Dataset Information

Increasing muscle co-contraction speeds up internal model acquisition during dynamic motor learning.

ABSTRACT: During reaching movements in the presence of novel dynamics, participants initially co-contract their muscles to reduce kinematic errors and improve task performance. As learning proceeds, muscle co-contraction decreases as an accurate internal model develops. The initial co-contraction could affect the learning of the internal model in several ways. By ensuring the limb remains close to the target state, co-contraction could speed up learning. Conversely, by reducing kinematic errors, a key training signal, it could slow down learning. Alternatively, given that the effects of muscle co-contraction on kinematic errors are predictable and could be discounted when assessing the internal model error, it could have no effect on learning. Using a sequence of force pulses, we pretrained two groups to either co-contract (stiff group) or relax (relaxed group) their arm muscles in the presence of dynamic perturbations. A third group (control group) was not pretrained. All groups performed reaching movements in a velocity-dependent curl field. We measured adaptation using channel trials and found greater adaptation in the stiff group during early learning. We also found a positive correlation between muscle co-contraction, as measured by surface electromyography, and adaptation. These results show that muscle co-contraction accelerates the rate of dynamic motor learning.

SUBMITTER: Heald JB

PROVIDER: S-EPMC6218508 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Increasing muscle co-contraction speeds up internal model acquisition during dynamic motor learning.

Heald James B JB Franklin David W DW Wolpert Daniel M DM

Scientific reports 20181105 1

During reaching movements in the presence of novel dynamics, participants initially co-contract their muscles to reduce kinematic errors and improve task performance. As learning proceeds, muscle co-contraction decreases as an accurate internal model develops. The initial co-contraction could affect the learning of the internal model in several ways. By ensuring the limb remains close to the target state, co-contraction could speed up learning. Conversely, by reducing kinematic errors, a key tra ...[more]

PMID: 30397273

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Project description:Manipulative actions involving unstable interactions with the environment require controlling mechanical impedance through muscle co-contraction. While much research has focused on how the central nervous system (CNS) selects the muscle patterns underlying a desired movement or end-point force, the coordination strategies used to achieve a desired end-point impedance have received considerably less attention. We recorded isometric forces at the hand and electromyographic (EMG) signals in subjects performing a reaching task with an external disturbance. In a virtual environment, subjects displaced a cursor by applying isometric forces and were instructed to reach targets in 20 spatial locations. The motion of the cursor was then perturbed by disturbances whose effects could be attenuated by increasing co-contraction. All subjects could voluntarily modulate co-contraction when disturbances of different magnitudes were applied. For most muscles, activation was modulated by target direction according to a cosine tuning function with an offset and an amplitude increasing with disturbance magnitude. Co-contraction was characterized by projecting the muscle activation vector onto the null space of the EMG-to-force mapping. Even in the baseline the magnitude of the null space projection was larger than the minimum magnitude required for non-negative muscle activations. Moreover, the increase in co-contraction was not obtained by scaling the baseline null space projection, scaling the difference between the null space projections in any block and the projection of the non-negative minimum-norm muscle vector, or scaling the difference between the null space projections in the perturbed blocks and the baseline null space projection. However, the null space projections in the perturbed blocks were obtained by linear combination of the baseline null space projection and the muscle activation used to increase co-contraction without generating any force. The failure of scaling rules in explaining voluntary modulation of arm co-contraction suggests that muscle pattern generation may be constrained by muscle synergies.

Project description:Enhanced muscle weakness is commonly experienced following stroke and may be accompanied by increased susceptibility to fatigue. To examine the contributions of central and peripheral factors to isometric muscle fatigue in stroke survivors, this study investigates changes in motor unit (MU) mean firing rate, and action potential duration during, and directly following, a sustained submaximal fatiguing contraction at 30% maximum voluntary contraction (MVC). A series of short contractions of the first dorsal interosseous muscle were performed pre- and post-fatigue at 20% MVC, and again following a 10-min recovery period, by 12 chronic stroke survivors. Individual MU firing times were extracted using surface EMG decomposition and used to obtain the spike-triggered average MU action potential waveforms. During the sustained fatiguing contraction, the mean rate of change in firing rate across all detected MUs was greater on the affected side (-0.02 ± 0.03 Hz/s) than on the less-affected side (-0.004 ± 0.003 Hz/s, p = 0.045). The change in firing rate immediately post-fatigue was also greater on the affected side than less-affected side (-13.5 ± 20 and 0.1 ± 19%, p = 0.04). Mean MU firing rates increased following the recovery period on the less-affected side when compared to the affected side (19.3 ± 17 and 0.5 ± 20%, respectively, p = 0.03). MU action potential duration increased post-fatigue on both sides (10.3 ± 1.2 to 11.2 ± 1.3 ms on the affected side and 9.9 ± 1.7 to 11.2 ± 1.9 ms on the less-affected side, p = 0.001 and p = 0.02, respectively), and changes in action potential duration tended to be smaller in subjects with greater impairment (p = 0.04). This study presents evidence of both central and peripheral fatigue at the MU level during isometric fatiguing contraction for the first time in stroke survivors. Together, these preliminary observations indicate that the response to an isometric fatiguing contraction differs between the affected and less-affected side post-stroke, and may suggest that central mechanisms observed here as changes in firing rate are the dominant processes leading to task failure on the affected side.

Project description:Walking after stroke is often described as requiring excessive muscle co-contraction, yet, evidence that co-contraction is a ubiquitous motor control strategy for this population remains inconclusive. Co-contraction, the simultaneous activation of agonist and antagonist muscles, can be assessed with electromyography (EMG) but is often described qualitatively. Here, our goal is to determine if co-contraction is associated with gait impairments following stroke. Fifteen individuals with chronic stroke and nine healthy controls walked on an instrumented treadmill at self-selected speed. Surface EMGs were collected from the medial gastrocnemius (MG), soleus (SOL), and tibialis anterior (TA) of each leg. EMG envelope amplitudes were assessed in three ways: (1) no normalization, (2) normalization to the maximum value across the gait cycle, or (3) normalization to maximal M-wave. Three co-contraction indices were calculated across each agonist/antagonist muscle pair (MG/TA and SOL/TA) to assess the effect of using various metrics to quantify co-contraction. Two factor ANOVAs were used to compare effects of group and normalization for each metric. Co-contraction during the terminal stance (TSt) phase of gait is not different between healthy controls and the paretic leg of individuals post-stroke, regardless of the metric used to quantify co-contraction. Interestingly, co-contraction was similar between M-max and non-normalized EMG; however, normalization does not impact the ability to resolve group differences. While a modest correlation is revealed between the amount of TSt co-contraction and walking speed, the relationship is not sufficiently strong to motivate further exploration of a causal link between co-contraction and walking function after stroke. Co-contraction does not appear to be a common strategy employed by individuals after stroke. We recommend exploration of alternative EMG analysis approaches in an effort to learn more about the causal mechanisms of gait impairment following stroke.

Dataset Information

Increasing muscle co-contraction speeds up internal model acquisition during dynamic motor learning.

Publications

Increasing muscle co-contraction speeds up internal model acquisition during dynamic motor learning.

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