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Abstract Study ObjectivesTo investigate associations between social jet lag and the developing adolescent brain. MethodsN = 3507 youth (median (IQR) age = 12.0 (1.1) years; 50.9% females) from the Adolescent Brain Cognitive Development cohort were studied. Social jet lag (adjusted for sleep debt [SJLSC] vs. nonadjusted [SJL]), topological properties and intrinsic dynamics of resting-state networks, and morphometric brain characteristics were analyzed. ResultsOver 35% of participants had SJLSC ≥ 2.0 h. Boys, Hispanic and Black non-Hispanic youth, and/or those at later pubertal stages had longer SJLSC (β = 0.06–0.68, CI = [0.02, 0.83], p ≤ .02), which was also associated with higher Body Mass Index (BMI) (β = 0.13, CI = [0.08, 0.18], p < .01). SJLSC and SJL were associated with lower strength of thalamic connections (β = −0.22, CI = [−0.39, −0.05], p = .03). Longer SJLSC was also associated with lower topological resilience and lower connectivity of the salience network (β = −0.04, CI = [−0.08, −0.01], p = .04), and lower thickness and/or volume of structures overlapping with this and other networks supporting emotional and reward processing and social function (β =−0.08 to −0.05, CI = [−0.12, −0.01], p < .05). Longer SJL was associated with lower connectivity and efficiency of the dorsal attention network (β = −0.05, CI = [−0.10, −0.01], p < .05). Finally, SJLSC and SJL were associated with alterations in spontaneously coordinated brain activity and lower information transfer between regions supporting sensorimotor integration, social function, and emotion regulation (β = −0.07 to −0.05, CI = [−0.12, −0.01], p < .04). ConclusionsMisaligned sleep is associated with widespread alterations in adolescent brain structures, circuit organization, and dynamics of regions that play critical roles in cognitive (including social) function, and emotion and reward regulation. 

Abstract Intrinsic brain dynamics play a fundamental role in cognitive function, but their development is incompletely understood. We investigated pubertal changes in temporal fluctuations of intrinsic network topologies (focusing on the strongest connections and coordination patterns) and signals, in an early longitudinal sample from the Adolescent Brain Cognitive Development (ABCD) study, with resting-state fMRI (n = 4,099 at baseline; n = 3,376 at follow-up [median age = 10.0 (1.1) and 12.0 (1.1) years; n = 2,116 with both assessments]). Reproducible, inverse associations between low-frequency signal and topological fluctuations were estimated (p < 0.05, β = −0.20 to −0.02, 95% confidence interval (CI) = [−0.23, −0.001]). Signal (but not topological) fluctuations increased in somatomotor and prefrontal areas with pubertal stage (p < 0.03, β = 0.06–0.07, 95% CI = [0.03, 0.11]), but decreased in orbitofrontal, insular, and cingulate cortices, as well as cerebellum, hippocampus, amygdala, and thalamus (p < 0.05, β = −0.09 to −0.03, 95% CI = [−0.15, −0.001]). Higher temporal signal and topological variability in spatially distributed regions were estimated in girls. In racial/ethnic minorities, several associations between signal and topological fluctuations were in the opposite direction of those in the entire sample, suggesting potential racial differences. Our findings indicate that during puberty, intrinsic signal dynamics change significantly in developed and developing brain regions, but their strongest coordination patterns may already be sufficiently developed and remain temporally consistent. 

Abstract Social isolation during development, especially in adolescence, has detrimental but incompletely understood effects on the brain. This study investigated the neural correlates of preference for solitude and social withdrawal in a sample of 2809 youth [median (IQR) age = 12.0 (1.1) years, 1440 (51.26%) females] from the Adolescent Brain Cognitive Development study. Older youth whose parents had mental health issues more frequently preferred solitude and/or were socially withdrawn (β = 0.04 to 0.14, CI = [0.002, 0.19], P < 0.05), both of which were associated with internalizing and externalizing behaviors, depression, and anxiety (β = 0.25 to 0.45, CI = [0.20, 0.49], P < 0.05). Youth who preferred solitude and/or were socially withdrawn had lower cortical thickness in regions involved in social function (cuneus, insula, anterior cingulate, and superior temporal gyri) and/or mental health (β = −0.09 to −0.02, CI = [−0.14, −0.003], P < 0.05), and higher amygdala, entorhinal cortex, parahippocampal gyrus, and basal ganglia volume (β = 2.62 to 668.10, CI = [0.13, 668.10], P < 0.05). Youth who often preferred solitude had more topologically segregated dorsal attention, temporoparietal, and social networks (β = 0.07 to 0.10, CI = [0.02, 0.14], P ≤ 0.03). Socially withdrawn youth had a less topologically robust and efficient (β = −0.05 to −0.80, CI = [−1.34,−0.01], P < 0.03) and more fragile cerebellum (β = 0.04, CI = [0.01, 0.07], P < 0.05). These findings suggest that social isolation in adolescence may be a risk factor for widespread alterations in brain regions supporting social function and mental health. 

Abstract Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method‐dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting‐state fMRI from = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), and = 5338 synthetic networks with heterogeneous, data‐inspired topologies, with the goal to investigate and compare three classes of community detection methods: (i) modularity maximization‐based (Newman and Louvain), (ii) probabilistic (Bayesian inference within the framework of stochastic block modeling (SBM)), and (iii) geometric (based on graph Ricci flow). Extensive comparisons between methods and their individual accuracy (relative to the ground truth in synthetic networks), and reliability (when applied to multiple fMRI runs from the same brains) suggest that the underlying brain network topology plays a critical role in the accuracy, reliability and agreement of community detection methods. Consistent method (dis)similarities, and their correlations with topological properties, were estimated across fMRI runs. Based on synthetic graphs, most methods performed similarly and had comparable high accuracy only in some topological regimes, specifically those corresponding to developed connectomes with at least quasi‐optimal community organization. In contrast, in densely and/or weakly connected networks with difficult to detect communities, the methods yielded highly dissimilar results, with Bayesian inference within SBM having significantly higher accuracy compared to all others. Associations between method‐specific modularity and demographic, anthropometric, physiological and cognitive parameters showed mostly method invariance but some method dependence as well. Although method sensitivity to different levels of community structure may in part explain method‐dependent associations between modularity estimates and parameters of interest, method dependence also highlights potential issues of reliability and reproducibility. These findings suggest that a probabilistic approach, such as Bayesian inference in the framework of SBM, may provide consistently reliable estimates of community structure across network topologies. In addition, to maximize robustness of biological inferences, identified network communities and their cognitive, behavioral and other correlates should be confirmed with multiple reliable detection methods. 

Abstract The human brain is a complex system whose function depends on interactions between neurons and their ensembles across scales of organization. These interactions are restricted by anatomical and energetic constraints, and facilitate information processing and integration in response to cognitive demands. In this work, we considered the brain as a closed loop dynamic system under sparse feedback control. This controller design considered simultaneously control performance and feedback (communication) cost. As proof of principle, we applied this framework to structural and functional brain networks. Under high feedback cost only a small number of highly connected network nodes were controlled, which suggests that a small subset of brain regions may play a central role in the control of neural circuits, through a trade‐off between performance and communication cost.