Imaging genetics of mood disorders

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.

Neuroanatomical Correlates of Mild-to-Moderate Depression: Memory Ability Mediates the Association Between Gray Matter Volume and Antidepressant Treatment Outcome

Mild-to-moderate depression (MMD) is frequently encountered in clinical practice. Investigating the brain mechanism and its relationship with symptoms in patients with MMD can help us understand the occurrence and development of depression, thus optimizing the prevention and treatment of depression. Shugan Jieyu capsule (SG), a traditional Chinese medicine, is commonly used to ameliorate emotional and cognitive symptoms induced by patients with MMD. Combining clinical assessments and magnetic resonance imaging (MRI), we obtained the emotional and cognitive status of MMD patients and also explored the structural and functional alterations in MMD patients after SG treatments. Structural MRI demonstrated that the gray matter volumes of the left thalamus, right thalamus, and right amygdala in MMD patients were significantly smaller than in healthy controls, and the right amygdala volume was negatively related to depression symptoms in MMD patients. Resting-state functional MRI data demonstrated that MMD patients exhibited decreased temporal coupling between the right amygdala and nucleus accumbens, which was further associated with the severity of depression. Furthermore, right amygdala volume at baseline served as a significant predictor to identify the treatment outcome after 8 weeks of SG treatment in the patients’ group, and importantly, the memory ability mediated the relationship from right amygdala volume to the treatment outcome. These data revealed the structural and functional deficits in the right amygdala, which were highly correlated with the symptoms of depression and its cognitive ability, likely predicting treatment outcome. Therefore, this study strengthened our understanding of the pathogenesis of MMD, which is hoped that it will contribute to tailoring a personalized method for treating the patients.

Migraine Is More Than Just Headache: Is the Link to Chronic Fatigue and Mood Disorders Simply Due to Shared Biological Systems?

Migraine is a symptomatically heterogeneous condition, of which headache is just one manifestation. Migraine is a disorder of altered sensory thresholding, with hypersensitivity among sufferers to sensory input. Advances in functional neuroimaging have highlighted that several brain areas are involved even prior to pain onset. Clinically, patients can experience symptoms hours to days prior to migraine pain, which can warn of impending headache. These symptoms can include mood and cognitive change, fatigue, and neck discomfort. Some epidemiological studies have suggested that migraine is associated in a bidirectional fashion with other disorders, such as mood disorders and chronic fatigue, as well as with other pain conditions such as fibromyalgia. This review will focus on the literature surrounding alterations in fatigue, mood, and cognition in particular, in association with migraine, and the suggested links to disorders such as chronic fatigue syndrome and depression. We hypothesize that migraine should be considered a neural disorder of brain function, in which alterations in aminergic networks integrating the limbic system with the sensory and homeostatic systems occur early and persist after headache resolution and perhaps interictally. The associations with some of these other disorders may allude to the inherent sensory sensitivity of the migraine brain and shared neurobiology and neurotransmitter systems rather than true co-morbidity.

Negative Parenting Affects Adolescent Internalizing Symptoms Through Alterations in Amygdala-Prefrontal Circuitry: A Longitudinal Twin Study

BACKGROUND

The synergic interaction of risk genes and environmental factors has been thought to play a critical role in mediating emotion-related brain circuitry function and dysfunction in depression and anxiety disorders. Little, however, is known regarding neurodevelopmental bases underlying how maternal negative parenting affects emotion-related brain circuitry linking to adolescent internalizing symptoms and whether this neurobehavioral association is heritable during adolescence.

METHODS

The effects of maternal parenting on amygdala-based emotional circuitry and internalizing symptoms were examined by using longitudinal functional magnetic resonance imaging among 100 monozygotic twins and 78 dizygotic twins from early adolescence (age 13 years) to mid-adolescence (age 16 years). The mediation effects among variables of interest and their heritability were assessed by structural equation modeling and quantitative genetic analysis, respectively.

RESULTS

Exposure to maternal negative parenting was positively predictive of stronger functional connectivity of the amygdala with the ventrolateral prefrontal cortex. This neural pathway mediated the association between negative parenting and adolescent depressive symptoms and exhibited moderate heritability (21%).

CONCLUSIONS

These findings highlight that maternal negative parenting in early adolescence is associated with the development of atypical amygdala-prefrontal connectivity in relation to internalizing depressive symptoms in mid-adolescence. Such abnormality of emotion-related brain circuitry is heritable to a moderate degree.

Neuregulins in Neurodegenerative Diseases

Neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS), are typically characterized by progressive neuronal loss and neurological dysfunctions in the nervous system, affecting both memory and motor functions. Neuregulins (NRGs) belong to the epidermal growth factor (EGF)-like family of extracellular ligands and they play an important role in the development, maintenance, and repair of both the central nervous system (CNS) and peripheral nervous system (PNS) through the ErbB signaling pathway. They also regulate multiple intercellular signal transduction and participate in a wide range of biological processes, such as differentiation, migration, and myelination. In this review article, we summarized research on the changes and roles of NRGs in neurodegenerative diseases, especially in AD. We elaborated on the structural features of each NRG subtype and roles of NRG/ErbB signaling networks in neurodegenerative diseases. We also discussed the therapeutic potential of NRGs in the symptom remission of neurodegenerative diseases, which may offer hope for advancing related treatment.

The Consortium on Vulnerability to Externalizing Disorders and Addictions (c-VEDA): an accelerated longitudinal cohort of children and adolescents in India

The global burden of disease attributable to externalizing disorders such as alcohol misuse calls urgently for effective prevention and intervention. As our current knowledge is mainly derived from high-income countries such in Europe and North-America, it is difficult to address the wider socio-cultural, psychosocial context, and genetic factors in which risk and resilience are embedded in low- and medium-income countries. c-VEDA was established as the first and largest India-based multi-site cohort investigating the vulnerabilities for the development of externalizing disorders, addictions, and other mental health problems. Using a harmonised data collection plan coordinated with multiple cohorts in China, USA, and Europe, baseline data were collected from seven study sites between November 2016 and May 2019. Nine thousand and ten participants between the ages of 6 and 23 were assessed during this time, amongst which 1278 participants underwent more intensive assessments including MRI scans. Both waves of follow-ups have started according to the accelerated cohort structure with planned missingness design. Here, we present descriptive statistics on several key domains of assessments, and the full baseline dataset will be made accessible for researchers outside the consortium in September 2019. More details can be found on our website [cveda.org].

The association of catechol-O-methyltransferase (COMT) rs4680 polymorphisms and generalized anxiety disorder in the Chinese Han population

The catechol-O-methyltransferase (COMT) Val158Met polymorphism has been reported to be implicated in generalized anxiety disorder (GAD) as well as the treatment response to antidepressants in patients with GAD, but the findings are inconsistent. In this study, we explore the association among COMT, GAD, and the antidepressant response in the Chinese Han population. One hundred and two patients with GAD and 120 healthy controls (HC) were recruited. All the patients were treated with escitalopram or venlafaxine for 8 weeks. The Hamilton Rating Scale for Anxiety (HAMA) was used to assess the treatment response. All the participants were genotyped for the COMT Val158Met polymorphism using the polymerase chain reaction method. No significant differences in the frequency of the COMT rs4680 polymorphism were found between the GAD and HC groups, or between patients with different genders. Further, we found no significant correlation between the COMT rs4680 polymorphism, gender, and the antidepressant treatment outcomes after eight weeks in the GAD patients. This study indicated that the COMT rs4680 genotype might not be related to GAD or to the genders of the GAD patients, nor did it have any effect on the antidepressant therapeutic response in the GAD patients. Even so, our research will be helpful by providing guidance and direction for future, more in depth, research.

L2RM: Low-rank Linear Regression Models for High-dimensional Matrix Responses

The aim of this paper is to develop a low-rank linear regression model (L2RM) to correlate a high-dimensional response matrix with a high dimensional vector of covariates when coefficient matrices have low-rank structures. We propose a fast and efficient screening procedure based on the spectral norm of each coefficient matrix in order to deal with the case when the number of covariates is extremely large. We develop an efficient estimation procedure based on the trace norm regularization, which explicitly imposes the low rank structure of coefficient matrices. When both the dimension of response matrix and that of covariate vector diverge at the exponential order of the sample size, we investigate the sure independence screening property under some mild conditions. We also systematically investigate some theoretical properties of our estimation procedure including estimation consistency, rank consistency and non-asymptotic error bound under some mild conditions. We further establish a theoretical guarantee for the overall solution of our two-step screening and estimation procedure. We examine the finite-sample performance of our screening and estimation methods using simulations and a large-scale imaging genetic dataset collected by the Philadelphia Neurodevelopmental Cohort (PNC) study.

Effects of the LHPP gene polymorphism on the functional and structural changes of gray matter in major depressive disorder

Background

A single-nucleotide polymorphism (SNP) of the LHPP gene (rs35936514) has been reported to be associated with major depressive disorder (MDD) in genome-wide association studies. However, the systems-level neural effects of rs35936514 that mediate the association are unknown. We hypothesized that variations in rs35936514 would be associated with structural and functional changes in gray matter (GM) at rest in MDD patients.

Methods

A total of 50 MDD patients and 113 healthy controls (HCs) were studied. Functional connectivity (FC) was analyzed by defining the bilateral hippocampus as the seed region. Voxel-based morphometry (VBM) was performed to assess the patterns of GM volume. The subjects were further divided into two groups: a CC homozygous group (CC; 24 MDD and 56 HC) and a risk T-allele carrier group (CT/TT genotypes; 26 MDD and 57 HC). A 2×2 analysis of variance (ANOVA: diagnosis × genotype) was used to determine the interaction effects and main effect (P<0.05).

Results

Significant diagnosis × genotype interaction effects on brain morphology and FC were noted. Compared to other subgroups, the MDD patients with the T allele showed an increased hippocampal FC in the bilateral calcarine cortex and cuneus and a decreased hippocampal FC in the right dorsolateral prefrontal cortex (DLPFC), bilateral anterior cingulate cortex (ACC), and medial prefrontal cortex (MPFC), in addition to reduced GM volume in the right DLPFC, bilateral temporal cortex, and posterior cingulate cortex (PCC).

Conclusions

LHPP gene polymorphisms may affect functional and structural changes in the GM at rest and may play an important role in the pathophysiological mechanisms of MDD.

Association of LHPP genetic variation (rs35936514) with structural and functional connectivity of hippocampal-corticolimbic neural circuitry

A single nucleotide polymorphism at the LHPP gene (rs35936514) has been reported to be associated with major depressive disorder (MDD) in genome-wide association studies. We conducted a neuroimaging analysis to explore whether and which brain neural systems are affected by LHPP variation. Since LHPP variants seem to be associated with the hippocampus, we assessed the relationship between rs35936514 variation and structural-functional connectivity within a hippocampal-corticolimbic neural system implicated in MDD. A total of 122 Chinese subjects were divided into a CC homozygous group (CC genotype, n = 60) and a T allele-carrier group (CT/TT genotypes, n = 62). All subjects participated in resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) scans. Structural and functional connectivity data analyses were then performed. Compared to the CC group, the T allele-carrier group showed significantly higher fractional anisotropy (FA) values in the fornix as well as increased functional connectivity from the hippocampus to the rostral part of the anterior cingulate cortex (rACC). Moreover, a significant negative correlation between fornix FA value and hippocampus-rACC functional connectivity was identified (P < 0.05). These findings suggest that there is a relationship between rs35936514 variation and both structural and functional hippocampal-corticolimbic neural system involvement in MDD. LHPP may play an important role in the neuropathophysiology of MDD.

Structured Genome-Wide Association Studies with Bayesian Hierarchical Variable Selection

It becomes increasingly important in using genome-wide association studies (GWAS) to select important genetic information associated with qualitative or quantitative traits. Currently, the discovery of biological association among SNPs motivates various strategies to construct SNP-sets along the genome and to incorporate such set information into selection procedure for a higher selection power, while facilitating more biologically meaningful results. The aim of this paper is to propose a novel Bayesian framework for hierarchical variable selection at both SNP-set (group) level and SNP (within group) level. We overcome a key limitation of existing posterior updating scheme in most Bayesian variable selection methods by proposing a novel sampling scheme to explicitly accommodate the ultrahigh-dimensionality of genetic data. Specifically, by constructing an auxiliary variable selection model under SNP-set level, the new procedure utilizes the posterior samples of the auxiliary model to subsequently guide the posterior inference for the targeted hierarchical selection model. We apply the proposed method to a variety of simulation studies and show that our method is computationally efficient and achieves substantially better performance than competing approaches in both SNP-set and SNP selection. Applying the method to the Alzheimers Disease Neuroimaging Initiative (ADNI) data, we identify biologically meaningful genetic factors under several neuroimaging volumetric phenotypes. Our method is general and readily to be applied to a wide range of biomedical studies.

Spatial correlations exploitation based on nonlocal voxel-wise GWAS for biomarker detection of AD

Potential biomarker detection is a crucial area of study for the prediction, diagnosis, and monitoring of Alzheimer's disease (AD). The voxelwise genome-wide association study (vGWAS) is widely used in imaging genomics studies that is usually applied to the detection of AD biomarkers in both imaging and genetic data. However, performing vGWAS remains a challenge because of the computational complexity of the technique and our ignorance of the spatial correlations within the imaging data. In this paper, we propose a novel method based on the exploitation of spatial correlations that may help to detect potential AD biomarkers using a fast vGWAS. To incorporate spatial correlations, we applied a nonlocal method that supposed that a given voxel could be represented by weighting the sum of the other voxels. Three commonly used weighting methods were adopted to calculate the weights among different voxels in this study. Then, a fast vGWAS approach was used to assess the association between the image and the genetic data. The proposed method was estimated using both simulated and real data. In the simulation studies, we designed a set of experiments to evaluate the effectiveness of the nonlocal method for incorporating spatial correlations in vGWAS. The experiments showed that incorporating spatial correlations by the nonlocal method could improve the detecting accuracy of AD biomarkers. For real data, we successfully identified three genes, namely, ANK3, MEIS2, and TLR4, which have significant associations with mental retardation, learning disabilities and age according to previous research. These genes have profound impacts on AD or other neurodegenerative diseases. Our results indicated that our method might be an effective and valuable tool for detecting potential biomarkers of AD.

Identifying new susceptibility genes on dopaminergic and serotonergic pathways for the framing effect in decision-making

The framing effect refers the tendency to be risk-averse when options are presented positively but be risk-seeking when the same options are presented negatively during decision-making. This effect has been found to be modulated by the serotonin transporter gene (SLC6A4) and the catechol-o-methyltransferase gene (COMT) polymorphisms, which are on the dopaminergic and serotonergic pathways and which are associated with affective processing. The current study aimed to identify new genetic variations of genes on dopaminergic and serotonergic pathways that may contribute to individual differences in the susceptibility to framing. Using genome-wide association data and the gene-based principal components regression method, we examined genetic variations of 26 genes on the pathways in 1317 Chinese Han participants. Consistent with previous studies, we found that the genetic variations of the SLC6A4 gene and the COMT gene were associated with the framing effect. More importantly, we demonstrated that the genetic variations of the aromatic-L-amino-acid decarboxylase (DDC) gene, which is involved in the synthesis of both dopamine and serotonin, contributed to individual differences in the susceptibility to framing. Our findings shed light on the understanding of the genetic basis of affective decision-making.

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.

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.

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.

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 genetics of anxiety-related negative valence system traits

NIMH's Research Domain Criteria (RDoC) domain of negative valence systems (NVS) captures constructs of negative affect such as fear and distress traditionally subsumed under the various internalizing disorders. Through its aims to capture dimensional measures that cut across diagnostic categories and are linked to underlying neurobiological systems, a large number of phenotypic constructs have been proposed as potential research targets. Since "genes" represent a central "unit of analysis" in the RDoC matrix, it is important for studies going forward to apply what is known about the genetics of these phenotypes as well as fill in the gaps of existing knowledge. This article reviews the extant genetic epidemiological data (twin studies, heritability) and molecular genetic association findings for a broad range of putative NVS phenotypic measures. We find that scant genetic epidemiological data is available for experimentally derived measures such as attentional bias, peripheral physiology, or brain-based measures of threat response. The molecular genetic basis of NVS phenotypes is in its infancy, since most studies have focused on a small number of candidate genes selected for putative association to anxiety disorders (ADs). Thus, more research is required to provide a firm understanding of the genetic aspects of anxiety-related NVS constructs. © 2016 Wiley Periodicals, Inc.

Catechol-O-methyltransferase Val(108/158)Met polymorphism affects fronto-limbic connectivity during emotional processing in bipolar disorder

Catechol-O-methyltransferase (COMT) inactivates catecholamines, Val/Val genotype was associated to an increased amygdala (Amy) response to negative stimuli and can influence the symptoms severity and the outcome of bipolar disorder, probably mediated by the COMT polymorphism (rs4680) interaction between cortical and subcortical dopaminergic neurotransmission. The aim of this study is to explore how rs4680 and implicit emotional processing of negative emotional stimuli could interact in affecting the Amy connectivity in bipolar depression. Forty-five BD patients (34 Met carriers vs. 11 Val/Val) underwent fMRI scanning during implicit processing of fearful and angry faces. We explore the effect of rs4680 on the strength of functional connectivity from the amygdalae to whole brain. Val/Val and Met carriers significantly differed for the connectivity between Amy and dorsolateral prefrontal cortex (DLPFC) and supramarginal gyrus. Val/Val patients showed a significant positive connectivity for all of these areas, where Met carriers presented a significant negative one for the connection between DLPFC and Amy. Our findings reveal a COMT genotype-dependent difference in corticolimbic connectivity during affective regulation, possibly identifying a neurobiological underpinning of clinical and prognostic outcome of BD. Specifically, a worse antidepressant recovery and clinical outcome previously detected in Val/Val patients could be associated to a specific increased sensitivity to negative emotional stimuli.

Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies

To perform a joint analysis of multivariate neuroimaging phenotypes and candidate genetic markers obtained from longitudinal studies, we develop a Bayesian longitudinal low-rank regression (L2R2) model. The L2R2 model integrates three key methodologies: a low-rank matrix for approximating the high-dimensional regression coefficient matrices corresponding to the genetic main effects and their interactions with time, penalized splines for characterizing the overall time effect, and a sparse factor analysis model coupled with random effects for capturing within-subject spatio-temporal correlations of longitudinal phenotypes. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. Simulations show that the L2R2 model outperforms several other competing methods. We apply the L2R2 model to investigate the effect of single nucleotide polymorphisms (SNPs) on the top 10 and top 40 previously reported Alzheimer disease-associated genes. We also identify associations between the interactions of these SNPs with patient age and the tissue volumes of 93 regions of interest from patients' brain images obtained from the Alzheimer's Disease Neuroimaging Initiative.

Relationship between the LHPP Gene Polymorphism and Resting-State Brain Activity in Major Depressive Disorder

A single-nucleotide polymorphism at the LHPP gene (rs35936514) has been reported in genome-wide association studies to be associated with major depressive disorder (MDD). However, the neural system effects of rs35936514 that mediate the association are unknown. The present work explores whether the LHPP rs35936514 polymorphism moderates brain regional activity in MDD. A total of 160 subjects were studied: a CC group homozygous for the C allele (23 individuals with MDD and 57 controls) and a T-carrier group carrying the high risk T allele (CT/TT genotypes; 22 MDD and 58 controls). All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning. Brain activity was assessed using the amplitudes of low-frequency fluctuations (ALFF). MDD patients showed a significant increased ALFF in the left middle temporal gyrus and occipital cortex. The T-carrier group showed increased ALFF in the left superior temporal gyrus. Significant diagnosis × genotype interaction was noted in the bilateral lingual gyri, bilateral dorsal lateral prefrontal cortex (dlPFC), and left medial prefrontal cortex (mPFC) (P < 0.05, corrected). Results demonstrated that MDD patients with LHPP rs35936514 CT/TT genotype may influence the regional brain activity. These findings implicate the effects of the rs35936514 variation on the neural system in MDD.

Adolescent neurobiological susceptibility to social context

Adolescence has been characterized as a period of heightened sensitivity to social contexts. However, adolescents vary in how their social contexts affect them. According to neurobiological susceptibility models, endogenous, biological factors confer some individuals, relative to others, with greater susceptibility to environmental influences, whereby more susceptible individuals fare the best or worst of all individuals, depending on the environment encountered (e.g., high vs. low parental warmth). Until recently, research guided by these theoretical frameworks has not incorporated direct measures of brain structure or function to index this sensitivity. Drawing on prevailing models of adolescent neurodevelopment and a growing number of neuroimaging studies on the interrelations among social contexts, the brain, and developmental outcomes, we review research that supports the idea of adolescent neurobiological susceptibility to social context for understanding why and how adolescents differ in development and well-being. We propose that adolescent development is shaped by brain-based individual differences in sensitivity to social contexts - be they positive or negative - such as those created through relationships with parents/caregivers and peers. Ultimately, we recommend that future research measure brain function and structure to operationalize susceptibility factors that moderate the influence of social contexts on developmental outcomes.

Reproducibility of Brain Morphometry from Short-Term Repeat Clinical MRI Examinations: A Retrospective Study

Purpose

To assess the inter session reproducibility of automatic segmented MRI-derived measures by FreeSurfer in a group of subjects with normal-appearing MR images.

Materials and Methods

After retrospectively reviewing a brain MRI database from our institute consisting of 14,758 adults, those subjects who had repeat scans and had no history of neurodegenerative disorders were selected for morphometry analysis using FreeSurfer. A total of 34 subjects were grouped by MRI scanner model. After automatic segmentation using FreeSurfer, label-wise comparison (involving area, thickness, and volume) was performed on all segmented results. An intraclass correlation coefficient was used to estimate the agreement between sessions. Wilcoxon signed rank test was used to assess the population mean rank differences across sessions. Mean-difference analysis was used to evaluate the difference intervals across scanners. Absolute percent difference was used to estimate the reproducibility errors across the MRI models. Kruskal-Wallis test was used to determine the across-scanner effect.

Results

The agreement in segmentation results for area, volume, and thickness measurements of all segmented anatomical labels was generally higher in Signa Excite and Verio models when compared with Sonata and TrioTim models. There were significant rank differences found across sessions in some labels of different measures. Smaller difference intervals in global volume measurements were noted on images acquired by Signa Excite and Verio models. For some brain regions, significant MRI model effects were observed on certain segmentation results.

Conclusions

Short-term scan-rescan reliability of automatic brain MRI morphometry is feasible in the clinical setting. However, since repeatability of software performance is contingent on the reproducibility of the scanner performance, the scanner performance must be calibrated before conducting such studies or before using such software for retrospective reviewing.

Imaging genetics studies on monoaminergic genes in major depressive disorder

Although depression is the leading cause of disability worldwide, current understanding of the neurobiology of depression has failed to be translated into clinical practice. Major depressive disorder (MDD) pathogenesis is considered to be significantly influenced by multiple risk genes, however genetic effects are not simply expressed at a behavioral level. Therefore the concept of endophenotype has been applied in psychiatric genetics. Imaging genetics applies anatomical or functional imaging technologies as phenotypic assays to evaluate genetic variation and their impact on behavior. This paper attempts to provide a comprehensive review of available imaging genetics studies, including reports on genetic variants that have most frequently been linked to MDD, such as the monoaminergic genes (serotonin transporter gene, monoamine oxidase A gene, tryptophan hydroxylase-2 gene, serotonin receptor 1A gene and catechol-O-methyl transferase gene), with regard to key structures involved in emotion processing, such as the hippocampus, amygdala, anterior cingulate cortex and orbitofrontal cortex.

GWAS-identified risk variants for major depressive disorder: Preliminary support for an association with late-life depressive symptoms and brain structural alterations

A number of genome-wide association studies (GWAS) have investigated risk factors for major depressive disorder (MDD), however there has been little attempt to replicate these findings in population-based studies of depressive symptoms. Variants within three genes, BICC1, PCLO and GRM7 were selected for replication in our study based on the following criteria: they were identified in a prior MDD GWAS study; a subsequent study found evidence that they influenced depression risk; and there is a solid biological basis for a role in depression. We firstly investigated whether these variants were associated with depressive symptoms in our population-based cohort of 929 elderly (238 with clinical depressive symptoms and 691 controls), and secondly to investigate associations with structural brain alterations. A number of nominally significant associations were identified, but none reached Bonferroni-corrected significance levels. Common SNPs in BICC1 and PCLO were associated with a 50% and 30% decreased risk of depression, respectively. PCLO rs2522833 was also associated with the volume of grey matter (p=1.6×10(-3)), and to a lesser extent with hippocampal volume and white matter lesions. Among depressed individuals rs9870680 (GRM7) was associated with the volume of grey and white matter (p=10(-4) and 8.3×10(-3), respectively). Our results provide some support for the involvement of BICC1 and PCLO in late-life depressive disorders and preliminary evidence that these genetic variants may also influence brain structural volumes. However effect sizes remain modest and associations did not reach corrected significance levels. Further large imaging studies are needed to confirm our findings.

Multiple SNP Set Analysis for Genome-Wide Association Studies Through Bayesian Latent Variable Selection

The power of genome-wide association studies (GWAS) for mapping complex traits with single-SNP analysis (where SNP is single-nucleotide polymorphism) may be undermined by modest SNP effect sizes, unobserved causal SNPs, correlation among adjacent SNPs, and SNP-SNP interactions. Alternative approaches for testing the association between a single SNP set and individual phenotypes have been shown to be promising for improving the power of GWAS. We propose a Bayesian latent variable selection (BLVS) method to simultaneously model the joint association mapping between a large number of SNP sets and complex traits. Compared with single SNP set analysis, such joint association mapping not only accounts for the correlation among SNP sets but also is capable of detecting causal SNP sets that are marginally uncorrelated with traits. The spike-and-slab prior assigned to the effects of SNP sets can greatly reduce the dimension of effective SNP sets, while speeding up computation. An efficient Markov chain Monte Carlo algorithm is developed. Simulations demonstrate that BLVS outperforms several competing variable selection methods in some important scenarios.

DNA methylation of the serotonin transporter gene (SLC6A4) is associated with brain function involved in processing emotional stimuli

BACKGROUND

The aim of the present study was to investigate the association of fMRI blood oxygen-level dependent (BOLD) reactivity with the level of epigenetic methylation of SLC6A4 in blood DNA from a sample of healthy participants and patients with major depressive disorder (MDD).

METHODS

We investigated patients with MDD and healthy controls using fMRI and an emotional attention-shifting task. We assessed site-specific DNA methylation of a previously characterized SLC6A4 region in peripheral blood DNA using pyrosequencing.

RESULTS

Our study involved 25 patients with MDD and 35 healthy controls. Activation in the anterior insula elicited by negative emotional content was significantly positively associated with the degree of SLC6A4 methylation. Significantly negative associations were observed between activation in the posterior insula and the degree of SLC6A4 methylation when judging the geometry of pictures after seeing negative in contrast to positive emotional stimuli. Healthy controls with a high degree of SLC6A4 methylation depicted significantly more activity elicited by positive stimuli in limbic regions and more activity elicited by negative stimuli in limbic as well as cognitive control regions than those with a low degree of SLC6A4 methylation.

LIMITATIONS

It is impossible to measure methylation directly in the brain and thus we assessed peripheral methylation of SLC6A4. Since the association was cross-sectional, no conclusion about cause and effect can be drawn.

CONCLUSION

Our study provides further support to the hypothesis that particular DNA methylation states that are associated with brain function during emotion processing are detectable in the periphery.

BDNF Val(66)Met and 5-HTTLPR Genotype are Each Associated with Visual Scanning Patterns of Faces in Young Children

Previous studies have documented both neuroplasticity-related BDNF Val(66)Met and emotion regulation-related 5-HTTLPR polymorphisms as genetic variants that contribute to the processing of emotions from faces. More specifically, research has shown the BDNF Met allele and the 5-HTTLPR Short allele to be associated with mechanisms of negative affectivity that relate to susceptibility for psychopathology. We examined visual scanning pathways in response to angry, happy, and neutral faces in relation to BDNF Val(66)Met and 5-HTTLPR genotyping in 49 children aged 4-7 years. Analyses revealed that variations in the visual processing of facial expressions of anger interacted with BDNF Val(66)Met genotype, such that children who carried at least one low neuroplasticity Met allele exhibited a vigilance-avoidance pattern of visual scanning compared to homozygotes for the high neuroplasticity Val allele. In a separate investigation of eye gaze towards the eye versus mouth regions of neutral faces, we observed that short allele 5-HTTLPR carriers exhibited reduced looking at the eye region compared with those with the higher serotonin uptake Long allele. Together, these findings suggest that genetic mechanisms early in life may influence the establishment of patterns of visual scanning of environmental stressors, which in conjunction with other factors such as negative life events, may lead to psychological difficulties and disorders in the later adolescent and adult years.

SPReM: Sparse Projection Regression Model For High-dimensional Linear Regression

The aim of this paper is to develop a sparse projection regression modeling (SPReM) framework to perform multivariate regression modeling with a large number of responses and a multivariate covariate of interest. We propose two novel heritability ratios to simultaneously perform dimension reduction, response selection, estimation, and testing, while explicitly accounting for correlations among multivariate responses. Our SPReM is devised to specifically address the low statistical power issue of many standard statistical approaches, such as the Hotelling's T² test statistic or a mass univariate analysis, for high-dimensional data. We formulate the estimation problem of SPREM as a novel sparse unit rank projection (SURP) problem and propose a fast optimization algorithm for SURP. Furthermore, we extend SURP to the sparse multi-rank projection (SMURP) by adopting a sequential SURP approximation. Theoretically, we have systematically investigated the convergence properties of SURP and the convergence rate of SURP estimates. Our simulation results and real data analysis have shown that SPReM out-performs other state-of-the-art methods.

Monoamine-sensitive developmental periods impacting adult emotional and cognitive behaviors

Development passes through sensitive periods, during which plasticity allows for genetic and environmental factors to exert indelible influence on the maturation of the organism. In the context of central nervous system development, such sensitive periods shape the formation of neurocircuits that mediate, regulate, and control behavior. This general mechanism allows for development to be guided by both the genetic blueprint as well as the environmental context. While allowing for adaptation, such sensitive periods are also vulnerability windows during which external and internal factors can confer risk to disorders by derailing otherwise resilient developmental programs. Here we review developmental periods that are sensitive to monoamine signaling and impact adult behaviors of relevance to psychiatry. Specifically, we review (1) a serotonin-sensitive period that impacts sensory system development, (2) a serotonin-sensitive period that impacts cognition, anxiety- and depression-related behaviors, and (3) a dopamine- and serotonin-sensitive period affecting aggression, impulsivity and behavioral response to psychostimulants. We discuss preclinical data to provide mechanistic insight, as well as epidemiological and clinical data to point out translational relevance. The field of translational developmental neuroscience has progressed exponentially providing solid conceptual advances and unprecedented mechanistic insight. With such knowledge at hand and important methodological innovation ongoing, the field is poised for breakthroughs elucidating the developmental origins of neuropsychiatric disorders, and thus understanding pathophysiology. Such knowledge of sensitive periods that determine the developmental trajectory of complex behaviors is a necessary step towards improving prevention and treatment approaches for neuropsychiatric disorders.

Association of TMEM132D, COMT, and GABRA6 genotypes with cingulate, frontal cortex and hippocampal emotional processing in panic and major depressive disorder

OBJECTIVE

The aim of the study was to evaluate the association of transmembrane protein 132D (TMEM132D), catechol-O-methyltransferase (COMT), and gamma-aminobutyric acid (GABA) receptor alpha 6 subunit (GABRA6) genotypes with cingulate, frontal cortex and hippocampal emotional processing in panic disorder (PD) and major depressive disorder (MDD).

METHOD

The single nucleotide polymorphisms (SNPs) in TMEM132D, COMT, and GABRA6 were examined in patients with MDD, PD, and healthy controls. Functional magnetic resonance imaging (fMRI) was performed in patients with MDD, PD, and healthy controls.

RESULTS

rs4680 in COMT and rs3219151 in GABRA6 showed positive associations with PD and MDD. A dynamic fearful face was shown to the participants during fMRI scanning. In PD patients, responses in the bilateral anterior cingulate were stronger in carriers of the AA genotype of SNP rs11060369 in TMEM132D compared with carriers of the AC + CC genotype, and stronger in CT + TT genotype carriers of SNP rs3219151 in GABRA6 compared with carriers of the CC genotype. The response in the medial orbital frontal cortex was stronger in carriers of the CT + TT genotypes of SNP rs3219151 in PD. In MDD patients, the response in the right parahippocampus of carriers of the GG genotype of rs4680 in COMT was stronger than that of carriers of the AA + AG genotype.

CONCLUSION

These results suggest that TMEM132D, GABRA6, and COMT variants may increase vulnerability to panic.

Association between serotonin transporter genotype, brain structure and adolescent-onset major depressive disorder: a longitudinal prospective study

The extent to which brain structural abnormalities might serve as neurobiological endophenotypes that mediate the link between the variation in the promoter of the serotonin transporter gene (5-HTTLPR) and depression is currently unknown. We therefore investigated whether variation in hippocampus, amygdala, orbitofrontal cortex (OFC) and anterior cingulate cortex volumes at age 12 years mediated a putative association between 5-HTTLPR genotype and first onset of major depressive disorder (MDD) between age 13-19 years, in a longitudinal study of 174 adolescents (48% males). Increasing copies of S-alleles were found to predict smaller left hippocampal volume, which in turn was associated with increased risk of experiencing a first onset of MDD. Increasing copies of S-alleles also predicted both smaller left and right medial OFC volumes, although neither left nor right medial OFC volumes were prospectively associated with a first episode of MDD during adolescence. The findings therefore suggest that structural abnormalities in the left hippocampus may be present prior to the onset of depression during adolescence and may be partly responsible for an indirect association between 5-HTTLPR genotype and depressive illness. 5-HTTLPR genotype may also impact upon other regions of the brain, such as the OFC, but structural differences in these regions in early adolescence may not necessarily alter the risk for onset of depression during later adolescence.

Treatment of premenstrual dysphoric disorder

Premenstrual dysphoric disorder (PMDD) is comprised of a cluster of affective, behavioral and somatic symptoms recurring monthly during the luteal phase of the menstrual cycle. The disorder affects 3-8% of menstruating women and represents the more severe and disabling end of the spectrum of premenstrual disorders, which includes premenstrual syndrome and premenstrual aggravation of underlying affective disorder. Rigorous and specific diagnostic criteria for PMDD were specified in the Diagnostic and Statistical Manual of Mental Disorders IV (1994) and reaffirmed in the Diagnostic and Statistical Manual of Mental Disorders V (2013) and, consequently, there has been a marked increase in well-designed, placebo-controlled studies evaluating treatment modalities. Although the exact pathogenesis of PMDD is still elusive, treatment of PMDD and severe premenstrual syndrome has centered on neuromodulation via serotonin reuptake inhibitor antidepressants, and ovulation suppression utilizing various contraceptive and hormonal preparations. Unlike the approach to the treatment of depression, serotonergic antidepressants need not be given daily, but can be effective when used cyclically, only in the luteal phase or even limited to the duration of the monthly symptoms. Less, well-substantiated alternative treatments, such as calcium supplementation, agnus castus (chasteberry), Hypericum perforatum (St John's wort) and cognitive/behavioral/relaxation therapies, may be useful adjuncts in the treatment of PMDD. This review provides an overview of current information on the treatment of PMDD.

TOMM40 rs2075650 may represent a new candidate gene for vulnerability to major depressive disorder

Evidence suggests that depression is a risk factor for dementia; however, the relationship between the two conditions is not fully understood. A novel gene (TOMM40) has been consistently associated with Alzheimer's disease (AD), but has received no attention in depression. We conducted a three-level cross-sectional study to investigate the association of the TOMM40 rs2075650 SNP with depression. We recruited a community sample of 1220 participants (571 controls, 649 lifetime depression) to complete a psychiatric background questionnaire, the Brief Symptom Inventory, and Big Five Inventory at Level-1, 243 (102 controls, 97 remitted, 44 currently depressed) to complete a face-to-face clinical interview and neuropsychological testing at Level-2 and 58 (33 controls, 25 remitted) to complete an emotional face-processing task during fMRI at Level-3. Our results indicated that the TOMM40 rs2075650 G allele was a significant risk factor for lifetime depression (p = 0.00006) and, in depressed subjects, was a significant predictor of low extraversion (p = 0.009). Currently depressed risk allele carriers showed subtle executive dysfunction (p = 0.004) and decreased positive memory bias (p = 0.021) together with reduced activity in the posterior (p(FWE) = 0.045) and anterior (p(FWE) = 0.041) cingulate during sad face emotion processing. Our results suggest that TOMM40 rs2075650 may be a risk factor for the development of depression characterized by reduced extraversion, impaired executive function, and decreased positive emotional recall, and reduced top-down cortical control during sad emotion processing.

Fronto-limbic disconnection in bipolar disorder

BACKGROUND

Bipolar disorder (BD) is a severe, disabling and life-threatening illness. Disturbances in emotion and affective processing are core features of the disorder with affective instability being paralleled by mood-congruent biases in information processing that influence evaluative processes and social judgment. Several lines of evidence, coming from neuropsychological and imaging studies, suggest that disrupted neural connectivity could play a role in the mechanistic explanation of these cognitive and emotional symptoms. The aim of the present study is to investigate the effective connectivity in a sample of bipolar patients.

METHODS

Dynamic causal modeling (DCM) technique was used to study 52 inpatients affected by bipolar disorders consecutively admitted to San Raffaele hospital in Milano and forty healthy subjects. A face-matching task was used as activation paradigm.

RESULTS

Patients with BD showed a significantly reduced endogenous connectivity in the DLPFC to Amy connection. There was no significant group effect upon the endogenous connection from Amy to ACC, from ACC to Amy and from DLPFC to ACC.

CONCLUSIONS

Both DLPFC and ACC are part of a network implicated in emotion regulation and share strong reciprocal connections with the amygdale. The pattern of abnormal or reduced connectivity between DLPFC and amygdala may reflect abnormal modulation of mood and emotion typical of bipolar patients.

Common variants in psychiatric risk genes predict brain structure at birth

Studies in adolescents and adults have demonstrated that polymorphisms in putative psychiatric risk genes are associated with differences in brain structure, but cannot address when in development these relationships arise. To determine if common genetic variants in disrupted-in-schizophrenia-1 (DISC1; rs821616 and rs6675281), catechol-O-methyltransferase (COMT; rs4680), neuregulin 1 (NRG1; rs35753505 and rs6994992), apolipoprotein E (APOE; ε3ε4 vs. ε3ε3), estrogen receptor alpha (ESR1; rs9340799 and rs2234693), brain-derived neurotrophic factor (BDNF; rs6265), and glutamate decarboxylase 1 (GAD1; rs2270335) are associated with individual differences in brain tissue volumes in neonates, we applied both automated region-of-interest volumetry and tensor-based morphometry to a sample of 272 neonates who had received high-resolution magnetic resonance imaging scans. ESR1 (rs9340799) predicted intracranial volume. Local variation in gray matter (GM) volume was significantly associated with polymorphisms in DISC1 (rs821616), COMT, NRG1, APOE, ESR1 (rs9340799), and BDNF. No associations were identified for DISC1 (rs6675281), ESR1 (rs2234693), or GAD1. Of note, neonates homozygous for the DISC1 (rs821616) serine allele exhibited numerous large clusters of reduced GM in the frontal lobes, and neonates homozygous for the COMT valine allele exhibited reduced GM in the temporal cortex and hippocampus, mirroring findings in adults. The results highlight the importance of prenatal brain development in mediating psychiatric risk.

Bayesian Generalized Low Rank Regression Models for Neuroimaging Phenotypes and Genetic Markers

We propose a Bayesian generalized low rank regression model (GLRR) for the analysis of both high-dimensional responses and covariates. This development is motivated by performing searches for associations between genetic variants and brain imaging phenotypes. GLRR integrates a low rank matrix to approximate the high-dimensional regression coefficient matrix of GLRR and a dynamic factor model to model the high-dimensional covariance matrix of brain imaging phenotypes. Local hypothesis testing is developed to identify significant covariates on high-dimensional responses. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of GLRR and its comparison with several competing approaches. We apply GLRR to investigate the impact of 1,071 SNPs on top 40 genes reported by AlzGene database on the volumes of 93 regions of interest (ROI) obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI).

Serotonin transporter genotype (5-HTTLPR) and electrocortical responses indicating the sensitivity to negative emotional cues

Growing literature indicates that emotional reactivity and regulation are strongly linked to genetic modulation of serotonergic neurotransmission. However, until now, most studies have focused on the relationship between genotypic markers, in particular the serotonin transporter-linked polymorphic region (5-HTTLPR), and neural structures using MRI. The current study aimed to bridge the gap between the relevant MRI literature on the effects of the 5-HTTLPR genotype and the research tradition focusing on transient lateralized changes of electrocortical activity in the prefrontal cortex using electroencephalography (EEG). Lateral shifts of EEG alpha asymmetry in response to an aversive film consisting of scenes of real injury and death were assessed in healthy participants (n = 165). To evaluate the specificity of the 5-HTTLPR effect, participants were also tested for the COMT Val158Met polymorphism which is linked to dopamine inactivation. While viewing the film, individuals homozygous for the 5-HTTLPR short allele displayed a clear lateral shift of dorsolateral frontal activity to the right, which was virtually absent in participants carrying the long allele. The heightened electrocortical response to the aversive stimulation and its direction indicates a greater propensity of s/s homozygotes to experience withdrawal oriented affect in response to negative emotion cues in the environment. Moreover, together with previous research the findings support the notion of a link between the serotonergic system and self-regulation related to avoidance motivation, and a link between the dopaminergic system and self-regulation related to approach motivation.

Impact of COMT genotype on serotonin-1A receptor binding investigated with PET

Alterations of the inhibitory serotonin-1A receptor (5-HT1A) constitute a solid finding in neuropsychiatric research, particularly in the field of mood and anxiety disorders. Manifold factors influencing the density of this receptor have been identified, e.g., steroid hormones, sunlight exposure and genetic variants of serotonin-related genes. Given the close interactions between serotonergic and dopaminergic neurotransmission, we investigated whether a common single-nucleotide-polymorphism of the catechol-O-methyltransferase (COMT) gene (VAL158MET or rs4680) coding for a key enzyme of the dopamine network that is associated with the pathogenesis of mood disorders and antidepressant treatment response, directly affects 5-HT1A receptor binding potential. Fifty-two healthy individuals (38 female, mean age ± standard deviation = 40.48 ± 14.87) were measured via positron emission tomography using the radioligand [carbonyl-(11)C]WAY-100635. Genotyping for rs4680 was performed using DNA isolated from whole blood with the MassARRAY platform of the software SEQUENOM(®). Whole brain voxel-wise ANOVA resulted in a main effect of genotype on 5-HT1A binding. Compared to A carriers (AA + AG) of rs4680, homozygote G subjects showed higher 5-HT1A binding potential in the posterior cingulate cortex (F (2,49) = 17.7, p = 0.05, FWE corrected), the orbitofrontal cortex, the anterior cingulate cortex, the insula, the amygdala and the hippocampus (voxel-level: p < 0.01 uncorrected, t > 2.4; cluster-level: p < 0.05 FWE corrected). In light of the frequently reported alterations of 5-HT1A binding in anxiety and mood disorders, this study proposes a potential implication of the COMT genotype, more specifically the VAL158MET polymorphism, via modulation of the serotonergic neurotransmission.

Genetic modulation of the response bias towards facial displays of anger and happiness

BACKGROUND

Investigating genetic modulation of emotion processing may contribute to the understanding of heritable mechanisms of emotional disorders. The aim of the present study was to test the effects of catechol-O-methyltransferase (COMT) val158met and serotonin-transporter-linked promoter region (5-HTTLPR) polymorphisms on facial emotion processing in healthy individuals.

METHODS

Two hundred and seventy five (167 female) participants were asked to complete a computerized facial affect recognition task, which involved four experimental conditions, each containing one type of emotional face (fearful, angry, sad or happy) intermixed with neutral faces. Participants were asked to indicate whether the face displayed an emotion or was neutral. The COMT-val158met and 5-HTTLPR polymorphisms were genotyped.

RESULTS

Met homozygotes (COMT) showed a stronger bias to perceive neutral faces as expressions of anger, compared with val homozygotes. However, the S-homozygotes (5-HTTLPR) showed a reduced bias to perceive neutral faces as expressions of happiness, compared to L-homozygotes. No interaction between 5-HTTLPR and COMT was found.

CONCLUSIONS

These results add to the knowledge of individual differences in social cognition that are modulated via serotonergic and dopaminergic systems. This potentially could contribute to the understanding of the mechanisms of susceptibility to emotional disorders.