Imaging genetics: perspectives from studies of genetically driven variation in serotonin function and corticolimbic affective processing

Individuals being high in their sensitivity to the environment: Are sensitive period changes in play?

All individuals on planet earth are sensitive to the environment, but some more than others. These individual differences in sensitivity to environments are seen across many animal species including humans, and can influence personalities as well as vulnerability and resilience to mental disorders. Yet, little is known about the underlying brain mechanisms. Key genes that contribute to individual differences in environmental sensitivity are the serotonin transporter, dopamine D4 receptor and brain-derived neurotrophic factor genes. By synthesizing neurodevelopmental findings of these genetic factors, and discussing them through the lens of mechanisms related to sensitive periods, which are phases of heightened neuronal plasticity during which a certain network is being finetuned by experiences, we propose that these genetic factors delay but extend postnatal sensitive periods. This may explain why sensitive individuals show behavioral features that are characteristic of a young brain state at the level of sensory information processing, such as reduced filtering or blockade of irrelevant information, resulting in a sensory processing system that 'keeps all options open'.

Organic cation transporters in psychiatric and substance use disorders

Psychiatric and substance use disorders inflict major public health burdens worldwide. Their widespread burden is compounded by a dearth of effective treatments, underscoring a dire need to uncover novel therapeutic targets. In this review, we summarize the literature implicating organic cation transporters (OCTs), including three subtypes of OCTs (OCT1, OCT2, and OCT3) and the plasma membrane monoamine transporter (PMAT), in the neurobiology of psychiatric and substance use disorders with an emphasis on mood and anxiety disorders, alcohol use disorder, and psychostimulant use disorder. OCTs transport monoamines with a low affinity but high capacity, situating them to play a central role in regulating monoamine homeostasis. Preclinical evidence discussed here suggests that OCTs may serve as promising targets for treatment of psychiatric and substance use disorders and encourage future research into their therapeutic potential.

The Genetic Side of the Mood: A Scientometric Review of the Genetic Basis of Mood Disorders

Mood disorders are highly heritable psychiatric disorders. Over the years, many genetic polymorphisms have been identified to pose a higher risk for the development of mood disorders. To overview the literature on the genetics of mood disorders, a scientometric analysis was performed on a sample of 5342 documents downloaded from Scopus. The most active countries and the most impactful documents in the field were identified. Furthermore, a total of 13 main thematic clusters emerged in the literature. From the qualitative inspection of clusters, it emerged that the research interest moved from a monogenic to a polygenic risk framework. Researchers have moved from the study of single genes in the early 1990s to conducting genome-wide association studies around 2015. In this way, genetic overlaps between mood disorders and other psychiatric conditions emerged too. Furthermore, around the 2010s, the interaction between genes and environmental factors emerged as pivotal in understanding the risk for mood disorders. The inspection of thematic clusters provides a valuable insight into the past and recent trends of research in the genetics of mood disorders and sheds light onto future lines of research.

A serotonergic biobehavioral signature differentiates cocaine use disorder participants administered mirtazapine

Cocaine use disorder (CUD) patients display heterogenous symptoms and unforeseeable responses to available treatment approaches, highlighting the need to identify objective, accessible biobehavioral signatures to predict clinical trial success in this population. In the present experiments, we employed a task-based behavioral and pharmacogenetic-fMRI approach to address this gap. Craving, an intense desire to take cocaine, can be evoked by exposure to cocaine-associated stimuli which can trigger relapse during attempted recovery. Attentional bias towards cocaine-associated words is linked to enhanced effective connectivity (EC) from the anterior cingulate cortex (ACC) to hippocampus in CUD participants, an observation which was replicated in a new cohort of participants in the present studies. Serotonin regulates attentional bias to cocaine and the serotonergic antagonist mirtazapine decreased activated EC associated with attentional bias, with greater effectiveness in those CUD participants carrying the wild-type 5-HT2CR gene relative to a 5-HT2CR single nucleotide polymorphism (rs6318). These data suggest that the wild-type 5-HT2CR is necessary for the efficacy of mirtazapine to decrease activated EC in CUD participants and that mirtazapine may serve as an abstinence enhancer to mitigate brain substrates of craving in response to cocaine-associated stimuli in participants with this pharmacogenetic descriptor. These results are distinctive in outlining a richer "fingerprint" of the complex neurocircuitry, behavior and pharmacogenetics profile of CUD participants which may provide insight into success of future medications development projects.

Group Sparse Joint Non-Negative Matrix Factorization on Orthogonal Subspace for Multi-Modal Imaging Genetics Data Analysis

With the development of multi-model neuroimaging technology and gene detection technology, the efforts of integrating multi-model imaging genetics data to explore the virulence factors of schizophrenia (SZ) are still limited. To address this issue, we propose a novel algorithm called group sparse of joint non-negative matrix factorization on orthogonal subspace (GJNMFO). Our algorithm fuses single nucleotide polymorphism (SNP) data, function magnetic resonance imaging (fMRI) data and epigenetic factors (DNA methylation) by projecting three-model data into a common basis matrix and three different coefficient matrices to identify risk genes, epigenetic factors and abnormal brain regions associated with SZ. Specifically, we introduce orthogonal constraints on the basis matrix to discard unimportant features in the row of coefficient matrices. Since imaging genetics data have rich group information, we draw into group sparse on three coefficient matrices to make the extracted features more accurate. Both the simulated and real Mind Clinical Imaging Consortium (MCIC) datasets are performed to validate our approach. Simulation results show that our algorithm works better than other competing methods. Through the experiments of MCIC datasets, GJNMFO reveals a set of risk genes, epigenetic factors and abnormal brain functional regions, which have been verified to be both statistically and biologically significant.

Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments

A growing body of evidence now proposes that machine learning and deep learning techniques can serve as a vital foundation for the pharmacogenomics of antidepressant treatments in patients with major depressive disorder (MDD). In this review, we focus on the latest developments for pharmacogenomics research using machine learning and deep learning approaches together with neuroimaging and multi-omics data. First, we review relevant pharmacogenomics studies that leverage numerous machine learning and deep learning techniques to determine treatment prediction and potential biomarkers for antidepressant treatments in MDD. In addition, we depict some neuroimaging pharmacogenomics studies that utilize various machine learning approaches to predict antidepressant treatment outcomes in MDD based on the integration of research on pharmacogenomics and neuroimaging. Moreover, we summarize the limitations in regard to the past pharmacogenomics studies of antidepressant treatments in MDD. Finally, we outline a discussion of challenges and directions for future research. In light of latest advancements in neuroimaging and multi-omics, various genomic variants and biomarkers associated with antidepressant treatments in MDD are being identified in pharmacogenomics research by employing machine learning and deep learning algorithms.

Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer's disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

Monoaminergic Genetic Variants, Prefrontal Cortex-Amygdala Circuit, and Emotional Symptoms in Children With ADHD: Exploration Based on the Gene-Brain-Behavior Relationship

Objective: This study aimed to explore the association between monoaminergic genetic variants and emotional lability (EL) symptoms in children with ADHD. In addition, genetic effects on prefrontal cortex (PFC)-amygdala functional connectivity (FC) were investigated. Method: Children with ADHD and controls were genotyped for five monoaminergic genetic variants and were evaluated for EL symptoms. Imaging genetic exploration was conducted with previously reported aberrant PFC-amygdala resting-state functional connectivities (RSFCs) as target features. Results: A genotypic effect on EL symptoms was only found for NET1-rs3785143, indicating higher EL symptoms in TT genotype carriers than in C-allele carriers. Imaging genetic analyses indicated a marginal effect of NET1-rs3785143 on ADHD-altered FC between the superficial amygdala (SFA) and middle frontal gyrus (MFG). Mediation analysis suggested potential effects of NET1-rs3785143 via RSFC (SFA-MFG) on EL. Conclusion:NET1 variants might participate in the pathogenesis of EL in children with ADHD by influencing the function of the PFC-amygdala circuit.

A Latent Gaussian Copula Model for Mixed Data Analysis in Brain Imaging Genetics

Recent advances in imaging genetics make it possible to combine different types of data including medical images like functional magnetic resonance imaging (fMRI) and genetic data like single nucleotide polymorphisms (SNPs) for comprehensive diagnosis of mental disorders. Understanding complex interactions among these heterogeneous data may give rise to a new perspective, while at the same time demand statistical models for their integration. Various graphical models have been proposed for the study of interaction or association networks with continuous, binary, and count data as well as the mixture of them. However, limited efforts have been made for the multinomial case, for instance, SNP data. Our goal is therefore to fill the void by developing a graphical model for the integration of fMRI image and SNP data, which can provide deeper understanding of the unknown neurogenetic mechanism. In this article, we propose a latent Gaussian copula model for mixed data containing multinomial components. We assume that the discrete variable is obtained by discretizing a latent (unobserved) continuous variable and then create a semi-rank based estimator of the graph structure. The simulation results demonstrate that the proposed latent correlation has more steady and accurate performance than several existing methods in detecting graph structure. When applying to a real schizophrenia data consisting of SNP array and fMRI image collected by the Mind Clinical Imaging Consortium (MCIC), the proposed method reveals a set of distinct SNP-brain associations, which are verified to be biologically significant. The proposed model is statistically promising in handling mixed types of data including multinomial components, which can find widespread applications. To promote reproducible research, the R code is available at https://github.com/Aiying0512/LGCM.

Imaging Genetics Study Based on a Temporal Group Sparse Regression and Additive Model for Biomarker Detection of Alzheimer's Disease

Imaging genetics is an effective tool used to detect potential biomarkers of Alzheimer's disease (AD) in imaging and genetic data. Most existing imaging genetics methods analyze the association between brain imaging quantitative traits (QTs) and genetic data [e.g., single nucleotide polymorphism (SNP)] by using a linear model, ignoring correlations between a set of QTs and SNP groups, and disregarding the varied associations between longitudinal imaging QTs and SNPs. To solve these problems, we propose a novel temporal group sparsity regression and additive model (T-GSRAM) to identify associations between longitudinal imaging QTs and SNPs for detection of potential AD biomarkers. We first construct a nonparametric regression model to analyze the nonlinear association between QTs and SNPs, which can accurately model the complex influence of SNPs on QTs. We then use longitudinal QTs to identify the trajectory of imaging genetic patterns over time. Moreover, the SNP information of group and individual levels are incorporated into the proposed method to boost the power of biomarker detection. Finally, we propose an efficient algorithm to solve the whole T-GSRAM model. We evaluated our method using simulation data and real data obtained from AD neuroimaging initiative. Experimental results show that our proposed method outperforms several state-of-the-art methods in terms of the receiver operating characteristic curves and area under the curve. Moreover, the detection of AD-related genes and QTs has been confirmed in previous studies, thereby further verifying the effectiveness of our approach and helping understand the genetic basis over time during disease progression.

Serotonin transporter genotype modulates resting state and predator stress-induced amygdala perfusion in mice in a sex-dependent manner

The serotonin transporter (5-HTT) is a key molecule of serotoninergic neurotransmission and target of many anxiolytics and antidepressants. In humans, 5-HTT gene variants resulting in lower expression levels are associated with behavioral traits of anxiety. Furthermore, functional magnetic resonance imaging (fMRI) studies reported increased cerebral blood flow (CBF) during resting state (RS) and amygdala hyperreactivity. 5-HTT deficient mice as an established animal model for anxiety disorders seem to be well suited for investigating amygdala (re-)activity in an fMRI study. We investigated wildtype (5-HTT+/+), heterozygous (5-HTT+/-), and homozygous 5-HTT-knockout mice (5-HTT-/-) of both sexes in an ultra-high-field 17.6 Tesla magnetic resonance scanner. CBF was measured with continuous arterial spin labeling during RS, stimulation state (SS; with odor of rats as aversive stimulus), and post-stimulation state (PS). Subsequently, post mortem c-Fos immunohistochemistry elucidated neural activation on cellular level. The results showed that in reaction to the aversive odor CBF in total brain and amygdala of all mice significantly increased. In male 5-HTT+/+ mice amygdala RS CBF levels were found to be significantly lower than in 5-HTT+/- mice. From RS to SS 5-HTT+/+ amygdala perfusion significantly increased compared to both 5-HTT+/- and 5-HTT-/- mice. Perfusion level changes of male mice correlated with the density of c-Fos-immunoreactive cells in the amygdaloid nuclei. In female mice the perfusion was not modulated by the 5-Htt-genotype, but by estrous cycle stages. We conclude that amygdala reactivity is modulated by the 5-Htt genotype in males. In females, gonadal hormones have an impact which might have obscured genotype effects. Furthermore, our results demonstrate experimental support for the tonic model of 5-HTTLPR function.

Dissecting diagnostic heterogeneity in depression by integrating neuroimaging and genetics

Depression is a heterogeneous and etiologically complex psychiatric syndrome, not a unitary disease entity, encompassing a broad spectrum of psychopathology arising from distinct pathophysiological mechanisms. Motivated by a need to advance our understanding of these mechanisms and develop new treatment strategies, there is a renewed interest in investigating the neurobiological basis of heterogeneity in depression and rethinking our approach to diagnosis for research purposes. Large-scale genome-wide association studies have now identified multiple genetic risk variants implicating excitatory neurotransmission and synapse function and underscoring a highly polygenic inheritance pattern that may be another important contributor to heterogeneity in depression. Here, we review various sources of phenotypic heterogeneity and approaches to defining and studying depression subtypes, including symptom-based subtypes and biology-based approaches to decomposing the depression syndrome. We review "dimensional," "categorical," and "hybrid" approaches to parsing phenotypic heterogeneity in depression and defining subtypes using functional neuroimaging. Next, we review recent progress in neuroimaging genetics (correlating neuroimaging patterns of brain function with genetic data) and its potential utility for generating testable hypotheses concerning molecular and circuit-level mechanisms. We discuss how genetic variants and transcriptomic profiles may confer risk for depression by modulating brain structure and function. We conclude by highlighting several promising areas for future research into the neurobiological underpinnings of heterogeneity, including efforts to understand sexually dimorphic mechanisms, the longitudinal dynamics of depressive episodes, and strategies for developing personalized treatments and facilitating clinical decision-making.

Research on the Effects of Occupational Stress and the DRD2 Gene on the Psychological Health of Workers in the Xinjiang Desert Oil Field

Background: This study investigated the relationship between occupational stress and the mental health of people working in oil fields in the arid desert environment of Xinjiang, and revealed the causal relationship between occupational stress and psychological disorders, while furthermore exploring the relationship between psychological disorders and genetic levels. Methods: The participants of this study included oil field company workers from the Xinjiang Petroleum Administration of Karamay City, Xinjiang, who underwent occupational health examinations. The Occupational Stress Inventory Revised Edition (OSI-R) was used to measure the occupational stress of the oil workers. The mental health status of oil workers was evaluated using the Symptoms Checklist-90. Results: Occupational tasks: The total scores of the personal strain and mental health questionnaires were positively correlated with somatization, obsessive-compulsive symptoms, interpersonal sensitivity, depression, anxiety, hostility, terror, paranoia, and psychosis (P < 0.05). Individual coping resources and the mental health total score was negatively correlated with somatization, obsessive-compulsive symptoms, interpersonal sensitivity, anxiety, hostility, terror, paranoia, and psychosis. The following factors were identified as mental health risk factors: female gender; age 45 and above (relative to ≤30 years old); high scores on the personal strain questionnaire; occupational stress; external effort; internal investment; and high effort-low return. The following factors were identified as protective factors for mental health: Han nationality; oil transportation (relative to drilling); individual resilience; and work returns. In respect to the abnormal psychological group and the normal psychological group, statistically significant differences were found in the distribution of genotypes and allele frequencies at the rs1800497 locus (P < 0.05). The depression and paranoia scores observed between different genotype groups at the rs1800497 locus were statistically significant (P < 0.05). Conclusions: This study shows that occupational stress and the D2 dopamine receptor (DRD2) gene have an impact on the mental health of oil field workers in the arid desert environment of Xinjiang. Effort-reward imbalance and occupational stress were identified as risk factors for mental health, while rewards for work were protective factors. Higher levels of occupational stress may lead to depression and other psychological disorders, adversely affecting mental health. In oil field operators in the arid desert environment of Xinjiang, the AA genotype of the DRD2 gene in the rs1800497 locus was identified as a genotype specific to susceptibility to mental health problems, and a correlation was found between the A allele and an increased risk of psychological problems. Therefore, it is necessary to devise relevant measures to alleviate occupational stress among oil workers and increase their job rewards, so as to improve their mental health.

Stress management in obesity during a thermal spa residential programme (ObesiStress): protocol for a randomised controlled trial study

INTRODUCTION

Stress and obesity are two public health issues. The relationship between obesity and stress is biological through the actions of stress on the major hormones that regulate appetite (leptin and ghrelin). Many spa resorts in France specialise in the treatment of obesity, but no thermal spa currently proposes a specific programme to manage stress in obesity. The ObesiStress protocol has been designed to offer a new residential stress management programme. This thermal spa treatment of obesity implements stress management strategies as suggested by international recommendations.

METHODS AND ANALYSIS

140 overweight or obese participants with a Body Mass Index of >25 kg/m² and aged over 18 years will be recruited. Participants will be randomised into two groups: a control group of usual practice (restrictive diet, physical activity and thermal spa treatment) and an intervention group with stress management in addition to the usual practice. In the present protocol, parameters will be measured on five occasions (at inclusion, at the beginning of the spa (day 0), at the end of the spa (day 21), and at 6 and 12 months). The study will assess the participants' heart rate variability, cardiac remodelling and function, electrodermal activity, blood markers, anthropometric profile, body composition, psychology and quality of life via the use of questionnaires and bone parameters.

ETHICS AND DISSEMINATION

The ObesiStress protocol complies with the ethics guidelines for Clinical Research and has been approved by the ethics committee (CPP Sud-Est VI, Clermont-Ferrand - ANSM: 2016-A01774-47). This study aimed to highlight the efficacy of a 21-day thermal spa residential programme of stress management in obesity through objective measurements of well-being and cardiovascular morbidity. Results will be disseminated during several research conferences and articles published in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT03578757.

Effects of a short residential thermal spa program to prevent work-related stress/burnout on stress biomarkers: the ThermStress proof of concept study

Objective

Work-related stress is a public health issue. Stress has multiple physical and psychological consequences, the most serious of which are increased mortality and cardiovascular morbidity. The ThermStress protocol was designed to offer a short residential thermal spa program for work-related stress prevention that is compatible with a professional context.

Methods

Participants will be 56 male and female workers aged 18 years or above. All participants will undergo a 6-day residential spa program comprising psychological intervention, physical activity, thermal spa treatment, health education, eating disorder therapy and a follow-up. On six occasions, participants’ heart rate variability, cardiac remodelling and function, electrodermal activity, blood markers, anthropometry and body composition, psychology and quality of life will be measured using questionnaires and bone parameters.

Results

This study protocol reports the planned and ongoing research for this intervention.

Discussion

The ThermStress protocol has been approved by an institutional ethics committee (ANSM: 2016 A02082 49). It is expected that this proof of concept study will highlight the effect of a short-term specific residential thermal spa program on the prevention of occupational burnout and work-related stress. The findings will be disseminated at several research conferences and in published articles in peer-reviewed journals.

Trial Registration: ClinicalTrials.gov ( NCT 03536624, 24/05/2018)

Depression genetic risk score is associated with anhedonia-related markers across units of analysis

Investigations of pathophysiological mechanisms implicated in vulnerability to depression have been negatively impacted by the significant heterogeneity characteristic of psychiatric syndromes. Such challenges are also reflected in numerous null findings emerging from genome-wide association studies (GWAS) of depression. Bolstered by increasing sample sizes, recent GWAS studies have identified genetics variants linked to MDD. Among them, Okbay and colleagues (Nat. Genet. 2016 Jun;48(6):624-33) identified genetic variants associated with three well-validated depression-related phenotypes: subjective well-being, depressive symptoms, and neuroticism. Despite this progress, little is known about psychopathological and neurobiological mechanisms underlying such risk. To fill this gap, a genetic risk score (GRS) was computed from the Okbay's study for a sample of 88 psychiatrically healthy females. Across two sessions, participants underwent two well-validated psychosocial stressors, and performed two separate tasks probing reward learning both before and after stress. Analyses tested whether GRS scores predicted anhedonia-related phenotypes across three units of analyses: self-report (Snaith Hamilton Pleasure Scale), behavior (stress-induced changes in reward learning), and circuits (stress-induced changes in striatal reward prediction error; striatal volume). GRS scores were negatively associated with anhedonia-related phenotypes across all units of analyses but only circuit-level variables were significant. In addition, the amount of explained variance was systematically larger as variables were putatively closer to the effects of genes (self-report < behavior < neural circuitry). Collectively, findings implicate anhedonia-related phenotypes and neurobiological mechanisms in increased depression vulnerability, and highlight the value of focusing on fundamental dimensions of functioning across different units of analyses.

The Translational Potential of Neuroimaging Genomic Analyses To Diagnosis And Treatment In The Mental Disorders

Imaging genomics focuses on characterizing genomic influence on the variation of neurobiological traits, holding promise for illuminating the pathogenesis, reforming the diagnostic system, and precision medicine of mental disorders. This paper aims to provide an overall picture of the current status of neuroimaging-genomic analyses in mental disorders, and how we can increase their translational potential into clinical practice. The review is organized around three perspectives. (a) Towards reliability, generalizability and interpretability, where we summarize the multivariate models and discuss the considerations and trade-offs of using these methods and how reliable findings may be reached, to serve as ground for further delineation. (b) Towards improved diagnosis, where we outline the advantages and challenges of constructing a dimensional transdiagnostic model and how imaging genomic analyses map into this framework to aid in deconstructing heterogeneity and achieving an optimal stratification of patients that better inform treatment planning. (c) Towards improved treatment. Here we highlight recent efforts and progress in elucidating the functional annotations that bridge between genomic risk and neurobiological abnormalities, in detecting genomic predisposition and prodromal neurodevelopmental changes, as well as in identifying imaging genomic biomarkers for predicting treatment response. Providing an overview of the challenges and promises, this review hopefully motivates imaging genomic studies with multivariate, dimensional and transdiagnostic designs for generalizable and interpretable findings that facilitate development of personalized treatment.

The brain-derived neurotrophic factor Val66Met genotype does not influence the grey or white matter structures underlying recognition memory

A single nucleotide polymorphism (SNP) in the gene coding for brain-derived neurotrophic factor (BDNF) has previously been associated with a reduction in recognition memory performance. While previous findings have highlighted that this SNP contributes to recognition memory, little is known about its influence on subprocesses of recognition, familiarity and recollection. Previous research has reported reduced hippocampal volume and decreased fractional anisotropy in carriers of the Met allele across a range of white matter tracts, including those networks that may support recognition memory. Here, in a sample of 61 healthy young adults, we used a source memory task to measure accuracy on each recognition subprocess, in order to determine whether the Val66Met SNP (rs6265) influences these equally. Additionally, we compared grey matter volume between these groups for structures that underpin familiarity and recollection separately. Finally, we used probabilistic tractography to reconstruct tracts that subserve each of these two recognition systems. Behaviourally, we found group differences on the familiarity measure, but not on recollection. However, we did not find any group difference on grey- or white-matter structures. Together, these results suggest a functional influence of the Val66Met SNP that is independent of coarse structural changes, and nuance previous research highlighting the relationship between BDNF, brain structure, and behaviour.

Cognitive Control as a 5-HT1A-Based Domain That Is Disrupted in Major Depressive Disorder

Heterogeneity within Major Depressive Disorder (MDD) has hampered identification of biological markers (e.g., intermediate phenotypes, IPs) that might increase risk for the disorder or reflect closer links to the genes underlying the disease process. The newer characterizations of dimensions of MDD within Research Domain Criteria (RDoC) domains may align well with the goal of defining IPs. We compare a sample of 25 individuals with MDD compared to 29 age and education matched controls in multimodal assessment. The multimodal RDoC assessment included the primary IP biomarker, positron emission tomography (PET) with a selective radiotracer for 5-HT1A [(11C)WAY-100635], as well as event-related functional MRI with a Go/No-go task targeting the Cognitive Control network, neuropsychological assessment of affective perception, negative memory bias and Cognitive Control domains. There was also an exploratory genetic analysis with the serotonin transporter (5-HTTLPR) and monamine oxidase A (MAO-A) genes. In regression analyses, lower 5-HT1A binding potential (BP) in the MDD group was related to diminished engagement of the Cognitive Control network, slowed resolution of interfering cognitive stimuli, one element of Cognitive Control. In contrast, higher/normative levels of 5-HT1A BP in MDD (only) was related to a substantial memory bias toward negative information, but intact resolution of interfering cognitive stimuli and greater engagement of Cognitive Control circuitry. The serotonin transporter risk allele was associated with lower 1a BP and the corresponding imaging and cognitive IPs in MDD. Lowered 5HT 1a BP was present in half of the MDD group relative to the control group. Lowered 5HT 1a BP may represent a subtype including decreased engagement of Cognitive Control network and impaired resolution of interfering cognitive stimuli. Future investigations might link lowered 1a BP to neurobiological pathways and markers, as well as probing subtype-specific treatment targets.

Variation in the CACNB2 gene is associated with functional connectivity of the Hippocampus in bipolar disorder

BACKGROUND

Calcium voltage-gated channel auxiliary subunit β2 is a protein that, in humans, is encoded by the CACNB2 gene. The β2 subunit is an auxiliary protein of voltage-gated calcium channels, which is predominantly expressed in hippocampal pyramidal neurons. A single-nucleotide polymorphism at the CACNB2 gene (rs11013860) has been reported in genome-wide association studies to be associated with bipolar disorder (BD). However, the neural effects of rs11013860 expression are unknown. Thus, the current study investigated the mechanisms of how the CACNB2 gene influences hippocampal-cortical limbic circuits in patients with bipolar disorder (BD).

METHODS

A total of 202 subjects were studied [69 BD patients and 133 healthy controls (HC)]. Participants agreed to undergo resting-state functional magnetic resonance imaging (rs-fMRI) and have blood drawn for genetic testing. Participants were found to belong to either a CC group homozygous for the C-allele (17 BD, 41 HC), or an A-carrier group carrying the high risk A-allele (AA/CA genotypes; 52 BD, 92 HC). Brain activity was assessed using resting-state functional connectivity (rs-FC) analyses.

RESULTS

A main effect of genotype showed that the rs-FC of the AA/CA group was elevated more than that of the CC-group between the hippocampus and the regions of right-inferior temporal, fusiform, and left-inferior occipital gyri. Additionally, a significant diagnosis × genotype interaction was noted between the hippocampus and right pars triangularis. Furthermore, in BD patients, the AA/CA group showed lower rs-FC when compared to that of the CC group. Additionally, individuals from HC within the AA/CA group showed higher rs-FC than that of the CC group. Finally, within C-allele-carrying groups, individuals with BD showed significantly increased rs-FC compared to that of HC.

CONCLUSIONS

Our study demonstrates that BD patients with the CACNB2 rs11013860 AA/CA genotype may exhibit altered hippocampal-cortical connectivity.

Personality and Coping: Individual Differences in Responses to Emotion

Abundant evidence links personality with emotion via coping. Alternatively, personality can be viewed as an emergent property of responses to the experience of emotion. Dispositions to control, approach, escape, and avoid one's emotional experience underlie diverse traits, including positive and negative urgency, trait emotional approach and avoidance, alexithymia, and emotional expressiveness. In this review, we consider the neurobiological underpinnings of these dispositions and the nature (e.g., stability) and adaptiveness of the associated traits. Important future directions for research in this area include the roles of development, intraindividual variability and flexibility, and the intensity and structure of emotional experience.

Longitudinal Genotype-Phenotype Association Study through Temporal Structure Auto-Learning Predictive Model

With the rapid development of high-throughput genotyping and neuroimaging techniques, imaging genetics has drawn significant attention in the study of complex brain diseases such as Alzheimer's disease (AD). Research on the associations between genotype and phenotype improves the understanding of the genetic basis and biological mechanisms of brain structure and function. AD is a progressive neurodegenerative disease; therefore, the study on the relationship between single nucleotide polymorphism (SNP) and longitudinal variations of neuroimaging phenotype is crucial. Although some machine learning models have recently been proposed to capture longitudinal patterns in genotype-phenotype association studies, most machine-learning models base the learning on fixed structure among longitudinal prediction tasks rather than automatically learning the interrelationships. In response to this challenge, we propose a new automated time structure learning model to automatically reveal the longitudinal genotype-phenotype interactions and exploits such learned structure to enhance the phenotypic predictions. We proposed an efficient optimization algorithm for our model and provided rigorous theoretical convergence proof. We performed experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort for longitudinal phenotype prediction, including 3123 SNPs and 2 biomarkers (Voxel-Based Morphometry and FreeSurfer). The empirical results validate that our proposed model is superior to the counterparts. In addition, the best SNPs identified by our model have been replicated in the literature, which justifies our prediction.

Negative self-referential processing is associated with genetic variation in the serotonin transporter-linked polymorphic region (5-HTTLPR): Evidence from two independent studies

The current research examined whether carriers of the short 5-HTTLPR allele (in SLC6A4), who have been shown to selectively attend to negative information, exhibit a bias towards negative self-referent processing. The self-referent encoding task (SRET) was used to measure self-referential processing of positive and negative adjectives. Ratcliff's diffusion model isolated and extracted decision-making components from SRET responses and reaction times. Across the initial (N = 183) and replication (N = 137) studies, results indicated that short 5-HTTLPR allele carriers more easily categorized negative adjectives as self-referential (i.e., higher drift rate). Further, drift rate was associated with recall of negative self-referential stimuli. Findings across both studies provide further evidence that genetic variation may contribute to the etiology of negatively biased processing of self-referent information. Large scale studies examining the genetic contributions to negative self-referent processing may be warranted.

Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis

Motivation

Neuroimaging genetics identifies the relationships between genetic variants (i.e., the single nucleotide polymorphisms) and brain imaging data to reveal the associations from genotypes to phenotypes. So far, most existing machine-learning approaches are widely used to detect the effective associations between genetic variants and brain imaging data at one time-point. However, those associations are based on static phenotypes and ignore the temporal dynamics of the phenotypical changes. The phenotypes across multiple time-points may exhibit temporal patterns that can be used to facilitate the understanding of the degenerative process. In this article, we propose a novel temporally constrained group sparse canonical correlation analysis (TGSCCA) framework to identify genetic associations with longitudinal phenotypic markers.

Results

The proposed TGSCCA method is able to capture the temporal changes in brain from longitudinal phenotypes by incorporating the fused penalty, which requires that the differences between two consecutive canonical weight vectors from adjacent time-points should be small. A new efficient optimization algorithm is designed to solve the objective function. Furthermore, we demonstrate the effectiveness of our algorithm on both synthetic and real data (i.e., the Alzheimer’s Disease Neuroimaging Initiative cohort, including progressive mild cognitive impairment, stable MCI and Normal Control participants). In comparison with conventional SCCA, our proposed method can achieve strong associations and discover phenotypic biomarkers across multiple time-points to guide disease-progressive interpretation.

Availability and implementation

The Matlab code is available at https://sourceforge.net/projects/ibrain-cn/files/.

A Clinician's Perspective on Biomarkers

Psychiatrists and mental health professionals regularly perform various clinical tasks (e.g., detection, differential diagnosis, prognostication, treatment selection and implementation). How well they perform each of these tasks has a direct impact on patient outcomes. Measurement-based care has brought greater precision to these tasks and has improved outcomes. This article provides an overview of the types of biomeasures and biomarkers, the clinical uses of biomarkers, and the challenges in their development and clinical use. Although still in their infancy, biomarkers hold the promise of bringing even greater precision and even better outcomes in mental health. Biomeasures that could become biomarkers include genetic, proteomic, metabolomic, and immunologic measures, as well as physiological, functional, and brain structural measures. Mechanistic markers reflect and are based on the specific pathobiological processes that are involved in the development of a clinically defined condition. Some clinically relevant biomarkers may rely on this mechanistic understanding while others may not. Clinical biomarkers serve three broadly defined goals. Diagnostic markers define what is wrong. Prognostic markers define what will happen in the natural course of the condition, although they may also predict the course of illness during treatment. Theranostic markers address issues pertinent to treatment by defining whether, when, whom, and how to treat. Other biomarkers may be used to monitor the overall effect of treatment regardless of the therapeutic effects or to monitor the specific therapeutic effects of the intervention on the disorder itself. Biomarkers can also be used to estimate susceptibility/risk of developing the condition or the biological consequences of having had the disorder.

GPU Accelerated Browser for Neuroimaging Genomics

Neuroimaging genomics is an emerging field that provides exciting opportunities to understand the genetic basis of brain structure and function. The unprecedented scale and complexity of the imaging and genomics data, however, have presented critical computational bottlenecks. In this work we present our initial efforts towards building an interactive visual exploratory system for mining big data in neuroimaging genomics. A GPU accelerated browsing tool for neuroimaging genomics is created that implements the ANOVA algorithm for single nucleotide polymorphism (SNP) based analysis and the VEGAS algorithm for gene-based analysis, and executes them at interactive rates. The ANOVA algorithm is 110 times faster than the 4-core OpenMP version, while the VEGAS algorithm is 375 times faster than its 4-core OpenMP counter part. This approach lays a solid foundation for researchers to address the challenges of mining large-scale imaging genomics datasets via interactive visual exploration.

Discovery biology of neuropsychiatric syndromes (DBNS): a center for integrating clinical medicine and basic science

BACKGROUND

There is emerging evidence that there are shared genetic, environmental and developmental risk factors in psychiatry, that cut across traditional diagnostic boundaries. With this background, the Discovery biology of neuropsychiatric syndromes (DBNS) proposes to recruit patients from five different syndromes (schizophrenia, bipolar disorder, obsessive compulsive disorder, Alzheimer's dementia and substance use disorders), identify those with multiple affected relatives, and invite these families to participate in this study. The families will be assessed: 1) To compare neuro-endophenotype measures between patients, first degree relatives (FDR) and healthy controls., 2) To identify cellular phenotypes which differentiate the groups., 3) To examine the longitudinal course of neuro-endophenotype measures., 4) To identify measures which correlate with outcome, and 5) To create a unified digital database and biorepository.

METHODS

The identification of the index participants will occur at well-established specialty clinics. The selected individuals will have a strong family history (with at least another affected FDR) of mental illness. We will also recruit healthy controls without family history of such illness. All recruited individuals (N = 4500) will undergo brief clinical assessments and a blood sample will be drawn for isolation of DNA and peripheral blood mononuclear cells (PBMCs). From among this set, a subset of 1500 individuals (300 families and 300 controls) will be assessed on several additional assessments [detailed clinical assessments, endophenotype measures (neuroimaging- structural and functional, neuropsychology, psychophysics-electroencephalography, functional near infrared spectroscopy, eye movement tracking)], with the intention of conducting repeated measurements every alternate year. PBMCs from this set will be used to generate lymphoblastoid cell lines, and a subset of these would be converted to induced pluripotent stem cell lines and also undergo whole exome sequencing.

DISCUSSION

We hope to identify unique and overlapping brain endophenotypes for major psychiatric syndromes. In a proportion of subjects, we expect these neuro-endophenotypes to progress over time and to predict treatment outcome. Similarly, cellular assays could differentiate cell lines derived from such groups. The repository of biomaterials as well as digital datasets of clinical parameters, will serve as a valuable resource for the broader scientific community who wish to address research questions in the area.

Fast and Accurate Detection of Complex Imaging Genetics Associations Based on Greedy Projected Distance Correlation

Recent advances in imaging genetics produce large amounts of data including functional MRI images, single nucleotide polymorphisms (SNPs), and cognitive assessments. Understanding the complex interactions among these heterogeneous and complementary data has the potential to help with diagnosis and prevention of mental disorders. However, limited efforts have been made due to the high dimensionality, group structure, and mixed type of these data. In this paper we present a novel method to detect conditional associations between imaging genetics data. We use projected distance correlation to build a conditional dependency graph among high-dimensional mixed data, then use multiple testing to detect significant group level associations (e.g., ROI-gene). In addition, we introduce a scalable algorithm based on orthogonal greedy algorithm, yielding the greedy projected distance correlation (G-PDC). This can reduce the computational cost, which is critical for analyzing large-volume of imaging genomics data. The results from our simulations demonstrate a higher degree of accuracy with GPDC than distance correlation, Pearson's correlation and partial correlation, especially when the correlation is nonlinear. Finally, we apply our method to the Philadelphia Neurodevelopmental data cohort with 866 samples including fMRI images and SNP profiles. The results uncover several statistically significant and biologically interesting interactions, which are further validated with many existing studies. The Matlab code is available at https://sites.google.com/site/jianfang86/gPDC.

Influence of the DRD2/ANKK1 Taq1A polymorphism on caudate volume in older adults without dementia

Dopaminergic neuromodulation is critically important for brain and cognitive integrity. The DRD2/ANKK1 Taq1A polymorphism is associated with striatal dopamine (DA) D2 receptor availability. Some previous studies have found that the A allele of the Taq1A polymorphism influences brain structure, but the results are inconsistent, likely due to population heterogeneity and small sample sizes. We investigated the genetic effect on caudate volume in a large sample of older adults without dementia. Results show that A-allele carriers have smaller caudate volume compared to non-carriers in relatively older adults (n = 167; Mage = 77.8 years), whereas the genotype did not influence caudate volume in a younger age group (n = 220; Mage = 62.8 years). Cognitive performance was not significantly affected by the DRD2 gene. Our findings extend previous observations by showing magnified genetic effects on brain volume in old age, and provide evidence for a link between a DA-related genetic polymorphism and grey matter volume in a brain region within the nigrostriatal dopaminergic pathway.

Genetic Variation in S100B Modulates Neural Processing of Visual Scenes in Han Chinese

Spatial navigation is a crucial ability for living. Previous animal studies have shown that the S100B gene is causally related to spatial navigation performance in mice. However, the genetic factors influencing human navigation and its neural substrates remain unclear. Here, we provided the first evidence that the S100B gene modulates neural processing of navigationally relevant scenes in humans. First, with a novel protocol, we demonstrated that the spatial pattern of S100B gene expression in postmortem brains was associated with brain activation pattern for spatial navigation in general, and for scene processing in particular. Further, in a large fMRI cohort of healthy adults of Han Chinese (N = 202), we found that S100B gene polymorphisms modulated scene selectivity in the retrosplenial cortex (RSC) and parahippocampal place area. Finally, the serum levels of S100B protein mediated the association between S100B gene polymorphism and scene selectivity in the RSC. Our study takes the first step toward understanding the neurogenetic mechanism of human spatial navigation and suggests a novel approach to discover candidate genes modulating cognitive functions.

Stress responsivity and the structure of common mental disorders: Transdiagnostic internalizing and externalizing dimensions are associated with contrasting stress appraisal biases

Biased stress appraisals critically relate to the origins and temporal course of many-perhaps most-forms of psychopathology. We hypothesized that aberrant stress appraisals are linked directly to latent internalizing and externalizing traits that, in turn, predispose to multiple disorders. A high-risk community sample of 815 adolescents underwent semistructured interviews to assess clinical disorders and naturalistic stressors at ages 15 and 20. Participants and blind rating teams separately evaluated the threat associated with acute stressors occurring in the past year, and an appraisal bias index (i.e., discrepancy between subjective and team-rated threat) was generated. A 2-factor (Internalizing and Externalizing) latent variable model provided an excellent fit to the diagnostic correlations. After adjusting for the covariation between the factors, adolescents' threat overestimation prospectively predicted higher standing on Internalizing, whereas threat underestimation prospectively predicted elevations on Externalizing. Cross-sectional analyses replicated this pattern in early adulthood. Appraisals were not related to the residual portions of any diagnosis in the latent variable model, suggesting that the transdiagnostic dimensions mediated the connections between stress appraisal bias and disorder entities. We discuss implications for enhancing the efficiency of emerging research on the stress response and speculate how these findings, if replicated, might guide refinements to psychological treatments for stress-linked disorders. (PsycINFO Database Record

Maternal depressive symptoms during pregnancy are associated with amygdala hyperresponsivity in children

Depression during pregnancy is highly prevalent and has a multitude of potential risks of the offspring. Among confirmed consequences is a higher risk of psychopathology. However, it is unknown how maternal depression may impact the child's brain to mediate this vulnerability. Here we studied amygdala functioning, using task-based functional MRI, in children aged 6-9 years as a function of prenatal maternal depressive symptoms selected from a prospective population-based sample (The Generation R Study). We show that children exposed to clinically relevant maternal depressive symptoms during pregnancy (N = 19) have increased amygdala responses to negative emotional faces compared to control children (N = 20) [F(1,36) 7.02, p = 0.022]. Strikingly, postnatal maternal depressive symptoms, obtained at 3 years after birth, did not explain this relation. Our findings are in line with a model in which prenatal depressive symptoms of the mother are associated with amygdala hyperresponsivity in her offspring, which may represent a risk factor for later-life psychopathology.

Pattern Discovery in Brain Imaging Genetics via SCCA Modeling with a Generic Non-convex Penalty

Brain imaging genetics intends to uncover associations between genetic markers and neuroimaging quantitative traits. Sparse canonical correlation analysis (SCCA) can discover bi-multivariate associations and select relevant features, and is becoming popular in imaging genetic studies. The L1-norm function is not only convex, but also singular at the origin, which is a necessary condition for sparsity. Thus most SCCA methods impose [Formula: see text]-norm onto the individual feature or the structure level of features to pursuit corresponding sparsity. However, the [Formula: see text]-norm penalty over-penalizes large coefficients and may incurs estimation bias. A number of non-convex penalties are proposed to reduce the estimation bias in regression tasks. But using them in SCCA remains largely unexplored. In this paper, we design a unified non-convex SCCA model, based on seven non-convex functions, for unbiased estimation and stable feature selection simultaneously. We also propose an efficient optimization algorithm. The proposed method obtains both higher correlation coefficients and better canonical loading patterns. Specifically, these SCCA methods with non-convex penalties discover a strong association between the APOE e4 rs429358 SNP and the hippocampus region of the brain. They both are Alzheimer's disease related biomarkers, indicating the potential and power of the non-convex methods in brain imaging genetics.

Prefrontal mechanisms of comorbidity from a transdiagnostic and ontogenic perspective

Accumulating behavioral and genetic research suggests that most forms of psychopathology share common genetic and neural vulnerabilities and are manifestations of a relatively few core underlying processes. These findings support the view that comorbidity mostly arises, not from true co-occurrence of distinct disorders, but from the behavioral expression of shared vulnerability processes across the life span. The purpose of this review is to examine the role of the prefrontal cortex (PFC) in the shared vulnerability mechanisms underlying the clinical phenomena of comorbidity from a transdiagnostic and ontogenic perspective. In adopting this perspective, we suggest complex transactions between neurobiologically rooted vulnerabilities inherent in PFC circuitry and environmental factors (e.g., parenting, peers, stress, and substance use) across development converge on three key PFC-mediated processes: executive functioning, emotion regulation, and reward processing. We propose that individual differences and impairments in these PFC-mediated functions provide intermediate mechanisms for transdiagnostic symptoms and underlie behavioral tendencies that evoke and interact with environmental risk factors to further potentiate vulnerability.

Genetic moderation of multiple pathways linking early cumulative socioeconomic adversity and young adults' cardiometabolic disease risk

Recent research suggests that psychosocial resources and life stressors are mediating pathways explaining socioeconomic variation in young adults' health risks. However, less research has examined both these pathways simultaneously and their genetic moderation. A nationally representative sample of 11,030 respondents with prospective data collected over 13 years from the National Study of Adolescent to Adult Health was examined. First, the association between early cumulative socioeconomic adversity and young adults' (ages 25-34) cardiometabolic disease risk, as measured by 10 biomarkers, through psychosocial resources (educational attainment) and life stressors (accelerated transition to adulthood) was examined. Second, moderation of these pathways by the serotonin transporter linked polymorphic region gene (5-HTTLPR) was examined. There was evidence for the association between early socioeconomic adversity and young adults' cardiometabolic disease risk directly and indirectly through educational attainment and accelerated transitions. These direct and mediating pathways were amplified by the 5-HTTLPR polymorphism. These findings elucidate how early adversity can have an enduring influence on young adults' cardiometabolic disease risk directly and indirectly through psychosocial resources and life stressors and their genetic moderation. This information suggests that effective intervention and prevention programs should focus on early adversity, youth educational attainment, and their transition to young adulthood.

Longitudinal Genotype-Phenotype Association Study via Temporal Structure Auto-Learning Predictive Model

With rapid progress in high-throughput genotyping and neuroimaging, imaging genetics has gained significant attention in the research of complex brain disorders, such as Alzheimer's Disease (AD). The genotype-phenotype association study using imaging genetic data has the potential to reveal genetic basis and biological mechanism of brain structure and function. AD is a progressive neurodegenerative disease, thus, it is crucial to look into the relations between SNPs and longitudinal variations of neuroimaging phenotypes. Although some machine learning models were newly presented to capture the longitudinal patterns in genotype-phenotype association study, most of them required fixed longitudinal structures of prediction tasks and could not automatically learn the interrelations among longitudinal prediction tasks. To address this challenge, we proposed a novel temporal structure auto-learning model to automatically uncover longitudinal genotype-phenotype interrelations and utilized such interrelated structures to enhance phenotype prediction in the meantime. We conducted longitudinal phenotype prediction experiments on the ADNI cohort including 3,123 SNPs and 2 types of biomarkers, VBM and FreeSurfer. Empirical results demonstrated advantages of our proposed model over the counterparts. Moreover, available literature was identified for our top selected SNPs, which demonstrated the rationality of our prediction results. An executable program is available online at https://github.com/littleq1991/sparse_lowRank_regression.

A Set-Based Mixed Effect Model for Gene-Environment Interaction and Its Application to Neuroimaging Phenotypes

Imaging genetics is an emerging field for the investigation of neuro-mechanisms linked to genetic variation. Although imaging genetics has recently shown great promise in understanding biological mechanisms for brain development and psychiatric disorders, studying the link between genetic variants and neuroimaging phenotypes remains statistically challenging due to the high-dimensionality of both genetic and neuroimaging data. This becomes even more challenging when studying gene-environment interaction (G×E) on neuroimaging phenotypes. In this study, we proposed a set-based mixed effect model for gene-environment interaction (MixGE) on neuroimaging phenotypes, such as structural volumes and tensor-based morphometry (TBM). MixGE incorporates both fixed and random effects of G×E to investigate homogeneous and heterogeneous contributions of multiple genetic variants and their interaction with environmental risks to phenotypes. We discuss the construction of score statistics for the terms associated with fixed and random effects of G×E to avoid direct parameter estimation in the MixGE model, which would greatly increase computational cost. We also describe how the score statistics can be combined into a single significance value to increase statistical power. We evaluated MixGE using simulated and real Alzheimer's Disease Neuroimaging Initiative (ADNI) data, and showed statistical power superior to other burden and variance component methods. We then demonstrated the use of MixGE for exploring the voxelwise effect of G×E on TBM, made feasible by the computational efficiency of MixGE. Through this, we discovered a potential interaction effect of gene ABCA7 and cardiovascular risk on local volume change of the right superior parietal cortex, which warrants further investigation.

The Inverse Relationship between the Microstructural Variability of Amygdala-Prefrontal Pathways and Trait Anxiety Is Moderated by Sex

Anxiety impacts the quality of everyday life and may facilitate the development of affective disorders, possibly through concurrent alterations in neural circuitry. Findings from multimodal neuroimaging studies suggest that trait-anxious individuals may have a reduced capacity for efficient communication between the amygdala and the ventral prefrontal cortex (vPFC). A diffusion-weighted imaging protocol with 61 directions was used to identify lateral and medial amygdala-vPFC white matter pathways. The structural integrity of both pathways was inversely correlated with self-reported levels of trait anxiety. When this mask from our first dataset was then applied to an independent validation dataset, both pathways again showed a consistent inverse relationship with trait anxiety. Importantly, a moderating effect of sex was found, demonstrating that the observed brain-anxiety relationship was stronger in females. These data reveal a potential neuroanatomical mediator of previously documented functional alterations in amygdala-prefrontal connectivity that is associated with trait anxiety, which might prove informative for future studies of psychopathology.

Joint sparse canonical correlation analysis for detecting differential imaging genetics modules

MOTIVATION

Imaging genetics combines brain imaging and genetic information to identify the relationships between genetic variants and brain activities. When the data samples belong to different classes (e.g. disease status), the relationships may exhibit class-specific patterns that can be used to facilitate the understanding of a disease. Conventional approaches often perform separate analysis on each class and report the differences, but ignore important shared patterns.

RESULTS

In this paper, we develop a multivariate method to analyze the differential dependency across multiple classes. We propose a joint sparse canonical correlation analysis method, which uses a generalized fused lasso penalty to jointly estimate multiple pairs of canonical vectors with both shared and class-specific patterns. Using a data fusion approach, the method is able to detect differentially correlated modules effectively and efficiently. The results from simulation studies demonstrate its higher accuracy in discovering both common and differential canonical correlations compared to conventional sparse CCA. Using a schizophrenia dataset with 92 cases and 116 controls including a single nucleotide polymorphism (SNP) array and functional magnetic resonance imaging data, the proposed method reveals a set of distinct SNP-voxel interaction modules for the schizophrenia patients, which are verified to be both statistically and biologically significant.

AVAILABILITY AND IMPLEMENTATION

The Matlab code is available at https://sites.google.com/site/jianfang86/JSCCA CONTACT: wyp@tulane.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Dysfunctional stress responses in chronic pain

Many dysfunctional and chronic pain conditions overlap. This review describes the different modes of chronic deregulation of the adaptive response to stress which may be a common factor for these conditions. Several types of dysfunction can be identified within the hypothalamo-pituitary-adrenal axis: basal hypercortisolism, hyper-reactivity, basal hypocortisolism and hypo-reactivity. Neuroactive steroid synthesis is another component of the adaptive response to stress. Dehydroepiandrosterone (DHEA) and its sulfated form DHEA-S, and progesterone and its derivatives are synthetized in cutaneous, nervous, and adipose cells. They are neuroactive factors that act locally. They may have a role in the localization of the symptoms and their levels can vary both in the central nervous system and in the periphery. Persistent changes in neuroactive steroid levels or precursors can induce localized neurodegeneration. The autonomic nervous system is another component of the stress response. Its dysfunction in chronic stress responses can be expressed by decreased basal parasympathethic activity, increased basal sympathetic activity or sympathetic hyporeactivity to a stressful stimulus. The immune and genetic systems also participate. The helper-T cells Th1 secrete pro-inflammatory cytokines such as IL-1-β, IL-2, IL-6, IL-8, IL-12, IFN-γ, and TNF-α, whereas Th2 secrete anti-inflammatory cytokines: IL-4, IL-10, IGF-10, IL-13. Chronic deregulation of the Th1/Th2 balance can occur in favor of anti- or pro-inflammatory direction, locally or systemically. Individual vulnerability to stress can be due to environmental factors but can also be genetically influenced. Genetic polymorphisms and epigenetics are the main keys to understanding the influence of genetics on the response of individuals to constraints.

Integrating affect and impulsivity: The role of positive and negative urgency in substance use risk

BACKGROUND

The personality traits of positive and negative urgency refer to the tendencies to act rashly when experiencing unusually positive or negative emotions, respectively.

METHODS

The authors review recent empirical work testing urgency theory (Cyders and Smith, 2008a) and consider advances in theory related to these traits.

RESULTS

Empirical findings indicate that (a) the urgency traits are particularly important predictors of the onset of, and increases in, substance use in both children and young adults; (b) they appear to operate in part by biasing psychosocial learning; (c) pubertal onset is associated with increases in negative urgency, which in turn predict increases in adolescent drinking behavior; (d) variation in negative urgency trait levels are associated with variations in the functioning of an identified brain system; and (e) variations in the serotonin transporter gene, known to influence the relevant brain system, relate to variations in the urgency traits.

CONCLUSION

A recent model (Carver et al., 2008) proposes the urgency traits to be markers of a tendency to respond reflexively to emotion, whether through impulsive action or ill-advised inaction (the latter leading to depressive symptoms); this model has received empirical support. The authors discuss new directions for research on the urgency traits.

Biological mechanisms underlying evolutionary origins of psychotic and mood disorders

Psychotic and mood disorders are brain dysfunctions that are caused by gene environment interactions. Although these disorders are disadvantageous and involve behavioral phenotypes that decrease the reproductive success of afflicted individuals in the modern human society, the prevalence of these disorders have remained constant in the population. Here, we propose several biological mechanisms by which the genes associated with psychotic and mood disorders could be selected for in specific environmental conditions that provide evolutionary bases for explanations of when, why, and where these disorders emerged and have been maintained in humans. We discuss the evolutionary origins of psychotic and mood disorders with specific focuses on the roles of dopamine and serotonin in the conditions of social competitiveness/hierarchy and maternal care and other potential mechanisms, such as social network homophily and symbiosis.

Emotion moderates the association between HTR2A (rs6313) genotype and antisaccade latency

The serotonin system is heavily involved in cognitive and emotional control processes. Previous work has typically investigated this system's role in control processes separately for cognitive and emotional domains, yet it has become clear the two are linked. The present study, therefore, examined whether variation in a serotonin receptor gene (HTR2A, rs6313) moderated effects of emotion on inhibitory control. An emotional antisaccade task was used in which participants looked toward (prosaccade) or away (antisaccade) from a target presented to the left or right of a happy, angry, or neutral face. Overall, antisaccade latencies were slower for rs6313 C allele homozygotes than T allele carriers, with no effect of genotype on prosaccade latencies. Thus, C allele homozygotes showed relatively weak inhibitory control but intact reflexive control. Importantly, the emotional stimulus was either present during target presentation (overlap trials) or absent (gap trials). The gap effect (slowed latency in overlap versus gap trials) in antisaccade trials was larger with angry versus neutral faces in C allele homozygotes. This impairing effect of negative valence on inhibitory control was larger in C allele homozygotes than T allele carriers, suggesting that angry faces disrupted/competed with the control processes needed to generate an antisaccade to a greater degree in these individuals. The genotype difference in the negative valence effect on antisaccade latency was attenuated when trial N-1 was an antisaccade, indicating top-down regulation of emotional influence. This effect was reduced in C/C versus T/_ individuals, suggesting a weaker capacity to downregulate emotional processing of task-irrelevant stimuli.

Genetics of anxiety: would the genome recognize the DSM?

The nosology of anxiety disorders has undergone substantial evolution over the past several decades. The modern classification of these disorders dates to the publication of Diagnostic and Statistical Manual-III (DSM-III) in 1980, but the validity of the current diagnostic categories has been the subject of controversy. Genetic research can help clarify the boundaries of diagnostic categories by examining the etiologic relationships among them. The question posed in the title of this article asks to what degree the DSM-IV definitions of the anxiety disorders are supported by the evolving body of research on the genetic basis of pathologic anxiety. With DSM-V on the horizon, there is a renewed imperative to examine the structure of these disorders. In this article, we address this issue by, first, providing a brief update about the current status of genetic research on anxiety disorders and then considering whether the evidence suggests that genetic influences conform to or transcend DSM definitions. Finally, we discuss future directions for the genetic dissection of anxiety disorders.

Developmental psychopathology in an era of molecular genetics and neuroimaging: A developmental neurogenetics approach

The emerging field of neurogenetics seeks to model the complex pathways from gene to brain to behavior. This field has focused on imaging genetics techniques that examine how variability in common genetic polymorphisms predict differences in brain structure and function. These studies are informed by other complimentary techniques (e.g., animal models and multimodal imaging) and have recently begun to incorporate the environment through examination of Imaging Gene × Environment interactions. Though neurogenetics has the potential to inform our understanding of the development of psychopathology, there has been little integration between principles of neurogenetics and developmental psychopathology. The paper describes a neurogenetics and Imaging Gene × Environment approach and how these approaches have been usefully applied to the study of psychopathology. Six tenets of developmental psychopathology (the structure of phenotypes, the importance of exploring mechanisms, the conditional nature of risk, the complexity of multilevel pathways, the role of development, and the importance of who is studied) are identified, and how these principles can further neurogenetics applications to understanding the development of psychopathology is discussed. A major issue of this piece is how neurogenetics and current imaging and molecular genetics approaches can be incorporated into developmental psychopathology perspectives with a goal of providing models for better understanding pathways from among genes, environments, the brain, and behavior.

An oxytocin receptor polymorphism predicts amygdala reactivity and antisocial behavior in men

Variability in oxytocin (OXT) signaling is associated with individual differences in sex-specific social behavior across species. The effects of OXT signaling on social behavior are, in part, mediated through its modulation of amygdala function. Here, we use imaging genetics to examine sex-specific effects of three single-nucleotide polymorphisms in the human oxytocin receptor gene (OXTR; rs1042778, rs53576 and rs2254298) on threat-related amygdala reactivity and social behavior in 406 Caucasians. Analyses revealed that among men but not women, OXTR rs1042778 TT genotype was associated with increased right amygdala reactivity to angry facial expressions, which was uniquely related to higher levels of antisocial behavior among men. Moderated meditation analysis suggested a trending indirect effect of OXTR rs1042778 TT genotype on higher antisocial behavior via increased right amygdala reactivity to angry facial expressions in men. Our results provide evidence linking genetic variation in OXT signaling to individual differences in amygdala function. The results further suggest that these pathways may be uniquely important in shaping antisocial behavior in men.