Reduction of spontaneous cortical beta bursts in Parkinson’s disease is linked to symptom severity

Abstract

<jats:title>Abstract</jats:title><jats:p>Parkinson’s disease is characterized by a gradual loss of dopaminergic neurons, which are associated with altered neuronal activity in the beta band (13-30 Hz). Assessing beta band activity typically involves transforming the time-series to get the power of the signal in the frequency-domain. Such transformation assumes that the time-series can be reduced to a combination of steady-state sine-and cosine waves. However, recent studies have suggested that this approach masks relevant biophysical features in the beta band activity—for example, that the beta band exhibits transient bursts of high-amplitude activity.</jats:p><jats:p>In an exploratory study we used magnetoencephalography (MEG) to record cortical beta band activity to characterize how spontaneous cortical beta bursts manifest in Parkinson’s patients ON and OFF dopaminergic medication, and compare this to matched healthy controls. From three minutes of MEG data, we extracted the time-course of beta band activity from the sensorimotor cortex and characterized high-amplitude epochs in the signal to test if they exhibited burst like properties. We then compared the rate, duration, inter-burst interval, and peak amplitude of the high-amplitude epochs between the Parkinson’s patients and healthy controls.</jats:p><jats:p>Our results show that Parkinson’s patients OFF medication had a 6-17% lower beta bursts rate compared to healthy controls, while both the duration and the amplitude of the bursts were the same for Parkinson’s patients and healthy controls and medicated state of the Parkinson’s patients. These data thus support the view that beta bursts are fundamental underlying features of beta band activity, and show that changes in cortical beta band power in PD can be explained primarily by changes in the underlying burst rate. Importantly, our results also revealed a relationship between beta bursts rate and motor symptom severity in PD: a lower burst rate scaled with increased in severity of bradykinesia and postural/kinetic tremor. Beta burst rate might thus serve as neuromarker for Parkinson’s disease that can help in the assessment of symptom severity in Parkinson’s disease or evaluate treatment effectiveness.</jats:p>

Neural oscillatory activity serving sensorimotor control is predicted by superoxide-sensitive mitochondrial redox environments

“…Peak voxel values (i.e., pseudot) were then extracted from the left M1 per time bin, and these were used for subsequent statistical modeling. In addition, time series were extracted from the peak voxel and used to compute the beta event count and peak event power, which are the most-common metrics in the beta bursting framework ( 35 , 39 ). These two bursting parameters were used in statistical models parallel to those computed for the more common beta oscillatory strength (see Statistical Analysis in the Materials and Methods section below).…”

“…The notion that such beta oscillations should be conceptualized in a bursting framework has been studied across sensory modalities (e.g., somatosensory and motor), species (e.g., animals and humans), and clinical populations (e.g., Parkinson’s disease). Further, premovement (i.e., baseline) burst event parameters (e.g., peak event power and event count/rate) may be a consistent modulator of behavioral performance ( 34 – 39 ). Thus, to complement our analysis of the widely studied, temporally sustained beta oscillations, we conducted supplementary analyses within the beta-burst framework.…”

A Single Case Feasibility Study of Sensorimotor Rhythm Neurofeedback in Parkinson’s Disease

“…Burst characteristics were calculated offline with the method described in Vinding et al (2020) . One threshold was used for each ROI of the source signal (see Supplementary Methods and Supplementary Figure 1 in Supplementary Material ).…”

New methods for oscillation analyses push new theories of discrete cognition

“…While burst analysis highlights the nature of cognitive processes, it can also be a useful clinical tool as it allows more precise diagnostics of abnormal dynamics in cognitive diseases, such as Parkinson's disease (Follett et al., 2010; Kühn et al., 2006; Lofredi et al., 2019; McCarthy et al., 2011; Schmidt et al., 2019; Tinkhauser et al., 2017; Vinding et al., 2020). As in the above working memory experiments, it is possible to examine in greater detail the underlying mechanisms for the observed difference in beta power between patients and healthy controls (see the special section “Advantages of burst analyses”).…”