Poster E3, Thursday, August 22, 2019, 3:45 – 5:30 pm, Restaurant Hall
Language Abilities as a Function of Resting-State EEG Trajectories: an 11-Year Longitudinal Study
Lars Meyer1, Xenia Dmitrieva2, Caroline Beese2,3, Vadim Nikulin4,5,6, Claudia Männel2,4,7, Angela D. Friederici2, Gesa Schaadt2,4,7;1Research Group Language Cycles, Max Planck Institute for Human Cognitive and Brain Sciences, 2Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 3Center for Lifespan Psychology, Max Planck Institute for Human Development, 4Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, 5Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, 6Neurophysics Group, Department of Neurology, Charité-University Medicine Berlin, Campus Benjamin Franklin, 7Day Clinic for Cognitive Neurology, University of Leipzig
Language development rests on complex neurophysiological development. Here, we sought to predict language outcome from the 11-year trajectory of children’s resting-state electroencephalogram (RS EEG), indexing the state of an individual’s brain networks. Forty-six children were followed across three recording time points (i.e., 2 months, 4.5 years, and 11 years of age). We focused on alpha-band oscillations, which have been implied in early cognitive development previously. We first identified individual alpha-band peak frequency (IAF) and power (IAP), using an automatic frequency-domain search within age-adapted frequency ranges (2-months range = 4–9 Hz, 4.5-years range = 6–12 Hz, 11-years range = 7–13 Hz). We then modeled individual longitudinal trajectories of IAF and IAP with latent growth curves. Based on the linear appearance of the IAF trajectory, we fitted a linear model to IAF data; in contrast, based on the inverted-u-shape trajectory of the IAP data, a quadratic model was fitted to the IAP data. Latent scores from the IAF and IAP growth curves, as well as their interaction, were then entered as predictors into a multivariate multiple regression analysis on the data from each recording electrode. Dependent outcome measures were general cognitive abilities (i.e., non-verbal intelligence) and language abilities (i.e., phonological awareness, age of writing onset, writing skills, reading speed and comprehension, and vocabulary size), which had been acquired at 10–13 years of age. After correcting for multiple comparisons across electrodes, we found the quadratic IAP trajectory to predict non-verbal intelligence, phonological awareness, writing skills, and vocabulary size. Specifically, high behavioral performance was associated with a continuing IAP increase into late childhood, rather than a downward slope of the quadratic function. To characterize the structure amongst these correlations, we performed a systematic analysis of the correlation matrix. We found that non-verbal intelligence and phonological awareness correlated more strongly with the IAP trajectory than with the other behavioral measures. In contrast, correlations between the IAP trajectory and the other behavioral measures were weaker than the correlations amongst the behavioral measures themselves. In particular, writing skills were most strongly correlated with phonological awareness and vocabulary size was most strongly correlated with writing skills. Our results suggest that a u-shaped trajectory of IAP from birth to late childhood associates with general cognitive outcome and basic linguistic outcome (i.e., phonological awareness), cascading into more specific linguistic abilities. While we replicated the inverted-u-shaped IAP trajectory across the first decade of life, we also note that a good cognitive outcome in late childhood may require a continuing IAP increase, rather than a downward slope.
Themes: Development, Methods
Method: Electrophysiology (MEG/EEG/ECOG)