Neuroimaging-genetic paradigms: a new approach to investigate the pathophysiology and treatment of cognitive deficits in schizophrenia

Association between schizophrenia polygenic risk and neural correlates of emotion perception

The neural correlates of emotion perception have been shown to be significantly altered in schizophrenia (SCZ) patients as well as their healthy relatives, possibly reflecting genetic susceptibility to the disease. The aim of the study was to investigate the association between SCZ polygenic risk and brain activity whilst testing perception of multisensory, dynamic emotional stimuli. We created SCZ polygenic risk scores (PRS) for a sample of twenty-eight healthy individuals. The PRS was based on data from the Psychiatric Genomics Consortium and was used as a regressor score in the neuroimaging analysis. The results of a multivariate brain-behaviour analysis show that higher SCZ PRS are related to increased activity in brain regions critical for emotion during the perception of threatening (angry) emotions. These results suggest that individuals with higher SCZ PRS over-activate the neural correlates underlying emotion during perception of threat, perhaps due to an increased experience of fear or neural inefficiency in emotion-regulation areas. Moreover, over-recruitment of emotion regulation regions might function as a compensation to maintain normal emotion regulation during threat perception. If replicated in larger studies, these findings may have important implications for understanding the neurophysiological biomarkers relevant in SCZ.

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

BACKGROUND

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

METHODS

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

DISCUSSION

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

The association between gene variants and longitudinal structural brain changes in psychosis: a systematic review of longitudinal neuroimaging genetics studies

Evidence suggests that genetic variation might influence structural brain alterations in psychotic disorders. Longitudinal genetic neuroimaging (G-NI) studies are designed to assess the association between genetic variants, disease progression and brain changes. There is a paucity of reviews of longitudinal G-NI studies in psychotic disorders. A systematic search of PubMed from inception until November 2016 was conducted to identify longitudinal G-NI studies examining the link between Magnetic Resonance Imaging (MRI) and Diffusion Tensor Imaging (DTI)-based brain measurements and specific gene variants (SNPs, microsatellites, haplotypes) in patients with psychosis. Eleven studies examined seven genes: BDNF, COMT, NRG1, DISC1, CNR1, GAD1, and G72. Eight of these studies reported at least one association between a specific gene variant and longitudinal structural brain changes. Genetic variants associated with longitudinal brain volume or cortical thickness loss included a 4-marker haplotype in G72, a microsatellite and a SNP in NRG1, and individual SNPs in DISC1, CNR1, BDNF, COMT and GAD1. Associations between genotype and progressive brain changes were most frequently observed in frontal regions, with five studies reporting significant interactions. Effect sizes for significant associations were generally of small or intermediate magnitude (Cohen’s d < 0.8). Only two genes (BDNF and NRG1) were assessed in more than one study, with great heterogeneity of the results. Replication studies and studies exploring additional genetic variants identified by large-scale genetic analysis are warranted to further ascertain the role of genetic variants in longitudinal brain changes in psychosis.

Partitioning heritability analysis reveals a shared genetic basis of brain anatomy and schizophrenia

Schizophrenia is a devastating neurodevelopmental disorder with a complex genetic etiology. Widespread cortical gray matter loss has been observed in patients and prodromal samples. However, it remains unresolved whether schizophrenia-associated cortical structure variations arise due to disease etiology or secondary to the illness. Here we address this question using a partitioning-based heritability analysis of genome-wide single-nucleotide polymorphism (SNP) and neuroimaging data from 1750 healthy individuals. We find that schizophrenia-associated genetic variants explain a significantly enriched proportion of trait heritability in eight brain phenotypes (false discovery rate=10%). In particular, intracranial volume and left superior frontal gyrus thickness exhibit significant and robust associations with schizophrenia genetic risk under varying SNP selection conditions. Cross-disorder comparison suggests that the neurogenetic architecture of schizophrenia-associated brain regions is, at least in part, shared with other psychiatric disorders. Our study highlights key neuroanatomical correlates of schizophrenia genetic risk in the general population. These may provide fundamental insights into the complex pathophysiology of the illness, and a potential link to neurocognitive deficits shaping the disorder.

Psychopathology research in the RDoC era: Unanswered questions and the importance of the psychophysiological unit of analysis

The NIMH Research Domain Criteria (RDoC) initiative seeks to re-conceptualize psychopathology by identifying transdiagnostic constructs that reflect core mechanisms of psychopathology. Although the RDoC framework has been discussed in many prior papers, there are several methodological and conceptual points that have yet to be fully specified. For example, little discussion exists on the importance of distinguishing each construct's nomological network and linking it to risk for psychopathology. It has also been unclear the extent to which RDoC constructs (within and across systems) should relate to one another and how these associations may differ as a function of developmental period. These are important questions as we enter the RDoC era and psychophysiological measures represent an exciting tool to address these issues. In this paper, we discuss the currently un- (or under-)specified aspects of the RDoC initiative and highlight the advantages of the psychophysiological 'unit of analysis.' We also briefly review existing psychophysiological studies, within the positive and negative valence systems, that exemplify the RDoC approach and make recommendations for how future studies can help the field progress in this mission.

Evaluation of relationship between GRM3 polymorphisms and cognitive function in schizophrenia of Han Chinese

Recently, the novel SNP rs12704290 in GRM3 was identified in a genome-wide association study on schizophrenia susceptibility. Our study was to investigate the association of 29 selected SNPs (including rs12704290) with schizophrenia and to evaluate any possible relationship between them and cognition related to schizophrenia. The SNPs were analyzed in 1115 unrelated schizophrenic patients and 2289 healthy controls. The results showed significant associations between these SNPs and schizophrenia as well as with changes in cognition.

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.

Cannabis and psychosis: Neurobiology

Cannabis is a known risk factor for schizophrenia, although the exact neurobiological process through which the effects on psychosis occur is not well-understood. In this review, we attempt to develop and discuss a possible pathway for the development of psychosis. We examine the neurobiological changes due to cannabis to see if these changes are similar to those seen in schizophrenic patients the findings show similarities; however, these mere similarities cannot establish a 'cause-effect' relationship as a number of people with similar changes do not develop schizophrenia. Therefore, the 'transition-to-psychosis' due to cannabis, despite being a strong risk factor, remains uncertain based upon neurobiological changes. It appears that other multiple factors might be involved in these processes which are beyond neurobiological factors. Major advances have been made in understanding the underpinning of marijuana dependence, and the role of the cannabinoid system, which is a major area for targeting medications to treat marijuana withdrawal and dependence, as well as other addictions is of now, it is clear that some of the similarities in the neurobiology of cannabis and schizophrenia may indicate a mechanism for the development of psychosis, but its trajectories are undetermined.

Genetic variation throughout the folate metabolic pathway influences negative symptom severity in schizophrenia

Low serum folate levels previously have been associated with negative symptom risk in schizophrenia, as has the hypofunctional 677C>T variant of the MTHFR gene. This study examined whether other missense polymorphisms in folate-regulating enzymes, in concert with MTHFR, influence negative symptoms in schizophrenia, and whether total risk allele load interacts with serum folate status to further stratify negative symptom risk. Medicated outpatients with schizophrenia (n = 219), all of European origin and some included in a previous report, were rated with the Positive and Negative Syndrome Scale. A subset of 82 patients also underwent nonfasting serum folate testing. Patients were genotyped for the MTHFR 677C>T (rs1801133), MTHFR 1298A>C (rs1801131), MTR 2756A>G (rs1805087), MTRR 203A>G (rs1801394), FOLH1 484T>C (rs202676), RFC 80A>G (rs1051266), and COMT 675G>A (rs4680) polymorphisms. All genotypes were entered into a linear regression model to determine significant predictors of negative symptoms, and risk scores were calculated based on total risk allele dose. Four variants, MTHFR 677T, MTR 2756A, FOLH1 484C, and COMT 675A, emerged as significant independent predictors of negative symptom severity, accounting for significantly greater variance in negative symptoms than MTHFR 677C>T alone. Total allele dose across the 4 variants predicted negative symptom severity only among patients with low folate levels. These findings indicate that multiple genetic variants within the folate metabolic pathway contribute to negative symptoms of schizophrenia. A relationship between folate level and negative symptom severity among patients with greater genetic vulnerability is biologically plausible and suggests the utility of folate supplementation in these patients.

Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse Partial Least Squares

Brain imaging is increasingly recognised as an intermediate phenotype to understand the complex path between genetics and behavioural or clinical phenotypes. In this context, a first goal is to propose methods to identify the part of genetic variability that explains some neuroimaging variability. Classical univariate approaches often ignore the potential joint effects that may exist between genes or the potential covariations between brain regions. In this paper, we propose instead to investigate an exploratory multivariate method in order to identify a set of Single Nucleotide Polymorphisms (SNPs) covarying with a set of neuroimaging phenotypes derived from functional Magnetic Resonance Imaging (fMRI). Recently, Partial Least Squares (PLS) regression or Canonical Correlation Analysis (CCA) have been proposed to analyse DNA and transcriptomics. Here, we propose to transpose this idea to the DNA vs. imaging context. However, in very high-dimensional settings like in imaging genetics studies, such multivariate methods may encounter overfitting issues. Thus we investigate the use of different strategies of regularisation and dimension reduction techniques combined with PLS or CCA to face the very high dimensionality of imaging genetics studies. We propose a comparison study of the different strategies on a simulated dataset first and then on a real dataset composed of 94 subjects, around 600,000 SNPs and 34 functional MRI lateralisation indexes computed from reading and speech comprehension contrast maps. We estimate the generalisability of the multivariate association with a cross-validation scheme and demonstrate the significance of this link, using a permutation procedure. Univariate selection appears to be necessary to reduce the dimensionality. However, the significant association uncovered by this two-step approach combining univariate filtering and L1-regularised PLS suggests that discovering meaningful genetic associations calls for a multivariate approach.

A hypomethylating variant of MTHFR, 677C>T, blunts the neural response to errors in patients with schizophrenia and healthy individuals

BACKGROUND

Responding to errors is a critical first step in learning from mistakes, a process that is abnormal in schizophrenia. To gain insight into the neural and molecular mechanisms of error processing, we used functional MRI to examine effects of a genetic variant in methylenetetrahydrofolate reductase (MTHFR 677C>T, rs1801133) that increases risk for schizophrenia and that has been specifically associated with increased perseverative errors among patients. MTHFR is a key regulator of the intracellular one-carbon milieu, including DNA methylation, and each copy of the 677T allele reduces MTHFR activity by 35%.

METHODOLOGY/PRINCIPAL FINDINGS

Using an antisaccade paradigm, we found that the 677T allele induces a dose-dependent blunting of dorsal anterior cingulate cortex (dACC) activation in response to errors, a pattern that was identical in healthy individuals and patients with schizophrenia. Further, the normal relationship between dACC activation and error rate was disrupted among carriers of the 677T allele.

CONCLUSIONS/SIGNIFICANCE

These findings implicate an epigenetic mechanism in the neural response to errors, and provide insight into normal cognitive variation through a schizophrenia risk gene.

Habituation in prepulse inhibition is affected by a polymorphism on the NMDA receptor 2B subunit gene (GRIN2B)

OBJECTIVES

To identify the reliable connectivity between causal genes or variants with an abnormality expressed in a certain endophenotype has been viewed as a crucial step in unraveling the etiology of schizophrenia because of the considerable heterogeneity in this disorder.

METHODS

According to this practical and scientific demand, we aimed to investigate the relationship between seven top-ranked variants in the SZgene database [120-bpTR in DRD4, rs1801028 and rs6277 in DRD2, rs1019385 (T200G) in GRIN2B, rs1800532 in TPH1, rs1801133 (C677T) in MTHFR, rs2619528 (P1765) in DTNBP1] and prepulse inhibition (PPI) and habituation after acoustic stimulus (HAB).

RESULTS

Both PPI and HAB were decreased significantly in patients with schizophrenia. In addition, we observed a significant effect of GRIN2B (human NMDA receptor 2B subunit gene, NR2B) genotype on HAB (P<0.05, not corrected).

CONCLUSION

Although these findings need to be replicated in other samples, an underlying mechanism of impaired biological reaction may be influenced by NMDA hypofunctioning in schizophrenia.

False positives in neuroimaging genetics using voxel-based morphometry data

Voxel-wise statistical inference is commonly used to identify significant experimental effects or group differences in both functional and structural studies of the living brain. Tests based on the size of spatially extended clusters of contiguous suprathreshold voxels are also widely used due to their typically increased statistical power. In "imaging genetics", such tests are used to identify regions of the brain that are associated with genetic variation. However, concerns have been raised about the adequate control of rejection rates in studies of this type. A previous study tested the effect of a set of 'null' SNPs on brain structure and function, and found that false positive rates were well-controlled. However, no similar analysis of false positive rates in an imaging genetic study using cluster size inference has yet been undertaken. We measured false positive rates in an investigation of the effect of 700 pre-selected null SNPs on grey matter volume using voxel-based morphometry (VBM). As VBM data exhibit spatially-varying smoothness, we used both non-stationary and stationary cluster size tests in our analysis. Image and genotype data on 181 subjects with mild cognitive impairment were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). At a nominal significance level of 5%, false positive rates were found to be well-controlled (3.9-5.6%), using a relatively high cluster-forming threshold, α(c)=0.001, on images smoothed with a 12 mm Gaussian kernel. Tests were however anticonservative at lower cluster-forming thresholds (α(c)=0.01, 0.05), and for images smoothed using a 6mm Gaussian kernel. Here false positive rates ranged from 9.8 to 67.6%. In a further analysis, false positive rates using simulated data were observed to be well-controlled across a wide range of conditions. While motivated by imaging genetics, our findings apply to any VBM study, and suggest that parametric cluster size inference should only be used with high cluster-forming thresholds and smoothness. We would advocate the use of nonparametric methods in other cases.

Identifying gene regulatory networks in schizophrenia

The imaging genetics approach to studying the genetic basis of disease leverages the individual strengths of both neuroimaging and genetic studies by visualizing and quantifying the brain activation patterns in the context of genetic background. Brain imaging as an intermediate phenotype can help clarify the functional link among genes, the molecular networks in which they participate, and brain circuitry and function. Integrating genetic data from a genome-wide association study (GWAS) with brain imaging as a quantitative trait (QT) phenotype can increase the statistical power to identify risk genes. A QT analysis using brain imaging (DLPFC activation during a working memory task) as a quantitative trait has identified unanticipated risk genes for schizophrenia. Several of these genes (RSRC1, ARHGAP18, ROBO1-ROBO2, GPC1, TNIK, and CTXN3-SLC12A2) have functions related to progenitor cell proliferation, migration, and differentiation, cytoskeleton reorganization, axonal connectivity, and development of forebrain structures. These genes, however, do not function in isolation but rather through gene regulatory networks. To obtain a deeper understanding how the GWAS-identified genes participate in larger gene regulatory networks, we measured correlations among transcript levels in the mouse and human postmortem tissue and performed a gene set enrichment analysis (GSEA) that identified several microRNA associated with schizophrenia (448, 218, 137). The results of such computational approaches can be further validated in animal experiments in which the networks are experimentally studied and perturbed with specific compounds. Glypican 1 and FGF17 mouse models for example, can be used to study such gene regulatory networks. The model demonstrates epistatic interactions between FGF and glypican on brain development and may be a useful model of negative symptom schizophrenia.

Association between genetic variants of the metabotropic glutamate receptor 3 (GRM3) and cognitive set shifting in healthy individuals

Set-shifting and maintenance are complex cognitive processes, which are often impaired in schizophrenia. The genetic basis of these processes is poorly understood. We aimed to investigate the association between genetic variants of the metabotropic glutamate receptor 3 (GRM3) and cognitive set-shifting in healthy individuals. The relationship between 14 selected single nucleotide polymorphisms (SNPs) of the GRM3 gene and cognitive set-shifting as measured by perseverative errors using the modified card sorting test (MCST) was analysed in a sample of N = 98 young healthy individuals (mean age in years: 22.7 +/- 0.19). Results show that SNP rs17676277 is related to the performance on the MCST. Subjects with the TT genotype showed significantly less perseverative errors as compared with the AA (P = 0.025) and AT (P = 0.0005) and combined AA/AT genotypes (P = 0.0005). Haplotype analyses suggest the involvement of various SNPs of the GRM3 gene in perseverative error processing in a dominant model of inheritance. The findings strongly suggest that the genetic variation (rs17676277 and three haplotypes) in the metabotropic GRM3 is related to cognitive set-shifting in healthy individuals independent of working memory. However, because of a relatively small sample size for a genetic association study, the present results are tentative and require replication.

[From identification of neurofunctional systems to individualization of treatment for schizophrenic disorders]

This article outlines the role of systemic neuroscience in research of the pathogenesis and pathophysiology of schizophrenic and other psychotic disorders and in the development of diagnostic and prognostic tools to permit individualized and optimized pharmacotherapy for these disorders. Based on preclinical and clinical studies of the physiology and pathophysiology of human working memory, it is possible to establish intermediate neurofunctional phenotypes that, given the current lack of identified pathological substrates, may serve as biological markers for diagnosing factual disease entities. In this way the use of functional neuroimaging techniques may allow the identification of subtypes of the psychotic disorders that so far are uniformly diagnosed according to current psychiatric classification systems. This may permit more precise diagnosis and a specification and optimization of pharmacotherapy.

Association between catechol-O-methyltrasferase Val108/158Met genotype and prefrontal hemodynamic response in schizophrenia

Background

“Imaging genetics” studies have shown that brain function by neuroimaging is a sensitive intermediate phenotype that bridges the gap between genes and psychiatric conditions. Although the evidence of association between functional val108/158met polymorphism of the catechol-O-methyltransferase gene (COMT) and increasing risk for developing schizophrenia from genetic association studies remains to be elucidated, one of the most topical findings from imaging genetics studies is the association between COMT genotype and prefrontal function in schizophrenia. The next important step in the translational approach is to establish a useful neuroimaging tool in clinical settings that is sensitive to COMT variation, so that the clinician could use the index to predict clinical response such as improvement in cognitive dysfunction by medication. Here, we investigated spatiotemporal characteristics of the association between prefrontal hemodynamic activation and the COMT genotype using a noninvasive neuroimaging technique, near-infrared spectroscopy (NIRS).

Methodology/Principal Findings

Study participants included 45 patients with schizophrenia and 60 healthy controls matched for age and gender. Signals that are assumed to reflect regional cerebral blood volume were monitored over prefrontal regions from 52-channel NIRS and compared between two COMT genotype subgroups (Met carriers and Val/Val individuals) matched for age, gender, premorbid IQ, and task performance. The [oxy-Hb] increase in the Met carriers during the verbal fluency task was significantly greater than that in the Val/Val individuals in the frontopolar prefrontal cortex of patients with schizophrenia, although neither medication nor clinical symptoms differed significantly between the two subgroups. These differences were not found to be significant in healthy controls.

Conclusions/Significance

These data suggest that the prefrontal NIRS signals can noninvasively detect the impact of COMT variation in patients with schizophrenia. NIRS may be a promising candidate translational approach in psychiatric neuroimaging.

Association between sigma-1 receptor gene polymorphism and prefrontal hemodynamic response induced by cognitive activation in schizophrenia

The molecular biological role of the sigma-1 receptor (Sig-1R) has attracted much attention. Evidence suggests that the Sig-1R engaged in modulating NMDA and dopamine receptors is involved in the pathophysiology of schizophrenia and the mechanism of psychotropic drug efficacy. However, whether the Sig-1R genotype affects brain function in schizophrenia in vivo remains unknown. We investigated the association between Sig-1R functional polymorphism (Gln2Pro) and brain function in schizophrenia. The subjects were 40 patients with schizophrenia and 60 healthy controls, all right-handed, who gave written informed consent to participate. Signals, detected from prefrontal regions by 52-channel near-infrared spectroscopy (NIRS) during cognitive activation, were compared between two Sig1-R genotype subgroups (Gln/Gln individuals and Pro carriers) matched for age, gender, premorbid IQ and task performance. The prefrontal hemodynamic response of healthy controls during the verbal fluency task was higher than that of patients with schizophrenia. For the patients with schizophrenia, even after controlling the effect of medication, the [oxy-Hb] increase in the prefrontal cortex of the Gln/Gln genotype group was significantly greater than that of the Pro carriers (false discovery rate corrected p<0.05). Clinical symptoms were not significantly different between the two Sig-1R genotype subgroups. These differences were not significant in the healthy controls. This is the first functional imaging genetics study that implicated the association between Sig-1R genotype and prefrontal cortical function in schizophrenia in vivo. Our findings also suggest that the prefrontal hemodynamic response assessed by noninvasive and less demanding NIRS is a useful intermediate phenotype for translational research in schizophrenia.

Gene discovery through imaging genetics: identification of two novel genes associated with schizophrenia

We have discovered two genes, RSRC1 and ARHGAP18, associated with schizophrenia and in an independent study provided additional support for this association. We have both discovered and verified the association of two genes, RSRC1 and ARHGAP18, with schizophrenia. We combined a genome-wide screening strategy with neuroimaging measures as the quantitative phenotype and identified the single nucleotide polymorphisms (SNPs) related to these genes as consistently associated with the phenotypic variation. To control for the risk of false positives, the empirical P-value for association significance was calculated using permutation testing. The quantitative phenotype was Blood-Oxygen-Level Dependent (BOLD) Contrast activation in the left dorsal lateral prefrontal cortex measured during a working memory task. The differential distribution of SNPs associated with these two genes in cases and controls was then corroborated in a larger, independent sample of patients with schizophrenia (n=82) and healthy controls (n=91), thus suggesting a putative etiological function for both genes in schizophrenia. Up until now these genes have not been linked to any neuropsychiatric illness, although both genes have a function in prenatal brain development. We introduce the use of functional magnetic resonance imaging activation as a quantitative phenotype in conjunction with genome-wide association as a gene discovery tool.

Genetic determinants of target and novelty-related event-related potentials in the auditory oddball response

Processing of novel and target stimuli in the auditory target detection or 'oddball' task encompasses the chronometry of perception, attention and working memory and is reflected in scalp recorded event-related potentials (ERPs). A variety of ERP components related to target and novelty processing have been described and extensively studied, and linked to deficits of cognitive processing. However, little is known about associations of genotypes with ERP endophenotypes. Here we sought to elucidate the genetic underpinnings of auditory oddball ERP components using a novel data analysis technique. A parallel independent component analysis of the electrophysiology and single nucleotide polymorphism (SNP) data was used to extract relations between patterns of ERP components and SNP associations purely based on an analysis incorporating higher order statistics. The method allows for broader associations of genotypes with phenotypes than traditional hypothesis-driven univariate correlational analyses. We show that target detection and processing of novel stimuli are both associated with a shared cluster of genes linked to the adrenergic and dopaminergic pathways. These results provide evidence of genetic influences on normal patterns of ERP generation during auditory target detection and novelty processing at the SNP association level.

Combining fMRI and SNP data to investigate connections between brain function and genetics using parallel ICA

There is current interest in understanding genetic influences on both healthy and disordered brain function. We assessed brain function with functional magnetic resonance imaging (fMRI) data collected during an auditory oddball task--detecting an infrequent sound within a series of frequent sounds. Then, task-related imaging findings were utilized as potential intermediate phenotypes (endophenotypes) to investigate genomic factors derived from a single nucleotide polymorphism (SNP) array. Our target is the linkage of these genomic factors to normal/abnormal brain functionality. We explored parallel independent component analysis (paraICA) as a new method for analyzing multimodal data. The method was aimed to identify simultaneously independent components of each modality and the relationships between them. When 43 healthy controls and 20 schizophrenia patients, all Caucasian, were studied, we found a correlation of 0.38 between one fMRI component and one SNP component. This fMRI component consisted mainly of parietal lobe activations. The relevant SNP component was contributed to significantly by 10 SNPs located in genes, including those coding for the nicotinic alpha-7 cholinergic receptor, aromatic amino acid decarboxylase, disrupted in schizophrenia 1, among others. Both fMRI and SNP components showed significant differences in loading parameters between the schizophrenia and control groups (P = 0.0006 for the fMRI component; P = 0.001 for the SNP component). In summary, we constructed a framework to identify interactions between brain functional and genetic information; our findings provide a proof-of-concept that genomic SNP factors can be investigated by using endophenotypic imaging findings in a multivariate format.

A genome-wide association study of schizophrenia using brain activation as a quantitative phenotype

BACKGROUND

Genome-wide association studies (GWASs) are increasingly used to identify risk genes for complex illnesses including schizophrenia. These studies may require thousands of subjects to obtain sufficient power. We present an alternative strategy with increased statistical power over a case-control study that uses brain imaging as a quantitative trait (QT) in the context of a GWAS in schizophrenia.

METHODS

Sixty-four subjects with chronic schizophrenia and 74 matched controls were recruited from the Functional Biomedical Informatics Research Network (FBIRN) consortium. Subjects were genotyped using the Illumina HumanHap300 BeadArray and were scanned while performing a Sternberg Item Recognition Paradigm in which they learned and then recognized target sets of digits in an functional magnetic resonance imaging protocol. The QT was the mean blood oxygen level-dependent signal in the dorsolateral prefrontal cortex during the probe condition for a memory load of 3 items.

RESULTS

Three genes or chromosomal regions were identified by having 2 single-nucleotide polymorphisms (SNPs) each significant at P < 10(-6) for the interaction between the imaging QT and the diagnosis (ROBO1-ROBO2, TNIK, and CTXN3-SLC12A2). Three other genes had a significant SNP at <10(-6) (POU3F2, TRAF, and GPC1). Together, these 6 genes/regions identified pathways involved in neurodevelopment and response to stress.

CONCLUSION

Combining imaging and genetic data from a GWAS identified genes related to forebrain development and stress response, already implicated in schizophrenic dysfunction, as affecting prefrontal efficiency. Although the identified genes require confirmation in an independent sample, our approach is a screening method over the whole genome to identify novel SNPs related to risk for schizophrenia.

Genome-wide strategies for discovering genetic influences on cognition and cognitive disorders: methodological considerations

INTRODUCTION

Genes play a well-documented role in determining normal cognitive function. This paper focuses on reviewing strategies for the identification of common genetic variation in genes that modulate normal and abnormal cognition with a genome-wide association scan (GWAS). GWASs make it possible to survey the entire genome to discover important but unanticipated genetic influences.

METHODS

The use of a quantitative phenotype in combination with a GWAS provides many advantages over a case-control design, both in power and in physiological understanding of the underlying cognitive processes. We review the major features of this approach, and show how, using a General Linear Model method, the contribution of each Single Nucleotide Polymorphism (SNP) to the phenotype is determined, and adjustments then made for multiple tests. An example of the strategy is presented, in which fMRI measures of cortical inefficiency while performing a working memory task are used as the quantitative phenotype. We estimate power under different effect sizes (10-30%) and variations in allelic frequency for a Quantitative Trait (QT) (10-20%), and compare them to a case-control design with an Odds Ratio (OR) of 1.5, showing how a QT approach is superior to a traditional case-control. In the presented example, this method identifies putative susceptibility genes for schizophrenia which affect prefrontal efficiency and have functions related to cell migration, forebrain development and stress response.

CONCLUSION

The use of QT as phenotypes provide increased statistical power over categorical association approaches and when combined with a GWAS creates a strategy for identification of unanticipated genes that modulate cognitive processes and cognitive disorders.

In the mind's eye: provider and patient attitudes on functional brain imaging

Success in functional neuroimaging has brought the promise of quantitative data in the form of brain images to the diagnosis of disorders of the central nervous system for which only qualitative clinical criteria have previously existed. Even though the translation of research to clinical neuroimaging for conditions such as major depression may not be available yet, rapid innovation along this trajectory of discovery to implementation compels exploration of how such information will eventually affect providers and patients. Clinical neuroethics is devoted to elucidating ethical challenges prior to and during the transfer of new research capabilities to the bedside. Through a model of proactive ethics, clinical neuroethics promotes the development of responsible social and public policies in response to new diagnostic and prognostic capabilities for the benefit of patients and their families, and for providers within the health care systems in which they practice. To examine views about the potential interaction of clinical neuroimaging and depression, we surveyed both mental health providers and outpatients and inpatients diagnosed with major depressive disorder. From responses of 52 providers and 72 patients, we found high receptivity to brain scans for treatment tailoring and choice, for improving understanding of and coping with disease, and for mitigating the effects of stigma and self-blame. Our results suggest that, once ready, roll out of the fully validated technology has significant potential to reduce social burden associated with highly stigmatized illnesses like depression.

Proton magnetic resonance spectroscopy in alcohol use disorders: a potential new endophenotype?

BACKGROUND

Current effort is directed at defining new classification schemes for alcohol use disorders (AUD) based on genetic/biological, physiological, and behavioral endophenotypes.

METHODS

We describe briefly findings of in vivo brain proton magnetic resonance spectroscopy ((1)H MRS) studies in AUD and propose that they be further explored and expanded regarding their value as a potential endophenotype for AUD.

RESULTS

In vivo (1)H MRS, as part of the emerging field of "imaging genomics," may provide readily accessible, objective, functionally significant and region-specific neurobiological measures that successfully link genotypes to neurocognition and to psychiatric symptomatology in relatively small patient cohorts. We discuss several functional gene variants that may affect specific (1)H MRS-detectable metabolites and provide recent data from our own work that supports the view of genetic effects on metabolite measures.

CONCLUSIONS

MRS-genetics research will not only offer clues to the functional significance and downstream effects of genetic differences in AUD, but, via monitoring and/or predicting the efficacy of pharmacological and behavioral interventions as a function of genotype, has the potential to influence future clinical management of AUD.

Differentiating cortical patterns of cognitive dysfunction in schizophrenia and posttraumatic stress disorder

Comparative studies are needed to determine whether the cognitive impairments found in various psychiatric disorders are specific to those disorders, or are a more universal consequence of mental illness. This study compares the patterns of cognitive dysfunction in two conditions characterized by working memory dysfunction, schizophrenia and posttraumatic stress disorder (PTSD). Three matched groups (Schizophrenia, PTSD, Control) of 16 subjects had event related potentials recorded, using a 27 electrode array, while they performed a working memory auditory target detection task. Both disorders were associated with impaired task performance, with greater impairment in schizophrenia. Reduction in N1 amplitude was found only in schizophrenia, and an increase in target N2 amplitude and latency was found only in PTSD. Both patient groups showed a reduction in the amplitude of the non-target and target P3, but the groups were distinguished by a reduction in non-target parietal P3 amplitude in the schizophrenia group and a reduction in target P3 amplitude over the left posterior parietal region in the PTSD Group. This study demonstrates that there are specific patterns of cognitive dysfunction associated with schizophrenia and with PTSD.

A Parallel Independent Component Analysis Approach to Investigate Genomic Influence on Brain Function

Relationships between genomic data and functional brain images are of great interest but require new analysis approaches to integrate the high-dimensional data types. This letter presents an extension of a technique called parallel independent component analysis (paraICA), which enables the joint analysis of multiple modalities including interconnections between them. We extend our earlier work by allowing for multiple interconnections and by providing important overfitting controls. Performance was assessed by simulations under different conditions, and indicated reliable results can be extracted by properly balancing overfitting and underfitting. An application to functional magnetic resonance images and single nucleotide polymorphism array produced interesting findings.

Functional MRI in ADHD: a systematic literature review

Functional MRI (fMRI) research in attention-deficit/hyperactivity disorder (ADHD) is a fast developing and very complex field. Every study appears to show differences in patterns of brain activation between cases and controls, but the interpretation of such differences is not as straightforward as it may seem. We present here a systematic review of the fMRI literature in ADHD; areas covered include executive functions, reward processing, the effects of methylphenidate, comorbidity and spontaneous brain activity in the resting state. To facilitate the interpretation of research in this area, we discuss important conceptual issues, such as the need to take group differences in performance into account or to consider the role of errors. We present common themes that emerge from these studies and we discuss possible reasons for the many discrepancies that were observed. Finally, based on existing literature and current advancements in fMRI research, we discuss the role that fMRI could play in the future as a diagnostic tool or in treatment outcome predictions, and we make predictions for the future directions of research in this field.

Translational research in late-life mood disorders: implications for future intervention and prevention research

Clinical and epidemiological studies have consistently observed the heterogeneous symptomatology and course of geriatric depression. Given the importance of genetic and environmental risk factors, aging processes, neurodegenerative and cerebrovascular disease processes, and medical comorbidity, the integration of basic and clinical neuroscience research approaches is critical for the understanding of the variability in illness course, as well as the development of prevention and intervention strategies that are more effective. These considerations were the impetus for a workshop, sponsored by the Geriatrics Research Branch in the Division of Adult Translational Research and Treatment Development of the National Institute of Mental Health that was held on September 7-8, 2005. The primary goal of the workshop was to bring together investigators in geriatric psychiatry research with researchers in specific topic areas outside of geriatric mental health to identify priority areas to advance translational research in geriatric depression. As described in this report, the workshop focused on a discussion of the development and application of integrative approaches combining genetics and neuroimaging methods to understand such complex issues as treatment response variability, the role of medical comorbidity in depression, and the potential overlap between depression and dementia. Future directions for integrative research were identified. Understanding the nature of geriatric depression requires the application of translational research and interdisciplinary research approaches. Geriatric depression could serve as a model for translational research integrating basic and clinical neuroscience approaches that would have implications for the study of other neuropsychiatric disorders.

Interactive memory systems and category learning in schizophrenia

Schizophrenia is a devastating mental disorder with multiple facets, including the impairment of learning and memory. Recent evidence suggests that information is processed and represented by multiple interacting memory systems in the brain, including prefrontal cortex, basal ganglia, and medial temporal lobe. These structures are critical in the pathophysiology of schizophrenia. Whereas executive and declarative memory dysfunctions are well known in schizophrenia, habit learning deficits related to the basal ganglia are less clear, despite the fact that dopaminergic and other neurochemical processes in the basal ganglia may play a crucial role in the pathophysiology and pharmacology of schizophrenia. In this article, I propose that the investigation of different classification learning functions, including reward- and feedback-guided learning and acquired equivalence learning, may shed light on the neuropsychology, pathophysiology, pharmacology, and behavioral genetics of schizophrenia.

BDNF variant linked to anxiety-related behaviors

Brain-derived neurotrophic factor (BDNF) is the most-abundant neurotrophin in the brain. In mammals, it is synthesized as a precursor called proBDNF, which is proteolytically cleaved to generate mature BDNF. The BDNF gene is located on chromosome 11p13, and a functional single nucleotide polymorphism (SNP) of this gene has been shown to produce a valine (Val)-to-methionine (Met) substitution in the proBDNF protein at codon 66 (Val66Met). Several papers suggest that this SNP is related to decreased hippocampal volume and hippocampus-mediated memory performance in humans. Recently, Chen et al. generated a variant BDNF mouse (BDNF(Met/Met)) that reproduces the phenotypic hallmarks in humans with a variant Met allele. In the behavioral analysis, BDNF(Met/Met) mice show increased anxiety-related behaviors. This mini-review examines the impact of Met substitution of proBDNF on anxiety-related behaviors.

Conceptual issues in psychiatric neuroimaging

PURPOSE OF REVIEW

Psychiatric neuroimaging has made a dramatic impact on the understanding of the brain in mental illness in a relatively brief period of time and continues to be evolving in terms of methodology, analysis and utilization of the combination of techniques. Given the level of sophistication of the techniques and the importance of imaging in current academic psychiatry, it is timely to review its conceptual influence on psychopathology.

RECENT FINDINGS

The study will review scientific advances in psychiatric neuromaging, around the themes of functional connectivity, diffusion tensor imaging, magnetoencephalography, modality integration, meta-analyses and mega-analyses of data and discuss recent influential findings in contemporary research. We then focus on more conceptual issues relating to biological psychiatry and its relationship with cognitive neuroscience. We discuss the dominant paradigm of scientific psychopathology, namely cognitive neuropsychiatry and how it relates more broadly to imaging and cognitive science and elaborate on the philosophical positions of the paradigm and how it views abnormal mental states.

SUMMARY

We conclude that despite the advances in biological psychiatry and the power of the cognitive neuropsychiatry paradigm, its findings are logically contingent upon psychopathology and the normatively defined terms employed therein.

Endophenotype approach to developmental psychopathology: implications for autism research

This paper discusses the utility of the endophenotype approach in the study of developmental psychopathology. It is argued that endophenotype research holds considerable promise for the study of gene-brain/cognition-behaviour pathways for developmental disorders. This paper outlines the criteria for determining useful endophenotypes. Possible endophenotypes for autism are discussed as an example of an area where endophenotype research on developmental disorders may be fruitful. It is concluded that although the endophenotype approach holds promise for the study of gene-brain/cognition-behaviour pathways, much work remains to be done in order to validate endophenotype measures. It is also noted that the changing nature of any developmental psychopathology poses a particular challenge to this type of research.