Detection and analysis of cortical beta bursts in developmental EEG data

Abstract

Developmental EEG research often involves analyzing signals within various frequency bands, based on the assumption that these signals represent oscillatory neural activity. However, growing evidence suggests that certain frequency bands are dominated by transient burst events in single trials rather than sustained oscillations. This is especially true for the beta band, with adult ‘beta burst’ timing a better predictor of motor behavior than slow changes in average beta amplitude. No developmental research thus far has looked at beta bursts, with techniques used to investigate frequency-specific activity structure rarely even applied to such data. Therefore, we aimed to: i) provide a tutorial for developmental EEG researchers on the application of methods for evaluating the rhythmic versus transient nature of frequency-specific activity; and ii) use these techniques to investigate the existence of sensorimotor beta bursts in infants. We found that beta activity in 12-month-olds did occur in bursts, however differences were also revealed in terms of duration, amplitude, and rate during grasping compared to adults. Application of the techniques illustrated here will be critical for clarifying the functional roles of frequency-specific activity across early development, including the role of beta activity in motor processing and its contribution to differing developmental motor trajectories.

Diverse beta burst waveform motifs characterize movement-related cortical dynamics

“…Studies of sensorimotor activity in developing populations typically focus on the alpha or mu frequency bands, and little is known about the development of beta band activity in infancy (Cuevas et al, 2014; Perone & Gartstein, 2019). It has recently been shown that, similar to alpha, there are age-related changes in beta frequency and power (He et al, 2019; Johnson et al, 2019; Rayson et al, 2022; Trevarrow et al, 2019) from infancy to adulthood. In infancy, the peak beta frequency is 15 Hz (Rayson et al, 2022), but movement-related artifacts from facial and arm movements also occur around this frequency (Georgieva et al, 2020).…”

“…It has recently been shown that, similar to alpha, there are age-related changes in beta frequency and power (He et al, 2019; Johnson et al, 2019; Rayson et al, 2022; Trevarrow et al, 2019) from infancy to adulthood. In infancy, the peak beta frequency is 15 Hz (Rayson et al, 2022), but movement-related artifacts from facial and arm movements also occur around this frequency (Georgieva et al, 2020). This makes it extremely difficult to separate movement-related neural activity from artifactual activity using TF-based analyses.…”

“…However, it is becoming increasingly apparent that sensorimotor beta activity does not occur as sustained oscillations in single trials, but as transient burst events that occur with varying probability in time (Cagnan et al, 2019;Diesburg et al, 2021;Feingold et al, 2015;Haufler et al, 2022;Howe et al, 2011;Karvat et al, 2020;Kosciessa et al, 2020;Little et al, 2019;Lofredi et al, 2019;Sherman et al, 2016;Shin et al, 2017;Sporn et al, 2020;Torrecillos et al, 2018;Wessel, 2020). These variations in burst probability are reflected in the slow changes in average spectral power (Feingold et al, 2015;Howe et al, 2011;Little et al, 2019;Rayson et al, 2022;Sherman et al, 2016), but suggest that single trial beta activity is more dynamic than classically thought. The rate and timing of single-trial beta events are tightly linked to behavior (Diesburg et al, 2021;Echeverria-Altuna et al, 2021;Enz et al, 2021;Feingold et al, 2015;Haufler et al, 2022;Heideman et al, 2020;Howe et al, 2011;Karvat et al, 2020;Khawaldeh et al, 2020;Kosciessa et al, 2020;Law et al, 2022;Little et al, 2019;Sherman et al, 2016;Shin et al, 2017;Sporn et al, 2020;Torrecillos et al, 2018;Walsh et al, 2022;Wessel, 2020;…”

Early development of electrophysiological activity: contribution of periodic and aperiodic components of the EEG signal

“…Also, especially in infancy, oscillatory brain activity appears in bursts, and is not constant across the entire register (Jones, 2016). Therefore, testing the appearance of those bursts and computing the power with them would probably increase the reliability of results and give other parameters of interest (Cole & Voytek, 2019; Ostlun et al, 2022; Rayson, et al, 2022). Finally, as infants protocols incorporate an active baseline, registering peripheral and behavior measures related to infants’ cognitive state could give us information and increase the comparability between infant and other developmental studies (Orekhova, Stroganova, & Posikera, 2001; Xie, Mallin, & Richards, 2018).…”

Overcoming harmonic hurdles: genuine beta-band rhythms vs. contributions of alpha-band waveform shape

“…Recently, identification of harmonics via the instantaneous frequency has been put forward (Fabus et al, 2022). The distinction between harmonic and genuine beta bursts is especially of interest for analyses performed in sensor space, as often done in developmental data (Rayson et al, 2022). If a rhythm is attributed to a harmonic, it may be beneficial to continue the analysis using the underlying base-frequency band instead of the harmonic frequency-band for further analyses, in order to profit from a much higher signal-to-noise ratio of the base frequency rhythm.…”