Collaborative networks and data sharing initiatives are broadening the opportunities for the advancement of science. These initiatives offer greater transparency in science, with the opportunity for external research groups to reproduce, replicate, and extend research findings. Further, larger datasets offer the opportunity to identify homogeneous patterns within subgroups of individuals, where these patterns may be obscured by the heterogeneity of the neurobiological measure in smaller samples.However, data sharing and data pooling initiatives are not without their challenges, especially with new laws that may at first glance appear quite restrictive for open science initiatives. Interestingly, what is key to some of these new laws (i.e, the European Union's general data protection regulation) is that they provide greater control of data to those who "give" their data for research purposes. Thus, the most important element in data sharing is allowing the participants to make informed decisions about how they want their data to be used, and, within the law of the specific country, to follow the participants' wishes. This framework encompasses obtaining thorough informed consent and allowing the participant to determine the extent that they want their data shared, many of the ethical and legal obstacles are reduced to just monsters under the bed. In this manuscript we discuss the many options and obstacles for data sharing, from fully open, to federated learning, to fully closed. Importantly, we highlight the intersection of data sharing, privacy, and data ownership and highlight specific examples that we believe are informative to the neuroimaging community.

Under the influence of genes and a varying environment, human brain structure changes throughout the lifespan. Even in adulthood, when the brain seems relatively stable, individuals differ in the profile and rate of brain changes 1 . Longitudinal studies are crucial to identify genetic and environmental factors that influence the rate of these brain changes throughout development 2 and aging 3 . Inter-individual differences in brain development are associated with general cognitive function 4,5 and risk for psychiatric disorders 6,7 and neurological diseases 8,9 . Genetic factors involved in brain development and aging overlap with those for cognition 10 and risk for neuropsychiatric disorders 11 . A recent cross-sectional study showed brain age to be advanced in several brain disorders. Brain age is an estimate of biological age based on brain structure, which can deviate from chronological age. Several shared loci were found between the genome-wide association study (GWAS) summary statistics for advanced brain age and psychiatric disorders 12 . However, information is still lacking on which genetic variants influence an individual's brain changes throughout life, because this requires longitudinal data. Discovering genetic factors that explain variation between individuals in brain structural changes may reveal key biological pathways that drive normal development and aging and may contribute to identifying disease risk and resilience-a crucial goal given the urgent need for new treatments for aberrant brain development and aging worldwide.As part of the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium 13 , the ENIGMA Plasticity Working Group quantified the overall genetic contribution to longitudinal brain changes by combining evidence from multiple twin cohorts across the world 14 . Most global and subcortical brain measures showed genetic influences on change over time, with a higher genetic contribution in the elderly (heritability, 16-42%). Genetic factors that influence longitudinal changes were partially independent of those that influence baseline volumes of brain structures, suggesting that there might be genetic variants that specifically affect the rate of development or aging. However, the genes involved in these processes are still not known, with only a single, small-scale GWAS performed for longitudinal volume change in gray and white matter of the cerebrum, basal ganglia and cerebellum 15 . In this study, we set out to find genetic variants that may influence rates of brain changes over time, using genome-wide analysis in individuals scanned with magnetic resonance imaging (MRI) on more than one occasion. We also aimed to identify references

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Background Sleep problems, altered sleep patterns and mental health difficulties often co-occur in the pediatric population. Different assessment methods for sleep exist, however, many studies only use one measure of sleep or focus on one specific mental health problem. In this population-based study, we assessed different aspects of sleep and mother-reported mental health to provide a broad overview of the associations between reported and actigraphic sleep characteristics and mental health. Methods This cross-sectional study included 788 children 10-11-year-old children (52.5% girls) and 344 13–14-year-old children (55.2% girls). Mothers and children reported on the sleep of the child and wrist actigraphy was used to assess the child’s sleep patterns and 24 h activity rhythm. Mental health was assessed via mother-report and covered internalizing, externalizing and a combined phenotype of internalizing and externalizing symptoms, the dysregulation profile. Results Higher reported sleep problems were related to more symptoms of mental health problems in 10–11- and 13–14-year-old adolescents, with standardized ß-estimates ranging between 0.11 and 0.35. There was no association between actigraphy-estimated sleep and most mental health problems, but earlier sleep onset was associated with more internalizing problems (ß = − 0.09, SE = 0.03, p-value = 0.002), and higher intra-daily variability of the 24 h activity rhythm was associated with more dysregulation profile symptoms at age 10–11 (ß = 0.11, SE = 0.04, p-value = 0.002). Discussion Reported sleep problems across informants were related to all domains of mental health problems, providing evidence that sleep can be an important topic to discuss for clinicians seeing children with mental health problems. Actigraphy-estimated sleep characteristics were not associated with most mental health problems. The discrepancy between reported and actigraphic sleep measures strengthens the idea that these two measures tap into distinct constructs of sleep.