Analytical strategies for large imaging genetic datasets: experiences from the IMAGEN study

A Developmental Perspective on Facets of Impulsivity and Brain Activity Correlates From Adolescence to Adulthood

BACKGROUND

On a theoretical level, impulsivity represents a multidimensional construct associated with acting without foresight, inefficient inhibitory response control, and alterations in reward processing. On an empirical level, relationships and changes in associations between different measures of impulsivity from adolescence into young adulthood and their relation to neural activity during inhibitory control and reward anticipation have not been fully understood.

METHODS

We used data from IMAGEN, a longitudinal multicenter, population-based cohort study in which 2034 healthy adolescents were investigated at age 14, and 1383 were reassessed as young adults at age 19. We measured the construct of trait impulsivity using self-report questionnaires and neurocognitive indices of decisional impulsivity. With functional magnetic resonance imaging, we assessed brain activity during inhibition error processing using the stop signal task and during reward anticipation in the monetary incentive delay task. Correlations were analyzed, and mixed-effect models were fitted to explore developmental and predictive effects.

RESULTS

All self-report and neurocognitive measures of impulsivity proved to be correlated during adolescence and young adulthood. Further, pre-supplementary motor area and inferior frontal gyrus activity during inhibition error processing was associated with trait impulsivity in adolescence, whereas in young adulthood, a trend-level association with reward anticipation activity in the ventral striatum was found. For adult delay discounting, a trend-level predictive effect of adolescent neural activity during inhibition error processing emerged.

CONCLUSIONS

Our findings help to inform theories of impulsivity about the development of its multidimensional nature and associated brain activity patterns and highlight the need for taking functional brain development into account when evaluating neuromarker candidates.

New drug targets in psychiatry: Neurobiological considerations in the genomics era

After a period of withdrawal, pharmaceutical companies have begun to reinvest in neuropsychiatric disorders, due to improvements in our understanding of these disorders, stimulated in part by genomic studies. However, translating this information into disease insights and ultimately into tractable therapeutic targets is a major challenge. Here we consider how different sources of information might be integrated to guide this process. We review how an understanding of neurobiology has been used to advance therapeutic candidates identified in the pre-genomic era, using catechol-O-methyltransferase (COMT) as an exemplar. We then contrast with ZNF804A, the first genome-wide significant schizophrenia gene, and draw on some of the lessons that these and other examples provide. We highlight that, at least in the short term, the translation of potential targets for which there is orthogonal neurobiological support is likely to be more straightforward and productive than that those relying solely on genomic information. Although we focus here on information from genomic studies of schizophrenia, the points are broadly applicable across major psychiatric disorders and their symptoms.

Ethical issues in global neuroimaging genetics collaborations

Neuroimaging genetics is a rapidly developing field that combines neuropsychiatric genetics studies with imaging modalities to investigate how genetic variation influences brain structure and function. As both genetic and imaging technologies improve further, their combined power may hold translational potential in terms of improving psychiatric nosology, diagnosis, and treatment. While neuroimaging genetics studies offer a number of scientific advantages, they also face challenges. In response to some of these challenges, global neuroimaging genetics collaborations have been created to pool and compare brain data and replicate study findings. Attention has been paid to ethical issues in genetics, neuroimaging, and multi-site collaborative research, respectively, but there have been few substantive discussions of the ethical issues generated by the confluence of these areas in global neuroimaging genetics collaborations. Our discussion focuses on two areas: benefits and risks of global neuroimaging genetics collaborations and the potential impact of neuroimaging genetics research findings in low- and middle-income countries. Global neuroimaging genetics collaborations have the potential to enhance relations between countries and address global mental health challenges, however there are risks regarding inequity, exploitation and data sharing. Moreover, neuroimaging genetics research in low- and middle-income countries must address the issue of feedback of findings and the risk of essentializing and stigmatizing interpretations of mental disorders. We conclude by examining how the notion of solidarity, informed by an African Ethics framework, may justify some of the suggestions made in our discussion.

Genetic associations with childhood brain growth, defined in two longitudinal cohorts

Genome-wide association studies (GWASs) are unraveling the genetics of adult brain neuroanatomy as measured by cross-sectional anatomic magnetic resonance imaging (aMRI). However, the genetic mechanisms that shape childhood brain development are, as yet, largely unexplored. In this study we identify common genetic variants associated with childhood brain development as defined by longitudinal aMRI. Genome-wide single nucleotide polymorphism (SNP) data were determined in two cohorts: one enriched for attention-deficit/hyperactivity disorder (ADHD) (LONG cohort: 458 participants; 119 with ADHD) and the other from a population-based cohort (Generation R: 257 participants). The growth of the brain's major regions (cerebral cortex, white matter, basal ganglia, and cerebellum) and one region of interest (the right lateral prefrontal cortex) were defined on all individuals from two aMRIs, and a GWAS and a pathway analysis were performed. In addition, association between polygenic risk for ADHD and brain growth was determined for the LONG cohort. For white matter growth, GWAS meta-analysis identified a genome-wide significant intergenic SNP (rs12386571, P = 9.09 × 10^-9 ), near AKR1B10. This gene is part of the aldo-keto reductase superfamily and shows neural expression. No enrichment of neural pathways was detected and polygenic risk for ADHD was not associated with the brain growth phenotypes in the LONG cohort that was enriched for the diagnosis of ADHD. The study illustrates the use of a novel brain growth phenotype defined in vivo for further study.

GABRB1 Single Nucleotide Polymorphism Associated with Altered Brain Responses (but not Performance) during Measures of Impulsivity and Reward Sensitivity in Human Adolescents

Variations in genes encoding several GABA_A receptors have been associated with human drug and alcohol abuse. Among these, a number of human studies have suggested an association between GABRB1, the gene encoding GABA_A receptor β1 subunits, with Alcohol dependence (AD), both on its own and comorbid with other substance dependence and psychiatric illnesses. In the present study, we hypothesized that the GABRB1 genetically-associated increased risk for developing alcoholism may be associated with impaired behavioral control and altered sensitivity to reward, as a consequence of altered brain function. Exploiting the IMAGEN database (Schumann et al., 2010), we explored in a human adolescent population whether possession of the minor (T) variant of the single nucleotide polymorphism (SNP) rs2044081 is associated with performance of tasks measuring aspects of impulsivity, and reward sensitivity that are implicated in drug and alcohol abuse. Allelic variation did not associate with altered performance in either a stop-signal task (SST), measuring one aspect of impulsivity, or a monetary incentive delay (MID) task assessing reward anticipation. However, increased functional magnetic resonance imaging (fMRI) blood-oxygen-level dependent (BOLD) response in the right hemisphere inferior frontal gyrus (IFG), left hemisphere caudate/insula and left hemisphere inferior temporal gyrus (ITG) during MID performance was higher in the minor (T) allelic group. In contrast, during SST performance, the BOLD response found in the right hemisphere supramarginal gyrus, right hemisphere lingual and left hemisphere inferior parietal gyrus indicated reduced responses in the minor genotype. We suggest that β1-containing GABA_A receptors may play a role in excitability of brain regions important in controlling reward-related behavior, which may contribute to susceptibility to addictive behavior.

Addictions Neuroclinical Assessment: A Neuroscience-Based Framework for Addictive Disorders

This article proposes a heuristic framework for the Addictions Neuroclinical Assessment that incorporates key functional domains derived from the neurocircuitry of addiction. We review how addictive disorders (ADs) are presently diagnosed and the need for new neuroclinical measures to differentiate patients who meet clinical criteria for addiction to the same agent while differing in etiology, prognosis, and treatment response. The need for a better understanding of the mechanisms provoking and maintaining addiction, as evidenced by the limitations of current treatments and within-diagnosis clinical heterogeneity, is articulated. In addition, recent changes in the nosology of ADs, challenges to current classification systems, and prior attempts to subtype individuals with ADs are described. Complementary initiatives, including the Research Domain Criteria project, that have established frameworks for the neuroscience of psychiatric disorders are discussed. Three domains-executive function, incentive salience, and negative emotionality-tied to different phases in the cycle of addiction form the core functional elements of ADs. Measurement of these domains in epidemiologic, genetic, clinical, and treatment studies will provide the underpinnings for an understanding of cross-population and temporal variation in addictions, shared mechanisms in addictive disorders, impact of changing environmental influences, and gene identification. Finally, we show that it is practical to implement such a deep neuroclinical assessment using a combination of neuroimaging and performance measures. Neuroclinical assessment is key to reconceptualizing the nosology of ADs on the basis of process and etiology, an advance that can lead to improved prevention and treatment.

Imaging genetics and psychiatric disorders

Imaging genetics is an integrated research method that uses neuroimaging and genetics to assess the impact of genetic variation on brain function and structure. Imaging genetics is both a tool for the discovery of risk genes for psychiatric disorders and a strategy for characterizing the neural systems affected by risk gene variants to elucidate quantitative and mechanistic aspects of brain function implicated in psychiatric disease. Early studies of imaging genetics included association analyses between brain morphology and single nucleotide polymorphisms whose function is well known, such as catechol-Omethyltransferase (COMT) and brain-derived neurotrophic factor (BDNF). GWAS of psychiatric disorders have identified genes with unknown functions, such as ZNF804A, and imaging genetics has been used to investigate clues of the biological function of these genes. The difficulty in replicating the findings of studies with small sample sizes has motivated the creation of largescale collaborative consortiums, such as ENIGMA, CHARGE and IMAGEN, to collect thousands of images. In a genome-wide association study, the ENIGMA consortium successfully identified common variants in the genome associated with hippocampal volume at 12q24, and the CHARGE consortium replicated this finding. The new era of imaging genetics has just begun, and the next challenge we face is the discovery of small effect size signals from large data sets obtained from genetics and neuroimaging. New methods and technologies for data reduction with appropriate statistical thresholds, such as polygenic analysis and parallel independent component analysis (ICA), are warranted. Future advances in imaging genetics will aid in the discovery of genes and provide mechanistic insight into psychiatric disorders.

Are we doing enough to extract genomic information from our data?

Genome-wide studies have been successful in identifying stable associations of single genes, albeit at the price of a high false negative rate. The promise of endophenotypes to increase power of genome-wide association studies has only been partially fulfilled. To optimize the investigation of genetic influences on behavioral (endo-)phenotypes, the development of novel phenotypical characterizations and methods to describe the relation between genotype and phenotype are needed. This will require the development of innovative analytical strategies, as well as corroborative approaches linking association studies with functional characterizations. The sole reliance on canonical genome-wide significance thresholds is not sufficient to describe the complex relation of genotype and phenotype.

An introductory review of parallel independent component analysis (p-ICA) and a guide to applying p-ICA to genetic data and imaging phenotypes to identify disease-associated biological pathways and systems in common complex disorders

Complex inherited phenotypes, including those for many common medical and psychiatric diseases, are most likely underpinned by multiple genes contributing to interlocking molecular biological processes, along with environmental factors (Owen et al., 2010). Despite this, genotyping strategies for complex, inherited, disease-related phenotypes mostly employ univariate analyses, e.g., genome wide association. Such procedures most often identify isolated risk-related SNPs or loci, not the underlying biological pathways necessary to help guide the development of novel treatment approaches. This article focuses on the multivariate analysis strategy of parallel (i.e., simultaneous combination of SNP and neuroimage information) independent component analysis (p-ICA), which typically yields large clusters of functionally related SNPs statistically correlated with phenotype components, whose overall molecular biologic relevance is inferred subsequently using annotation software suites. Because this is a novel approach, whose details are relatively new to the field we summarize its underlying principles and address conceptual questions regarding interpretation of resulting data and provide practical illustrations of the method.

Association of norepinephrine transporter (NET, SLC6A2) genotype with ADHD-related phenotypes: findings of a longitudinal study from birth to adolescence

Variation in the gene encoding for the norepinephrine transporter (NET, SLC6A2) has repeatedly been linked with ADHD, although there is some inconsistency regarding the association with specific genes. The variants for which most consistent association has been found are the NET variants rs3785157 and rs28386840. Here, we tested for their association with ADHD diagnosis and ADHD-related phenotypes during development in a longitudinal German community sample. Children were followed from age 4 to age 15, using diagnostic interviews to assess ADHD. Between the ages of 8 and 15 years, the Child Behavior Checklist (CBCL) was administered to the primary caregivers. The continuous performance task (CPT) was performed at age 15. Controlling for possible confounders, we found that homozygous carriers of the major A allele of the functional promoter variant rs28386840 displayed a higher rate of ADHD lifetime diagnosis. Moreover, homozygous carriers of the minor T allele of rs3785157 were more likely to develop ADHD and showed higher scores on the CBCL externalizing behavior scales. Additionally, we found that individuals heterozygous for rs3785157 made fewer omission errors in the CPT than homozygotes. This is the first longitudinal study to report associations between specific NET variants and ADHD-related phenotypes during the course of development.

Epidemiology of child psychopathology: major milestones

Child psychiatric epidemiology has developed rapidly from descriptive, cross-sectional studies in the 1960s to the current large-scale prospective cohorts that unravel aetiological mechanisms. The objective of the study was to give an overview of epidemiological studies that have influenced child psychiatry. A chronological overview of selected major milestone studies was obtained to demonstrate the development of child psychiatric epidemiology, with a more in-depth discussion of findings and methodological issues exemplified in one cohort, the Generation R Study. Epidemiological studies have been successful in describing the frequency and course of child psychiatric problems. The high expectations that biological factors can be used to better explain, diagnose or predict child psychiatric problems have not been met. More ambitious large-scale child psychiatric cohort studies are needed, carefully applying genetics, neuroscience or other molecular research to better understand how the brain produces maladaptive behaviour. Progress will only be attained if the basic sciences are systematically integrated in cohorts with rigorous epidemiological designs rather than hurriedly inserted in child psychiatric studies.

Sparse models for correlative and integrative analysis of imaging and genetic data

The development of advanced medical imaging technologies and high-throughput genomic measurements has enhanced our ability to understand their interplay as well as their relationship with human behavior by integrating these two types of datasets. However, the high dimensionality and heterogeneity of these datasets presents a challenge to conventional statistical methods; there is a high demand for the development of both correlative and integrative analysis approaches. Here, we review our recent work on developing sparse representation based approaches to address this challenge. We show how sparse models are applied to the correlation and integration of imaging and genetic data for biomarker identification. We present examples on how these approaches are used for the detection of risk genes and classification of complex diseases such as schizophrenia. Finally, we discuss future directions on the integration of multiple imaging and genomic datasets including their interactions such as epistasis.

DRD2/ANKK1 polymorphism modulates the effect of ventral striatal activation on working memory performance

Motivation is important for learning and cognition. Although dopaminergic (D2) transmission in the ventral striatum (VS) is associated with motivation, learning, and cognition are more strongly associated with function of the dorsal striatum, including activation in the caudate nucleus. A recent study found an interaction between intrinsic motivation and the DRD2/ANKK1 polymorphism (rs1800497), suggesting that A-carriers of rs1800497 are significantly more sensitive to motivation in order to improve during working memory (WM) training. Using data from the two large-scale imaging genetic data sets, IMAGEN (n=1080, age 13-15 years) and BrainChild (n∼300, age 6-27), we investigated whether rs1800497 is associated with WM. In the IMAGEN data set, we tested whether VS/caudate activation during reward anticipation was associated with WM performance and whether rs1800497 and VS/caudate activation interact to affect WM performance. We found that rs1800497 was associated with WM performance in IMAGEN and BrainChild. Higher VS and caudate activation during reward processing were significantly associated with higher WM performance (p<0.0001). An interaction was found between the DRD2/ANKK1 polymorphism rs1800497 and VS activation during reward anticipation on WM (p<0.01), such that carriers of the minor allele (A) showed a significant correlation between VS activation and WM, whereas the GG-homozygotes did not, suggesting that the effect of VS BOLD on WM is modified by inter-individual genetic differences related to D2 dopaminergic transmission.

Imaging genetics in obsessive-compulsive disorder: linking genetic variations to alterations in neuroimaging

Obsessive-compulsive disorder (OCD) occurs in ∼1-3% of the general population, and its often rather early onset causes major disabilities in the everyday lives of patients. Although the heritability of OCD is between 35 and 65%, many linkage, association, and genome-wide association studies have failed to identify single genes that exhibit high effect sizes. Several neuroimaging studies have revealed structural and functional alterations mainly in cortico-striato-thalamic loops. However, there is also marked heterogeneity across studies. These inconsistencies in genetic and neuroimaging studies may be due to the heterogeneous and complex phenotypes of OCD. Under the consideration that genetic variants may also influence neuroimaging in OCD, researchers have started to combine both domains in the field of imaging genetics. Here, we conducted a systematic search of PubMed and Google Scholar literature for articles that address genetic imaging in OCD and related disorders (published through March 2014). We selected 8 publications that describe the combination of imaging genetics with OCD, and extended it with 43 publications of comorbid psychiatric disorders. The most promising findings of this systematic review point to the involvement of variants in genes involved in the serotonergic (5-HTTLPR, HTR2A), dopaminergic (COMT, DAT), and glutamatergic (SLC1A1, SAPAP) systems. However, the field of imaging genetics must be further explored, best through investigations that combine multimodal imaging techniques with genetic profiling, particularly profiling techniques that employ polygenetic approaches, with much larger sample sizes than have been used up to now.

Whole-genome analyses of whole-brain data: working within an expanded search space

Large-scale comparisons of patients and healthy controls have unearthed genetic risk factors associated with a range of neurological and psychiatric illnesses. Meanwhile, brain imaging studies are increasing in size and scope, revealing disease and genetic effects on brain structure and function, and implicating neural pathways and causal mechanisms. With the advent of global neuroimaging consortia, imaging studies are now well powered to discover genetic variants that reliably affect the brain. Genetic analyses of brain measures from tens of thousands of people are being extended to test genetic associations with signals at millions of locations in the brain, and connectome-wide, genome-wide scans can jointly screen brain circuits and genomes; these analyses and others present new statistical challenges. There is a growing need for the community to establish and enforce standards in this developing field to ensure robust findings. Here we discuss how neuroimagers and geneticists have formed alliances to discover how genetic factors affect the brain. The field is rapidly advancing with ultra-high-resolution imaging and whole-genome sequencing. We recommend a rigorous approach to neuroimaging genomics that capitalizes on its recent successes and ensures the reliability of future discoveries.

A systems medicine research approach for studying alcohol addiction

According to the World Health Organization, about 2 billion people drink alcohol. Excessive alcohol consumption can result in alcohol addiction, which is one of the most prevalent neuropsychiatric diseases afflicting our society today. Prevention and intervention of alcohol binging in adolescents and treatment of alcoholism are major unmet challenges affecting our health-care system and society alike. Our newly formed German SysMedAlcoholism consortium is using a new systems medicine approach and intends (1) to define individual neurobehavioral risk profiles in adolescents that are predictive of alcohol use disorders later in life and (2) to identify new pharmacological targets and molecules for the treatment of alcoholism. To achieve these goals, we will use omics-information from epigenomics, genetics transcriptomics, neurodynamics, global neurochemical connectomes and neuroimaging (IMAGEN; Schumann et al. ) to feed mathematical prediction modules provided by two Bernstein Centers for Computational Neurosciences (Berlin and Heidelberg/Mannheim), the results of which will subsequently be functionally validated in independent clinical samples and appropriate animal models. This approach will lead to new early intervention strategies and identify innovative molecules for relapse prevention that will be tested in experimental human studies. This research program will ultimately help in consolidating addiction research clusters in Germany that can effectively conduct large clinical trials, implement early intervention strategies and impact political and healthcare decision makers.

Neuroimaging as a potential biomarker to optimize psychiatric research and treatment

Complex, polygenic phenotypes in psychiatry hamper our understanding of the underlying molecular pathways and mechanisms of many diseases. The unknown aetiology, together with symptoms which often show a large variability both across individuals and over time and also tend to respond comparatively slowly to medication, can be a problem for patient treatment and drug development. We argue that neuroimaging has the potential to improve psychiatric treatment in two ways. First, by reducing phenotypic complexity, neuroimaging intermediate phenotypes can help to identify disease-related genes and can shed light into the biological mechanisms of known risk genes. Second, quantitative neuroimaging markers - reflecting the spectrum of impairment on a brain-based level - can be used as a more sensitive, reliable and immediate treatment response biomarker. In the end, enhancing both our understanding of the pathophysiology of psychiatric disorders and the prediction of treatment success could eventually optimise current therapy plans.