bioRxivNeurosciencePreprint
Dopaminergic Medication Improves Force Control and Modifies Motor Unit Activity During the Steadiest and Least Steady Periods in Parkinsons Disease
Parisa Alaei, Aleš HolobariD, Daryl J. WileiD, Jennifer M. JakobiiD
Abstract
Parkinsons disease (PD) impairs steady force production, but how dopaminergic medication alters force control and the motor unit (MU) activity is unclear. This study examined force steadiness and biceps brachii MU behavior in 16 people with PD ON and OFF dopaminergic medication. Participants performed sustained isometric elbow flexion contractions at 5%, 10%, 15%, and 25% of maximum voluntary contraction (MVC). High density surface electromyography was decomposed into MU spike trains. Force steadiness and MU discharge properties were quantified during nonoverlapping 7-s steadiest and least steady contraction periods, whereas MU coherence was assessed across the force plateau. In the ON state, force steadiness improved by 17_19% during both contraction periods at 5_15% MVC. MU interspike interval variability decreased by [~]12% across all forces during the least steady contraction period and by 17% at 15% MVC during the steadiest contraction period, whereas mean MU discharge rate was unchanged. Recruitment thresholds were higher at 10% and 25% MVC in the ON state, whereas derecruitment thresholds did not differ. Across the entire plateau, alpha- and tremor-band MU coherence decreased by 14% and 18%, respectively, whereas delta-band coherence was unchanged. These findings demonstrate that dopaminergic medication improves both the steadiest and least steady periods of low-force control, accompanied by reduced MU discharge variability and frequency-specific changes in plateau-wide MU coherence rather than changes in mean discharge rate. Resolving within-contraction variation reveals medication-sensitive features of force control that are obscured by conventional plateau-wide analyses, providing a new approach for evaluating motor output in PD.
- organism: human
- assay: electronic health records
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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