Xie, H.-B. | bioRxiv Science

bioRxiv2021

EEG-based clusters differentiate psychological distress, sleep quality and cognitive function in adolescents

1IntroductionTo better understand the relationships between brain activity, cognitive function and mental health risk in adolescence there is value in identifying data-driven subgroups based on measurements of brain activity and function, and then comparing cognition and mental health symptoms between such subgroups. MethodsHere we implement a multi-stage analysis pipeline to identify data-driven clusters of 12-year-olds (M = 12.64, SD = 0.32) based on frequency characteristics calculated from resting state, eyes-closed electroencephalography (EEG) recordings. EEG data was collected from 59 individuals as part of their baseline assessment in the Longitudinal Adolescent Brain Study (LABS) being undertaken in Queensland, Australia. Applying multiple unsupervised clustering algorithms to these EEG features, we identified well-separated subgroups of individuals. To study patterns of difference in cognitive function and mental health symptoms between core clusters, we applied Bayesian regression models to probabilistically identify differences in these measures between clusters. ResultsWe identified 5 core clusters which were associated with distinct subtypes of resting state EEG frequency content. EEG features that were influential in differentiating clusters included Individual Alpha Frequency, relative power in 4 Hz bands up to 16 Hz, and 95% Spectral Edge Frequency. Bayesian models demonstrated substantial differences in psychological distress, sleep quality and cognitive function between these clusters. By examining associations between neurophysiology and health measures across clusters, we have identified preliminary risk and protective profiles linked to EEG characteristics. ConclusionIn this work we have developed a flexible and scaleable pipeline to identify subgroups of individuals in early adolescence on the basis of resting state EEG activity. These findings provide new clues about neurophysiological subgroups of adolescents in the general population, and associated patterns of health and cognition that are not observed at the whole group level. This approach offers potential utility in clinical risk prediction for mental and cognitive health outcomes throughout adolescent development.

neuroscience↗

bioRxiv2019

Convergent genomic signatures of high altitude adaptation among domestic mammals

Abundant and diverse domestic mammals living on the Tibetan Plateau provide useful materials for investigating adaptive evolution and genetic convergence. Here, we utilized 327 genomes from horses, sheep, goats, cattle, pigs and dogs living at both high and low altitudes, including 73 genomes generated for this study, to disentangle the genetic mechanisms underlying local adaptation of domestic mammals. Although molecular convergence is comparatively rare at the DNA sequence level, we found convergent signature of positive selection at the gene level, particularly EPAS1 gene in these Tibetan domestic mammals. We also reported a potential function in response to hypoxia for the gene C10orf67, which underwent positive selection in three of the domestic mammals. Our data provides insight into adaptive evolution of high-altitude domestic mammals, and should facilitate the search for additional novel genes involved in the hypoxia response pathway.

evolutionary biology↗