β-bursting as a sensitive neural marker of inhibitory control in healthy older adults: a linear mixed-effects modelling and threshold-free cluster approach
Warden, A. C. M.; Cruse, D.; McAllister, C.; MacDonald, H. J.
Show abstract
Inhibitory control is essential for adaptive behaviour and declines with age, yet the underlying neural dynamics remain poorly understood. The {beta}-rhythm (15-29 Hz) is thought to reflect inhibitory signalling within the fronto-basal ganglia network. Recent evidence suggests that transient {beta}-bursts support inhibitory performance, often masked by conventional analyses of trial-averaged {beta}-power. To reveal the link between trial-by-trial {beta}-bursting and inhibition, we applied a recently developed analysis framework combining linear mixed-effects modelling (LMM) with threshold-free cluster enhancement (TFCE) during response inhibition and initiation in older adults. Twenty healthy older adults performed a bimanual anticipatory response inhibition task, while electroencephalography and electromyography were recorded to capture {beta}-activity ({beta}-burst rate/duration; averaged {beta}-power) and muscle bursting dynamics, respectively. Our analysis revealed distinct {beta}-bursting signatures absent in averaged {beta}-power data. Following the stop-signal, parieto-occipital {beta}-bursting presented before a temporal cascade from attentional to inhibitory processes. In addition to expected right fronto-central and bilateral sensorimotor activity, we observed left prefrontal {beta}-bursting, indexing broader inhibitory network engagement during bimanual response inhibition. Moreover, we established a functional link between right sensorimotor {beta}-bursting and muscle bursts during stopping, indicating rapid cortical suppression of initiated motor output. These results help clarify the mechanistic role of {beta}-oscillations and underscore the sensitivity of {beta}-bursting to both the timing and context of inhibitory demands in healthy older adults. Future research will help establish the potential of {beta}-bursting, combined with LMM-TFCE analysis, as a clinically relevant marker of impulse control dysfunction. Significance statementOur novel application of an advanced statistical framework revealed distinct spatiotemporal {beta}-bursting patterns during response inhibition and response withholding in healthy older adults, which were not captured by averaged {beta}-power. Identifying a further link between cortical {beta}-bursting and muscle-level suppression, the findings offer a mechanistic account of how the brain halts action in real time in older adults. This work provides a sensitive, trial-level framework for studying {beta}-bursting measures in general, as well as inhibitory control across aging and clinical populations.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Transcallosal generation of phase aligned beta-bursts underlies TMS-induced interhemispheric inhibition 95%
- Validating genuine changes in Heartbeat Evoked Potentials using Pseudotrials and Surrogate Procedures 95%
- Uncovering the Neural Correlates of the Urge-to-Blink: A Study Utilising Subjective Urge Ratings and Paradigm Free Mapping 95%
Similar papers in this journal
- Mobile EEG reveals functionally dissociable dynamic processes supporting real-world ambulatory obstacle avoidance: Evidence for early proactive control 95%
- Cholinergic modulation of motor sequence learning 95%
- The Readiness Potential reflects the internal source of action, rather than decision uncertainty 94%
Similar papers in this journal
- Inhibitory control of gait initiation in humans: an electroencephalography study 97%
- Cortical, muscular, and kinetic activity underpinning attentional focus strategies during visuomotor control 95%
- Reconsidering electrophysiological markers of response inhibition in light of trigger failures in the stop-signal task 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.