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bioRxiv2020

EEG Alpha Power Predicts the Temporal Sensitivity of Multisensory Integration

Pre-stimulus EEG oscillations, especially in the alpha range (8-13 Hz), can affect the integration of stimulus features into a coherent percept. The effects of alpha power are often explained in terms of alphas inhibitory functions, whereas effects of alpha frequency have bolstered theories of discrete perceptual cycles, where the length of a cycle, or window of integration, is determined by alpha frequency. Such studies typically employ visual detection paradigms with near-threshold or even illusory stimuli. It is unclear whether such results generalize to above-threshold stimuli. Here, we recorded electroencephalography, while measuring temporal discrimination sensitivity in a temporal order judgement task using above-threshold auditory and visual stimuli. We tested whether pre-stimulus oscillations predict audio-visual temporal discrimination sensitivity on a trial-by-trial basis. By applying a jackknife procedure to link single-trial pre-stimulus oscillatory power and instantaneous frequency to psychometric measures, we identified two highly overlapping clusters over posterior sites. One where lower alpha power was associated with higher temporal sensitivity of audiovisual discrimination, and another where higher instantaneous alpha-frequency predicted higher temporal discrimination sensitivity. A follow-up analysis revealed that these effects were not independent, and that the effect of instantaneous frequency could be explained by power modulations in the lower alpha band. These results suggest that temporal sensitivity for above-threshold multisensory stimuli changes spontaneously from moment to moment and is likely related to fluctuations in cortical excitability. Moreover, our results caution against interpreting instantaneous frequency effects as independent from power effects if these effects overlap in time and space.

neuroscience↗

bioRxiv2018

Frequency and power of human alpha oscillations drift systematically and independently 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 hour), 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 (~8-10Hz), while components with unique changes oscillated primarily in the higher alpha-band (~9-13Hz). 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.

neuroscience↗