Diverse beta burst waveform motifs characterize movement-related cortical dynamics

• Published in: Progress in neurobiology 2023-09, Vol.228, p.102490-102490, Article 102490
• Main Authors: Szul, Maciej J., Papadopoulos, Sotirios, Alavizadeh, Sanaz, Daligaut, Sébastien, Schwartz, Denis, Mattout, Jérémie, Bonaiuto, James J.
• Format: Article
• Language: English
• Subjects: Beta bursts; Biological Physics; Life Sciences; MEG; Neurons and Cognition; Physics; Sensorimotor cortex
• ISSN: 0301-0082; 1873-5118
• DOI: 10.1016/j.pneurobio.2023.102490

Classical analyses of induced, frequency-specific neural activity typically average band-limited power over trials. More recently, it has become widely appreciated that in individual trials, beta band activity occurs as transient bursts rather than amplitude-modulated oscillations. Most studies of beta bursts treat them as unitary, and having a stereotyped waveform. However, we show there is a wide diversity of burst shapes. Using a biophysical model of burst generation, we demonstrate that waveform variability is predicted by variability in the synaptic drives that generate beta bursts. We then use a novel, adaptive burst detection algorithm to identify bursts from human MEG sensor data recorded during a joystick-based reaching task, and apply principal component analysis to burst waveforms to define a set of dimensions, or motifs, that best explain waveform variance. Finally, we show that bursts with a particular range of waveform motifs, ones not fully accounted for by the biophysical model, differentially contribute to movement-related beta dynamics. Sensorimotor beta bursts are therefore not homogeneous events and likely reflect distinct computational processes. •Activity in the Beta frequency band (13-33 Hz) is dominated by transient events (bursts).•Beta bursts are not homogenous, displaying great variability of the underlying waveform.•Diversity of those events exceeds characteristics found in time-frequency representation of the signal.•Biophysical modelling shows that variability of synaptic inputs contributes to variability in the burst waveform shape.•Novel, adaptive, single-trial burst detection algorithm allows to find a greater range of events with differing amplitudes.•We found waveform shape motifs that are dynamically regulated by the task demands.•Counterintuitively, some of the Beta burst waveform shape motifs increase their rate during the movement.•Varied response rates of described waveform motifs likely reflect a plethora of functions ascribed to Beta band activity.