Common genetic variants influence human subcortical brain structures

COINSTAC: A Privacy Enabled Model and Prototype for Leveraging and Processing Decentralized Brain Imaging Data

The field of neuroimaging has embraced the need for sharing and collaboration. Data sharing mandates from public funding agencies and major journal publishers have spurred the development of data repositories and neuroinformatics consortia. However, efficient and effective data sharing still faces several hurdles. For example, open data sharing is on the rise but is not suitable for sensitive data that are not easily shared, such as genetics. Current approaches can be cumbersome (such as negotiating multiple data sharing agreements). There are also significant data transfer, organization and computational challenges. Centralized repositories only partially address the issues. We propose a dynamic, decentralized platform for large scale analyses called the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC). The COINSTAC solution can include data missing from central repositories, allows pooling of both open and "closed" repositories by developing privacy-preserving versions of widely-used algorithms, and incorporates the tools within an easy-to-use platform enabling distributed computation. We present an initial prototype system which we demonstrate on two multi-site data sets, without aggregating the data. In addition, by iterating across sites, the COINSTAC model enables meta-analytic solutions to converge to "pooled-data" solutions (i.e., as if the entire data were in hand). More advanced approaches such as feature generation, matrix factorization models, and preprocessing can be incorporated into such a model. In sum, COINSTAC enables access to the many currently unavailable data sets, a user friendly privacy enabled interface for decentralized analysis, and a powerful solution that complements existing data sharing solutions.

A study of genetic and environmental contributions to structural brain changes over time in twins concordant and discordant for bipolar disorder

This is the first longitudinal twin study examining genetic and environmental contributions to the association between liability to bipolar disorder (BD) and changes over time in global brain volumes, and global and regional measures of cortical surface area, cortical thickness and cortical volume. A total of 50 twins from pairs discordant or concordant for BD (monozygotic: 8 discordant and 3 concordant pairs, and 1 patient and 3 co-twins from incomplete pairs; dizygotic: 6 discordant and 2 concordant pairs, and 1 patient and 7 co-twins from incomplete pairs) underwent magnetic resonance imaging twice. In addition, 57 twins from healthy twin pairs (15 monozygotic and 10 dizygotic pairs, and 4 monozygotic and 3 dizygotic subjects from incomplete pairs) were also scanned twice. Mean follow-up duration for all twins was 7.5 years (standard deviation: 1.5 years). Data were analyzed using structural equation modeling software OpenMx. The liability to BD was not associated with global or regional structural brain changes over time. Although we observed a subtle increase in cerebral white matter in BD patients, this effect disappeared after correction for multiple comparisons. Heritability of brain changes over time was generally low to moderate. Structural brain changes appear to follow similar trajectories in BD patients and healthy controls. Existing brain abnormalities in BD do not appear to progressively change over time, but this requires additional confirmation. Further study with large cohorts is recommended to assess genetic and environmental influences on structural brain abnormalities in BD, while taking into account the influence of lithium on the brain.

Impact of polygenic schizophrenia-related risk and hippocampal volumes on the onset of psychosis

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

Conservation of Distinct Genetically-Mediated Human Cortical Pattern

The many subcomponents of the human cortex are known to follow an anatomical pattern and functional relationship that appears to be highly conserved between individuals. This suggests that this pattern and the relationship among cortical regions are important for cortical function and likely shaped by genetic factors, although the degree to which genetic factors contribute to this pattern is unknown. We assessed the genetic relationships among 12 cortical surface areas using brain images and genotype information on 2,364 unrelated individuals, brain images on 466 twin pairs, and transcriptome data on 6 postmortem brains in order to determine whether a consistent and biologically meaningful pattern could be identified from these very different data sets. We find that the patterns revealed by each data set are highly consistent (p<10⁻³), and are biologically meaningful on several fronts. For example, close genetic relationships are seen in cortical regions within the same lobes and, the frontal lobe, a region showing great evolutionary expansion and functional complexity, has the most distant genetic relationship with other lobes. The frontal lobe also exhibits the most distinct expression pattern relative to the other regions, implicating a number of genes with known functions mediating immune and related processes. Our analyses reflect one of the first attempts to provide an assessment of the biological consistency of a genetic phenomenon involving the brain that leverages very different types of data, and therefore is not just statistical replication which purposefully use very similar data sets.

Although functional and anatomical connections among cortical regions have been intensively explored, genetically-mediated relationships between cortical regions have not been pursued to the same degree. Identifying genetic factors that mediate these relationships among different brain subcomponents can provide insight into how the human brain is organized and functions. We have assessed the genetic relationships among cortical regions using an integrated approach that considers twin data, genotype information among a large set of unrelated individuals, and gene expression measurements from postmortem neural tissues. We looked for evidence that subsets of cortical brain regions are under common or unique genetic control. We found that the patterns of genetic relationships are highly consistent across three independent data sets and multiple lines of evidence, suggesting that the patterning of cortical surface area is strongly mediated by genetic factors and, furthermore, likely reflects underlying anatomical and possibly functional relationships among cortical brain regions.

Polygenic risk of Alzheimer disease is associated with early- and late-life processes

OBJECTIVE

To examine associations between aggregate genetic risk and Alzheimer disease (AD) markers in stages preceding the clinical symptoms of dementia using data from 2 large observational cohort studies.

METHODS

We computed polygenic risk scores (PGRS) using summary statistics from the International Genomics of Alzheimer's Project genome-wide association study of AD. Associations between PGRS and AD markers (cognitive decline, clinical progression, hippocampus volume, and β-amyloid) were assessed within older participants with dementia. Associations between PGRS and hippocampus volume were additionally examined within healthy younger participants (age 18-35 years).

RESULTS

Within participants without dementia, elevated PGRS was associated with worse memory (p = 0.002) and smaller hippocampus (p = 0.002) at baseline, as well as greater longitudinal cognitive decline (memory: p = 0.0005, executive function: p = 0.01) and clinical progression (p < 0.00001). High PGRS was associated with AD-like levels of β-amyloid burden as measured with florbetapir PET (p = 0.03) but did not reach statistical significance for CSF β-amyloid (p = 0.11). Within the younger group, higher PGRS was associated with smaller hippocampus volume (p = 0.05). This pattern was evident when examining a PGRS that included many loci below the genome-wide association study (GWAS)-level significance threshold (16,123 single nucleotide polymorphisms), but not when PGRS was restricted to GWAS-level significant loci (18 single nucleotide polymorphisms).

CONCLUSIONS

Effects related to common genetic risk loci distributed throughout the genome are detectable among individuals without dementia. The influence of this genetic risk may begin in early life and make an individual more susceptible to cognitive impairment in late life. Future refinement of polygenic risk scores may help identify individuals at risk for AD dementia.

Limited Evidence for Association of Genome-Wide Schizophrenia Risk Variants on Cortical Neuroimaging Phenotypes

BACKGROUND

There are now over 100 established genetic risk variants for schizophrenia; however, their influence on brain structure and circuitry across the human lifespan are not known.

METHODS

We examined healthy individuals 8-86 years of age, from the Centre for Addiction and Mental Health, the Zucker Hillside Hospital, and the Philadelphia Neurodevelopmental Cohort. Following thorough quality control procedures, we investigated associations of established genetic risk variants with heritable neuroimaging phenotypes relevant to schizophrenia, namely thickness of frontal and temporal cortical regions (n = 565) and frontotemporal and interhemispheric white matter tract fractional anisotropy (FA) (n = 530).

RESULTS

There was little evidence for association of risk variants with imaging phenotypes. No association with cortical thickness of any region was present. Only rs12148337, near a long noncoding RNA region, was associated with white matter FA (splenium of corpus callosum) following multiple comparison correction (corrected p = .012); this single nucleotide polymorphism was also associated with genu FA and superior longitudinal fasciculus FA at p <.005 (uncorrected). There was no association of polygenic risk score with white matter FA or cortical thickness.

CONCLUSIONS

In sum, our findings provide limited evidence for association of schizophrenia risk variants with cortical thickness or diffusion imaging white matter phenotypes. When taken with recent lack of association of these variants with subcortical brain volumes, our results either suggest that structural neuroimaging approaches at current resolution are not sufficiently sensitive to detect effects of these risk variants or that multiple comparison correction in correlated phenotypes is too stringent, potentially "eliminating" biologically important signals.

Additive sex-specific influence of common non-synonymous DISC1 variants on amygdala, basal ganglia, and white cortical surface area in healthy young adults

The disrupted-in-schizophrenia-1 (DISC1) gene is known for its role in the development of mental disorders. It is also involved in neurodevelopment, cognition, and memory. To investigate the association between DISC1 variants and brain morphology, we analyzed the influence of the three common non-synonymous polymorphisms in DISC1 on specific brain structures in healthy young adults. The volumes of brain regions were determined in 145 subjects by magnetic resonance imaging and automated analysis using FreeSurfer. Genotyping was performed by high resolution melting of amplified products. In an additive genetic model, rs6675281 (Leu607Phe), rs3738401 (Arg264Gln), and rs821616 (Ser704Cys) significantly explained the volume variance of the amygdala (p = 0.007) and the pallidum (p = 0.004). A higher cumulative portion of minor alleles was associated with larger volumes of the amygdala (p = 0.005), the pallidum (p = 0.001), the caudate (p = 0.024), and the putamen (p = 0.007). Sex-stratified analysis revealed a strong genetic effect of rs6675281 on putamen and pallidum in females but not in males and an opposite influence of rs3738401 on the white cortical surface in females compared to males. The strongest single association was found for rs821616 and the amygdala volume in male subjects (p < 0.001). No effect was detected for the nucleus accumbens. We report-to our knowledge-for the first time a significant and sex-specific influence of common DISC1 variants on volumes of the basal ganglia, the amygdala and on the cortical surface area. Our results demonstrate that the additive model of all three polymorphisms outperforms their single analysis.

Genetic variants in Alzheimer disease - molecular and brain network approaches

Genetic studies in late-onset Alzheimer disease (LOAD) are aimed at identifying core disease mechanisms and providing potential biomarkers and drug candidates to improve clinical care of AD. However, owing to the complexity of LOAD, including pathological heterogeneity and disease polygenicity, extraction of actionable guidance from LOAD genetics has been challenging. Past attempts to summarize the effects of LOAD-associated genetic variants have used pathway analysis and collections of small-scale experiments to hypothesize functional convergence across several variants. In this Review, we discuss how the study of molecular, cellular and brain networks provides additional information on the effects of LOAD-associated genetic variants. We then discuss emerging combinations of these omic data sets into multiscale models, which provide a more comprehensive representation of the effects of LOAD-associated genetic variants at multiple biophysical scales. Furthermore, we highlight the clinical potential of mechanistically coupling genetic variants and disease phenotypes with multiscale brain models.

Impact of PICALM and CLU on hippocampal degeneration

PICALM and CLU are two major risk genes of late-onset Alzheimer's disease (LOAD), and there is strong molecular evidence suggesting their interaction on amyloid-beta deposition, hence finding functional dependency between their risk genotypes may lead to better understanding of their roles in LOAD development and greater clinical utility. In this study, we mainly investigated interaction effects of risk loci PICALM rs3581179 and CLU rs11136000 on hippocampal degeneration in both young and elderly adults in order to understand their neural mechanism on aging process, which may help identify robust biomarkers for early diagnosis and intervention. Besides volume we also assessed hippocampal shape phenotypes derived from diffeomorphic metric mapping and nonlinear dimensionality reduction. In elderly individuals (75.6 ± 6.7 years) significant interaction effects existed on hippocampal volume (P < 0.001), whereas in young healthy adults (19.4 ± 1.1 years) such effects existed on a shape phenotype (P = 0.01) indicating significant variation at hippocampal head and tail that mirror most AD vulnerable regions. Voxel-wise analysis also pointed to the same regions but lacked statistical power. In both cohorts, PICALM protective genotype AA only exhibited protective effects on hippocampal degeneration and cognitive performance when combined with CLU protective T allele, but adverse effects with CLU risk CC. This study revealed novel PICALM and CLU interaction effects on hippocampal degeneration along aging, and validated effectiveness of diffeomorphometry in imaging genetics study. Hum Brain Mapp 37:2419-2430, 2016. © 2016 Wiley Periodicals, Inc.

Gene-set and multivariate genome-wide association analysis of oppositional defiant behavior subtypes in attention-deficit/hyperactivity disorder

Oppositional defiant disorder (ODD) is a frequent psychiatric disorder seen in children and adolescents with attention-deficit-hyperactivity disorder (ADHD). ODD is also a common antecedent to both affective disorders and aggressive behaviors. Although the heritability of ODD has been estimated to be around 0.60, there has been little research into the molecular genetics of ODD. The present study examined the association of irritable and defiant/vindictive dimensions and categorical subtypes of ODD (based on latent class analyses) with previously described specific polymorphisms (DRD4 exon3 VNTR, 5-HTTLPR, and seven OXTR SNPs) as well as with dopamine, serotonin, and oxytocin genes and pathways in a clinical sample of children and adolescents with ADHD. In addition, we performed a multivariate genome-wide association study (GWAS) of the aforementioned ODD dimensions and subtypes. Apart from adjusting the analyses for age and sex, we controlled for "parental ability to cope with disruptive behavior." None of the hypothesis-driven analyses revealed a significant association with ODD dimensions and subtypes. Inadequate parenting behavior was significantly associated with all ODD dimensions and subtypes, most strongly with defiant/vindictive behaviors. In addition, the GWAS did not result in genome-wide significant findings but bioinformatics and literature analyses revealed that the proteins encoded by 28 of the 53 top-ranked genes functionally interact in a molecular landscape centered around Beta-catenin signaling and involved in the regulation of neurite outgrowth. Our findings provide new insights into the molecular basis of ODD and inform future genetic studies of oppositional behavior. © 2015 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.

Heritability of cortical thickness changes over time in twin pairs discordant for schizophrenia

BACKGROUND

Cortical thickness and surface area changes have repeatedly been found in schizophrenia. Whether progressive loss in cortical thickness and surface area are mediated by genetic or disease related factors is unknown. Here we investigate to what extent genetic and/or environmental factors contribute to the association between change in cortical thickness and surface area and liability to develop schizophrenia.

METHOD

Longitudinal magnetic resonance imaging study over a 5-year interval. Monozygotic (MZ) and dizygotic (DZ) twin pairs discordant for schizophrenia were compared with healthy control twin pairs using repeated measures analysis of variance (RM-ANOVA) and structural equation modeling (SEM). Twins discordant for schizophrenia and healthy control twins were recruited from the twin cohort at the University Medical Centre Utrecht, The Netherlands. A total of 90 individuals from 46 same sex twin pairs were included: 9 MZ and 10 DZ discordant for schizophrenia and 14 MZ and 13 (11 complete and 2 incomplete) DZ healthy twin-pairs. Age varied between 19 and 57years.

RESULTS

Higher genetic liability for schizophrenia was associated with progressive global thinning of the cortex, particularly of the left superior temporal cortex. Higher environmental liability for schizophrenia was associated with global attenuated thinning of the cortex, and including of the left superior temporal cortex. Cortical surface area change was heritable, but not significantly associated with higher genetic or environmental liability for schizophrenia.

CONCLUSIONS

Excessive cortical thinning, particularly of the left superior temporal cortex, may represent a genetic risk marker for schizophrenia.

Genetic and Environmental Factors Associated with Cannabis Involvement

Approximately 50-70% of the variation in cannabis use and use disorders can be attributed to heritable factors. For cannabis use, the remaining variance can be parsed in to familial and person-specific environmental factors while for use disorders, only the latter contribute. While numerous candidate gene studies have identified the role of common variation influencing liability to cannabis involvement, replication has been elusive. To date, no genomewide association study has been sufficiently powered to identify significant loci. Despite this, studies adopting polygenic techniques and integrating genetic variation with neural phenotypes and measures of environmental risk, such as childhood adversity, are providing promising new leads. It is likely that the small effect sizes associated with variants related to cannabis involvement will only be robustly identified in substantially larger samples. Results of such large-scale efforts will provide valuable single variant targets for translational research in neurogenetic, pharmacogenetic and non-human animal models as well as polygenic risk indices that can be used to explore a host of other genetic hypotheses related to cannabis use and misuse.

Genome-wide association study of cognitive functions and educational attainment in UK Biobank (N=112 151)

People's differences in cognitive functions are partly heritable and are associated with important life outcomes. Previous genome-wide association (GWA) studies of cognitive functions have found evidence for polygenic effects yet, to date, there are few replicated genetic associations. Here we use data from the UK Biobank sample to investigate the genetic contributions to variation in tests of three cognitive functions and in educational attainment. GWA analyses were performed for verbal-numerical reasoning (N=36 035), memory (N=112 067), reaction time (N=111 483) and for the attainment of a college or a university degree (N=111 114). We report genome-wide significant single-nucleotide polymorphism (SNP)-based associations in 20 genomic regions, and significant gene-based findings in 46 regions. These include findings in the ATXN2, CYP2DG, APBA1 and CADM2 genes. We report replication of these hits in published GWA studies of cognitive function, educational attainment and childhood intelligence. There is also replication, in UK Biobank, of SNP hits reported previously in GWA studies of educational attainment and cognitive function. GCTA-GREML analyses, using common SNPs (minor allele frequency>0.01), indicated significant SNP-based heritabilities of 31% (s.e.m.=1.8%) for verbal-numerical reasoning, 5% (s.e.m.=0.6%) for memory, 11% (s.e.m.=0.6%) for reaction time and 21% (s.e.m.=0.6%) for educational attainment. Polygenic score analyses indicate that up to 5% of the variance in cognitive test scores can be predicted in an independent cohort. The genomic regions identified include several novel loci, some of which have been associated with intracranial volume, neurodegeneration, Alzheimer's disease and schizophrenia.

Genome research in pre-dementia stages of Alzheimer's disease

Genetic characterization of individuals at risk of Alzheimer's disease (AD), i.e. people having amyloid deposits in the brain without symptoms, people suffering from subjective cognitive decline (SCD) or mild cognitive impairment (MCI), has spurred the interests of researchers. However, their pre-dementia genetic profile remains mostly unexplored. In this study, we reviewed the loci related to phenotypes of AD, MCI and SCD from literature and performed the first meta-analyses evaluating the role of apolipoprotein E (APOE) in the risk of conversion from a healthy status to MCI and SCD. For AD dementia risk, an increased number of loci have been identified; to date, 28 genes have been associated with Late Onset AD. In MCI syndrome, APOE is confirmed as a pheno-conversion factor leading from MCI to AD, and clusterin is a promising candidate. Additionally, our meta-analyses revealed APOE as genetic risk factor to convert from a healthy status to MCI [OR = 1.849 (1.587-2.153); P = 2.80  × 10-15] and to a lesser extent from healthy status to SCD [OR = 1.151 (1.015-1.304); P = 0.028]. Thus, we believe that genetic studies in longitudinal SCD and MCI series may provide new therapeutic targets and improve the existing knowledge of AD. This type of studies must be completed on healthy subjects to better understand the natural disease resistance to brain insults and neurodegeneration.

Longitudinal Evidence for Smaller Hippocampus Volume as a Vulnerability Factor for Perceived Stress

Hippocampal volume has been found to be smaller in individuals with stress-related disorders, but it remains unclear whether smaller volume is a consequence of stress or rather a vulnerability factor. Here, we examined this issue by relating stress levels to hippocampal volumes in healthy participants examined every 5 years in a longitudinal population-based study. Based on scores of 25- to 60-year–old participants on the perceived stress questionnaire, we defined moderately to high (n = 35) and low (n = 76) stress groups. The groups were re-examined after 5 years (at the 6th study wave). Historical data on subjective stress were available up to 10 years prior to Wave 5. At the first MRI session, the moderately to high stress group had a significantly smaller hippocampal volume, as measured by FreeSurfer (version 5.3), compared with the low-stress group. At follow-up, group differences in stress levels and hippocampal volume remained unchanged. In retrospective analyses of subjective stress, the observed group difference in stress was found to be stable. The long-term stability of group differences in perceived stress and hippocampal volume suggests that a small hippocampal volume may be a vulnerability factor for stress-related disorders.

Developmentally Sensitive Interaction Effects of Genes and the Social Environment on Total and Subcortical Brain Volumes

Smaller total brain and subcortical volumes have been linked to psychopathology including attention-deficit/hyperactivity disorder (ADHD). Identifying mechanisms underlying these alterations, therefore, is of great importance. We investigated the role of gene-environment interactions (GxE) in interindividual variability of total gray matter (GM), caudate, and putamen volumes. Brain volumes were derived from structural magnetic resonance imaging scans in participants with (N = 312) and without ADHD (N = 437) from N = 402 families (age M = 17.00, SD = 3.60). GxE effects between DAT1, 5-HTT, and DRD4 and social environments (maternal expressed warmth and criticism; positive and deviant peer affiliation) as well as the possible moderating effect of age were examined using linear mixed modeling. We also tested whether findings depended on ADHD severity. Deviant peer affiliation was associated with lower caudate volume. Participants with low deviant peer affiliations had larger total GM volumes with increasing age. Likewise, developmentally sensitive GxE effects were found on total GM and putamen volume. For total GM, differential age effects were found for DAT1 9-repeat and HTTLPR L/L genotypes, depending on the amount of positive peer affiliation. For putamen volume, DRD4 7-repeat carriers and DAT1 10/10 homozygotes showed opposite age relations depending on positive peer affiliation and maternal criticism, respectively. All results were independent of ADHD severity. The presence of differential age-dependent GxE effects might explain the diverse and sometimes opposing results of environmental and genetic effects on brain volumes observed so far.

Gene networks and developmental context: the importance of understanding complex gene expression patterns in evolution

Animal development is the product of distinct components and interactions-genes, regulatory networks, and cells-and it exhibits emergent properties that cannot be inferred from the components in isolation. Often the focus is on the genotype-to-phenotype map, overlooking the process of development that turns one into the other. We propose a move toward micro-evolutionary analysis of development, incorporating new tools that enable cell type resolution and single-cell microscopy. Using the sex determination pathway in Drosophila to illustrate potential avenues of research, we highlight some of the questions that these emerging technologies can address. For example, they provide an unprecedented opportunity to study heterogeneity within cell populations, and the potential to add the dimension of time to gene regulatory network analysis. Challenges still remain in developing methods to analyze this data and to increase the throughput. However this line of research has the potential to bridge the gaps between previously more disparate fields, such as population genetics and development, opening up new avenues of research.

Corticolimbic anatomical characteristics predetermine risk for chronic pain

SEE TRACEY DOI101093/BRAIN/AWW147 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Mechanisms of chronic pain remain poorly understood. We tracked brain properties in subacute back pain patients longitudinally for 3 years as they either recovered from or transitioned to chronic pain. Whole-brain comparisons indicated corticolimbic, but not pain-related circuitry, white matter connections predisposed patients to chronic pain. Intra-corticolimbic white matter connectivity analysis identified three segregated communities: dorsal medial prefrontal cortex-amygdala-accumbens, ventral medial prefrontal cortex-amygdala, and orbitofrontal cortex-amygdala-hippocampus. Higher incidence of white matter and functional connections within the dorsal medial prefrontal cortex-amygdala-accumbens circuit, as well as smaller amygdala volume, represented independent risk factors, together accounting for 60% of the variance for pain persistence. Opioid gene polymorphisms and negative mood contributed indirectly through corticolimbic anatomical factors, to risk for chronic pain. Our results imply that persistence of chronic pain is predetermined by corticolimbic neuroanatomical factors.

Structural Brain Abnormalities in Youth With Psychosis Spectrum Symptoms

IMPORTANCE

Structural brain abnormalities are prominent in psychotic disorders, including schizophrenia. However, it is unclear when aberrations emerge in the disease process and if such deficits are present in association with less severe psychosis spectrum (PS) symptoms in youth.

OBJECTIVE

To investigate the presence of structural brain abnormalities in youth with PS symptoms.

DESIGN, SETTING, AND PARTICIPANTS

The Philadelphia Neurodevelopmental Cohort is a prospectively accrued, community-based sample of 9498 youth who received a structured psychiatric evaluation. A subsample of 1601 individuals underwent neuroimaging, including structural magnetic resonance imaging, at an academic and children's hospital health care network between November 1, 2009, and November 30, 2011.

MAIN OUTCOMES AND MEASURES

Measures of brain volume derived from T1-weighted structural neuroimaging at 3 T. Analyses were conducted at global, regional, and voxelwise levels. Regional volumes were estimated with an advanced multiatlas regional segmentation procedure, and voxelwise volumetric analyses were conducted as well. Nonlinear developmental patterns were examined using penalized splines within a general additive model. Psychosis spectrum (PS) symptom severity was summarized using factor analysis and evaluated dimensionally.

RESULTS

Following exclusions due to comorbidity and image quality assurance, the final sample included 791 participants aged youth 8 to 22 years. Fifty percent (n = 393) were female. After structured interviews, 391 participants were identified as having PS features (PS group) and 400 participants were identified as typically developing comparison individuals without significant psychopathology (TD group). Compared with the TD group, the PS group had diminished whole-brain gray matter volume (P = 1.8 × 10-10) and expanded white matter volume (P = 2.8 × 10-11). Voxelwise analyses revealed significantly lower gray matter volume in the medial temporal lobe (maximum z score = 5.2 and cluster size of 1225 for the right and maximum z score = 4.5 and cluster size of 310 for the left) as well as in frontal, temporal, and parietal cortex. Volumetric reduction in the medial temporal lobe was correlated with PS symptom severity.

CONCLUSIONS AND RELEVANCE

Structural brain abnormalities that have been commonly reported in adults with psychosis are present early in life in youth with PS symptoms and are not due to medication effects. Future longitudinal studies could use the presence of such abnormalities in conjunction with clinical presentation, cognitive profile, and genomics to predict risk and aid in stratification to guide early interventions.

The CHRM3 gene is implicated in abnormal thalamo-orbital frontal cortex functional connectivity in first-episode treatment-naive patients with schizophrenia

BACKGROUND

The genetic influences in human brain structure and function and impaired functional connectivities are the hallmarks of the schizophrenic brain. To explore how common genetic variants affect the connectivities in schizophrenia, we applied genome-wide association studies assaying the abnormal neural connectivities in schizophrenia as quantitative traits.

METHOD

We recruited 161 first-onset and treatment-naive patients with schizophrenia and 150 healthy controls. All the participants underwent scanning with a 3 T-magnetic resonance imaging scanner to acquire structural and functional imaging data and genotyping using the HumanOmniZhongHua-8 BeadChip. The brain-wide association study approach was employed to account for the inherent modular nature of brain connectivities.

RESULTS

We found differences in four abnormal functional connectivities [left rectus to left thalamus (REC.L-THA.L), left rectus to right thalamus (REC.L-THA.R), left superior orbital cortex to left thalamus (ORBsup.L-THA.L) and left superior orbital cortex to right thalamus (ORBsup.L-THA.R)] between the two groups. Univariate single nucleotide polymorphism (SNP)-based association revealed that the SNP rs6800381, located nearest to the CHRM3 (cholinergic receptor, muscarinic 3) gene, reached genomic significance (p = 1.768 × 10-8) using REC.L-THA.R as the phenotype. Multivariate gene-based association revealed that the FAM12A (family with sequence similarity 12, member A) gene nearly reached genomic significance (nominal p = 2.22 × 10-6, corrected p = 0.05).

CONCLUSIONS

Overall, we identified the first evidence that the CHRM3 gene plays a role in abnormal thalamo-orbital frontal cortex functional connectivity in first-episode treatment-naive patients with schizophrenia. Identification of these genetic variants using neuroimaging genetics provides insights into the causes of variability in human brain development, and may help us determine the mechanisms of dysfunction in schizophrenia.

Magnetic resonance imaging in Alzheimer's Disease Neuroimaging Initiative 2

INTRODUCTION

Alzheimer's Disease Neuroimaging Initiative (ADNI) is now in its 10th year. The primary objective of the magnetic resonance imaging (MRI) core of ADNI has been to improve methods for clinical trials in Alzheimer's disease (AD) and related disorders.

METHODS

We review the contributions of the MRI core from present and past cycles of ADNI (ADNI-1, -Grand Opportunity and -2). We also review plans for the future-ADNI-3.

RESULTS

Contributions of the MRI core include creating standardized acquisition protocols and quality control methods; examining the effect of technical features of image acquisition and analysis on outcome metrics; deriving sample size estimates for future trials based on those outcomes; and piloting the potential utility of MR perfusion, diffusion, and functional connectivity measures in multicenter clinical trials.

DISCUSSION

Over the past decade the MRI core of ADNI has fulfilled its mandate of improving methods for clinical trials in AD and will continue to do so in the future.

An Association Study Between Genetic Polymorphisms in Functional Regions of Five Genes and the Risk of Schizophrenia

Schizophrenia is a severe mental disorder that is likely to be strongly determined by genetic factors. To identify markers of disks, large homolog 2 (DLG2), FAT atypical cadherin 3 (FAT3), kinectin1 (KTN1), deleted in colorectal carcinoma (DCC), and glycogen synthase kinase-3β (GSK3β) that contribute to the genetic susceptibility to schizophrenia, we systematically screened for polymorphisms in the functional regions of these genes. A total of 22 functional single-nucleotide polymorphisms (SNPs) in 940 Chinese subjects were genotyped using SNaPshot. The results first suggested that the allelic and genotypic frequencies of the DCC polymorphism rs2229080 were nominally associated with schizophrenia. The patients were significantly less likely to be CC homozygous (P = 0.005, odds ratio [OR] = 0.635, 95 % confidence interval [95 % CI] = 0.462-0.873), and the schizophrenia subjects exhibited lower frequency of the C allele (P = 0.024, OR = 0.811, 95 % CI = 0.676-0.972). Regarding GSK3β, there was a significant difference in genotype distribution of rs3755557 between schizophrenia and healthy control subjects (P = 0.009). The patients exhibited a significantly lower frequency of the T allele of rs3755557 (P = 0.002, OR = 0.654, 95 % CI = 0.498-0.860). Our results point to the polymorphisms of DCC and GSK3β as contributors to the genetic basis of individual differences in the susceptibility to schizophrenia.

Gene Prioritization for Imaging Genetics Studies Using Gene Ontology and a Stratified False Discovery Rate Approach

Imaging genetics is an emerging field in which the association between genes and neuroimaging-based quantitative phenotypes are used to explore the functional role of genes in neuroanatomy and neurophysiology in the context of healthy function and neuropsychiatric disorders. The main obstacle for researchers in the field is the high dimensionality of the data in both the imaging phenotypes and the genetic variants commonly typed. In this article, we develop a novel method that utilizes Gene Ontology, an online database, to select and prioritize certain genes, employing a stratified false discovery rate (sFDR) approach to investigate their associations with imaging phenotypes. sFDR has the potential to increase power in genome wide association studies (GWAS), and is quickly gaining traction as a method for multiple testing correction. Our novel approach addresses both the pressing need in genetic research to move beyond candidate gene studies, while not being overburdened with a loss of power due to multiple testing. As an example of our methodology, we perform a GWAS of hippocampal volume using both the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA2) and the Alzheimer's Disease Neuroimaging Initiative datasets. The analysis of ENIGMA2 data yielded a set of SNPs with sFDR values between 10 and 20%. Our approach demonstrates a potential method to prioritize genes based on biological systems impaired in a disease.

Toward an integrative science of the developing human mind and brain: Focus on the developing cortex

Based on the Huttenlocher lecture, this article describes the need for a more integrative scientific paradigm for addressing important questions raised by key observations made over 2 decades ago. Among these are the early descriptions by Huttenlocher of variability in synaptic density in cortex of postmortem brains of children of different ages and the almost simultaneous reports of cortical volume reductions on MR imaging in children and adolescents. In spite of much progress in developmental neurobiology, developmental cognitive neuroscience, and behavioral and imaging genetics, we still do not know how these early observations relate to each other. It is argued that large scale, collaborative research programs are needed to establish the associations between behavioral differences among children and imaging biomarkers, and to link the latter to cellular changes in the developing brain. Examples of progress and challenges remaining are illustrated with data from the Pediatric Imaging, Neurocognition, and Genetics Project (PING).

Sex-specific mediation effect of the right fusiform face area volume on the association between variants in repeat length of AVPR1A RS3 and altruistic behavior in healthy adults

Microsatellite variants in the arginine vasopressin receptor 1A gene (AVPR1A) RS3 have been associated with normal social behaviors variation and autism spectrum disorders (ASDs) in a sex-specific manner. However, neural mechanisms underlying these associations remain largely unknown. We hypothesized that AVPR1A RS3 variants affect altruistic behavior by modulating the gray matter volume (GMV) of specific brain regions in a sex-specific manner. We investigated 278 young healthy adults using the Dictator Game to assess altruistic behavior. All subjects were genotyped and main effect of AVPR1A RS3 repeat polymorphisms and interaction of genotype-by-sex on the GMV were assessed in a voxel-wise manner. We observed that male subjects with relatively short repeats allocated less money to others and exhibited a significantly smaller GMV in the right fusiform face area (FFA) compared with male long homozygotes. In male subjects, the GMV of the right FFA exhibited a significant positive correlation with altruistic behavior. A mixed mediation and moderation analysis further revealed both a significant mediation effect of the GMV of the right FFA on the association between AVPR1A RS3 repeat polymorphisms and allocation sums and a significant moderation effect of sex (only in males) on the mediation effect. Post hoc analysis showed that the GMV of the right FFA was significantly smaller in male subjects carrying allele 426 than in non-426 carriers. These results suggest that the GMV of the right FFA may be a potential mediator whereby the genetic variants in AVPR1A RS3 affect altruistic behavior in healthy male subjects. Hum Brain Mapp 37:2700-2709, 2016. © 2016 Wiley Periodicals, Inc.

"RADIOTRANSCRIPTOMICS": A synergy of imaging and transcriptomics in clinical assessment

Recent advances in quantitative imaging and "omics" technology have generated a wealth of mineable biological "big data". With the push towards a P4 "predictive, preventive, personalized, and participatory" approach to medicine, researchers began integrating complementary tools to further tune existing diagnostic and therapeutic models. The field of radiogenomics has long pioneered such multidisciplinary investigations in neuroscience and oncology, correlating genotypic and phenotypic signatures to study structural and functional changes in relation to altered molecular behavior. Given the innate dynamic nature of complex disorders and the role of environmental and epigenetic factors in pathogenesis, the transcriptome can further elucidate serial modifications undetected at the genome level. We therefore propose "radiotranscriptomics" as a new member of the P4 medicine initiative, combining transcriptome information, including gene expression and isoform variation, and quantitative image annotations.

Genetic influences on schizophrenia and subcortical brain volumes: large-scale proof of concept

Schizophrenia is a devastating psychiatric illness with high heritability. Brain structure and function differ, on average, between people with schizophrenia and healthy individuals. As common genetic associations are emerging for both schizophrenia and brain imaging phenotypes, we can now use genome-wide data to investigate genetic overlap. Here we integrated results from common variant studies of schizophrenia (33,636 cases, 43,008 controls) and volumes of several (mainly subcortical) brain structures (11,840 subjects). We did not find evidence of genetic overlap between schizophrenia risk and subcortical volume measures either at the level of common variant genetic architecture or for single genetic markers. These results provide a proof of concept (albeit based on a limited set of structural brain measures) and define a roadmap for future studies investigating the genetic covariance between structural or functional brain phenotypes and risk for psychiatric disorders.

Heritability and reliability of automatically segmented human hippocampal formation subregions

The human hippocampal formation can be divided into a set of cytoarchitecturally and functionally distinct subregions, involved in different aspects of memory formation. Neuroanatomical disruptions within these subregions are associated with several debilitating brain disorders including Alzheimer's disease, major depression, schizophrenia, and bipolar disorder. Multi-center brain imaging consortia, such as the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) consortium, are interested in studying disease effects on these subregions, and in the genetic factors that affect them. For large-scale studies, automated extraction and subsequent genomic association studies of these hippocampal subregion measures may provide additional insight. Here, we evaluated the test-retest reliability and transplatform reliability (1.5T versus 3T) of the subregion segmentation module in the FreeSurfer software package using three independent cohorts of healthy adults, one young (Queensland Twins Imaging Study, N=39), another elderly (Alzheimer's Disease Neuroimaging Initiative, ADNI-2, N=163) and another mixed cohort of healthy and depressed participants (Max Planck Institute, MPIP, N=598). We also investigated agreement between the most recent version of this algorithm (v6.0) and an older version (v5.3), again using the ADNI-2 and MPIP cohorts in addition to a sample from the Netherlands Study for Depression and Anxiety (NESDA) (N=221). Finally, we estimated the heritability (h(2)) of the segmented subregion volumes using the full sample of young, healthy QTIM twins (N=728). Test-retest reliability was high for all twelve subregions in the 3T ADNI-2 sample (intraclass correlation coefficient (ICC)=0.70-0.97) and moderate-to-high in the 4T QTIM sample (ICC=0.5-0.89). Transplatform reliability was strong for eleven of the twelve subregions (ICC=0.66-0.96); however, the hippocampal fissure was not consistently reconstructed across 1.5T and 3T field strengths (ICC=0.47-0.57). Between-version agreement was moderate for the hippocampal tail, subiculum and presubiculum (ICC=0.78-0.84; Dice Similarity Coefficient (DSC)=0.55-0.70), and poor for all other subregions (ICC=0.34-0.81; DSC=0.28-0.51). All hippocampal subregion volumes were highly heritable (h(2)=0.67-0.91). Our findings indicate that eleven of the twelve human hippocampal subregions segmented using FreeSurfer version 6.0 may serve as reliable and informative quantitative phenotypes for future multi-site imaging genetics initiatives such as those of the ENIGMA consortium.

Genetics of Bipolar Disorder: Recent Update and Future Directions

Although genetic studies of Bipolar Disorder have been pursued for decades, it has only been in the last several years that clearly replicated findings have emerged. These findings, typically of modest effects, point to a polygenic genetic architecture consisting of multiple common and rare susceptibility variants. While larger genome-wide association studies are ongoing, the advent of whole exome and genome sequencing should lead to the identification of rare, and potentially more penetrant, variants. Progress along both fronts will provide novel insights into the biology of Bipolar Disorder and help usher in a new era of personalized medicine and improved treatments.

Annual Research Review: Discovery science strategies in studies of the pathophysiology of child and adolescent psychiatric disorders--promises and limitations

BACKGROUND

Psychiatric science remains descriptive, with a categorical nosology intended to enhance interobserver reliability. Increased awareness of the mismatch between categorical classifications and the complexity of biological systems drives the search for novel frameworks including discovery science in Big Data. In this review, we provide an overview of incipient approaches, primarily focused on classically categorical diagnoses such as schizophrenia (SZ), autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD), but also reference convincing, if focal, advances in cancer biology, to describe the challenges of Big Data and discovery science, and outline approaches being formulated to overcome existing obstacles.

FINDINGS

A paradigm shift from categorical diagnoses to a domain/structure-based nosology and from linear causal chains to complex causal network models of brain-behavior relationship is ongoing. This (r)evolution involves appreciating the complexity, dimensionality, and heterogeneity of neuropsychiatric data collected from multiple sources ('broad' data) along with data obtained at multiple levels of analysis, ranging from genes to molecules, cells, circuits, and behaviors ('deep' data). Both of these types of Big Data landscapes require the use and development of robust and powerful informatics and statistical approaches. Thus, we describe Big Data analysis pipelines and the promise and potential limitations in using Big Data approaches to study psychiatric disorders.

CONCLUSIONS

We highlight key resources available for psychopathological studies and call for the application and development of Big Data approaches to dissect the causes and mechanisms of neuropsychiatric disorders and identify corresponding biomarkers for early diagnosis.

Modulative effects of COMT haplotype on age-related associations with brain morphology

Catechol-O-methyltransferase (COMT), located on chromosome 22q11.2, encodes an enzyme critical for dopamine flux in the prefrontal cortex. Genetic variants of COMT have been suggested to functionally manipulate prefrontal morphology and function in healthy adults. This study aims to investigate modulative roles of individuals COMT SNPs (rs737865, val158met, rs165599) and its haplotypes in age-related brain morphology using an Asian sample with 174 adults aged from 21 to 80 years. We showed an age-related decline in cortical thickness of the dorsal visual pathway, including the left dorsolateral prefrontal cortex, bilateral angular gyrus, right superior frontal cortex, and age-related shape compression in the basal ganglia as a function of the genotypes of the individual COMT SNPs, especially COMT val158met. Using haplotype trend regression analysis, COMT haplotype probabilities were estimated and further revealed an age-related decline in cortical thickness in the default mode network (DMN), including the posterior cingulate, precuneus, supramarginal and paracentral cortex, and the ventral visual system, including the occipital cortex and left inferior temporal cortex, as a function of the COMT haplotype. Our results provided new evidence on an antagonistic pleiotropic effect in COMT, suggesting that genetically programmed neural benefits in early life may have a potential bearing towards neural susceptibility in later life. Hum Brain Mapp 37:2068-2082, 2016. © 2016 Wiley Periodicals, Inc.

Early developmental gene enhancers affect subcortical volumes in the adult human brain

Genome-wide association screens aim to identify common genetic variants contributing to the phenotypic variability of complex traits, such as human height or brain morphology. The identified genetic variants are mostly within noncoding genomic regions and the biology of the genotype-phenotype association typically remains unclear. In this article, we propose a complementary targeted strategy to reveal the genetic underpinnings of variability in subcortical brain volumes, by specifically selecting genomic loci that are experimentally validated forebrain enhancers, active in early embryonic development. We hypothesized that genetic variation within these enhancers may affect the development and ultimately the structure of subcortical brain regions in adults. We tested whether variants in forebrain enhancer regions showed an overall enrichment of association with volumetric variation in subcortical structures of >13,000 healthy adults. We observed significant enrichment of genomic loci that affect the volume of the hippocampus within forebrain enhancers (empirical P = 0.0015), a finding which robustly passed the adjusted threshold for testing of multiple brain phenotypes (cutoff of P < 0.0083 at an alpha of 0.05). In analyses of individual single nucleotide polymorphisms (SNPs), we identified an association upstream of the ID2 gene with rs7588305 and variation in hippocampal volume. This SNP-based association survived multiple-testing correction for the number of SNPs analyzed but not for the number of subcortical structures. Targeting known regulatory regions offers a way to understand the underlying biology that connects genotypes to phenotypes, particularly in the context of neuroimaging genetics. This biology-driven approach generates testable hypotheses regarding the functional biology of identified associations. Hum Brain Mapp 37:1788-1800, 2016. © 2016 Wiley Periodicals, Inc.

Estimating Effect Sizes and Expected Replication Probabilities from GWAS Summary Statistics

Genome-wide Association Studies (GWAS) result in millions of summary statistics (“z-scores”) for single nucleotide polymorphism (SNP) associations with phenotypes. These rich datasets afford deep insights into the nature and extent of genetic contributions to complex phenotypes such as psychiatric disorders, which are understood to have substantial genetic components that arise from very large numbers of SNPs. The complexity of the datasets, however, poses a significant challenge to maximizing their utility. This is reflected in a need for better understanding the landscape of z-scores, as such knowledge would enhance causal SNP and gene discovery, help elucidate mechanistic pathways, and inform future study design. Here we present a parsimonious methodology for modeling effect sizes and replication probabilities, relying only on summary statistics from GWAS substudies, and a scheme allowing for direct empirical validation. We show that modeling z-scores as a mixture of Gaussians is conceptually appropriate, in particular taking into account ubiquitous non-null effects that are likely in the datasets due to weak linkage disequilibrium with causal SNPs. The four-parameter model allows for estimating the degree of polygenicity of the phenotype and predicting the proportion of chip heritability explainable by genome-wide significant SNPs in future studies with larger sample sizes. We apply the model to recent GWAS of schizophrenia (N = 82,315) and putamen volume (N = 12,596), with approximately 9.3 million SNP z-scores in both cases. We show that, over a broad range of z-scores and sample sizes, the model accurately predicts expectation estimates of true effect sizes and replication probabilities in multistage GWAS designs. We assess the degree to which effect sizes are over-estimated when based on linear-regression association coefficients. We estimate the polygenicity of schizophrenia to be 0.037 and the putamen to be 0.001, while the respective sample sizes required to approach fully explaining the chip heritability are 10⁶ and 10⁵. The model can be extended to incorporate prior knowledge such as pleiotropy and SNP annotation. The current findings suggest that the model is applicable to a broad array of complex phenotypes and will enhance understanding of their genetic architectures.

Neurobiological origin of spurious brain morphological changes: A quantitative MRI study

The high gray‐white matter contrast and spatial resolution provided by T1‐weighted magnetic resonance imaging (MRI) has made it a widely used imaging protocol for computational anatomy studies of the brain. While the image intensity in T1‐weighted images is predominantly driven by T1, other MRI parameters affect the image contrast, and hence brain morphological measures derived from the data. Because MRI parameters are correlates of different histological properties of brain tissue, this mixed contribution hampers the neurobiological interpretation of morphometry findings, an issue which remains largely ignored in the community. We acquired quantitative maps of the MRI parameters that determine signal intensities in T1‐weighted images (R₁ (=1/T1), R₂*, and PD) in a large cohort of healthy subjects (n = 120, aged 18–87 years). Synthetic T1‐weighted images were calculated from these quantitative maps and used to extract morphometry features—gray matter volume and cortical thickness. We observed significant variations in morphometry measures obtained from synthetic images derived from different subsets of MRI parameters. We also detected a modulation of these variations by age. Our findings highlight the impact of microstructural properties of brain tissue—myelination, iron, and water content—on automated measures of brain morphology and show that microstructural tissue changes might lead to the detection of spurious morphological changes in computational anatomy studies. They motivate a review of previous morphological results obtained from standard anatomical MRI images and highlight the value of quantitative MRI data for the inference of microscopic tissue changes in the healthy and diseased brain. Hum Brain Mapp 37:1801–1815, 2016. © 2016 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.

MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection

Atlas-based automated anatomical labeling is a fundamental tool in medical image segmentation, as it defines regions of interest for subsequent analysis of structural and functional image data. The extensive investigation of multi-atlas warping and fusion techniques over the past 5 or more years has clearly demonstrated the advantages of consensus-based segmentation. However, the common approach is to use multiple atlases with a single registration method and parameter set, which is not necessarily optimal for every individual scan, anatomical region, and problem/data-type. Different registration criteria and parameter sets yield different solutions, each providing complementary information. Herein, we present a consensus labeling framework that generates a broad ensemble of labeled atlases in target image space via the use of several warping algorithms, regularization parameters, and atlases. The label fusion integrates two complementary sources of information: a local similarity ranking to select locally optimal atlases and a boundary modulation term to refine the segmentation consistently with the target image's intensity profile. The ensemble approach consistently outperforms segmentations using individual warping methods alone, achieving high accuracy on several benchmark datasets. The MUSE methodology has been used for processing thousands of scans from various datasets, producing robust and consistent results. MUSE is publicly available both as a downloadable software package, and as an application that can be run on the CBICA Image Processing Portal (https://ipp.cbica.upenn.edu), a web based platform for remote processing of medical images.

Genetics of structural connectivity and information processing in the brain

Understanding the genetic factors underlying brain structural connectivity is a major challenge in imaging genetics. Here, we present results from genome-wide association studies (GWASs) of whole-brain white matter (WM) fractional anisotropy (FA), an index of microstructural coherence measured using diffusion tensor imaging. Data from independent GWASs of 355 Swedish and 250 Norwegian healthy adults were integrated by meta-analysis to enhance power. Complementary GWASs on behavioral data reflecting processing speed, which is related to microstructural properties of WM pathways, were performed and integrated with WM FA results via multimodal analysis to identify shared genetic associations. One locus on chromosome 17 (rs145994492) showed genome-wide significant association with WM FA (meta P value = 1.87 × 10^-08). Suggestive associations (Meta P value <1 × 10^-06) were observed for 12 loci, including one containing ZFPM2 (lowest meta P value = 7.44 × 10^-08). This locus was also implicated in multimodal analysis of WM FA and processing speed (lowest Fisher P value = 8.56 × 10^-07). ZFPM2 is relevant in specification of corticothalamic neurons during brain development. Analysis of SNPs associated with processing speed revealed association with a locus that included SSPO (lowest meta P value = 4.37 × 10^-08), which has been linked to commissural axon growth. An intergenic SNP (rs183854424) 14 kb downstream of CSMD1, which is implicated in schizophrenia, showed suggestive evidence of association in the WM FA meta-analysis (meta P value = 1.43 × 10^-07) and the multimodal analysis (Fisher P value = 1 × 10^-07). These findings provide novel data on the genetics of WM pathways and processing speed, and highlight a role of ZFPM2 and CSMD1 in information processing in the brain.

Leveraging Genomic Annotations and Pleiotropic Enrichment for Improved Replication Rates in Schizophrenia GWAS

Most of the genetic architecture of schizophrenia (SCZ) has not yet been identified. Here, we apply a novel statistical algorithm called Covariate-Modulated Mixture Modeling (CM3), which incorporates auxiliary information (heterozygosity, total linkage disequilibrium, genomic annotations, pleiotropy) for each single nucleotide polymorphism (SNP) to enable more accurate estimation of replication probabilities, conditional on the observed test statistic (“z-score”) of the SNP. We use a multiple logistic regression on z-scores to combine information from auxiliary information to derive a “relative enrichment score” for each SNP. For each stratum of these relative enrichment scores, we obtain nonparametric estimates of posterior expected test statistics and replication probabilities as a function of discovery z-scores, using a resampling-based approach that repeatedly and randomly partitions meta-analysis sub-studies into training and replication samples. We fit a scale mixture of two Gaussians model to each stratum, obtaining parameter estimates that minimize the sum of squared differences of the scale-mixture model with the stratified nonparametric estimates. We apply this approach to the recent genome-wide association study (GWAS) of SCZ (n = 82,315), obtaining a good fit between the model-based and observed effect sizes and replication probabilities. We observed that SNPs with low enrichment scores replicate with a lower probability than SNPs with high enrichment scores even when both they are genome-wide significant (p < 5x10^-8). There were 693 and 219 independent loci with model-based replication rates ≥80% and ≥90%, respectively. Compared to analyses not incorporating relative enrichment scores, CM3 increased out-of-sample yield for SNPs that replicate at a given rate. This demonstrates that replication probabilities can be more accurately estimated using prior enrichment information with CM3.

Genome-wide association studies (GWAS) have thus far identified only a small fraction of the heritability of common complex disorders, such as schizophrenia. Here, we demonstrate that by using auxiliary information we can improve estimates of replication probabilities from GWAS summary statistics. The proposed Covariate-Modulated Mixture Model (CM3) incorporates auxiliary information to construct an “enrichment score” for each single nucleotide polymorphism (SNP). We show that a scale mixture of two Gaussians provides a good fit to the observed effect size distribution stratified by the predicted enrichment score when applied the method to a recent genome-wide association study (GWAS) of SCZ (n = 82,315). Compared to estimates performed not using auxiliary information, the CM3 more accurately models the observed replication rates by stratifying on covariate-modulated enrichment scores. We observed that SNPs with low enrichment scores replicate with a lower probability compared to SNPs with high enrichment scores, even when both are genome-wide significant (p < 5x10^-8). At model-based replication rates ≥80% and ≥90% there were 693 and 219 independent loci, respectively. Increased out-of-sample yield for SNPs ranked according to CM3 demonstrate the utility of incorporating auxiliary information via CM3.

Genetic Moderation of Stress Effects on Corticolimbic Circuitry

Stress exposure is associated with individual differences in corticolimbic structure and function that often mirror patterns observed in psychopathology. Gene x environment interaction research suggests that genetic variation moderates the impact of stress on risk for psychopathology. On the basis of these findings, imaging genetics, which attempts to link variability in DNA sequence and structure to neural phenotypes, has begun to incorporate measures of the environment. This research paradigm, known as imaging gene x environment interaction (iGxE), is beginning to contribute to our understanding of the neural mechanisms through which genetic variation and stress increase psychopathology risk. Although awaiting replication, evidence suggests that genetic variation within the canonical neuroendocrine stress hormone system, the hypothalamic-pituitary-adrenal axis, contributes to variability in stress-related corticolimbic structure and function, which, in turn, confers risk for psychopathology. For iGxE research to reach its full potential it will have to address many challenges, of which we discuss: (i) small effects, (ii) measuring the environment and neural phenotypes, (iii) the absence of detailed mechanisms, and (iv) incorporating development. By actively addressing these challenges, iGxE research is poised to help identify the neural mechanisms underlying genetic and environmental associations with psychopathology.

Genetic influences on the neural and physiological bases of acute threat: A research domain criteria (RDoC) perspective

The NIMH Research Domain Criteria (RDoC) initiative aims to describe key dimensional constructs underlying mental function across multiple units of analysis-from genes to observable behaviors-in order to better understand psychopathology. The acute threat ("fear") construct of the RDoC Negative Valence System has been studied extensively from a translational perspective, and is highly pertinent to numerous psychiatric conditions, including anxiety and trauma-related disorders. We examined genetic contributions to the construct of acute threat at two units of analysis within the RDoC framework: (1) neural circuits and (2) physiology. Specifically, we focused on genetic influences on activation patterns of frontolimbic neural circuitry and on startle, skin conductance, and heart rate responses. Research on the heritability of activation in threat-related frontolimbic neural circuitry is lacking, but physiological indicators of acute threat have been found to be moderately heritable (35-50%). Genetic studies of the neural circuitry and physiology of acute threat have almost exclusively relied on the candidate gene method and, as in the broader psychiatric genetics literature, most findings have failed to replicate. The most robust support has been demonstrated for associations between variation in the serotonin transporter (SLC6A4) and catechol-O-methyltransferase (COMT) genes with threat-related neural activation and physiological responses. However, unbiased genome-wide approaches using very large samples are needed for gene discovery, and these can be accomplished with collaborative consortium-based research efforts, such as those of the Psychiatric Genomics Consortium (PGC) and Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium.

Differing brain structural correlates of familial and environmental risk for major depressive disorder revealed by a combined VBM/pattern recognition approach

BACKGROUND

Neuroimaging traits of either familial or environmental risk for major depressive disorder (MDD) have been interpreted as possibly useful vulnerability markers. However, the simultaneous occurrence of familial and environmental risk might prove to be a major obstacle in the attempt of recent studies to confine the precise impact of each of these conditions on brain structure. Moreover, the exclusive use of group-level analyses does not permit prediction of individual illness risk which would be the basic requirement for the clinical application of imaging vulnerability markers. Hence, we aimed to distinguish between brain structural characteristics of familial predisposition and environmental stress by using both group- and individual-level analyses.

METHOD

We investigated grey matter alterations between 20 healthy control subjects (HC) and 20 MDD patients; 16 healthy first-degree relatives of MDD patients (FH+) and 20 healthy subjects exposed to former childhood maltreatment (CM+) by using a combined VBM/pattern recognition approach.

RESULTS

We found similar grey matter reductions in the insula and the orbitofrontal cortex in patients and FH+ subjects and in the hippocampus in patients and CM+ subjects. No direct overlap in grey matter alterations was found between FH+ and CM+ subjects. Pattern classification successfully detected subjects at risk for the disease even by strictly focusing on morphological traits of MDD.

CONCLUSIONS

Familial and environmental risk factors for MDD are associated with differing morphometric anomalies. Pattern recognition might be a promising instrument in the search for and future application of vulnerability markers for MDD.

The Genetics of Stress-Related Disorders: PTSD, Depression, and Anxiety Disorders

Research into the causes of psychopathology has largely focused on two broad etiologic factors: genetic vulnerability and environmental stressors. An important role for familial/heritable factors in the etiology of a broad range of psychiatric disorders was established well before the modern era of genomic research. This review focuses on the genetic basis of three disorder categories-posttraumatic stress disorder (PTSD), major depressive disorder (MDD), and the anxiety disorders-for which environmental stressors and stress responses are understood to be central to pathogenesis. Each of these disorders aggregates in families and is moderately heritable. More recently, molecular genetic approaches, including genome-wide studies of genetic variation, have been applied to identify specific risk variants. In this review, I summarize evidence for genetic contributions to PTSD, MDD, and the anxiety disorders including genetic epidemiology, the role of common genetic variation, the role of rare and structural variation, and the role of gene-environment interaction. Available data suggest that stress-related disorders are highly complex and polygenic and, despite substantial progress in other areas of psychiatric genetics, few risk loci have been identified for these disorders. Progress in this area will likely require analysis of much larger sample sizes than have been reported to date. The phenotypic complexity and genetic overlap among these disorders present further challenges. The review concludes with a discussion of prospects for clinical translation of genetic findings and future directions for research.

Measuring Global Brain Atrophy with the Brain Volume/Cerebrospinal Fluid Index: Normative Values, Cut-Offs and Clinical Associations

BACKGROUND

Global brain atrophy is present in normal aging and different neurodegenerative disorders such as Alzheimer's disease (AD) and is becoming widely used to monitor disease progression.

SUMMARY

The brain volume/cerebrospinal fluid index (BV/CSF index) is validated in this study as a measurement of global brain atrophy. We tested the ability of the BV/CSF index to detect global brain atrophy, investigated the influence of confounders, provided normative values and cut-offs for mild, moderate and severe brain atrophy, and studied associations with different outcome variables. A total of 1,009 individuals were included [324 healthy controls, 408 patients with mild cognitive impairment (MCI) and 277 patients with AD]. Magnetic resonance images were segmented using FreeSurfer, and the BV/CSF index was calculated and studied both cross-sectionally and longitudinally (1-year follow-up). Both AD patients and MCI patients who progressed to AD showed greater global brain atrophy compared to stable MCI patients and controls. Atrophy was associated with older age, larger intracranial volume, less education and presence of the ApoE ε4 allele. Significant correlations were found with clinical variables, CSF biomarkers and several cognitive tests.

KEY MESSAGES

The BV/CSF index may be useful for staging individuals according to the degree of global brain atrophy, and for monitoring disease progression. It also shows potential for predicting clinical changes and for being used in the clinical routine.

Genetic sex and the volumes of the caudate-putamen, nucleus accumbens core and shell: original data and a review

Sex differences are widespread across vertebrate nervous systems. Such differences are sometimes reflected in the neural substrate via neuroanatomical differences in brain region volume. One brain region that displays sex differences in its associated functions and pathologies is the striatum, including the caudate-putamen (dorsal striatum), nucleus accumbens core and shell (ventral striatum). The extent to which these differences can be attributed to alterations in volume is unclear. We thus tested whether the volumes of the caudate-putamen, nucleus accumbens core, and nucleus accumbens shell differed by region, sex, and hemisphere in adult Sprague-Dawley rats. As a positive control for detecting sex differences in brain region volume, we measured the sexually dimorphic nucleus of the medial preoptic area (SDN-POA). As expected, SDN-POA volume was larger in males than in females. No sex differences were detected in the volumes of the caudate-putamen, nucleus accumbens core or shell. Nucleus accumbens core volume was larger in the right than left hemisphere across males and females. These findings complement previous reports of lateralized nucleus accumbens volume in humans, and suggest that this may possibly be driven via hemispheric differences in nucleus accumbens core volume. In contrast, striatal sex differences seem to be mediated by factors other than striatal region volume. This conclusion is presented within the context of a detailed review of studies addressing sex differences and similarities in striatal neuroanatomy.

The Rotterdam Scan Study: design update 2016 and main findings

Imaging plays an essential role in research on neurological diseases in the elderly. The Rotterdam Scan Study was initiated as part of the ongoing Rotterdam Study with the aim to elucidate the causes of neurological disease by performing imaging of the brain in a prospective population-based setting. Initially, in 1995 and 1999, random subsamples of participants from the Rotterdam Study underwent neuroimaging, whereas from 2005 onwards MRI has been implemented into the core protocol of the Rotterdam Study. In this paper, we discuss the background and rationale of the Rotterdam Scan Study. Moreover, we describe the imaging protocol, image post-processing techniques, and the main findings to date. Finally, we provide recommendations for future research, which will also be topics of investigation in the Rotterdam Scan Study.

Contribution of genes and unique environment to cross-sectional and longitudinal measures of subcortical volumes in bipolar disorder

The influence of genes and environment on the association between bipolar disorder (BD) and volumes of subcortical brain regions involved in emotion processing has rarely been studied. Furthermore, as far as we know, longitudinal twin studies of subcortical brain volume change in BD have not been carried out at all. In this study, we focused on the genetic and environmental contributions to cross-sectional and longitudinal measures of subcortical brain volumes in BD. A total of 99 twins from monozygotic and dizygotic pairs concordant or discordant for BD and 129 twins from monozygotic and dizygotic healthy control pairs underwent magnetic resonance imaging at baseline. Longitudinal assessment was carried out in 48 twins from monozygotic and dizygotic patient pairs and 52 twins from monozygotic and dizygotic control pairs. Subcortical volume measures were obtained with Freesurfer software and analyzed with structural equation modeling software OpenMx. At baseline, BD was phenotypically and genetically associated with smaller volumes of the thalamus, putamen and nucleus accumbens. BD was not associated with subcortical brain volume change over time in any of the examined regions. Heritability of subcortical volumes at baseline was high, whereas subcortical volume change had low heritability. Genes contributing to BD showed overlap with those associated with smaller volumes of the thalamus, putamen and nucleus accumbens at baseline. Further evaluation of genetic contributions to abnormalities in subcortical brain regions assumed to be involved in emotion processing is recommended.

Genetic imaging consortium for addiction medicine: From neuroimaging to genes

Since the sample size of a typical neuroimaging study lacks sufficient statistical power to explore unknown genomic associations with brain phenotypes, several international genetic imaging consortia have been organized in recent years to pool data across sites. The challenges and achievements of these consortia are considered here with the goal of leveraging these resources to study addiction. The authors of this review have joined together to form an Addiction working group within the framework of the ENIGMA project, a meta-analytic approach to multisite genetic imaging data. Collectively, the Addiction working group possesses neuroimaging and genomic data obtained from over 10,000 subjects. The deadline for contributing data to the first round of analyses occurred at the beginning of May 2015. The studies performed on this data should significantly impact our understanding of the genetic and neurobiological basis of addiction.