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•The transition from childhood to adolescence is defined by social development.•Prosocial behavior increases with age, yet empathy peaks in late childhood.•Ventral striatum activity during prosocial behavior shows a peak in late childhood.•dACC, insula and striatum activity dips in late childhood when being socially included.•Changes in VS and mPFC activity co-occur with changes in prosocial behavior. Acting prosocially and feeling socially included are important factors for developing social relations. However, little is known about the development of neural trajectories of prosocial behavior and social inclusion in the transition from middle childhood to early adolescence. In this pre-registered study, we investigated the development of prosocial behavior, social inclusion, and their neural mechanisms in a three-wave longitudinal design (ages 7–13 years; NT1 = 512; NT2 = 456; NT3 = 336). We used the Prosocial Cyberball Game, a ball tossing game in which one player is excluded, to measure prosocial compensating behavior. Prosocial compensating behavior showed a linear developmental increase, similar to parent-reported prosocial behavior, whereas parent-reported empathy showed a quadratic trajectory with highest levels in late childhood. On a neural level we found a peak in ventral striatum activity during prosocial compensating behavior. Neural activity during social inclusion showed quadratic age effects in anterior cingulate cortex, insula, striatum, and precuneus, and a linear increase in temporo-parietal junction. Finally, changes in prosocial compensating behavior were negatively associated with changes in ventral striatum and mPFC activity during social inclusion, indicating an important co-occurrence between development in brain and social behavior. Together these findings shed a light on the mechanisms underlying social development from childhood into adolescence.

Prior studies demonstrated contributions of the insula and dorsal anterior cingulate cortex (dACC) for both experiencing and observing social exclusion, but it is not yet well understood how the brain processes the compensation of exclusion, as is observed in prosocial helping. Here, we tested if social brain regions, specifically the medial prefrontal cortex (mPFC) and temporal parietal junction (TPJ) are involved when individuals show prosocial behavior towards excluded others. For this purpose, 23 female participants played a four-player Cyberball Game in which participants could toss balls to each other. During the exclusion game, two players excluded one of the other players. When participants observed exclusion by others, they showed elevated activity in the insula, consistent with prior studies. However, when they tossed the ball to the excluded player, they showed increased activation in the TPJ, consistent with the hypothesis that prosocial behavior is associated with social reasoning. In addition, tossing to the excluded player was associated with increased activity in the nucleus accumbens (NAcc). Given that prior studies reported that the NAcc is involved in experiencing rewards, this may suggest a warm glow for showing prosocial compensation behavior when helping excluded others.

Connectivity between limbic/subcortical and prefrontal-cortical brain regions develops considerably across childhood, but less is known about the heritability of these networks at this age. We tested the heritability of limbic/subcortical-cortical and limbic/subcortical-subcortical functional brain connectivity in 7- to 9-year-old twins (N = 220), focusing on two key limbic/subcortical structures: the ventral striatum and the amygdala, given their combined influence on changing incentivised behavior during childhood and adolescence. Whole brain analyses with ventral striatum (VS) and amygdala as seeds in genetically independent groups showed replicable functional connectivity patterns. The behavioral genetic analyses revealed that in general VS and amygdala connectivity showed distinct influences of genetics and environment. VS-prefrontal cortex connections were best described by genetic and unique environmental factors (the latter including measurement error), whereas amygdala-prefrontal cortex connectivity was mainly explained by environmental influences. Similarities were also found: connectivity between both the VS and amygdala and ventral anterior cingulate cortex (vACC) showed influences of shared environment, while connectivity with the orbitofrontal cortex (OFC) showed heritability. These findings may inform future interventions that target behavioral control and emotion regulation, by taking into account genetic dispositions as well as shared and unique environmental factors such as child rearing. •Whole brain analyses with limbic seeds show replicable connectivity patterns.•VS-PFC connectivity is best described by genetic factors.•Amygdala -PFC connectivity is best explained by environmental factors.•The findings may inform future intervention studies.

Performing quality control to detect image artifacts and data-processing errors is crucial in structural magnetic resonance imaging, especially in developmental studies. Currently, many studies rely on visual inspection by trained raters for quality control. The subjectivity of these manual procedures lessens comparability between studies, and with growing study sizes quality control is increasingly time consuming. In addition, both inter-rater as well as intra-rater variability of manual quality control is high and may lead to inclusion of poor quality scans and exclusion of scans of usable quality. In the current study we present the Qoala-T tool, which is an easy and free to use supervised-learning model to reduce rater bias and misclassification in manual quality control procedures using FreeSurfer-processed scans. First, we manually rated quality of N = 784 FreeSurfer-processed T1-weighted scans acquired in three different waves in a longitudinal study. Different supervised-learning models were then compared to predict manual quality ratings using FreeSurfer segmented output data. Results show that the Qoala-T tool using random forests is able to predict scan quality with both high sensitivity and specificity (mean area under the curve (AUC) = 0.98). In addition, the Qoala-T tool was also able to adequately predict the quality of two novel unseen datasets (total N = 872). Finally, analyses of age effects showed that younger participants were more likely to have lower scan quality, underlining that scan quality might confound findings attributed to age effects. These outcomes indicate that this procedure could further help to reduce variability related to manual quality control, thereby benefiting the comparability of data quality between studies. [Display omitted] •Variability of manual quality control procedures reduces comparability among studies.•We introduce Qoala-T to automatically assess quality of FreeSurfer-processed scans.•Scan quality of novel datasets was adequately predicted using the Qoala-T tool.•In three different datasets younger age was associated with lower scan quality.•Qoala-T is publicly available and easy-to-use as an add-on to manual QC.

How neural correlates of self‐concept are influenced by environmental versus genetic factors is currently not fully understood. We investigated heritability estimates of behavioral and neural correlates of self‐concept in middle childhood since this phase is an important time window for taking on new social roles in academic and social contexts. To do so, a validated self‐concept fMRI task was applied in a twin sample of 345 participants aged between 7 and 9 years. In the self‐concept condition, participants were asked to indicate whether academic and social traits applied to them whereas the control condition required trait categorization. The self‐processing activation analyses (n = 234) revealed stronger medial prefrontal cortex (mPFC) activation for self than for control conditions. This effect was more pronounced for social‐self than academic self‐traits, whereas stronger dorsolateral prefrontal cortex (DLPFC) activation was observed for academic versus social self‐evaluations. Behavioral genetic modeling (166 complete twin pairs) revealed that 25–52% of the variation in academic self‐evaluations was explained by genetic factors, whereas 16–49% of the variation in social self‐evaluations was explained by shared environmental factors. Neural genetic modeling (91 complete twin pairs) for variation in mPFC and anterior prefrontal cortex (PFC) activation for academic self‐evaluations confirmed genetic and unique environmental influences, whereas anterior PFC activation for social self‐evaluations was additionally influenced by shared environmental influences. This indicates that environmental context possibly has a larger impact on the behavioral and neural correlates of social self‐concept at a young age. This is the first study demonstrating in a young twin sample that self‐concept depends on both genetic and environmental factors, depending on the specific domain. We investigated heritability estimates of behavioral and neural correlates of self‐concept in middle childhood since this phase is an important time window for taking on new social roles in academic and social contexts. To do so, a validated self‐concept fMRI task was applied in a twin sample of 345 participants aged between 7 and 9 years. This is the first study demonstrating in a young twin sample that self‐concept depends on both genetic and environmental factors, with stronger environmental influences in the social versus academic domain.

Media’s prevailing thin-body ideal plays a vital role in adolescent girls’ body image development, but the co-occurring impact of peer feedback is understudied. The present study used functional magnetic resonance imaging (fMRI) to test media imagery and peer feedback combinations on neural activity related to thin-body ideals. Twenty-four healthy female late adolescents rated precategorized body sizes of bikini models ( too thin or normal ), directly followed by ostensible peer feedback ( too thin or normal ). Consistent with prior studies on social feedback processing, results showed increased brain activity in the dorsal medial prefrontal cortex (dmPFC)/anterior cingulate cortex (ACC) and bilateral insula in incongruent situations: when participants rated media models’ body size as normal while peer feedback indicated the models as too thin (or vice versa). This effect was stronger for girls with lower self-esteem. A subsequent behavioral study ( N = 34 female late adolescents, separate sample) demonstrated that participants changed behavior in the direction of the peer feedback: precategorized normal sized models were rated as too thin more often after receiving too thin peer feedback. This suggests that the neural responses upon peer feedback may influence subsequent choice. Our results show that media-by-peer interactions have pronounced effects on girls’ body ideals.

Performing quality control to detect image artifacts and data-processing errors is crucial in structural magnetic resonance imaging, especially in developmental studies. Currently, many studies rely on visual inspection by trained raters for quality control. The subjectivity of these manual procedures lessens comparability between studies, and with growing study sizes quality control is increasingly time consuming. In addition, both inter-rater as well as intra-rater variability of manual quality control is high and may lead to inclusion of poor quality scans and exclusion of scans of usable quality. In the current study we present the Qoala-T tool, which is an easy and free to use supervised-learning model to reduce rater bias and misclassification in manual quality control procedures using FreeSurfer-processed scans. First, we manually rated quality of N = 784 FreeSurfer-processed T1-weighted scans acquired in three different waves in a longitudinal study. Different supervised-learning models were then compared to predict manual quality ratings. Results show that the Qoala-T tool using random forests is able to predict scan quality with both high sensitivity and specificity (mean area under the curve (AUC) = 0.98). In addition, the Qoala-T tool was also able to adequately predict the quality of two novel unseen datasets (total N = 873). Finally, analyses of age effects showed that younger participants were more likely to have lower scan quality, underlining that scan quality might confound findings attributed to age effects. These outcomes indicate that this procedure could further help to reduce variability related to manual quality control, thereby benefiting the comparability of data quality between studies.

Introduction To guide good practices in patient and public involvement (PPI), several calls have been made to share detailed accounts of practical experiences. We describe our collaboration with young people with a chronic condition (YPCC) in the development, testing and implementation of the digital communication tool MyBoT (Map your Burden of Treatment). Methods MyBoT was developed by a team of academic researchers, some of whom were practising care providers, YPCC and designers. In addition to the two co‐researchers in the research team, various groups of YPCC were involved in decision‐making through participation in a design session, workshops and a dialogue session. The Involvement Matrix was used to reflect on the PPI of all YPCC. Results Initially, the two co‐researchers were involved in the roles of informer and co‐thinker, but their decision‐making power within the study increased over time. In the final stages of the study, the co‐researchers and researchers became partners. The other YPCC who participated in the different sessions and workshops were co‐thinkers in all stages of the study. Conclusion The PPI of two YPCCs as co‐researchers within the research team ensured continuous involvement, whereas the PPI of various groups of YPCCs guaranteed a representative and inclusive approach. Researchers play an essential role in bringing all perspectives together, integrating them within the technical and financial constraints and ultimately building a tool that is tailored to its users' needs. Patient or Public Contribution YPCC played a significant role in the present study. Two YPCC—who are also co‐authors of this paper—were involved in all stages of this project as members of the research team. In addition, various YPCCs were involved in the development, testing and implementation stage of MyBoT by organizing design sessions, workshops and a dialogue session.