Frequency and power of human alpha oscillations drift systematically with time-on-task

Oscillatory neural activity is a fundamental characteristic of the mammalian brain spanning multiple levels of spatial and temporal scale. Current theories of neural oscillations and analysis techniques employed to investigate their functional significance are based on an often implicit assumption: In the absence of experimental manipulation, the spectral content of any given EEG- or MEG-recorded neural oscillator remains approximately stationary over the course of a typical experimental session (∼1 h), spontaneously fluctuating only around its dominant frequency. Here, we examined this assumption for ongoing neural oscillations in the alpha-band (8–13 Hz). We found that alpha peak frequency systematically decreased over time, while alpha-power increased. Intriguingly, these systematic changes showed partial independence of each other: Statistical source separation (independent component analysis) revealed that while some alpha components displayed concomitant power increases and peak frequency decreases, other components showed either unique power increases or frequency decreases. Interestingly, we also found these components to differ in frequency. Components that showed mixed frequency/power changes oscillated primarily in the lower alpha-band (∼8–10 Hz), while components with unique changes oscillated primarily in the higher alpha-band (∼9–13 Hz). Our findings provide novel clues on the time-varying intrinsic properties of large-scale neural networks as measured by M/EEG, with implications for the analysis and interpretation of studies that aim at identifying functionally relevant oscillatory networks or at driving them through external stimulation

Evoked responses to rhythmic visual stimulation vary across sources of intrinsic alpha activity in humans

Rhythmic flickering visual stimulation produces steady-state visually evoked potentials (SSVEPs) in electroencephalogram (EEG) recordings. Based on electrode-level analyses, two dichotomous models of the underpinning mechanisms leading to SSVEP generation have been proposed: entrainment or superposition, i.e., phase-alignment or independence of endogenous brain oscillations from flicker-induced oscillations, respectively. Electrode-level analyses, however, represent an averaged view of underlying ‘source-level’ activity, at which variability in SSVEPs may lie, possibly suggesting the co-existence of multiple mechanisms. To probe this idea, we investigated the variability of SSVEPs derived from the sources underpinning scalp EEG responses during presentation of a flickering radial checkerboard. Flicker was presented between 6 and 12 Hz in 1 Hz steps, and at individual alpha frequency (IAF i.e., the dominant frequency of endogenous alpha oscillatory activity). We tested whether sources of endogenous alpha activity could be dissociated according to evoked responses to different flicker frequencies relative to IAF. Occipitoparietal sources were identified by temporal independent component analysis, maximal resting-state alpha power at IAF and source localisation. The pattern of SSVEPs to rhythmic flicker relative to IAF was estimated by correlation coefficients, describing the correlation between the peak-to-peak amplitude of the SSVEP and the absolute distance of the flicker frequency from IAF across flicker conditions. We observed extreme variability in correlation coefficients across sources, ranging from −0.84 to 0.93, with sources showing largely different coefficients co-existing within subjects. This result demonstrates variation in evoked responses to flicker across sources of endogenous alpha oscillatory activity. Data support the idea of multiple SSVEP mechanisms

A Comparison of Closed Loop vs. Fixed Frequency tACS on Modulating Brain Oscillations and Visual Detection

Transcranial alternating current stimulation has emerged as an effective tool for the exploration of brain oscillations. By applying a weak alternating current between electrodes placed on the scalp matched to the endogenous frequency, tACS enables the specific modulation of targeted brain oscillations This results in alterations in cognitive functions or persistent physiological changes. Most studies that utilize tACS determine a fixed stimulation frequency prior to the stimulation that is kept constant throughout the experiment. Yet it is known that brain rhythms can encounter shifts in their endogenous frequency. This could potentially move the ongoing brain oscillations into a frequency region where it is no longer affected by the stimulation, thereby decreasing or negating the effect of tACS. Such an effect of a mismatch between stimulation frequency and endogenous frequency on the outcome of stimulation has been shown before for the parietal alpha-activity. In this study, we employed an intermittent closed loop stimulation protocol, where the stimulation is divided into short epochs, between which an EEG is recorded and rapidly analyzed to determine a new stimulation frequency for the next stimulation epoch. This stimulation protocol was tested in a three-group study against a classical fixed stimulation protocol and a sham-treatment. We targeted the parietal alpha rhythm and hypothesized that this setup will ensure a constant close match between the frequencies of tACS and alpha activity. This closer match should lead to an increased modulation of detection of visual luminance changes depending on the phase of the tACS and an increased rise in alpha peak power post stimulation when compared to a protocol with fixed pre-determined stimulation frequency. Contrary to our hypothesis, our results show that only a fixed stimulation protocol leads to a persistent increase in post-stimulation alpha power as compared to sham. Furthermore, in none of the stimulated groups significant modulation of detection performance occurred. While the lack of behavioral effects is inconclusive due to the short selection of different phase bins and trials, the physiological results suggest that a constant stimulation with a fixed frequency is actually beneficial, when the goal is to produce persistent synaptic changes

The GLM-spectrum:A multilevel framework for spectrum analysis with covariate and confound modelling

The frequency spectrum is a central method for representing the dynamics within electrophysiological data. Some widely used spectrum estimators make use of averaging across time segments to reduce noise in the final spectrum. The core of this approach has not changed substantially since the 1960s, though many advances in the field of regression modelling and statistics have been made during this time. Here, we propose a new approach, the General Linear Model (GLM) Spectrum, which reframes time averaged spectral estimation as multiple regression. This brings several benefits, including the ability to do confound modelling, hierarchical modelling, and significance testing via non-parametric statistics. We apply the approach to a dataset of EEG recordings of participants who alternate between eyes-open and eyes-closed resting state. The GLM-Spectrum can model both conditions, quantify their differences, and perform denoising through confound regression in a single step. This application is scaled up from a single channel to a whole head recording and, finally, applied to quantify age differences across a large group-level dataset. We show that the GLM-Spectrum lends itself to rigorous modelling of within- and between-subject contrasts as well as their interactions, and that the use of model-projected spectra provides an intuitive visualisation. The GLM-Spectrum is a flexible framework for robust multilevel analysis of power spectra, with adaptive covariate and confound modelling

Long‐term (statistically learnt) and short‐term (inter‐trial) distractor‐location effects arise at different pre‐ and post‐selective processing stages

A salient distractor interferes less with visual search if it appears at a location where it is likely to occur, referred to as distractor-location probability cueing. Conversely, if the current target appears at the same location as a distractor on the preceding trial, search is impeded. While these two location-specific “suppression” effects reflect long-term, statistically learnt and short-term, inter-trial adaptations of the system to distractors, it is unclear at what stage(s) of processing they arise. Here, we adopted the additional-singleton paradigm and examined lateralized event-related potentials (L-ERPs) and lateralized alpha (8–12 Hz) power to track the temporal dynamics of these effects. Behaviorally, we confirmed both effects: reaction times (RTs) interference was reduced for distractors at frequent versus rare (distractor) locations, and RTs were delayed for targets that appeared at previous distractor versus non-distractor locations. Electrophysiologically, the statistical-learning effect was not associated with lateralized alpha power during the pre-stimulus period. Rather, it was seen in an early N1pc referenced to the frequent distractor location (whether or not a distractor or a target occurred there), indicative of a learnt top-down prioritization of this location. This early top-down influence was systematically modulated by (competing) target- and distractor-generated bottom-up saliency signals in the display. In contrast, the inter-trial effect was reflected in an enhanced SPCN when the target was preceded by a distractor at its location. This suggests that establishing that an attentionally selected item is a task-relevant target, rather than an irrelevant distractor, is more demanding at a previously “rejected” distractor location

Coupling of pupil- and neuronal population dynamics reveals diverse influences of arousal on cortical processing

Fluctuations in arousal, controlled by subcortical neuromodulatory systems, continuously shape cortical state, with profound consequences for information processing. Yet, how arousal signals influence cortical population activity in detail has so far only been characterized for a few selected brain regions. Traditional accounts conceptualize arousal as a homogeneous modulator of neural population activity across the cerebral cortex. Recent insights, however, point to a higher specificity of arousal effects on different components of neural activity and across cortical regions. Here, we provide a comprehensive account of the relationships between fluctuations in arousal and neuronal population activity across the human brain. Exploiting the established link between pupil size and central arousal systems, we performed concurrent magnetoencephalographic (MEG) and pupillographic recordings in a large number of participants, pooled across three laboratories. We found a cascade of effects relative to the peak timing of spontaneous pupil dilations: Decreases in low-frequency (2-8 Hz) activity in temporal and lateral frontal cortex, followed by increased high-frequency (>64 Hz) activity in mid-frontal regions, followed by monotonic and inverted U relationships with intermediate frequency-range activity (8-32 Hz) in occipito-parietal regions. Pupil-linked arousal also coincided with widespread changes in the structure of the aperiodic component of cortical population activity, indicative of changes in the excitation-inhibition balance in underlying microcircuits. Our results provide a novel basis for studying the arousal modulation of cognitive computations in cortical circuits

Frequency and power of human alpha oscillations drift systematically with time-on-task

EEG Dat

Mapping the Spatial and Temporal Dynamics of Visual Percepts Elicited by a Non-Invasive Brain Stimulation Technique

While many of us rely on vision to interact with and experience the world, for people with damage or disease to the eye or visual cortex, experience through this modality is extremely limited. Brain and retinal stimulation devices show exciting promise for restoring vision, but little is understood about where and when vision percepts can be induced through stimulation. Using a non-invasive brain stimulation technique called transcranial magnetic stimulation (TMS), we characterized the spatial and temporal dynamics of perception induced through brain stimulation. In the first set of experiments, we explore the importance of higher visual and non-visual areas vs. early visual areas in generating perception. We demonstrate that stimulation of even non-visual areas can evoke percepts in some subjects, but that this perception is likely a consequence of direct or indirect stimulation of early visual regions. In the second set of experiments, we demonstrate that percepts evoked from stimulation of this non-visual area likely arise from excitation of the optic nerve, an early visual structure. This reinforces the importance of early visual areas, not later ones, in generating perception, and suggests that induction of perception must involve activity in early visual structures. In the last set of experiments, we investigated the temporal dynamics of percepts induced through non-invasive stimulation. We show that the latency and duration of percepts evoked through brain stimulation are highly variable across individuals. Furthermore, we demonstrate that perception of these percepts is not instantaneous, but rather requires additional feedback processing for conscious awareness. Together, our results bridge a fundamental gap in our understanding of the some of the most fundamental characteristics of perception induced through non-invasive brain stimulation