Oscillatory Bursting as a Mechanism for Temporal Coupling and Information Coding

Abstract

Even the simplest cognitive processes involve interactions between cortical regions. To study these processes, we usually rely on averaging across several repetitions of a task or across long segments of data to reach a statistically valid conclusion. Neuronal oscillations reflect synchronized excitability fluctuations in ensembles of neurons and can be observed in electrophysiological recordings in the presence or absence of an external stimulus. Oscillatory brain activity has been viewed as sustained increase in power at specific frequency bands. However, this perspective has been challenged in recent years by the notion that oscillations may occur as transient burst-like events that occur in individual trials and may only appear as sustained activity when multiple trials are averaged together. In this review, we examine the idea that oscillatory activity can manifest as a transient burst as well as a sustained increase in power. We discuss the technical challenges involved in the detection and characterization of transient events at the single trial level, the mechanisms that might generate them and the features that can be extracted from these events to study single-trial dynamics of neuronal ensemble activity.

When the time is right: Temporal dynamics of brain activity in healthy aging and dementia

“…Dynamic neural activity can be investigated, not just in individual brain areas or couplings, but also across entire networks. In order to evaluate brain networks' characteristics over time, Hidden Markov Models represent brain activity as a succession of discrete states (i.e., characterized by matrices of specific couplings between brain regions), with each state in the finite set of states being dominant at certain time points and not at others (e.g., Tal et al, 2020). Graph theory analyses have also been conducted to examine changes with aging (e.g., Van Straaten & Stam, 2013;Hinault et al, 2021).…”

Detection and analysis of cortical beta bursts in developmental EEG data

“…Classical analyses of frequency-specific activity in magneto/electroencephalography (M/EEG) data from developmental and adult populations start with the assumption that such activity is oscillatory, with amplitude time series in various frequency bands then averaged over trials. Recently, however, it has become clear that such trial-averaged analyses can mask the temporal structure of frequency-specific activity in individual trials, and moreover, that activity in certain frequency bands may occur as discrete, transient bursts rather than as oscillations ( Jones, 2016 , Lundqvist et al, 2018 , Quinn et al, 2019 , Seedat et al, 2020 , Sherman et al, 2016 , Tal et al, 2020 , van Ede et al, 2018 ).…”

“…periodicity, a primary characteristic of oscillatory activity) of frequency-specific activity and for detecting bursts of activity; and 2) to apply these methods to infant and adult EEG datasets collected during the same grasping task in order to provide evidence for infant sensorimotor beta bursts, and to compare burst properties and their modulation in infants and adults. Specifically, we illustrate the use of power spectral densities combined with lagged coherence to determine frequency band limits and examine the rhythmicity of beta band activity, and a variant of the widely-used ‘p-episode method’ ( Caplan et al, 2001 , Hannah et al, 2020 , Little et al, 2019 , Lundqvist et al, 2016 , Sherman et al, 2016 , Shin et al, 2017 , Tal et al, 2020 , Wessel, 2020 ) for the identification of beta burst timing, duration, and amplitude. All source code used for tutorial and subsequent analyses have been made publically available.…”

Taxonomy of neural oscillation events in primate auditory cortex

“…Intrinsic cortical oscillations consist of both rhythmic and brief, pulse-like neuronal activity patterns, which co-occur in electrophysiological recordings [1][2][3]. These patterns manifest differently across multiple frequency bands and different brain regions during various task-dependent brain states [4].…”

“…The presence of high spectral power in a neural signal does not necessarily indicate an intrinsic oscillation. This is true particularly for one-or two-cycle events which could arise stochastically [2,3,20]. For example, a high-amplitude, single-cycle waveform with 50 ms duration could be mistaken for a 20 Hz oscillation [2,21].…”