Age-Related Differences in Prestimulus EEG Affect ERPs and Behaviour in the Equiprobable Go/NoGo Task.

Detailed studies of the equiprobable auditory Go/NoGo task have allowed for the development of a sequential-processing model of the perceptual and cognitive processes involved. These processes are reflected in various components differentiating the Go and NoGo event-related potentials (ERPs). It has long been established that electroencephalography (EEG) changes through normal lifespan development. It is also known that ERPs and behaviour in the equiprobable auditory Go/NoGo task change from children to young adults, and again in older adults. Here, we provide a novel examination of links between in-task prestimulus EEG, poststimulus ERPs, and behaviour in three gender-matched groups: children (8-12 years), young adults (18-24 years), and older adults (59-74 years). We used a frequency Principal Component Analysis (f-PCA) to estimate prestimulus EEG components and a temporal Principal Component Analysis (t-PCA) to separately estimate poststimulus ERP Go and NoGo components in each age group to avoid misallocation of variance. The links between EEG components, ERP components, and behavioural measures differed markedly between the groups. The young adults performed best and accomplished this with the simplest EEG-ERP-behaviour brain dynamics pattern. The children performed worst, and this was reflected in the most complex brain dynamics pattern. The older adults showed some reduction in performance, reflected in an EEG-ERP-behaviour pattern with intermediate complexity between those of the children and young adults. These novel brain dynamics patterns hold promise for future developmental research.

Natural alpha frequency components in resting EEG and their relation to arousal.

OBJECTIVE: Global EEG alpha activity is negatively correlated with skin conductance level (SCL), supporting alpha as an inverse marker of arousal. Frequency Principal Components Analysis (f-PCA) of resting EEG amplitude spectra has demonstrated natural components in the alpha band of healthy persons. This is a preliminary exploration of whether such components differ with arousal, possibly underpinning the anomalous ADHD hypoarousal link to reduced alpha. METHOD: Twenty-seven right-handed undergraduate students participated in three 2 minute blocks of resting eyes-open/closed EEG and SCL: EO1, EC, EO2. For each condition, mean EEG spectra were submitted to separate f-PCAs. RESULTS: The inverse alpha/SCL relationship was confirmed for band amplitudes. EO had two alpha components; both correlated negatively with SCL. EC alpha contained four components, but only one had a substantial negative correlation with SCL; two had no relationship, suggesting natural alpha components with different non-arousal functionality in EC. CONCLUSION: Some alpha components in both EC and EO reflect arousal, with other non-arousal components in EC. Our f-PCA approach offers insight into previously-noted alpha anomalies in disorders such as ADHD. SIGNIFICANCE: This proof of concept demonstration in typical participants may provide the basis for a new research effort in clinical disorders involving atypical arousal patterns.

Frontal theta and posterior alpha in resting EEG: A critical examination of convergent and discriminant validity.

Prior research has identified two resting EEG biomarkers with potential for predicting functional outcomes in depression: theta current density in frontal brain regions (especially rostral anterior cingulate cortex) and alpha power over posterior scalp regions. As little is known about the discriminant and convergent validity of these putative biomarkers, a thorough evaluation of these psychometric properties was conducted toward the goal of improving clinical utility of these markers. Resting 71-channel EEG recorded from 35 healthy adults at two sessions (1-week retest) were used to systematically compare different quantification techniques for theta and alpha sources at scalp (surface Laplacian or current source density [CSD]) and brain (distributed inverse; exact low resolution electromagnetic tomography [eLORETA]) level. Signal quality was evaluated with signal-to-noise ratio, participant-level spectra, and frequency PCA covariance decomposition. Convergent and discriminant validity were assessed within a multitrait-multimethod framework. Posterior alpha was reliably identified as two spectral components, each with unique spatial patterns and condition effects (eyes open/closed), high signal quality, and good convergent and discriminant validity. In contrast, frontal theta was characterized by one low-variance component, low signal quality, lack of a distinct spectral peak, and mixed validity. Correlations between candidate biomarkers suggest that posterior alpha components constitute reliable, convergent, and discriminant biometrics in healthy adults. Component-based identification of spectral activity (CSD/eLORETA-fPCA) was superior to fixed, a priori frequency bands. Improved quantification and conceptualization of frontal theta is necessary to determine clinical utility.

EEG frequency PCA in EEG-ERP dynamics.

Principal components analysis (PCA) has long been used to decompose the ERP into components, and these mathematical entities are increasingly accepted as meaningful and useful representatives of the electrophysiological components constituting the ERP. A similar expansion appears to be beginning in regard to decomposition of the EEG amplitude spectrum into frequency components via frequency PCA. However, to date, there has been no exploration of the brain's dynamic EEG-ERP linkages using PCA decomposition to assess components in each measure. Here, we recorded intrinsic EEG in both eyes-closed and eyes-open resting conditions, followed by an equiprobable go/no-go task. Frequency PCA of the EEG, including the nontask resting and within-task prestimulus periods, found seven frequency components within the delta to beta range. These differentially predicted PCA-derived go and no-go N1 and P3 ERP components. This demonstration suggests that it may be beneficial in future brain dynamics studies to implement PCA for the derivation of data-driven components from both the ERP and EEG.