No abstract

The aim of this study was to define the pattern of reduction in absolute power spectral density (PSD) of magnetoencephalography (MEG) signals throughout development. Specifically, we wanted to explore whether the human skull's high permeability for electromagnetic fields would allow us to question whether the pattern of absolute PSD reduction observed in the human electroencephalogram is due to an increase in the skull's resistive properties with age. Furthermore, the topography of the MEG signals during maturation was explored, providing additional insights about the areas and brain rhythms related to late maturation in the human brain. To attain these goals, spontaneous MEG activity was recorded from 148 sensors in a sample of 59 subjects divided into three age groups: children/adolescents (7-14 years), young adults (17-20 years) and adults (21-26 years). Statistical testing was carried out by means of an analysis of variance (ANOVA), with "age group" as between-subject factor and "sensor group" as within-subject factor. Additionally, correlations of absolute PSD with age were computed to assess the influence of age on the spectral content of MEG signals. Results showed a broadband PSD decrease in frontal areas, which suggests the late maturation of this region, but also a mild increase in high frequency PSD with age in posterior areas. These findings suggest that the intensity of the neural sources during spontaneous brain activity decreases with age, which may be related to synaptic pruning.

It has been described that the frequency ranges at which theta, mu and alpha rhythms oscillate is increasing with age. The present report, by analyzing the spontaneous EEG, tries to demonstrate whether there is an increase with age in the frequency at which the cortical structures oscillate. A topographical approach was followed. The spontaneous EEG of one hundredand seventy subjects was recorded. The spectral power (from 0.5 to 45.5 Hz) was obtained by means of the Fast Fourier Transform. Correlations of spatial topographies among the different age groups showed that older groups presented the same topographical maps as younger groups, but oscillating at higher frequencies. The results suggest that the same brain areas oscillate at lower frequencies in children than in older groups, for a broad frequency range. This shift to a higher frequency with age would be a trend in spontaneous brain rhythm development.

No abstract

In this study, we asked whether the event-related potentials associated to cue and target stimuli of a Central Cue Posner Paradigm (CCPP) may encode key parameters of Bayesian inference – prior expectation and surprise – on a trial-by-trial basis. Thirty-two EEG channel were recorded in a sample of 19 young adult subjects while performing a CCPP, in which a cue indicated (validly or invalidly) the position of an incoming auditory target. Three different types of blocks with validities of 50%, 64%, and 88%, respectively, were presented. Estimates of prior expectation and surprise were obtained on a trial-by-trial basis from participants’ responses, using a computational model implementing Bayesian learning. These two values were correlated on a trial-by-trial basis with the EEG values in all the electrodes and time bins. Therefore, a Spearman correlation metrics of the relationship between Bayesian parameters and the EEG was obtained. We report that the surprise parameter was able to classify the different validity blocks. Furthermore, the prior expectation parameter showed a significant correlation with the EEG in the cue-target period, in which the Contingent Negative Variation develops. Finally, in the post-target period the surprise parameter showed a significant correlation in the latencies and electrodes in which different event-related potentials are induced. Our results suggest that Bayesian parameters are coded in the EEG signals; and namely, the CNV would be related to prior expectation, while the post-target components P2a, P2, P3a, P3b, and SW would be related to surprise. This study thus provides novel support to the idea that human electrophysiological neural activity may implement a (Bayesian) predictive processing scheme.