Exploring Age-Related Changes in Dynamical Non-Stationarity in Electroencephalographic Signals during Early Adolescence

Abstract

Dynamics of brain signals such as electroencephalogram (EEG) can be characterized as a sequence of quasi-stable patterns. Such patterns in the brain signals can be associated with coordinated neural oscillations, which can be modeled by non-linear systems. Further, these patterns can be quantified through dynamical non-stationarity based on detection of qualitative changes in the state of the systems underlying the observed brain signals. This study explored age-related changes in dynamical non-stationarity of the brain signals recorded at rest, longitudinally with 128-channel EEG during early adolescence (10 to 13 years of age, 56 participants). Dynamical non-stationarity was analyzed based on segmentation of the time series with subsequent grouping of the segments into clusters with similar dynamics. Age-related changes in dynamical non-stationarity were described in terms of the number of stationary states and the duration of the stationary segments. We found that the EEG signal became more non-stationary with age. Specifically, the number of states increased whereas the mean duration of the stationary segment decreased with age. These two effects had global and parieto-occipital distribution, respectively, with the later effect being most dominant in the alpha (around 10 Hz) frequency band.

Developmental changes in spontaneous electrocortical activity and network organization from early to late childhood

“…One consequence of cortical networks with increased global integration appears to be their capacity to support a greater repertoire of functional cortical states (McIntosh et al, 2008; Vakorin et al, 2011, 2013). Indeed, in our data, older children exhibited functional alpha networks that were less spatially homogenous compared to those of younger children.…”

“…The diverse and continuously changing ensemble of states that is explored by cortical circuits in the absence of stimulus input is shaped both by the intrinsic oscillatory properties of neurons as well as by the spatial spreading of synchronization due to recurrent connectivity (Hipp et al,2012; Sporns, 2011). Brain development is associated with a general increase in the diversity of spontaneous cortical states, a fact that may underlie increases in sophistication of information processing into adolescence and early adulthood (Vakorin et al, 2011, 2013; Koening et al, 2002; Lippé, Kovacevic, & McIntosh, 2009; McIntosh et al, 2008). Accordingly, charting the maturational profiles of spontaneous neuroelectrical activity and the development of large-scale cortical oscillatory networks has emerged as an active area of research inquiry (Palva & Palva, 2012; Uhlhaas et al, 2010), one that can reveal the neural infrastructure that underlies both healthy and disordered perceptual, cognitive and affective capacities.…”

Detecting Mild Traumatic Brain Injury Using Resting State Magnetoencephalographic Connectivity

“…Significance of the contrast can be tested with permutation tests, whereas the robustness of the contribution of specific connections and frequencies to the identified contrast can be tested with bootstrap procedures. Here we give a brief description of the technique [55–58], which was previously applied in a number of EEG and MEG studies to characterize changes in the brain signals [59–61]. …”

Charting moment-to-moment brain signal variability from early to late childhood

“…On the basis of the evidence reviewed above, there has been a rapidly growing interest in treating intraindividual brain signal variability as a new frontier for studies examining neural development, learning, and nervous system pathology (Garrett et al, 2013; McIntosh et al, 2010; Takahashi, 2013; Vakorin et al, 2013). Although there exist numerous ways of quantifying brain signal variability, one prominent class of computational methods that exhibit superior performance (as compared to conventional estimates based on simple mean and variance) involves computing regularity statistics that capture the degree of unpredictability inherent in time series recordings (Garrett et al, 2013; Takahashi, 2013; Vakorin & McIntosh, 2012).…”