EMG feedback tasks reduce reflexive stiffness during force and position perturbations

Force and position perturbations are widely applied to identify muscular and reflexive contributions to posture maintenance of the arm. Both task instruction (force vs. position) and the inherently linked perturbation type (i.e., force perturbations-position task and position perturbations-force tasks) affect these contributions and their mutual balance. The goal of this study is to explore the modulation of muscular and reflexive contributions in shoulder muscles using EMG biofeedback. The EMG biofeedback provides a harmonized task instruction to facilitate the investigation of perturbation type effects irrespective of task instruction. External continuous force and position perturbations with a bandwidth of 0.5–20 Hz were applied at the hand while subjects maintained prescribed constant levels of muscular co-activation using visual feedback of an EMG biofeedback signal. Joint admittance and reflexive impedance were identified in the frequency domain, and parametric identification separated intrinsic muscular and reflexive feedback properties. In tests with EMG biofeedback, perturbation type (position and force) had no effect on joint admittance and reflexive impedance, indicating task as the dominant factor. A reduction in muscular and reflexive stiffness was observed when performing the EMG biofeedback task relative to the position task. Reflexive position feedback was effectively suppressed during the equivalent EMG biofeedback task, while velocity and acceleration feedback were both decreased by approximately 37%. This indicates that force perturbations with position tasks are a more effective paradigm to investigate complete dynamic motor control of the arm, while EMG tasks tend to reduce the reflexive contribution

A Novel Approach for Modeling Neural Responses to Joint Perturbations Using the NARMAX Method and a Hierarchical Neural Network

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

Dynamic Information Flow Based on EEG and Diffusion MRI in Stroke: A Proof-of-Principle Study

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

Mri-based visualisation of orbital fat deformation during eye motion

Summary. Orbital fat, or the fat behind the eye, plays an important role in eye movements. In order to gain a better understanding of orbital fat mobility during eye motion, MRI datasets of the eyes of two healthy subjects were acquired respectively in seven and fourteen different directions of gaze. After semi-automatic rigid registration, the Demons deformable registration algorithm was used to derive timedependent three-dimensional deformation vector fields from these datasets. Visualisation techniques were applied to these datasets in order to investigate fat mobility in specific regions of interest in the first subject. A qualitative analysis of the first subject showed that in two of the three regions of interest, fat moved half as much as the embedded structures. In other words, when the muscles and the optic nerve that are embedded in the fat move, the fat partly moves along with these structures and partly flows around them. In the second subject, a quantitative analysis was performed which showed a relation between the distance behind the sclera and the extent to which fat moves along with the optic nerve

Comparison of Two Methods for In Vivo Estimation of the Glenohumeral Joint Rotation Center (GH-JRC) of the Patients with Shoulder Hemiarthroplasty

Determination of an accurate glenohumeral-joint rotation center (GH-JRC) from marker data is essential for kinematic and dynamic analysis of shoulder motions. Previous studies have focused on the evaluation of the different functional methods for the estimation of the GH-JRC for healthy subjects. The goal of this paper is to compare two widely used functional methods, namely the instantaneous helical axis (IHA) and symmetrical center of rotation (SCoRE) methods, for estimating the GH-JRC in vivo for patients with implanted shoulder hemiarthroplasty. The motion data of five patients were recorded while performing three different dynamic motions (circumduction, abduction, and forward flexion). The GH-JRC was determined using the CT-images of the subjects (geometric GH-JRC) and was also estimated using the two IHA and SCoRE methods. The rotation centers determined using the IHA and SCoRE methods were on average 1.47±0.62 cm and 2.07±0.55 cm away from geometric GH-JRC, respectively. The two methods differed significantly (two-tailed p-value from paired t-Test ∼0.02, post-hoc power ∼0.30). The SCoRE method showed a significant lower (two-tailed p-value from paired t-Test ∼0.03, post-hoc power ∼0.68) repeatability error calculated between the different trials of each motion and each subject and averaged across all measured subjects (0.62±0.10 cm for IHA vs. 0.43±0.12 cm for SCoRE). It is concluded that the SCoRE appeared to be a more repeatable method whereas the IHA method resulted in a more accurate estimation of the GH-JRC for patients with endoprostheses