Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity

Rehabilitative devices for a top-down approach

In recent years, neurorehabilitation has moved from a "bottom-up" to a "top down" approach. This change has also involved the technological devices developed for motor and cognitive rehabilitation. It implies that during a task or during therapeutic exercises, new "top-down" approaches are being used to stimulate the brain in a more direct way to elicit plasticity-mediated motor re-learning. This is opposed to "Bottom up" approaches, which act at the physical level and attempt to bring about changes at the level of the central neural system. Areas covered: In the present unsystematic review, we present the most promising innovative technological devices that can effectively support rehabilitation based on a top-down approach, according to the most recent neuroscientific and neurocognitive findings. In particular, we explore if and how the use of new technological devices comprising serious exergames, virtual reality, robots, brain computer interfaces, rhythmic music and biofeedback devices might provide a top-down based approach. Expert commentary: Motor and cognitive systems are strongly harnessed in humans and thus cannot be separated in neurorehabilitation. Recently developed technologies in motor-cognitive rehabilitation might have a greater positive effect than conventional therapies

Augmenting Sensorimotor Control Using “Goal-Aware” Vibrotactile Stimulation during Reaching and Manipulation Behaviors

We describe two sets of experiments that examine the ability of vibrotactile encoding of simple position error and combined object states (calculated from an optimal controller) to enhance performance of reaching and manipulation tasks in healthy human adults. The goal of the first experiment (tracking) was to follow a moving target with a cursor on a computer screen. Visual and/or vibrotactile cues were provided in this experiment, and vibrotactile feedback was redundant with visual feedback in that it did not encode any information above and beyond what was already available via vision. After only 10 minutes of practice using vibrotactile feedback to guide performance, subjects tracked the moving target with response latency and movement accuracy values approaching those observed under visually guided reaching. Unlike previous reports on multisensory enhancement, combining vibrotactile and visual feedback of performance errors conferred neither positive nor negative effects on task performance. In the second experiment (balancing), vibrotactile feedback encoded a corrective motor command as a linear combination of object states (derived from a linear-quadratic regulator implementing a trade-off between kinematic and energetic performance) to teach subjects how to balance a simulated inverted pendulum. Here, the tactile feedback signal differed from visual feedback in that it provided information that was not readily available from visual feedback alone. Immediately after applying this novel “goal-aware” vibrotactile feedback, time to failure was improved by a factor of three. Additionally, the effect of vibrotactile training persisted after the feedback was removed. These results suggest that vibrotactile encoding of appropriate combinations of state information may be an effective form of augmented sensory feedback that can be applied, among other purposes, to compensate for lost or compromised proprioception as commonly observed, for example, in stroke survivors

Visual Error Augmentation for Enhancing Motor Learning and Rehabilitative Relearning

We developed a real-time controller for a 2 degree-of-freedom robotic system using xPC Target. This system was used to investigate how different methods of performance error feedback can lead to faster and more complete motor learning in individuals asked to compensate for a novel visuo-motor transformation (a 30 degree rotation). Four groups of normal human subjects were asked to reach with their unseen arm to visual targets surrounding a central starting location. A cursor tracking hand motion was provided during each reach. For one group of subjects, deviations from the ideal compensatory hand movement (i.e. trajectory errors) were amplified with a gain of 2 whereas another group was provided visual feedback with a gain of 3.1. Yet another group was provided cursor feedback wherein the cursor was rotated by an additional (constant) offset angle. We compared the rates at which the hand paths converged to the steady-state trajectories. Our results demonstrate that error-augmentation can improve the rate and extent of motor learning of visuomotor rotations in healthy subjects. We also tested this method on straightening the movements of stroke subjects, and our early results suggest that error amplification can facilitate neurorehabilitation strategies in brain injuries such as stroke

Stroke Survivors Control the Temporal Structure of Variability During Reaching in Dynamic Environments

Learning to control forces is known to reduce the amount of movement variability (e.g., standard deviation; SD) while also altering the temporal structure of movement variability (e.g., approximate entropy; ApEn). Such variability control has not been explored in stroke survivors during reaching movements in dynamic environments. Whether augmented feedback affects such variability control, is also unknown. Chronic stroke survivors, assigned randomly to a control/experimental group, learned reaching movements in a dynamically changing environment while receiving either true feedback of their movement (control) or augmented visual feedback (experimental). Hand movement variability was analyzed using SD and ApEn. A significant change in variability was determined for both SD and ApEn. Post hoc tests revealed that the significant decrease in SD was not retained after a week. However, the significant increase in ApEn, determined on both days of training, showed significant retention effects. In dynamically changing environments, chronic stroke survivors reduced the amount of movement variability and made their movement patterns less repeatable and possibly more flexible. These changes were not affected by augmented visual feedback. Moreover, the learning patterns characteristically involved the control of the nonlinear dynamics rather than the amount of hand movement variability. The absence of transfer effects demonstrated that variability control of hand movement after a stroke is specific to the task and the environment

Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices

A multilevel model for movement rehabilitation in Traumatic Brain Injury (TBI) using virtual environments

This paper presents a conceptual model for movement rehabilitation of traumatic brain injury (TBI) using virtual environments. This hybrid model integrates principles from ecological systems theory with recent advances in cognitive neuroscience, and supports a multilevel approach to both assessment and treatment. Performance outcomes at any stage of recovery are determined by the interplay of task, individual, and environmental/contextual factors. We argue that any system of rehabilitation should provide enough flexibility for task and context factors to be varied systematically, based on the current neuromotor and biomechanical capabilities of the performer or patient. Thus, in order to understand how treatment modalities are to be designed and implemented, there is a need to understand the function of brain systems that support learning at a given stage of recovery, and the inherent plasticity of the system. We know that virtual reality (VR) systems allow training environments to be presented in a highly automated, reliable, and scalable way. Presentation of these virtual environments (VEs) should permit movement analysis at three fundamental levels of behaviour: (i) neurocognitive bases of performance (we focus in particular on the development and use of internal models for action which support adaptive, on-line control); (ii) movement forms and patterns that describe the patients' movement signature at a given stage of recovery (i.e, kinetic and kinematic markers of movement proficiency), (iii) functional outcomes of the movement. Each level of analysis can also map quite seamlessly to different modes of treatment. At the neurocognitive level, for example, semi-immersive VEs can help retrain internal modeling processes by reinforcing the patients' sense of multimodal space (via augmented feedback), their position within it, and the ability to predict and control actions flexibly (via movement simulation and imagery training). More specifically, we derive four - key therapeutic environment concepts (or Elements) presented using VR technologies: Embodiment (simulation and imagery), Spatial Sense (augmenting position sense), Procedural (automaticity and dual-task control), and Participatory (self-initiated action). The use of tangible media/objects, force transduction, and vision-based tracking systems for the augmentation of gestures and physical presence will be discussed in this context

Education and transfer of water competencies: An ecological dynamics approach

© The Author(s) 2020. To cope in various aquatic environments (i.e. swimming pools, lakes, rivers, oceans), learners require a wide repertoire of self-regulatory behaviours such as awareness of obstacles and water properties, floating and moving from point to point with different strokes, decision making, emotional control and breathing efficiently. By experiencing different learning situations in stable indoor pool environments, it is assumed that children strengthen aquatic competencies that should be transferable to functioning in open water environments, where prevalence of drowning is high. However, this fundamental assumption may be misleading. Here, we propose the application of a clear, related methodology and theoretical framework that could be useful to help physical education curriculum specialists (re)shape and (re)design appropriate aquatic learning situations to facilitate better transfer of learning. We discuss the need for more representativeness in a learning environment, proposing how the many different task and environmental constraints on aquatic actions may bound the emergence of functional, self-regulatory behaviours in learners. Ideas in ecological dynamics suggest that physical educators should design learning environments that offer a rich landscape of opportunities for action for learners. As illustration, three practice interventions are described for developing functional and transferrable skills in indoor aquatic environments. It is important that aquatic educators focus not just upon ‘learning to swim’, but particularly on relevant transferable skills and self-regulatory behaviours deemed necessary for functioning in dynamic, outdoor aquatic environments

Education and transfer of water competencies: An ecological dynamics approach

© The Author(s) 2020. To cope in various aquatic environments (i.e. swimming pools, lakes, rivers, oceans), learners require a wide repertoire of self-regulatory behaviours such as awareness of obstacles and water properties, floating and moving from point to point with different strokes, decision making, emotional control and breathing efficiently. By experiencing different learning situations in stable indoor pool environments, it is assumed that children strengthen aquatic competencies that should be transferable to functioning in open water environments, where prevalence of drowning is high. However, this fundamental assumption may be misleading. Here, we propose the application of a clear, related methodology and theoretical framework that could be useful to help physical education curriculum specialists (re)shape and (re)design appropriate aquatic learning situations to facilitate better transfer of learning. We discuss the need for more representativeness in a learning environment, proposing how the many different task and environmental constraints on aquatic actions may bound the emergence of functional, self-regulatory behaviours in learners. Ideas in ecological dynamics suggest that physical educators should design learning environments that offer a rich landscape of opportunities for action for learners. As illustration, three practice interventions are described for developing functional and transferrable skills in indoor aquatic environments. It is important that aquatic educators focus not just upon ‘learning to swim’, but particularly on relevant transferable skills and self-regulatory behaviours deemed necessary for functioning in dynamic, outdoor aquatic environments