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Background: Dopamine plays an important role in orienting, response anticipation and movement evaluation. Thus, we examined the influence of functional variants related to dopamine inactivation in the dopamine transporter (DAT1) and catechol-O-methyltransferase genes (COMT) on the time-course of motor processing in a contingent negative variation (CNV) task.
Methods: 64-channel EEG recordings were obtained from 195 healthy adolescents of a community-based sample during a continuous performance task (A-X version). Early and late CNV as well as motor postimperative negative variation were assessed. Adolescents were genotyped for the COMT Val158Met and two DAT1 polymorphisms (variable number tandem repeats in the 3′-untranslated region and in intron 8).
Results: The results revealed a significant interaction between COMT and DAT1, indicating that COMT exerted stronger effects on lateralized motor post-processing (centro-parietal motor postimperative negative variation) in homozygous carriers of a DAT1 haplotype increasing DAT1 expression. Source analysis showed that the time interval 500–1000 ms after the motor response was specifically affected in contrast to preceding movement anticipation and programming stages, which were not altered.
Conclusions: Motor slow negative waves allow the genomic imaging of dopamine inactivation effects on cortical motor post-processing during response evaluation. This is the first report to point towards epistatic effects in the motor system during response evaluation, i.e. during the post-processing of an already executed movement rather than during movement programming.
Background: Dopamine plays an important role in orienting and the regulation of selective attention to relevant stimulus characteristics. Thus, we examined the influences of functional variants related to dopamine inactivation in the dopamine transporter (DAT1) and catechol-O-methyltransferase genes (COMT) on the time-course of visual processing in a contingent negative variation (CNV) task.
Methods: 64-channel EEG recordings were obtained from 195 healthy adolescents of a community-based sample during a continuous performance task (A-X version). Early and late CNV as well as preceding visual evoked potential components were assessed.
Results: Significant additive main effects of DAT1 and COMT on the occipito-temporal early CNV were observed. In addition, there was a trend towards an interaction between the two polymorphisms. Source analysis showed early CNV generators in the ventral visual stream and in frontal regions. There was a strong negative correlation between occipito-temporal visual post-processing and the frontal early CNV component. The early CNV time interval 500–1000 ms after the visual cue was specifically affected while the preceding visual perception stages were not influenced.
Conclusions: Late visual potentials allow the genomic imaging of dopamine inactivation effects on visual post-processing. The same specific time-interval has been found to be affected by DAT1 and COMT during motor post-processing but not motor preparation. We propose the hypothesis that similar dopaminergic mechanisms modulate working memory encoding in both the visual and motor and perhaps other systems.
Bipolar disorder (BD) is a major psychiatric illness affecting around 1% of the global population. BD is characterized by recurrent manic and depressive episodes, and has an estimated heritability of around 70%. Research has identified the first BD susceptibility genes. However, the underlying pathways and regulatory networks remain largely unknown. Research suggests that the cumulative impact of common alleles with small effects explains only around 25–38% of the phenotypic variance for BD. A plausible hypothesis therefore is that rare, high penetrance variants may contribute to BD risk. The present study investigated the role of rare, nonsynonymous, and potentially functional variants via whole exome sequencing in 15 BD cases from two large, multiply affected families from Cuba. The high prevalence of BD in these pedigrees renders them promising in terms of the identification of genetic risk variants with large effect sizes. In addition, SNP array data were used to calculate polygenic risk scores for affected and unaffected family members. After correction for multiple testing, no significant increase in polygenic risk scores for common, BD-associated genetic variants was found in BD cases compared to healthy relatives. Exome sequencing identified a total of 17 rare and potentially damaging variants in 17 genes. The identified variants were shared by all investigated BD cases in the respective pedigree. The most promising variant was located in the gene SERPING1 (p.L349F), which has been reported previously as a genome-wide significant risk gene for schizophrenia. The present data suggest novel candidate genes for BD susceptibility, and may facilitate the discovery of disease-relevant pathways and regulatory networks.
Major mood disorders, which primarily include bipolar disorder and major depressive disorder, are the leading cause of disability worldwide and pose a major challenge in identifying robust risk genes. Here, we present data from independent large-scale clinical data sets (including 29 557 cases and 32 056 controls) revealing brain expressed protocadherin 17 (PCDH17) as a susceptibility gene for major mood disorders. Single-nucleotide polymorphisms (SNPs) spanning the PCDH17 region are significantly associated with major mood disorders; subjects carrying the risk allele showed impaired cognitive abilities, increased vulnerable personality features, decreased amygdala volume and altered amygdala function as compared with non-carriers. The risk allele predicted higher transcriptional levels of PCDH17 mRNA in postmortem brain samples, which is consistent with increased gene expression in patients with bipolar disorder compared with healthy subjects. Further, overexpression of PCDH17 in primary cortical neurons revealed significantly decreased spine density and abnormal dendritic morphology compared with control groups, which again is consistent with the clinical observations of reduced numbers of dendritic spines in the brains of patients with major mood disorders. Given that synaptic spines are dynamic structures which regulate neuronal plasticity and have crucial roles in myriad brain functions, this study reveals a potential underlying biological mechanism of a novel risk gene for major mood disorders involved in synaptic function and related intermediate phenotypes.
Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy.
Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression.
Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification.
Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research.
Multiplex families with a high prevalence of a psychiatric disorder are often examined to identify rare genetic variants with large effect sizes. In the present study, we analysed whether the risk for bipolar disorder (BD) in BD multiplex families is influenced by common genetic variants. Furthermore, we investigated whether this risk is conferred mainly by BD-specific risk variants or by variants also associated with the susceptibility to schizophrenia or major depression. In total, 395 individuals from 33 Andalusian BD multiplex families (166 BD, 78 major depressive disorder, 151 unaffected) as well as 438 subjects from an independent, BD case/control cohort (161 unrelated BD, 277 unrelated controls) were analysed. Polygenic risk scores (PRS) for BD, schizophrenia (SCZ), and major depression were calculated and compared between the cohorts. Both the familial BD cases and unaffected family members had higher PRS for all three psychiatric disorders than the independent controls, with BD and SCZ being significant after correction for multiple testing, suggesting a high baseline risk for several psychiatric disorders in the families. Moreover, familial BD cases showed significantly higher BD PRS than unaffected family members and unrelated BD cases. A plausible hypothesis is that, in multiplex families with a general increase in risk for psychiatric disease, BD development is attributable to a high burden of common variants that confer a specific risk for BD. The present analyses demonstrated that common genetic risk variants for psychiatric disorders are likely to contribute to the high incidence of affective psychiatric disorders in the multiplex families. However, the PRS explained only part of the observed phenotypic variance, and rare variants might have also contributed to disease development.
Multiplex families with a high prevalence of a psychiatric disorder are often examined to identify rare genetic variants with large effect sizes. In the present study, we analysed whether the risk for bipolar disorder (BD) in BD multiplex families is influenced by common genetic variants. Furthermore, we investigated whether this risk is conferred mainly by BD-specific risk variants or by variants also associated with the susceptibility to schizophrenia or major depression. In total, 395 individuals from 33 Andalusian BD multiplex families as well as 438 subjects from an independent, sporadic BD case-control cohort were analysed. Polygenic risk scores (PRS) for BD, schizophrenia, and major depression were calculated and compared between the cohorts. Both the familial BD cases and unaffected family members had significantly higher PRS for all three psychiatric disorders than the independent controls, suggesting a high baseline risk for several psychiatric disorders in the families. Moreover, familial BD cases showed significantly higher BD PRS than unaffected family members and sporadic BD cases. A plausible hypothesis is that, in multiplex families with a general increase in risk for psychiatric disease, BD development is attributable to a high burden of common variants that confer a specific risk for BD. The present analyses, therefore, demonstrated that common genetic risk variants for psychiatric disorders are likely to contribute to the high incidence of affective psychiatric disorders in the multiplex families. The PRS explained only part of the observed phenotypic variance and rare variants might have also contributed to disease development.
Growing up in cities is associated with increased risk for developing mental health problems. Stress exposure and altered stress regulation have been proposed as mechanisms linking urbanicity and psychopathology, with most research conducted in adult populations. Here, we focus on early childhood, and investigate urbanicity, behavior problems and the regulation of the hypothalamus-pituitary-adrenal (HPA) axis, a central circuit of the stress system, in a sample of N = 399 preschoolers aged 45 months. Urbanicity was coded dichotomously distinguishing between residences with more or less than 100,000 inhabitants. Behavior problems were measured using the Child Behavior Checklist (CBCL) 1½ - 5. Cortisol stress reactivity was assessed using an age-appropriated game-like stress task, and cortisol in the first morning urine was measured to assess nocturnal HPA axis activity. Urbanicity was not associated with behavior problems, urinary cortisol or the cortisol stress response. Neither urinary cortisol nor salivary cortisol response after stress exposure were identified as mediators of the relationship between urbanicity and behavior problems. The findings suggest no strong association of urbanicity with behavior problems and HPA axis regulation in preschool age. To our knowledge, this is the youngest sample to date studying the relationship between urbanicity and behavior problems as well as HPA axis regulation. Future research should examine at which age associations can first be identified and which mechanisms contribute to these relationships.
Bipolar disorder (BD) is a highly heritable neuropsychiatric disease characterized by recurrent episodes of mania and depression. BD shows substantial clinical and genetic overlap with other psychiatric disorders, in particular schizophrenia (SCZ). The genes underlying this etiological overlap remain largely unknown. A recent SCZ genome wide association study (GWAS) by the Psychiatric Genomics Consortium identified 128 independent genome-wide significant single nucleotide polymorphisms (SNPs). The present study investigated whether these SCZ-associated SNPs also contribute to BD development through the performance of association testing in a large BD GWAS dataset (9747 patients, 14278 controls). After re-imputation and correction for sample overlap, 22 of 107 investigated SCZ SNPs showed nominal association with BD. The number of shared SCZ-BD SNPs was significantly higher than expected (p = 1.46x10-8). This provides further evidence that SCZ-associated loci contribute to the development of BD. Two SNPs remained significant after Bonferroni correction. The most strongly associated SNP was located near TRANK1, which is a reported genome-wide significant risk gene for BD. Pathway analyses for all shared SCZ-BD SNPs revealed 25 nominally enriched gene-sets, which showed partial overlap in terms of the underlying genes. The enriched gene-sets included calcium- and glutamate signaling, neuropathic pain signaling in dorsal horn neurons, and calmodulin binding. The present data provide further insights into shared risk loci and disease-associated pathways for BD and SCZ. This may suggest new research directions for the treatment and prevention of these two major psychiatric disorders.