Longitudinal study of EEG frequency maturation and power changes in children on the Russian North

Abstract

The aim of the study was to reveal longitudinal changes in electroencephalogram spectral power and frequency (percentage frequency composition of EEG and alpha peak frequency) patterns in normal children from northern Russia. Fifteen children (9 girls and 6 boys) participated in the study. The resting state (eyes closed) EEGs were recorded yearly (2005-2013) from age 8 to age 16-17 for each child. EEG frequency patterns were estimated as the percentages of waves with a 1 Hz step revealed by measuring the interval durations between points crossing zero (isoline) by a curve. EEG spectral power changes were analyzed for delta (1.5-4 Hz), theta (4-7.5 Hz), alpha-1 (7.5-9.5 Hz), alpha-2 (9.5-12.5 Hz), beta-1 (12.5-18 Hz) and beta-2 (18-30 Hz) bands. According to the frequency composition of the EEG signals fast synchronous, polymorphous synchronous, polymorphous desynchronous and slow synchronous types of children EEG were revealed. These EEG types were relatively stable during adolescence. In these EEG types, the frequency patterns and spectral power dynamics with age had several common and specific features. Slow wave percentage and spectral power in the delta band remarkably decreased with age in all groups. Starting from the theta band the EEG types were characterized by different EEG spectral power changes with age. In fast synchronous EEG type, the theta and alpha-1 EEG power decreased, and the alpha-2 power increased in the occipital and parietal areas. The polymorphous synchronous type was characterized by increased both the alpha-1 and alpha-2 power with regional peculiarities. In the polymorphous desynchronous type spectral power in all bands decreased with age, and in the slow synchronous type, the alpha-1 power massively increased with age. Obtained results suggest predictive strength of the spatial-frequency patterns in EEG for its following maturation through the years.

Heightened Delta Power during Slow-Wave-Sleep in Patients with Rett Syndrome Associated with Poor Sleep Efficiency

“…Studies have shown that from early childhood to late adolescence [healthy human subjects (2.4–19.4 years)], the location of maximal SWS activity during sleep shifts from posterior to anterior regions as the brain matures [ 17 , 48 ]. Our automated algorithm identified occipital predominance of SWS in qEEG of RTT and control females below 10 yrs.…”

The EEG Split Alpha Peak: Phenomenological Origins and Methodological Aspects of Detection and Evaluation

“…All these factors can have an impact on the separation of theta and alpha bands (Klimesch, 1999 ; Garn et al, 2012 ; Grandy et al, 2013a ; Bazanova and Vernon, 2014 ). Research shows that alpha power is lower in early childhood and in the elderly than in adulthood (Chiang et al, 2011 ; Cottone et al, 2013 ; Grandy et al, 2013b ; Ponomareva et al, 2013 ; Soroko et al, 2014 ; Vysata et al, 2014 ; Miskovic et al, 2015 ; Zappasodi et al, 2015 ), and that alpha peak frequency is higher in females than in males in posterior parts of the brain (Langrova et al, 2012 ; Garces et al, 2013 ). Moreover, alpha power decreases and theta power increases during the transition from wakefulness to sleep (Klimesch, 1999 ).…”

EEG “Signs” of Verbal Creative Task Fulfillment with and without Overcoming Self-Induced Stereotypes

“…A number of averaged non-artifact epochs for the power spectra analysis were balanced within the subjects and were set to 45. The estimations of the absolute power of the EEG were calculated and averaged in the following frequency bands: delta (1.5-4 Hz), theta (4-7.5 Hz), alpha1 (7.5-9.5 Hz), alpha2 (9.5-12.5 Hz), beta1 (12.5-18 Hz) and beta2 (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Estimation arrays were averaged for each subject in each year and were subjected to standardization by means of transformations, Y = log X for power [27], for further statistical analysis.…”

“…Since the spectral power of EEG rhythms is influenced not only by the index of these rhythms, but also by the amplitude-consideration of the frequency structure and spectral power of EEG gives a broader perspective to the estimation of the neurophysiological mechanisms of cognitive activities and individual differences. For the frequency structure evaluation, the interval durations were measured between the points crossing the zero (isoline) using a curve by means of S.S. Bekshaev's (IEPhB RAS) program [20,28]. The interval durations (ms) between points on the isoline intersections were transformed into frequencies (Hz) and evaluated across all analyzed EEG intervals (no less than 3 min).…”

“…This study aimed to explore rearrangements in EEG frequency percentages (as suggested in [20]) and EEG spectral power changes during creative task performance, with and without mental effort, while overcoming self-induced stereotypes. We wanted to determine what is crucial for creative task performance and for overcoming self-created stereotypes.…”