The future of fMRI and genetics research

Efficient multi-phenotype genome-wide analysis identifies genetic associations for unsupervised deep-learning-derived high-dimensional brain imaging phenotypes

Brain imaging is a high-content modality that offers dense insights into the structure and pathology of the brain. Existing genetic association studies of brain imaging, typically focusing on a number of individual image-derived phenotypes (IDPs), have successfully identified many genetic loci. Previously, we have created a 128-dimensional Unsupervised Deep learning derived Imaging Phenotypes (UDIPs), and identified multiple loci from single-phenotype genome-wide association studies (GWAS) for individual UDIP dimensions, using data from the UK Biobank (UKB). However, this approach may miss genetic associations where one single nucleotide polymorphism (SNP) is moderately associated with multiple UDIP dimensions. Here, we present Joint Analysis of multi-phenotype GWAS (JAGWAS), a new tool that can efficiently calculate multivariate association statistics using single-phenotype summary statistics for hundreds of phenotypes. When applied to UDIPs of T1 and T2 brain magnetic resonance imaging (MRI) on discovery and replication cohorts from the UKB, JAGWAS identified 195/168 independently replicated genomic loci for T1/T2, 6 times more than those from the single-phenotype GWAS. The replicated loci were mapped into 555/494 genes, and 217/188 genes overlapped with the expression quantitative trait loci (eQTL) of brain tissues. Gene enrichment analysis indicated that the genes mapped are closely related to neurobiological functions. Our results suggested that multi-phenotype GWAS is a powerful approach for genetic discovery using high-dimensional UDIPs.

Neurogenetics of Brain Connectivity: Current Approaches to the Study (Review)

Owing to the advances of neuroimaging techniques, a number of functional brain networks associated both with specific functions and the state of relative inactivity has been distinguished. A sufficient bulk of information has been accumulated on changes in connectivity (links between brain regions) in psychopathologies, for example, depression, schizophrenia, autism. Their genetic markers are being actively investigated using a candidate-gene approach or a genome-wide association study. At the same time, there is not much data considering connectivity as an intermediate link in the genotype-pathology chain, although it seems to be a reliable endophenotype, since it demonstrates a high stability and high heritability. In the present review, we consider the results of investigations devoted to the search for biomarkers, molecular and genetic associations of functional, partially anatomical, and effective connectivity. The main approaches to the evaluation of connectivity neurogenetics have been described, as well as specific genetic variants, for which the association with brain connectivity in psychiatric pathologies has been detected.

Research on the Pathogenesis of Cognitive and Neurofunctional Impairments in Patients with Noonan Syndrome: The Role of Rat Sarcoma-Mitogen Activated Protein Kinase Signaling Pathway Gene Disturbances

Noonan syndrome (NS) is one of the most common genetic conditions inherited mostly in an autosomal dominant manner with vast heterogeneity in clinical and genetic features. Patients with NS might have speech disturbances, memory and attention deficits, limitations in daily functioning, and decreased overall intelligence. Here, 34 patients with Noonan syndrome and 23 healthy controls were enrolled in a study involving gray and white matter volume evaluation using voxel-based morphometry (VBM), white matter connectivity measurements using diffusion tensor imaging (DTI), and resting-state functional magnetic resonance imaging (rs-fMRI). Fractional anisotropy (FA) and mean diffusivity (MD) probability distributions were calculated. Cognitive abilities were assessed using the Stanford Binet Intelligence Scales. Reductions in white matter connectivity were detected using DTI in NS patients. The rs-fMRI revealed hyper-connectivity in NS patients between the sensorimotor network and language network and between the sensorimotor network and salience network in comparison to healthy controls. NS patients exhibited decreased verbal and nonverbal IQ compared to healthy controls. The assessment of the microstructural alterations of white matter as well as the resting-state functional connectivity (rsFC) analysis in patients with NS may shed light on the mechanisms responsible for cognitive and neurofunctional impairments.

Neuroimaging feature extraction using a neural network classifier for imaging genetics

BACKGROUND

Dealing with the high dimension of both neuroimaging data and genetic data is a difficult problem in the association of genetic data to neuroimaging. In this article, we tackle the latter problem with an eye toward developing solutions that are relevant for disease prediction. Supported by a vast literature on the predictive power of neural networks, our proposed solution uses neural networks to extract from neuroimaging data features that are relevant for predicting Alzheimer's Disease (AD) for subsequent relation to genetics. The neuroimaging-genetic pipeline we propose is comprised of image processing, neuroimaging feature extraction and genetic association steps. We present a neural network classifier for extracting neuroimaging features that are related with the disease. The proposed method is data-driven and requires no expert advice or a priori selection of regions of interest. We further propose a multivariate regression with priors specified in the Bayesian framework that allows for group sparsity at multiple levels including SNPs and genes.

RESULTS

We find the features extracted with our proposed method are better predictors of AD than features used previously in the literature suggesting that single nucleotide polymorphisms (SNPs) related to the features extracted by our proposed method are also more relevant for AD. Our neuroimaging-genetic pipeline lead to the identification of some overlapping and more importantly some different SNPs when compared to those identified with previously used features.

CONCLUSIONS

The pipeline we propose combines machine learning and statistical methods to benefit from the strong predictive performance of blackbox models to extract relevant features while preserving the interpretation provided by Bayesian models for genetic association. Finally, we argue in favour of using automatic feature extraction, such as the method we propose, in addition to ROI or voxelwise analysis to find potentially novel disease-relevant SNPs that may not be detected when using ROIs or voxels alone.

White-Matter Integrity and Working Memory: Links to Aging and Dopamine-Related Genes

Working memory, a core function underlying many higher-level cognitive processes, requires cooperation of multiple brain regions. White matter refers to myelinated axons, which are critical to interregional brain communication. Past studies on the association between white-matter integrity and working memory have yielded mixed findings. Using voxelwise tract-based spatial statistics analysis, we investigated this relationship in a sample of 328 healthy adults from 25 to 80 years of age. Given the important role of dopamine (DA) in working-memory functioning and white matter, we also analyzed the effects of dopamine-related genes on them. There were associations between white-matter integrity and working memory in multiple tracts, indicating that working-memory functioning relies on global connections between different brain areas across the adult life span. Moreover, a mediation analysis suggested that white-matter integrity contributes to age-related differences in working memory. Finally, there was an effect of the COMT Val158Met polymorphism on white-matter integrity, such that Val/Val carriers had lower fractional anisotropy values than any Met carriers in the internal capsule, corona radiata, and posterior thalamic radiation. As this polymorphism has been associated with dopaminergic tone in the prefrontal cortex, this result provides evidence for a link between DA neurotransmission and white matter. Together, the results support a link between white-matter integrity and working memory, and provide evidence for its interplay with age- and DA-related genes.

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.

Strategies for multivariate analyses of imaging genetics study in Alzheimer's disease

Alzheimer's disease (AD) is an incurable neurodegenerative disease primarily affecting the elderly population. Early diagnosis of AD is critical for the management of this disease. Imaging genetics examines the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on brain structure and function and many novel approaches of imaging genetics are proposed for studying AD. We review and synthesize the Alzheimer's Disease Neuroimaging Initiative (ADNI) genetic associations with quantitative disease endophenotypes including structural and functional neuroimaging, diffusion tensor imaging (DTI), positron emission tomography (PET), and fluid biomarker assays. In this review, we survey recent publications using neuroimaging and genetic data of AD, with a focus on methods capturing multivariate effects accommodating the large number variables from both imaging data and genetic data. We review methods focused on bridging the imaging and genetic data by establishing genotype-phenotype association, including sparse canonical correlation analysis, parallel independent component analysis, sparse reduced rank regression, sparse partial least squares, genome-wide association study, and so on. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future pharmaceutical therapy and biomarker development.

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.

Genetic Influences on Hippocampal Subfields: An Emerging Area of Neuroscience Research

There is clear evidence that hippocampal subfield volumes have partly distinct genetic determinants associated with specific biological processes. The identification of genetic correlates of hippocampal subfield volumes may help to elucidate the mechanisms of neurologic diseases, as well as aging and neurodegenerative processes. However, despite the emerging interest in this area of research, the current knowledge of the genetic architecture of hippocampal subfields has not yet been consolidated. We aimed to provide a review of the current evidence from genetic studies of hippocampal subfields, highlighting current priorities and upcoming challenges. The limited number of studies investigating the influential genetic effects on hippocampal subfields, a lack of replicated results and longitudinal designs, and modest sample sizes combined with insufficient standardization of protocols are identified as the most pressing challenges in this emerging area of research.

A knowledge-based multivariate statistical method for examining gene-brain-behavioral/cognitive relationships: Imaging genetics generalized structured component analysis

With advances in neuroimaging and genetics, imaging genetics is a naturally emerging field that combines genetic and neuroimaging data with behavioral or cognitive outcomes to examine genetic influence on altered brain functions associated with behavioral or cognitive variation. We propose a statistical approach, termed imaging genetics generalized structured component analysis (IG-GSCA), which allows researchers to investigate such gene-brain-behavior/cognitive associations, taking into account well-documented biological characteristics (e.g., genetic pathways, gene-environment interactions, etc.) and methodological complexities (e.g., multicollinearity) in imaging genetic studies. We begin by describing the conceptual and technical underpinnings of IG-GSCA. We then apply the approach for investigating how nine depression-related genes and their interactions with an environmental variable (experience of potentially traumatic events) influence the thickness variations of 53 brain regions, which in turn affect depression severity in a sample of Korean participants. Our analysis shows that a dopamine receptor gene and an interaction between a serotonin transporter gene and the environment variable have statistically significant effects on a few brain regions' variations that have statistically significant negative impacts on depression severity. These relationships are largely supported by previous studies. We also conduct a simulation study to safeguard whether IG-GSCA can recover parameters as expected in a similar situation.

Independent Multiple Factor Association Analysis for Multiblock Data in Imaging Genetics

Multivariate methods have the potential to better capture complex relationships that may exist between different biological levels. Multiple Factor Analysis (MFA) is one of the most popular methods to obtain factor scores and measures of discrepancy between data sets. However, singular value decomposition in MFA is based on PCA, which is adequate only if the data is normally distributed, linear or stationary. In addition, including strongly correlated variables can overemphasize the contribution of the estimated components. In this work, we introduced a novel method referred as Independent Multifactorial Analysis (ICA-MFA) to derive relevant features from multiscale data. This method is an extended implementation of MFA, where the component value decomposition is based on Independent Component Analysis. In addition, ICA-MFA incorporates a predictive step based on an Independent Component Regression. We evaluated and compared the performance of ICA-MFA with both, the MFA method and traditional univariate analyses, in a simulation study. We showed how ICA-MFA explained up to 10-fold more variance than MFA and univariate methods. We applied the proposed algorithm in a study of 4057 individuals belonging to the population-based Rotterdam Study with available genetic and neuroimaging data, as well as information about executive cognitive functioning. Specifically, we used ICA-MFA to detect relevant genetic features related to structural brain regions, which in turn were involved, in the mechanisms of executive cognitive function. The proposed strategy makes it possible to determine the degree to which the whole set of genetic and/or neuroimaging markers contribute to the variability of the symptomatology jointly, rather than individually. While univariate results and MFA combinations only explained a limited proportion of variance (less than 2%), our method increased the explained variance (10%) and allowed the identification of significant components that maximize the variance explained in the model. The potential application of the ICA-MFA algorithm constitutes an important aspect of integrating multivariate multiscale data, specifically in the field of Neurogenetics.

Associations between genetic risk, functional brain network organization and neuroticism

Neuroticism and genetic variation in the serotonin-transporter (SLC6A4) and catechol-O-methyltransferase (COMT) gene are risk factors for psychopathology. Alterations in the functional integration and segregation of neural circuits have recently been found in individuals scoring higher on neuroticism. The aim of the current study was to investigate how genetic risk factors impact functional network organization and whether genetic risk factors moderate the association between neuroticism and functional network organization. We applied graph theory analysis on resting-state fMRI data in a sample of 120 women selected based on their neuroticism score, and genotyped two polymorphisms: 5-HTTLPR (S-carriers and L-homozygotes) and COMT (rs4680-rs165599; COMT risk group and COMT non-risk group). For the 5-HTTLPR polymorphism, we found that subnetworks related to cognitive control show less connections with other subnetworks in S-carriers compared to L-homozygotes. The COMT polymorphism moderated the association between neuroticism and functional network organization. We found that neuroticism was associated with lower efficiency coefficients in visual and somatosensory-motor subnetworks in the COMT risk group compared to the COMT non-risk group. The findings of altered topology of specific subnetworks point to different cognitive-emotional processes that may be affected in relation to the genetic risk factors, concerning emotion regulation in S-carriers (5-HTTLPR) and emotional salience processing in COMT risk carriers.

Strategies for integrated analysis in imaging genetics studies

Imaging Genetics (IG) integrates neuroimaging and genomic data from the same individual, deepening our knowledge of the biological mechanisms behind neurodevelopmental domains and neurological disorders. Although the literature on IG has exponentially grown over the past years, the majority of studies have mainly analyzed associations between candidate brain regions and individual genetic variants. However, this strategy is not designed to deal with the complexity of neurobiological mechanisms underlying behavioral and neurodevelopmental domains. Moreover, larger sample sizes and increased multidimensionality of this type of data represents a challenge for standardizing modeling procedures in IG research. This review provides a systematic update of the methods and strategies currently used in IG studies, and serves as an analytical framework for researchers working in this field. To complement the functionalities of the Neuroconductor framework, we also describe existing R packages that implement these methodologies. In addition, we present an overview of how these methodological approaches are applied in integrating neuroimaging and genetic data.

Past, present, and future of genetic research in borderline personality disorder

Borderline Personality Disorder (BPD) is a major mental illness with a lifetime prevalence of approximately 1-3%, characterized by a persistent pattern of instability in relationships, mood, impulse regulation, and sense of self. This results in impulsive self-damaging behavior, high suicide rates, and severe functional impairment. BPD has a complex, multifactorial etiology, resulting from an interaction among genetic and environmental substrates, and has moderate to high heritability based on twin and family studies. However, our understanding of the genetic architecture of BPD is very limited. This is a critical obstacle since genetics can pave the way for identifying new treatment targets and developing preventive and disease-modifying pharmacological treatments which are currently lacking. We review genetic studies in BPD, with a focus on limitations and challenges and future directions. Genetic research in BPD is still in its very early stages compared to other major psychiatric disorders. Most early genetic studies in BPD were non-replicated association studies in small samples, focused on single candidate genes. More recently, there has been one genome-wide linkage study and a genome-wide association study (GWAS) of subclinical BPD traits and a first GWAS in a relatively modest sample of patients fulfilling full diagnostic criteria for the disorder. Although there are adequate animal models for some of the core dimensions of BPD, there is a lack of translational research including data from animal models in BPD. Research in more pioneering fields, such as imaging genetics, deep sequencing and epigenetics, holds promise for elucidating the pathophysiology of BPD and identifying new treatment targets.

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.

Mapping the Schizophrenia Genes by Neuroimaging: The Opportunities and the Challenges

Schizophrenia (SZ) is a heritable brain disease originating from a complex interaction of genetic and environmental factors. The genes underpinning the neurobiology of SZ are largely unknown but recent data suggest strong evidence for genetic variations, such as single nucleotide polymorphisms, making the brain vulnerable to the risk of SZ. Structural and functional brain mapping of these genetic variations are essential for the development of agents and tools for better diagnosis, treatment and prevention of SZ. Addressing this, neuroimaging methods in combination with genetic analysis have been increasingly used for almost 20 years. So-called imaging genetics, the opportunities of this approach along with its limitations for SZ research will be outlined in this invited paper. While the problems such as reproducibility, genetic effect size, specificity and sensitivity exist, opportunities such as multivariate analysis, development of multisite consortia for large-scale data collection, emergence of non-candidate gene (hypothesis-free) approach of neuroimaging genetics are likely to contribute to a rapid progress for gene discovery besides to gene validation studies that are related to SZ.

Resting State Networks Mediate the Effect of Genotype by Environment Interaction on Mental Health

A number of studies have shown that the presence of short (S) allele of the serotonin transporter-linked polymorphic region (5-HTTLPR) is associated with a higher risk for depression following exposure to stressful life events. These findings are in line with neuroimaging studies showing that 5-HTTLPR polymorphism has an effect on the connectivity among key areas involved in emotion regulation. Here using mediated moderation analysis, we show that electrophysiological manifestations of resting state networks in the alpha frequency band mediate the effect of 5-HTTLPR by stress interaction on depression/anxiety symptoms in a nonclinical sample. Specifically, at the brain level, both L-allele homozygotes and S-allele carriers are similarly responsive to stress exposure. However, these brain responses seem to act as triggers of psychopathological symptoms in S-allele carriers, but as suppressors in L-allele homozygotes. This finding implies that the interpretation of the effect of gene by environment interaction on psychopathology seems more complicated than behavioral results alone would imply. It is not just differential sensitivity to stress, but rather different ways of coping with stress, which distinguish S-allele carriers and L-allele homozygotes.

Functional brain imaging in neuropsychology over the past 25 years

OBJECTIVE

Outline effects of functional neuroimaging on neuropsychology over the past 25 years.

METHOD

Functional neuroimaging methods and studies will be described that provide a historical context, offer examples of the utility of neuroimaging in specific domains, and discuss the limitations and future directions of neuroimaging in neuropsychology.

RESULTS

Tracking the history of publications on functional neuroimaging related to neuropsychology indicates early involvement of neuropsychologists in the development of these methodologies. Initial progress in neuropsychological application of functional neuroimaging has been hampered by costs and the exposure to ionizing radiation. With rapid evolution of functional methods-in particular functional MRI (fMRI)-neuroimaging has profoundly transformed our knowledge of the brain. Its current applications span the spectrum of normative development to clinical applications. The field is moving toward applying sophisticated statistical approaches that will help elucidate distinct neural activation networks associated with specific behavioral domains. The impact of functional neuroimaging on clinical neuropsychology is more circumscribed, but the prospects remain enticing.

CONCLUSIONS

The theoretical insights and empirical findings of functional neuroimaging have been led by many neuropsychologists and have transformed the field of behavioral neuroscience. Thus far they have had limited effects on the clinical practices of neuropsychologists. Perhaps it is time to add training in functional neuroimaging to the clinical neuropsychologist's toolkit and from there to the clinic or bedside. (PsycINFO Database Record

Brain circuit-gene expression relationships and neuroplasticity of multisensory cortices in blind children

Sensory deprivation reorganizes neurocircuits in the human brain. The biological basis of such neuroplastic adaptations remains elusive. In this study, we applied two complementary graph theory-based functional connectivity analyses, one to evaluate whole-brain functional connectivity relationships and the second to specifically delineate distributed network connectivity profiles downstream of primary sensory cortices, to investigate neural reorganization in blind children compared with sighted controls. We also examined the relationship between connectivity changes and neuroplasticity-related gene expression profiles in the cerebral cortex. We observed that multisensory integration areas exhibited enhanced functional connectivity in blind children and that this reorganization was spatially associated with the transcription levels of specific members of the cAMP Response Element Binding protein gene family. Using systems-level analyses, this study advances our understanding of human neuroplasticity and its genetic underpinnings following sensory deprivation.

Brain-derived neurotrophic factor Val66Met genotype modulates amygdala habituation

A deficit in amygdala habituation to repeated emotional stimuli may be an endophenotype of disorders characterized by emotion dysregulation, such as borderline personality disorder (BPD). Amygdala reactivity to emotional stimuli is genetically modulated by brain-derived neurotrophic factor (BDNF) variants. Whether amygdala habituation itself is also modulated by BDNF genotypes remains unknown. We used imaging-genetics to examine the effect of BDNF Val66Met genotypes on amygdala habituation to repeated emotional stimuli. We used functional magnetic resonance imaging (fMRI) in 57 subjects (19 BPD patients, 18 patients with schizotypal personality disorder [SPD] and 20 healthy controls [HC]) during a task involving viewing of unpleasant, neutral, and pleasant pictures, each presented twice to measure habituation. Amygdala responses across genotypes (Val66Met SNP Met allele-carriers vs. Non-Met carriers) and diagnoses (HC, BPD, SPD) were examined with ANOVA. The BDNF 66Met allele was significantly associated with a deficit in amygdala habituation, particularly for emotional pictures. The association of the 66Met allele with a deficit in habituation to unpleasant emotional pictures remained significant in the subsample of BPD patients. Using imaging-genetics, we found preliminary evidence that deficient amygdala habituation may be modulated by BDNF genotype.

[The German research network for mental disorders]

Mental disorders are among the greatest medical and social challenges facing us. They can occur at all stages of life and are among the most important commonly occurring diseases. In Germany 28 % of the population suffer from a mental disorder every year, while the lifetime risk of suffering from a mental disorder is almost 50 %. Mental disorders cause great suffering for those affected and their social network. Quantitatively speaking, they can be considered to be among those diseases creating the greatest burden for society due to reduced productivity, absence from work and premature retirement. The Federal Ministry of Education and Research is funding a new research network from 2015 to 2019 with up to 35 million euros to investigate mental disorders in order to devise and develop better therapeutic measures and strategies for this population by means of basic and translational clinical research. This is the result of a competitive call for research proposals entitled research network for mental diseases. It is a nationwide network of nine consortia with up to ten psychiatric and clinical psychology partner institutions from largely university-based research facilities for adults and/or children and adolescents. Furthermore, three cross-consortia platform projects will seek to identify shared causes of diseases and new diagnostic modalities for anxiety disorders, attention deficit hyperactivity disorders (ADHS), autism, bipolar disorders, depression, schizophrenia and psychotic disorders as well as substance-related and addictive disorders. The spectrum of therapeutic approaches to be examined ranges from innovative pharmacological and psychotherapeutic treatment to novel brain stimulation procedures. In light of the enormous burden such diseases represent for society as a whole, a sustainable improvement in the financial support for those researching mental disorders seems essential. This network aims to become a nucleus for long overdue and sustained support for a German center for mental disorders.

Hippocampal-prefrontal connectivity as a translational phenotype for schizophrenia

Finding novel biological targets in psychiatry has been difficult, partly because current diagnostic categories are not defined by pathophysiology and difficult to model in animals. The study of species-conserved systems-level mechanisms implicated in psychiatric disease could be a promising strategy to address some of these difficulties. Altered hippocampal-prefrontal (HC-PFC) connectivity during working memory (WM) processing is a candidate for such a translational phenotype as it has been repeatedly associated with impaired cognition in schizophrenia patients and animal models for psychiatric risk factors. Specifically, persistent hippocampus-dorsolateral prefrontal cortex (HC-DLPFC) coupling during WM is an intermediate phenotype for schizophrenia that has been observed in patients, healthy relatives and carriers of two different risk polymorphisms identified in genome-wide association studies. Rodent studies report reduced coherence between HC and PFC during anesthesia, sleep and task performance in both genetic, environmental and neurodevelopmental models for schizophrenia. We discuss several challenges for translation including differences in anatomy, recording modalities and WM paradigms and suggest that a better understanding of HC-PFC coupling across species can be achieved if translational neuroimaging is used to control for task differences. The evidence for potential neurobiological substrates underlying HC-PFC dysconnectivity is evaluated and research strategies are proposed that aim to bridge the gap between findings from large-scale association studies and disease mechanisms.

A method for integrating neuroimaging into genetic models of learning performance

Specific learning disorders (SLD) are an archetypal example of how clinical neuropsychological (NP) traits can differ from underlying genetic and neurobiological risk factors. Disparate environmental influences and pathologies impact learning performance assessed through cognitive examinations and clinical evaluations, the primary diagnostic tools for SLD. We propose a neurobiological risk for SLD with neuroimaging biomarkers, which is integrated into a genome-wide association study (GWAS) of learning performance in a cohort of 479 European individuals between 8 and 21 years of age. We first identified six regions of interest (ROIs) in temporal and anterior cingulate regions where the group diagnosed with learning disability has the least overall variation, relative to the other group, in thickness, area, and volume measurements. Although we used the three imaging measures, the thickness was the leading contributor. Hence, we calculated the Euclidean distances between any two individuals based on their thickness measures in the six ROIs. Then, we defined the relative similarity of one individual according to the averaged ranking of pairwise distances from the individuals to those in the SLD group. The inverse of this relative similarity is called the neurobiological risk for the individual. Single nucleotide polymorphisms in the AGBL1 gene on chromosome 15 had a significant association with learning performance at a genome-wide level. This finding was supported in an independent cohort of 2,327 individuals of the same demographic profile. Our statistical approach for integrating genetic and neuroimaging biomarkers can be extended into studying the biological basis of other NP traits.

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.

Inhibition of PARP-1 participates in the mechanisms of propofol-induced amnesia in mice and human

Poly(ADP-ribose) polymerase 1 (PARP-1) has emerged as an important regulator in learning and memory. Propofol leads to amnesia, however, the mechanism remains unclear. The present study was designed to examine whether and how PARP-1 plays a role in propofol-induced amnesia. Mice were injected intraperitoneally with propofol before acquisition training. Cognitive function was evaluated by object recognition test. PARP-1 and PAR expression was determined through Western blot. The protein and mRNA levels of Arc and c-Fos were detected by Western blot and real-time PCR. Thirty volunteers were assigned to three groups according to codon 762 variation of PARP-1 gene (rs1136410). They learned word lists awake and during propofol sedation. Their cognitive traits were evaluated through fMRI. Rodent data demonstrated that propofol inhibited acquisition-induced increase in PARP-1 and PAR, thereby suppressing Arc and c-Fos, which impaired object recognition 24h after learning. Consistent with this, carriers of a low-catalyzing function PARP-1 variant (Val762Ala) exhibited decreased retrieval-induced hippocampal reactivity 24h after learning under propofol-sedative condition. These findings suggested that inhibition of PARP-1 might participate in the mechanism of propofol-induced amnesia in mice and human. More generally, our approach illustrated a potential translational research bridging animal models and human studies.

A Genetic Polymorphism of the Human Dopamine Transporter Determines the Impact of Sleep Deprivation on Brain Responses to Rewards and Punishments

Despite an emerging link between alterations in motivated behavior and a lack of sleep, the impact of sleep deprivation on human brain mechanisms of reward and punishment remain largely unknown, as does the role of trait dopamine activity in modulating such effects in the mesolimbic system. Combining fMRI with an established incentive paradigm and individual genotyping, here, we test the hypothesis that trait differences in the human dopamine transporter (DAT) gene-associated with altered synaptic dopamine signalling-govern the impact of sleep deprivation on neural sensitivity to impending monetary gains and losses. Consistent with this framework, markedly different striatal reward responses were observed following sleep loss depending on the DAT functional polymorphisms. Only participants carrying a copy of the nine-repeat DAT allele-linked to higher phasic dopamine activity-expressed amplified striatal response during anticipation of monetary gain following sleep deprivation. Moreover, participants homozygous for the ten-repeat DAT allele-linked to lower phasic dopamine activity-selectively demonstrated an increase in sensitivity to monetary loss within anterior insula following sleep loss. Together, these data reveal a mechanistic dependency on human of trait dopaminergic function in determining the interaction between sleep deprivation and neural processing of rewards and punishments. Such findings have clinical implications in disorders where the DAT genetic polymorphism presents a known risk factor with comorbid sleep disruption, including attention hyperactive deficit disorder and substance abuse.

Human fear acquisition deficits in relation to genetic variants of the corticotropin-releasing hormone receptor 1 and the serotonin transporter--revisited

We recently showed that a genetic polymorphism (rs878886) in the human corticotropin-releasing hormone receptor 1 (CRHR1) is associated with reduced fear-conditioned responses to a threat cue. This is a potentially important finding considering that the failure to acquire fear contingencies can leave an individual in a maladaptive state of more generalized anxiety. Consistent with that idea, the CRHR1-dependent fear acquisition deficit translated into heightened contextual anxiety when taking genetic variability within the serotonin transporter long polymorphic region (5-HTTLPR) into account. To replicate our previous findings, we conducted a replication study in 224 healthy medication-free human subjects using the exact same cue and context virtual reality fear-conditioning procedure as in study by Heitland et al. (2013). In the replication study, consistent with the original findings, CRHR1 rs878886 G-allele carriers showed reduced acquisition of cue-specific fear-conditioned responses compared with C/C homozygotes. Also, in this larger sample the cue acquisition deficit of G-allele carriers translated into heightened contextual anxiety, even independent of 5-HTT gene variation. In contrast to our earlier findings, there was an additional interaction effect of CRHR1 rs878886 and the triallelic 5-HTTLPR/rs25531 variant on cued fear acquisition. In summary, this study replicated the initially reported association of the CRHR1 rs878886 G-allele with cued fear acquisition deficits, albeit with a different pattern of results regarding the interaction with 5-HTT variation. This further supports the notion that the human corticotropin-releasing hormone plays a role in the acquisition of fears.

Reliability of an fMRI paradigm for emotional processing in a multisite longitudinal study

Multisite neuroimaging studies can facilitate the investigation of brain-related changes in many contexts, including patient groups that are relatively rare in the general population. Though multisite studies have characterized the reliability of brain activation during working memory and motor functional magnetic resonance imaging tasks, emotion processing tasks, pertinent to many clinical populations, remain less explored. A traveling participants study was conducted with eight healthy volunteers scanned twice on consecutive days at each of the eight North American Longitudinal Prodrome Study sites. Tests derived from generalizability theory showed excellent reliability in the amygdala ( Eρ2 = 0.82), inferior frontal gyrus (IFG; Eρ2 = 0.83), anterior cingulate cortex (ACC; Eρ2 = 0.76), insula ( Eρ2 = 0.85), and fusiform gyrus ( Eρ2 = 0.91) for maximum activation and fair to excellent reliability in the amygdala ( Eρ2 = 0.44), IFG ( Eρ2 = 0.48), ACC ( Eρ2 = 0.55), insula ( Eρ2 = 0.42), and fusiform gyrus ( Eρ2 = 0.83) for mean activation across sites and test days. For the amygdala, habituation ( Eρ2 = 0.71) was more stable than mean activation. In a second investigation, data from 111 healthy individuals across sites were aggregated in a voxelwise, quantitative meta-analysis. When compared with a mixed effects model controlling for site, both approaches identified robust activation in regions consistent with expected results based on prior single-site research. Overall, regions central to emotion processing showed strong reliability in the traveling participants study and robust activation in the aggregation study. These results support the reliability of blood oxygen level-dependent signal in emotion processing areas across different sites and scanners and may inform future efforts to increase efficiency and enhance knowledge of rare conditions in the population through multisite neuroimaging paradigms.

Comparison of variants of canonical correlation analysis and partial least squares for combined analysis of MRI and genetic data

The standard analysis approach in neuroimaging genetics studies is the mass-univariate linear modeling (MULM) approach. From a statistical view, however, this approach is disadvantageous, as it is computationally intensive, cannot account for complex multivariate relationships, and has to be corrected for multiple testing. In contrast, multivariate methods offer the opportunity to include combined information from multiple variants to discover meaningful associations between genetic and brain imaging data. We assessed three multivariate techniques, partial least squares correlation (PLSC), sparse canonical correlation analysis (sparse CCA) and Bayesian inter-battery factor analysis (Bayesian IBFA), with respect to their ability to detect multivariate genotype-phenotype associations. Our goal was to systematically compare these three approaches with respect to their performance and to assess their suitability for high-dimensional and multi-collinearly dependent data as is the case in neuroimaging genetics studies. In a series of simulations using both linearly independent and multi-collinear data, we show that sparse CCA and PLSC are suitable even for very high-dimensional collinear imaging data sets. Among those two, the predictive power was higher for sparse CCA when voxel numbers were below 400 times sample size and candidate SNPs were considered. Accordingly, we recommend Sparse CCA for candidate phenotype, candidate SNP studies. When voxel numbers exceeded 500 times sample size, the predictive power was the highest for PLSC. Therefore, PLSC can be considered a promising technique for multivariate modeling of high-dimensional brain-SNP-associations. In contrast, Bayesian IBFA cannot be recommended, since additional post-processing steps were necessary to detect causal relations. To verify the applicability of sparse CCA and PLSC, we applied them to an experimental imaging genetics data set provided for us. Most importantly, application of both methods replicated the findings of this data set.

The impact of cue learning, trait anxiety and genetic variation in the serotonin 1A receptor on contextual fear

In everyday life, aversive events are usually associated with certain predictive cues. Normally, the acquisition of these contingencies enables organisms to appropriately respond to threat. Presence of a threat cue clearly signals 'danger', whereas absence of such cues signals a period of 'safety'. Failure to identify threat cues may lead to chronic states of anxious apprehension in the context in which the threat has been imminent, which may be instrumental in the pathogenesis of anxiety disorders. In this study, existing data from 150 healthy volunteers in a cue and context virtual reality fear conditioning paradigm were reanalyzed. The aim was to further characterize the impact of cue acquisition and trait anxiety, and of a single nucleotide polymorphism in the serotonin 1A receptor gene (5-HTR1A, rs6295), on cued fear and contextual anxiety before and after fear contingencies were explicitly introduced. Fear conditioned responding was quantified with fear potentiation of the eyeblink startle reflex and subjective fear ratings. First, we replicated previous findings that the inability to identify danger cues during acquisition leads to heightened anxious apprehension in the threat context. Second, in subjects who did not identify the danger cue initially, contextual fear was associated with trait anxiety after the contingencies were explicitly instructed. Third, genetic variability within 5-HTR1A (rs6295) was associated with contextual fear independent of awareness or trait anxiety. These findings confirm that failure to acquire cue contingencies impacts contextual fear responding, in association with trait anxiety. The observed 5-HTR1A effect is in line with models of anxiety, but needs further replication.

Behavior genetics: past, present, future

The disciplines of developmental psychopathology and behavior genetics are concerned with many of the same questions about the etiology and course of normal and abnormal behavior and about the factors that promote typical development despite the presence of risk. The goal of this paper is to summarize how research in behavior genetics has shed light on questions that are central to developmental psychopathology. We briefly review the origins of behavior genetics, summarize the findings that have been gleaned from several decades of quantitative and molecular genetics research, and describe future directions for research that will delineate gene function as well as pathways from genes to brain to behavior. The importance of environmental contributions, at both genetic and epigenetic levels, will be discussed. We conclude that behavior genetics has made significant contributions to developmental psychopathology by documenting the interplay among risk and protective factors at multiple levels of the organism, by clarifying the causal status of risk exposures, and by identifying factors that account for change and stability in psychopathology. As the tools to identify gene function become increasingly sophisticated, and as behavioral geneticists become increasingly interdisciplinary in their scope, the field is poised to make ever greater contributions to our understanding of typical and atypical development.

Imbalanced hippocampal functional networks associated with remitted geriatric depression and apolipoprotein E ε4 allele in nondemented elderly: a preliminary study

BACKGROUND

Apolipoprotein E (APOE) ε4 allele and a history of geriatric depression are confirmed risk factors of Alzheimer׳s disease (AD). Coexistence of both factors could notably enhance the risk of cognitive impairment in nondemented elderly. However, neural basis of the association remains unclear.

METHODS

Thirty-one remitted geriatric depression (RGD) patients and 29 cognitively normal subjects were recruited and underwent resting-state functional MRI scans. They were further divided into four groups according to their APOE genotypes. Hippocampal seed-based network analysis and two-way factorial analysis of covariance were employed to detect the main effects and interactive effects of RGD and APOE ε4 allele on the hippocampal functional connectivity (HFC) networks. Partial correlation analysis was applied to examine the cognitive significance of these altered HFC networks.

RESULTS

The HFC networks of RGD patients were decreased in the dorsal frontal and increased in the right temporal-occipital regions. For APOE ε4 carriers, the HFC networks were reduced primarily in medial prefrontal regions and enhanced in the bilateral insula. Additionally, when both factors coexisted, the left HFC network was significantly disrupted in the dorsal anterior cingulate cortex and increased in somatomotor and occipital regions. Importantly, the extent of network alterations was linked to inferior cognitive performances in RGD patients and APOE ε4 carriers.

LIMITATIONS

The small sample size may limit the generalizability of our findings.

CONCLUSIONS

RGD and APOE ε4 allele, and their interaction, are associated with the imbalanced HFC network, which may contribute to cognitive deterioration for subjects with a high risk of AD.

A review of multivariate analyses in imaging genetics

Recent advances in neuroimaging technology and molecular genetics provide the unique opportunity to investigate genetic influence on the variation of brain attributes. Since the year 2000, when the initial publication on brain imaging and genetics was released, imaging genetics has been a rapidly growing research approach with increasing publications every year. Several reviews have been offered to the research community focusing on various study designs. In addition to study design, analytic tools and their proper implementation are also critical to the success of a study. In this review, we survey recent publications using data from neuroimaging and genetics, focusing on methods capturing multivariate effects accommodating the large number of variables from both imaging data and genetic data. We group the analyses of genetic or genomic data into either a priori driven or data driven approach, including gene-set enrichment analysis, multifactor dimensionality reduction, principal component analysis, independent component analysis (ICA), and clustering. For the analyses of imaging data, ICA and extensions of ICA are the most widely used multivariate methods. Given detailed reviews of multivariate analyses of imaging data available elsewhere, we provide a brief summary here that includes a recently proposed method known as independent vector analysis. Finally, we review methods focused on bridging the imaging and genetic data by establishing multivariate and multiple genotype-phenotype-associations, including sparse partial least squares, sparse canonical correlation analysis, sparse reduced rank regression and parallel ICA. These methods are designed to extract latent variables from both genetic and imaging data, which become new genotypes and phenotypes, and the links between the new genotype-phenotype pairs are maximized using different cost functions. The relationship between these methods along with their assumptions, advantages, and limitations are discussed.

Additive genetic effect of APOE and BDNF on hippocampus activity

Human memory is a highly heritable polygenic trait with complex inheritance patterns. To study the genetics of memory and memory-related diseases, hippocampal functioning has served as an intermediate phenotype. The importance of investigating gene-gene effects on complex phenotypes has been emphasized, but most imaging studies still focus on single polymorphisms. APOE ε4 and BDNF Met, two of the most studied gene variants for variability in memory performance and neuropsychiatric disorders, have both separately been related to poorer episodic memory and altered hippocampal functioning. Here, we investigated the combined effect of APOE and BDNF on hippocampal activation (N=151). No non-additive interaction effects were seen. Instead, the results revealed decreased activation in bilateral hippocampus and parahippocampus as a function of the number of APOE ε4 and BDNF Met alleles present (neither, one, or both). The combined effect was stronger than either of the individual effects, and both gene variables explained significant proportions of variance in BOLD signal change. Thus, there was an additive gene-gene effect of APOE and BDNF on medial temporal lobe (MTL) activation, showing that a larger proportion of variance in brain activation attributed to genetics can be explained by considering more than one gene variant. This effect might be relevant for the understanding of normal variability in memory function as well as memory-related disorders associated with APOE and BDNF.

A systems medicine research approach for studying alcohol addiction

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

Random forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes

MOTIVATION

Multivariate quantitative traits arise naturally in recent neuroimaging genetics studies, in which both structural and functional variability of the human brain is measured non-invasively through techniques such as magnetic resonance imaging (MRI). There is growing interest in detecting genetic variants associated with such multivariate traits, especially in genome-wide studies. Random forests (RFs) classifiers, which are ensembles of decision trees, are amongst the best performing machine learning algorithms and have been successfully employed for the prioritisation of genetic variants in case-control studies. RFs can also be applied to produce gene rankings in association studies with multivariate quantitative traits, and to estimate genetic similarities measures that are predictive of the trait. However, in studies involving hundreds of thousands of SNPs and high-dimensional traits, a very large ensemble of trees must be inferred from the data in order to obtain reliable rankings, which makes the application of these algorithms computationally prohibitive.

RESULTS

We have developed a parallel version of the RF algorithm for regression and genetic similarity learning tasks in large-scale population genetic association studies involving multivariate traits, called PaRFR (Parallel Random Forest Regression). Our implementation takes advantage of the MapReduce programming model and is deployed on Hadoop, an open-source software framework that supports data-intensive distributed applications. Notable speed-ups are obtained by introducing a distance-based criterion for node splitting in the tree estimation process. PaRFR has been applied to a genome-wide association study on Alzheimer's disease (AD) in which the quantitative trait consists of a high-dimensional neuroimaging phenotype describing longitudinal changes in the human brain structure. PaRFR provides a ranking of SNPs associated to this trait, and produces pair-wise measures of genetic proximity that can be directly compared to pair-wise measures of phenotypic proximity. Several known AD-related variants have been identified, including APOE4 and TOMM40. We also present experimental evidence supporting the hypothesis of a linear relationship between the number of top-ranked mutated states, or frequent mutation patterns, and an indicator of disease severity.

AVAILABILITY

The Java codes are freely available at http://www2.imperial.ac.uk/~gmontana.

Human fear acquisition deficits in relation to genetic variants of the corticotropin releasing hormone receptor 1 and the serotonin transporter

The ability to identify predictors of aversive events allows organisms to appropriately respond to these events, and failure to acquire these fear contingencies can lead to maladaptive contextual anxiety. Recently, preclinical studies demonstrated that the corticotropin-releasing factor and serotonin systems are interactively involved in adaptive fear acquisition. Here, 150 healthy medication-free human subjects completed a cue and context fear conditioning procedure in a virtual reality environment. Fear potentiation of the eyeblink startle reflex (FPS) was measured to assess both uninstructed fear acquisition and instructed fear expression. All participants were genotyped for polymorphisms located within regulatory regions of the corticotropin releasing hormone receptor 1 (CRHR1 - rs878886) and the serotonin transporter (5HTTLPR). These polymorphisms have previously been linked to panic disorder and anxious symptomology and personality, respectively. G-allele carriers of CRHR1 (rs878886) showed no acquisition of fear conditioned responses (FPS) to the threat cue in the uninstructed phase, whereas fear acquisition was present in C/C homozygotes. Moreover, carrying the risk alleles of both rs878886 (G-allele) and 5HTTLPR (short allele) was associated with increased FPS to the threat context during this phase. After explicit instructions regarding the threat contingency were given, the cue FPS and context FPS normalized in all genotype groups. The present results indicate that genetic variability in the corticotropin-releasing hormone receptor 1, especially in interaction with the 5HTTLPR, is involved in the acquisition of fear in humans. This translates prior animal findings to the human realm.

A neurogenetics approach to understanding individual differences in brain, behavior, and risk for psychopathology

Neurogenetics research has begun to advance our understanding of how genetic variation gives rise to individual differences in brain function, which, in turn, shapes behavior and risk for psychopathology. Despite these advancements, neurogenetics research is currently confronted by three major challenges: (1) conducting research on individual variables with small effects, (2) absence of detailed mechanisms, and (3) a need to translate findings toward greater clinical relevance. In this review, we showcase techniques and developments that address these challenges and highlight the benefits of a neurogenetics approach to understanding brain, behavior and psychopathology. To address the challenge of small effects, we explore approaches including incorporating the environment, modeling epistatic relationships and using multilocus profiles. To address the challenge of mechanism, we explore how non-human animal research, epigenetics research and genome-wide association studies can inform our mechanistic understanding of behaviorally relevant brain function. Finally, to address the challenge of clinical relevance, we examine how neurogenetics research can identify novel therapeutic targets and for whom treatments work best. By addressing these challenges, neurogenetics research is poised to exponentially increase our understanding of how genetic variation interacts with the environment to shape the brain, behavior and risk for psychopathology.

Neurodevelopmental model of schizophrenia: update 2012

The neurodevelopmental model of schizophrenia, which posits that the illness is the end state of abnormal neurodevelopmental processes that started years before the illness onset, is widely accepted, and has long been dominant for childhood-onset neuropsychiatric disorders. This selective review updates our 2005 review of recent studies that have impacted, or have the greatest potential to modify or extend, the neurodevelopmental model of schizophrenia. Longitudinal whole-population studies support a dimensional, rather than categorical, concept of psychosis. New studies suggest that placental pathology could be a key measure in future prenatal high-risk studies. Both common and rare genetic variants have proved surprisingly diagnostically nonspecific, and copy number variants (CNVs) associated with schizophrenia are often also associated with autism, epilepsy and intellectual deficiency. Large post-mortem gene expression studies and prospective developmental multi-modal brain imaging studies are providing critical data for future clinical and high-risk developmental brain studies. Whether there can be greater molecular specificity for phenotypic characterization is a subject of current intense study and debate, as is the possibility of neuronal phenotyping using human pluripotent-inducible stem cells. Biological nonspecificity, such as in timing or nature of early brain development, carries the possibility of new targets for broad preventive treatments.

Gene, brains, and environment-genetic neuroimaging of depression

Depression, conceptualized as major depressive disorder (MDD), is a complex psychiatric disorder with multiple behavioral changes and alterations in various brain regions. Biochemically, serotonin and others substances like GABA, glutamate, norepinephrin, adrenaline/noradrenaline play an essential role in the pathogenesis of MDD. The paper reviews recent human neuroimaging findings on how the genes underlying these biochemical substances modulate neural activity, behavior, and ultimately clinical symptoms. Current data provide solid evidence that genes related to serotonin impact emotion-related neural activity in the amygdala and the anterior cingulate cortex. By contrast, evidence is not as strong for genes related to biochemical substances other than serotonin and other regions of the brain. The review concludes with discussing future genetic, neural, and clinical challenges that point out the central role of gene × environment and brain × environment interactions as genetic and neural predispositions of depression.