As interest in the mechanistic roles of neural oscillations and neural entrainment in perception and cognition increases, so does interest in the bounding conditions for entrainment. The degree of temporal regularity is an intuitive feature to consider – entrainment to a completely periodic stimulus is clear, but entrainment to a completely structureless stimulus is impossible by definition. Somewhere in between are behaviorally relevant stimuli, such as music and speech in the auditory modality or lip movements and gestures in the visual modality. A number of papers have used time-domain measures that allow for more dynamic measures of entrainment, such cerebro-acoustic phase lag or mutual information. However, approaches making use of “steady-state evoked potentials” or “frequency-tagging” methods to measure entrainment typically transform long epochs of time-domain neural data to the frequency domain and use the height of peaks in the frequency spectrum to index the strength of entrainment at the corresponding rate. This approach effectively discards dynamics of entrainment and, as demonstrated in a new paper by Keitel, Thut, and Gross in NeuroImage, may lead to underestimations of entrainment strength when the stimulus rate varies over time.
The authors constructed well-controlled visual stimuli for which contrast fluctuated independently in the two hemifields within theta- (4–7 Hz), alpha- (8–13 Hz), or beta-band (14–20 Hz) ranges. At the same time, the frequency of modulation in each hemifield was itself modulated according to random, continuous modulation functions that were uncorrelated across hemifields, leading to “quasi-rhythmic” visual stimulation (and perhaps the first time “quasi-rhythmic” has been concretely, operationally defined!). An attention manipulation (“attend left” vs. “attend right”) allowed the authors to compare (using EEG) entrainment strength and strength of attentional modulation for quasi-rhythmic stimuli with modulations in each frequency band and to compare these data to fixed-frequency sinusoidal modulations in the alpha range (10 Hz on the left and 12 Hz on the right).
Although there are very many interesting findings reported in the paper (and I encourage anyone reading this blog to check out the paper!), I’d like to focus on an important methodological issue that the paper confronts as well as its implications. A critical feature of quasi-rhythmic stimuli in any modality is that the instantaneous frequency wanders around over time. For that reason, converting a whole time series of electrophysiological data to the frequency domain reduces the signal-to-noise ratio for any single frequency compared to fixed-frequency stimulation (and violates the stationarity assumption of the Fourier transform). The authors elegantly demonstrate this by analyzing EEG responses to quasi-rhythmic stimulation in two ways. First, they use an approach based on calculating the cross-coherence between short segments of narrow-band EEG (multi-taper method) and the corresponding segments of the stimulus. This technique leaves temporal dynamics intact, and demonstrates entrainment of frequency-band-specific neural activity to quasi-rhythmic stimuli. Analyzing the same EEG data using an approach that considers only power of short data segments and then averages those frequency-domain representations (if I’ve read that correctly; ignoring the fact that frequency may change over the time course of stimulation) failed to reveal entrainment, and instead looked like the power spectrum that might be expected during a resting-state measurement, regardless of the frequency range of the visual stimulation.
This demonstration potentially reconciles conflicting results in the literature regarding strength of entrainment to perfectly regular versus quasi-rhythmic stimuli. Moreover, this finding highlights the importance of not ignoring the dynamic, nonstationary nature of behaviorally relevant stimuli and the neural activity that synchronizes to such stimuli. Approaches focusing on steady-state evoked potentials and frequency-tagging often convert long stretches of time-domain data to the frequency domain without considering dynamics – which may be a close enough approximation when the stimulus has a single frequency, but certainly doesn’t represent the way that brains work generally or how entrainment to quasi-rhythmic, behaviorally relevant stimuli works more specifically. In order to really understand neural “dynamics” and how they are related to perception and cognition, making use of analysis techniques that don’t obscure dynamics will be critical. I’m optimistic that demonstrations like the current one – that not all analysis approaches preserve the dynamic nature of entrainment to quasi-rhythmic stimuli and that this matters for interpretation – will allow us to better understand the roles of neural entrainment in perception and cognition in naturalistic situations.
–Molly Henry, University of Western Ontario