Characterization of information-based learning benefits with submovement dynamics and muscular rhythmicity

Submovements in manual tracking: people with Parkinson's disease produce more submovements than age-matched controls

BACKGROUND

In general, people are unable to produce slow, smooth movements - as movements become slower (i.e., with longer durations), they become jerkier. A hallmark feature of Parkinson's disease is bradykinesia - slowness of movement. In this study, we investigate the intersection of these two observations - how do people with Parkinson's disease (PwP) perform in a slow tracking task, and how does it vary as a function of movement frequency? On the one hand, as PwP move more slowly in day-to-day life, they may be better in a slow tracking task. On the other hand, their general impairment in movement production may lead to worse tracking outcomes.

METHODS

We used a well-tested tracking task known as the one-person mirror game, where participants control the left-right movement of an ellipse on a graphics tablet. They did so using a stylus and were instructed to match the horizontal location of a stimulus, an ellipse moving in a sinusoidal fashion at different movement frequencies and peak velocities. We calculated the submovement rate, identifying both type 2 (acceleration zero crossings) and type 3 (jerk zero crossings) from the trajectories, as well as relative position error (dX) and mean timing error (dT). To account for age-related performance decline, we tested three groups: PwP (N = 31), age-matched controls (OC; N = 29), and younger controls (YC; N = 30) in a cross-sectional study, and used mixed-design ANOVAs to compare across groups and movement frequencies.

RESULTS

We reproduced earlier results showing that slow movements (i.e., with lower frequencies) require more submovements to track. PwP also generally performed more submovements than the other two groups, but only for type 3 submovements, whereas OC and YC performed submovements at a similar rate. Younger controls (YC) performed fewer tracking errors than older participants (both PwP and OC), and OC performed better than PwP.

CONCLUSIONS

The ability to smoothly track showed an age-related decline, with PwP producing more errors and using more submovements. This may be due to reduced automaticity in movement production. The findings of the study can be used to guide optimal movement frequencies for motor training for older adults and PwP.

Improving Precision Force Control With Low-Frequency Error Amplification Feedback: Behavioral and Neurophysiological Mechanisms

Although error amplification (EA) feedback has been shown to improve performance on visuomotor tasks, the challenge of EA is that it concurrently magnifies task-irrelevant information that may impair visuomotor control. The purpose of this study was to improve the force control in a static task by preclusion of high-oscillatory components in EA feedback that cannot be timely used for error correction by the visuomotor system. Along with motor unit behaviors and corticomuscular coherence, force fluctuations (Fc) were modeled with non-linear SDA to contrast the reliance of the feedback process and underlying neurophysiological mechanisms by using real feedback, EA, and low-frequency error amplification (LF-EA). During the static force task in the experiment, the EA feedback virtually potentiated the size of visual error, whereas the LF-EA did not channel high-frequency errors above 0.8 Hz into the amplification process. The results showed that task accuracy was greater with the LF-EA than with the real and EA feedback modes, and that LF-EA led to smaller and more complex Fc. LF-EA generally led to smaller SDA variables of Fc (critical time points, critical point of Fc, the short-term effective diffusion coefficient, and short-term exponent scaling) than did real feedback and EA. The use of LF-EA feedback increased the irregularity of the ISIs of MUs but decreased the RMS of the mean discharge rate, estimated with pooled MU spike trains. Beta-range EEG–EMG coherence spectra (13–35 Hz) in the LF-EA condition were the greatest among the three feedback conditions. In summary, amplification of low-frequency errors improves force control by shifting the relative significances of the feedforward and feedback processes. The functional benefit arises from the increase in the common descending drive to promote a stable state of MU discharges.