van 't Ent, D. | bioRxiv Science

bioRxiv2020

Greater male than female variability in regional brain structure across the lifespan

For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.

neuroscience↗

bioRxiv2018

EEG-based age-prediction models as stable and heritable indicators of brain maturational level in children and adolescents

The human brain shows remarkable development of functional brain activity from childhood to adolescence. Here, we investigated whether electroencephalogram (EEG) recordings are suitable for predicting the age of children and adolescents. Moreover, we investigated whether over-or underestimation of age was stable over longer time periods, as stable prediction error can be interpreted as reflecting individual brain maturational level. Finally, we established whether the age-prediction error was genetically determined. Three minutes eyes-closed resting state EEG data from the longitudinal EEG studies of Netherlands Twin Register (n=836) and Washington University in St. Louis (n = 702) were used at ages 5, 7, 12, 14, 16 and 18. Longitudinal data were available within childhood and adolescence. We calculated power in 1 Hz wide bins (1 to 24 Hz). Random Forest regression and Relevance Vector Machine with 6-fold cross-validation were applied. The best mean absolute prediction error was obtained with Random Forest (1.22 years). Classification of childhood vs. adolescence reached over 94% accuracy. Prediction errors were moderately to highly stable over periods of 1.5 to 2.1 years (0.53 < r < 0.74) and signifcantly affected by genetic factors (heritability between 42% and 79%). Our results show that age prediction from low-cost EEG recordings is comparable in accuracy to those obtained with MRI. Children and adolescents showed stable over- or underestimation of their age, which means that some participants have stable brain activity patterns that reflect those of an older or younger age, and could therefore reflect individual brain maturational level. This prediction error is heritable, suggesting that genes underlie maturational level of functional brain activity. We propose that age prediction based on EEG recordings can be used for tracking neurodevelopment in typically developing children, in preterm children, and in children with neurodevelopmental disorders.

neuroscience↗