Prestimulus amplitudes modulate P1 latencies and evoked traveling alpha waves

Just a phase? Causal probing reveals spurious phasic dependence of sustained attention

For over a decade, electrophysiological studies have reported correlations between attention / perception and the phase of spontaneous brain oscillations. To date, these findings have been interpreted as evidence that the brain uses neural oscillations to sample and predict upcoming stimuli. Yet, evidence from simulations have shown that analysis artefacts could also lead to spurious pre-stimulus oscillations that appear to predict future brain responses. To address this discrepancy, we conducted an experiment in which visual stimuli were presented in time to specific phases of spontaneous alpha and theta oscillations. This allowed us to causally probe the role of ongoing neural activity in visual processing independent of the stimulus-evoked dynamics. Our findings did not support a causal link between spontaneous alpha / theta rhythms and behaviour. However, spurious correlations between theta phase and behaviour emerged offline using gold-standard time-frequency analyses. These findings are a reminder that care should be taken when inferring causal relationships between neural activity and behaviour using acausal analysis methods.

Pre-stimulus alpha activity modulates long-lasting unconscious feature integration

Pre-stimulus alpha (α) activity can influence perception of shortly presented, low-contrast stimuli. The underlying mechanisms are often thought to affect perception exactly at the time of presentation. In addition, it is suggested that α cycles determine temporal windows of integration. However, in everyday situations, stimuli are usually presented for periods longer than ∼100 ms and perception is often an integration of information across space and time. Moving objects are just one example. Hence, the question is whether α activity plays a role also in temporal integration, especially when stimuli are integrated over several α cycles. Using electroencephalography (EEG), we investigated the relationship between pre-stimulus brain activity and long-lasting integration in the sequential metacontrast paradigm (SQM), where two opposite vernier offsets, embedded in a stream of lines, are unconsciously integrated into a single percept. We show that increases in α power, even 300 ms before the stimulus, affected the probability of reporting the first offset, shown at the very beginning of the SQM. This effect was mediated by the systematic slowing of the α rhythm that followed the peak in α power. No phase effects were found. Together, our results demonstrate a cascade of neural changes, following spontaneous bursts of α activity and extending beyond a single moment, which influences the sensory representation of visual features for hundreds of milliseconds. Crucially, as feature integration in the SQM occurs before a conscious percept is elicited, this also provides evidence that α activity is linked to mechanisms regulating unconscious processing.

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.

Cognitive neurophysiology: Event-related potentials

Event-related potentials (ERPs) are one of the most commonly used tools to assess cognitive processing with a high temporal resolution. We provide an updated view of the cortical origins of evoked responses and discuss potential mechanisms contributing to ERP generation. In particular, we focus on the relationship between evoked and ongoing oscillatory activity and discuss the differences between ERPs and cortical activation as indexed by high-frequency activity in human intracranial electroencephalography (EEG). We highlight several possibilities for how ERPs can precisely index human perception and behavior in nontraditional approaches, such as neuronal entrainment through steady-state evoked potentials, multivariate decoding, and cross-frequency correlations. We argue that analyses of time-locked responses are beneficial to assess nonlinear and nonsinusoidal neuronal activity on a fine-grained temporal scale, since analyses in the time domain are less susceptible to artifacts than spectral decomposition techniques. Taken together, the current review provides a state-of-the-art overview of ERPs and their application in cognitive and clinical neurophysiology.

The frequency architecture of brain and brain body oscillations: an analysis

Research on brain oscillations has brought up a picture of coupled oscillators. Some of the most important questions that will be analyzed are, how many frequencies are there, what are the coupling principles, what their functional meaning is, and whether body oscillations follow similar coupling principles. It is argued that physiologically, two basic coupling principles govern brain as well as body oscillations: (i) amplitude (envelope) modulation between any frequencies m and n, where the phase of the slower frequency m modulates the envelope of the faster frequency n, and (ii) phase coupling between m and n, where the frequency of n is a harmonic multiple of m. An analysis of the center frequency of traditional frequency bands and their coupling principles suggest a binary hierarchy of frequencies. This principle leads to the foundation of the binary hierarchy brain body oscillation theory. Its central hypotheses are that the frequencies of body oscillations can be predicted from brain oscillations and that brain and body oscillations are aligned to each other. The empirical evaluation of the predicted frequencies for body oscillations is discussed on the basis of findings for heart rate, heart rate variability, breathing frequencies, fluctuations in the BOLD signal, and other body oscillations. The conclusion is that brain and many body oscillations can be described by a single system, where the cross talk - reflecting communication - within and between brain and body oscillations is governed by m : n phase to envelope and phase to phase coupling.

On the Physiological Modulation and Potential Mechanisms Underlying Parieto-Occipital Alpha Oscillations

The parieto-occipital alpha (8–13 Hz) rhythm is by far the strongest spectral fingerprint in the human brain. Almost 90 years later, its physiological origin is still far from clear. In this Research Topic I review human pharmacological studies using electroencephalography (EEG) and magnetoencephalography (MEG) that investigated the physiological mechanisms behind posterior alpha. Based on results from classical and recent experimental studies, I find a wide spectrum of drugs that modulate parieto-occipital alpha power. Alpha frequency is rarely affected, but this might be due to the range of drug dosages employed. Animal and human pharmacological findings suggest that both GABA enhancers and NMDA blockers systematically decrease posterior alpha power. Surprisingly, most of the theoretical frameworks do not seem to embrace these empirical findings and the debate on the functional role of alpha oscillations has been polarized between the inhibition vs. active poles hypotheses. Here, I speculate that the functional role of alpha might depend on physiological excitation as much as on physiological inhibition. This is supported by animal and human pharmacological work showing that GABAergic, glutamatergic, cholinergic, and serotonergic receptors in the thalamus and the cortex play a key role in the regulation of alpha power and frequency. This myriad of physiological modulations fit with the view that the alpha rhythm is a complex rhythm with multiple sources supported by both thalamo-cortical and cortico-cortical loops. Finally, I briefly discuss how future research combining experimental measurements derived from theoretical predictions based of biophysically realistic computational models will be crucial to the reconciliation of these disparate findings.

Two Sides of the Same Coin: ERP and Wavelet Analyses of Visual Potentials Evoked and Induced by Task-Relevant Faces

New analysis techniques of the electroencephalogram (EEG) such as wavelet analysis open the possibility to address questions that may largely improve our understanding of the EEG and clarify its relation with related potentials (ER Ps). Three issues were addressed. 1) To what extent can early ERERP components be described as transient evoked oscillations in specific frequency bands? 2) Total EEG power (TP) after a stimulus consists of pre-stimulus baseline power (BP), evoked power (EP), and induced power (IP), but what are their respective contributions? 3) The Phase Reset model proposes that BP predicts EP, while the evoked model holds that BP is unrelated to EP; which model is the most valid one? EEG results on NoGo trials for 123 individuals that took part in an experiment with emotional facial expressions were examined by computing ERPs and by performing wavelet analyses on the raw EEG and on ER Ps. After performing several multiple regression analyses, we obtained the following answers. First, the P1, N1, and P2 components can by and large be described as transient oscillations in the α and θ bands. Secondly, it appears possible to estimate the separate contributions of EP, BP, and IP to TP, and importantly, the contribution of IP is mostly larger than that of EP. Finally, no strong support was obtained for either the Phase Reset or the Evoked model. Recent models are discussed that may better explain the relation between raw EEG and ERPs.