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Background: Bipolar disorder is highly heritable and polygenic. The polygenic risk for bipolar disorder overlaps with that of schizophrenia, and polygenic scores are normally distributed in the population. Bipolar disorder has been associated with structural brain abnormalities, but it is unknown how these are linked to genetic risk factors for psychotic disorders. Methods: We tested whether polygenic risk scores for bipolar disorder and schizophrenia predict structural brain alterations in 98 patients with bipolar disorder and 81 healthy controls. We derived brain cortical thickness, surface area and volume from structural MRI scans. In post-hoc analyses, we correlated polygenic risk with functional hub strength, derived from resting-state functional MRI and brain connectomics. Results: Higher polygenic risk scores for both bipolar disorder and schizophrenia were associated with a thinner ventromedial prefrontal cortex (vmPFC). We found these associations in the combined group, and separately in patients and drug-naive controls. Polygenic risk for bipolar disorder was correlated with the functional hub strength of the vmPFC within the default mode network. Limitations: Polygenic risk is a cumulative measure of genomic burden. Detailed genetic mechanisms underlying brain alterations and their cognitive consequences still need to be determined. Conclusion: Our multimodal neuroimaging study linked genomic burden and brain endophenotype by demonstrating an association between polygenic risk scores for bipolar disorder and schizophrenia and the structure and function of the vmPFC. Our findings suggest that genetic factors might confer risk for psychotic disorders by influencing the integrity of the vmPFC, a brain region involved in self-referential processes and emotional regulation. Our study may also provide an imaging-genetics vulnerability marker that can be used to help identify individuals at risk for developing bipolar disorder., Introduction Bipolar disorder is a highly heritable (1) and severe psychiatric disorder characterized by recurrent episodes of (hypo)mania and depression. (2) Psychotic symptoms similar to those seen in schizophrenia are [...]

Background: Long-term pain is a common health problem that results in disability for patients of all ages, leading to an enormous economic burden. Over 20% of the population suffer from long-term pain. Unfortunately, there are no clinical tests that predicts who will develop long-term pain. The overall aim is to predict future pain incidence based on brain function, pain behavior, health status, and genetic variability. Method: PrePain utilizes a superstruct design, which involves recruiting participants from ongoing research projects. Eligible individuals for participation in PrePain were over 18 years old and free from long-term pain. During the baseline visit, participants provide pain and health-related questionnaires, undergo structural and functional MRI scans, and provide a saliva sample for DNA extraction. Individual baseline measures are then routinely followed-up via national registries. Result: We present quality-assessed data from over 300 participants. The average age was 34 years, and most participants were women (75%). Participants rated their pain sensitivity above average and reported low avoidance. Catastrophizing thoughts during painful episodes were rated as moderate. Assessments of (f)MRI data indicated generally good image quality. In this first follow-up, we found that 45 participants had a pain-related diagnoses. Conclusion: Results indicate that a superstruct design is feasible for collecting a large number of high-quality data. The incidence of long-term pain indicates that a sufficient number of participants have been recruited to complete the prediction analyses. PrePain is a unique prospective pain database with a fair prognosis to determine risk factors of long-term pain. [ABSTRACT FROM AUTHOR]

As various methods for precision inventories, including light detection and ranging (LiDAR), are becoming increasingly common in forestry, planning at the individual-tree level is becoming more viable. In this study, we present a method for finding the optimal thinning times for individual trees from an economic perspective. The method utilizes a forest growth model based on individual trees that has been fitted to Norway spruce (Picea abies (L.) Karst.) stands in northern Sweden. We find that the optimal management strategy is to thin from above (i.e., harvesting trees that are larger than average). We compare our optimal strategy with a conventional management strategy and find that the optimal strategy results in approximately 20% higher land expectation value. Furthermore, we find that for the optimal strategy, increasing the discount rate will reduce the final harvest age and increase the basal area reduction. Decreasing the cost to initiate a thinning (e.g., machinery-related transportation costs) increases the number of thinnings and delays the first thinning. Key words: forest management, optimization, precision forestry, simulation, thinning. La planification a l'echelle de l'arbre devient de plus en plus viable a mesure qu'augmente le recours a diverses methodes d'inventaire de precision en foresterie, telles que le lidar (<>). Dans cette etude, nous presentons une methode visant a determiner les periodes optimales d'application d'une eclaircie pour les arbres individuels d'un point de vue economique. La methode utilise un modele de croissance forestiere a l'echelle de l'arbre qui a ete ajuste a des peuplements d'epicea commun (Picea abies (L.) Karst.) du nord de la Suede. Nous avons trouve que la strategie d'amenagement optimale consiste a eclaircir par le haut, c'est-a-dire a recolter des arbres plus gros que la moyenne. Nous avons compare notre strategie optimale a une strategie d'amenagement conventionnelle et avons constate qu'elle produit une augmentation de la valeur actualisee des terres d'environ 20 %. De plus, lorsque la strategie optimale est associee a une augmentation du taux d'actualisation, l'age de la recolte finale diminue et le prelevement en surface terriere augmente. Une diminution des couts associes a l'eclaircie (p. ex., les couts de transport mecanise) augmente le nombre d'eclaircies et retarde l'application de la premiere eclaircie. Mots-cles: amenagement forestier, optimisation, foresterie de precision, simulation, eclaircie., Introduction Given the ecological and economic importance of forests, it is not surprising that much effort has been invested in developing and improving forestry practices. Increasingly common use of remote [...]

Current evidence suggests the phenotype of autism spectrum disorder to be driven by a complex interaction of genetic and environmental factors impacting onto brain maturation, synaptic function, and cortical networks. However, findings are heterogeneous, and the exact neurobiological pathways of autism spectrum disorder still remain poorly understood. The co-twin control or twin-difference design is a potentially powerful tool to disentangle causal genetic and environmental contributions on neurodevelopment in autism spectrum disorder. To this end, monozygotic twins discordant for this condition provide unique means for the maximum control of potentially confounding factors. Unfortunately, only few studies of a rather narrow scope, and limited sample size, have been conducted. In an attempt to highlight the great potential of combining the brain connectome approach with monozygotic twin design, we first give an overview of the existing neurobiological evidence for autism spectrum disorder and its cognitive correlates. Then, a special focus is made onto the brain imaging findings reported within populations of monozygotic twins phenotypically discordant for autism spectrum disorder. Finally, we introduce the brain connectome model and describe an ongoing project using this approach among the largest cohort of monozygotic twins discordant for autism spectrum disorder ever recruited.