The utility of twins in developmental cognitive neuroscience research: How twins strengthen the ABCD research design

The ABCD twin study will elucidate the genetic and environmental contributions to a wide range of mental and physical health outcomes in children, including substance use, brain and behavioral development, and their interrelationship. Comparisons within and between monozygotic and dizygotic twin pairs, further powered by multiple assessments, provide information about genetic and environmental contributions to developmental associations, and enable stronger tests of causal hypotheses, than do comparisons involving unrelated children. Thus a sub-study of 800 pairs of same-sex twins was embedded within the overall Adolescent Brain and Cognitive Development (ABCD) design. The ABCD Twin Hub comprises four leading centers for twin research in Minnesota, Colorado, Virginia, and Missouri. Each site is enrolling 200 twin pairs, as well as singletons. The twins are recruited from registries of all twin births in each State during 2006–2008. Singletons at each site are recruited following the same school-based procedures as the rest of the ABCD study. This paper describes the background and rationale for the ABCD twin study, the ascertainment of twin pairs and implementation strategy at each site, and the details of the proposed analytic strategies to quantify genetic and environmental influences and test hypotheses critical to the aims of the ABCD study. Keywords: Twins, Heritability, Environment, Substance use, Brain structure, Brain functio

The Quantitative Genetics of Neurodevelopment: A Magnetic Resonance Imaging Study of Childhood and Adolescence

Understanding the causes of individual differences in brain structure may give clues about the etiology of cognition, personality, and psychopathology, and also may identify endophenotypes for molecular genetic studies on brain development. We performed a comprehensive statistical genetic study of anatomic neuroimaging data from a large pediatric sample (N=600+) of twins and family members from the Child Psychiatry Branch at the NIMH. These analyses included variance decomposition of structural volumetric endophenotypes at several levels of resolution, voxel-level analysis of cortical thickness, assessment of gene by age interaction, several multivariate genetic analyses, and a search for genetically-mediated brain-behavioral relationships. These analyses found strong evidence for a genetic role in the generation of individual differences in brain volumes, with the exception of the cerebellum and the lateral ventricles. Subsequent multivariate analyses demonstrated that most of the genetic variance in large volumes shares a common source. More subtle analyses suggest that although this global genetic factor is the principal determinant of neuroanatomic variability, genetic factors also mediate regional variability in cortical thickness and are different for gray and white matter volumes. Models using graph theory show that brain structure follows small-world architectural rules, and that these relationships are genetically-determined. Structural homologues appeared to be strongly related genetically, which was further confirmed using novel methods for semi-multivariate quantitative genetic analysis at the voxel level. Studies on interactions with age were mixed. We found evidence of gene by age interaction on frontal and temporal lobar volumes, indicating that the role of genetic factors on these structures is dynamic during childhood. Analyses on cortical thickness at a finer scale, however, showed that environmental factors are more important in childhood, and environmental changes were responsible for most of the changes in heritability over this age range. When assessing the relationship between brain and behavior, we found weak negative genetic correlations and positive environmental correlations between IQ and cortical thickness, which appear to partially cancel each other out. More complex models allowing for age interactions suggest that high and low IQ groups have different patterns of gene by age interactions in concordance with prior literature on cortical phenotypes

Genetic network properties of the human cortex based on regional thickness and surface area measures

We examined network properties of genetic covariance between average cortical thickness (CT) and surface area (SA) within genetically-identified cortical parcellations that we previously derived from human cortical genetic maps using vertex-wise fuzzy clustering analysis with high spatial resolution. There were 24 hierarchical parcellations based on vertex-wise CT and 24 based on vertex-wise SA expansion/contraction; in both cases the 12 parcellations per hemisphere were largely symmetrical. We utilized three techniques—biometrical genetic modeling, cluster analysis, and graph theory—to examine genetic relationships and network properties within and between the 48 parcellation measures. Biometrical modeling indicated significant shared genetic covariance between size of several of the genetic parcellations. Cluster analysis suggested small distinct groupings of genetic covariance; networks highlighted several significant negative and positive genetic correlations between bilateral parcellations. Graph theoretical analysis suggested that small world, but not rich club, network properties may characterize the genetic relationships between these regional size measures. These findings suggest that cortical genetic parcellations exhibit short characteristic path lengths across a broad network of connections. This property may be protective against network failure. In contrast, previous research with structural data has observed strong rich club properties with tightly interconnected hub networks. Future studies of these genetic networks might provide powerful phenotypes for genetic studies of normal and pathological brain development, aging, and function

Genetics of brain fiber architecture and intellectual performance

The study is the first to analyze genetic and environmental factors that affect brain fiber architecture and its genetic linkage with cognitive function. We assessed white matter integrity voxelwise using diffusion tensor imaging at high magnetic field (4 Tesla), in 92 identical and fraternal twins. White matter integrity, quantified using fractional anisotropy (FA), was used to fit structural equation models (SEM) at each point in the brain, generating three-dimensional maps of heritability. We visualized the anatomical profile of correlations between white matter integrity and full-scale, verbal, and performance intelligence quotients (FIQ, VIQ, and PIQ). White matter integrity (FA) was under strong genetic control and was highly heritable in bilateral frontal (a2 = 0.55, p = 0.04, left; a2 = 0.74, p = 0.006, right), bilateral parietal (a2 = 0.85, p < 0.001, left; a2 = 0.84, p < 0.001, right), and left occipital (a2 = 0.76, p = 0.003) lobes, and was correlated with FIQ and PIQ in the cingulum, optic radiations, superior fronto-occipital fasciculus, internal capsule, callosal isthmus, and the corona radiata (p = 0.04 for FIQ and p = 0.01 for PIQ, corrected for multiple comparisons). In a cross-trait mapping approach, common genetic factors mediated the correlation between IQ and white matter integrity, suggesting a common physiological mechanism for both, and common genetic determination. These genetic brain maps reveal heritable aspects of white matter integrity and should expedite the discovery of single-nucleotide polymorphisms affecting fiber connectivity and cognition

Systems genomics analysis of complex cognitive traits

The study of the genetic underpinnings of human cognitive traits is deemed an important tool to increase our understanding of molecular processes related to physiological and pathological cognitive functioning. The polygenic architecture of such complex traits implies that multiple naturally occurring genetic variations, each of small effect size, are likely to influence jointly the biological processes underlying cognitive ability. Genetic association results are yet devoid of biological context, thus limiting both the identification and functional interpretation of susceptibility variants. This biological gap can be reduced by the integrative analysis of intermediate molecular traits, as mediators of genomic action. In this thesis, I present results from two such systems genomics analyses, as attempts to identify molecular patterns underlying cognitive trait variability. In the first study, we adopted a system-level approach to investigate the relationship between global age-related patterns of epigenetic variation and cortical thickness, a brain morphometric measure that is linked to cognitive functioning. The integration of both genome-wide methylomic and genetic profiles allowed the identification of a peripheral molecular signature that showed association with both cortical thickness and episodic memory performance. In the second study, we explicitly modeled the interdependencies between local genetic markers and peripherally measured epigenetic variations. We thus generated robust estimators of epigenetic regulation and showed that these estimators resulted in the identification of epigenetic underpinnings of schizophrenia, a common genetically complex disorder. These results underscore the potential of systems genomics approaches, capitalizing on the integration of high-dimensional multi-layered molecular data, for the study of brain- related complex traits

Genetic Influences on the Development of Cerebral Cortical Thickness During Childhood and Adolescence in a Dutch Longitudinal Twin Sample:The Brainscale Study

Previous studies have demonstrated that cortical thickness (CT) is under strong genetic control across the life span. However, little is known about genetic influences that cause changes in cortical thickness (ΔCT) during brain development. We obtained 482 longitudinal MRI scans at ages 9, 12, and 17 years from 215 twins and applied structural equation modeling to estimate genetic influences on (1) cortical thickness between regions and across time, and (2) changes in cortical thickness between ages. Although cortical thickness is largely mediated by the same genetic factor throughout late childhood and adolescence, we found evidence for influences of distinct genetic factors on regions across space and time. In addition, we found genetic influences for cortical thinning during adolescence that is mostly due to fluctuating influences from the same genetic factor, with evidence of local influences from a second emerging genetic factor. This fluctuating core genetic factor and emerging novel genetic factor might be implicated in the rapid cognitive and behavioral development during childhood and adolescence, and could potentially be targets for investigation into the manifestation of psychiatric disorders that have their origin in childhood and adolescence

Dimensionality reduction and unsupervised learning techniques applied to clinical psychiatric and neuroimaging phenotypes

Unsupervised learning and other multivariate analysis techniques are increasingly recognized in neuropsychiatric research. Here, finite mixture models and random forests were applied to clinical observations of patients with major depression to detect and validate treatment response subgroups. Further, independent component analysis and agglomerative hierarchical clustering were combined to build a brain parcellation solely on structural covariance information of magnetic resonance brain images. Übersetzte Kurzfassung: Unüberwachtes Lernen und andere multivariate Analyseverfahren werden zunehmend auf neuropsychiatrische Fragestellungen angewendet. Finite mixture Modelle wurden auf klinische Skalen von Patienten mit schwerer Depression appliziert, um Therapieantwortklassen zu bilden und mit Random Forests zu validieren. Unabhängigkeitsanalysen und agglomeratives hierarchisches Clustering wurden kombiniert, um die strukturelle Kovarianz von Magnetresonanz­tomographie-Bildern für eine Hirnparzellierung zu nutzen

Mapping Genetic Influence on Brain Structure

Neuroimaging is playing an increasingly crucial role in delineating pathological conditions that cannot be typically defined by non-specific clinical symptom. The goal of this thesis was to characterize the genetic influence on grey and white matter indices and evaluate their potential as a reliable “structural MRI signatures”. We first assessed the effects of spatial resolution and smoothing on heritability estimation (Chapter 3). We then investigated heritability patterns of MRI measures of grey and white matter (Chapters 4-5). We then performed a cross-sectional evaluation of how heritability changes over the lifespan for both grey and white matter (Chapter 6). Finally, multivariate structural equation modeling was used to investigate the genetic correlation between grey matter structure and white matter connectivity (Chapter 7), in the default mode network (DMN). Our results show that several key brain structures were moderate to highly heritable and that this heritability was both spatially and temporally heterogeneous. At a network level, the DMN was found to have distinct genetic factors that modulated the grey matter regions and white matter tracts separately. We conclude that the spatial and temporal heterogeneity are likely to reflect gene expression patterns that are related to the developmental of specific brain regions and circuits over time