The Effects of Mirror Feedback during Target Directed Movements on Ipsilateral Corticospinal Excitability

Mirror visual feedback (MVF) training is a promising technique to promote activation in the lesioned hemisphere following stroke, and aid recovery. However, current outcomes of MVF training are mixed, in part, due to variability in the task undertaken during MVF. The present study investigated the hypothesis that movements directed toward visual targets may enhance MVF modulation of motor cortex (M1) excitability ipsilateral to the trained hand compared to movements without visual targets. Ten healthy subjects participated in a 2 × 2 factorial design in which feedback (veridical, mirror) and presence of a visual target (target present, target absent) for a right index-finger flexion task were systematically manipulated in a virtual environment. To measure M1 excitability, transcranial magnetic stimulation (TMS) was applied to the hemisphere ipsilateral to the trained hand to elicit motor evoked potentials (MEPs) in the untrained first dorsal interosseous (FDI) and abductor digiti minimi (ADM) muscles at rest prior to and following each of four 2-min blocks of 30 movements (B1–B4). Targeted movement kinematics without visual feedback was measured before and after training to assess learning and transfer. FDI MEPs were decreased in B1 and B2 when movements were made with veridical feedback and visual targets were absent. FDI MEPs were decreased in B2 and B3 when movements were made with mirror feedback and visual targets were absent. FDI MEPs were increased in B3 when movements were made with mirror feedback and visual targets were present. Significant MEP changes were not present for the uninvolved ADM, suggesting a task-specific effect. Analysis of kinematics revealed learning occurred in visual target-directed conditions, but transfer was not sensitive to mirror feedback. Results are discussed with respect to current theoretical mechanisms underlying MVF-induced changes in ipsilateral excitability

Neural Patterns of Reorganization after Intensive Robot-Assisted Virtual Reality Therapy and Repetitive Task Practice in Patients with Chronic Stroke

Several approaches to rehabilitation of the hand following a stroke have emerged over the last two decades. These treatments, including repetitive task practice (RTP), robotically assisted rehabilitation and virtual rehabilitation activities, produce improvements in hand function but have yet to reinstate function to pre-stroke levels—which likely depends on developing the therapies to impact cortical reorganization in a manner that favors or supports recovery. Understanding cortical reorganization that underlies the above interventions is therefore critical to inform how such therapies can be utilized and improved and is the focus of the current investigation. Specifically, we compare neural reorganization elicited in stroke patients participating in two interventions: a hybrid of robot-assisted virtual reality (RAVR) rehabilitation training and a program of RTP training. Ten chronic stroke subjects participated in eight 3-h sessions of RAVR therapy. Another group of nine stroke subjects participated in eight sessions of matched RTP therapy. Functional magnetic resonance imaging (fMRI) data were acquired during paretic hand movement, before and after training. We compared the difference between groups and sessions (before and after training) in terms of BOLD intensity, laterality index of activation in sensorimotor areas, and the effective connectivity between ipsilesional motor cortex (iMC), contralesional motor cortex, ipsilesional primary somatosensory cortex (iS1), ipsilesional ventral premotor area (iPMv), and ipsilesional supplementary motor area. Last, we analyzed the relationship between changes in fMRI data and functional improvement measured by the Jebsen Taylor Hand Function Test (JTHFT), in an attempt to identify how neurophysiological changes are related to motor improvement. Subjects in both groups demonstrated motor recovery after training, but fMRI data revealed RAVR-specific changes in neural reorganization patterns. First, BOLD signal in multiple regions of interest was reduced and re-lateralized to the ipsilesional side. Second, these changes correlated with improvement in JTHFT scores. Our findings suggest that RAVR training may lead to different neurophysiological changes when compared with traditional therapy. This effect may be attributed to the influence that augmented visual and haptic feedback during RAVR training exerts over higher-order somatosensory and visuomotor areas

Parietal Activation Associated With Target-Directed Right Hand Movement Is Lateralized by Mirror Feedback to the Ipsilateral Hemisphere

Current research shows promise in restoring impaired hand function after stroke with the help of Mirror Visual Feedback (MVF), putatively by facilitating activation of sensorimotor areas of the brain ipsilateral to the moving limb. However, the MVF related clinical effects show variability across studies. MVF tasks that have been used place varying amounts of visuomotor demand on one’s ability to complete the task. Therefore, we ask here whether varying visuomotor demand during MVF may translate to differences in brain activation patterns. If so, we argue that this may provide a mechanistic explanation for variable clinical effects. To address this, we used functional magnetic resonance imaging (fMRI) to investigate the interaction of target directed movement and MVF on the activation of, and functional connectivity between, regions within the visuomotor network. In an event-related fMRI design, twenty healthy subjects performed finger flexion movements using their dominant right hand, with feedback presented in a virtual reality (VR) environment. Visual feedback was presented in real time VR as either veridical feedback with and without a target (VT+ and VT-, respectively), or MVF with and without a target (MT+ and MT-, respectively). fMRI contrasts revealed predominantly activation in the ipsilateral intraparietal sulcus for the main effect of MVF and bilateral superior parietal activation for the main effect of target. Importantly, we noted significant and robust activation lateralized to the ipsilateral parietal cortex alone in the MT+ contrast with respect to the other conditions. This suggests that combining MVF with targeted movements performed using the right hand may redirect enhanced bilateral parietal activation due to target presentation to the ipsilateral cortex. Moreover, functional connectivity analysis revealed that the interaction between the ipsilateral parietal lobe and the motor cortex was significantly greater during target-directed movements with mirror feedback compared to veridical feedback. These findings provide a normative basis to investigate the integrity of these networks in patient populations. Identification of the brain regions involved in target directed movement with MVF in stroke may have important implications for optimal delivery of MVF based therapy

The Association Between Reorganization of Bilateral M1 Topography and Function in Response to Early Intensive Hand Focused Upper Limb Rehabilitation Following Stroke Is Dependent on Ipsilesional Corticospinal Tract Integrity

Transcranial magnetic stimulation (TMS) induced motor evoked potentials (MEPs) are an established proxy of corticospinal excitability. As a binary measure, the presence (MEP+) or absence (MEP-) of ipsilesional hemisphere MEPs early following stroke is a robust indicator of long-term recovery, however this measure does not provide information about spatial cortical reorganization. MEPs have been systematically acquired over the sensorimotor cortex to “map” motor topography. In this investigation we compared the degree to which functional improvements resulting from early (<3 months post-stroke) intensive hand focused upper limb rehabilitation correlate with changes in motor topography between MEP+ and MEP- individuals. Following informed consent, 17 individuals (4 Female, 60.3 ± 9.4 years, 24.6 ± 24.01 days post first time stroke) received 8 one hour-sessions of training with virtual reality (VR)/Robotic simulations. Clinical tests [Box and Blocks Test (BBT), Wolf Motor Function Test (WMFT), Upper Extremity Fugl-Meyer (UEFMA)], kinematic and kinetic assessments [finger Active Range of Motion (finger AROM), Maximum Pinch Force (MPF)], and bilateral TMS mapping of 5 hand muscles were performed prior to (PRE), directly following (POST), and 1 month following (1M) training. Participants were divided into two groups (MEP+, MEP-) based on whether an MEP was present in the affected first dorsal interosseous (FDI) at any time point. MEP+ individuals improved significantly more than MEP- individuals from PRE to 1M on the WMFT, BBT, and finger AROM scores. Ipsilesional hemisphere FDI area increased significantly with time in the MEP+ group. FDI area of the contralesional hemisphere was not significantly different across time points or groups. In the MEP+ group, significant correlations were observed between PRE-1M changes in ipsilesional FDI area and WMFT, BBT, and finger AROM, and contralesional FDI area and UEFMA and MPF. In the MEP- group, no significant correlations were found between changes in contralesional FDI area and functional outcomes. We report preliminary evidence in a small sample that patterns of recovery and the association of recovery to bilateral changes in motor topography may depend on integrity of the ipsilesional cortical spinal tract as assessed by the presence of TMS evoked MEPs

Fast and Expressive Gesture Recognition using a Combination-Homomorphic Electromyogram Encoder

We study the task of gesture recognition from electromyography (EMG), with the goal of enabling expressive human-computer interaction at high accuracy, while minimizing the time required for new subjects to provide calibration data. To fulfill these goals, we define combination gestures consisting of a direction component and a modifier component. New subjects only demonstrate the single component gestures and we seek to extrapolate from these to all possible single or combination gestures. We extrapolate to unseen combination gestures by combining the feature vectors of real single gestures to produce synthetic training data. This strategy allows us to provide a large and flexible gesture vocabulary, while not requiring new subjects to demonstrate combinatorially many example gestures. We pre-train an encoder and a combination operator using self-supervision, so that we can produce useful synthetic training data for unseen test subjects. To evaluate the proposed method, we collect a real-world EMG dataset, and measure the effect of augmented supervision against two baselines: a partially-supervised model trained with only single gesture data from the unseen subject, and a fully-supervised model trained with real single and real combination gesture data from the unseen subject. We find that the proposed method provides a dramatic improvement over the partially-supervised model, and achieves a useful classification accuracy that in some cases approaches the performance of the fully-supervised model.Comment: 24 pages, 7 figures, 6 tables V2: add link to code, fix bibliograph

User Training with Error Augmentation for Electromyogram-based Gesture Classification

We designed and tested a system for real-time control of a user interface by extracting surface electromyographic (sEMG) activity from eight electrodes in a wrist-band configuration. sEMG data were streamed into a machine-learning algorithm that classified hand gestures in real-time. After an initial model calibration, participants were presented with one of three types of feedback during a human-learning stage: veridical feedback, in which predicted probabilities from the gesture classification algorithm were displayed without alteration, modified feedback, in which we applied a hidden augmentation of error to these probabilities, and no feedback. User performance was then evaluated in a series of minigames, in which subjects were required to use eight gestures to manipulate their game avatar to complete a task. Experimental results indicated that, relative to baseline, the modified feedback condition led to significantly improved accuracy and improved gesture class separation. These findings suggest that real-time feedback in a gamified user interface with manipulation of feedback may enable intuitive, rapid, and accurate task acquisition for sEMG-based gesture recognition applications.Comment: 10 pages, 10 figure