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

Motor control-based assessment of therapy effects in individuals post-stroke: implications for prediction of response and subject-specific modifications

Producing a coordinated motion such as walking is, at its root, the result of healthy communication pathways between the central nervous system and the musculoskeletal system. The central nervous system produces an electrical signal responsible for the excitation of a muscle, and the musculoskeletal system contains the necessary equipment for producing a movement-driving force to achieve a desired motion. Motor control refers to the ability an individual has to produce a desired motion, and the complexity of motor control is a mathematical concept stemming from how the electrical signals from the central nervous system translate to muscle activations. Exercising a high-level complexity of motor control is critical to producing a smooth motion. However, the occurrence of a sudden, detrimental neurological event like a stroke damages these connecting pathways between these two systems, and the result is a motion that is uncoordinated and energy-inefficient due to diminished motor control complexity. Stroke is a leading cause of disability with nearly 800,000 stroke victims each year in the U.S. alone, amounting to an estimated cost of $45.5B. Impaired mobility following a stroke is a widespread effect, with more than half of survivors over the age of 65 affected in this way, and up to 80% of survivors at some point experiencing hemiparesis during post-stroke recovery. As such, given the importance of independent mobility for quality of life, improving gait mechanics and mobility of stroke survivors has been the goal of rehabilitation efforts for decades. In this work, we mold together the forefronts of statistics and computational physics-based modeling to obtain insight and information about post-stroke hemiparetic gait mechanics and what drives them that would otherwise be unavailable. We expand upon previous work to quantify motor control complexity as it relates to the health of the neuromuscular system and analyze the effect of a specific therapy on motor control of individuals post-stroke. Secondly, we aim to develop a predictive model to conclude whether an individual will respond to the therapy based on kinematic and dynamic features from pre-therapy recordings. Lastly, we will determine how to individually tailor this therapy in order to achieve maximum improvement in motor control complexity in order to improve gait mechanics in individuals post-stroke

Reciprocal Inhibition Post-stroke is Related to Reflex Excitability and Movement Ability

Objective Decreased reciprocal inhibition (RI) of motor neurons may contribute to spasticity after stroke. However, decreased RI is not a uniform observation among stroke survivors, suggesting that this spinal circuit may be influenced by other stroke-related characteristics. The purpose of this study was to measure RI post-stroke and to examine the relationship between RI and other features of stroke. Methods RI was examined in 15 stroke survivors (PAR) and 10 control subjects by quantifying the effect of peroneal nerve stimulation on soleus H-reflex amplitude. The relationship between RI and age, time post-stroke, lesion side, walking velocity, Fugl-Meyer, Ashworth, and Achilles reflex scores was examined. Results RI was absent and replaced by reciprocal facilitation in 10 of 15 PAR individuals. Reciprocal facilitation was associated with low Fugl-Meyer scores and slow walking velocities but not with hyperactive Achilles tendon reflexes. There was no relationship between RI or reciprocal facilitation and time post-stroke, lesion side, or Ashworth score. Conclusions Decreased RI is not a uniform finding post-stroke and is more closely related to walking ability and movement impairment than to spasticity. Significance Phenomena other than decreased RI may contribute to post-stroke spasticity

Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions.

Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject's self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot-ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject's walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject's walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject's walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations

Brain Activation During Passive and Volitional Pedaling After Stroke

Background: Prior work indicates that pedaling-related brain activation is lower in people with stroke than in controls. We asked whether this observation could be explained by between-group differences in volitional motor commands and pedaling performance. Methods: Individuals with and without stroke performed passive and volitional pedaling while brain activation was recorded with functional magnetic resonance imaging. The passive condition eliminated motor commands to pedal and minimized between-group differences in pedaling performance. Volume, intensity, and laterality of brain activation were compared across conditions and groups. Results: There were no significant effects of condition and no Group × Condition interactions for any measure of brain activation. Only 53% of subjects could minimize muscle activity for passive pedaling. Conclusions: Altered motor commands and pedaling performance are unlikely to account for reduced pedaling-related brain activation poststroke. Instead, this phenomenon may be due to functional or structural brain changes. Passive pedaling can be difficult to achieve and may require inhibition of excitatory descending drive

The Investigation of Motor Primitives During Human Reaching Movements and the Quantification of Post-Stroke Motor Impairment

Movement is a complex task, requiring precise and coordinated muscle contractions. The forces and torques produced during multi-segmental movement of the upper limbs in humans, must be controlled, in order for movement to be achieved successfully. Although a critical aspect of everyday life, there remain questions regarding the specific controller used by the central nervous system to govern movement. Furthermore, how this system is affected by neurological injuries such as stroke also remains in question. It was the goal of this thesis to examine the neurological control of movement in healthy individuals and apply these findings to the further investigation of chronically motor impaired stroke patients. Additionally, this work aimed at providing clinicians with a more reliable, easy to use, and inexpensive approach to quantify post-stroke motor impairment