Contralateral manual compensation for velocity-dependent force perturbations

It is not yet clear how the temporal structure of a voluntary action is coded allowing coordinated bimanual responses. This study focuses on the adaptation to and compensation for a force profile presented to one stationary arm which is proportional to the velocity of the other moving arm. We hypothesised that subjects would exhibit predictive coordinative responses which would co-vary with the state of the moving arm. Our null hypothesis is that they develop a time-dependent template of forces appropriate to compensate for the imposed perturbation. Subjects were trained to make 500 ms duration reaching movements with their dominant right arm to a visual target. A force generated with a robotic arm that was proportional to the velocity of the moving arm and perpendicular to movement direction acted on their stationary left hand, either at the same time as the movement or delayed by 250 or 500 ms. Subjects rapidly learnt to minimise the final end-point error. In the delay conditions, the left hand moved in advance of the onset of the perturbing force. In test conditions with faster or slower movement of the right hand, the predictive actions of the left hand co-varied with movement speed. Compensation for movement-related forces appeared to be predictive but not based on an accurate force profile that was equal and opposite to the imposed perturbatio

Asymmetric interlimb transfer of concurrent adaptation to opposing dynamic forces

Interlimb transfer of a novel dynamic force has been well documented. It has also been shown that unimanual adaptation to opposing novel environments is possible if they are associated with different workspaces. The main aim of this study was to test if adaptation to opposing velocity dependent viscous forces with one arm could improve the initial performance of the other arm. The study also examined whether this interlimb transfer occurred across an extrinsic, spatial, coordinative system or an intrinsic, joint based, coordinative system. Subjects initially adapted to opposing viscous forces separated by target location. Our measure of performance was the correlation between the speed profiles of each movement within a force condition and an ‘average’ trajectory within null force conditions. Adaptation to the opposing forces was seen during initial acquisition with a significantly improved coefficient in epoch eight compared to epoch one. We then tested interlimb transfer from the dominant to non-dominant arm (D → ND) and vice-versa (ND → D) across either an extrinsic or intrinsic coordinative system. Interlimb transfer was only seen from the dominant to the non-dominant limb across an intrinsic coordinative system. These results support previous studies involving adaptation to a single dynamic force but also indicate that interlimb transfer of multiple opposing states is possible. This suggests that the information available at the level of representation allowing interlimb transfer can be more intricate than a general movement goal or a single perceived directional error

Estimating the Relevance of World Disturbances to Explain Savings, Interference and Long-Term Motor Adaptation Effects

Recent studies suggest that motor adaptation is the result of multiple, perhaps linear processes each with distinct time scales. While these models are consistent with some motor phenomena, they can neither explain the relatively fast re-adaptation after a long washout period, nor savings on a subsequent day. Here we examined if these effects can be explained if we assume that the CNS stores and retrieves movement parameters based on their possible relevance. We formalize this idea with a model that infers not only the sources of potential motor errors, but also their relevance to the current motor circumstances. In our model adaptation is the process of re-estimating parameters that represent the body and the world. The likelihood of a world parameter being relevant is then based on the mismatch between an observed movement and that predicted when not compensating for the estimated world disturbance. As such, adapting to large motor errors in a laboratory setting should alert subjects that disturbances are being imposed on them, even after motor performance has returned to baseline. Estimates of this external disturbance should be relevant both now and in future laboratory settings. Estimated properties of our bodies on the other hand should always be relevant. Our model demonstrates savings, interference, spontaneous rebound and differences between adaptation to sudden and gradual disturbances. We suggest that many issues concerning savings and interference can be understood when adaptation is conditioned on the relevance of parameters

A new method for tracking of motor skill learning through practical application of Fitts’ law

This article is made available through the Brunel Open Access Publishing Fund.A novel upper limb motor skill measure, task productivity rate (TPR) was developed integrating speed and spatial error, delivered by a practical motor skill rehabilitation task (MSRT). This prototype task involved placement of 5 short pegs horizontally on a spatially configured rail array. The stability of TPR was tested on 18 healthy right-handed adults (10 women, 8 men, median age 29 years) in a prospective single-session quantitative within-subjects study design. Manipulations of movement rate 10% faster and slower relative to normative states did not significantly affect TPR, F(1.387, 25.009) = 2.465, p = .121. A significant linear association between completion time and error was highest during the normative state condition (Pearson's r = .455, p < .05). Findings provided evidence that improvements in TPR over time reflected motor learning with possible changes in coregulation behavior underlying practice under different conditions. These findings extend Fitts’ law theory to tracking of practical motor skill using a dexterity task, which could have potential clinical applications in rehabilitation

Skin cancer precursor immunotherapy for squamous cell carcinoma prevention

BACKGROUND: Topical calcipotriol plus 5-fluorouracil (5-FU) combination is an effective immunotherapy against actinic keratosis (AK), which is a precursor to squamous cell carcinoma (SCC). However, the long-term effectiveness of calcipotriol plus 5-FU treatment for SCC prevention is unknown. METHODS: We performed a blinded prospective cohort study on participants of a randomized double-blind clinical trial in which a 4-day course of topical calcipotriol plus 5-FU combination was compared to Vaseline plus 5-FU (control) for AK treatment. SCC and basal cell carcinoma (BCC) incidences were assessed at 1, 2, and 3 years after trial. Tissues were analyzed for calcipotriol plus 5-FU-induced T cell immunity in the skin. RESULTS: Calcipotriol plus 5-FU-induced tissue-resident memory T (Trm) cell formation in face and scalp skin associated with significantly higher erythema scores compared with control (P \u3c 0.01). Importantly, more participants in the test cohort remained SCC-free over the more than 1,500-day follow-up period (P = 0.0765), and significantly fewer developed SCC on the treated face and scalp within 3 years (2 of 30 [7%] versus 11 of 40 [28%] in control group, hazard ratio 0.215 [95% CI: 0.048-0.972], P = 0.032). Accordingly, significantly more epidermal Trm cells persisted in the calcipotriol plus 5-FU-treated face and scalp skin compared with control (P = 0.0028). There was no significant difference in BCC incidence between the treatment groups. CONCLUSION: A short course of calcipotriol plus 5-FU treatment on the face and scalp is associated with induction of robust T cell immunity and Trm formation against AKs and significantly lowers the risk of SCC development within 3 years of treatment. FUNDING: This research was supported by internal academic funds and by grants from the Burroughs Wellcome Fund, Sidney Kimmel Foundation, Cancer Research Institute, and NIH

Visually targeted reaching in horse-head grasshoppers

Visually targeted reaching to a specific object is a demanding neuronal task requiring the translation of the location of the object from a two-dimensionsal set of retinotopic coordinates to a motor pattern that guides a limb to that point in three-dimensional space. This sensorimotor transformation has been intensively studied in mammals, but was not previously thought to occur in animals with smaller nervous systems such as insects. We studied horse-head grasshoppers (Orthoptera: Proscopididae) crossing gaps and found that visual inputs are sufficient for them to target their forelimbs to a foothold on the opposite side of the gap. High-speed video analysis showed that these reaches were targeted accurately and directly to footholds at different locations within the visual field through changes in forelimb trajectory and body position, and did not involve stereotyped searching movements. The proscopids estimated distant locations using peering to generate motion parallax, a monocular distance cue, but appeared to use binocular visual cues to estimate the distance of nearby footholds. Following occlusion of regions of binocular overlap, the proscopids resorted to peering to target reaches even to nearby locations. Monocular cues were sufficient for accurate targeting of the ipsilateral but not the contralateral forelimb. Thus, proscopids are capable not only of the sensorimotor transformations necessary for visually targeted reaching with their forelimbs but also of flexibly using different visual cues to target reaches. © 2012 The Royal Society

Iterative Learning Control as a Framework for Human-Inspired Control with Bio-mimetic Actuators

The synergy between musculoskeletal and central nervous systems empowers humans to achieve a high level of motor performance, which is still unmatched in bio-inspired robotic systems. Literature already presents a wide range of robots that mimic the human body. However, under a control point of view, substantial advancements are still needed to fully exploit the new possibilities provided by these systems. In this paper, we test experimentally that an Iterative Learning Control algorithm can be used to reproduce functionalities of the human central nervous system - i.e. learning by repetition, after-effect on known trajectories and anticipatory behavior - while controlling a bio-mimetically actuated robotic arm

The resting human brain and motor learning.

Functionally related brain networks are engaged even in the absence of an overt behavior. The role of this resting state activity, evident as low-frequency fluctuations of BOLD (see [1] for review, [2-4]) or electrical [5, 6] signals, is unclear. Two major proposals are that resting state activity supports introspective thought or supports responses to future events [7]. An alternative perspective is that the resting brain actively and selectively processes previous experiences [8]. Here we show that motor learning can modulate subsequent activity within resting networks. BOLD signal was recorded during rest periods before and after an 11 min visuomotor training session. Motor learning but not motor performance modulated a fronto-parietal resting state network (RSN). Along with the fronto-parietal network, a cerebellar network not previously reported as an RSN was also specifically altered by learning. Both of these networks are engaged during learning of similar visuomotor tasks [9-22]. Thus, we provide the first description of the modulation of specific RSNs by prior learning--but not by prior performance--revealing a novel connection between the neuroplastic mechanisms of learning and resting state activity. Our approach may provide a powerful tool for exploration of the systems involved in memory consolidation

A biologically inspired neural network controller for ballistic arm movements

<p>Abstract</p> <p>Background</p> <p>In humans, the implementation of multijoint tasks of the arm implies a highly complex integration of sensory information, sensorimotor transformations and motor planning. Computational models can be profitably used to better understand the mechanisms sub-serving motor control, thus providing useful perspectives and investigating different control hypotheses. To this purpose, the use of Artificial Neural Networks has been proposed to represent and interpret the movement of upper limb. In this paper, a neural network approach to the modelling of the motor control of a human arm during planar ballistic movements is presented.</p> <p>Methods</p> <p>The developed system is composed of three main computational blocks: 1) a parallel distributed learning scheme that aims at simulating the internal inverse model in the trajectory formation process; 2) a pulse generator, which is responsible for the creation of muscular synergies; and 3) a limb model based on two joints (two degrees of freedom) and six muscle-like actuators, that can accommodate for the biomechanical parameters of the arm. The learning paradigm of the neural controller is based on a pure exploration of the working space with no feedback signal. Kinematics provided by the system have been compared with those obtained in literature from experimental data of humans.</p> <p>Results</p> <p>The model reproduces kinematics of arm movements, with bell-shaped wrist velocity profiles and approximately straight trajectories, and gives rise to the generation of synergies for the execution of movements. The model allows achieving amplitude and direction errors of respectively 0.52 cm and 0.2 radians.</p> <p>Curvature values are similar to those encountered in experimental measures with humans.</p> <p>The neural controller also manages environmental modifications such as the insertion of different force fields acting on the end-effector.</p> <p>Conclusion</p> <p>The proposed system has been shown to properly simulate the development of internal models and to control the generation and execution of ballistic planar arm movements. Since the neural controller learns to manage movements on the basis of kinematic information and arm characteristics, it could in perspective command a neuroprosthesis instead of a biomechanical model of a human upper limb, and it could thus give rise to novel rehabilitation techniques.</p