Modulation of Beta Bursts in the Subthalamic Nucleus Predicts Motor Performance

Abstract

Considerable evidence suggests a role of beta-band oscillations in voluntary movements. However, most of the studies linking beta power to motor performance are based on data averaged across trials that ignore the fast dynamics of oscillatory activity and trial-to-trial variations in motor responses. Recently, emphasis has shifted from the functional implications of the mean beta power to the presence and nature of episodic bursts of beta activity. Here we test the hypothesis that beta bursts, though short in duration in more physiological state, may help explain spontaneous variations in motor behavior of human adults at the single-trial level. To this end, we recorded local field potential activity from the subthalamic nucleus of parkinsonian patients of both genders whose motor behavior had been normalized as far as possible through treatment with the dopamine prodrug, levodopa. We found that beta bursts present in a time-limited window well before movement onset in the contralateral subthalamic nucleus reduce the peak velocity of that movement and that this effect is further amplified by the amplitude of the burst. Additionally, prolonged reaction times are observed when bursts occur immediately after the GO cue. Together, these results suggest that the modulation of the timing and amplitude of beta bursts might serve to dynamically adapt motor performance. These results offer new insight in the pathology of Parkinson's disease, and suggest that beta bursts whose presence and nature are modulated by context may have a physiological role in modulating behavior.SIGNIFICANCE STATEMENT Beta oscillations (∼13-30 Hz) have been increasingly interpreted as transient bursts rather than as rhythmically sustained oscillations (Feingold et al., 2015). Prolonged and increased probability of beta bursts in the subthalamic nucleus correlates with the severity of motor impairment in Parkinson's disease (Tinkhauser et al., 2017a, b). However, it remains unclear whether beta bursts act to modify motor performance on a trial-by-trial basis under more physiological condition. Here, we found that, according to the time window in which they fall, beta bursts reduced the velocity of the forthcoming movement or prolonged the reaction time. These results offer new insight in the pathology of Parkinson's disease and also suggest that the modulation of beta bursts might serve to dynamically adapt motor performance.

Keywords: Parkinson's disease; beta bursts; beta oscillations; motor performance; reaching movement; subthalamic nucleus.

Copyright © 2018 the authors 0270-6474/18/388905-13$15.00/0.

Figures

Figure 1.

Task and behavioral results. A, Visual stimuli in the joystick task and timeline of each trial. Single-trial beta oscillations were analyzed in the premovement period, from −600 ms before the GO cue to −200 ms before movement onset (yellow shading). The dashed circle outlines were not visible to the subject. During movement, only the endpoint feedback of the red cursor position was shown. B, Velocity profiles averaged across all trials for each subject (gray) and the grand average computed across all subjects (black). The time is normalized between two consecutive GO cues (100%) to average trials of different duration. Inset, How the RT and the amplitude of the velocity peak (VelPA) were defined for each trial. C, Mean peak velocity of each subject and their CV. D, Velocity profiles of all individual trials and all subjects (n = 506 trials, 12 subjects) relative to the GO cue. E, Mean RTs of each subject and their CV.

Figure 2.

Definition of beta bursts. A, Single-trial data for 1 subject sorted by RTs. The beta power time courses were computed by averaging over a 6 Hz frequency band centered on the individual beta frequency peak. Then bursts were defined as beta amplitude exceeding the 75th percentile threshold with a minimum duration of 2 cycles. Black dots represent GO cue. Red dots represent movement onset. B, Positive correlation between the burst duration and amplitude in 1 example subject (same as for A; r = 0.56, p < 0.001). C, Mean burst duration and amplitude and positive correlations between the 2 for the 12 subjects. For all plots, only the contralateral STN was considered.

Figure 3.

Effect of bursts before and overlapping with the GO cue on the amplitude of the peak velocity and impact of burst detection threshold. A, Mean peak velocity in burst trials normalized (z score) to the mean velocity of all trials for all subjects. A negative value indicates a reduction of peak velocity in burst trials. Trials are divided according to the presence of a burst in a 600 ms window before the GO cue where bursts are only included if more than half of their duration falls in the time window. Bursts were defined with the default threshold of 75th percentile. B, Impact of burst detection threshold on the peak velocity reduction. For each subject, the velocity peak of each trial is normalized (z scores) as described for A. C, Estimated effects and 95% CIs derived from the linear mixed-effects models testing the impact of bursts occurring before or overlapping with the GO cue on peak velocity. Burst detection thresholds stop at 85th as too few trials with bursts were identified for the next 90th threshold. For the modeling, the peak velocities were power transformed (see Materials and Methods). *Significant model (p < 0.05).

Figure 4.

Single-trial data in individual subjects illustrating the relationship between last burst amplitude and peak velocity. The linear mixed-effects model showed a negative relationship between the amplitude of the last burst before or overlapping the GO cue, and the peak velocity (25 ± 1.8 burst trials per subject; b = −0.013, t(287) = −2.5, p = 0.014). Only the burst trials of the contralateral STN are considered.

Figure 5.

Bursts affect the velocity peak when they are in a critical peri-GO window, with a maximal effect when realigned to movement onset. A, Estimated effects and 95% CIs derived from the linear mixed-effects model testing the impact of bursts in 50 ms bins on peak velocity. Bins are defined relative to the GO cue, which is indicated by the bold vertical line. B, Estimated effects and 95% CIs derived from the same linear mixed-effects model when bins were defined relative to the movement onset. Pair of bold vertical lines indicates the range in which the GO cue would have fallen. For the modeling, the velocity peaks are power transformed (see Materials and Methods). *Significant model (p < 0.05) when bins are considered in isolation. Blue shading represents significant bins after FDR correction. C, D, The majority of the beta bursts occurring in the significant window aligned to movement onset (B, blue shading) end before the GO cue or right after (yet still have more than half of their duration before the GO). C, The percentage of these across subjects is shown (Before GO). D, The timing of the burst termination points for each subject. ***p < 0.001.

Figure 6.

Bursts after the GO cue increase the RT, with a maximal effect when realigned to GO. A, Estimated effects and 95% CIs derived from the linear mixed-effects model testing the impact of bursts in 50 ms bins on RT. Bins were defined relative to the GO cue, which is indicated by the bold vertical line. B, Mean RTs in burst trials normalized (z score) to the mean RT of all trials for all subjects. A positive value indicates an increase in RT in burst trials. Trials are divided according to the presence of a burst in the 200 ms after GO. C, Estimated effects and 95% CIs derived from the linear mixed-effects model when bins were defined relative to the movement onset. Pair of bold vertical lines indicates the range in which the GO cue would have fallen. For the modeling, the RTs were log transformed. *Significant model (p < 0.05) when bins are considered in isolation. Purple shading represents significant bins after FDR correction.