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Title: EEG-based age-prediction models as stable and heritable indicators of brain maturational level in children and adolescents
Author(s): M.M.L.J.Z. Vandenbosch, D. van 't Ent, D.I. Boomsma, A.P. Anokhin and D.J.A. Smit
Journal: Human Brain Mapping
Year: 2019
Month: April
Day: 15
Volume: 40
Issue: 6
Pages: 1919-1926
DOI: 10.1002/hbm.24501
File URL: http://www.tweelingenregister.org/nederlands/verslaggeving/NTR-publicaties_2019/Vandenbosch_HBM_2019.pdf
Web URL: http://www.tweelingenregister.org/publicaties/wetenschappelijke-publicaties/supplementary-materials/
Keywords: age prediction; brain age; brain maturation; development; electroencephalography (EEG); machine learning
Abstract: 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 overestimation 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. Then, 3?min eyes?closed resting?state EEG data from the longitudinal EEG studies of Netherlands Twin Register (NTR; 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 (5–7?years) and adolescence (16–18 years). We calculated power in 1 Hz wide bins (1–24?Hz). Random forest (RF) regression and relevance vector machine with sixfold cross?validation were applied. The best mean absolute prediction error was obtained with RF (1.22 years). Classification of childhood versus puberty/adolescence reached over 94% accuracy. Prediction errors were moderately to highly stable over periods of 1.5–2.1?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 magnetic resonance imaging. Children and adolescents showed stable overestimation 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.

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