This issue on the genetics of brain imaging phenotypes is a celebration of the happy marriage between two of science's highly interesting fields: neuroscience and genetics. The articles collected here are ample evidence that a good deal of synergy exists in this marriage. A wide selection of papers is presented that provide many different perspectives on how genes cause variation in brain structure and function, which in turn influence behavioral phenotypes (including psychopathology). They are examples of the many different methodologies in contemporary genetics and neuroscience research. Genetic methodology includes genome-wide association (GWA), candidate-gene association, and twin studies. Sources of data on brain phenotypes include cortical gray matter (GM) structural/volumetric measures from magnetic resonance imaging (MRI); white matter (WM) measures from diffusion tensor imaging (DTI), such as fractional anisotropy; functional- (activity-) based measures from electroencephalography (EEG), and functional MRI (fMRI). Together, they reflect a combination of scientific fields that have seen great technological advances, whether it is the single-nucleotide polymorphism (SNP) array in genetics, the increasingly high-resolution MRI imaging, or high angular resolution diffusion imaging technique for measuring WM connective properties.

As the amygdala is part of the phylogenetic old brain, and its anatomical and functional properties are conserved across species, it is reasonable to assume genetic influence on its activity. A large corpus of candidate gene studies indicate that individual differences in amygdala activity may be caused by genetic variants within monoaminergic signaling pathways such as dopamine, serotonin, and norepinephrine. However, to our knowledge, the use of genome-wide data to discover genetic variants underlying individual differences in adult amygdala activity is novel. In the present study, the combination of genome-wide data and functional imaging phenotypes from an emotional faces task yielded a significant association between rs10014254 and the amygdala using a region of interest approach. This single nucleotide polymorphism is located in a regulatory region upstream of the Paired-like homeobox 2b (PHOX2B) gene; therefore it could affect the expression of this gene. PHOX2B regulates the expression of enzymes necessary for the synthesis of several monoamines and is essential for the development of the autonomic nervous system. However, an attempt to replicate the finding in an independent sample from North America did not succeed. The synthesis of functional magnetic resonance imaging (fMRI) and genome-wide data takes a hypothesis-free approach as to which genetic variants are of interest. Therefore, we believe that an undirected finding within such a plausible region is of interest, and that our results add further support to the hypothesis that monoaminergic signaling pathways play a central role in regulating amygdala activity.

The development of late-onset Alzheimer's disease (LOAD) is under strong genetic control and there is great interest in the genetic variants that confer increased risk. The Alzheimer's disease risk gene, growth factor receptor bound protein 2-associated protein (GAB2), has been shown to provide a 1.27–1.51 increased odds of developing LOAD for rs7101429 major allele carriers, in case-control analysis. GAB2 is expressed across the brain throughout life, and its role in LOAD pathology is well understood. Recent studies have begun to examine the effect of genetic variation in the GAB2 gene on differences in the brain. However, the effect of GAB2 on the young adult brain has yet to be considered. Here we found a significant association between the GAB2 gene and morphological brain differences in 755 young adult twins (469 females) (M = 23.1, SD = 3.1 years), using a gene-based test with principal components regression (PCReg). Detectable differences in brain morphology are therefore associated with variation in the GAB2 gene, even in young adults, long before the typical age of onset of Alzheimer's disease.

A single nucleotide polymorphism rs12807809 located upstream of the neurogranin (NRGN) gene has been identified as a risk variant for schizophrenia in recent genome-wide association studies. To date, there has been little investigation of the endophenotypic consequences of this variant, and our own investigations have suggested that the effects of this gene are not apparent at the level of cognitive function in patients or controls. Because the impact of risk variants may be more apparent at the level of brain, the aim of this investigation was to delineate whether NRGN genotype predicted variability in brain structure and/or function. Healthy individuals participated in structural (N = 140) and/or functional (N = 36) magnetic resonance imaging (s/fMRI). Voxel-based morphometry was used to compare gray and white matter volumes between carriers of the non-risk C allele (i.e., CC/CT) and those who were homozygous for the risk T allele. Functional imaging data were acquired during the performance of a spatial working memory task, and were also analyzed with respect to the difference between C carriers and T homozygotes. There was no effect of the NRGN variant rs12807809 on behavioral performance or brain structure. However, there was a main effect of genotype on brain activity during performance of the working memory task, such that while C carriers exhibited a load-independent decrease in left superior frontal gyrus/BA10, TT individuals failed to show a similar decrease in activity. The failure to disengage this ventromedial prefrontal region, despite preserved performance, may be indicative of a reduction in processing efficiency in healthy TT carriers. Although it remains to be established whether this holds true in larger samples and in patient cohorts, if valid, this suggests a potential mechanism by which NRGN variability might contribute to schizophrenia risk.

Understanding the genetic and environmental contributions to measures of brain structure such as surface area and cortical thickness is important for a better understanding of the nature of brain-behavior relationships and changes due to development or disease. Continuous spatial maps of genetic influences on these structural features can contribute to our understanding of regional patterns of heritability, since it remains to be seen whether genetic contributions to brain structure respect the boundaries of any traditional parcellation approaches. Using data from magnetic resonance imaging scans collected on a large sample of monozygotic and dizygotic twins in the Vietnam Era Twin Study of Aging, we created maps of the heritability of areal expansion (a vertex-based area measure) and cortical thickness and examined the degree to which these maps were affected by adjustment for total surface area and mean cortical thickness. We also compared the approach of estimating regional heritability based on the average heritability of vertices within the region to the more traditional region-of-interest (ROI)-based approach. The results suggested high heritability across the cortex for areal expansion and, to a slightly lesser degree, for cortical thickness. There was a great deal of genetic overlap between global and regional measures for surface area, so maps of region-specific genetic influences on surface area revealed more modest heritabilities. There was greater inter-regional variability in heritabilities when calculated using the traditional ROI-based approach compared to summarizing vertex-by-vertex heritabilities within regions. Discrepancies between the approaches were greatest in small regions and tended to be larger for surface area than for cortical thickness measures. Implications regarding brain phenotypes for future genetic association studies are discussed.

The degree to which genes and environment determine variations in brain structure and function is fundamentally important to understanding normal and disease-related patterns of neural organization and activity. We studied genetic contributions to the midsagittal area of the corpus callosum (CC) in pedigreed baboons (68 males, 112 females) to replicate findings of high genetic contribution to that area of the CC reported in humans, and to determine if the heritability of the CC midsagittal area in adults was modulated by fetal development rate. Measurements of callosal area were obtained from high-resolution MRI scans. Heritability was estimated from pedigree-based maximum likelihood estimation of genetic and non-genetic variance components as implemented in Sequential Oligogenic Linkage Analysis Routines (SOLAR). Our analyses revealed significant heritability for the total area of the CC and all of its subdivisions, with h2 = .46 for the total CC, and h2 = .54, .37, .62, .56, and .29 for genu, anterior midbody, medial midbody, posterior midbody and splenium, respectively. Genetic correlation analysis demonstrated that the individual subdivisions shared between 41% and 98% of genetic variability. Combined with previous research reporting high heritability of other brain structures in baboons, these results reveal a consistent pattern of high heritability for brain morphometric measures in baboons.

Structural brain measures are employed as endophenotypes in the search for schizophrenia susceptibility genes. We analyzed two independent structural imaging datasets with voxel-based morphometry and with source-based morphometry, a multivariate, independent components analysis, to determine the stability and heritability of regional gray matter concentration abnormalities in schizophrenia. The samples comprised 209 and 102 patients with schizophrenia and 208 and 96 healthy volunteers, respectively. The second sample additionally included non-ill siblings of participants with and without schizophrenia. A standard voxel-based analysis showed reproducible regional gray matter deficits in the affected participants compared with unrelated, unaffected controls in both datasets: patients showed significant gray matter concentration deficits in cortical frontal, temporal, and insular lobes. Source-based morphometry (SBM) was applied to the gray matter images of the entire sample to determine the effects of diagnosis on networks of covarying structures. The SBM analysis extracted 24 significant sets of covarying regions (components). Four of these components showed significantly lower gray matter concentrations in patients (p < .05). We determined the familiality of the observed SBM components based on 66 sibling pairs (25 discordant for schizophrenia). Two components, one including the medial frontal, insular, inferior frontal, and temporal lobes, and the other including the posterior occipital lobe, showed significant familiality (p < .05). We conclude that structural brain deficits in schizophrenia are replicable, and that SBM can extract unique familial and likely heritable components. SBM provides a useful data reduction technique that can provide measures that may serve as endophenotypes for schizophrenia.

Understanding genetic and environmental effects on white matter development in the first years of life is of great interest, as it provides insights into the etiology of neurodevelopmental disorders. In this study, the genetic and environmental effects on white matter were estimated using data from 173 neonatal twin subjects. Diffusion tensor imaging scans were acquired around 40 days after birth and were non-rigidly registered to a group-specific atlas and parcellated into 98 ROIs. A model of additive genetic, and common and specific environmental variance components was used to estimate overall and regional genetic and environmental contributions to diffusion parameters of fractional anisotropy, radial diffusivity, and axial diffusivity. Correlations between the regional heritability values and diffusion parameters were also examined. Results indicate that individual differences in overall white matter microstructure, represented by the average diffusion parameters over the whole brain, are heritable, and estimates are higher than found in studies in adults. Estimates of genetic and environmental variance components vary considerably across different white matter regions. Significant positive correlations between radial diffusivity heritability and radial diffusivity values are consistent with regional genetic variation being modulated by maturation status in the neonatal brain: the more mature the region is, the less genetic variation it shows. Common environmental effects are present in a few regions that tend to be characterized by low radial diffusivity. Results from the joint diffusion parameter analysis suggest that multivariate modeling approaches might be promising to better estimate maturation status and its relationship with genetic and environmental effects.

Because brain structure and function are affected in neurological and psychiatric disorders, it is important to disentangle the sources of variation in these phenotypes. Over the past 15 years, twin studies have found evidence for both genetic and environmental influences on neuroimaging phenotypes, but considerable variation across studies makes it difficult to draw clear conclusions about the relative magnitude of these influences. Here we performed the first meta-analysis of structural MRI data from 48 studies on >1,250 twin pairs, and diffusion tensor imaging data from 10 studies on 444 twin pairs. The proportion of total variance accounted for by genes (A), shared environment (C), and unshared environment (E), was calculated by averaging A, C, and E estimates across studies from independent twin cohorts and weighting by sample size. The results indicated that additive genetic estimates were significantly different from zero for all meta-analyzed phenotypes, with the exception of fractional anisotropy (FA) of the callosal splenium, and cortical thickness (CT) of the uncus, left parahippocampal gyrus, and insula. For many phenotypes there was also a significant influence of C. We now have good estimates of heritability for many regional and lobar CT measures, in addition to the global volumes. Confidence intervals are wide and number of individuals small for many of the other phenotypes. In conclusion, while our meta-analysis shows that imaging measures are strongly influenced by genes, and that novel phenotypes such as CT measures, FA measures, and brain activation measures look especially promising, replication across independent samples and demographic groups is necessary.

One of the core behavioral features associated with obsessive compulsive symptomatology is the inability to inhibit thoughts and/or behaviors. Neuroimaging studies have indicated abnormalities in frontostriatal and dorsolateral prefrontal – anterior cingulate circuits during inhibitory control in patients with obsessive compulsive disorder compared with controls. In the present study, task performance and brain activation during Stroop color-word and Flanker interference were compared within monozygotic twin pairs discordant for obsessive compulsive symptoms and between groups of pairs scoring very low or very high on obsessive compulsive symptoms, in order to examine the differential impact of non-shared environmental versus genetic risk factors for obsessive compulsive symptomatology on inhibitory control related functional brain activation. Although performance was intact, brain activation during inhibition of distracting information differed between obsessive compulsive symptom high-scoring compared to low-scoring subjects. Regions affected in the discordant group (e.g., temporal and anterior cingulate gyrus) were partly different from those observed to be affected in the concordant groups (e.g., parietal gyrus and thalamus). A robust increase in dorsolateral prefrontal activity during response interference was observed in both the high-scoring twins of the discordant sample and the high-scoring twins of the concordant sample, marking this structure as a possible key region for disturbances in inhibitory control in obsessive compulsive disorder.

The human electroencephalogram (EEG) consists of oscillations that reflect the summation of postsynaptic potentials at the dendritic tree of cortical neurons. The strength of the oscillations (EEG power) is a highly genetic trait that has been related to individual differences in many phenotypes, including intelligence and liability for psychopathology. Here, we investigated whether brain anatomy underlies these EEG power differences by correlating it to gray and white matter volumes (GMV, WMV), and additionally investigated whether this association can be attributed to genes or environmental factors. EEG was measured in a sample of 405 young adult twins and their siblings, and power in the theta (~4 Hz), alpha (~10 Hz), and beta (~20 Hz) frequency bands determined. A subset of 121 subjects were also scanned in a 1.5 T MRI scanner, and gray and white matter volumes defined as the total of cortical and subcortical volumes, excluding cerebellum. Both MRI-based volumes and EEG power spectra were highly heritable. GMV and WMV correlated .25 to .29 with EEG power for the slower oscillations (theta, alpha). Moreover, these phenotypic correlations largely reflected genetic covariation, irrespective of oscillation frequency and volume type. Genetic correlations (.31 < rA < .43) revealed that only moderate proportions of the heritable variance overlapped between MRI volumes and EEG power. The results suggest that MRI volumes and EEG power share genetic sources of variation, which may reflect such processes as myelination, synaptic density, and dendritic outgrowth.

A new genetic factor model for multivariate phenotypic time series, iFACE, is presented which allows for the estimation of subject-specific model parameters of genetic and environmental factors. The iFACE was applied to multivariate EEG registrations obtained with single dizygotic twin pairs. The results showed evidence for considerable subject-specificity in heritabilities and environmental effects. The assumption that the population is homogeneous (i.e., that each case in the population obeys the same parametric model), does not hold for these psychophysiological data, and its use should be critically reconsidered. We conclude that the iFACE provides a powerful new methodology to assess heterogeneity (subject-specificity) based on phenotypic observations.

The longitudinal fissure separates the human brain into two hemispheres that remain connected through the corpus callosum. The left and the right halves of the brain resemble each other, and almost every structure present in one side has an equivalent structure in the other. Despite this exceptional correspondence, the two hemispheres also display important anatomical differences and there is marked lateralization of certain cognitive and motor functions such as language and handedness. However, the mechanisms that underlie the establishment of these hemispheric specializations, as well as their physiological and behavioral implications, remain largely unknown. Thanks to recent advances in neuroimaging, a series of studies documenting variation in symmetry and asymmetry as a function of age, gender, brain region, and pathological state, have been published in the past decade. Here, we review evidence of normal and atypical cerebral asymmetry, and the factors that influence it at the macrostructural level. Given the prominent role that cerebral asymmetry plays in the organization of the brain, and its possible implication in neurodevelopmental and psychiatric conditions, further research in this area is anticipated.

In an attempt to increase power to detect genetic associations with brain phenotypes derived from human neuroimaging data, we recently conducted a large-scale, genome-wide association meta-analysis of hippocampal, brain, and intracranial volume through the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium. Here, we present a freely available online interactive tool, EnigmaVis, which makes it easy to visualize the association results generated by the consortium alongside allele frequency, genes, and functional annotations. EnigmaVis runs natively within the web browser, and generates plots that show the level of association between brain phenotypes at user-specified genomic positions. Uniquely, EnigmaVis is dynamic; users can interact with elements on the plot in real time. This software will be useful when exploring the effect on brain structure of particular genetic variants influencing neuropsychiatric illness and cognitive function. Future projects of the consortium and updates to EnigmaVis will also be displayed on the site. EnigmaVis is freely available online at http://enigma.loni.ucla.edu/enigma-vis/

Despite the significant advancements being made in the neurogenetics for mental health, the identification and validation of potential endophenotype markers of risk and resilience remain to be confirmed. The TWIN-E study (The Twin study in Wellbeing using Integrative Neuroscience of Emotion) aims to validate endophenotype markers of mental health across cognitive, brain, and autonomic measures by testing the heritability, clinical plausibility, and reliability of each of these measures in a large adult twin cohort. The specific gene and environmental mechanisms that moderate prospective links between endophenotype-phenotype markers and the final outcome of wellbeing will also be identified. TWIN-E is a national prospective study with three phases: I) baseline testing on a battery of online questionnaires and cognitive tasks, and EEG, MRI, and autonomic testing; II) 12-month follow-up testing on the online assessments; and III) randomized controlled trial of brain training. Minimum target numbers include 1,500 male/female twins (18–65 years) for the online assessments (Phase I and II), 300 twins for the EEG testing component, and 244 twins for the MRI testing component. For Phase III, each twin out of the pair will be randomized to either the treatment or waitlist control group to test the effects of brain training on mental health over a 30-day period, and to confirm the gene–environment and endophenotype contributions to treatment response. Preliminary heritability results are provided for the first 50% of the MRI subgroup (n = 142) for the grey matter volume, thickness, and surface area measures, and white matter diffuse tensor imaging fractional anisotropy.

Data collection for the Norwegian Cognitive NeuroGenetics sample (NCNG) was initiated in 2003 with a research grant (to Ivar Reinvang) to study cognitive aging, brain function, and genetic risk factors. The original focus was on the effects of aging (from middle age and up) and candidate genes (e.g., APOE, CHRNA4) in cross-sectional and longitudinal designs, with the cognitive and MRI-based data primarily being used for this purpose. However, as the main topic of the project broadened from cognitive aging to imaging and cognitive genetics more generally, the sample size, age range of the participants, and scope of available phenotypes and genotypes, have developed beyond the initial project. In 2009, a genome-wide association (GWA) study was undertaken, and the NCNG proper was established to study the genetics of cognitive and brain function more comprehensively. The NCNG is now controlled by the NCNG Study Group, which consists of the present authors. Prominent features of the NCNG are the adult life-span coverage of healthy participants with high-dimensional imaging, and cognitive data from a genetically homogenous sample. Another unique property is the large-scale (sample size 300–700) use of experimental cognitive tasks focusing on attention and working memory. The NCNG data is now used in numerous ongoing GWA-based studies and has contributed to several international consortia on imaging and cognitive genetics. The objective of the following presentation is to give other researchers the information necessary to evaluate possible contributions from the NCNG to various multi-sample data analyses.

From childhood into adolescence, the child's brain undergoes considerable changes in both structure and function. Twin studies are of great value to explore to what extent genetic and environmental factors explain individual differences in brain development and cognition. In The Netherlands, we initiated a longitudinal study in which twins, their siblings and their parents are assessed at three year intervals. The participants were recruited from The Netherlands Twin Register (NTR) and at baseline consisted of 112 families, with 9-year-old twins and an older sibling. Three years later, 89 families returned for follow-up assessment. Data collection included psychometric IQ tests, a comprehensive neuropsychological testing protocol, and parental and self-ratings of behavioral and emotional problems. Physical maturation was measured through assessment of Tanner stages. Hormonal levels (cortisol, luteinizing hormone, follicle-stimulating hormone, testosterone, and estrogens) were assessed in urine and saliva. Brain scans were acquired using 1.5 Tesla Magnetic Resonance Imaging (MRI), which provided volumetric measures and measures of cortical thickness. Buccal swabs were collected for DNA isolation for future candidate gene and genome-wide analysis studies. This article gives an overview of the study and the main findings. Participants will return for a third assessment when the twins are around 16 years old. Longitudinal twin-sibling studies that map brain development and cognitive function at well-defined ages aid in the understanding of genetic influences on normative brain development.

Naturally occurring co-twin control designs can assess associations between environmental hazards and disease risk because the genotypes of monozygous twins are the same. The first population-based study to explore links between solvent exposure and Parkinson's disease used 49 discordant monozygous twin pairs, as well as a comparison group of 50 discordant dizygous twin pairs. The methods, findings and implications from this important study are reviewed. Reviews of twin research concerning gender identity disorder, prenatal development and the VATER syndrome follow. A sampling of engaging and informative twin-related news items is also presented.