Abstract
An emerging perspective describes beta-band (15−28 Hz) activity as consisting of short-lived high-amplitude events that only appear sustained in conventional measures of trial-average power. This has important implications for characterising abnormalities observed in beta-band activity in disorders like Parkinson’s disease. Measuring parameters associated with beta-event dynamics may yield more sensitive measures, provide more selective diagnostic neural markers, and provide greater mechanistic insight into the breakdown of brain dynamics in this disease. Here, we used magnetoencephalography in eighteen Parkinson’s disease participants off dopaminergic medication and eighteen healthy control participants to investigate beta-event dynamics during timed movement preparation. We used the Hidden Markov Model to classify event dynamics in a data-driven manner and derived three parameters of beta events: (1) beta-state amplitude, (2) beta-state lifetime, and (3) beta-state interval time. Of these, changes in beta-state interval time explained the overall decreases in beta power during timed movement preparation and uniquely captured the impairment in such preparation in patients with Parkinson’s disease. Thus, the increased granularity of the Hidden Markov Model analysis (compared with conventional analysis of power) provides increased sensitivity and suggests a possible reason for impairments of timed movement preparation in Parkinson’s disease.
Highlights
Beta-band activity (15–28 Hz) is one of the most prevalent frequency-specific patterns of activity across both cortical and subcortical areas in the human brain
This study investigated changes in high-amplitude beta events during temporally-cued movement preparation in Parkinson’s disease
We show that beta-state interval time is the main variable that increases between stronger vs. weaker movement preparation, providing a more mechanistic insight into beta activity during task performance than accounts that focus on beta power alone
Summary
Beta-band activity (15–28 Hz) is one of the most prevalent frequency-specific patterns of activity across both cortical and subcortical areas in the human brain. It has been suggested that frequency-specific patterns of brain activity consist of short-lived, isolated, high-amplitude events (Feingold et al, 2015; Jones, 2016; Lundqvist et al, 2016; Sherman et al, 2016; Vidaurre et al, 2016; Shin et al, 2017; Tinkhauser et al, 2017a, 2017b; 2018; van Ede et al, 2018; Little et al, 2019), which only appear sustained when averaged across trials By adopting this perspective, it is possible to consider multiple parameters that may influence overall beta power, like changes in event amplitude, in event duration, or in the time between subsequent events (see Fig. 1). To investigate highamplitude beta events we need methods that can distinguish such events from surrounding ongoing (lower amplitude) activity with high temporal resolution, on a single-trial level
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