Imaging genetics and development: challenges and promises

A Systems Biology Approach for Hypothesizing the Effect of Genetic Variants on Neuroimaging Features in Alzheimer's Disease

BACKGROUND

Neuroimaging markers provide quantitative insight into brain structure and function in neurodegenerative diseases, such as Alzheimer's disease, where we lack mechanistic insights to explain pathophysiology. These mechanisms are often mediated by genes and genetic variations and are often studied through the lens of genome-wide association studies. Linking these two disparate layers (i.e., imaging and genetic variation) through causal relationships between biological entities involved in the disease's etiology would pave the way to large-scale mechanistic reasoning and interpretation.

OBJECTIVE

We explore how genetic variants may lead to functional alterations of intermediate molecular traits, which can further impact neuroimaging hallmarks over a series of biological processes across multiple scales.

METHODS

We present an approach in which knowledge pertaining to single nucleotide polymorphisms and imaging readouts is extracted from the literature, encoded in Biological Expression Language, and used in a novel workflow to assist in the functional interpretation of SNPs in a clinical context.

RESULTS

We demonstrate our approach in a case scenario which proposes KANSL1 as a candidate gene that accounts for the clinically reported correlation between the incidence of the genetic variants and hippocampal atrophy. We find that the workflow prioritizes multiple mechanisms reported in the literature through which KANSL1 may have an impact on hippocampal atrophy such as through the dysregulation of cell proliferation, synaptic plasticity, and metabolic processes.

CONCLUSION

We have presented an approach that enables pinpointing relevant genetic variants as well as investigating their functional role in biological processes spanning across several, diverse biological scales.

Genetic Variation of a DRD2 Co-expression Network is Associated with Changes in Prefrontal Function After D2 Receptors Stimulation

Dopamine D2 receptors (D2Rs) contribute to the inverted U-shaped relationship between dopamine signaling and prefrontal function. Genetic networks from post-mortem human brain revealed 84 partner genes co-expressed with DRD2. Moreover, eight functional single nucleotide polymorphisms combined into a polygenic co-expression index (PCI) predicted co-expression of this DRD2 network and were associated with prefrontal function in humans. Here, we investigated the non-linear association of the PCI with behavioral and Working Memory (WM) related brain response to pharmacological D2Rs stimulation. Fifty healthy volunteers took part in a double-blind, placebo-controlled, functional MRI (fMRI) study with bromocriptine and performed the N-Back task. The PCI by drug interaction was significant on both WM behavioral scores (P = 0.046) and related prefrontal activity (all corrected P < 0.05) using a polynomial PCI model. Non-linear responses under placebo were reversed by bromocriptine administration. fMRI results on placebo were replicated in an independent sample of 50 participants who did not receive drug administration (P = 0.034). These results match earlier evidence in non-human primates and confirm the physiological relevance of this DRD2 co-expression network. Results show that in healthy subjects, different alleles evaluated as an ensemble are associated with non-linear prefrontal responses. Therefore, brain response to a dopaminergic drug may depend on a complex system of allelic patterns associated with DRD2 co-expression.

Genetic influence on ageing-related changes in resting-state brain functional networks in healthy adults: A systematic review

This systematic review examines the genetic and epigenetic factors associated with resting-state functional connectivity (RSFC) in healthy human adult brains across the lifespan, with a focus on genes associated with Alzheimer's disease (AD). There were 58 studies included. The key findings are: (i) genetic factors have a low to moderate contribution; (ii) the apolipoprotein E ε2/3/4 polymorphism was the most studied genetic variant, with the APOE-ε4 allele most consistently associated with deficits of the default mode network, but there were insufficient studies to determine the relationships with other AD candidate risk genes; (iii) a single genome-wide association study identified several variants related to RSFC; (iv) two epigenetic independent studies showed a positive relationship between blood DNA methylation of the SLC6A4 promoter and RSFC measures. Thus, there is emerging evidence that genetic and epigenetic variation influence the brain's functional organisation and connectivity over the adult lifespan. However, more studies are required to elucidate the roles genetic and epigenetic factors play in RSFC measures across the adult lifespan.

Pubertal maturation and sex effects on the default-mode network connectivity implicated in mood dysregulation

This study examines the effects of puberty and sex on the intrinsic functional connectivity (iFC) of brain networks, with a focus on the default-mode network (DMN). Consistently implicated in depressive disorders, the DMN's function may interact with puberty and sex in the development of these disorders, whose onsets peak in adolescence, and which show strong sex disproportionality (females > males). The main question concerns how the DMN evolves with puberty as a function of sex. These effects are expected to involve within- and between-network iFC, particularly, the salience and the central-executive networks, consistent with the Triple-Network Model. Resting-state scans of an adolescent community sample (n = 304, male/female: 157/147; mean/std age: 14.6/0.41 years), from the IMAGEN database, were analyzed using the AFNI software suite and a data reduction strategy for the effects of puberty and sex. Three midline regions (medial prefrontal, pregenual anterior cingulate, and posterior cingulate), within the DMN and consistently implicated in mood disorders, were selected as seeds. Within- and between-network clusters of the DMN iFC changed with pubertal maturation differently in boys and girls (puberty-X-sex). Specifically, pubertal maturation predicted weaker iFC in girls and stronger iFC in boys. Finally, iFC was stronger in boys than girls independently of puberty. Brain-behavior associations indicated that lower connectivity of the anterior cingulate seed predicted higher internalizing symptoms at 2-year follow-up. In conclusion, weaker iFC of the anterior DMN may signal disconnections among circuits supporting mood regulation, conferring risk for internalizing disorders.

FGWAS: Functional genome wide association analysis

Functional phenotypes (e.g., subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e.g., functional smoothness and correlation). The aim of this paper is to develop a functional genome-wide association analysis (FGWAS) framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genome-wide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. We have successfully applied FGWAS to large-scale analysis of data from the Alzheimer's Disease Neuroimaging Initiative for 708 subjects, 30,000 vertices on the left and right hippocampal surfaces, and 501,584 SNPs.

Spatially Weighted Principal Component Analysis for Imaging Classification

The aim of this paper is to develop a supervised dimension reduction framework, called Spatially Weighted Principal Component Analysis (SWPCA), for high dimensional imaging classification. Two main challenges in imaging classification are the high dimensionality of the feature space and the complex spatial structure of imaging data. In SWPCA, we introduce two sets of novel weights including global and local spatial weights, which enable a selective treatment of individual features and incorporation of the spatial structure of imaging data and class label information. We develop an e cient two-stage iterative SWPCA algorithm and its penalized version along with the associated weight determination. We use both simulation studies and real data analysis to evaluate the finite-sample performance of our SWPCA. The results show that SWPCA outperforms several competing principal component analysis (PCA) methods, such as supervised PCA (SPCA), and other competing methods, such as sparse discriminant analysis (SDA).

Neural correlates of high-risk behavior tendencies and impulsivity in an emotional Go/NoGo fMRI task

Improved neuroscientific understanding of high-risk behaviors such as alcohol binging, drug use, and unsafe sex will lead to therapeutic advances for high-risk groups. High-risk behavior often occurs in an emotionally-charged context, and behavioral inhibition and emotion regulation play important roles in risk-related decision making. High impulsivity is an important potential contributor to high-risk behavior tendencies. We explored the relationships between high-risk behavior tendencies, impulsivity, and fMRI brain activations in an emotional Go/NoGo task. This task presented emotional distractor pictures (aversive vs. neutral) simultaneously with Go/NoGo stimuli (square vs. circle) that required a button press or withholding of the press, respectively. Participants' risk behavior tendencies were assessed with the Cognitive Appraisal of Risky Events (CARE) scale. The Barratt Impulsivity Scale 11 (BIS) was used to assess participant impulsivity. Individuals with higher CARE risk scores exhibited reduced activation related to response inhibition (NoGo-Go) in right orbital frontal cortex (OFC) and ventromedial prefrontal cortex. These regions did not show a significant relationship with impulsivity scores. Conversely, more impulsive individuals showed reduced emotion-related activity (aversive-neutral distractors) in dorsomedial prefrontal cortex, perigenual anterior cingulate cortex, and right posterior OFC. There were distinct neural correlates of high-risk behavior tendency and impulsivity in terms of brain activity in the emotional Go/NoGo task. This dissociation supports the conception of high-risk behavior tendency as a distinct construct from that of impulsivity. Our results suggest that treatment for high-risk behavior may be more effective with a nuanced approach that does not conflate high impulsivity necessarily with high-risk behavior tendencies.

Environmental and Genetic Influences on Neurocognitive Development: The Importance of Multiple Methodologies and Time-Dependent Intervention

Genetic mutations and environmental factors dynamically influence gene expression and developmental trajectories at the neural, cognitive, and behavioral levels. The examples in this article cover different periods of neurocognitive development-early childhood, adolescence, and adulthood-and focus on studies in which researchers have used a variety of methodologies to illustrate the early effects of socioeconomic status and stress on brain function, as well as how allelic differences explain why some individuals respond to intervention and others do not. These studies highlight how similar behaviors can be driven by different underlying neural processes and show how a neurocomputational model of early development can account for neurodevelopmental syndromes, such as autism spectrum disorders, with novel implications for intervention. Finally, these studies illustrate the importance of the timing of environmental and genetic factors on development, consistent with our view that phenotypes are emergent, not predetermined.

Developmental imaging genetics: linking dopamine function to adolescent behavior

Adolescence is a period of development characterized by numerous neurobiological changes that significantly influence behavior and brain function. Adolescence is of particular interest due to the alarming statistics indicating that mortality rates increase two to three-fold during this time compared to childhood, due largely to a peak in risk-taking behaviors resulting from increased impulsivity and sensation seeking. Furthermore, there exists large unexplained variability in these behaviors that are in part mediated by biological factors. Recent advances in molecular genetics and functional neuroimaging have provided a unique and exciting opportunity to non-invasively study the influence of genetic factors on brain function in humans. While genes do not code for specific behaviors, they do determine the structure and function of proteins that are essential to the neuronal processes that underlie behavior. Therefore, studying the interaction of genotype with measures of brain function over development could shed light on critical time points when biologically mediated individual differences in complex behaviors emerge. Here we review animal and human literature examining the neurobiological basis of adolescent development related to dopamine neurotransmission. Dopamine is of critical importance because of (1) its role in cognitive and affective behaviors, (2) its role in the pathogenesis of major psychopathology, and (3) the protracted development of dopamine signaling pathways over adolescence. We will then focus on current research examining the role of dopamine-related genes on brain function. We propose the use of imaging genetics to examine the influence of genetically mediated dopamine variability on brain function during adolescence, keeping in mind the limitations of this approach.

Translational developmental studies of stress on brain and behavior: implications for adolescent mental health and illness?

Adolescence is the transition from childhood to adulthood, with onset marked by puberty and the offset by relative independence from parents. Across species, it is a time of incredible change that carries increased risks and rewards. The ability of the individual to respond adequately to the mental, physical and emotional stresses of life during this time is a function of both their early environment and their present state. In this article, we focus on the effects that acute threat and chronic stress have on the brain and behavior in humans and rodents. First, we highlight developmental changes in frontolimbic function as healthy individuals transition into and out of adolescence. Second, we examine genetic factors that may enhance susceptibility to stress in one individual over another using translation from genetic mouse models to human neuroimaging. Third, we examine how the timing and nature of stress varies in its impact on brain and behavior. These findings are discussed in the context of implications for adolescent mental health and illness.

Tensor Regression with Applications in Neuroimaging Data Analysis

Classical regression methods treat covariates as a vector and estimate a corresponding vector of regression coefficients. Modern applications in medical imaging generate covariates of more complex form such as multidimensional arrays (tensors). Traditional statistical and computational methods are proving insufficient for analysis of these high-throughput data due to their ultrahigh dimensionality as well as complex structure. In this article, we propose a new family of tensor regression models that efficiently exploit the special structure of tensor covariates. Under this framework, ultrahigh dimensionality is reduced to a manageable level, resulting in efficient estimation and prediction. A fast and highly scalable estimation algorithm is proposed for maximum likelihood estimation and its associated asymptotic properties are studied. Effectiveness of the new methods is demonstrated on both synthetic and real MRI imaging data.

Treating the developing brain: implications from human imaging and mouse genetics

A fundamental issue in psychiatric medicine is the lack of empirical evidence indicating when, during development, a treatment will be most effective for a patient. We review behavioral and brain changes that occur across development, focusing on the period of adolescence, when there is a peak in diagnosis of many psychiatric disorders. We use anxiety disorders as an example because of their high prevalence in youth (affecting as many as 1 in 10). Basic forms of fear learning, which are at the core of anxiety disorders and are the targets of behavioral therapeutics, are examined as a function of age. We also discuss how fear learning has been genetically modulated in mice and humans. Based on these findings, we provide future directions for determining the efficacy of innovative therapies and preventive strategies for anxiety disorders as a function of age and potential genetic effects inferred from mice and humans.

On the biology of technostress

Despite the fact that human society has greatly benefited from the availability of information and communication technologies (ICT), both the use and ubiquity of ICT may also have a "dark side." Direct human interaction with ICT, as well as perceptions, emotions, and thoughts regarding the implementation of ICT in organizations and its pervasiveness in society in general, may lead to notable stress perceptions--a type of stress referred to as technostress. Analysis of the information systems (IS) literature reveals that technostress has hardly been addressed from a biological perspective. This is problematic, because biology not only provides objective measurements, but also, to a notable degree, determines human behavior toward ICT. Most important, biological measures (e.g., stress hormone levels, cardiovascular activity) are crucial predictors of human health, making them an indispensable complement to self-reports on stress perceptions. Against this background, the present article reviews the technostress research based on biological approaches that has been published in various disciplines such as human-computer interaction, medicine, biological psychology, and ergonomics. With the goal of developing a "big-picture" view of technostress and biology, this article integrates a body of highly fragmented work. The review reveals significant negative biological effects that develop from human interaction with ICT (e.g., increased activity of the cardiovascular system, or elevated levels of stress hormones such as adrenaline and cortisol). However, the review also indicates that countermeasures, which may positively affect biological parameters (e.g., reduced levels of stress hormones), do exist. Drawing on the literature review, this article also specifies a research agenda for future technostress research. The agenda is organized along three themes (theory and methods, design science and engineering, health and coping strategies), and proposes fifteen research questions (topics) that can be addressed in future investigations.

Transitional and translational studies of risk for anxiety

Adolescence reflects a period of increased rates of anxiety, depression, and suicide. Yet most teens emerge from this period with a healthy, positive outcome. In this article, we identify biological factors that may increase risk for some individuals during this developmental period by: (1) examining changes in neural circuitry underlying core phenotypic features of anxiety as healthy individuals transition into and out of adolescence; (2) examining genetic factors that may enhance the risk for psychopathology in one individual over another using translation from mouse models to human neuroimaging and behavior; and (3) examining the effects of early experiences on core phenotypic features of anxiety using human neuroimaging and behavioral approaches. Each of these approaches alone provides only limited information on genetic and environmental influences on complex human behavior across development. Together, they reflect an emerging field of translational developmental neuroscience in forming important bridges between animal models of neurodevelopmental and neuropsychiatric disorders.

Genetically informative research on adolescent substance use: methods, findings, and challenges

OBJECTIVE

To provide an overview of the genetic epidemiology of substance use and misuse in adolescents.

METHOD

A selective review of genetically informative research strategies, their limitations, and key findings examining issues related to the heritability of substance use and substance use disorders in children and adolescents is presented.

RESULTS

Adoption, twin, and extended-family designs have established there is a strong heritable component to liability to nicotine, alcohol, and illicit drug dependence in adults. However, shared environmental influences are relatively stronger in youth samples and at earlier stages of substance involvement (e.g., use). There is considerable overlap in the genetic influences associated with the abuse/dependence across drug classes, and shared genetic influences contribute to the commonly observed associations between substance-use disorders and externalizing and, to a lesser extent, internalizing psychopathology. Rapid technologic advances have made the identification of specific gene variants that influence risks for substance-use disorders feasible, and linkage and association (including genomewide association studies) have identified promising candidate genes implicated in the development of substance-use disorders.

CONCLUSIONS

Studies using genetically informative research designs, including those that examine aggregate genetic factors and those examining specific gene variants, individually and in interaction with environmental influences, offer promising avenues not only for delineating genetic effects on substance-use disorders but also for understanding the unfolding of risk across development and the interaction between environmental and genetic factors in the etiology of these disorders.

The storm and stress of adolescence: insights from human imaging and mouse genetics

The characterization of adolescence as a time of "storm and stress" remains an open debate. Intense and frequent negative affect during this period has been hypothesized to explain the increased rates of affective disorders, suicide, and accidental death during this time of life. Yet some teens emerge from adolescence with minimal turmoil. We provide a neurobiological model of adolescence that proposes an imbalance in the development of subcortical limbic (e.g., amygdala) relative to prefrontal cortical regions as a potential mechanism for heightened emotionality during this period. Empirical support for this model is provided from recent behavioral and human imaging studies on the development of emotion regulation. We then provide examples of environmental factors that may exacerbate imbalances in amygdala-ventrofrontal function increasing risk for anxiety related behaviors. Finally we present data from human and mouse studies to illustrate how genetic factors may enhance or diminish this risk. Together, these studies provide a converging methods approach for understanding the highly variable stress and turmoil experienced in adolescence.