Causal evidence that intrinsic beta-frequency is relevant for enhanced signal propagation in the motor system as shown through rhythmic TMS

Evidence is accumulating that beta-band neural oscillations (~13–30 Hz) are related to temporal prediction in the context of auditory rhythm perception. Since beta oscillations are faster than any musical rhythms in which we would be interested (more in the 1–5 Hz range), they can’t phase lock to the temporal structure of auditory rhythms. Instead, fluctuations in beta power synchronize with auditory rhythms. For example, during listening to an isochronous tone sequence (think, a metronome), beta oscillations weaken, or desynchronize (or both), after each tone, but then get stronger, or resynchronize (or both) in anticipation of the next tone. This pattern scales with tempo, meaning that the faster the tone sequence goes, the faster beta power fluctuates. And if you take away the temporal structure by randomizing the inter-tone intervals, patterned beta-power fluctuations go away. Beta power also differs for individual tones that are imagined as emphasized versus those that are not, suggesting a role in beat/meter perception. Beta oscillations are often linked to the motor system, and become pathological in Parkinson’s disease, which is meaningful because Parkinson’s patients (in addition to having well-described motor problems) have trouble discriminating rhythms with a regular beat.

The motor system (including the basal ganglia) is thought to be important for rhythm and beat perception. Given the tight association between the motor system and beta-band neural oscillations, one interesting possibility is to interfere with beta oscillations using non-invasive brain stimulation in a way that would be predicted to disrupt (or enhance) rhythm and beat perception. Which brings me to a recent paper by Romei et al., which actually has nothing to do with rhythm perception (but potentially opens a lot of doors for those of us who are interested in the topic).

The authors first measured the individual peak beta frequency for each participant during finger tapping (this by itself is very cool, as relatively few papers investigate what individual differences in neural oscillator properties actually mean). Then, they applied rhythmic transcranial magnetic stimulation (rTMS) to left M1. The critical thing is that rTMS was applied at the individual peak frequency, or at higher and lower frequencies that still fell within the beta range (±3 Hz, ± 6 Hz). Simultaneously, both EEG (electroencephalography) and EMG (electromyography) were measured (the latter from the right hand).

Cortical beta oscillations measured by EEG were stronger (power) and more synchronized with the rTMS (phase locking) when the rTMS matched the individual peak beta frequency (less power and less synchronization for off-best-frequency rTMS, and even less for sham stimulation). I interpret this to mean that the individual peak beta frequency reflects the resonance frequency of a neural oscillator, which can be enhanced by even weak (sub-threshold) noninvasive brain stimulation. EMG data showed a similar pattern (weaker, yes, but I’m not the authors, so I’m free to interpret the p=.07 and p=.11 interaction effects [in the theoretically predicted pattern] as meaningful). That is, EMG power and phase locking were enhanced in particular when rTMS was applied to motor cortex at the individual peak frequency. The authors interpret this finding to mean that signal propagation from the central to the peripheral motor system is dependent on beta oscillations, and proceeds most efficiently at the individual peak frequency within the beta band. Finally, coupling between EEG and EMG (cortico-spinal coherence) was observed basically exclusively for the situation where rTMS was applied at the individual peak frequency.

These results are great news (and a lesson) for those of us interested in the role of beta oscillations in rhythm and beat perception. We can use noninvasive brain stimulation techniques like rTMS to modify beta oscillations during listening to different types of rhythmic stimuli (which area we stimulate, and whether M1 is necessarily the right target for this type of question are issues that I’m not discussing here). And then we can start to ask questions about the causal role and dynamics of beta oscillations in rhythm and beat perception. The lesson here is that blindly applying a catch-all 20-Hz beta stimulation might lead to null effects, and it wouldn’t necessarily be fair to treat those null effects as evidence of absence. Instead, this paper demonstrates that it’s important to take into account individual differences in neural oscillator properties for our manipulations to work the way we’d like them to. (And I’d argue that this is a lesson that can be extended beyond this particular study or frequency band – the more we start to understand when and why these individual differences are important, the faster we’ll be able to make gains in understanding what neural oscillations in particular frequency bands are doing for us in what situations.)

–source article: Romei, Bauer, Brooks, Economides, Penney, Thut, Driver, & Bestmann. Causal evidence that intrinsic beta-frequency is relevant for enhanced signal propagation in the motor system as shown through rhythmic TMS. NeuroImage.

Author: Argie