Spinal circuits for motor learning

Brownstone, Robert M.; Bui, Tuan V.; Stifani, Nicolas

doi:10.1016/j.conb.2015.04.007

Cited by 34 publications

(36 citation statements)

References 80 publications

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“…For instance, the spinal cord is known for hosting neural circuits that generate patterns of muscle activity (Duysens and Van de Crommert 1998;Weiler et al 2019) . In addition, the spinal cord's involvement in learning motor behaviours has been demonstrated in studies involving spinal cord transection, in which subsequent training improves locomotor activity (Brownstone et al 2015;Harkema et al 2012;Leblond et al 2003) . Thus, the reduction in shoulder activity might be a reflection of changes at the spinal level.…”

Section: Neural Structures For Learning Arm Dynamicsmentioning

confidence: 99%

Explicit feedback and instruction do not change shoulder muscle activity reduction after shoulder fixation

Maeda

Zdybal

Gribble

et al. 2020

Preprint

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Generating pure elbow rotation requires contracting muscles at both the shoulder and elbow joints to counter torques that arise at the shoulder when the forearm rotates (i.e., intersegmental dynamics). Previous work has shown that human participants learn to reduce their shoulder muscle activity if the same elbow movement is performed after the shoulder joint is mechanically locked, which is appropriate because locking the shoulder joint eliminates the torques that arise at the shoulder when the forearm rotates. However, this learning is slow (i.e., it unfolds over hundreds of trials) and incomple te (i.e., shoulder activity is not fully eliminated). Here we investigated whether and how the addit ion of explicit strategies and biofeedback modulate this type of learning. Three groups of human participants (N = 55) performed voluntary pure elbow rotations using a robotic exoskeleton that permits shoulder and elbow rotation in a horizontal plane. Participants did the task with the shoulder free to move (baseline), then with the shoulder joint locked by the robotic manipulandum (adaptation), and then with the shoulder free to move again (post-adaptation). The first group of participants performed this protocol and received no instructions about what to do after their shoulder was locked. The second group of participants received visual feedback about their shoulder muscle activity after each movement and was instructed to reduce their shoulder activity to zero. The third group of participants also received visual biofeedback, but it was removed part way through the experiment. We found that, although all groups learned, the rate and magnitude of learning was not reliably different across the groups. Taken together, our results suggest that learning new arm dynamics, unlike other motor learning paradigms, unfolds independent of explicit instructions, biofeedback and task instructions.

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Section: Neural Structures For Learning Arm Dynamicsmentioning

confidence: 99%

Explicit feedback and instruction do not change shoulder muscle activity reduction after shoulder fixation

Maeda

Zdybal

Gribble

et al. 2020

Preprint

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“…Despite an important role for the cerebellum in locomotor adaptation, other neural structures might also participate. In particular, spinal circuits, which play an essential role in producing the basic locomotor pattern (Brownstone & Wilson, 2008;McCrea & Rybak, 2008;Grillner & Jessell, 2009;Frigon, 2012;Kiehn, 2016), were shown to produce motor adaptation and have the basic neuronal elements for learning (Brownstone et al 2015). Indeed, the limb trajectory of spinal cats and rats was altered in response to external perturbations (e.g.…”

Section: Introductionmentioning

confidence: 99%

Lack of adaptation during prolonged split‐belt locomotion in the intact and spinal cat

Kuczynski

Telonio

Thibaudier

et al. 2017

The Journal of Physiology

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In humans, gait adapts to prolonged walking on a split-belt treadmill, where one leg steps faster than the other, by gradually restoring the symmetry of interlimb kinematic variables, such as double support periods and step lengths, and by reducing muscle activity (EMG, electromyography). The adaptation is also characterized by reversing the asymmetry of interlimb variables observed during the early split-belt period when returning to tied-belt locomotion, termed an after-effect. To determine if cats adapt to prolonged split-belt locomotion and to assess if spinal locomotor circuits participate in the adaptation, we measured interlimb variables and EMG in intact and spinal-transected cats before, during and after 10 min of split-belt locomotion. In spinal cats, only the hindlimbs performed stepping with the forelimbs stationary. In intact and spinal cats, step lengths and double support periods were, on average, symmetric, during tied-belt locomotion. They became asymmetric during split-belt locomotion and remained asymmetric throughout the split-belt period. Upon returning to tied-belt locomotion, symmetry was immediately restored. In intact cats, the mean EMG amplitude of hindlimb extensors increased during split-belt locomotion and remained increased throughout the split-belt period, whereas in spinal cats, EMG amplitude did not change. Therefore, the results indicate that the locomotor pattern of cats does not adapt to prolonged split-belt locomotion, suggesting an important physiological difference in the control of locomotion between cats and humans. We propose that restoring left-right symmetry is not required to maintain balance during prolonged asymmetric locomotion in the cat, a quadruped, as opposed to human bipedal locomotion.

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“…Long-term changes, however, must be accompanied by homeostatic plastic mechanisms that prevent instability induced by positive feedback (Turrigiano, 1999; Desai, 2003; Quartarone and Hallett, 2013). These mechanisms could include, for example, changes in connectivity, synaptic strength, and/or morphology of spinal neurons (Brownstone et al, 2015). Such changes have been proposed to underlie spontaneous or training-induced changes in motor output in spinal cord injury patients (Harkema, 2008; Knikou, 2010; Dietz, 2012) and in animal models of spinal cord injury (Côté and Gossard, 2004; Frigon et al, 2009; Tillakaratne et al, 2010; Martin, 2012; van den Brand et al, 2012; Houle and Côté, 2013; Takeoka et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

“…Predictive inputs arise from forward models derived from a copy of the motor command – an efference copy. By comparing these two inputs, comparator neurons calculate the ‘sensory prediction error,’ which is then used to modify circuit function, either for corrective responses or sustained learning (Shadmehr et al, 2010; Requarth and Sawtell, 2014; Brownstone et al, 2015). Most dI3 INs receive excitatory instructive inputs from a variety of sensory afferents (Bui et al, 2013), as well as inhibitory rhythmic input from locomotor circuits.…”

Section: Discussionmentioning

confidence: 99%

“…Through training, the sensory prediction error is iteratively calculated by dI3 INs, and would lead to synaptic and/or cellular changes in locomotor circuits, leading to sustained recovery of motor function (Brownstone et al, 2015). Therefore, functional removal of dI3 INs from the circuits results in the loss of sensory prediction error signals, and thus prevents the benefit of locomotor training following spinal transection (Figure 6C).…”

Section: Discussionmentioning

confidence: 99%

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Spinal microcircuits comprising dI3 interneurons are necessary for motor functional recovery following spinal cord transection

Bui

Stifani

Akay

et al. 2016

eLife

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The spinal cord has the capacity to coordinate motor activities such as locomotion. Following spinal transection, functional activity can be regained, to a degree, following motor training. To identify microcircuits involved in this recovery, we studied a population of mouse spinal interneurons known to receive direct afferent inputs and project to intermediate and ventral regions of the spinal cord. We demonstrate that while dI3 interneurons are not necessary for normal locomotor activity, locomotor circuits rhythmically inhibit them and dI3 interneurons can activate these circuits. Removing dI3 interneurons from spinal microcircuits by eliminating their synaptic transmission left locomotion more or less unchanged, but abolished functional recovery, indicating that dI3 interneurons are a necessary cellular substrate for motor system plasticity following transection. We suggest that dI3 interneurons compare inputs from locomotor circuits with sensory afferent inputs to compute sensory prediction errors that then modify locomotor circuits to effect motor recovery.DOI: http://dx.doi.org/10.7554/eLife.21715.001

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Spinal circuits for motor learning

Cited by 34 publications

References 80 publications

Explicit feedback and instruction do not change shoulder muscle activity reduction after shoulder fixation

Explicit feedback and instruction do not change shoulder muscle activity reduction after shoulder fixation

Lack of adaptation during prolonged split‐belt locomotion in the intact and spinal cat

Spinal microcircuits comprising dI3 interneurons are necessary for motor functional recovery following spinal cord transection

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