Decoding hand and wrist movement intention from chronic stroke survivors with hemiparesis using a user-friendly, wearable EMG-based neural interface.

OBJECTIVE: Seventy-five percent of stroke survivors, caregivers, and health care professionals (HCP) believe current therapy practices are insufficient, specifically calling out the upper extremity as an area where innovation is needed to develop highly usable prosthetics/orthotics for the stroke population. A promising method for controlling upper extremity technologies is to infer movement intention non-invasively from surface electromyography (EMG). However, existing technologies are often limited to research settings and struggle to meet user needs. APPROACH: To address these limitations, we have developed the NeuroLife® EMG System, an investigational device which consists of a wearable forearm sleeve with 150 embedded electrodes and associated hardware and software to record and decode surface EMG. Here, we demonstrate accurate decoding of 12 functional hand, wrist, and forearm movements in chronic stroke survivors, including multiple types of grasps from participants with varying levels of impairment. We also collected usability data to assess how the system meets user needs to inform future design considerations. MAIN RESULTS: Our decoding algorithm trained on historical- and within-session data produced an overall accuracy of 77.1 ± 5.6% across 12 movements and rest in stroke participants. For individuals with severe hand impairment, we demonstrate the ability to decode a subset of two fundamental movements and rest at 85.4 ± 6.4% accuracy. In online scenarios, two stroke survivors achieved 91.34 ± 1.53% across three movements and rest, highlighting the potential as a control mechanism for assistive technologies. Feedback from stroke survivors who tested the system indicates that the sleeve's design meets various user needs, including being comfortable, portable, and lightweight. The sleeve is in a form factor such that it can be used at home without an expert technician and can be worn for multiple hours without discomfort. SIGNIFICANCE: The NeuroLife EMG System represents a platform technology to record and decode high-resolution EMG for the real-time control of assistive devices in a form factor designed to meet user needs. The NeuroLife EMG System is currently limited by U.S. federal law to investigational use.

Vagus Nerve Stimulation Enhances Stable Plasticity and Generalization of Stroke Recovery.

BACKGROUND AND PURPOSE: Chronic impairment of the arm and hand is a common consequence of stroke. Animal and human studies indicate that brief bursts of vagus nerve stimulation (VNS) in conjunction with rehabilitative training improve recovery of motor function after stroke. In this study, we tested whether VNS could promote generalization, long-lasting recovery, and structural plasticity in motor networks. METHODS: Rats were trained on a fully automated, quantitative task that measures forelimb supination. On task proficiency, unilateral cortical and subcortical ischemic lesions were administered. One week after ischemic lesion, rats were randomly assigned to receive 6 weeks of rehabilitative training on the supination task with or without VNS. Rats then underwent 4 weeks of testing on a task assessing forelimb strength to test generalization of recovery. Finally, the durability of VNS benefits was tested on the supination task 2 months after the cessation of VNS. After the conclusion of behavioral testing, viral tracing was performed to assess synaptic connectivity in motor networks. RESULTS: VNS enhances plasticity in corticospinal motor networks to increase synaptic connectivity to musculature of the rehabilitated forelimb. Adding VNS more than doubled the benefit of rehabilitative training, and the improvements lasted months after the end of VNS. Pairing VNS with supination training also significantly improved performance on a similar, but untrained task that emphasized volitional forelimb strength, suggesting generalization of forelimb recovery. CONCLUSIONS: This study provides the first evidence that VNS paired with rehabilitative training after stroke (1) doubles long-lasting recovery on a complex task involving forelimb supination, (2) doubles recovery on a simple motor task that was not paired with VNS, and (3) enhances structural plasticity in motor networks.

Median and ulnar nerve injuries reduce volitional forelimb strength in rats.

INTRODUCTION: Peripheral nerve injuries (PNI) are among the leading causes of physical disability in the United States. The majority of injuries occur in the upper extremities, and functional recovery is often limited. Robust animal models are critical first steps for developing effective therapies to restore function after PNI. METHODS: We developed an automated behavioral assay that provides quantitative measurements of volitional forelimb strength in rats. Multiple forelimb PNI models involving the median and ulnar nerves were used to assess forelimb function for up to 13 weeks postinjury. RESULTS: Despite multiple weeks of task-oriented training following injury, rats exhibit significant reductions in multiple quantitative parameters of forelimb function, including maximal pull force and speed of force generation. DISCUSSION: This study demonstrates that the isometric pull task is an effective method of evaluating forelimb function following PNI and may aid in development of therapeutic interventions to restore function. Muscle Nerve 56: 1149-1154, 2017.

Characterization of an Algorithm for Autonomous, Closed-Loop Neuromodulation During Motor Rehabilitation.

BACKGROUND: Recent evidence demonstrates that manually triggered vagus nerve stimulation (VNS) combined with rehabilitation leads to increased recovery of upper limb motor function after stroke. This approach is premised on studies demonstrating that the timing of stimulation relative to movements is a key determinant in the effectiveness of this approach. OBJECTIVE: The overall goal of the study was to identify an algorithm that could be used to automatically trigger VNS on the best movements during rehabilitative exercises while maintaining a desired interval between stimulations to reduce the burden of manual stimulation triggering. METHODS: To develop the algorithm, we analyzed movement data collected from patients with a history of neurological injury. We applied 3 different algorithms to the signal, analyzed their triggering choices, and then validated the best algorithm by comparing triggering choices to those selected by a therapist delivering VNS therapy. RESULTS: The dynamic algorithm triggered above the 95th percentile of maximum movement at a rate of 5.09 (interquartile range [IQR] = 0.74) triggers per minute. The periodic algorithm produces stimulation at set intervals but low movement selectivity (34.05%, IQR = 7.47), while the static threshold algorithm produces long interstimulus intervals (27.16 ± 2.01 seconds) with selectivity of 64.49% (IQR = 25.38). On average, the dynamic algorithm selects movements that are 54 ± 3% larger than therapist-selected movements. CONCLUSIONS: This study shows that a dynamic algorithm is an effective strategy to trigger VNS during the best movements at a reliable triggering rate.

Usage of RePlay as a Take-Home System to Support High-Repetition Motor Rehabilitation After Neurological Injury.

Stroke is a leading cause of chronic motor disability. While physical rehabilitation can promote functional recovery, several barriers prevent patients from receiving optimal rehabilitative care. Easy access to at-home rehabilitative tools could increase patients' ability to participate in rehabilitative exercises, which may lead to improved outcomes. Toward achieving this goal, we developed RePlay: a novel system that facilitates unsupervised rehabilitative exercises at home. RePlay leverages available consumer technology to provide a simple tool that allows users to perform common rehabilitative exercises in a gameplay environment. RePlay collects quantitative time series force and movement data from handheld devices, which provide therapists the ability to quantify gains and individualize rehabilitative regimens. RePlay was developed in C# using Visual Studio. In this feasibility study, we assessed whether participants with neurological injury are capable of using the RePlay system in both a supervised in-office setting and an unsupervised at-home setting, and we assessed their adherence to the unsupervised at-home rehabilitation assignment. All participants were assigned a set of 18 games and exercises to play each day. Participants produced on average 698 ± 36 discrete movements during the initial 1 hour in-office visit. A subset of participants who used the system at home produced 1593 ± 197 discrete movements per day. Participants demonstrated a high degree of engagement while using the system at home, typically completing nearly double the number of assigned exercises per day. These findings indicate that the open-source RePlay system may be a feasible tool to facilitate access to rehabilitative exercises and potentially improve overall patient outcomes.

Increasing Robustness of Brain-Computer Interfaces Through Automatic Detection and Removal of Corrupted Input Signals.

For brain-computer interfaces (BCIs) to be viable for long-term daily usage, they must be able to quickly identify and adapt to signal disruptions. Furthermore, the detection and mitigation steps need to occur automatically and without the need for user intervention while also being computationally tractable for the low-power hardware that will be used in a deployed BCI system. Here, we focus on disruptions that are likely to occur during chronic use that cause some recording channels to fail but leave the remaining channels unaffected. In these cases, the algorithm that translates recorded neural activity into actions, the neural decoder, should seamlessly identify and adjust to the altered neural signals with minimal inconvenience to the user. First, we introduce an adapted statistical process control (SPC) method that automatically identifies disrupted channels so that both decoding algorithms can be adjusted, and technicians can be alerted. Next, after identifying corrupted channels, we demonstrate the automated and rapid removal of channels from a neural network decoder using a masking approach that does not change the decoding architecture, making it amenable for transfer learning. Finally, using transfer and unsupervised learning techniques, we update the model weights to adjust for the corrupted channels without requiring the user to collect additional calibration data. We demonstrate with both real and simulated neural data that our approach can maintain high-performance while simultaneously minimizing computation time and data storage requirements. This framework is invisible to the user but can dramatically increase BCI robustness and usability.

Dynamic detection and reversal of myocardial ischemia using an artificially intelligent bioelectronic medicine.

Myocardial ischemia is spontaneous, frequently asymptomatic, and contributes to fatal cardiovascular consequences. Importantly, myocardial sensory networks cannot reliably detect and correct myocardial ischemia on their own. Here, we demonstrate an artificially intelligent and responsive bioelectronic medicine, where an artificial neural network (ANN) supplements myocardial sensory networks, enabling reliable detection and correction of myocardial ischemia. ANNs were first trained to decode spontaneous cardiovascular stress and myocardial ischemia with an overall accuracy of ~92%. ANN-controlled vagus nerve stimulation (VNS) significantly mitigated major physiological features of myocardial ischemia, including ST depression and arrhythmias. In contrast, open-loop VNS or ANN-controlled VNS following a caudal vagotomy essentially failed to reverse cardiovascular pathophysiology. Last, variants of ANNs were used to meet clinically relevant needs, including interpretable visualizations and unsupervised detection of emerging cardiovascular stress. Overall, these preclinical results suggest that ANNs can potentially supplement deficient myocardial sensory networks via an artificially intelligent bioelectronic medicine system.

Classifying Intracortical Brain-Machine Interface Signal Disruptions Based on System Performance and Applicable Compensatory Strategies: A Review.

Brain-machine interfaces (BMIs) record and translate neural activity into a control signal for assistive or other devices. Intracortical microelectrode arrays (MEAs) enable high degree-of-freedom BMI control for complex tasks by providing fine-resolution neural recording. However, chronically implanted MEAs are subject to a dynamic in vivo environment where transient or systematic disruptions can interfere with neural recording and degrade BMI performance. Typically, neural implant failure modes have been categorized as biological, material, or mechanical. While this categorization provides insight into a disruption's causal etiology, it is less helpful for understanding degree of impact on BMI function or possible strategies for compensation. Therefore, we propose a complementary classification framework for intracortical recording disruptions that is based on duration of impact on BMI performance and requirement for and responsiveness to interventions: (1) Transient disruptions interfere with recordings on the time scale of minutes to hours and can resolve spontaneously; (2) Reversible disruptions cause persistent interference in recordings but the root cause can be remedied by an appropriate intervention; (3) Irreversible compensable disruptions cause persistent or progressive decline in signal quality, but their effects on BMI performance can be mitigated algorithmically; and (4) Irreversible non-compensable disruptions cause permanent signal loss that is not amenable to remediation or compensation. This conceptualization of intracortical BMI disruption types is useful for highlighting specific areas for potential hardware improvements and also identifying opportunities for algorithmic interventions. We review recording disruptions that have been reported for MEAs and demonstrate how biological, material, and mechanical mechanisms of disruption can be further categorized according to their impact on signal characteristics. Then we discuss potential compensatory protocols for each of the proposed disruption classes. Specifically, transient disruptions may be minimized by using robust neural decoder features, data augmentation methods, adaptive machine learning models, and specialized signal referencing techniques. Statistical Process Control methods can identify reparable disruptions for rapid intervention. In-vivo diagnostics such as impedance spectroscopy can inform neural feature selection and decoding models to compensate for irreversible disruptions. Additional compensatory strategies for irreversible disruptions include information salvage techniques, data augmentation during decoder training, and adaptive decoding methods to down-weight damaged channels.

Enhancing plasticity in central networks improves motor and sensory recovery after nerve damage.

Nerve damage can cause chronic, debilitating problems including loss of motor control and paresthesia, and generates maladaptive neuroplasticity as central networks attempt to compensate for the loss of peripheral connectivity. However, it remains unclear if this is a critical feature responsible for the expression of symptoms. Here, we use brief bursts of closed-loop vagus nerve stimulation (CL-VNS) delivered during rehabilitation to reverse the aberrant central plasticity resulting from forelimb nerve transection. CL-VNS therapy drives extensive synaptic reorganization in central networks paralleled by improved sensorimotor recovery without any observable changes in the nerve or muscle. Depleting cortical acetylcholine blocks the plasticity-enhancing effects of CL-VNS and consequently eliminates recovery, indicating a critical role for brain circuits in recovery. These findings demonstrate that manipulations to enhance central plasticity can improve sensorimotor recovery and define CL-VNS as a readily translatable therapy to restore function after nerve damage.

The M-Maze task: An automated method for studying fear memory in rats exposed to protracted aversive conditioning.

BACKGROUND: Fear conditioning (FC) in rodents is the most used animal model to investigate the neurobiology of posttraumatic stress disorder (PTSD). Although research using FC has generated a better understanding of fear memories, studies often rely on mild or moderate FC training and behavioral analysis generally focuses on measuring freezing responses within few test sessions. NEW METHOD: We introduce the M-Maze task, a system that measures extinction of conditioned fear using suppression of operant behavior. The apparatus consists of an M-shaped maze where rats are trained to alternate nose poking at two pellet dispensers. Proximity sensors measure the animal's locomotion, as well as the latencies and number of operant behaviors. Here we also describe the protracted aversive conditioning (PAC), a rat model of severe fear that induces resistant extinction following a 4-day conditioning protocol that combines delay, unpredictable, and short- and long-trace conditioning. RESULTS: An intense one-day auditory FC protocol induced a sharp elevation in transit time and suppression of nose pokes by conditioned cues, but in contrast to what is found in PTSD patients, fear extinction was rapidly observed. On the other hand, PAC alone or in combination with exposure to single prolonged stress induced persistent extinction impairments in M-Maze tests, as well as enhanced anxiety, and social withdrawal. COMPARISON WITH OTHER EXISTING METHODS: The M-Maze task is fully automated and allows multiple animals to be tested simultaneously in long-term experiments. Moreover, PAC training can be an alternative approach to study extinction-resistant fear. CONCLUSIONS: The M-Maze task allows rapid and unbiased measurements of fear-induced suppression. We suggest that long-term assessment of extinction impairments would lead to a better understanding of the neurobiology of persistent fear and the screening for new therapies.

Closed-loop neuromodulation restores network connectivity and motor control after spinal cord injury.

Recovery from serious neurological injury requires substantial rewiring of neural circuits. Precisely-timed electrical stimulation could be used to restore corrective feedback mechanisms and promote adaptive plasticity after neurological insult, such as spinal cord injury (SCI) or stroke. This study provides the first evidence that closed-loop vagus nerve stimulation (CLV) based on the synaptic eligibility trace leads to dramatic recovery from the most common forms of SCI. The addition of CLV to rehabilitation promoted substantially more recovery of forelimb function compared to rehabilitation alone following chronic unilateral or bilateral cervical SCI in a rat model. Triggering stimulation on the most successful movements is critical to maximize recovery. CLV enhances recovery by strengthening synaptic connectivity from remaining motor networks to the grasping muscles in the forelimb. The benefits of CLV persist long after the end of stimulation because connectivity in critical neural circuits has been restored.