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OBJECTIVE: Deep brain stimulation (DBS) is an effective treatment for refractory obsessive-compulsive disorder (OCD) yet neural markers of optimized stimulation parameters are largely unknown. We aimed to describe (sub-)cortical electrophysiological responses to acute DBS at various voltages in OCD. METHODS: We explored how DBS doses between 3-5 V delivered to the ventral anterior limb of the internal capsule of five OCD patients affected electroencephalograms and intracranial local field potentials (LFPs). We focused on theta power/ phase-stability, given their previously established role in DBS for OCD. RESULTS: Cortical theta power and theta phase-stability did not increase significantly with DBS voltage. DBS-induced theta power peaks were seen at the previously defined individualized therapeutic voltage. Although LFP power generally increased with DBS voltages, this occurred mostly in frequency peaks that overlapped with stimulation artifacts limiting its interpretability. Though highly idiosyncratic, three subjects showed significant acute DBS effects on electroencephalogram theta power and four subjects showed significant carry-over effects (pre-vs post DBS, unstimulated) on LFP and electroencephalogram theta power. CONCLUSIONS: Our findings challenge the presence of a consistent dose-response relationship between stimulation voltage and brain activity. SIGNIFICANCE: Theta power may be investigated further as a neurophysiological marker to aid personalized DBS voltage optimization in OCD.

While 1-2% of individuals meet the criteria for a clinical diagnosis of obsessive-compulsive disorder (OCD), many more (∼15-40%) experience subclinical obsessive-compulsive symptoms (OCS) during their life. To characterize the genetic underpinnings of OCS and its genetic relationship to OCD, we conducted the largest genome-wide association study (GWAS) meta-analysis of parent- or self-reported OCS to date (N = 33,943 with complete phenotypic and genome-wide data), combining the results from seven large-scale population-based cohorts from Sweden, the Netherlands, England, and Canada (including six twin cohorts and one cohort of unrelated individuals). We found no genome-wide significant associations on the SNP or gene-level, but a polygenic risk score (PRS) based on the OCD GWAS previously published by the Psychiatric Genetics Consortium (PGC-OCD) was significantly associated with OCS (Pfixed= 3.06 ×10−5). Also, one curated gene set (Mootha Gluconeogenesis) reached Bonferroni-corrected significance (Ngenes= 28, Beta = 0.79, SE = 0.16, Pbon= 0.008). Expression of genes in this set is high at sites of insulin-mediated glucose disposal. Dysregulated insulin signaling in the etiology of OCS has been suggested by a previous study describing a genetic overlap of OCS with insulin signaling-related traits in children and adolescents. We report a SNP heritability of 4.1% (P = 0.0044) in the meta-analyzed GWAS, and heritability estimates based on the twin cohorts of 33% - 43%. Genetic correlation analysis showed that OCS were most strongly associated with OCD (rG= 0.72, p = 0.0007) among all tested psychiatric disorders (N = 11). Of all 97 tested phenotypes, 24 showed a significant genetic correlation with OCS, and 66 traits showed concordant directions of effect with OCS and OCD. OCS have a significant polygenic contribution and share genetic risk with diagnosed OCD, supporting the hypothesis that OCD represents the extreme end of widely distributed OCS in the population.

Patients with misophonia experience strong negative emotional responses to human-produced sounds at a level disrupting normal social interaction. The exact nature of the disorder remains a matter of debate. Here, we investigated the genetic aetiology of misophonia in order to understand contributing factors and shed light on the nosology of the disorder. For misophonia, we used an unpublished genome-wide association study (GWAS) from 23andMe on a self-report item probing a common misophonic symptom: the occurrence of rage when others produce eating sounds.. We used gene-based and to functional annotation analyses to establish its neurobiological determinants. Next, we calculated genetic correlations (rG) of this misophonia GWAS with a wide range of traits and disorders from audiology (tinnitus, hearing performance and hearing trauma), psychiatry, neurology, and personality traits. Misophonia was significantly correlated with tinnitus, major depression disorder, post-traumatic stress disorder, and generalized anxiety disorder (0.12 < rG < 0.22). Stronger genetic correlations (0.21 < rG < 0.42) were observed for two clusters of personality traits: a neuroticism/guilt and an irritability/sensitivity cluster. Our results showed no genetic correlation with ADHD, OCD, and psychotic disorders. A negative correlation with autism spectrum disorder (ASD) was found, which may be surprising given the previously reported comorbidities and the sensory sensitivity reported in ASD. Clustering algorithms showed that misophonia consistently clustered with MDD, generalized anxiety, PTSD and related personality traits. We conclude that — based on genetics — misophonia most strongly clusters with psychiatric disorders and specific personality profile that matches those for anxiety and PTSD.

Hoarding Disorder (HD) is a mental disorder characterized by persistent difficulties discarding or parting with possessions, often resulting in cluttered living spaces, distress, and impairment. Its etiology is largely unknown, but twin studies suggest that it is moderately heritable. In this study, we pooled phenotypic and genomic data from seven international cohorts (N = 27 537 individuals) and conducted a genome wide association study (GWAS) meta-analysis of parent- or self-reported hoarding symptoms (HS). We followed up the results with gene-based and gene-set analyses, as well as leave-one-out HS polygenic risk score (PRS) analyses. To examine a possible genetic association between hoarding symptoms and other phenotypes we conducted cross-trait PRS analyses. Though we did not report any genome-wide significant SNPs, we found a significant contribution of common genetic factors to HS, as indicated by substantial SNP-based twin-heritability estimates ranging between 26% and 48% and a SNP-heritability of 11% for one sub-cohort. Cross-trait PRS analyses showed that the genetic risk for schizophrenia and autism spectrum disorder were significantly associated with hoarding symptoms. We also found suggestive evidence for an association with educational attainment. There were no significant associations with other phenotypes previously linked to HD, such as obsessive-compulsive disorder, depression, anxiety, or attention-deficit hyperactivity disorder. To conclude, we found that HS are heritable, confirming and extending previous twin studies but we had limited power to detect any genome-wide significant loci. Much larger samples will be needed to further extend these findings and reach a “gene discovery zone”. To move the field forward, future research should not only include genetic analyses of quantitative hoarding traits in larger samples, but also in samples of individuals meeting strict diagnostic criteria for HD, and more ethnically diverse samples.

We investigated whether obsessive compulsive (OC) symptoms from a population-based sample could be analyzed to detect genetic variants influencing OCD. We performed a GWAS on the obsession (rumination and impulsions) and compulsion (checking, washing, and ordering/precision) subscales of an abbreviated version of the Padua Inventory (N=8267 with genome-wide genotyping and phenotyping). The compulsion subscale showed a substantial and significant positive genetic correlation with an OCD case-control GWAS (rG=0.61, p=0.017) previously published by the Psychiatric Genomics Consortium (PGC-OCD). The obsession subscale and the total Padua score showed no significant genetic correlations (rG=–0.02 and rG=0.42, respectively). A meta-analysis of the compulsive symptoms GWAS with the PGC-OCD revealed no genome-wide significant SNPs (combined N=17992, indicating that the power is still low for individual SNP effects). A gene-based association analysis, however, yielded two novel genes (WDR7 and ADCK1). The top 250 genes in the gene-based test also showed significant increase in enrichment for psychiatric and brain-expressed genes. S-Predixcan testing showed that for genes expressed in hippocampus, amygdala, and caudate nucleus significance increased in the meta-analysis with compulsive symptoms compared to the original PGC-OCD GWAS. Thus, inclusion of dimensional symptom data in genome-wide association on clinical case-control GWAS of OCD may be useful to find genes for OCD if the data are based on quantitative indices of compulsive behavior. SNP-level power increases were limited, but aggregate, gene-level analyses showed increased enrichment for brain-expressed genes related to psychiatric disorders, and increased association with gene-expression in brain tissues with known emotional, reward processing, memory, and fear-formation functions.

Background.Frequency and quantity of alcohol consumption are metrics commonly used to measure alcohol consumption behaviors. Epidemiological studies indicate that these alcohol consumption measures are differentially associated with (mental) health outcomes and socioeconomic status (SES). The current study aims to elucidate to what extent genetic risk factors are shared between frequency and quantity of alcohol consumption, and how these alcohol consumption measures are genetically associated with four broad phenotypic categories: (i) SES; (ii) substance use disorders; (iii) other psychiatric disorders; and (iv) psychological/personality traits.Methods.Genome-Wide Association analyses were conducted to test genetic associations with alcohol consumption frequency (N= 438 308) and alcohol consumption quantity (N= 307 098 regular alcohol drinkers) within UK Biobank. For the other phenotypes, we used genome-wide association studies summary statistics. Genetic correlations (rg) between the alcohol measures and other phenotypes were estimated using LD score regression.Results.We found a substantial genetic correlation between the frequency and quantity of alcohol consumption (rg= 0.52). Nevertheless, both measures consistently showed opposite genetic correlations with SES traits, and many substance use, psychiatric, and psychological/personality traits. High alcohol consumption frequency was genetically associated with high SES and low risk of substance use disorders and other psychiatric disorders, whereas the opposite applies for high alcohol consumption quantity.Conclusions.Although the frequency and quantity of alcohol consumption show substantial genetic overlap, they consistently show opposite patterns of genetic associations with SES-related phenotypes. Future studies should carefully consider the potential influence of SES on the shared genetic etiology between alcohol and adverse (mental) health outcomes.

Background: Mental health and cognitive achievement are partly heritable, highly polygenic, and associated with brain variations in structure and function. However, the underlying neural mechanisms remain unclear. Methods: We investigated the association between genetic predispositions to various mental health and cognitive traits and a large set of structural and functional brain measures from the UK Biobank (N = 36,799). We also applied linkage disequilibrium score regression to estimate the genetic correlations between various traits and brain measures based on genome-wide data. To decompose the complex association patterns, we performed a multivariate partial least squares model of the genetic and imaging modalities. Results: The univariate analyses showed that certain traits were related to brain structure (significant genetic correlations with total cortical surface area from rg = −0.101 for smoking initiation to rg = 0.230 for cognitive ability), while other traits were related to brain function (significant genetic correlations with functional connectivity from rg = −0.161 for educational attainment to rg = 0.318 for schizophrenia). The multivariate analysis showed that genetic predispositions to attention-deficit/hyperactivity disorder, smoking initiation, and cognitive traits had stronger associations with brain structure than with brain function, whereas genetic predispositions to most other psychiatric disorders had stronger associations with brain function than with brain structure. Conclusions: These results reveal that genetic predispositions to mental health and cognitive traits have distinct brain profiles.

This study aims to disentangle the contribution of genetic liability, educational attainment (EA), and their overlap and interaction in lifetime smoking. We conducted genome-wide association studies (GWASs) in UK Biobank (N = 394,718) to (i) capture variants for lifetime smoking, (ii) variants for EA, and (iii) variants that contribute to lifetime smoking independently from EA (‘smoking-without-EA’). Based on the GWASs, three polygenic scores (PGSs) were created for individuals from the Netherlands Twin Register (NTR, N = 17,805) and the Netherlands Mental Health Survey and Incidence Study-2 (NEMESIS-2, N = 3090). We tested gene–environment (G × E) interactions between each PGS, neighborhood socioeconomic status (SES) and EA on lifetime smoking. To assess if the PGS effects were specific to smoking or had broader implications, we repeated the analyses with measures of mental health. After subtracting EA effects from the smoking GWAS, the SNP-based heritability decreased from 9.2 to 7.2%. The genetic correlation between smoking and SES characteristics was reduced, whereas overlap with smoking traits was less affected by subtracting EA. The PGSs for smoking, EA, and smoking-without-EA all predicted smoking. For mental health, only the PGS for EA was a reliable predictor. There were suggestions for G × E for some relationships, but there were no clear patterns per PGS type. This study showed that the genetic architecture of smoking has an EA component in addition to other, possibly more direct components. PGSs based on EA and smoking-without-EA had distinct predictive profiles. This study shows how disentangling different models of genetic liability and interplay can contribute to our understanding of the etiology of smoking.

Misophonia is a newly described condition in which specific ordinary sounds provoke disproportionately strong negative affect. Since evidence for neurobiological abnormalities underlying misophonia is scarce, we tested whether misophonia patients differed from healthy controls in grey matter volumes and resting-state functional connectivity. We collected structural magnetic resonance imaging and resting-state functional magnetic resonance imaging data from 24 misophonia patients and 25 matched controls. Compared to controls, voxel-based morphometry showed larger right amygdala volume in misophonia patients. Follow-up seed-based functional connectivity analysis of the amygdala showed a different pattern of connectivity with the cerebellum, driven by greater connectivity with the left amygdala. Additional data-driven independent component analysis showed greater connectivity within lateral occipital cortices and fusiform gyri in the ventral attention network. We propose that the amygdala enlargement may be associated with heightened emotional reactivity in misophonia. The higher connectivity between left amygdala and cerebellum might be linked to a tendency to exhibit reflex-like physical reactions to triggers. Higher attention network connectivity may reflect sensory enhancement of visual triggers or visual imagery related to trigger sounds. In sum, we found structural and functional abnormalities which implicate dysfunction of emotional and attentional systems in misophonia.

Obsessive-compulsive disorder (OCD) is a heritable disorder, but no definitive, replicated OCD susceptibility loci have yet been identified by any genome-wide association study (GWAS). Here, we report results from a GWAS in the largest OCD case-control sample (N = 14,140 OCD cases and N = 562,117 controls) to date. We explored the genetic architecture of OCD, including its genetic relationships to other psychiatric and non-psychiatric phenotypes. In the GWAS analysis, we identified one SNP associated with OCD at a genome-wide significant level. Subsequent gene-based analyses identified additional two genes as potentially implicated in OCD pathogenesis. All SNPs combined explained 16% of the heritability of OCD. We show sub-stantial positive genetic correlations between OCD and a range of psychiatric disorders, including anxiety disorders, anorexia nervosa, and major depression. We thus for the first time provide evidence of a genome-wide locus implicated in OCD and strengthen previous literature suggesting a polygenic nature of this disorder.

Background and purpose The ENIGMA‐EEG working group was established to enable large‐scale international collaborations among cohorts that investigate the genetics of brain function measured with electroencephalography (EEG). In this perspective, we will discuss why analyzing the genetics of functional brain activity may be crucial for understanding how neurological and psychiatric liability genes affect the brain. Methods We summarize how we have performed our currently largest genome‐wide association study of oscillatory brain activity in EEG recordings by meta‐analyzing the results across five participating cohorts, resulting in the first genome‐wide significant hits for oscillatory brain function located in/near genes that were previously associated with psychiatric disorders. We describe how we have tackled methodological issues surrounding genetic meta‐analysis of EEG features. We discuss the importance of harmonizing EEG signal processing, cleaning, and feature extraction. Finally, we explain our selection of EEG features currently being investigated, including the temporal dynamics of oscillations and the connectivity network based on synchronization of oscillations. Results We present data that show how to perform systematic quality control and evaluate how choices in reference electrode and montage affect individual differences in EEG parameters. Conclusion The long list of potential challenges to our large‐scale meta‐analytic approach requires extensive effort and organization between participating cohorts; however, our perspective shows that these challenges are surmountable. Our perspective argues that elucidating the genetic of EEG oscillatory activity is a worthwhile effort in order to elucidate the pathway from gene to disease liability., We present the perspective of the ENIGMA‐EEG workgroup on our goal to elucidate the genetics of brain activity measured by electroencephalography. We explain the issues that arise when performing an international multicohort study and the solutions to these problems. We summarize our initial results and lay out our plans for future electroencephalographic features that are crucial to investigate in order to link liability genes to brain activity.

Mind wandering constitutes a major part of everyday experience and is inherently related to how we feel and identify ourselves. Thus, probing the character and content of thoughts and feelings experienced during mind-wandering episodes could lead to a better understanding of the human mind in health and disease. How mind wandering and spontaneous thought processes are affected in disorders such as autism is poorly understood. Here, we used the eyes-closed rest condition to stimulate mind wandering and quantified the subjective experiences using the Amsterdam Resting-State Questionnaire—which quantifies subjective psychological states of resting-state cognition across 10 domains—in 88 adults with autism spectrum disorder and 90 controls. We observed an atypical pattern of both thoughts and feelings in the autism spectrum disorder cohort, specifically in the domains of Theory of Mind, Comfort, and Discontinuity of Mind. We propose that the use of the Amsterdam Resting-State Questionnaire as a standardized cognitive instrument could advance our understanding of thoughts and feelings in autism spectrum disorder as well as in a wide variety of other brain disorders and how these may change due to therapeutic interventions. Lay abstract Everyone knows the feeling of letting one’s mind wander freely in a quiet moment. The thoughts and feelings experienced in those moments have been shown to influence our well-being—and vice versa. In this study, we looked at which thoughts and feelings are being experienced by adults with autism spectrum disorder and compared them to adults without autism spectrum disorder. In total, 88 adults with autism spectrum disorder and 90 adults without autism spectrum disorder were asked to rest for 5 min with their eyes closed and let their mind wander. Directly after, they filled in the Amsterdam Resting-State Questionnaire, which probes what participants were feeling and thinking during the period of rest. We found that adults with autism spectrum disorder tend to think less about others, felt less comfortable, and had more disrupted thoughts during the rest compared to adults without autism spectrum disorder. Interestingly, autism spectrum disorder participants reporting lower levels of comfort during the rest also reported more autism spectrum disorder symptoms, specifically in social behaviors and skills, attention switching, and imagination. We propose to use the eyes-closed rest condition in combination with the Amsterdam Resting-State Questionnaire more widely to shed light on aberrant thoughts and feelings in brain disorders and to study the effect of therapeutic interventions.

The human brain shows remarkable development of functional brain activity from childhood to adolescence. Here, we investigated whether electroencephalogram (EEG) recordings are suitable for predicting the age of children and adolescents. Moreover, we investigated whether overestimation or underestimation of age was stable over longer time periods, as stable prediction error can be interpreted as reflecting individual brain maturational level. Finally, we established whether the age-prediction error was genetically determined. Then, 3 min eyes-closed resting-state EEG data from the longitudinal EEG studies of Netherlands Twin Register (NTR; n = 836) and Washington University in St. Louis (n = 702) were used at ages 5, 7, 12, 14, 16, and 18. Longitudinal data were available within childhood (5-7 years) and adolescence (16-18 years). We calculated power in 1 Hz wide bins (1-24 Hz). Random forest (RF) regression and relevance vector machine with sixfold cross-validation were applied. The best mean absolute prediction error was obtained with RF (1.22 years). Classification of childhood versus puberty/adolescence reached over 94% accuracy. Prediction errors were moderately to highly stable over periods of 1.5-2.1 years (0.53

Deep brain stimulation is a treatment option for patients with refractory obsessive-compulsive disorder. A new generation of stimulators hold promise for closed loop stimulation, with adaptive stimulation in response to biological signals Here we aimed to discover a suitable biomarker in the ventral striatum in patients with obsessive compulsive disorder using local field potentials. We induced obsessions and compulsions and trained a deep learning model on the recorded time series. Average classification sensitivities were 47% for obsessions and 66% for compulsions for patient specific models at 25% chance level. Sensitivity for obsessions reached over 90% in one patient, whereas performance was near chance level when the model was trained across patients. Optimal sensitivity for obsessions and compulsions was obtained at different recording sites. This study shows that closed loop stimulation is a viable option for OCD, but that intracranial biomarkers for obsessive-compulsive disorder are patient and not disorder specific.

Although substantial research into genetics of psychotic disorders has been conducted, a large proportion of their genetic architecture has remained unresolved. Electroencephalographical intermediate phenotypes (EIP) have the potential to constitute a valuable tool when studying genetic risk loci for schizophrenia, in particular P3b amplitude, P50 suppression, mismatch negativity (MMN) and resting state power spectra of the electroencephalogram (EEG). Here, we systematically reviewed studies investigating the association of single nucleotide polymorphisms (SNPs) with these EIPs and meta-analysed them when appropriate. We retrieved 45 studies (N = 34,971 study participants). Four SNPs investigated in more than one study were genome-wide significant for an association with schizophrenia and three were genome-wide suggestive, based on a lookup in the influential 2014 GWAS (Ripke et al., 2014). However, in our meta-analyses, rs1625579 failed to reach a statistically significant association with p3b amplitude decrease and rs4680 risk allele carrier status was not associated with p3b amplitude decrease or with impaired p50 suppression. In conclusion, evidence for SNP associations with EIPs remains limited to individual studies. Careful selection of EIPs and SNPs, combined with consistent reporting of effect sizes, directions of effect and p-values would aid future meta-analyses.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of “big data” (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA’s activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material.

Background and aimsA recent study investigated the genetic associations and latent genetic structure among eight psychiatric disorders using findings from genome-wide association studies (GWASs). No data from substance use disorders were included, while these represent an important category of mental disorders and could influence the latent genetic structure. We extended the original paper by recreating the genetic relationship matrix, graph, and latent genetic factor structure, including additional data from substance use disorders.MethodsWe used GWAS summary statistics of 11 psychiatric disorders, including alcohol dependence, nicotine dependence, and cannabis use disorder. We estimated genetic correlations between all traits in Linkage Disequilibrium-score regression. A graph was created to illustrate the network of genetic correlations. We then used the genetic relationships to model a latent genetic factor structure.ResultsAlcohol and nicotine dependence showed significant genetic correlations with several other psychiatric disorders, including ADHD, schizophrenia, and major depression. Cannabis use disorder was only significantly associated with ADHD. The addition of substance use disorders resulted in some changes in the latent structure of the factor model when compared to the original model including eight disorders. All substance use disorders contributed mostly to Factor 3, a heterogeneous factor with also loadings from ADHD, major depression, Autism Spectrum disorders, and Tourette Syndrome.ConclusionsAlcohol and nicotine dependence show widespread genetic correlations with other psychiatric disorders. Including substance use disorders in the factor analysis results in some changes in the underlying genetic factor structure. Given the instability of such models, identified structures should be interpreted with caution.

Vascular contribution to cognitive impairment (VCI) and dementia is related to etiologies that may affect the neurophysiological mechanisms regulating brain arousal and generating electroencephalographic (EEG) activity. A multidisciplinary expert panel reviewed the clinical literature and reached consensus about the EEG measures consistently found as abnormal in VCI patients with dementia. As compared to cognitively unimpaired individuals, those VCI patients showed (1) smaller amplitude of resting state alpha (8-12 Hz) rhythms dominant in posterior regions; (2) widespread increases in amplitude of delta (< 4 Hz) and theta (4-8 Hz) rhythms; and (3) delayed N20 0/P30 0 peak latencies in averaged event-related potentials, especially during the detection of auditory rare target stimuli requiring participants' responses in "oddball" paradigms. The expert panel formulated the following recommendations: (1) the above EEG measures are not specific for VCI and should not be used for its diagnosis; (2) they may be considered as "neural synchronization" biomarkers to enlighten the relationships between features of the VCI-related cerebrovascular lesions and abnormalities in neurophysiological brain mechanisms; and (3) they may be tested in future clinical trials as prognostic biomarkers and endpoints of interventions aimed at normalizing background brain excitability and vigilance in wakefulness.

Background Patients with psychiatric disorders, such as major depressive disorder, schizophrenia or obsessive-compulsive disorder, often suffer from cognitive dysfunction. The nature of these dysfunctions and their relation with clinical symptoms and biological parameters is not yet clear. Traditionally, cognitive dysfunction is studied in patients with specific psychiatric disorders, disregarding the fact that cognitive deficits are shared across disorders. The Across study aims to investigate cognitive functioning and its relation with psychiatric symptoms and biological parameters transdiagnostically and longitudinally. Methods The study recruits patients diagnosed with a variety of psychiatric disorders and has a longitudinal cohort design with an assessment at baseline and at one-year follow-up. The primary outcome measure is cognitive functioning. The secondary outcome measures include clinical symptoms, electroencephalographic, genetic and blood markers (e.g., fatty acids), and hair cortisol concentration levels. Discussion The Across study provides an opportunity for a transdiagnostic, bottom-up, data-driven approach of investigating cognition in relation to symptoms and biological parameters longitudinally in patients with psychiatric disorders. The study may help to find new clusters of symptoms, biological markers, and cognitive dysfunctions that have better prognostic value than the current diagnostic categories. Furthermore, increased insight into the relationship among cognitive deficits, biological parameters, and psychiatric symptoms can lead to new treatment possibilities. Trial registration Netherlands Trial Register (NTR): NL8170.

Recent human electrophysiological evidence implicated θ-band communication between the nucleus accumbens (NAc) and frontal and parietal cortex in cognitive flexibility. Since the NAc is connected with the motor system, we tested whether phase and amplitude-based NAc-cortical connectivity and power modulation likewise underlie flexibility in motor action control. We combined concurrently recorded intracranial and extracranial electroencephalograms from seven psychiatric patients implanted with deep brain stimulation (DBS) electrodes who performed a stop signal task (SST). Inhibition success, as opposed to failure, was associated with greater prestimulus information flow from right NAc to medial frontal cortex through phase coupling of θ oscillations. Inhibition failure evoked θ power increases in the left NAc and medial frontal cortex, whereas parieto-occipital cortex showed an α power decrease. We conclude that NAc-to-frontal θ connectivity, possibly facilitating processing of task-relevant information, and α and θ power modulations, possibly reflecting post-error engagement of cognitive control, contribute to adaptive behavior pertaining motor control.

Epidemiological studies show high comorbidity between different mental health problems, indicating that individuals with a diagnosis of one disorder are more likely to develop other mental health problems. Genetic studies reveal substantial sharing of genetic risk factors across mental health traits. However, mental health is genetically correlated with socio-economic status (SES) and it is therefore important to investigate and disentangle the genetic relationship between mental health and SES. We used summary statistics from large genome-wide association studies (average N∼160,000) to estimate the genetic overlap across nine psychiatric disorders and seven substance use traits and explored the genetic influence of three different indicators of SES. Using Genomic SEM, we show significant changes in patterns of genetic correlations after partialling out SES-associated genetic variation. Our approach allows the separation of disease-specific genetic variation and genetic variation shared with SES, thereby improving our understanding of the genetic architecture of mental health.

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Contains fulltext : 216629.pdf (Publisher’s version ) (Open Access) Background: Little is known about the functional mechanisms through which genetic loci associated with substance use traits ascertain their effect. This study aims to identify and functionally annotate loci associated with substance use traits based on their role in genetic regulation of gene expression. Methods: We evaluated expression Quantitative Trait Loci (eQTLs) from 13 brain regions and whole blood of the Genotype-Tissue Expression (GTEx) database, and from whole blood of the Depression Genes and Networks (DGN) database. The role of single eQTLs was examined for six substance use traits: alcohol consumption (N = 537,349), cigarettes per day (CPD; N = 263,954), former vs. current smoker (N = 312,821), age of smoking initiation (N = 262,990), ever smoker (N = 632,802), and cocaine dependence (N = 4,769). Subsequently, we conducted a gene level analysis of gene expression on these substance use traits using S-PrediXcan. Results: Using an FDR-adjusted p-value < 0.05 we found 2,976 novel candidate genetic loci for substance use traits, and identified genes and tissues through which these loci potentially exert their effects. Using S-PrediXcan, we identified significantly associated genes for all substance traits. Discussion: Annotating genes based on transcriptomic regulation improves the identification and functional characterization of candidate loci and genes for substance use traits. 9 p.

BackgroundPoor sleep quality and insomnia have been associated with the use of tobacco, alcohol, and cannabis, but it is unclear if there is a causal link. In this Mendelian Randomization (MR) study we examine if insomnia causes substance use and/or if substance use causes insomnia.MethodsMR uses summary effect estimates from a genome-wide association study (GWAS) to create a genetic instrumental variable for a proposed ‘exposure’ variable and then identifies that same genetic instrument in an ‘outcome’ GWAS. With data of GWAS of insomnia, smoking (initiation, heaviness, cessation), alcohol use (drinks per week, dependence), and cannabis initiation, bi-directional causal effects were tested. Multiple sensitivity analyses were applied to assess the robustness of the findings.ResultsThere was strong evidence for positive causal effects of insomnia on all substance use phenotypes (smoking traits, alcohol dependence, cannabis initiation), except alcohol per week. The effects on alcohol dependence and cannabis initiation were attenuated after filtering out pleiotropic SNPs. In the other direction, there was strong evidence that smoking initiation increased chances of insomnia (smoking heaviness and cessation could not be tested as exposures). We found no evidence that alcohol use per week, alcohol dependence, or cannabis initiation causally affect insomnia.ConclusionsThere were unidirectional effects of insomnia on alcohol dependence and cannabis initiation, and bidirectional effects between insomnia and smoking measures. Bidirectional effects between smoking and insomnia might give rise to a vicious circle. Future research should investigate if interventions aimed at insomnia are beneficial for substance use treatment.

Oscillatory activity is crucial for information processing in the brain, and has a long history as a biomarker for psychopathology. Variation in oscillatory activity is highly heritable, but the involvement of specific genetic variants, genes, and brain expression pathways remains elusive. Here, we present a genome-wide association study (GWAS) for the power of oscillations at standard frequencies (~2 Hz delta, ~6 Hz theta, ~10 Hz alpha, and ~20 Hz beta) of the electroencephalogram (EEG), followed up with gene-based and brain-expression analyses based on the extraction of RNA expression quantitative trait loci (eQTL) and imputation of gene expression in brain tissues by machine learning (Metaxcan). Five cohorts with eyes-closed resting EEG and genome-wide single nucleotide polymorphism (SNP) were included with data for 8425 participants. GWAS revealed a significant SNP for alpha oscillation power intronic to protein-coding gene PRKG2. PRKG2 is amongst the genes deleted in the 4q21 microdeletion syndrome which results in speech and mental retardation. GABRA2 -- a known genetic marker for alcohol use disorder and epilepsy -- significantly affected beta power, consistent with the relation between GABAA interneuron activity and beta oscillations, and between beta oscillations and alcohol use disorders. Twenty-four genes in region 3p21.1 -- previously linked to schizophrenia -- reached significance for alpha power, which fits well with observed aberrant oscillatory activity in schizophrenia. SNPs in this region were eQTLs for GLYCTK in hippocampal tissue, and eQTLs for GNL3 and ITIH4 in the frontal cortex. SNP-based imputation of hippocampal GABRA2 expression was significantly associated with beta oscillations and indicated associations of immune genes IL18R1 and IL1RL1 in the Hippocampus and Putamen with alpha oscillation strength. In conclusion, we successfully associated genes and genetic variants with oscillatory brain activity, some of which were previously associated with psychopathology. The results show how psychopathological liability genes affect brain functioning via cortical and subcortical brain expression.

Part of the symptomatology of post-traumatic stress disorder (PTSD) are alterations in arousal and reactivity which could be related to a maladaptive increase in the automated sensory change detection system of the brain. In the current EEG study we investigated whether the brain’s response to a simple auditory sensory change was altered in patients with PTSD relative to trauma-exposed matched controls who did not develop the disorder. Thirteen male PTSD patients and trauma-exposed controls matched for age and educational level were presented with regular auditory pure tones (1000 Hz, 200 ms duration), with 11% of the tones deviating in both duration (50 ms) and frequency (1200 Hz) while watching a silent movie. Relative to the controls, patients who had developed PTSD showed enhanced mismatch negativity (MMN), increased theta power (5–7 Hz), and stronger suppression of upper alpha activity (13–15 Hz) after deviant vs. standard tones. Behaviourally, the alpha suppression in PTSD correlated with decreased spatial working memory performance suggesting it might reflect enhanced stimulus-feature representations in auditory memory. These results taken together suggest that PTSD patients and trauma-exposed controls can be distinguished by enhanced involuntary attention to changes in sensory patterns.

ObjectiveParoxysmal epileptiform abnormalities on electroencephalography (EEG) are the hallmark of epilepsies, but it is uncertain to what extent epilepsy and background EEG oscillations share neurobiological underpinnings. Here, we aimed to assess the genetic correlation between epilepsy and background EEG oscillations.MethodsConfounding factors, including the heterogeneous etiology of epilepsies and medication effects hamper studies on background brain activity in people with epilepsy. To overcome this limitation, we compared genetic data from a GWAS on epilepsy (n=12,803 people with epilepsy and 24,218 controls) with that from a GWAS on background EEG (n=8,425 subjects without epilepsy), in which background EEG oscillation power was quantified in four different frequency bands: alpha, beta, delta and theta. We replicated our findings in an independent epilepsy replication dataset (n=4851 people with epilepsy and 20,428 controls). To assess the genetic overlap between these phenotypes, we performed genetic correlation analyses using linkage disequilibrium score regression, polygenic risk scores and Mendelian randomization analyses.ResultsOur analyses show strong genetic correlations between genetic generalized epilepsy (GGE) with background EEG oscillations, primarily with the beta frequency band. Furthermore, we show that subjects with higher beta and theta polygenic risk scores have a significantly higher risk of having generalized epilepsy. Mendelian randomization analyses suggest a causal effect of GGE genetic liability on beta oscillations.SignificanceOur results point to shared biological mechanisms underlying background EEG oscillations and the susceptibility for GGE, opening avenues to investigate the clinical utility of background EEG oscillations in the diagnostic work-up of epilepsy.

Recent studies suggest that individuals with dyslexia may be impaired in probability learning and performance monitoring. These observations are consistent with findings indicating atypical neural activations in frontostriatal circuits in the brain, which are important for associative learning. The current study further examined probability learning and performance monitoring in adult individuals with dyslexia (n = 23) and typical readers (n = 31) using two varieties of a typical probabilistic learning task. In addition to performance measures, we measured heart rate, focusing on cardiac slowing with negative feedback as a manifestation of the automatic performance monitoring system. One task required participants to learn associations between artificial script and speech sounds and the other task required them to learn associations between geometric forms and bird sounds. Corrective feedback (informative or random) was provided in both tasks. Performance results indicated that individuals with dyslexia and typical readers learned the associations equally well in contrast to expectations. We found the typical cardiac response associated with feedback processing consisting of a heart rate slowing with the presentation of the feedback and a return to baseline thereafter. Interestingly, the heart rate slowing associated with feedback was less pronounced and the return to baseline was delayed in individuals with dyslexia relative to typical readers. These findings were interpreted in relation to current theorizing of performance monitoring linking the salience network in the brain to autonomic functioning.

The pleasure of music listening regulates daily behaviour and promotes rehabilitation in healthcare. Human behaviour emerges from the modulation of spontaneous timely coordinated neuronal networks. Too little is known about the physical properties and neurophysiological underpinnings of music to understand its perception, its health benefit and to deploy personalized or standardized music-therapy. Prior studies revealed how macroscopic neuronal and music patterns scale with frequency according to a 1/fα relationship, where a is the scaling exponent. Here, we examine how this hallmark in music and neuronal dynamics relate to pleasure. Using electroencephalography, electrocardiography and behavioural data in healthy subjects, we show that music listening decreases the scaling exponent of neuronal activity and—in temporal areas—this change is linked to pleasure. Default-state scaling exponents of the most pleased individuals were higher and approached those found in music loudness fluctuations. Furthermore, the scaling in selective regions and timescales and the average heart rate were largely proportional to the scaling of the melody. The scaling behaviour of heartbeat and neuronal fluctuations were associated during music listening. Our results point to a 1/fresonance between brain and music and a temporal rescaling of neuronal activity in the temporal cortex as mechanisms underlying music appreciation.

Sensory symptoms were recently added to the diagnostic criteria of autism spectrum disorder and may be a mechanism underlying the broad phenotype of autism spectrum disorder. To measure sensory symptoms based on perceptual rather than affective, regulative, or attention components, the Sensory Perception Quotient (SPQ) measuring five modalities of sensory sensitivity has been developed. In this study, the Dutch translation of the abridged SPQ-Short was investigated in a large sample of adults with ( n = 657) and without autism spectrum disorder ( n = 585). Its hypothesized factor structure, combining modality specific and one modality-independent factor, was assessed in a hierarchical model. Results show that modality-specific subscales are indeed present in the short version. Furthermore, its reliability is high and comparable to the original English version. The autism spectrum disorder group reported higher sensory sensitivities than the comparison group, and women with autism spectrum disorder reported higher sensitivities compared with men with autism spectrum disorder. The SPQ-Short correlates with all Autism Quotient (AQ)-Short subscales, except for the “imagination” subscale. The SPQ-Short seems suitable to further explore the relationship between basic sensory sensitivities in autism spectrum disorder and their related symptoms such as over- and under-responsivity to sensory stimulation. Lay Abstract Individuals on the autism spectrum often experience heightened or reduced sensory sensitivities. This feature was recently added to the diagnostic manual for autism ( Diagnostic and Statistical Manual of Mental Disorders, 5th ed. (DSM-5)). To measure sensory sensitivities, the Sensory Perception Quotient (SPQ) has been developed. In this study, we tested whether a Dutch translation of the abridged SPQ-Short yields similar results as the original English version. We also tested whether this questionnaire can measure modality specific sensitivities. To this end, 657 adults with autism spectrum disorder and 585 adults without an autism spectrum disorder diagnosis filled out the Dutch SPQ-Short. The Dutch questionnaire data were very similar to the original English version: adults with autism spectrum disorder were more sensitive compared with adults without autism spectrum disorder. Women with autism spectrum disorder are more sensitive compared with men with autism spectrum disorder. Gender did not have an effect in the group without autism spectrum disorder. Individuals reporting higher sensory sensitivities also reported more autistic traits (such as lower social interests, or increased fascination for patterns). Finally, we found that the Dutch SPQ-Short is suited to measure modality-specific sensitivities. We conclude that the Dutch translation is a viable tool to measure sensory sensitivities in adults with and without autism spectrum disorder and can be used to further our understanding of differences in perception in people with or without autism spectrum disorder.

Stimuli in simple oddball target detection paradigms cause evoked responses in brain potential. These responses are heritable traits, and potential endophenotypes for clinical phenotypes. These stimuli also cause responses in oscillatory activity, both evoked responses phase-locked to stimulus presentation and phase-independent induced responses. Here, we investigate whether phase-locked and phase-independent oscillatory responses are heritable traits. Oscillatory responses were examined in EEG recordings from 213 twin pairs (91 monozygotic and 122 dizygotic twins) performing a visual oddball task. After group Independent Component Analysis (group-ICA) and time-frequency decomposition, individual differences in evoked and induced oscillatory responses were compared between MZ and DZ twin pairs. Induced (phase-independent) oscillatory responses consistently showed the highest heritability (24-55%) compared to evoked (phase-locked) oscillatory responses and spectral energy, which revealed lower heritability at 1-35.6% and 4.5-32.3%, respectively. Since the phase-independent induced response encodes functional aspects of the brain response to target stimuli different from evoked responses, we conclude that the modulation of ongoing oscillatory activity may serve as an additional endophenotype for behavioral phenotypes and psychiatric genetics.

The neuronal mechanisms underlying creativity are poorly understood. Here, we investigated whether temporal dynamics of functional brain activity is a biomarker of creative ideation. Specifically, we investigated whether long-range temporal correlations in fluctuating resting-state alpha oscillations predict human creativity. Because lower temporal correlations reflect faster brain state switching, and faster state switching may be associated with increased flexibility of mind, we hypothesized that subjects with lower temporal correlations would show higher creativity. Creativity was measured by self-rating, examiner-rating and the alternative uses task in 40 healthy young adults, and scored on dimensions of verbal fluency, originality, elaboration, usefulness, and flexibility. For each dimension, the total number of subject-reported alternative uses that matched the criterium was noted (the quantity measure), as well as the proportion of uses that matched the dimensional criterium. A principal components analysis confirmed the two-component structure of quantity and quality. Eyes-open resting-state brain activity was measured by electroencephalogram (EEG) with 128 channels. Scaling exponents β were derived from spectral analysis of the amplitude modulation of 8-13 Hz oscillations, where high exponents β reflect lower decay of autocorrelation and slower switching of brain. Partial correlation analysis was used controlling for gender and age, and a cluster permutation test was performed correcting for multiple testing. Significant negative correlations between creativity and temporal correlations were observed, most prominent in right central/temporal brain areas. To our knowledge, this is the first demonstration that individual variation in the intrinsic dynamics of the brain may offer a neuronal explanation for individual variation in both the quality and quantity of creative ideation.

Contains fulltext : 193359.pdf (Publisher’s version ) (Open Access) BACKGROUND: Alcohol and tobacco use are heritable phenotypes. However, only a small number of common genetic variants have been identified, and common variants account for a modest proportion of the heritability. Therefore, this study aims to investigate the role of low-frequency and rare variants in alcohol and tobacco use. METHODS: We meta-analyzed ExomeChip association results from eight discovery cohorts and included 12,466 subjects and 7432 smokers in the analysis of alcohol consumption and tobacco use, respectively. The ExomeChip interrogates low-frequency and rare exonic variants, and in addition a small pool of common variants. We investigated top variants in an independent sample in which ICD-9 diagnoses of "alcoholism" (N=25,508) and "tobacco use disorder" (N=27,068) had been assessed. In addition to the single variant analysis, we performed gene-based, polygenic risk score (PRS), and pathway analyses. RESULTS: The meta-analysis did not yield exome-wide significant results. When we jointly analyzed our top results with the independent sample, no low-frequency or rare variants reached significance for alcohol consumption or tobacco use. However, two common variants that were present on the ExomeChip, rs16969968 (p=2.39x10(-7)) and rs8034191 (p=6.31x10(-7)) located in CHRNA5 and AGPHD1 at 15q25.1, showed evidence for association with tobacco use. DISCUSSION: Low-frequency and rare exonic variants with large effects do not play a major role in alcohol and tobacco use, nor does the aggregate effect of ExomeChip variants. However, our results confirmed the role of the CHRNA5-CHRNA3-CHRNB4 cluster of nicotinic acetylcholine receptor subunit genes in tobacco use. 8 p.

Tic disorders are moderately heritable common psychiatric disorders that can be highly troubling, both in childhood and in adulthood. In this study, we report results obtained in the first epigenome-wide association study (EWAS) of tic disorders. The subjects are participants in surveys at the Netherlands Twin Register (NTR) and the NTR biobank project. Tic disorders were measured with a self-report version of the Yale Global Tic Severity Scale Abbreviated version (YGTSS-ABBR), included in the 8th wave NTR data collection (2008). DNA methylation data consisted of 411,169 autosomal methylation sites assessed by the Illumina Infinium HumanMethylation450 BeadChip Kit (HM450k array). Phenotype and DNA methylation data were available in 1,678 subjects (mean age = 41.5). No probes reached genome-wide significance (p < 1.2 × 10−7). The strongest associated probe was cg15583738, located in an intergenic region on chromosome 8 (p = 1.98 × 10−6). Several of the top ranking probes (p < 1 × 10−4) were in or nearby genes previously associated with neurological disorders (e.g., GABBRI, BLM, and ADAM10), warranting their further investigation in relation to tic disorders. The top significantly enriched gene ontology (GO) terms among higher ranking methylation sites included anatomical structure morphogenesis (GO:0009653, p = 4.6 × 10−15) developmental process (GO:0032502, p = 2.96 × 10−12), and cellular developmental process (GO:0048869, p = 1.96 × 10−12). Overall, these results provide a first insight into the epigenetic mechanisms of tic disorders. This first study assesses the role of DNA methylation in tic disorders, and it lays the foundations for future work aiming to unravel the biological mechanisms underlying the architecture of this disorder.

We examined the genetic architecture of functional brain connectivity measures in resting state electroencephalographic (EEG) recordings. Previous studies in Dutch twins have suggested that genetic factors are a main source of variance in functional brain connectivity derived from EEG recordings. In addition, qualitative descriptors of the brain network derived from graph analysis — network clustering and average path length — are also heritable traits. Here we replicated previous findings for connectivity, quantified by the synchronization likelihood, and the graph theoretical parameters cluster coefficient and path length in an Australian sample of 16-year-old twins (879) and their siblings (93). Modeling of monozygotic and dizygotic twins and sibling resemblance indicated heritability estimates of the synchronization likelihood (27–74%) and cluster coefficient and path length in the alpha and theta band (40–44% and 23–40% respectively) and path length in the beta band frequency (41%). This corroborates synchronization likelihood and its graph theoretical derivatives cluster coefficient and path length as potential endophenotypes for behavioral traits and neurological disorders.

The child brain is a small-world network, which is hypothesized to change toward more ordered configurations with development. In graph theoretical studies, comparing network topologies under different conditions remains a critical point. Constructing a minimum spanning tree (MST) might present a solution, since it does not require setting a threshold and uses a fixed number of nodes and edges. In this study, the MST method is introduced to examine developmental changes in functional brain network topology in young children. Resting-state electroencephalography was recorded from 227 children twice at 5 and 7 years of age. Synchronization likelihood (SL) weighted matrices were calculated in three different frequency bands from which MSTs were constructed, which represent constructs of the most important routes for information flow in a network. From these trees, several parameters were calculated to characterize developmental change in network organization. The MST diameter and eccentricity significantly increased, while the leaf number and hierarchy significantly decreased in the alpha band with development. Boys showed significant higher leaf number, betweenness, degree and hierarchy and significant lower SL, diameter, and eccentricity than girls in the theta band. The developmental changes indicate a shift toward more decentralized line-like trees, which supports the previously hypothesized increase toward regularity of brain networks with development. Additionally, girls showed more line-like decentralized configurations, which is consistent with the view that girls are ahead of boys in brain development. MST provides an elegant method sensitive to capture subtle developmental changes in network organization without the bias of network comparison.

The effects of inbreeding on the health of offspring can be studied by measuring genome-wide autozygosity as the proportion of the genome in runs of homozygosity (F roh) and relate F roh to outcomes such as psychiatric phenotypes. To successfully conduct these studies, the main patterns of variation for genome-wide autozygosity between and within populations should be well understood and accounted for. Within population variation was investigated in the Dutch population by comparing autozygosity between religious and non-religious groups. The Netherlands have a history of societal segregation and assortment based on religious affiliation, which may have increased parental relatedness within religious groups. Religion has been associated with several psychiatric phenotypes, such as major depressive disorder (MDD). We investigated whether there is an association between autozygosity and MDD, and the extent to which this association can be explained by religious affiliation. All F roh analyses included adjustment for ancestry-informative principal components (PCs) and geographic factors. Religious affiliation was significantly associated with autozygosity, showing that F roh has the ability to capture within population differences that are not captured by ancestry-informative PCs or geographic factors. The non-religious group had significantly lower F roh values and significantly more MDD cases, leading to a nominally significant negative association between autozygosity and depression. After accounting for religious affiliation, MDD was not associated with F roh, indicating that the relation between MDD and inbreeding was due to stratification. This study shows how past religious assortment and recent secularization can have genetic consequences in a relatively small country. This warrants accounting for the historical social context and its effects on genetic variation in association studies on psychiatric and other related traits.

The Netherlands Twin Register (NTR) began in 1987 with data collection in twins and their families, including families with newborn twins and triplets. Twenty-five years later, the NTR has collected at least one survey for 70,784 children, born after 1985. For the majority of twins, longitudinal data collection has been done by age-specific surveys. Shortly after giving birth, mothers receive a first survey with items on pregnancy and birth. At age 2, a survey on growth and achievement of milestones is sent. At ages 3, 7, 9/10, and 12 parents and teachers receive a series of surveys that are targeted at the development of emotional and behavior problems. From age 14 years onward, adolescent twins and their siblings report on their behavior problems, health, and lifestyle. When the twins are 18 years and older, parents are also invited to take part in survey studies. In sub-groups of different ages, in-depth phenotyping was done for IQ, electroencephalography , MRI, growth, hormones, neuropsychological assessments, and cardiovascular measures. DNA and biological samples have also been collected and large numbers of twin pairs and parents have been genotyped for zygosity by either micro-satellites or sets of short nucleotide polymorphisms and repeat polymorphisms in candidate genes. Subject recruitment and data collection is still ongoing and the longitudinal database is growing. Data collection by record linkage in the Netherlands is beginning and we expect these combined longitudinal data to provide increased insights into the genetic etiology of development of mental and physical health in children and adolescents. Copyright © The Authors 2012.

Variation in obsessive–compulsive symptoms (OCS) has a heritable basis, with genetic association studies starting to yield the first suggestive findings. We contribute to insights into the genetic basis of OCS by performing an extensive series of genetic analyses in a homogeneous, population-based sample from the Netherlands. First, phenotypic and genetic longitudinal correlations over a 6-year period were estimated by modeling OCS data from twins and siblings. Second, polygenic risk scores (PRS) for 6931 subjects with genotype and OCS data were calculated based on meta-analysis results from IOCDF-GC, to investigate their predictive value. Third, the contribution of measured single nucleotide polymorphisms (SNPs) to the heritability was estimated using random-effects modeling. Last, we performed an exploratory genome-wide association study (GWAS) of OCS, testing for SNP- and for gene-based associations. Stability in OCS (test–retest correlation 0.63) was mainly explained by genetic stability. The PRS based on clinical samples predicted OCS in our population-based twin-family sample. SNP-based heritability was estimated at 14%. GWAS revealed one SNP (rs8100480), located within the MEF2BNB gene, associated with OCS (P=2.56 × 10−8). Additional gene-based testing resulted in four significantly associated genes, which are located in the same chromosomal region on chromosome 19p13.11: MEF2BNB, RFXANK, MEF2BNB-MEF2B and MEF2B. Thus, common genetic variants explained a significant proportion of OCS trait variation. Genes significantly associated with OCS are expressed in the brain and involved in development and control of immune system functions (RFXANK) and regulation of gene expression of muscle-specific genes (MEF2BNB). MEF2BNB also showed a suggestive association with OCD in an independent case–control study, suggesting a role for this gene in the development of OCS.

Graph analysis of electroencephalography (EEG) has previously revealed developmental increases in connectivity between distant brain areas and a decrease in randomness and increased integration in the brain network with concurrent increased modularity. Comparisons of graph parameters across age groups, however, may be confounded with network degree distributions. In this study, we analyzed graph parameters from minimum spanning tree (MST) graphs and compared their developmental trajectories to those of graph parameters based on full graphs published previously. MST graphs are constructed by selecting only the strongest available connections avoiding loops, resulting in a backbone graph that is thought to reflect the major qualitative properties of the network, while allowing a better comparison across age groups by avoiding the degree of distribution confound. EEG was recorded in a large (n = 1500) population-based sample aged 5-71 years. Connectivity was assessed using phase lag index to reduce effects of volume conduction. Connectivity in the MST graph increased significantly from childhood to adolescence, continuing to grow nonsignificantly into adulthood, and decreasing significantly about 57 years of age. Leaf number, degree, degree correlation, and maximum centrality from the MST graph indicated a pattern of increased integration and decreased randomness from childhood into early adulthood. The observed development in network topology suggested that maturation at the neuronal level is aimed to increase connectivity as well as increase integration of the brain network. We confirm that brain network connectivity shows quantitative changes across the life span and additionally demonstrate parallel qualitative changes in the connectivity pattern.

This issue on the genetics of brain imaging phenotypes is a celebration of the happy marriage between two of science's highly interesting fields: neuroscience and genetics. The articles collected here are ample evidence that a good deal of synergy exists in this marriage. A wide selection of papers is presented that provide many different perspectives on how genes cause variation in brain structure and function, which in turn influence behavioral phenotypes (including psychopathology). They are examples of the many different methodologies in contemporary genetics and neuroscience research. Genetic methodology includes genome-wide association (GWA), candidate-gene association, and twin studies. Sources of data on brain phenotypes include cortical gray matter (GM) structural/volumetric measures from magnetic resonance imaging (MRI); white matter (WM) measures from diffusion tensor imaging (DTI), such as fractional anisotropy; functional- (activity-) based measures from electroencephalography (EEG), and functional MRI (fMRI). Together, they reflect a combination of scientific fields that have seen great technological advances, whether it is the single-nucleotide polymorphism (SNP) array in genetics, the increasingly high-resolution MRI imaging, or high angular resolution diffusion imaging technique for measuring WM connective properties.