Lifespan Trajectories of Resting State EEG power

Abstract

Trajectories of resting state monopolar EEG power were analyzed using the means, variances, and correlations of data in seven frequency bands obtained from 17 electrodes of the 10-20 system from a sample of 5238 participants from the Collaborative Study on the Genetics of Alcoholism (COGA) with 11906 observations from ages 12 through 70. The values for the individual observations were calculated by standard Fourier transform based methods. In order to make the study more useful for understanding EEG in the general population only subjects who were never diagnosed with alcohol use disorder were included in the study. The trajectories of power show a clear pattern of decrease in all frequency bands and both sexes from ages 12 to 20. Subsequently there is considerable amount of variation between frequency bands and between males and females. In females, the generally lower rate of decrease after age 20 is reversed at age 35 in anterior and central regions in the alpha and beta bands, and stabilized in the theta bands. In males, the decreases continue in the theta and high alpha bands, but there are elements of the reversal in the low alpha and beta bands. Examination of the derivatives of the trajectories show that sex differences begin in the mid-twenties in alpha and beta but not until the mid-thirties in theta. In contrast to the varied power value trajectories, the trajectories of inter-frequency correlation show a pervasive increase with age of the correlation between high alpha and each of the other frequency bands except high beta. This increase is primarily anterior in the alpha-theta correlations and more regionally uniform in the alpha-beta correlations. Sex differences are very small. Trajectories of intra-frequency correlations for comparable between region pairs and within region pairs were generally high and stable across age. This is the first of a series of studies which will provide similar analyses of bipolar EEG power and bipolar EEG coherence in this sample. We know of no other study of resting state EEG power which combines the analysis of power with the analysis of both inter-frequency and intra-frequency correlation of power values.