22q11.2 deletion syndrome (22q11DS) is associated with elevated levels of impulsivity, inattention, and distractibility, which may be related to underlying neurobiological dysfunction due to haploinsufficiency for genes involved in dopaminergic neurotransmission (i.e. catechol-O-methyltransferase). The Stop-signal task has been employed to probe the neural circuitry involved in response inhibition (RI); findings in healthy individuals indicate that a fronto-basal ganglia network underlies successful inhibition of a prepotent motor response. However, little is known about the neurobiological substrates of RI difficulties in 22q11DS. Here, we investigated this using functional magnetic resonance imaging while 45 adult participants (15 22q11DS patients, 30 matched controls) performed the Stop-signal task. Healthy controls showed significantly greater activation than 22q11DS patients within frontal cortical and basal ganglia regions during successful RI, whereas 22q11DS patients did not show increased neural activity relative to controls in any regions. Using the Barratt Impulsivity Scale, we also investigated whether neural dysfunction during RI was associated with cognitive impulsivity in 22q11DS patients. RI-related activity within left middle frontal gyrus and basal ganglia was associated with severity of self-reported cognitive impulsivity. These results suggest reduced engagement of RI-related brain regions in 22q11DS patients, which may be relevant to characteristic behavioral manifestations of the disorder.

•22q11DS offers a compelling model to understand the neural substrates of attentional dysfunction.•First study directly comparing neural function in 22q11DS vs. ADHD patients•22q11DS and ADHD patients show a shared deficit in RI-related activation.•ADHD patients showed greater activity in the middle frontal gyrus than 22q11DS during RI.•Neural activity is inversely correlated with self-reported Cognitive Impulsivity in 22q11DS.

Objective: Schizophrenia patients exhibit impaired working and episodic memory, but this may represent generalized impairment across memory modalities or performance deficits restricted to particular memory systems in subgroups of patients. Furthermore, it is unclear whether deficits are unique from those associated with other disorders. Method: Healthy controls (n = 1101) and patients with schizophrenia (n = 58), bipolar disorder (n = 49) and attention-deficit-hyperactivity-disorder (n = 46) performed 18 tasks addressing primarily verbal and spatial episodic and working memory. Effect sizes for group contrasts were compared across tasks and the consistency of subjects' distributional positions across memory domains was measured. Results: Schizophrenia patients performed poorly relative to the other groups on every test. While low to moderate correlation was found between memory domains ( r= .320), supporting modularity of these systems, there was limited agreement between measures regarding each individual's task performance (ICC = .292) and in identifying those individuals falling into the lowest quintile (kappa = 0.259). A general ability factor accounted for nearly all of the group differences in performance and agreement across measures in classifying low performers. Conclusions: Pathophysiological processes involved in schizophrenia appear to act primarily on general abilities required in all tasks rather than on specific abilities within different memory domains and modalities. These effects represent a general shift in the overall distribution of general ability (i.e., each case functioning at a lower level than they would have if not for the illness), rather than presence of a generally low-performing subgroup of patients. There is little evidence that memory impairments in schizophrenia are shared with bipolar disorder and ADHD.

This data descriptor outlines a shared neuroimaging dataset from the UCLA Consortium for Neuropsychiatric Phenomics, which focused on understanding the dimensional structure of memory and cognitive control (response inhibition) functions in both healthy individuals (130 subjects) and individuals with neuropsychiatric disorders including schizophrenia (50 subjects), bipolar disorder (49 subjects), and attention deficit/hyperactivity disorder (43 subjects). The dataset includes an extensive set of task-based fMRI assessments, resting fMRI, structural MRI, and high angular resolution diffusion MRI. The dataset is shared through the OpenfMRI project, and is formatted according to the Brain Imaging Data Structure (BIDS) standard.

Risky decision-making is a common, heritable endophenotype seen across many psychiatric disorders. Its underlying genetic architecture is incompletely explored. We examined behavior in the Balloon Analogue Risk Task (BART), which tests risky decision-making, in two independent samples of European ancestry. One sample (n = 1138) comprised healthy participants and some psychiatric patients (53 schizophrenia, 42 bipolar disorder, 47 ADHD); the other (n = 911) excluded for recent treatment of various psychiatric disorders but not ADHD. Participants provided DNA and performed the BART, indexed by mean adjusted pumps. We constructed a polygenic risk score (PRS) for discovery in each dataset and tested it in the other as replication. Subsequently, a genome-wide MEGA-analysis, combining both samples, tested genetic correlation with risk-taking self-report in the UK Biobank sample and psychiatric phenotypes characterized by risk-taking (ADHD, Bipolar Disorder, Alcohol Use Disorder, prior cannabis use) in the Psychiatric Genomics Consortium. The PRS for BART performance in one dataset predicted task performance in the replication sample (r = 0.13, p = 0.000012, pFDR = 0.000052), as did the reciprocal analysis (r = 0.09, p = 0.0083, pFDR=0.04). Excluding participants with psychiatric diagnoses produced similar results. The MEGA-GWAS identified a single SNP (rs12023073; p = 3.24 × 10-8) near IGSF21, a protein involved in inhibitory brain synapses; replication samples are needed to validate this result. A PRS for self-reported cannabis use (p = 0.00047, pFDR = 0.0053), but not self-reported risk-taking or psychiatric disorder status, predicted behavior on the BART in our MEGA-GWAS sample. The findings reveal polygenic architecture of risky decision-making as measured by the BART and highlight its overlap with cannabis use.

Temperament has a strongly heritable component, yet multiple independent genome-wide studies have failed to identify significant genetic associations. We have assembled the largest sample to date of persons with genome-wide genotype data, who have been assessed with Cloninger's Temperament and Character Inventory. Sum scores for novelty seeking, harm avoidance, reward dependence and persistence have been measured in over 11,000 persons collected in four different cohorts. Our study had >80% power to identify genome-wide significant loci (P<1.25 × 10(-8), with correction for testing four scales) accounting for ≥0.4% of the phenotypic variance in temperament scales. Using meta-analysis techniques, gene-based tests and pathway analysis we have tested over 1.2 million single-nucleotide polymorphisms (SNPs) for association to each of the four temperament dimensions. We did not discover any SNPs, genes, or pathways to be significantly related to the four temperament dimensions, after correcting for multiple testing. Less than 1% of the variability in any temperament dimension appears to be accounted for by a risk score derived from the SNPs showing strongest association to the temperament dimensions. Elucidation of genetic loci significantly influencing temperament and personality will require potentially very large samples, and/or a more refined phenotype. Item response theory methodology may be a way to incorporate data from cohorts assessed with multiple personality instruments, and might be a method by which a large sample of a more refined phenotype could be acquired.

The complex nature of human cognition has resulted in cognitive genomics lagging behind many other fields in terms of gene discovery using genome-wide association study (GWAS) methods. In an attempt to overcome these barriers, the current study utilized GWAS meta-analysis to examine the association of common genetic variation (~8M single-nucleotide polymorphisms (SNP) with minor allele frequency ⩾1%) to general cognitive function in a sample of 35 298 healthy individuals of European ancestry across 24 cohorts in the Cognitive Genomics Consortium (COGENT). In addition, we utilized individual SNP lookups and polygenic score analyses to identify genetic overlap with other relevant neurobehavioral phenotypes. Our primary GWAS meta-analysis identified two novel SNP loci (top SNPs: rs76114856 in the CENPO gene on chromosome 2 and rs6669072 near LOC105378853 on chromosome 1) associated with cognitive performance at the genome-wide significance level (P<5 × 10^-8). Gene-based analysis identified an additional three Bonferroni-corrected significant loci at chromosomes 17q21.31, 17p13.1 and 1p13.3. Altogether, common variation across the genome resulted in a conservatively estimated SNP heritability of 21.5% (s.e.=0.01%) for general cognitive function. Integration with prior GWAS of cognitive performance and educational attainment yielded several additional significant loci. Finally, we found robust polygenic correlations between cognitive performance and educational attainment, several psychiatric disorders, birth length/weight and smoking behavior, as well as a novel genetic association to the personality trait of openness. These data provide new insight into the genetics of neurocognitive function with relevance to understanding the pathophysiology of neuropsychiatric illness.

This corrects the article DOI: 10.1038/mp.2016.244.