia and bipolar disorder

Schizophrenia (SCZ) and bipolar disorder (BD) exhibited altered activation in several brain areas, including the prefrontal and temporal cortex; however, a less explored topic is how brain connectivity and functional disturbances occur in non-Caucasian samples of SCZ and BD. Individuals with SCZ (n=20), BD (n=21), and healthy controls (HC, n=21) from indigenous and African ethnicity were submitted to clinical screening and functional assessments. Mood, compulsive and psychotic symptoms were also correlated to network dysfunction in each group. Two distinct networks' subcomponents demonstrated significant lower global efficiency (GE) in SCZ versus HC, corresponding to left posterior dorsal attention and medial left ventral attention (VA) networks. Lower GE was found in BD versus controls in four subcomponents, including the left medial and right VA. Higher compulsion scores correlated in BD with lower GE in the left VA, whereas increased report of alcohol abuse was associated with higher GE in left default mode network. Although preliminary, differences in the activation of specific networks, notably the left hemisphere, in SCZ versus controls, and lower activation in VA areas, in BD versus controls. Results highlight default mode and salient network as relevant for the emotional processing of SCZ and BD of indigenous and black ethnicity. Abstract: schizophrenia, bipolar disorder, functional neuroimaging, ethnicity, default network.

Brain Structure and Function Show Distinct Relations With Genetic Predispositions to Mental Health and Cognition

BACKGROUND

Mental health and cognitive achievement are partly heritable, highly polygenic, and associated with brain variations in structure and function. However, the underlying neural mechanisms remain unclear.

METHODS

We investigated the association between genetic predispositions to various mental health and cognitive traits and a large set of structural and functional brain measures from the UK Biobank (N = 36,799). We also applied linkage disequilibrium score regression to estimate the genetic correlations between various traits and brain measures based on genome-wide data. To decompose the complex association patterns, we performed a multivariate partial least squares model of the genetic and imaging modalities.

RESULTS

The univariate analyses showed that certain traits were related to brain structure (significant genetic correlations with total cortical surface area from r_g = -0.101 for smoking initiation to r_g = 0.230 for cognitive ability), while other traits were related to brain function (significant genetic correlations with functional connectivity from r_g = -0.161 for educational attainment to r_g = 0.318 for schizophrenia). The multivariate analysis showed that genetic predispositions to attention-deficit/hyperactivity disorder, smoking initiation, and cognitive traits had stronger associations with brain structure than with brain function, whereas genetic predispositions to most other psychiatric disorders had stronger associations with brain function than with brain structure.

CONCLUSIONS

These results reveal that genetic predispositions to mental health and cognitive traits have distinct brain profiles.

Polygenic risk score for five major psychiatric disorders associated with volume of distinct brain regions in the general population

Risk genes and abnormal brain structural indices of psychiatric disorders have been extensively studied. However, whether genetic risk influences brain structure in the general population has been rarely studied. The current study enrolled 483 young Chinese adults, calculated their polygenic risk scores (PRS) for psychiatric disorders based on Psychiatric Genomics Consortium GWAS results, and examined the association between PRSs and brain volume. We found that PRSs were associated with the volume of many brain regions, with differences between PRS for different disorder, calculated at different threshold, and calculated using European or East Asian ancestry. Of them, the PRS for Major Depressive Disorder based on European ancestry was positively associated with right temporal gyrus; the PRS for schizophrenia based on East Asian ancestry was negatively associated with right precentral and postcentral gyrus; the PRS for schizophrenia based on European ancestry was positively associated with right superior temporal gyrus. All these brain regions are critical for corresponding disorders. However, no significant associations were found between PRS for Autism Spectrum Disorder / Bipolar Disorder and brain volume; and the association between PRS for Attention Deficit Hyperactivity Disorder at different thresholds and brain volume was inconsistent. These findings suggest distinct brain mechanisms underlying different psychiatric disorders.

Shared and disease-sensitive dysfunction across bipolar and unipolar disorder during depressive episodes: a transdiagnostic study

Patients with depressive episodes (PDE), such as unipolar disorder (UD) and bipolar disorder (BD), are often defined as distinct diagnostic categories, but increasing converging evidence indicated shared etiologies and pathophysiological characteristics across different clinical diagnoses. We explored whether these transdiagnostic deficits are caused by the common neural substrates across diseases or disease-sensitive mechanisms, or a combination of both. In this study, we utilized a Bayesian model to decompose the resting-state brain activity into multiple hyper- and hypo-activity patterns (refer to as "factors"), so as to explore the shared and disease-sensitive alteration patterns in PDE. The model was constructed over a total of 259 patients (131 UD and 128 BD) with 100 healthy controls as the reference. The other 32 initial depressive episode BD (IDE-BD) patients who had symptoms of mania or hypomania during follow-up were taken as an independent set to estimate the factor composition using the established model for further analysis. We revealed three transdiagnostic alteration factors in PDE. Based on the distribution of factors and the tendency of factor composition at the group level, these factors were defined as BD sensitive factor, UD sensitive factor and shared basic alteration factor. We further found that the factor composition and the ROIs-based alteration degree (mainly involving in orbitofrontal gyrus and part of parietal lobe) were associated with the bipolar index in IDE-BD patients. Our findings contributed to understanding the core transdiagnostic shared and disease-sensitive alterations in PDE and to predicting the risk of emotional state transition in IDE-BD patients.

Reduced Gray Matter Volume in Orbitofrontal Cortex Across Schizophrenia, Major Depressive Disorder, and Bipolar Disorder: A Comparative Imaging Study

Schizophrenia (SZ), major depressive disorder (MDD), and bipolar disorder (BD) are severe psychiatric disorders and share common characteristics not only in clinical symptoms but also in neuroimaging. The purpose of this study was to examine common and specific neuroanatomical features in individuals with these three psychiatric conditions. In this study, 70 patients with SZ, 85 patients with MDD, 42 patients with BD, and 95 healthy controls (HCs) were recruited. Voxel-based morphometry (VBM) analysis was used to explore brain imaging characteristics. Psychopathology was assessed using the Beck Depression Inventory (BDI), the Beck Anxiety Inventory (BAI), the Young Mania Rating Scale (YMRS), and the Positive and Negative Syndrome Scale (PANSS). Cognition was assessed using the digit symbol substitution test (DSST), forward-digital span (DS), backward-DS, and semantic fluency. Common reduced gray matter volume (GMV) in the orbitofrontal cortex (OFC) region was found across the SZ, MDD, and BD. Specific reduced GMV of brain regions was also found. For patients with SZ, we found reduced GMV in the frontal lobe, temporal pole, occipital lobe, thalamus, hippocampus, and cerebellum. For patients with MDD, we found reduced GMV in the frontal and temporal lobes, insular cortex, and occipital regions. Patients with BD had reduced GMV in the medial OFC, inferior temporal and fusiform regions, insular cortex, hippocampus, and cerebellum. Furthermore, the OFC GMV was correlated with processing speed as assessed with the DSST across four groups (r = 0.17, p = 0.004) and correlated with the PANSS positive symptoms sub-score in patients with SZ (r = − 0.27, p = 0.026). In conclusion, common OFC alterations in SZ, MDD, and BD provided evidence that this region dysregulation may play a critical role in the pathophysiology of these three psychiatric disorders.

Identification of Pathogenetic Brain Regions via Neuroimaging Data for Diagnosis of Autism Spectrum Disorders

Autism spectrum disorder (ASD) is a kind of neurodevelopmental disorder that often occurs in children and has a hidden onset. Patients usually have lagged development of communication ability and social behavior and thus suffer an unhealthy physical and mental state. Evidence has indicated that diseases related to ASD have commonalities in brain imaging characteristics. This study aims to study the pathogenesis of ASD based on brain imaging data to locate the ASD-related brain regions. Specifically, we collected the functional magnetic resonance image data of 479 patients with ASD and 478 normal subjects matched in age and gender and used a machine-learning framework named random support vector machine cluster to extract distinctive brain regions from the preprocessed data. According to the experimental results, compared with other existing approaches, the method used in this study can more accurately distinguish patients from normal individuals based on brain imaging data. At the same time, this study found that the development of ASD was highly correlated with certain brain regions, e.g., lingual gyrus, superior frontal gyrus, medial gyrus, insular lobe, and olfactory cortex. This study explores the effectiveness of a novel machine-learning approach in the study of ASD brain imaging and provides a reference brain area for the medical research and clinical treatment of ASD.

A meta-analysis of deep brain structural shape and asymmetry abnormalities in 2,833 individuals with schizophrenia compared with 3,929 healthy volunteers via the ENIGMA Consortium

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

Integration of structural MRI and epigenetic analyses hint at linked cellular defects of the subventricular zone and insular cortex in autism: Findings from a case study

Introduction

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social interaction, communication and repetitive, restrictive behaviors, features supported by cortical activity. Given the importance of the subventricular zone (SVZ) of the lateral ventrical to cortical development, we compared molecular, cellular, and structural differences in the SVZ and linked cortical regions in specimens of ASD cases and sex and age-matched unaffected brain.

Methods

We used magnetic resonance imaging (MRI) and diffusion tractography on ex vivo postmortem brain samples, which we further analyzed by Whole Genome Bisulfite Sequencing (WGBS), Flow Cytometry, and RT qPCR.

Results

Through MRI, we observed decreased tractography pathways from the dorsal SVZ, increased pathways from the posterior ventral SVZ to the insular cortex, and variable cortical thickness within the insular cortex in ASD diagnosed case relative to unaffected controls. Long-range tractography pathways from and to the insula were also reduced in the ASD case. FACS-based cell sorting revealed an increased population of proliferating cells in the SVZ of ASD case relative to the unaffected control. Targeted qPCR assays of SVZ tissue demonstrated significantly reduced expression levels of genes involved in differentiation and migration of neurons in ASD relative to the control counterpart. Finally, using genome-wide DNA methylation analyses, we identified 19 genes relevant to neurological development, function, and disease, 7 of which have not previously been described in ASD, that were significantly differentially methylated in autistic SVZ and insula specimens.

Conclusion

These findings suggest a hypothesis that epigenetic changes during neurodevelopment alter the trajectory of proliferation, migration, and differentiation in the SVZ, impacting cortical structure and function and resulting in ASD phenotypes.

Patterns of brain asymmetry associated with polygenic risks for autism and schizophrenia implicate language and executive functions but not brain masculinization

Autism spectrum disorder (ASD) and schizophrenia have been conceived as partly opposing disorders in terms of systemizing vs. empathizing cognitive styles, with resemblances to male vs. female average sex differences. Left-right asymmetry of the brain is an important aspect of its organization that shows average differences between the sexes and can be altered in both ASD and schizophrenia. Here we mapped multivariate associations of polygenic risk scores for ASD and schizophrenia with asymmetries of regional cerebral cortical surface area, thickness, and subcortical volume measures in 32,256 participants from the UK Biobank. Polygenic risks for the two disorders were positively correlated (r = 0.08, p = 7.13 × 10-50) and both were higher in females compared to males, consistent with biased participation against higher-risk males. Each polygenic risk score was associated with multivariate brain asymmetry after adjusting for sex, ASD r = 0.03, p = 2.17 × 10-9, and schizophrenia r = 0.04, p = 2.61 × 10-11, but the multivariate patterns were mostly distinct for the two polygenic risks and neither resembled average sex differences. Annotation based on meta-analyzed functional imaging data showed that both polygenic risks were associated with asymmetries of regions important for language and executive functions, consistent with behavioral associations that arose in phenome-wide association analysis. Overall, the results indicate that distinct patterns of subtly altered brain asymmetry may be functionally relevant manifestations of polygenic risks for ASD and schizophrenia, but do not support brain masculinization or feminization in their etiologies.

Where the genome meets the connectome: Understanding how genes shape human brain connectivity

The integration of modern neuroimaging methods with genetically informative designs and data can shed light on the molecular mechanisms underlying the structural and functional organization of the human connectome. Here, we review studies that have investigated the genetic basis of human brain network structure and function through three complementary frameworks: (1) the quantification of phenotypic heritability through classical twin designs; (2) the identification of specific DNA variants linked to phenotypic variation through association and related studies; and (3) the analysis of correlations between spatial variations in imaging phenotypes and gene expression profiles through the integration of neuroimaging and transcriptional atlas data. We consider the basic foundations, strengths, limitations, and discoveries associated with each approach. We present converging evidence to indicate that anatomical connectivity is under stronger genetic influence than functional connectivity and that genetic influences are not uniformly distributed throughout the brain, with phenotypic variation in certain regions and connections being under stronger genetic control than others. We also consider how the combination of imaging and genetics can be used to understand the ways in which genes may drive brain dysfunction in different clinical disorders.

Reaction Time and Visual Memory in Connection to Hazardous Drinking Polygenic Scores in Schizophrenia, Schizoaffective Disorder and Bipolar Disorder

The purpose of this study was to explore the association of cognition with hazardous drinking Polygenic Scores (PGS) in 2649 schizophrenia, 558 schizoaffective disorder, and 1125 bipolar disorder patients in Finland. Hazardous drinking PGS was computed using the LDPred program. Participants performed two computerized tasks from the Cambridge Automated Neuropsychological Test Battery (CANTAB) on a tablet computer: the 5-choice serial reaction time task, or Reaction Time (RT) test, and the Paired Associative Learning (PAL) test. The association between hazardous drinking PGS and cognition was measured using four cognition variables. Log-linear regression was used in Reaction Time (RT) assessment, and logistic regression was used in PAL assessment. All analyses were conducted separately for males and females. After adjustment of age, age of onset, education, household pattern, and depressive symptoms, hazardous drinking PGS was not associated with reaction time or visual memory in male or female patients with schizophrenia, schizoaffective, and bipolar disorder.

Toward a neurocircuit-based taxonomy to guide treatment of obsessive-compulsive disorder

An important challenge in mental health research is to translate findings from cognitive neuroscience and neuroimaging research into effective treatments that target the neurobiological alterations involved in psychiatric symptoms. To address this challenge, in this review we propose a heuristic neurocircuit-based taxonomy to guide the treatment of obsessive-compulsive disorder (OCD). We do this by integrating information from several sources. First, we provide case vignettes in which patients with OCD describe their symptoms and discuss different clinical profiles in the phenotypic expression of the condition. Second, we link variations in these clinical profiles to underlying neurocircuit dysfunctions, drawing on findings from neuropsychological and neuroimaging studies in OCD. Third, we consider behavioral, pharmacological, and neuromodulatory treatments that could target those specific neurocircuit dysfunctions. Finally, we suggest methods of testing this neurocircuit-based taxonomy as well as important limitations to this approach that should be considered in future research.

All-optical approaches to studying psychiatric disease

Improvements in all-optical means of monitoring and manipulating neural activity have generated new ways of studying psychiatric disease. The combination of calcium imaging techniques with optogenetics to concurrently record and manipulate neural activity has been used to create new disease models that link distinct circuit abnormalities to specific disease dimensions. These approaches represent a new path towards the development of more effective treatments, as they allow researchers to identify circuit manipulations that normalize pathological network activity. In this review we highlight the utility of all-optical approaches to generate new psychiatric disease models where the specific circuit abnormalities associated with disease symptomology can be assessed in vivo and in response to manipulations designed to normalize disease states. We then outline the principles underlying all-optical interrogations of neural circuits and discuss practical considerations for experimental design.

Smaller total brain volume but not subcortical structure volume related to common genetic risk for ADHD

BACKGROUND

Mechanistic endophenotypes can inform process models of psychopathology and aid interpretation of genetic risk factors. Smaller total brain and subcortical volumes are associated with attention-deficit hyperactivity disorder (ADHD) and provide clues to its development. This study evaluates whether common genetic risk for ADHD is associated with total brain volume (TBV) and hypothesized subcortical structures in children.

METHODS

Children 7-15 years old were recruited for a case-control study (N = 312, N = 199 ADHD). Children were assessed with a multi-informant, best-estimate diagnostic procedure and motion-corrected MRI measured brain volumes. Polygenic scores were computed based on discovery data from the Psychiatric Genomics Consortium (N = 19 099 ADHD, N = 34 194 controls) and the ENIGMA + CHARGE consortium (N = 26 577).

RESULTS

ADHD was associated with smaller TBV, and altered volumes of caudate, cerebellum, putamen, and thalamus after adjustment for TBV; however, effects were larger and statistically reliable only in boys. TBV was associated with an ADHD polygenic score [β = -0.147 (-0.27 to -0.03)], and mediated a small proportion of the effect of polygenic risk on ADHD diagnosis (average ACME = 0.0087, p = 0.012). This finding was stronger in boys (average ACME = 0.019, p = 0.008). In addition, we confirm genetic variation associated with whole brain volume, via an intracranial volume polygenic score.

CONCLUSION

Common genetic risk for ADHD is not expressed primarily as developmental alterations in subcortical brain volumes, but appears to alter brain development in other ways, as evidenced by TBV differences. This is among the first demonstrations of this effect using molecular genetic data. Potential sex differences in these effects warrant further examination.

Functional connectome-wide associations of schizophrenia polygenic risk

Schizophrenia is a highly heritable mental disorder characterized by functional dysconnectivity across the brain. However, the relationships between polygenic risk factors and connectome-wide neural mechanisms are unclear. Here, combining genetic and multiparadigm fMRI data of 623 healthy Caucasian adults drawn from the Human Connectome Project, we found that higher schizophrenia polygenic risk scores were significantly correlated with lower functional connectivity in a large-scale brain network primarily encompassing the visual system, default-mode system, and frontoparietal system. Such correlation was robustly observed across multiple fMRI paradigms, suggesting a brain-state-independent neural phenotype underlying individual genetic liability to schizophrenia. Moreover, using an independent clinical dataset acquired from the Consortium for Neuropsychiatric Phenomics, we further demonstrated that the connectivity of the identified network was reduced in patients with schizophrenia and significantly correlated with general cognitive ability. These findings provide the first evidence for connectome-wide associations of schizophrenia polygenic risk at the systems level and suggest that disrupted integration of sensori-cognitive information may be a hallmark of genetic effects on the brain that contributes to the pathogenesis of schizophrenia.

Impact of autism genetic risk on brain connectivity: a mechanism for the female protective effect

The biological mechanisms underlying the greater prevalence of autism spectrum disorder in males than females remain poorly understood. One hypothesis posits that this female protective effect arises from genetic load for autism spectrum disorder differentially impacting male and female brains. To test this hypothesis, we investigated the impact of cumulative genetic risk for autism spectrum disorder on functional brain connectivity in a balanced sample of boys and girls with autism spectrum disorder and typically developing boys and girls (127 youth, ages 8-17). Brain connectivity analyses focused on the salience network, a core intrinsic functional connectivity network which has previously been implicated in autism spectrum disorder. The effects of polygenic risk on salience network functional connectivity were significantly modulated by participant sex, with genetic load for autism spectrum disorder influencing functional connectivity in boys with and without autism spectrum disorder but not girls. These findings support the hypothesis that autism spectrum disorder risk genes interact with sex differential processes, thereby contributing to the male bias in autism prevalence and proposing an underlying neurobiological mechanism for the female protective effect.

Healthy individuals vs patients with bipolar or unipolar depression in gray matter volume

BACKGROUND

Previous studies using voxel-based morphometry (VBM) revealed changes in gray matter volume (GMV) of patients with depression, but the differences between patients with bipolar disorder (BD) and unipolar depression (UD) are less known.

AIM

To analyze the whole-brain GMV data of patients with untreated UD and BD compared with healthy controls.

METHODS

Fourteen patients with BD and 20 with UD were recruited from the Mental Health Center of Shantou University between August 2014 and July 2015, and 20 non-depressive controls were recruited. After routine three-plane positioning, axial T2WI scanning was performed. The connecting line between the anterior and posterior commissures was used as the scanning baseline. The scanning range extended from the cranial apex to the foramen magnum. Categorical data are presented as frequencies and were analyzed using the Fisher exact test.

RESULTS

There were no significant intergroup differences in gender, age, or years of education. Disease course, age at the first episode, and Hamilton depression rating scale scores were similar between patients with UD and those with BD. Compared with the non-depressive controls, patients with BD showed smaller GMVs in the right inferior temporal gyrus, left middle temporal gyrus, right middle occipital gyrus, and right superior parietal gyrus and larger GMVs in the midbrain, left superior frontal gyrus, and right cerebellum. In contrast, UD patients showed smaller GMVs than the controls in the right fusiform gyrus, left inferior occipital gyrus, left paracentral lobule, right superior and inferior temporal gyri, and the right posterior lobe of the cerebellum, and larger GMVs than the controls in the left posterior central gyrus and left middle frontal gyrus. There was no difference in GMV between patients with BD and UD.

CONCLUSION

Using VBM, the present study revealed that patients with UD and BD have different patterns of changes in GMV when compared with healthy controls.

A Whole-Brain and Cross-Diagnostic Perspective on Functional Brain Network Dysfunction

A wide variety of mental disorders have been associated with resting-state functional network alterations, which are thought to contribute to the cognitive changes underlying mental illness. These observations appear to support theories postulating large-scale disruptions of brain systems in mental illness. However, existing approaches isolate differences in network organization without putting those differences in a broad, whole-brain perspective. Using a graph distance approach-connectome-wide similarity-we found that whole-brain resting-state functional network organization is highly similar across groups of individuals with and without a variety of mental diseases. This similarity was observed across autism spectrum disorder, attention-deficit hyperactivity disorder, and schizophrenia. Nonetheless, subtle differences in network graph distance were predictive of diagnosis, suggesting that while functional connectomes differ little across health and disease, those differences are informative. These results suggest a need to reevaluate neurocognitive theories of mental illness, with a role for subtle functional brain network changes in the production of an array of mental diseases. Such small network alterations suggest the possibility that small, well-targeted alterations to brain network organization may provide meaningful improvements for a variety of mental disorders.

Correlations between immune and metabolic serum markers and schizophrenia/bipolar disorder polygenic risk score in first-episode psychosis

AIMS

There is a strong interest in identifying the biological mechanisms involved in the genetic risk for psychotic disorders. In this study, we evaluated the correlation between serum concentrations of specific molecular markers and the genetic component for schizophrenia and bipolar disorder.

METHODS

We analysed the association between the polygenic risk score (PRS) and the serum levels of different inflammatory/metabolic markers in a sample of 81 first-episode psychosis patients (FEP) with a diagnosis of schizophrenia or bipolar disorder and 33 controls.

RESULTS

A positive correlation of schizophrenia and bipolar disorder PRS with the inflammatory marker C-C Motif Chemokine Ligand 4 serum concentration (ρ = 0.42, P = 1.56 × 10^-04 and ρ = 0.40, P = 1.65 × 10^-03 , respectively) and a negative correlation with the serum ghrelin content (ρ = - 0.35, P = 4.27 × 10^-03 and ρ = - 0.45, P = 6.05 × 10^-04 , respectively) were observed.

CONCLUSION

These findings provide new insight into the biological underpinnings of the PRS component, thus supporting a role of the genetic liability on the inflammatory and metabolic alterations that characterize psychosis onset.

Methods and Challenges for Assessing Heterogeneity

The widely acknowledged homogeneity assumption limits progress in refining clinical diagnosis, understanding mechanisms, and developing new treatments for mental health disorders. This homogeneity assumption drives both a comorbidity and a heterogeneity problem, where two different approaches tackle the problems. One, a unifying approach, tackles the comorbidity problem by assuming that a single general psychopathology factor underlies multiple disorders. Another, a multifactorial approach, tackles the heterogeneity problem by assuming that disorders comprise multiple subtypes driven by multiple discrete factors. We show how each of these approaches can make useful contributions to mental health-related research and clinical practice. For example, the unifying approach can develop a rapid assessment tool that may be clinically valuable for triaging cases. The multifactorial approach can reveal subtypes that are differentially responsive to treatments and highlight distinct mechanisms leading to similar phenotypes. Because both approaches tackle different problems, both have different limitations. We describe the statistical frameworks that incorporate and adjudicate between both approaches (e.g., the bifactor model, normative modeling, and the functional random forest). Such frameworks can identify whether sets of disorders are more affected by heterogeneity or comorbidity. Therefore, future studies that incorporate such frameworks can provide further insight into the nature of psychopathology.

High polygenic burden is associated with blood DNA methylation changes in individuals with suicidal behavior

Suicidal behavior is result of the interaction of several contributors, including genetic and environmental factors. The integration of approaches considering the polygenic component of suicidal behavior, such as polygenic risk scores (PRS) and DNA methylation is promising for improving our understanding of the complex interplay between genetic and environmental factors in this behavior. The aim of this study was the evaluation of DNA methylation differences between individuals with high and low genetic burden for suicidality. The present study was divided into two phases. In the first phase, genotyping with the Psycharray chip was performed in a discovery sample of 568 Mexican individuals, of which 149 had suicidal behavior (64 individuals with suicidal ideation, 50 with suicide attempt and 35 with completed suicide). Then, a PRS analysis based on summary statistics from the Psychiatric Genomic Consortium was performed in the discovery sample. In a second phase, we evaluated DNA methylation differences between individuals with high and low genetic burden for suicidality in a sub-sample of the discovery sample (target sample) of 94 subjects. We identified 153 differentially methylated sites between individuals with low and high-PRS. Among genes mapped to differentially methylated sites, we found genes involved in neurodevelopment (CHD7, RFX4, KCNA1, PLCB1, PITX1, NUMBL) and ATP binding (KIF7, NUBP2, KIF6, ATP8B1, ATP11A, CLCN7, MYLK, MAP2K5). Our results suggest that genetic variants might increase the predisposition to epigenetic variations in genes involved in neurodevelopment. This study highlights the possible implication of polygenic burden in the alteration of epigenetic changes in suicidal behavior.

Genetics of ADHD: What Should the Clinician Know?

PURPOSE OF REVIEW

Attention deficit hyperactivity disorder (ADHD) shows high heritability in formal genetic studies. In our review article, we provide an overview on common and rare genetic risk variants for ADHD and their link to clinical practice.

RECENT FINDINGS

The formal heritability of ADHD is about 80% and therefore higher than most other psychiatric diseases. However, recent studies estimate the proportion of heritability based on singlenucleotide variants (SNPs) at 22%. It is a matter of debate which genetic mechanisms explain this huge difference. While frequent variants in first mega-analyses of genome-wideassociation study data containing several thousand patients give the first genome-wide results, explaining only little variance, the methodologically more difficult analyses of rare variants are still in their infancy. Some rare genetic syndromes show higher prevalence for ADHD indicating a potential role for a small number of patients. In contrast, polygenic risk scores (PRS) could potentially be applied to every patient. We give an overview how PRS explain different behavioral phenotypes in ADHD and how they could be used for diagnosis and therapy prediction. Knowledge about a patient's genetic makeup is not yet mandatory for ADHD therapy or diagnosis. PRS however have been introduced successfully in other areas of clinical medicine, and their application in psychiatry will begin within the next years. In order to ensure competent advice for patients, knowledge of the current state of research is useful forpsychiatrists.

Proof-of-concept study of a multi-gene risk score in adolescent bipolar disorder

BACKGROUND

Few studies have examined multiple genetic variants concurrently for the purpose of classifying bipolar disorder (BD); the literature among youth is particularly sparse. We selected 35 genetic variants, previously implicated in BD or associated characteristics, from which to identify the most robustly predictive group of genes.

METHODS

215 Caucasian adolescents (114 BD and 101 healthy controls (HC), ages 13-20 years) were included. Psychiatric diagnoses were determined based on semi-structured diagnostic interviews. Genomic DNA was extracted from saliva for genotyping. Two models were used to calculate a multi-gene risk score (MGRS). Model 1 used forward and backward regressions, and model 2 used a PLINK generated method.

RESULTS

In model 1, GPX3 rs3792797 was significant in the forward regression, DRD4 exonIII was significant in the backward regression; IL1β rs16944 and DISC1 rs821577 were significant in both the forward and backward regressions. These variants are involved in dopamine neurotransmission; inflammation and oxidative stress; and neuronal development. Model 1 MGRS did not significantly discriminate between BD and HC. In model 2, ZNF804A rs1344706 was significantly associated with BD; however, this association did not predict diagnosis when entered into the weighted model.

LIMITATIONS

This study was limited by the number of genetic variants examined and the modest sample size.

CONCLUSIONS

Whereas regression approaches identified four genetic variants that significantly discriminated between BD and HC, those same variants no longer discriminated between BD and HC when computed as a MGRS. Future larger studies are needed evaluating intermediate phenotypes such as neuroimaging and blood-based biomarkers.

Structural Brain Alterations in Youth With Psychosis and Bipolar Spectrum Symptoms

OBJECTIVE

Adults with established diagnoses of serious mental illness (bipolar disorder and schizophrenia) exhibit structural brain abnormalities, yet less is known about how such abnormalities manifest earlier in development.

METHOD

Cross-sectional data publicly available from the Philadelphia Neurodevelopmental Cohort (PNC) were analyzed. Structural magnetic resonance neuroimaging data were collected on a subset of the PNC (N = 989; 9-22 years old). Cortical thickness, surface area (SA), and subcortical volumes were calculated. Study participants were assessed for psychiatric symptomatology using a structured interview and the following groups were created: typically developing (n = 376), psychosis spectrum (PS; n = 113), bipolar spectrum (BP; n = 117), and BP + PS (n = 109). Group and developmental differences in structural magnetic resonance neuroimaging measures were examined. In addition, the extent to which any structural aberration was related to neurocognition, global functioning, and clinical symptomatology was examined.

RESULTS

Compared with other groups, PS youth exhibited significantly decreased SA in the orbitofrontal, cingulate, precentral, and postcentral regions. PS youth also exhibited deceased thalamic volume compared with all other groups. The strongest effects for precentral and posterior cingulate SA decreases were seen during early adolescence (13-15 years old) in PS youth. The strongest effects for decreases in thalamic volume and orbitofrontal and postcentral SA were observed in mid-adolescence (16-18 years) in PS youth. Across groups, better overall functioning was associated with increased lateral orbitofrontal SA. Increased postcentral SA was associated with better executive cognition and less severe negative symptoms in the entire sample.

CONCLUSION

In a community-based sample, decreased cortical SA and thalamic volume were present early in adolescent development in youth with PS symptoms, but not in youth with BP symptoms or with BP and PS symptoms. These findings point to potential biological distinctions between PS and BP conditions, which could suggest additional biomarkers relevant to early identification.

Multiscale Neuroscience of Psychiatric Disorders

The human brain comprises a multiscale network with multiple levels of organization. Neurons with dendritic and axonal connections form the microscale fabric of brain circuitry, and macroscale brain regions and white matter connections form the infrastructure for system-level brain communication and information integration. In this review, we discuss the emerging trend of multiscale neuroscience, the multidisciplinary field that brings together data from these different levels of nervous system organization to form a better understanding of between-scale relationships of brain structure, function, and behavior in health and disease. We provide a broad overview of this developing field and discuss recent findings of exemplary multiscale neuroscience studies that illustrate the importance of studying cross-scale interactions among the genetic, molecular, cellular, and macroscale levels of brain circuitry and connectivity and behavior. We particularly consider a central, overarching goal of these multiscale neuroscience studies of human brain connectivity: to obtain insight into how disease-related alterations at one level of organization may underlie alterations observed at other scales of brain network organization in mental disorders. We conclude by discussing the current limitations, challenges, and future directions of the field.

The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes

The imprecise nature of psychiatric nosology restricts progress towards characterizing and treating mental health disorders. One issue is the 'heterogeneity problem': different causal mechanisms may relate to the same disorder, and multiple outcomes of interest can occur within one individual. Our review tackles this heterogeneity problem, providing considerations, concepts, and approaches for investigators examining human cognition and mental health. We highlight the difficulty of pure dimensional approaches due to 'the curse of dimensionality'. Computationally, we consider supervised and unsupervised statistical approaches to identify putative subtypes within a population. However, we emphasize that subtype identification should be linked to a particular outcome or question. We conclude with novel hybrid approaches that can identify subtypes tied to outcomes, and may help advance precision diagnostic and treatment tools.

A systematic review of associations between functional MRI activity and polygenic risk for schizophrenia and bipolar disorder

Genetic factors account for up to 80% of the liability for schizophrenia (SCZ) and bipolar disorder (BD). Genome-wide association studies have successfully identified several genes associated with increased risk for both disorders. This has allowed researchers to model the aggregate effect of genes associated with disease status and create a polygenic risk score (PGRS) for each individual. The interest in imaging genetics using PGRS has grown in recent years, with several studies now published. We have conducted a systematic review to examine the effects of PGRS of SCZ, BD and cross psychiatric disorders on brain function and connectivity using fMRI data. Results indicate that the effect of genetic load for SCZ and BD on brain function affects task-related recruitment, with frontal areas having a more prominent role, independent of task. Additionally, the results suggest that the polygenic architecture of psychotic disorders is not regionally confined but impacts on the task-dependent recruitment of multiple brain regions. Future imaging genetics studies with large samples, especially population studies, would be uniquely informative in mapping the spatial distribution of the genetic risk to psychiatric disorders on brain processes during various cognitive tasks and may lead to the discovery of biological pathways that could be crucial in mediating the link between genetic factors and alterations in brain networks.

Comprehensive Analysis of Rare Variants of 101 Autism-Linked Genes in a Hungarian Cohort of Autism Spectrum Disorder Patients

Background

Autism spectrum disorder (ASD) is genetically and phenotypically heterogeneous. Former genetic studies suggested that both common and rare genetic variants play a role in the etiology. In this study, we aimed to analyze rare variants detected by next generation sequencing (NGS) in an autism cohort from Hungary.

Methods

We investigated the yield of NGS panel sequencing of an unselected ASD cohort (N = 174 ) for the detection of ASD associated syndromes. Besides, we analyzed rare variants in a common disease-rare variant framework and performed rare variant burden analysis and gene enrichment analysis in phenotype based clusters.

Results

We have diagnosed 13 molecularly proven syndromic autism cases. Strongest indicators of syndromic autism were intellectual disability, epilepsy or other neurological plus symptoms. Rare variant analysis on a cohort level confirmed the association of five genes with autism (AUTS2, NHS, NSD1, SLC9A9, and VPS13). We found no correlation between rare variant burden and number of minor malformation or autism severity. We identified four phenotypic clusters, but no specific gene was enriched in a given cluster.

Conclusion

Our study indicates that NGS panel gene sequencing can be useful, where the clinical picture suggests a clinically defined syndromic autism. In this group, targeted panel sequencing may provide reasonable diagnostic yield. Unselected NGS panel screening in the clinic remains controversial, because of uncertain utility, and difficulties of the variant interpretation. However, the detected rare variants may still significantly influence autism risk and subphenotypes in a polygenic model, but to detect the effects of these variants larger cohorts are needed.

Neural cell adhesion molecule Negr1 deficiency in mouse results in structural brain endophenotypes and behavioral deviations related to psychiatric disorders

Neuronal growth regulator 1 (NEGR1) belongs to the immunoglobulin (IgLON) superfamily of cell adhesion molecules involved in cortical layering. Recent functional and genomic studies implicate the role of NEGR1 in a wide spectrum of psychiatric disorders, such as major depression, schizophrenia and autism. Here, we investigated the impact of Negr1 deficiency on brain morphology, neuronal properties and social behavior of mice. In situ hybridization shows Negr1 expression in the brain nuclei which are central modulators of cortical-subcortical connectivity such as the island of Calleja and the reticular nucleus of thalamus. Brain morphological analysis revealed neuroanatomical abnormalities in Negr1^−/− mice, including enlargement of ventricles and decrease in the volume of the whole brain, corpus callosum, globus pallidus and hippocampus. Furthermore, decreased number of parvalbumin-positive inhibitory interneurons was evident in Negr1^−/− hippocampi. Behaviorally, Negr1^−/− mice displayed hyperactivity in social interactions and impairments in social hierarchy. Finally, Negr1 deficiency resulted in disrupted neurite sprouting during neuritogenesis. Our results provide evidence that NEGR1 is required for balancing the ratio of excitatory/inhibitory neurons and proper formation of brain structures, which is prerequisite for adaptive behavioral profiles. Therefore, Negr1^−/− mice have a high potential to provide new insights into the neural mechanisms of neuropsychiatric disorders.

The endocytic membrane trafficking pathway plays a major role in the risk of Parkinson's disease

BACKGROUND

PD is a complex polygenic disorder. In recent years, several genes from the endocytic membrane-trafficking pathway have been suggested to contribute to disease etiology. However, a systematic analysis of pathway-specific genetic risk factors is yet to be performed.

OBJECTIVES

To comprehensively study the role of the endocytic membrane-trafficking pathway in the risk of PD.

METHODS

Linkage disequilibrium score regression was used to estimate PD heritability explained by 252 genes involved in the endocytic membrane-trafficking pathway including genome-wide association studies data from 18,869 cases and 22,452 controls. We used pathway-specific single-nucleotide polymorphisms to construct a polygenic risk score reflecting the cumulative risk of common variants. To prioritize genes for follow-up functional studies, summary-data based Mendelian randomization analyses were applied to explore possible functional genomic associations with expression or methylation quantitative trait loci.

RESULTS

The heritability estimate attributed to endocytic membrane-trafficking pathway was 3.58% (standard error = 1.17). Excluding previously nominated PD endocytic membrane-trafficking pathway genes, the missing heritability was 2.21% (standard error = 0.42). Random heritability simulations were estimated to be 1.44% (standard deviation = 0.54), indicating that the unbiased total heritability explained by the endocytic membrane-trafficking pathway was 2.14%. Polygenic risk score based on endocytic membrane-trafficking pathway showed a 1.25 times increase of PD risk per standard deviation of genetic risk. Finally, Mendelian randomization identified 11 endocytic membrane-trafficking pathway genes showing functional consequence associated to PD risk.

CONCLUSIONS

We provide compelling genetic evidence that the endocytic membrane-trafficking pathway plays a relevant role in disease etiology. Further research on this pathway is warranted given that critical effort should be made to identify potential avenues within this biological process suitable for therapeutic interventions. © 2019 International Parkinson and Movement Disorder Society.

Interactome Studies of Psychiatric Disorders

High comorbidity and complexity have precluded reliable diagnostic assessment and treatment of psychiatric disorders. Impaired molecular interactions may be relevant for underlying mechanisms of psychiatric disorders but by and large remain unknown. With the help of a number of publicly available databases and various technological tools, recent research has filled the paucity of information by generating a novel dataset of psychiatric interactomes. Different technological platforms including yeast two-hybrid screen, co-immunoprecipitation-coupled with mass spectrometry-based proteomics, and transcriptomics have been widely used in combination with cellular and molecular techniques to interrogate the psychiatric interactome. Novel molecular interactions have been identified in association with different psychiatric disorders including autism spectrum disorders, schizophrenia, bipolar disorder, and major depressive disorder. However, more extensive and sophisticated interactome research needs to be conducted to overcome the current limitations such as incomplete interactome databases and a lack of functional information among components. Ultimately, integrated psychiatric interactome databases will contribute to the implementation of biomarkers and therapeutic intervention.

Zebrafish models for personalized psychiatry: Insights from individual, strain and sex differences, and modeling gene x environment interactions

Currently becoming widely recognized, personalized psychiatry focuses on unique physiological and genetic profiles of patients to best tailor their therapy. However, the role of individual differences, as well as genetic and environmental factors, in human psychiatric disorders remains poorly understood. Animal experimental models are a valuable tool to improve our understanding of disease pathophysiology and its molecular mechanisms. Due to high reproduction capability, fully sequenced genome, easy gene editing, and high genetic and physiological homology with humans, zebrafish (Danio rerio) are emerging as a novel powerful model in biomedicine. Mounting evidence supports zebrafish as a useful model organism in CNS research. Robustly expressed in these fish, individual, strain, and sex differences shape their CNS responses to genetic, environmental, and pharmacological manipulations. Here, we discuss zebrafish as a promising complementary translational tool to further advance patient-centered personalized psychiatry.

Depressive Temperament in Relatives of Patients with Schizophrenia Is Associated with Suicidality in Patients with Schizophrenia

Objective

Suicide is a major cause of death in patients with schizophrenia; thus, predicting and preventing suicide in patients with schizophrenia is examined in various studies. Affective temperaments which are accepted as precursors of mood disorders may be an important factor in predicting suicidality. This study investigated the relationship between affective temperaments of relatives of schizophrenia patients and suicidal thoughts and other clinical correlates of patients with schizophrenia.

Methods

Patients with schizophrenia and their first degree relatives are included to the study. All of the participants were evaluated with Structured Clinical Interview for DSM-IV axis I disorders and relatives with active psychiatric diagnosis were excluded. Positive and Negative Symptom Scale, Clinical Global Impression Scale, Turkish version of cognitive assessment interview were administered congruently to the patients. Relatives of the patients were evaluated with Temperament Evaluation of Memphis, Pisa, Paris and San Diego-Auto-questionnaire.

Results

Depressive temperament scores of relatives of schizophrenic patients who had suicidal thoughts were higher than the scores of the relatives of the patients who did not have suicidal thoughts. Depressive temperament also predicted number of suicide attempts in regression analysis. Number of suicide attempts was also related with number of hospitalization and functionality of the patient.

Conclusion

Suicidality in schizophrenia is related with relatives’ affective temperaments and patients’ own positive symptom scores. The relationship between suicidal thoughts and depressive temperament is high lightened in this study

Effects of Schizophrenia Polygenic Risk Scores on Brain Activity and Performance During Working Memory Subprocesses in Healthy Young Adults

Recent work has begun to shed light on the neural correlates and possible mechanisms of polygenic risk for schizophrenia. Here, we map a schizophrenia polygenic risk profile score (PRS) based on genome-wide association study significant loci onto variability in the activity and functional connectivity of a frontoparietal network supporting the manipulation versus maintenance of information during a numerical working memory (WM) task in healthy young adults (n = 99, mean age = 19.8). Our analyses revealed that higher PRS was associated with hypoactivity of the dorsolateral prefrontal cortex (dlPFC) during the manipulation but not maintenance of information in WM (r2 = .0576, P = .018). Post hoc analyses revealed that PRS-modulated dlPFC hypoactivity correlated with faster reaction times during WM manipulation (r2 = .0967, P = .002), and faster processing speed (r2 = .0967, P = .003) on a separate behavioral task. These PRS-associated patterns recapitulate dlPFC hypoactivity observed in patients with schizophrenia during central executive manipulation of information in WM on this task.

Genetics Factors in Major Depression Disease

Depressive disorders (DDs) are one of the most widespread forms of psychiatric pathology. According to the World Health Organization, about 350 million people in the world are affected by this condition. Family and twin studies have demonstrated that the contribution of genetic factors to the risk of the onset of DDs is quite large. Various methodological approaches (analysis of candidate genes, genome-wide association analysis, genome-wide sequencing) have been used, and a large number of the associations between genes and different clinical DD variants and DD subphenotypes have been published. However, in most cases, these associations have not been confirmed in replication studies, and only a small number of genes have been proven to be associated with DD development risk. To ascertain the role of genetic factors in DD pathogenesis, further investigations of the relevant conditions are required. Special consideration should be given to the polygenic characteristics noted in whole-genome studies of the heritability of the disorder without a pronounced effect of the major gene. These observations accentuate the relevance of the analysis of gene-interaction roles in DD development and progression. It is important that association studies of the inherited variants of the genome should be supported by analysis of dynamic changes during DD progression. Epigenetic changes that cause modifications of a gene's functional state without changing its coding sequence are of primary interest. However, the opportunities for studying changes in the epigenome, transcriptome, and proteome during DD are limited by the nature of the disease and the need for brain tissue analysis, which is possible only postmortem. Therefore, any association studies between DD pathogenesis and epigenetic factors must be supplemented through the use of different animal models of depression. A threefold approach comprising the combination of gene association studies, assessment of the epigenetic state in DD patients, and analysis of different "omic" changes in animal depression models will make it possible to evaluate the contribution of genetic, epigenetic, and environmental factors to the development of different forms of depression and to help develop ways to decrease the risk of depression and improve the treatment of DD.

Genomic and Imaging Biomarkers in Schizophrenia

Recent large-scale genomic studies have confirmed that schizophrenia is a polygenic syndrome and have implicated a number of biological pathways in its aetiology. Both common variants individually of small effect and rarer but more penetrant genetic variants have been shown to play a role in the pathogenesis of the disorder. No simple Mendelian forms of the condition have been identified, but progress has been made in stratifying risk on the basis of the polygenic burden of common variants individually of small effect, and the contribution of rarer variants of larger effect such as Copy Number Variants (CNVs). Pathway analysis of risk-associated variants has begun to identify specific biological processes implicated in risk for the disorder, including elements of the glutamatergic NMDA receptor complex and post synaptic density, voltage-gated calcium channels, targets of the Fragile X Mental Retardation Protein (FMRP targets) and immune pathways. Genetic studies have also been used to drive genomic imaging approaches to the investigation of brain markers associated with risk for the disorder. Genomic imaging approaches have been applied both to investigate the effect of polygenic risk and to study the impact of individual higher-penetrance variants such as CNVs. Both genomic and genomic imaging approaches offer potential for the stratification of patients and at-risk groups and the development of better biomarkers of risk and treatment response; however, further research is needed to integrate this work and realise the full potential of these approaches.

The YWHAE gene confers risk to major depressive disorder in the male group of Chinese Han population

Schizophrenia and major depressive disorder are two major psychiatric illnesses that may share specific genetic risk factors to a certain extent. Increasing evidence suggests that the two disorders might be more closely related than previously considered. To investigate whether YWHAE gene plays a significant role in major depressive disorder in Han Chinese population, we recruited 1135 unrelated major depressive disorder patients (485 males, 650 females) and 989 unrelated controls (296 males, 693 females) of Chinese Han origin. Eleven common SNPs were genotyped using TaqMan® technology. In male-group, the allele and genotype frequencies of rs34041110 differed significantly between patients and control (P_allele=0.036486, OR[95%CI]: 1.249442(1.013988-1.539571); P_genotype=0.045301). Also in this group, allele and genotype frequencies of rs1532976 differed significantly (P_allele=0.013242, OR[95%CI]: 1.302007(1.056501-1.604563); genotype: P=0.039152). Haplotype-analyses showed that, in male-group, positive association with major depressive disorder was found for the A-A-C-G haplotype of rs3752826-rs2131431-rs1873827-rs12452627 (χ²=20.397, P=6.38E-06, OR[95%CI]: 7.442 [2.691-20.583]), its C-A-C-G haplotype (χ²=19.122, P=1.24E-05, OR and 95%CI: 0.402 [0.264-0.612]), its C-C-T-G haplotype (χ²=9.766, P=0.001785, OR[95%CI]: 5.654 [1.664-19.211]). In female-group, positive association was found for the A-A-C-G haplotype of rs3752826-rs2131431-rs1873827-rs12452627 (χ²=78.628, P=7.94E-19, OR[95%CI]: 50.043 [11.087-225.876]), its A-C-T-G haplotype (χ²=38.806, P=4.83E-10, OR[95%CI]: 0.053 [0.015-0.192]), the C-A-C-G haplotype (χ²=18.930, P=1.37E-05, OR[95%CI]: 0.526 [0.392-0.705]), and the C-C-T-G haplotype (χ²=38.668, P=5.18E-10, OR[95%CI]: 6.130 [3.207-11.716]). Our findings support YWHAE being a risk gene for Major Depressive Disorder in the Han Chinese population.