Sensitivity of Motor Adaptation to the Statistical Properties of an Environmental Load

Linear, limited-memory models capture many important features of adaptive motor performance during reaching, stepping and pointing. A recent study in our lab found that a model fitted to data obtained from subjects reaching against elastic loads which varied from trial-to-trial later failed to fit the steady-state response behavior of subjects exposed to deterministic, step changes in load. Does motor adaptation depend on statistical properties of the environment (eg. mean load strength and variability)? Neurologically intact volunteers (n=14) made 6 blocks of 100 planar, ballistic, 10cm, out-and-back reaching movements against spring-like loads having equilibrium positions at the hand\u27s starting point. View of the limb was not allowed. Load stiffness varied trial-by-trial, and each block of movements differed in mean and/or variance such that three, 3-block contrasts were evaluated: increasing standard deviation (VAR), increasing mean (MEAN), and proportionally increasing standard deviation and mean (WEBER). In the VAR and MEAN contrasts, either the mean or the standard deviation of the load stiffness sequence was held constant while the other parameter varied systematically. In WEBER contrast, mean and standard deviation scaled proportionally over the contrast. The zero location of the transfer function moved toward the origin as variability increased. This trend in the zero location was the result of an unbalance in the decrease in the influence of previous load and the decrease of effective limb compliance with increasing variability. Specifically, the decrease in the influence of prior load was greater than the decrease in effective limb compliance. Effective limb compliance decreased to a larger extent in the MEAN and WEBER contrasts, which both presented an increase in mean load. In the MEAN contrast, the decrease in effective limb compliance with increasing mean load was balanced by an equivalent decrease in the influence of prior load, resulting in no significant change in the transfer function zero location. No changes in the influence of prior errors were observed in any of the contrasts. Thus, motor adaptation adjusts in two ways: the influence of prior load on subsequent movements decreases both when the environment is more variable and when effective limb compliance decreases with the mean load

Remembering Forward: Neural Correlates of Memory and Prediction in Human Motor Adaptation

We used functional MR imaging (FMRI), a robotic manipulandum and systems identification techniques to examine neural correlates of predictive compensation for spring-like loads during goal-directed wrist movements in neurologically-intact humans. Although load changed unpredictably from one trial to the next, subjects nevertheless used sensorimotor memories from recent movements to predict and compensate upcoming loads. Prediction enabled subjects to adapt performance so that the task was accomplished with minimum effort. Population analyses of functional images revealed a distributed, bilateral network of cortical and subcortical activity supporting predictive load compensation during visual target capture. Cortical regions – including prefrontal, parietal and hippocampal cortices – exhibited trial-by-trial fluctuations in BOLD signal consistent with the storage and recall of sensorimotor memories or “states” important for spatial working memory. Bilateral activations in associative regions of the striatum demonstrated temporal correlation with the magnitude of kinematic performance error (a signal that could drive reward-optimizing reinforcement learning and the prospective scaling of previously learned motor programs). BOLD signal correlations with load prediction were observed in the cerebellar cortex and red nuclei (consistent with the idea that these structures generate adaptive fusimotor signals facilitating cancelation of expected proprioceptive feedback, as required for conditional feedback adjustments to ongoing motor commands and feedback error learning). Analysis of single subject images revealed that predictive activity was at least as likely to be observed in more than one of these neural systems as in just one. We conclude therefore that motor adaptation is mediated by predictive compensations supported by multiple, distributed, cortical and subcortical structures

Principles of sensorimotor learning.

The exploits of Martina Navratilova and Roger Federer represent the pinnacle of motor learning. However, when considering the range and complexity of the processes that are involved in motor learning, even the mere mortals among us exhibit abilities that are impressive. We exercise these abilities when taking up new activities - whether it is snowboarding or ballroom dancing - but also engage in substantial motor learning on a daily basis as we adapt to changes in our environment, manipulate new objects and refine existing skills. Here we review recent research in human motor learning with an emphasis on the computational mechanisms that are involved

How virtual and mechanical coupling impact bimanual tracking.

Bilateral training systems look to promote the paretic hand's use in individuals with hemiplegia. Although this is normally achieved using mechanical coupling (i.e., a physical connection between the hands), a virtual reality system relying on virtual coupling (i.e., through a shared virtual object) would be simpler to use and prevent slacking. However, it is not clear whether different coupling modes differently impact task performance and effort distribution between the hands. We explored how 18 healthy right-handed participants changed their motor behaviors in response to the uninstructed addition of mechanical coupling, and virtual coupling using a shared cursor mapped to the average hands' position. In a second experiment, we then studied the impact of connection stiffness on performance, perception, and effort imbalance. The results indicated that both coupling types can induce the hands to actively contribute to the task. However, the task asymmetry introduced by using a cursor mapped to either the left or right hand only modulated the hands' contribution when not mechanically coupled. The tracking performance was similar for all coupling types, independent of the connection stiffness, although the mechanical coupling was preferred and induced the hands to move with greater correlation. These findings suggest that virtual coupling can induce the hands to actively contribute to a task in healthy participants without hindering their performance. Further investigation on the coupling types' impact on the performance and hands' effort distribution in patients with hemiplegia could allow for the design of simpler training systems that promote the affected hand's use.NEW & NOTEWORTHY We showed that the uninstructed addition of a virtual and/or a mechanical coupling can induce both hands to actively contribute in a continuous redundant bimanual tracking task without impacting performance. In addition, we showed that the task asymmetry can only alter the effort distribution when the hands are not connected, independent of the connection stiffness. Our findings suggest that virtual coupling could be used in the development of simpler VR-based training devices

Assessing Performance, Role Sharing, and Control Mechanisms in Human-Human Physical Interaction for Object Manipulation

abstract: Object manipulation is a common sensorimotor task that humans perform to interact with the physical world. The first aim of this dissertation was to characterize and identify the role of feedback and feedforward mechanisms for force control in object manipulation by introducing a new feature based on force trajectories to quantify the interaction between feedback- and feedforward control. This feature was applied on two grasp contexts: grasping the object at either (1) predetermined or (2) self-selected grasp locations (“constrained” and “unconstrained”, respectively), where unconstrained grasping is thought to involve feedback-driven force corrections to a greater extent than constrained grasping. This proposition was confirmed by force feature analysis. The second aim of this dissertation was to quantify whether force control mechanisms differ between dominant and non-dominant hands. The force feature analysis demonstrated that manipulation by the dominant hand relies on feedforward control more than the non-dominant hand. The third aim was to quantify coordination mechanisms underlying physical interaction by dyads in object manipulation. The results revealed that only individuals with worse solo performance benefit from interpersonal coordination through physical couplings, whereas the better individuals do not. This work showed that naturally emerging leader-follower roles, whereby the leader in dyadic manipulation exhibits significant greater force changes than the follower. Furthermore, brain activity measured through electroencephalography (EEG) could discriminate leader and follower roles as indicated power modulation in the alpha frequency band over centro-parietal areas. Lastly, this dissertation suggested that the relation between force and motion (arm impedance) could be an important means for communicating intended movement direction between biological agents.Dissertation/ThesisDoctoral Dissertation Biomedical Engineering 201

Sensorimotor learning under switching dynamics

Humans have a remarkable capacity to learn new motor behaviours without forgetting old ones. This capacity relies on the ability to acquire and express multiple motor memories without interference. Here we combine behavioural experiments and computational modelling to investigate how the sensorimotor system uses contextual information to create, update and recall motor memories. We first examine the role of muscle co-contraction in the learning of novel movement dynamics. We show that muscle co-contraction, as measured by surface electromyography, accelerates motor learning. We then explore the role of control points on objects in the formation of motor memories during object manipulation. We show that opposing dynamic perturbations, which interfere when controlling a single location on an object, can be learned when each is associated with a separate control point. To account for these results, we develop a parametric switching state-space model, in which the association between cues (control points) and contexts (dynamics) is learned from experience rather than fixed. We then extend this model to a Bayesian nonparametric switching state-space model, in which the number of contexts and cues are learned online rather than specified in advance. This model can instantiate new memories when novel perturbations are experienced and exhibits spontaneous recovery of a memory that has been ostensibly overwritten. To test the model, we perform an experiment in which we briefly present a previously experienced perturbation after behaviour has returned to baseline. As predicted, we observe a qualitatively distinct and more pronounced form of recovery, which we refer to as evoked recovery. Finally, we investigate Bayesian context estimation using single-trial learning. We show that people are able to learn novel associations between cues and contexts and that they use both contextual cues and state feedback to infer the current context and partition learning between memories. Taken together, these findings further the understanding of the behaviour and computational principles of sensorimotor learning under switching dynamics.Engineering and Physical Sciences Research Counci

Changes in Intramuscular Coherence in Tibialis Anterior Muscle following a Visuomotor Gait Task

openThe aim of the study is to measure changes in intramuscular coherence between two sections of the Tibialis anterior muscle (proximal and distal) before and after performing a visuomotor gait task. The study is conducted on typically developed adults and children as well as individuals with cerebral palsy

Generalization of Motor and Sensory Changes in Motor Learning

This dissertation examines the generalization of motor and sensory changes in motor learning. Chapter two describes the process of intermanual transfer of reach adaptation and proprioceptive recalibration. We exposed participants to a laterally translated cursor while reaching with one hand to three targets, and then we measured reach aftereffects and changes in felt hand position for the trained and untrained hand. We found reach adaptation transfer from right to left hand and no transfer of proprioceptive recalibration. This suggests that the intermanual transfer for motor adaptation is hand-dependant, and proprioceptive recalibration is specific to the trained hand. Chapter three describes the generalization patterns of reach adaptation and proprioceptive recalibration across different distances. Reach aftereffects and changes in estimates of hand position were measured following reach-training with a rotated visual feedback of the hand to a single target distance. We found that reach adaptation and proprioceptive recalibration transfer across near and far novel distances. However, proprioceptive recalibration generalization was significantly smaller at far novel locations. This suggests that, unlike motor adaptation, the extent of sensory changes generalization is distance-dependent. Chapter four describes the contribution of proprioceptive recalibration and updated efference-based sensory predictions in motor adaptation and changes in hand localization. We exposed our participants to only visual-proprioceptive discrepancy by removing volitional movements and having a robot move their hand passively. Then, we examined changes in hand localization in two hand movement conditions, i.e., active (self-generated) and passive (robot-generated). Results showed no significant difference in hand localization changes between active and passive conditions. This suggests changes in hand localization reflect mainly proprioceptive recalibration of the hand rather than updates in efference-based sensorypredictions, and entirely on proprioceptive recalibration when training does not include any volitional movements. Additionally, in Chapter four, we examined how reach adaptation and proprioceptive recalibration generalized across different directions in the workspace. We found that reach aftereffects generalized to neighboring novel targets in a pattern similar to proprioceptive recalibration generalization pattern. This suggests that some of the reach adaptation reflect proprioceptive changes. Our findings provide insight into the characteristics of proprioceptive recalibration and how this process influences motor learning. This should be taken into consideration when designing motor adaptation/learning paradigms, teaching a motor skill or designing a movement rehabilitation protocol

The Contribution of Cross-Sensory Error Signals to Reach Aftereffects and Proprioceptive Recalibration

Reaching with altered visual feedback leads to adaptation of internal motor plans, which result in aftereffects, deviated reaching without visual feedback and proprioceptive recalibration, a shift in perceived hand location (Cressman & Henriques, 2010). Zbib, Henriques, and Cressman (2016) found motor changes arise more quickly than proprioceptive changes, which required prolonged training to become significantly shifted. But their methodology may not have captured the finer incremental changes in aftereffects and proprioception. Our lab also investigated the time course of these changes using a much quicker method of proprioceptive assessment. Results suggest that both motor and proprioceptive recalibration occurred in as few as 6 rotated-cursor training trials (7.6 and 3.9 respectively). Our current study focuses on the specific contribution of cross-sensory error signals on reach aftereffects and proprioceptive recalibration. Participants moved their hand to a remembered target while they were constrained to a force channel. The cursor always moved straight to the target site, while the hand was abruptly deviated 30 CCW of the intended target (making the cursor rotation CW as per the previous study). This passive training resulted in significant aftereffects and change in felt hand position within 6 training trials. Reach aftereffects were even larger by the end of passive-training (10.6), which were expectedly smaller than those produced during volitional reaches (15.7). In addition, all participants recalibrated their sense of felt hand position equally (11.3), which was larger than the shift seen with volitional reaching (5.09). The time course of these sensory and motor changes differed slightly across experiments but more across the different measures (motor vs. sensory). Our results suggest that proprioception is much more important for motor learning, with even the mere discrepancy between felt and seen hand location being enough to drive robust motor adaptation

Multimodal Sensory Integration for Perception and Action in High Functioning Children with Autism Spectrum Disorder

Movement disorders are the earliest observed features of autism spectrum disorder (ASD) present in infancy. Yet we do not understand the neural basis for impaired goal-directed movements in this population. To reach for an object, it is necessary to perceive the state of the arm and the object using multiple sensory modalities (e.g. vision, proprioception), to integrate those sensations into a motor plan, to execute the plan, and to update the plan based on the sensory consequences of action. In this dissertation, I present three studies in which I recorded hand paths of children with ASD and typically developing (TD) controls as they grasped the handle of a robotic device to control a cursor displayed on a video screen. First, participants performed discrete and continuous movements to capture targets. Cursor feedback was perturbed from the hand\u27s actual position to introduce visuo-spatial conflict between sensory and proprioceptive feedback. Relative to controls, children with ASD made greater errors, consistent with deficits of sensorimotor adaptive and strategic compensations. Second, participants performed a two-interval forced-choice discrimination task in which they perceived two movements of the visual cursor and/or the robot handle and then indicated which of the two movements was more curved. Children with ASD were impaired in their ability to discriminate movement kinematics when provided visual and proprioceptive information simultaneously, suggesting deficits of visuo-proprioceptive integration. Finally, participants made goal-directed reaching movements against a load while undergoing simultaneous functional magnetic resonance imaging (MRI). The load remained constant (predictable) within an initial block of trials and then varied randomly within four additional blocks. Children with ASD exhibited greater movement variability compared to controls during both constant and randomly-varying loads. MRI analysis identified marked differences in the extent and intensity of the neural activities supporting goal-directed reaching in children with ASD compared to TD children in both environmental conditions. Taken together, the three studies revealed deficits of multimodal sensory integration in children with ASD during perception and execution of goal-directed movements and ASD-related motor performance deficits have a telltale neural signature, as revealed by functional MR imaging