The genetic and environmental determinants of the association between brain abnormalities and schizophrenia: the schizophrenia twins and relatives consortium

Based on neural network cascade abnormal texture information dissemination of classification of patients with schizophrenia and depression

This study used MRI brain image segmentation to identify novel magnetic resonance imaging (MRI) biomarkers to distinguish patients with schizophrenia (SCZ), major depressive disorder (MD), and healthy control (HC). Brain texture measurements, including entropy and contrast, were calculated to capture variability in adjacent MRI voxel intensity. These measures are then applied to group classification in deep learning techniques and combined with hierarchical correlations to locate results. Texture feature maps were extracted from segmented brain MRI scans of 141 patients with schizophrenia (SCZ), 103 patients with major depressive disorder (MD) and 238 healthy controls (HC). Gray scale coassociation matrix (GLCM) is a monomer matrix calculated in a voxel cube. Deep learning methods were evaluated to determine the application capability of texture feature mapping in binary classification (SCZ vs. HC, MD vs. HC, SCZ vs. MD). The method is implemented by repeated nesting and cross-validation for feature selection. Regions that show the highest correlation (positive or negative). In this study, the authors successfully classified SCZ, MD and HC. This suggests that texture analysis can be used as an effective feature extraction method to distinguish different disease states. Compared with other methods, texture analysis can capture richer image information and improve classification accuracy in some cases. The classification accuracy of SCZ and HC, MD and HC, SCZ and MD reached 84.6%, 86.4% and 76.21%, respectively. Among them, SCZ and HC are the most significant features with high sensitivity and specificity.

Neurological Soft Signs in Schizophrenia, a Picture of the Knowledge in the Last Decade: A Scoping Review

(1) Background: Neurological Soft Signs (NSS) are subtle neurological abnormalities that are more common in schizophrenia patients than in healthy individuals and have been regularly observed in neuroleptic-naive first-episode patients, supporting the hypothesis that they are an intrinsic component of schizophrenia. (2) Methods: a review of articles published in the last ten years (from January 2013 to January 2023) was carried out on articles published in ScienceDirect and PubMed, by following the PRISMA Statement extension for scoping reviews (PRISMA-ScR), which evaluated the impact of NSS in correlation with the symptomatology, neuroleptic treatment, and the cerebral structural changes of patients with schizophrenia. (3) Results: thirty articles were included, among them twelve included MRI structural evaluation and four studies with a longitudinal design. (4) Conclusions: interest in researching NSS has increased in recent years, but questions remain about their origin and relationship to schizophrenia symptoms, thus this study aims to fill in information gaps in the hope that future research will help provide individualized treatment. It is suggested that NSS in schizophrenia might have an inherited genetic relationship pattern, thus being in line with a trait viewpoint. Most of the research revealed that schizophrenia patients had higher NSS scores than healthy controls, however, they were rather similar to their first-degree relatives, thus, also arguing in favor of a trait perspective. The greatest improvement in scores is seen in those with a remitting course, as shown by declining NSS ratings concurrent with symptomatology.

Effect of schizophrenia common variants on infant brain volumes: cross-sectional study in 207 term neonates in developing Human Connectome Project

Increasing lines of evidence suggest deviations from the normal early developmental trajectory could give rise to the onset of schizophrenia during adolescence and young adulthood, but few studies have investigated brain imaging changes associated with schizophrenia common variants in neonates. This study compared the brain volumes of both grey and white matter regions with schizophrenia polygenic risk scores (PRS) for 207 healthy term-born infants of European ancestry. Linear regression was used to estimate the relationship between PRS and brain volumes, with gestational age at birth, postmenstrual age at scan, ancestral principal components, sex and intracranial volumes as covariates. The schizophrenia PRS were negatively associated with the grey (β = -0.08, p = 4.2 × 10^-3) and white (β = -0.13, p = 9.4 × 10^-3) matter superior temporal gyrus volumes, white frontal lobe volume (β = -0.09, p = 1.5 × 10^-3) and the total white matter volume (β = -0.062, p = 1.66 × 10^-2). This result also remained robust when incorporating individuals of Asian ancestry. Explorative functional analysis of the schizophrenia risk variants associated with the right frontal lobe white matter volume found enrichment in neurodevelopmental pathways. This preliminary result suggests possible involvement of schizophrenia risk genes in early brain growth, and potential early life structural alterations long before the average age of onset of the disease.

Asphyxia at birth affects brain structure in patients on the schizophrenia-bipolar disorder spectrum and healthy participants

BACKGROUND

Uncertainty exists about what causes brain structure alterations associated with schizophrenia (SZ) and bipolar disorder (BD). Whether a history of asphyxia-related obstetric complication (ASP) - a common but harmful condition for neural tissue - contributes to variations in adult brain structure is unclear. We investigated ASP and its relationship to intracranial (ICV), global brain volumes and regional cortical and subcortical structures.

METHODS

A total of 311 patients on the SZ - BD spectrum and 218 healthy control (HC) participants underwent structural magnetic resonance imaging. They were evaluated for ASP using prospective information obtained from the Medical Birth Registry of Norway.

RESULTS

In all groups, ASP was related to smaller ICV, total brain, white and gray matter volumes and total surface area, but not to cortical thickness. Smaller cortical surface areas were found across frontal, parietal, occipital, temporal and insular regions. Smaller hippocampal, amygdala, thalamus, caudate and putamen volumes were reported for all ASP subgroups. ASP effects did not survive ICV correction, except in the caudate, which remained significantly smaller in both patient ASP subgroups, but not in the HC.

CONCLUSIONS

Since ASP was associated with smaller brain volumes in all groups, the genetic risk of developing a severe mental illness, alone, cannot easily explain the smaller ICV. Only the smaller caudate volumes of ASP patients specifically suggest that injury from ASP can be related to disease development. Our findings give support for the ICV as a marker of aberrant neurodevelopment and ASP in the etiology of brain development in BD and SZ.

Heritable anisotropy associated with cognitive impairments among patients with schizophrenia and their non-psychotic relatives in multiplex families

BACKGROUND

To test the functional implications of impaired white matter (WM) connectivity among patients with schizophrenia and their relatives, we examined the heritability of fractional anisotropy (FA) measured on diffusion tensor imaging data acquired in Pittsburgh and Philadelphia, and its association with cognitive performance in a unique sample of 175 multigenerational non-psychotic relatives of 23 multiplex schizophrenia families and 240 unrelated controls (total = 438).

METHODS

We examined polygenic inheritance (h2r) of FA in 24 WM tracts bilaterally, and also pleiotropy to test whether heritability of FA in multiple WM tracts is secondary to genetic correlation among tracts using the Sequential Oligogenic Linkage Analysis Routines. Partial correlation tests examined the correlation of FA with performance on eight cognitive domains on the Penn Computerized Neurocognitive Battery, controlling for age, sex, site and mother's education, followed by multiple comparison corrections.

RESULTS

Significant total additive genetic heritability of FA was observed in all three-categories of WM tracts (association, commissural and projection fibers), in total 33/48 tracts. There were significant genetic correlations in 40% of tracts. Diagnostic group main effects were observed only in tracts with significantly heritable FA. Correlation of FA with neurocognitive impairments was observed mainly in heritable tracts.

CONCLUSIONS

Our data show significant heritability of all three-types of tracts among relatives of schizophrenia. Significant heritability of FA of multiple tracts was not entirely due to genetic correlations among the tracts. Diagnostic group main effect and correlation with neurocognitive performance were mainly restricted to tracts with heritable FA suggesting shared genetic effects on these traits.

Identification of voxel-based texture abnormalities as new biomarkers for schizophrenia and major depressive patients using layer-wise relevance propagation on deep learning decisions

Non-segmented MRI brain images are used for the identification of new Magnetic Resonance Imaging (MRI) biomarkers able to differentiate between schizophrenic patients (SCZ), major depressive patients (MD) and healthy controls (HC). Brain texture measures such as entropy and contrast, capturing the neighboring variation of MRI voxel intensities, were computed and fed into deep learning technique for group classification. Layer-wise relevance was applied for the localization of the classification results. Texture feature map of non-segmented brain MRI scans were extracted from 141 SCZ, 103 MD and 238 HC. The gray level co-occurrence matrix (GLCM) was calculated on a voxel-by-voxel basis in a cube of voxels. Deep learning tested if texture feature map could predict diagnostic group membership of three classes under a binary classification (SCZ vs. HC, MD vs. HC, SCZ vs. MD). The method was applied in a repeated nested cross-validation scheme and cross-validated feature selection. The regions with the highest relevance (positive/negative) are presented. The method was applied on non-segmented images reducing the computation complexity and the error associated with segmentation process.

Brain structural trajectories in youth at familial risk for schizophrenia or bipolar disorder according to development of psychosis spectrum symptoms

BACKGROUND

The evaluation of child and adolescent offspring of patients with schizophrenia (SzO) or bipolar disorder (BpO) may help understand changes taking place in the brain in individuals at heightened risk for disease during a key developmental period.

METHODS

One hundred twenty-eight individuals (33 SzO and 46 BpO, considered jointly as 'Familial High Risk' (FHR), and 49 controls) aged 6-17 years underwent clinical, cognitive and neuroimaging assessment at baseline, 2- and 4-year follow-up. Twenty FHR participants (11 SzO and 9 BpO) developed psychotic spectrum symptoms during follow-up, while 59 FHR participants did not. Magnetic resonance imaging was performed on a 3Tesla scanner; cortical surface reconstruction was applied to measure cortical thickness, surface area and grey matter volume.

RESULTS

FHR participants who developed psychotic spectrum symptoms over time showed greater time-related mean cortical thinning than those who did not and than controls. By subgroups, this effect was present in both BpO and SzO in the occipital cortex. At baseline, FHR participants who developed psychotic spectrum symptoms over time had smaller total surface area and grey matter volume than those who did not and than controls. Over time, all FHR participants showed less longitudinal decrease in surface area than controls. In those who developed psychotic spectrum symptoms over time, this effect was driven by BpO, while in those who did not, this was due to SzO, who also showed less grey matter volume reduction.

CONCLUSION

The emergence of psychotic spectrum symptoms in FHR was indexed by smaller cross-sectional surface area and progressive cortical thinning. Relative preservation of surface area over time may signal different processes according to familial risk. These findings lay the foundation for future studies aimed at stratification of FHR youth.

Brain features of nearly drug-naïve female monozygotic twins with first-episode schizophrenia and the classification accuracy of brain feature patterns: A pilot study

BACKGROUND

Data on differences in brain features between monozygotic (MZ) twins with and without schizophrenia are scarce.

METHODS

We compared brain features of female MZ twins with and without first-episode schizophrenia and healthy controls (n = 20 each). Voxel-based morphometry and tract-based spatial statistics were used to analyze differences in brain structure. Whole-brain effective connectivity (EC) and functional connectivity (FC) networks were constructed using resting-state functional magnetic resonance imaging (rs-fMRI) data.

RESULTS

Female twins with schizophrenia exhibited abnormal gray matter volume (GMV) in the basal ganglia and prefrontal and parietal cortices, impairments in the arcuate fasciculus, and significant disruptions (primarily decreases) in nine EC networks. They exhibited rs-EC alterations involving the limbic areas and subcortex. Combined rs-EC and rs-FC data distinguished twins with first-episode schizophrenia with high accuracy. Combined consideration of structural and functional features enabled the distinction of female MZ twins with schizophrenia from those without schizophrenia and healthy controls with 100% accuracy.

CONCLUSIONS

Female MZ twins with schizophrenia exhibited increased GMV, white matter impairment, and disruptions in EC and FC networks. The combination of rs-EC + rs-FC data could distinguish female twins with schizophrenia from twins without schizophrenia and healthy controls with 97.4% accuracy, and the addition of structural brain features yielded a 100% accuracy rate. These findings may provide pivotal insight for further study of the mechanisms underlying schizophrenia.

Twin studies of brain structure and cognition in schizophrenia

Twin studies in schizophrenia have been crucial in establishing estimates for the heritability and thus providing evidence for a genetic component in this disorder. Recent years have seen the application of the twin study paradigm to both putative intermediate phenotypes and biomarkers of disease as well as a diversification of its use in schizophrenia research. This review addressed studies of brain structure (T1 morphometry) and cognition in schizophrenia using twin study designs. We review major findings such as the overlap of genetic variance between schizophrenia and cognition as a model for the emergence of psychopathology. The use of novel hybrid models integrating molecular genetic risk markers, as well as the use of twin studies in epigenetics might prove to significantly enhance schizophrenia research in the post-GWAS era.

The Association Between Familial Risk and Brain Abnormalities Is Disease Specific: An ENIGMA-Relatives Study of Schizophrenia and Bipolar Disorder

BACKGROUND

Schizophrenia and bipolar disorder share genetic liability, and some structural brain abnormalities are common to both conditions. First-degree relatives of patients with schizophrenia (FDRs-SZ) show similar brain abnormalities to patients, albeit with smaller effect sizes. Imaging findings in first-degree relatives of patients with bipolar disorder (FDRs-BD) have been inconsistent in the past, but recent studies report regionally greater volumes compared with control subjects.

METHODS

We performed a meta-analysis of global and subcortical brain measures of 6008 individuals (1228 FDRs-SZ, 852 FDRs-BD, 2246 control subjects, 1016 patients with schizophrenia, 666 patients with bipolar disorder) from 34 schizophrenia and/or bipolar disorder family cohorts with standardized methods. Analyses were repeated with a correction for intracranial volume (ICV) and for the presence of any psychopathology in the relatives and control subjects.

RESULTS

FDRs-BD had significantly larger ICV (d = +0.16, q < .05 corrected), whereas FDRs-SZ showed smaller thalamic volumes than control subjects (d = -0.12, q < .05 corrected). ICV explained the enlargements in the brain measures in FDRs-BD. In FDRs-SZ, after correction for ICV, total brain, cortical gray matter, cerebral white matter, cerebellar gray and white matter, and thalamus volumes were significantly smaller; the cortex was thinner (d < -0.09, q < .05 corrected); and third ventricle was larger (d = +0.15, q < .05 corrected). The findings were not explained by psychopathology in the relatives or control subjects.

CONCLUSIONS

Despite shared genetic liability, FDRs-SZ and FDRs-BD show a differential pattern of structural brain abnormalities, specifically a divergent effect in ICV. This may imply that the neurodevelopmental trajectories leading to brain anomalies in schizophrenia or bipolar disorder are distinct.

Mapping cortical surface features in treatment resistant schizophrenia with in vivo structural MRI

Decreases in cortical volume (CV), thickness (CT) and surface area (SA) have been reported in individuals with schizophrenia by in vivo MRI studies. However, there are few studies that examine these cortical measures as potential biomarkers of treatment resistance (TR) and treatment response (NTR) in schizophrenia. This study used structural MRI to examine differences in CV, CT, and SA in 42 adults with schizophrenia (TR = 21, NTR = 21) and 23 healthy controls (HC) to test the hypothesis that individuals with TR schizophrenia have significantly greater reductions in these cortical measures compared to individuals with NTR schizophrenia. We found that individuals with TR schizophrenia showed significant reductions in CV and CT compared to individuals with NTR schizophrenia in right frontal and precentral regions, right parietal and occipital cortex, left temporal cortex and bilateral cingulate cortex. In line with previous literature, the temporal lobe and cingulate gyrus in both patient groups showed significant reductions of all three measures when compared to healthy controls. Taken together these results suggest that regional changes in CV and CT may index mechanisms specific to TR schizophrenia and potentially identify patients with TR schizophrenia for earlier treatment.

Heritability of cerebral glutamate levels and their association with schizophrenia spectrum disorders: a 1[H]-spectroscopy twin study

Research findings implicate cerebral glutamate in the pathophysiology of schizophrenia, including genetic studies reporting associations with glutamatergic neurotransmission. The extent to which aberrant glutamate levels can be explained by genetic factors is unknown, and if glutamate can serve as a marker of genetic susceptibility for schizophrenia remains to be established. We investigated the heritability of cerebral glutamate levels and whether a potential association with schizophrenia spectrum disorders could be explained by genetic factors. Twenty-three monozygotic (MZ) and 20 dizygotic (DZ) proband pairs con- or discordant for schizophrenia spectrum disorders, along with healthy control pairs (MZ = 28, DZ = 18) were recruited via the National Danish Twin Register and the Psychiatric Central Register (17 additional twins were scanned without their siblings). Glutamate levels in the left thalamus and the anterior cingulate cortex (ACC) were measured using 1[H]-magnetic resonance spectroscopy at 3 Tesla and analyzed by structural equation modeling. Glutamate levels in the left thalamus were heritable and positively correlated with liability for schizophrenia spectrum disorders (phenotypic correlation, 0.16, [0.02-0.29]; p = 0.010). The correlation was explained by common genes influencing both the levels of glutamate and liability for schizophrenia spectrum disorders. In the ACC, glutamate and glx levels were heritable, but not correlated to disease liability. Increases in thalamic glutamate levels found in schizophrenia spectrum disorders are explained by genetic influences related to the disease, and as such the measure could be a potential marker of genetic susceptibility, useful in early detection and stratification of patients with psychosis.

Associations between polygenic risk scores for four psychiatric illnesses and brain structure using multivariate pattern recognition

Psychiatric illnesses are complex and polygenic. They are associated with widespread alterations in the brain, which are partly influenced by genetic factors. There have been some attempts to relate polygenic risk scores (PRS) - a measure of the overall genetic risk an individual carries for a disorder - to brain structure using univariate methods. However, PRS are likely associated with distributed and covarying effects across the brain. We therefore used multivariate machine learning in this proof-of-principle study to investigate associations between brain structure and PRS for four psychiatric disorders; attention deficit-hyperactivity disorder (ADHD), autism, bipolar disorder and schizophrenia. The sample included 213 individuals comprising patients with depression (69), bipolar disorder (33), and healthy controls (111). The five psychiatric PRSs were calculated based on summary data from the Psychiatric Genomics Consortium. T1-weighted magnetic resonance images were obtained and voxel-based morphometry was implemented in SPM12. Multivariate relevance vector regression was implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo). Across the whole sample, a multivariate pattern of grey matter significantly predicted the PRS for autism (r = 0.20, pFDR = 0.03; MSE = 4.20 × 10-5, pFDR = 0.02). For the schizophrenia PRS, the MSE was significant (MSE = 1.30 × 10-5, pFDR = 0.02) although the correlation was not (r = 0.15, pFDR = 0.06). These results lend support to the hypothesis that polygenic liability for autism and schizophrenia is associated with widespread changes in grey matter concentrations. These associations were seen in individuals not affected by these disorders, indicating that this is not driven by the expression of the disease, but by the genetic risk captured by the PRSs.

Opportunities for an enhanced integration of neuroscience and genomics

Neuroimaging and genetics are two rapidly expanding fields of research. Thoughtful integration of these areas is critical for ongoing large-scale research into the genetic mechanisms underlying brain structure, function, and development. Neuroimaging genetics has been slow to evolve relative to psychiatric genetics research, and some may be unaware that new statistical methods allow for the genomic analysis of more modestly-sized imaging samples. We present a broad overview of the extant imaging genetics literature, provide an interpretation of the major problems surrounding the integration of neuroimaging and genetics, discuss the influence and impact of genetics consortia, and suggest statistical genetic analyses that expand the repertoire of imaging researchers amassing rich behavioral data in modestly-sized samples. Specific attention is paid to the creative use of polygenic risk scoring in imaging genetic analyses, with primers on the most current risk scoring applications.

Heritability of Neuropsychological Measures in Schizophrenia and Nonpsychiatric Populations: A Systematic Review and Meta-analysis

Schizophrenia is characterized by neuropsychological deficits across many cognitive domains. Cognitive phenotypes with high heritability and genetic overlap with schizophrenia liability can help elucidate the mechanisms leading from genes to psychopathology. We performed a meta-analysis of 170 published twin and family heritability studies of >800 000 nonpsychiatric and schizophrenia subjects to accurately estimate heritability across many neuropsychological tests and cognitive domains. The proportion of total variance of each phenotype due to additive genetic effects (A), shared environment (C), and unshared environment and error (E), was calculated by averaging A, C, and E estimates across studies and weighting by sample size. Heritability ranged across phenotypes, likely due to differences in genetic and environmental effects, with the highest heritability for General Cognitive Ability (32%-67%), Verbal Ability (43%-72%), Visuospatial Ability (20%-80%), and Attention/Processing Speed (28%-74%), while the lowest heritability was observed for Executive Function (20%-40%). These results confirm that many cognitive phenotypes are under strong genetic influences. Heritability estimates were comparable in nonpsychiatric and schizophrenia samples, suggesting that environmental factors and illness-related moderators (eg, medication) do not substantially decrease heritability in schizophrenia samples, and that genetic studies in schizophrenia samples are informative for elucidating the genetic basis of cognitive deficits. Substantial genetic overlap between cognitive phenotypes and schizophrenia liability (average rg = -.58) in twin studies supports partially shared genetic etiology. It will be important to conduct comparative studies in well-powered samples to determine whether the same or different genes and genetic variants influence cognition in schizophrenia patients and the general population.

Abnormal Rich-Club Organization Associated with Compromised Cognitive Function in Patients with Schizophrenia and Their Unaffected Parents

Schizophrenia is considered to be a disorder of brain connectivity, which might result from a disproportionally impaired rich-club organization. The rich-club is composed of highly interconnected hub regions that play crucial roles in integrating information between different brain regions. Few studies have yet investigated whether the structural rich-club organization is impaired in patients and their first-degree relatives. In this study, we established a weighted network model of white matter connections using diffusion tensor imaging of 19 patients and 39 unaffected parents, 22 young healthy controls for the patients, and 25 old healthy controls for the parents. Feeder edges between rich-club nodes and non-rich-club nodes were significantly decreased in both schizophrenic patients and their unaffected parents compared with controls. Furthermore, the feeder edges showed significant positive correlations with the scores in Category Fluency Test-animal naming in the unaffected parents. Specific feeder edges exhibited discriminative power with accuracy of 84.4% in distinguishing unaffected parents from old healthy controls. Our findings suggest that impaired rich-club organization, especially impaired feeder edges, may be related to familial vulnerability to schizophrenia, possibly reflecting a genetic predisposition for schizophrenia.

Familial and environmental influences on brain volumes in twins with schizophrenia

BACKGROUND

Reductions in whole brain and grey matter volumes are robust features of schizophrenia, yet their etiological influences are unclear.

METHODS

We investigated the association between the genetic and environmental risk for schizophrenia and brain volumes. Whole brain, grey matter and white matter volumes were established from structural MRIs from twins varying in their zygosity and concordance for schizophrenia. Hippocampal volumes were measured manually. We conducted between-group testing and full genetic modelling.

RESULTS

We included 168 twins in our study. Whole brain, grey matter, white matter and right hippocampal volumes were smaller in twins with schizophrenia. Twin correlations were larger for whole brain, grey matter and white matter volumes in monozygotic than dizygotic twins and were significantly heritable, whereas hippocampal volume was the most environmentally sensitive. There was a significant phenotypic correlation between schizophrenia and reductions in all the brain volumes except for that of the left hippocampus. For whole brain, grey matter and the right hippocampus the etiological links with schizophrenia were principally associated with the shared familial environment. Lower birth weight and perinatal hypoxia were both associated with lower whole brain volume and with lower white matter and grey matter volumes, respectively.

LIMITATIONS

Scan data were collected across 2 sites, and some groups were modest in size.

CONCLUSION

Whole brain, grey matter and right hippocampal volume reductions are linked to schizophrenia through correlated familial risk (i.e., the shared familial environment). The degree of influence of etiological factors varies between brain structures, leading to the possibility of a neuroanatomically specific etiological imprint.

Cognitive control deficit in patients with first-episode schizophrenia is associated with complex deviations of early brain development

BACKGROUND

Several clinical and radiological markers of early neurodevelopmental deviations have been independently associated with cognitive impairment in patients with schizophrenia. The aim of our study was to test the cumulative and/or interactive effects of these early neurodevelopmental factors on cognitive control (CC) deficit, a core feature of schizophrenia.

METHODS

We recruited patients with first-episode schizophrenia-spectrum disorders, who underwent structural MRI. We evaluated CC efficiency using the Trail Making Test (TMT). Several markers of early brain development were measured: neurological soft signs (NSS), handedness, sulcal pattern of the anterior cingulate cortex (ACC) and ventricle enlargement.

RESULTS

We included 41 patients with schizophrenia in our analysis, which revealed a main effect of ACC morphology (p = 0.041) as well as interactions between NSS and ACC morphology (p = 0.005), between NSS and handedness (p = 0.044) and between ACC morphology and cerebrospinal fluid (CSF) volume (p = 0.005) on CC measured using the TMT-B score - the TMT-A score.

LIMITATIONS

No 3- or 4-way interactions were detected between the 4 neurodevelopmental factors. The sample size was clearly adapted to detect main effects and 2-way interactions, but may have limited the statistical power to investigate higher-order interactions. The effects of treatment and illness duration were limited as the study design involved only patients with first-episode psychosis.

CONCLUSION

To our knowledge, our study provides the first evidence of cumulative and interactive effects of different neurodevelopmental markers on CC efficiency in patients with schizophrenia. Such findings, in line with the neurodevelopmental model of schizophrenia, support the notion that CC impairments in patients with schizophrenia may be the final common pathway of several early neurodevelopmental mechanisms.

Genetic Variation in Schizophrenia Liability is Shared With Intellectual Ability and Brain Structure

BACKGROUND

Alterations in intellectual ability and brain structure are important genetic markers for schizophrenia liability. How variations in these phenotypes interact with variance in schizophrenia liability due to genetic or environmental factors is an area of active investigation. Studying these genetic markers using a multivariate twin modeling approach can provide novel leads for (genetic) pathways of schizophrenia development.

METHODS

In a sample of 70 twins discordant for schizophrenia and 130 healthy control twins, structural equation modeling was applied to quantify unique contributions of genetic and environmental factors on human brain structure (cortical thickness, cortical surface and global white matter fractional anisotropy [FA]), intellectual ability and schizophrenia liability.

RESULTS

In total, up to 28.1% of the genetic variance (22.8% of total variance) in schizophrenia liability was shared with intelligence quotient (IQ), global-FA, cortical thickness, and cortical surface. The strongest contributor was IQ, sharing on average 16.4% of the genetic variance in schizophrenia liability, followed by cortical thickness (6.3%), global-FA (4.7%) and cortical surface (0.5%). Furthermore, we found that up to 57.4% of the variation due to environmental factors (4.6% of total variance) in schizophrenia was shared with IQ (34.2%) and cortical surface (13.4%).

CONCLUSIONS

Intellectual ability, FA and cortical thickness show significant and independent shared genetic variance with schizophrenia liability. This suggests that measuring brain-imaging phenotypes helps explain genetic variance in schizophrenia liability that is not captured by variation in IQ.

Heritability of the limbic networks

Individual differences in cognitive ability and social behaviour are influenced by the variability in the structure and function of the limbic system. A strong heritability of the limbic cortex has been previously reported, but little is known about how genetic factors influence specific limbic networks. We used diffusion tensor imaging tractography to investigate heritability of different limbic tracts in 52 monozygotic and 34 dizygotic healthy adult twins. We explored the connections that contribute to the activity of three distinct functional limbic networks, namely the dorsal cingulum (‘medial default-mode network’), the ventral cingulum and the fornix (‘hippocampal-diencephalic-retrosplenial network’) and the uncinate fasciculus (‘temporo-amygdala-orbitofrontal network’). Genetic and environmental variances were mapped for multiple tract-specific measures that reflect different aspects of the underlying anatomy. We report the highest heritability for the uncinate fasciculus, a tract that underpins emotion processing, semantic cognition, and social behaviour. High to moderate genetic and shared environmental effects were found for pathways important for social behaviour and memory, for example, fornix, dorsal and ventral cingulum. These findings indicate that within the limbic system inheritance of specific traits may rely on the anatomy of distinct networks and is higher for fronto-temporal pathways dedicated to complex social behaviour and emotional processing.

Motor dysfunction within the schizophrenia-spectrum: A dimensional step towards an underappreciated domain

At the beginning of the 20th century, genuine motor abnormalities (GMA) were considered to be intricately linked to schizophrenia. Subsequently, however, GMA have been increasingly regarded as unspecific transdiagnostic phenomena or related to side effects of antipsychotic treatment. Despite possible medication confounds, within the schizophrenia spectrum GMA have been categorized into three broad categories, i.e. neurological soft signs, abnormal involuntary movements and catatonia. Schizophrenia patients show a substantial overlap across a broad range of distinct motor signs and symptoms suggesting a prominent involvement of the motor system in disease pathophysiology. There have been several attempts to increase reliability and validity in diagnosing schizophrenia based on behavior and neurobiology, yet relatively little attention has been paid to the motor domain in the past. Nevertheless, accumulating neuroscientific evidence suggests the possibility of a motor endophenotype in schizophrenia, and that GMA could represent a specific dimension within the schizophrenia-spectrum. Here, we review current neuroimaging research on GMA in schizophrenia with an emphasis on distinct and common mechanisms of brain dysfunction. Based on a dimensional approach we show that multimodal neuroimaging combined with fine-grained clinical examination can result in a comprehensive characterization of structural and functional brain changes that are presumed to underlie core GMA in schizophrenia. We discuss the possibility of a distinct motor domain, together with its implications for future research. Investigating GMA by means of multimodal neuroimaging can essentially contribute at identifying novel and biologically reliable phenotypes in psychiatry.

Reciprocal causation models of cognitive vs volumetric cerebral intermediate phenotypes for schizophrenia in a pan-European twin cohort

In aetiologically complex illnesses such as schizophrenia, there is no direct link between genotype and phenotype. Intermediate phenotypes could help clarify the underlying biology and assist in the hunt for genetic vulnerability variants. We have previously shown that cognition shares substantial genetic variance with schizophrenia; however, it is unknown if this reflects pleiotropic effects, direct causality or some shared third factor that links both, for example, brain volume (BV) changes. We quantified the degree of net genetic overlap and tested the direction of causation between schizophrenia liability, brain structure and cognition in a pan-European schizophrenia twin cohort consisting of 1243 members from 626 pairs. Cognitive deficits lie upstream of the liability for schizophrenia with about a quarter of the variance in liability to schizophrenia explained by variation in cognitive function. BV changes lay downstream of schizophrenia liability, with 4% of BV variation explained directly by variation in liability. However, our power to determine the nature of the relationship between BV deviation and schizophrenia liability was more limited. Thus, while there was strong evidence that cognitive impairment is causal to schizophrenia liability, we are not in a position to make a similar statement about the relationship between liability and BV. This is the first study to demonstrate that schizophrenia liability is expressed partially through cognitive deficits. One prediction of the finding that BV changes lie downstream of the disease liability is that the risk loci that influence schizophrenia liability will thereafter influence BV and to a lesser extent. By way of contrast, cognitive function lies upstream of schizophrenia, thus the relevant loci will actually have a larger effect size on cognitive function than on schizophrenia. These are testable predictions.

Evaluating the relationship between reelin gene variants (rs7341475 and rs262355) and schizophrenia: A meta-analysis

Studies have suggested that reelin (RELN) polymorphism was associated with the susceptibility of schizophrenia (SZ), but the results remained controversial. Thus, we conducted this meta-analysis to determine whether RELN variants (rs7341475 and rs262355) were associated with SZ risk. Studies were identified through retrieving Web of Science, PubMed and Embase databases from inception to May 2015. The genotype data were extracted to calculate the odds ratios (ORs) and 95% confidence intervals (CIs). For rs7341475, five studies with 4741 SZ patients and 10075 controls are included and the results indicate that carriage of A allele is associated with decreased SZ risk in dominant genetic model (OR=0.90, 95%CI=0.83-0.98) and additive model (OR=0.90, 95% CI=0.84-0.97). Subgroup analysis indicates that the association between rs7341475 and SZ is only significant in Caucasian. For rs262355, four studies with 2017 SZ patients and 3274 controls are included, the results demonstrate that carriage of A allele is associated with increased risk of SZ only in Caucasian (dominant model: OR=1.17, 95%CI=1.01-1.37; additive model OR=1.13, 95%CI=1.02-1.27). This meta-analysis suggests that rs7341475 (A/G) and rs262355 (A/T) polymorphisms in RELN gene are inversely associated with SZ risk.

Converging models of schizophrenia--Network alterations of prefrontal cortex underlying cognitive impairments

The prefrontal cortex (PFC) and its connections with other brain areas are crucial for cognitive function. Cognitive impairments are one of the core symptoms associated with schizophrenia, and manifest even before the onset of the disorder. Altered neural networks involving PFC contribute to cognitive impairments in schizophrenia. Both genetic and environmental risk factors affect the development of the local circuitry within PFC as well as development of broader brain networks, and make the system vulnerable to further insults during adolescence, leading to the onset of the disorder in young adulthood. Since spared cognitive functions correlate with functional outcome and prognosis, a better understanding of the mechanisms underlying cognitive impairments will have important implications for novel therapeutics for schizophrenia focusing on cognitive functions. Multidisciplinary approaches, from basic neuroscience to clinical studies, are required to link molecules, circuitry, networks, and behavioral phenotypes. Close interactions among such fields by sharing a common language on connectomes, behavioral readouts, and other concepts are crucial for this goal.

The Effect of Antipsychotic Treatment on Cortical Gray Matter Changes in Schizophrenia: Does the Class Matter? A Meta-analysis and Meta-regression of Longitudinal Magnetic Resonance Imaging Studies

BACKGROUND

Deficits in cortical gray matter (GM) have been found in patients with schizophrenia, with evidence of progression over time. The aim of this study was to determine the role of potential moderators of such changes, in particular of the amount and type of antipsychotic medication intake.

METHODS

Longitudinal magnetic resonance imaging studies comparing changes in the volume of cortical GM over time between patients with schizophrenia and healthy control subjects published between January 1, 1983, and March 31, 2014, were analyzed. Hedges' g was calculated for each study and volume changes from baseline to follow-up were analyzed. Meta-regression statistics were applied to investigate the role of potential moderators of the effect sizes.

RESULTS

Eighteen studies involving 1155 patients with schizophrenia and 911 healthy control subjects were included. Over time, patients with schizophrenia showed a significantly higher loss of total cortical GM volume. This was related to cumulative antipsychotic intake during the interval between scans in the whole study sample. Subgroup meta-analyses of studies on patients treated with second-generation antipsychotics and first-generation antipsychotics revealed a different and contrasting moderating role of medication intake on cortical GM changes: more progressive GM loss correlated with higher mean daily antipsychotic intake in patients treated with at least one first-generation antipsychotic and less progressive GM loss with higher mean daily antipsychotic intake in patients treated only with second-generation antipsychotics.

CONCLUSIONS

These findings add useful information to the controversial debate on the brain structural effects of antipsychotic medication and may have both clinical relevance and theoretical implications.

Schizophrenia risk variants modulate white matter volume across the psychosis spectrum: evidence from two independent cohorts

Polygenic risk scores, based on risk variants identified in genome-wide-association-studies (GWAS), explain a considerable portion of the heritability for schizophrenia (SZ) and bipolar disorder (BD). However, little is known about the combined effects of these variants, although polygenic neuroimaging has developed into a powerful tool of translational neuroscience. In this study, we used genome wide significant SZ risk variants to test the predictive capacity of the polygenic model and explored potential associations with white matter volume, a key candidate in imaging phenotype for psychotic disorders. By calculating the combined additive schizophrenia risk of seven SNPs (significant hits from a recent schizophrenia GWAS study), we show that increased additive genetic risk for SZ was associated with reduced white matter volume in a group of participants (n = 94) consisting of healthy individuals, SZ first-degree relatives, SZ patients and BD patients. This effect was also seen in a second independent sample of healthy individuals (n = 89). We suggest that a moderate portion of variance (~4%) of white matter volume can be explained by the seven hits from the recent schizophrenia GWAS. These results provide evidence for associations between cumulative genetic risk for schizophrenia and intermediate neuroimaging phenotypes in models of psychosis. Our work contributes to a growing body of literature suggesting that polygenic risk may help to explain white matter alterations associated with familial risk for psychosis.

Abnormal white matter integrity in antipsychotic-naïve first-episode psychosis patients assessed by a DTI principal component analysis

INTRODUCTION

Diffusion tensor imaging (DTI) studies in patients with schizophrenia have shown abnormalities in the microstructure of white matter tracts. Specifically, reduced fractional anisotropy (FA) has been described across multiple white matter tracts, in studies that have mainly included patients treated with antipsychotic medications.

OBJECTIVE

To compare FA in antipsychotic-naïve patients experiencing a first episode of psychosis (FEP) to FA in healthy controls to demonstrate that the variance of FA can be grouped, in a coincidental manner, in four predetermined factors in accordance with a theoretical partition of the white matter tracts, using a principal components analysis (PCA).

METHODS

Thirty-five antipsychotic-naïve FEP patients and 35 age- and gender-matched healthy controls underwent DTI at 3T. Analysis was performed using a tract-based spatial statistics (TBSS) method and exploratory PCA.

RESULTS

DTI analysis showed extensive FA reduction in white matter tracts in FEP patients compared with the control group. The PCA grouped the white matter tracts into four factors explaining 66% of the total variance. Comparison of the FA values within each factor highlighted the differences between FEP patients and controls.

DISCUSSION

Our study confirms extensive white matter tracts anomalies in patients with schizophrenia, more specifically, in drug-naïve FEP patients. The results also indicate that a small number of white matter tracts share common FA anomalies that relate to deficit symptoms in FEP patients. Our study adds to a growing body of literature emphasizing the need for treatments targeting white matter function and structure in FEP patients.

The miR-137 schizophrenia susceptibility variant rs1625579 does not predict variability in brain volume in a sample of schizophrenic patients and healthy individuals

The micro RNA 137 (miR-137) variant rs1625579 has been identified as a genome-wide significant risk variant for schizophrenia. miR-137 has an established role in neurodevelopment and may mediate cognitive dysfunction in schizophrenia. This role of miR-137 may be related to changes in brain morphology for risk-related genotypes; however this has not yet been delineated. Here we considered whether rs1625579 genotype was predictive of indices of brain structure in patients with schizophrenia and healthy controls. Structural magnetic resonance imaging (sMRI) data (i.e. 3T T1-TFE or 1.5T T1-MPRAGE) were acquired from 150 healthy controls and 163 schizophrenic patients. Two volumetric analyses that considered the impact of miR-137/rs1625579 genotype were carried out on sMRI data. In the first analysis, voxel based morphometry was employed to consider genotype-related variability in local grey and white matter across the entire brain volume. Our secondary analysis utilized the FIRST protocol in FSL to consider the volume of subcortical structures (i.e. bilateral accumbens, amygdala, caudate, hippocampus, pallidum, putamen and thalamus). Several brain regions in both analyses demonstrated the expected main effect of participant group (i.e. schizophrenics < controls), yet there were no regions where we observed an impact of rs1635579 genotype on brain volume. Our analyses suggest that the mechanism by which miR-137 confers risk for schizophrenia and impacts upon cognitive function may not be mediated by changes in local brain volume. However, it remains to be determined whether or not alternative measures of brain structure are related to these functions of miR-137.

Impaired rich club connectivity in unaffected siblings of schizophrenia patients

Schizophrenia has been conceptualized as a disorder of brain connectivity. Recent studies suggest that brain connectivity may be disproportionally impaired among the so-called rich club. This small core of densely interconnected hub regions has been hypothesized to form an important infrastructure for global brain communication and integration of information across different systems of the brain. Given the heritable nature of the illness, we hypothesized that connectivity disturbances, including abnormal rich club connectivity, may be related to familial vulnerability for schizophrenia. To test this hypothesis, both schizophrenia patients and unaffected siblings of patients were investigated. Rich club organization was examined in networks derived from diffusion-weighted imaging in 40 schizophrenia patients, 54 unaffected siblings of patients, and 51 healthy control subjects. Connectivity between rich club hubs was differentially reduced across groups (P = .014), such that it was highest in controls, intermediate in siblings (7.9% reduced relative to controls), and lowest in patients (19.6% reduced compared to controls). Furthermore, in patients, lower levels of rich club connectivity were found to be related to longer duration of illness and worse overall functioning. Together, these findings suggest that impaired rich club connectivity is related to familial, possibly reflecting genetic, vulnerability for schizophrenia. Our findings support a central role for abnormal rich club organization in the etiology of schizophrenia.

Arguments for the sake of endophenotypes: examining common misconceptions about the use of endophenotypes in psychiatric genetics

Endophenotypes are measurable biomarkers that are correlated with an illness, at least in part, because of shared underlying genetic influences. Endophenotypes may improve our power to detect genes influencing risk of illness by being genetically simpler, closer to the level of gene action, and with larger genetic effect sizes or by providing added statistical power through their ability to quantitatively rank people within diagnostic categories. Furthermore, they also provide insight into the mechanisms underlying illness and will be valuable in developing biologically-based nosologies, through efforts such as RDoC, that seek to explain both the heterogeneity within current diagnostic categories and the overlapping clinical features between them. While neuroimaging, electrophysiological, and cognitive measures are currently most used in psychiatric genetic studies, researchers currently are attempting to identify candidate endophenotypes that are less genetically complex and potentially closer to the level of gene action, such as transcriptomic and proteomic phenotypes. Sifting through tens of thousands of such measures requires automated, high-throughput ways of assessing, and ranking potential endophenotypes, such as the Endophenotype Ranking Value. However, despite the potential utility of endophenotypes for gene characterization and discovery, there is considerable resistance to endophenotypic approaches in psychiatry. In this review, we address and clarify some of the common issues associated with the usage of endophenotypes in the psychiatric genetics community.

Association of a risk allele of ANK3 with cognitive performance and cortical thickness in patients with first-episode psychosis

BACKGROUND

The gene ANK3 is implicated in bipolar disorder and schizophrenia. The present study investigated the influence of this gene on cognitive performance and brain structure among individuals with first-episode psychosis (FEP). The brief illness duration of an FEP sample makes it well suited for studying the effects of genetic variation.

METHODS

We genotyped 2 single nucleotide polymorphisms (SNPs; rs1938526 and rs10994336) in ANK3 in patients with FEP. Multivariate analysis of variance compared risk allele carriers and noncarriers on 6 domains of cognition consistent with MATRICS consensus. A subsample of 82 patients was assessed using magnetic resonance imaging. We compared brain structure between carriers and noncarriers using cortical thickness analysis and voxel-based morphometry on white matter.

RESULTS

In the 173 patients with FEP included in our study, rs1938526 and rs10994336 were in very high linkage disequilibrium (d' = 0.95), and analyses were therefore only carried out on the SNP (rs1938526) with the highest minor allele frequency (G). Allele G of rs1938526, was associated with lower cognitive performance across domains (F6,164 = 2.38, p = 0.030) and significantly lower scores on the domains of verbal memory (p = 0.015), working memory (p = 0.006) and attention (p = 0.019). The significant effects of this SNP on cognition were not maintained when controlling for IQ. Cortical thinning was observed in risk allele carriers at diverse sites across cortical lobes bilaterally at a threshold of p < 0.01, false discovery rate-corrected. Risk-allele carriers did not show any regions of reduced white matter volume.

LIMITATIONS

The sample size is modest given that a low-frequency variant was being examined.

CONCLUSION

The ANK3 risk allele rs1938526 appears to be associated with general cognitive impairment and widespread cortical thinning in patients with FEP.

Schizophrenia genetic variants are not associated with intelligence

BACKGROUND

Schizophrenia is associated with lower pre-morbid intelligence (IQ) in addition to (pre-morbid) cognitive decline. Both schizophrenia and IQ are highly heritable traits. Therefore, we hypothesized that genetic variants associated with schizophrenia, including copy number variants (CNVs) and a polygenic schizophrenia (risk) score (PSS), may influence intelligence.

METHOD

IQ was estimated with the Wechsler Adult Intelligence Scale (WAIS). CNVs were determined from single nucleotide polymorphism (SNP) data using the QuantiSNP and PennCNV algorithms. For the PSS, odds ratios for genome-wide SNP data were calculated in a sample collected by the Psychiatric Genome-Wide Association Study (GWAS) Consortium (8690 schizophrenia patients and 11 831 controls). These were used to calculate individual PSSs in our independent sample of 350 schizophrenia patients and 322 healthy controls.

RESULTS

Although significantly more genes were disrupted by deletions in schizophrenia patients compared to controls (p = 0.009), there was no effect of CNV measures on IQ. The PSS was associated with disease status (R 2 = 0.055, p = 2.1 × 10-7) and with IQ in the entire sample (R 2 = 0.018, p = 0.0008) but the effect on IQ disappeared after correction for disease status.

CONCLUSIONS

Our data suggest that rare and common schizophrenia-associated variants do not explain the variation in IQ in healthy subjects or in schizophrenia patients. Thus, reductions in IQ in schizophrenia patients may be secondary to other processes related to schizophrenia risk.

Gene x environment interactions in schizophrenia and bipolar disorder: evidence from neuroimaging

INTRODUCTION

Schizophrenia (SZ) and Bipolar disorder (BD) are considered as severe multifactorial diseases, stemming from genetic and environmental influences. Growing evidence supports gene x environment (GxE) interactions in these disorders and neuroimaging studies can help us to understand how those factors mechanistically interact. No reviews synthesized the existing data of neuroimaging studies in these issues.

METHODS

We conduct a systematic review on the neuroimaging studies exploring GxE interactions relative to SZ or BD in PubMed.

RESULTS

First results of the influence of genetic and environmental risks on brain structures came from monozygotic twin pairs concordant and discordant for SZ or BD. Few structural magnetic resonance imaging (sMRI) studies have explored the GxE interactions. No other imaging methods were found. Two main GxE interactions on brain volumes have arisen. First, an interaction between genetic liability to SZ and obstetric complications on gray matter, cerebrospinal fluid, and hippocampal volumes. Second, cannabis use and genetic liability interaction effects on cortical thickness and white matter volumes.

CONCLUSION

Combining GxE interactions and neuroimaging domains is a promising approach. Genetic risk and environmental exposures such as cannabis or obstetrical complications seem to interact leading to specific neuroimaging cerebral alterations in SZ. They are suggestive of GxE interactions that confer phenotypic abnormalities in SZ and possibly BD. We need further, larger neuroimaging studies of GxE interactions for which we may propose a framework focusing on GxE interactions data already known to have a clinical effect such as infections, early stress, urbanicity, and substance abuse.

A review of neuroimaging studies of young relatives of individuals with schizophrenia: a developmental perspective from schizotaxia to schizophrenia

In an effort to identify the developing abnormalities preceding psychosis, Dr. Ming T. Tsuang and colleagues at Harvard expanded Meehl's concept of "schizotaxia," and examined brain structure and function in families affected by schizophrenia (SZ). Here, we systematically review genetic (familial) high-risk (HR) studies of SZ using magnetic resonance imaging (MRI), examine how findings inform models of SZ etiology, and suggest directions for future research. Neuroimaging studies of youth at HR for SZ through the age of 30 were identified through a MEDLINE (PubMed) search. There is substantial evidence of gray matter volume abnormalities in youth at HR compared to controls, with an accelerated volume reduction over time in association with symptoms and cognitive deficits. In structural neuroimaging studies, prefrontal cortex (PFC) alterations were the most consistently reported finding in HR. There was also consistent evidence of smaller hippocampal volume. In functional studies, hyperactivity of the right PFC during performance of diverse tasks with common executive demands was consistently reported. The only longitudinal fMRI study to date revealed increasing left middle temporal activity in association with the emergence of psychotic symptoms. There was preliminary evidence of cerebellar and default mode network alterations in association with symptoms. Brain abnormalities in structure, function and neurochemistry are observed in the premorbid period in youth at HR for SZ. Future research should focus on the genetic and environmental contributions to these alterations, determine how early they emerge, and determine whether they can be partially or fully remediated by innovative treatments.

Family-wise automatic classification in schizophrenia

Automatic classification of individuals at increased risk for schizophrenia can become an important screening method that allows for early intervention based on disease markers, if proven to be sufficiently accurate. Conventional classification methods typically consider information from single subjects, thereby ignoring (heritable) features of the person's relatives. In this paper we show that the inclusion of these features can lead to an increase in classification accuracy from 0.54 to 0.72 using a support vector machine model. This inclusion of contextual information is especially useful in diseases where the classification features carry a heritable component.

A family affair: brain abnormalities in siblings of patients with schizophrenia

Schizophrenia is a severe mental disorder that has a strong genetic basis. Converging evidence suggests that schizophrenia is a progressive neurodevelopmental disorder, with earlier onset cases resulting in more profound brain abnormalities. Siblings of patients with schizophrenia provide an invaluable resource for differentiating between trait and state markers, thus highlighting possible endophenotypes for ongoing research. However, findings from sibling studies have not been systematically put together in a coherent story across the broader age span. We review here the cortical grey matter abnormalities in siblings of patients with schizophrenia from childhood to adulthood, by reviewing sibling studies from both childhood-onset schizophrenia, and the more common adult-onset schizophrenia. When reviewed together, studies suggest that siblings of patients with schizophrenia display significant brain abnormalities that highlight both similarities and differences between the adult and childhood populations, with shared developmental risk patterns, and segregating trajectories. Based on current research it appears that the cortical grey matter abnormalities in siblings are likely to be an age-dependent endophenotype, which normalize by the typical age of onset of schizophrenia unless there has been more genetic or symptom burdening. With increased genetic burdening (e.g. discordant twins of patients) the grey matter abnormalities in (twin) siblings are progressive in adulthood. This synthesis of the literature clarifies the importance of brain plasticity in the pathophysiology of the illness, indicating that probands may lack protective factors critical for healthy development.

Variation in the major histocompatibility complex [MHC] gene family in schizophrenia: associations and functional implications

Schizophrenia is a chronic debilitating neuropsychiatric disorder with a complex genetic contribution. Although multiple genetic, immunological and environmental factors are known to contribute to schizophrenia susceptibility, the underlying neurobiological mechanism(s) is yet to be established. The immune system dysfunction theory of schizophrenia is experiencing a period of renewal due to a growth in evidence implicating components of the immune system in brain function and human behavior. Current evidence indicates that certain immune molecules such as Major Histocompatibility Complex (MHC) and cytokines, the key regulators of immunity and inflammation are directly involved in the neurobiological processes related to neurodevelopment, neuronal plasticity, learning, memory and behavior. However, the strongest support in favor of the immune hypothesis has recently emerged from on-going genome wide association studies advocating MHC region variants as major determinants of one's risk for developing schizophrenia. Further identification of the interacting partners and receptors of MHC molecules in the brain and their role in down-stream signaling pathways of neurotransmission have implicated these molecules as potential schizophrenia risk factors. More recently, combined brain imaging and genetic studies have revealed a relationship between genetic variations within the MHC region and neuromorphometric changes during schizophrenia. Furthermore, MHC molecules play a significant role in the immune-infective and neurodevelopmental pathogenetic pathways, currently hypothesized to contribute to the pathophysiology of schizophrenia. Herein, we review the immunological, genetic and expression studies assessing the role of the MHC in conferring risk for developing schizophrenia, we summarize and discuss the possible mechanisms involved, making note of the challenges to, and future directions of, immunogenetic research in schizophrenia.

Genetic schizophrenia risk variants jointly modulate total brain and white matter volume

BACKGROUND

Thousands of common single nucleotide polymorphisms (SNPs) are weakly associated with schizophrenia. It is likely that subsets of disease-associated SNPs are associated with distinct heritable disease-associated phenotypes. Therefore, we examined the shared genetic susceptibility modulating schizophrenia and brain volume.

METHODS

Odds ratios for genome-wide SNP data were calculated in the sample collected by the Psychiatric Genome-wide Association Study Consortium (8690 schizophrenia patients and 11,831 control subjects, excluding subjects from the present study). These were used to calculate individual polygenic schizophrenia (risk) scores in an independent sample of 152 schizophrenia patients and 142 healthy control subjects with available structural magnetic resonance imaging scans.

RESULTS

In the entire group, the polygenic schizophrenia score was significantly associated with total brain volume (R2 = .048, p = 1.6 × 10(-4)) and white matter volume (R2 = .051, p = 8.6 × 10(-5)) equally in patients and control subjects. The number of (independent) SNPs that substantially influenced both disease risk and white matter (n = 2020) was much smaller than the entire set of SNPs that modulated disease status (n = 14,751). From the set of 2020 SNPs, a group of 186 SNPs showed most evidence for association with white matter volume and an explorative functional analysis showed that these SNPs were located in genes with neuronal functions.

CONCLUSIONS

These results indicate that a relatively small subset of schizophrenia genetic risk variants is related to the (normal) development of white matter. This, in turn, suggests that disruptions in white matter growth increase the susceptibility to develop schizophrenia.

Association study of copy number variants with brain volume in schizophrenia patients and healthy controls

Schizophrenia patients have more copy number variations (CNVs) than healthy controls, and reduced brain volumes. Although this could suggest a causal relationship, we found no association between global CNV burden and three brain volume measures (on a MRI scan) in a sample of 173 schizophrenia patients and 176 healthy controls.