When brain rhythms aren't ‘rhythmic’: implication for their mechanisms and meaning
Introduction
Oscillations in electrical signatures of neural activity occur in almost all structures in the brain across electrophysiological scales ranging from rhythmic activities in single neurons to large-scale population dynamics as measured with magneto-encephalography and electro-encephalography (MEG/EEG). Modulations in rhythms correlate with behaviors such as perception, attention, memory and motor action [1, 2, 3, 4, 5•, 6, 7, 8, 9]. Their disruption is a biomarker of several disease states, most notably Parkinson's Disease [10], autism [11] and schizophrenia [12]. Causal manipulations of brain rhythms with techniques such as optogenetic [13], transcranial alternating current stimulation (tACS) [14], and repetitive transcranial magnetic stimulation (rTMS) [15] have shown that oscillatory entrainment can actively modulate behavior. While studies connecting rhythms to function are vast and rapidly growing, our understanding of their causal and/or epiphenomenal role in information processing is still highly debated. Some view rhythms as essential to temporally coordinating activity necessary for information processing [16, 17, 18, 19, 20], while others suggest rhythms are an epiphenomenal reflection of other key processes [21, 22, 23, 24, 25].
Essential to defining the role of rhythms in function is an accurate interpretation of the cellular and network level mechanisms underlying the neurophysiological data. In our view, this interpretation relies on a detailed understanding of the nature of the rhythm in the un-averaged, unfiltered time-domain signal. Typically, many levels of filtering and averaging are applied to time series data before a connection between rhythms and function is made, and it is uncommon for raw data to be shown in any form. While such analyses are often necessary to limit the scope of question, and/or show statistical significance of results, we argue here that these practices can lead to misinterpretations in tying a frequency band of activity to a particular mechanism or hypothesized function.
We review several lines of evidence investigating unaveraged data that demonstrate brain activity that shows functionally relevant changes in the power spectrum of averaged data is not always ‘rhythmic’, defined as exhibiting repeated cycles of activity with a reliable period. First, we review recent studies that reveal rhythmic activity on individual trials is often transient [26•, 27••, 28••, 29••]. The accumulation of these transient activations across trials results in a prolonged high-power oscillation in the average, creating the illusion of a sustained rhythm. As such, differences in averaged power across behavioral conditions can reflect a change in the accumulation of transients across trials rather than a change in the net amplitude or duration of the oscillations [28••, 29••].
Second, we review evidence demonstrating that peaks at specific frequencies in the spectrum can be created by dominant waveform features in the time domain rather than periodic ‘sinusoidal’ type oscillations. While the waveform may retain oscillatory components, peaks in the spectral domain can also emerge as a spurious consequence of specific waveform shapes [30•, 31••, 32••, 33••]. Lastly, we discuss how these findings imply a need to develop a new generation of analysis methods to study rhythms and suggest several techniques that may help uncover their meaning for function.
Section snippets
Sustained and high spectral power in the average can reflect the accumulation of transient ‘rhythmic’ events across trials
When we think of rhythms, we typically imagine repeated cycles of oscillatory activity. Indeed, there are many instances of rhythms in the brain where this is the case, including the well-known examples of eyes-closed (7–14 Hz) alpha rhythms over occipital cortex [34], sleep rhythms [35] and hippocampal (4–8 Hz) theta rhythms [17, 20, 36]. However, recent evidence shows that in many cases brain signals considered as belonging to a frequency-defined class of brain rhythms do not represent
Dominant waveform features can create rhythms that do not represent repeated bouts of activity with direct implications on their mechanism
Here, we review evidence suggesting features of the raw time-domain signal are also essential to consider when interpreting the mechanisms or meaning of a rhythm. Vastly different waveform shapes, created by different underlying mechanisms, can create identical peaks in spectral power and/or spurious high power activity.
Implications for next generation studies of rhythms
The reviewed results suggest several new directions to pursue in our quest to discover the mechanism and meaning of brain rhythms. First, the transient and stochastic nature of some rhythms (Figure 1) is reminiscent of a stochastic point process, albeit on a slower time scale than cell spiking. Thus, methods to study neural dynamics with point process techniques, typically applied to the study of spiking interactions, could be adapted to the study of rhythms. Several techniques have been
Conclusions
Defining a functional role for brain rhythms relies on accurate knowledge of the temporal features of the signals from which they emerge. We reviewed several studies that showed rhythms are not always ‘rhythmic’ in the sense of multiple repeated cycles of activity, and that vastly different waveforms can create similar peaks in spectral power. The implication of these facts on the interpretation of the mechanisms and thus the meaning of rhythms is profound. They emphasize the necessity for the
Conflict of interest
Nothing declared.
References and recommended reading
Papers of particular interest, published within the period of review, have been highlighted as:
• of special interest
•• of outstanding interest
Acknowledgements
Work and writing were supported by the National Institutes of Mental Health (R01MH106174) and the National Science Foundation (CRCNS1131850), the Brown Institute for Brain Sciences and the Norman Prince Neurosciences Institute. This material is based upon work supported in part by the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Rehabilitation Research and Development Service, Project N9228-C. The views expressed in this article are those
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