Muscle tissue is prone to changes in composition and architecture following stroke. Changes in muscle tissue of the extremities are thought to increase resistance to muscle elongation or joint torque under passive conditions. These effects likely compound neuromuscular impairments, exacerbating movement function. Unfortunately, conventional rehabilitation is devoid of precise measures and relies on subjective assessments of passive joint torques. Shear wave ultrasound elastography, a tool to measure muscle mechanical properties, may be readily available for use in the rehabilitation setting as a precise measure, albeit at the muscle-tissue level. To support this postulation, we evaluated the criterion validity of shear wave ultrasound elastography of the biceps brachii; we investigated its relationship with a laboratory-based criterion measure for quantifying elbow joint torque in individuals with moderate to severe chronic stroke. Additionally, we evaluated construct validity, with the specific sub-type of hypothesis testing of known groups, by testing the difference between arms. Measurements were performed under passive conditions at seven positions spanning the arc of elbow joint flexion-extension in both arms of nine individuals with hemiparetic stroke. Surface electromyography was utilized for threshold-based confirmation of muscle quiescence. A moderate relationship between the shear wave velocity and elbow joint torque was identified, and both metrics were greater in the paretic arm. Data supports the progression toward a clinical application of shear wave ultrasound elastography in evaluating altered muscle mechanical properties in stroke, while acknowledging that undetectable muscle activation or hypertonicity may contribute to the measurement. Shear wave ultrasound elastography may augment the conventional method of manually testing joint mobility by providing a high-resolution precise value. Tissue-level measurement may also assist in identifying new therapeutic targets for patient-specific impairment-based interventions.

In hemiparetic stroke, functional recovery of paretic limb may occur with the reorganization of neural networks in the brain. Neuroimaging techniques, such as magnetic resonance imaging (MRI), have a high spatial resolution which can be used to reveal anatomical changes in the brain following a stroke. However, low temporal resolution of MRI provides less insight of dynamic changes of brain activity. In contrast, electro-neurophysiological techniques, such as electroencephalography (EEG), have an excellent temporal resolution to measure such transient events, however are hindered by its low spatial resolution. This proof-of-principle study assessed a novel multimodal brain imaging technique namely Variational Bayesian Multimodal Encephalography (VBMEG), which aims to improve the spatial resolution of EEG for tracking the information flow inside the brain and its changes following a stroke. The limitations of EEG are complemented by constraints derived from anatomical MRI and diffusion weighted imaging (DWI). EEG data were acquired from individuals suffering from a stroke as well as able-bodied participants while electrical stimuli were delivered sequentially at their index finger in the left and right hand, respectively. The locations of active sources related to this stimulus were precisely identified, resulting in high Variance Accounted For (VAF above 80%). An accurate estimation of dynamic information flow between sources was achieved in this study, showing a high VAF (above 90%) in the cross-validation test. The estimated dynamic information flow was compared between chronic hemiparetic stroke and able-bodied individuals. The results demonstrate the feasibility of VBMEG method in revealing the changes of information flow in the brain after stroke. This study verified the VBMEG method as an advanced computational approach to track the dynamic information flow in the brain following a stroke. This may lead to the development of a quantitative tool for monitoring functional changes of the cortical neural networks after a unilateral brain injury and therefore facilitate the research into, and the practice of stroke rehabilitation.

The human nervous system is an ensemble of connected neuronal networks. Modeling and system identification of the human nervous system helps us understand how the brain processes sensory input and controls responses at the systems level. This study aims to propose an advanced approach based on a hierarchical neural network and non-linear system identification method to model neural activity in the nervous system in response to an external somatosensory input. The proposed approach incorporates basic concepts of Non-linear AutoRegressive Moving Average Model with eXogenous input (NARMAX) and neural network to acknowledge non-linear closed-loop neural interactions. Different from the commonly used polynomial NARMAX method, the proposed approach replaced the polynomial non-linear terms with a hierarchical neural network. The hierarchical neural network is built based on known neuroanatomical connections and corresponding transmission delays in neural pathways. The proposed method is applied to an experimental dataset, where cortical activities from ten young able-bodied individuals are extracted from electroencephalographic signals while applying mechanical perturbations to their wrist joint. The results yielded by the proposed method were compared with those obtained by the polynomial NARMAX and Volterra methods, evaluated by the variance accounted for (VAF). Both the proposed and polynomial NARMAX methods yielded much better modeling results than the Volterra model. Furthermore, the proposed method modeled cortical responded with a mean VAF of 69.35% for a three-step ahead prediction, which is significantly better than the VAF from a polynomial NARMAX model (mean VAF 47.09%). This study provides a novel approach for precise modeling of cortical responses to sensory input. The results indicate that the incorporation of knowledge of neuroanatomical connections in building a realistic model greatly improves the performance of system identification of the human nervous system.

Post hemiparetic stroke an individual may face difficulty performing bimanual tasks due to an asymmetry in their arms' strengths. Here, we determined whether participants with a strength asymmetry were impaired bi-directionally when matching torques between arms (i.e., paretic arm matches non-paretic arm, non-paretic arm matches paretic arm). Six participants with chronic hemiparetic stroke and four participants without neurological impairments partook in this study. First, we identified the maximum voluntary torque that participants could generate about each elbow joint (τ_mvt). Then, we determined how accurately and precisely participants could match, bidirectionally, submaximal isometric flexion torques (0.25 · τ_{MVT:Reference}) between arms. Results demonstrate that task directionality impacted the ability of our participants with stroke who had a strength asymmetry to match torques between arms; specifically, participants were unimpaired matching to a referenced non-paretic arm yet impaired in the opposite direction. Additionally, results reveal that the degree to which participants overshot the target torque when matching with their non-paretic arm could be predicted based on their strength asymmetry (R² _Adjusted = 0.67). We propose that individuals with stroke may avoid torque matching impairments during bimanual tasks by matching their paretic arm to their non-paretic arm.

Communication between neuronal populations is facilitated by synchronization of their oscillatory activity. Although nonlinearity has been observed in the sensorimotor system, its nonlinear connectivity has not been widely investigated yet. This study investigates nonlinear connectivity during the human stretch reflex based on neuronal synchronization. Healthy participants generated isotonic wrist flexion while receiving a periodic mechanical perturbation to the wrist. Using a novel cross-frequency phase coupling metric, we estimate directional nonlinear connectivity, including time delay, from the perturbation to brain and to muscle, as well as from brain to muscle. Nonlinear phase coupling is significantly stronger from the perturbation to the muscle than to the brain, with a shorter time delay. The time delay from the perturbation to the muscle is 33 ms, similar to the reported latency of the spinal stretch reflex at the wrist. Source localization of nonlinear phase coupling from the brain to the muscle suggests activity originating from the motor cortex, although its effect on the stretch reflex is weak. As such nonlinear phase coupling between the perturbation and muscle activity is dominated by the spinal reflex loop. This study provides new evidence of nonlinear neuronal synchronization in the stretch reflex at the wrist joint with respect to spinal and transcortical loops.