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studies from other species 12,13 , we have freely released all data and the generated code.

Background Disruptive behavior disorders (DBD) are heterogeneous at the clinical and the biological level. Therefore, the aims were to dissect the heterogeneous neurodevelopmental deviations of the affective brain circuitry and provide an integration of these differences across modalities. Methods We combined two novel approaches. First, normative modeling to map deviations from the typical age-related pattern at the level of the individual of (i) activity during emotion matching and (ii) of anatomical images derived from DBD cases (n = 77) and controls (n = 52) aged 8–18 years from the EU-funded Aggressotype and MATRICS consortia. Second, linked independent component analysis to integrate subject-specific deviations from both modalities. Results While cases exhibited on average a higher activity than would be expected for their age during face processing in regions such as the amygdala when compared to controls these positive deviations were widespread at the individual level. A multimodal integration of all functional and anatomical deviations explained 23% of the variance in the clinical DBD phenotype. Most notably, the top marker, encompassing the default mode network (DMN) and subcortical regions such as the amygdala and the striatum, was related to aggression across the whole sample. Conclusions Overall increased age-related deviations in the amygdala in DBD suggest a maturational delay, which has to be further validated in future studies. Further, the integration of individual deviation patterns from multiple imaging modalities allowed to dissect some of the heterogeneity of DBD and identified the DMN, the striatum and the amygdala as neural signatures that were associated with aggression.

Using a social robot has been proven to have multiple benefits for the training of children with Autism Spectrum Disorder (ASD). However, there is no clarity on the impact of the interaction quality between a child with ASD and a robot on the effectiveness of the therapy. Previous research showed that the use of a robot in Pivotal Response Treatment (PRT) could be an effective treatment component in diminishing ASD-related symptoms. Further analyzing the data from a randomized controlled trial of PRT treatment, we looked at the long-term effects of child–robot game interactions to see whether the interaction quality changes over time. The attention and the engagement of six children were measured through the observation of non-verbal behavior at three different stages in the treatment that took 20 sessions per child. The gaze and arm/hand behavior of the participants towards the robot, the game, and other present humans were observed. The analysis showed no significant decrease in the attention and the engagement of the children towards the robot and the game. However, the attention and engagement toward the parents of the children increased. We conclude that the main result of sustained attention and engagement with the robot is due to the personalization of the games to meet the specific needs of this user group. These specific needs are met through inclusion of variability to the level of development and personal choice of each participating child. We see the additional finding of increased attention towards the parents as especially positive since the children are expected to improve in human–human interaction as a result of this treatment.

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Tourette Syndrome (TS) is a complex neurodevelopmental disorder characterized by vocal and motor tics lasting more than a year. It is highly polygenic in nature with both rare and common previously associated variants. Epidemiological studies have shown TS to be correlated with other phenotypes, but large-scale phenome wide analyses in biobank level data have not been performed to date. In this study, we used the summary statistics from the latest meta-analysis of TS to calculate the polygenic risk score (PRS) of individuals in the UK Biobank data and applied a Phenome Wide Association Study (PheWAS) approach to determine the association of disease risk with a wide range of phenotypes. A total of 57 traits were found to be significantly associated with TS polygenic risk, including multiple psychosocial factors and mental health conditions such as anxiety disorder and depression. Additional associations were observed with complex non-psychiatric disorders such as Type 2 diabetes, heart palpitations, and respiratory conditions. Cross-disorder comparisons of phenotypic associations with genetic risk for other childhood-onset disorders (e.g.: attention deficit hyperactivity disorder [ADHD], autism spectrum disorder [ASD], and obsessive-compulsive disorder [OCD]) indicated an overlap in associations between TS and these disorders. ADHD and ASD had a similar direction of effect with TS while OCD had an opposite direction of effect for all traits except mental health factors. Sex-specific PheWAS analysis identified differences in the associations with TS genetic risk between males and females. Type 2 diabetes and heart palpitations were significantly associated with TS risk in males but not in females, whereas diseases of the respiratory system were associated with TS risk in females but not in males. This analysis provides further evidence of shared genetic and phenotypic architecture of different complex disorders.