ENIGMA-Sleep: Challenges, opportunities, and the road map.

Neuroimaging and genetics studies have advanced our understanding of the neurobiology of sleep and its disorders. However, individual studies usually have limitations to identifying consistent and reproducible effects, including modest sample sizes, heterogeneous clinical characteristics and varied methodologies. These issues call for a large-scale multi-centre effort in sleep research, in order to increase the number of samples, and harmonize the methods of data collection, preprocessing and analysis using pre-registered well-established protocols. The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium provides a powerful collaborative framework for combining datasets across individual sites. Recently, we have launched the ENIGMA-Sleep working group with the collaboration of several institutes from 15 countries to perform large-scale worldwide neuroimaging and genetics studies for better understanding the neurobiology of impaired sleep quality in population-based healthy individuals, the neural consequences of sleep deprivation, pathophysiology of sleep disorders, as well as neural correlates of sleep disturbances across various neuropsychiatric disorders. In this introductory review, we describe the details of our currently available datasets and our ongoing projects in the ENIGMA-Sleep group, and discuss both the potential challenges and opportunities of a collaborative initiative in sleep medicine.

16Up: Outline of a Study Investigating Wellbeing and Information and Communication Technology Use in Adolescent Twins.

The '16Up' study conducted at the QIMR Berghofer Medical Research Institute from January 2014 to December 2018 aimed to examine the physical and mental health of young Australian twins aged 16-18 years (N = 876; 371 twin pairs and 18 triplet sets). Measurements included online questionnaires covering physical and mental health as well as information and communication technology (ICT) use, actigraphy, sleep diaries and hair samples to determine cortisol concentrations. Study participants generally rated themselves as being in good physical (79%) and mental (73%) health and reported lower rates of psychological distress and exposure to alcohol, tobacco products or other substances than previously reported for this age group in the Australian population. Daily or near-daily online activity was almost universal among study participants, with no differences noted between males and females in terms of frequency or duration of internet access. Patterns of ICT use in this sample indicated that the respondents were more likely to use online information sources for researching physical health issues than for mental health or substance use issues, and that they generally reported partial levels of satisfaction with the mental health information they found online. This suggests that internet-based mental health resources can be readily accessed by adolescent Australians, and their computer literacy augurs well for future access to online health resources. In combination with other data collected as part of the ongoing Brisbane Longitudinal Twin Study, the 16Up project provides a valuable resource for the longitudinal investigation of genetic and environmental contributions to phenotypic variation in a variety of human traits.

A meta-analysis of the relationship between subjective sleep and depressive symptoms in adolescence.

BACKGROUND: Adolescence is a risk period for the development of mental illness, as well as a time for pronounced change in sleep behaviour. While prior studies, including several meta-analyses show a relationship between sleep and depressive symptoms, there were many inconsistences found in the literature. OBJECTIVE: To investigate the relationship between subjective sleep and depressive symptoms. METHODS: Following PRISMA guidelines, we conducted a literature search that yielded forty-nine recent studies (2014-2020) with adolescent samples aged 9 to 25-year-olds, and more than double the sample size of previous meta-analyses (N = 318,256). RESULTS: In a series of meta-analyses, we show that while several common categories of subjective sleep are associated with depressive symptoms in adolescents, the strength of this relationship varies. Measures of sleep perception: poor sleep quality (r = 0.41), insomnia (r = 0.37), sleep disturbances (r = 0.36), wake after sleep onset (r = 0.31), and daytime sleepiness (r = 0.30) correlated more strongly with depressive symptoms, than measures of sleep behaviour: sleep latency (r = 0.22), and sleep duration (r = -0.19). CONCLUSIONS: These findings suggest that in studies of depressive symptoms it may be important to assess an adolescent's perception about their sleep, in addition to their sleep/wake behaviours.

Genetic and environmental influences on sleep-wake behaviors in adolescence.

Study Objectives: To investigate the influence of genetic and environmental factors on sleep-wake behaviors across adolescence. Methods: Four hundred and ninety-five participants (aged 9-17; 55% females), including 93 monozygotic and 117 dizygotic twin pairs, and 75 unmatched twins, wore an accelerometry device and completed a sleep diary for 2 weeks. Results: Individual differences in sleep onset, wake time, and sleep midpoint were influenced by both additive genetic (44%-50% of total variance) and shared environmental (31%-42%) factors, with a predominant genetic influence for sleep duration (62%) and restorative sleep (43%). When stratified into younger (aged 9-14) and older (aged 16-17) subsamples, genetic sources were more prominent in older adolescents. The moderate correlation between sleep duration and midpoint (rP = -.43, rG = .54) was attributable to a common genetic source. Sleep-wake behaviors on school and nonschool nights were correlated (rP = .44-.72) and influenced by the same genetic and unique environmental factors. Genetic sources specific to night-type were also identified, for all behaviors except restorative sleep. Conclusions: There were strong genetic influences on sleep-wake phenotypes, particularly on sleep timing, in adolescence. Moreover, there may be common genetic influences underlying both sleep and circadian rhythms. The differences in sleep-wake behaviors on school and nonschool nights could be attributable to genetic factors involved in reactivity to environmental context.