Feedback Adaptation to Unpredictable Force Fields in 250 ms

Head-neck cooling effects on central and peripheral fatigue, motor accuracy, and blood markers of stress in men with multiple sclerosis and healthy men: A randomized crossover study

BACKGROUND

The present study aimed to investigate whether head and neck cooling (at 18 °C next to the skin) and fatiguing submaximal exercise at a thermoneutral ambient temperature can induce peripheral and central responses in healthy men and those with multiple sclerosis (MS).

METHODS

A local head-neck cooling (at 18 °C next to the skin) intervention in men with a relapsing-remitting form of MS (n = 18; age 30.9 ± 8.1 years) and healthy men (n = 22; age 26.7 ± 5.9 years) was assessed. Men in both groups performed 100 intermittent isometric knee extensions with 5 s contractions and 20 s of rest. The primary variables were measured before exercise, after 50 and 100 repetitions, and 1 h after recovery. The central activation ratio, maximal voluntary contraction, electrically induced force, electromyography, contractile properties, blood markers, muscle temperature, and perception of effort were measured.

RESULTS

Compared with noncooled conditions, head and neck cooling increased the central capacity to activate exercising muscles but resulted in greater exercise-induced peripheral fatigue in men with MS (p < 0.05). Local cooling did not affect motor accuracy or the amplitude of electromyography signals; however, these factors were related to the intensity of the motor task (p > 0.05). The changes in central and peripheral fatigability induced by local cooling during submaximal exercise were more pronounced in men with MS than in healthy men (p < 0.05).

CONCLUSION

Head and neck cooling enhances central activation of muscles during exercise, leading to improved exercise performance compared with noncooled conditions in men with MS.

Task-dependent coarticulation of movement sequences

Combining individual actions into sequences is a hallmark of everyday activities. Classical theories propose that the motor system forms a single specification of the sequence as a whole, leading to the coarticulation of the different elements. In contrast, recent neural recordings challenge this idea and suggest independent execution of each element specified separately. Here, we show that separate or coarticulated sequences can result from the same task-dependent controller, without implying different representations in the brain. Simulations show that planning for multiple reaches simultaneously allows separate or coarticulated sequences depending on instructions about intermediate goals. Human experiments in a two-reach sequence task validated this model. Furthermore, in co-articulated sequences, the second goal influenced long-latency stretch responses to external loads applied during the first reach, demonstrating the involvement of the sensorimotor network supporting fast feedback control. Overall, our study establishes a computational framework for sequence production that highlights the importance of feedback control in this essential motor skill.

Fast learning without synaptic plasticity in spiking neural networks

Spiking neural networks are of high current interest, both from the perspective of modelling neural networks of the brain and for porting their fast learning capability and energy efficiency into neuromorphic hardware. But so far we have not been able to reproduce fast learning capabilities of the brain in spiking neural networks. Biological data suggest that a synergy of synaptic plasticity on a slow time scale with network dynamics on a faster time scale is responsible for fast learning capabilities of the brain. We show here that a suitable orchestration of this synergy between synaptic plasticity and network dynamics does in fact reproduce fast learning capabilities of generic recurrent networks of spiking neurons. This points to the important role of recurrent connections in spiking networks, since these are necessary for enabling salient network dynamics. We show more specifically that the proposed synergy enables synaptic weights to encode more general information such as priors and task structures, since moment-to-moment processing of new information can be delegated to the network dynamics.

Advanced feedback enhances sensorimotor adaptation

It is widely recognized that sensorimotor adaptation is facilitated when feedback is provided throughout the movement compared with when it is provided at the end of the movement. However, the source of this advantage is unclear: continuous feedback is more ecological, dynamic, and available earlier than endpoint feedback. Here, we assess the relative merits of these factors using a method that allows us to manipulate feedback timing independent of actual hand position. By manipulating the onset time of "endpoint" feedback, we found that adaptation was modulated in a non-monotonic manner, with the peak of the function occurring in advance of the hand reaching the target. Moreover, at this optimal time, learning was of similar magnitude as that observed with continuous feedback. By varying movement duration, we demonstrate that this optimal time occurs at a relatively fixed time after movement onset, an interval we hypothesize corresponds to when the comparison of the sensory prediction and feedback generates the strongest error signal.

Human reaching control in dynamic environments

Closed-loop models of movement control have attracted growing interest in how the nervous system transforms sensory information into motor commands, and several brain structures have been identified as neural substrates for these computational operations. Recently, several studies have focused on how these models need to be updated when environmental parameters change. Current evidence suggests that when the task changes, rapid control updates enable flexible modifications of current actions and online decisions. At the same time, when movement dynamics change, humans use different strategies based on a combination of adaptation and modulation of controller sensitivity to exogenous perturbations (robust control). This review proposes a unified framework to capture these results based on online estimation of model parameters with dynamic updates in control. The reviewed studies also identify the time scales of associated behavioral mechanisms to guide future research on the neural basis of movement control.

The nervous system tunes sensorimotor gains when reaching in variable mechanical environments

Humans often move in the presence of mechanical disturbances that can vary in direction and amplitude throughout movement. These disturbances can jeopardize the outcomes of our actions, such as when drinking from a glass of water on a turbulent flight or carrying a cup of coffee while walking on a busy sidewalk. Here, we examine control strategies that allow the nervous system to maintain performance when reaching in the presence of mechanical disturbances that vary randomly throughout movement. Healthy participants altered their control strategies to make movements more robust against disturbances. The change in control was associated with faster reaching movements and increased responses to proprioceptive and visual feedback that were tuned to the variability of the disturbances. Our findings highlight that the nervous system exploits a continuum of control strategies to increase its responsiveness to sensory feedback when reaching in the presence of increasingly variable physical disturbances.

Stable visually guided reaching does not require an internal feedforward model to compensate for internal delay: Data and model

Visually guided reaches are performed in ≈1s. Given unstable feedback control with neural transmission delay, stable visually guided reaching is assumed to require internal feedforward models that generate simulated feedback without delay that combines with actual feedback for stability. We investigated whether stable visually guided reaching requires internal models to handle such delay. Participants performed rapid targeted reaches in a virtual environment with different mappings between speeds of the hand and hand avatar. First, participants reached with visual guidance and constant mapping. Second, feedforward reaches were performed with constant mapping and hand avatar only visible at reach start and end. Reaches were accurate. Third, participants performed reaches with visual guidance and different mappings every trial. We expected performance as in the first condition. Finally, feedforward reaches with variable mapping yielded large errors showing visual guidance in the previous condition was successful despite an ineffective internal model. We simulated reaches using a proportional rate model with disparity Tau controlling the virtual Equilibrium Point in an Equilibrium Point (EP) model. The time dimensioned information and dynamic remained stable with delayed feedback. Finally, we fit movement times using the proportional rate EP model with 0msec, 50msec, and 100msec delay. With the fitted model parameters, we compared the model reach trajectories with the behavioral trajectories. Stable visually guided reaching did not require an internal feedforward model.

Pupil diameter tracked during motor adaptation in humans

Pupil diameter, under constant illumination, is known to reflect individuals' internal states, such as surprise about observation and environmental uncertainty. Despite the growing use of pupillometry in cognitive learning studies as an additional measure for examining internal states, few studies have used pupillometry in human motor learning studies. Here, we provide the first detailed characterization of pupil diameter changes in a short-term reach adaptation paradigm. We measured pupil changes in 121 human participants while they adapted to abrupt, gradual, or switching force field conditions. Sudden increases in movement error caused by the introduction/reversal of the force field resulted in strong phasic pupil dilation during movement accompanied by a transient increase in tonic premovement baseline pupil diameter in subsequent trials. In contrast, pupil responses were reduced when the force field was gradually introduced, indicating that large, unexpected errors drove the changes in pupil responses. Interestingly, however, error-induced pupil responses gradually became insensitive after experiencing multiple force field reversals. We also found an association between baseline pupil diameter and incidental knowledge of the gradually introduced perturbation. Finally, in all experiments, we found a strong co-occurrence of larger baseline pupil diameter with slower reaction and movement times after each rest break. Collectively, these results suggest that tonic baseline pupil diameter reflects one's belief about environmental uncertainty, whereas phasic pupil dilation during movement reflects surprise about a sensory outcome (i.e., movement error), and both effects are modulated by novelty. Our results provide a new approach for nonverbally assessing participants' internal states during motor learning.NEW & NOTEWORTHY Pupil diameter is known as a noninvasive window into individuals' internal states. Despite the growing use of pupillometry in cognitive learning studies, it receives little attention in motor learning studies. Here, we characterized the pupil responses in a short-term reach adaptation paradigm by measuring pupil diameter of human participants while they adapted to abrupt, gradual, or switching force field conditions. Our results demonstrate how surprise and uncertainty reflected in pupil diameter develop during motor adaptation.

Adaptive Feedback Control in Human Reaching Adaptation to Force Fields

Sensorimotor adaptation is a central function of the nervous system, as it allows humans and other animals to flexibly anticipate their interaction with the environment. In the context of human reaching adaptation to force fields, studies have traditionally separated feedforward (FF) and feedback (FB) processes involved in the improvement of behavior. Here, we review computational models of FF adaptation to force fields and discuss them in light of recent evidence highlighting a clear involvement of feedback control. Instead of a model in which FF and FB mechanisms adapt in parallel, we discuss how online adaptation in the feedback control system can explain both trial-by-trial adaptation and improvements in online motor corrections. Importantly, this computational model combines sensorimotor control and short-term adaptation in a single framework, offering novel perspectives for our understanding of human reaching adaptation and control.

Individual Differences in Sensorimotor Adaptation Are Conserved Over Time and Across Force-Field Tasks

Sensorimotor adaptation enables the nervous system to modify actions for different conditions and environments. Many studies have investigated factors that influence adaptation at the group level. There is growing recognition that individuals vary in their ability to adapt motor skills and that a better understanding of individual differences in adaptation may inform how motor skills are taught and rehabilitated. Here we examined individual differences in the adaptation of upper-limb reaching movements. We quantified the extent to which participants adapted their movements to a velocity-dependent force field during an initial session, at 24 h, and again 1-week later. Participants (n = 28) displayed savings, which was expressed as greater initial adaptation when re-exposed to the force field. Individual differences in adaptation across various stages of the experiment displayed weak-strong reliability, such that individuals who adapted to a greater extent in the initial session tended to do so when re-exposed to the force field. Our second experiment investigated if individual differences in adaptation are also present when participants adapt to different force fields or a force field and visuomotor rotation. Separate groups of participants adapted to position- and velocity-dependent force fields (Experiment 2a; n = 20) or a velocity-dependent force field and visuomotor rotation in a single session (Experiment 2b; n = 20). Participants who adapted to a greater extent to velocity-dependent forces tended to show a greater extent of adaptation when exposed to position-dependent forces. In contrast, correlations were weak between various stages of adaptation to the force-field and visuomotor rotation. Collectively, our study reveals individual differences in adaptation that are reliable across repeated exposure to the same force field and present when adapting to different force fields.

Savings in Human Force Field Learning Supported by Feedback Adaptation

Savings have been described as the ability of healthy humans to relearn a previously acquired motor skill faster than the first time, which in the context of motor adaptation suggests that the learning rate in the brain could be adjusted when a perturbation is recognized. Alternatively, it has been argued that apparent savings were the consequence of a distinct process that instead of reflecting a change in the learning rate, revealed an explicit re-aiming strategy. Based on recent evidence that feedback adaptation may be central to both planning and control, we hypothesized that this component could genuinely accelerate relearning in human adaptation to force fields (FFs) during reaching. Consistent with our hypothesis, we observed that on re-exposure to a previously learned FF, the very first movement performed by healthy volunteers in the relearning context was better adapted to the external disturbance, and this occurred without any anticipation or cognitive strategy because the relearning session was started unexpectedly. We conclude that feedback adaptation is a medium by which the nervous system can genuinely accelerate learning across movements.

Rapid Changes in Movement Representations during Human Reaching Could Be Preserved in Memory for at Least 850 ms

Humans adapt to mechanical perturbations such as forcefields (FFs) during reaching within tens of trials. However, recent findings suggested that this adaptation may start within one single trial, i.e., online corrective movements can become tuned to the unanticipated perturbations within a trial. This was highlighted in previous works with a reaching experiment in which participants had to stop at a via-point (VP) located between the start and the goal. An FF was applied during the first and second parts of the movement and then occasionally unexpectedly switched off at the VP during catch trials. The results showed an after-effect during the second part of the movement when participants exited the VP. This behavioral result was interpreted as a standard after-effect, but it remained unclear how it was related to conventional trial-by-trial learning. The current study aimed to investigate how long do such changes in movement representations last in memory. For this, we have studied the same reaching task with VP in two situations: one with very short residing time in the VP and the second with an imposed minimum 500 ms dwell time in the VP. In both situations, during the unexpected absence of the FF after VP, after-effects were observed. This suggests that online corrections to the internal representation of reach dynamics can be preserved in memory for around 850 ms of resting time on average. Therefore, rapid changes occurring within movements can thus be preserved in memory long enough to influence trial-by-trial motor adaptation.

Time course of changes in the long-latency feedback response parallels the fast process of short-term motor adaptation

Adapting to novel dynamics involves modifying both feedforward and feedback control. We investigated whether the motor system alters feedback responses during adaptation to a novel force field in a manner similar to adjustments in feedforward control. We simultaneously tracked the time course of both feedforward and feedback systems via independent probes during a force field adaptation task. Participants (n = 35) grasped the handle of a robotic manipulandum and performed reaches to a visual target while the hand and arm were occluded. We introduced an abrupt counterclockwise velocity-dependent force field during a block of reaching trials. We measured movement kinematics and shoulder and elbow muscle activity with surface EMG electrodes. We tracked the feedback stretch response throughout the task. Using force channel trials, we measured overall learning, which was later decomposed into a fast and slow process. We found that the long-latency feedback response (LLFR) was upregulated in the early stages of learning and was correlated with the fast component of feedforward adaptation. The change in feedback response was specific to the long-latency epoch (50-100 ms after muscle stretch) and was observed only in the triceps muscle, which was the muscle required to counter the force field during adaptation. The similarity in time course for the LLFR and the estimated time course of the fast process suggests both are supported by common neural circuits. While some propose that the fast process reflects an explicit strategy, we argue instead that it may be a proxy for the feedback controller.NEW & NOTEWORTHY We investigated whether changes in the feedback stretch response were related to the proposed fast and slow processes of motor adaptation. We found that the long-latency component of the feedback stretch response was upregulated in the early stages of learning and the time course was correlated with the fast process. While some propose that the fast process reflects an explicit strategy, we argue instead that it may be a proxy for the feedback controller.