Verbal working memory and functional large-scale networks in schizophrenia

The aim of this study was to test whether bilinear and nonlinear effective connectivity (EC) measures of working memory fMRI data can differentiate between patients with schizophrenia (SZ) and healthy controls (HC). We applied bilinear and nonlinear Dynamic Causal Modeling (DCM) for the analysis of verbal working memory in 16 SZ and 21 HC. The connection strengths with nonlinear modulation between the dorsolateral prefrontal cortex (DLPFC) and the ventral tegmental area/substantia nigra (VTA/SN) were evaluated. We used Bayesian Model Selection at the group and family levels to compare the optimal bilinear and nonlinear models. Bayesian Model Averaging was used to assess the connection strengths with nonlinear modulation. The DCM analyses revealed that SZ and HC used different bilinear networks despite comparable behavioral performance. In addition, the connection strengths with nonlinear modulation between the DLPFC and the VTA/SN area showed differences between SZ and HC. The adoption of different functional networks in SZ and HC indicated neurobiological alterations underlying working memory performance, including different connection strengths with nonlinear modulation between the DLPFC and the VTA/SN area. These novel findings may increase our understanding of connectivity in working memory in schizophrenia

Improving Genetic Prediction by Leveraging Genetic Correlations Among Human Diseases and Traits

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7 for height to 47 for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. © 2018 The Author(s)

Identification of common genetic risk variants for autism spectrum disorder

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample-size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 individuals with ASD and 27,969 controls that identified five genome-wide-significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), we identified seven additional loci shared with other traits at equally strict significance levels. Dissecting the polygenic architecture, we found both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis, and establish that GWAS performed at scale will be much more productive in the near term in ASD.Peer reviewe

Disrupted-in-Schizophrenia-1

Chromosomal abnormalities can be powerful tools to identify genes that influence disease risk. The study of a chromosome translocation that segregated with severe psychiatric illness in a large family led directly to the discovery of a gene disrupted by a chromosomal breakpoint. Disrupted-in-Schizophrenia-1 (DISC1) is now an important candidate risk gene for schizophrenia and affective disorders. We review the work that led up to this discovery and the evidence that it is important in the wider population with schizophrenia and affective disorders. We also discuss the latest findings on the neuronal functions of the protein DISC1 encoded by the gene

Balanced translocation linked to psychiatric disorder, glutamate, and cortical structure/function.

Rare genetic variants of large effect can help elucidate the pathophysiology of brain disorders. Here we expand the clinical and genetic analyses of a family with a (1;11)(q42;q14.3) translocation multiply affected by major psychiatric illness and test the effect of the translocation on the structure and function of prefrontal, and temporal brain regions. The translocation showed significant linkage (LOD score 6.1) with a clinical phenotype that included schizophrenia, schizoaffective disorder, bipolar disorder, and recurrent major depressive disorder. Translocation carriers showed reduced cortical thickness in the left temporal lobe, which correlated with general psychopathology and positive psychotic symptom severity. They showed reduced gyrification in prefrontal cortex, which correlated with general psychopathology severity. Translocation carriers also showed significantly increased activation in the caudate nucleus on increasing verbal working memory load, as well as statistically significant reductions in the right dorsolateral prefrontal cortex glutamate concentrations. These findings confirm that the t(1;11) translocation is associated with a significantly increased risk of major psychiatric disorder and suggest a general vulnerability to psychopathology through altered cortical structure and function, and decreased glutamate levels

Suppression of kindling epileptogenesis in rats by intrahippocampal cholinergic grafts

Selective immunolesioning of the basal forebrain cholinergic system by 192 IgG-saporin, which leads to a dramatic loss of the cholinergic innervation in cortical and hippocampal regions, facilitates the development of hippocampal kindling in rats. The aim of the present study was to explore whether grafted cholinergic neurones are able to reverse the lesion-induced increase of seizure susceptibility. Intraventricular 192 IgG-saporin was administered to rats which 3 weeks later were implanted with rat embryonic, acetylcholine-rich septal-diagonal band tissue ('cholinergic grafts') or cortical tissue/vehicle ('sham grafts') bilaterally into the hippocampal formation. After 3 months, the grafted animals as well as non-lesioned control rats were subjected to daily hippocampal kindling stimulations. In the animals with cholinergic grafts, which had reinnervated the hippocampus and dentate gyrus bilaterally, there was a marked suppression of the development of seizures as compared with the hyperexcitable, sham-grafted rats. This effect was significantly correlated to the density of the graft-derived cholinergic innervation of the host hippocampal formation. The kindling rate in the rats with cholinergic grafts was similar to that in non-lesioned controls. These results provide further evidence that the intrinsic basal forebrain cholinergic system dampens kindling epileptogenesis and demonstrate that this function can be exerted also by grafted cholinergic neurones