Search

Spinal cord injury (SCI) is damage to any part of the spinal cord resulting in paralysis, bowel and/or bladder incontinence, and loss of sensation and other bodily functions. Current treatments for chronic SCI are focused on managing symptoms and preventing further damage to the spinal cord with limited neuro-restorative interventions. Recent research and independent clinical trials of spinal cord stimulation (SCS) or intensive neuro-rehabilitation including neuro-robotics in participants with SCI have suggested potential malleability of the neuronal networks for neurological recovery. We hypothesize that epidural electrical stimulation (EES) delivered via SCS in conjunction with mental imagery practice and robotic neuro-rehabilitation can synergistically improve volitional motor function below the level of injury in participants with chronic clinically motor-complete SCI. In our pilot clinical RESTORES trial (RESToration Of Rehabilitative function with Epidural spinal Stimulation), we investigate the feasibility of this combined multi-modal approach in restoring volitional motor control and achieving independent overground locomotion in participants with chronic motor complete thoracic SCI. Secondary aims are to assess the safety of this combination therapy including the off-label SCS usage as well as improving functional outcome measures. To our knowledge, this is the first clinical trial that investigates the combined impact of this multi-modal EES and rehabilitation strategy in participants with chronic motor complete SCI. Two participants with chronic motor-complete thoracic SCI were recruited for this pilot trial. Both participants have successfully regained volitional motor control below their level of SCI injury and achieved independent overground walking within a month of post-operative stimulation and rehabilitation. There were no adverse events noted in our trial and there was an improvement in post-operative truncal stability score. Results from this pilot study demonstrates the feasibility of combining EES, mental imagery practice and robotic rehabilitation in improving volitional motor control below level of SCI injury and restoring independent overground walking for participants with chronic motor-complete SCI. Our team believes that this provides very exciting promise in a field currently devoid of disease-modifying therapies. [ABSTRACT FROM AUTHOR]

Background. Brain–machine interfaces (BMIs) offer users the ability to directly communicate with digital devices through neural signals decoded with machine learning (ML)-based algorithms. Spiking Neural Networks (SNNs) are a type of Artificial Neural Network (ANN) that operate on neural spikes instead of continuous scalar outputs. Compared to traditional ANNs, SNNs perform fewer computations, use less memory, and mimic biological neurons better. However, SNNs only retain information for short durations, limiting their ability to capture long-term dependencies in time-variant data. Here, we propose a novel spike-weighted SNN with spiking long short-term memory (swSNN-SLSTM) for a regression problem. Spike-weighting captures neuronal firing rate instead of membrane potential, and the SLSTM layer captures long-term dependencies. Methods. We compared the performance of various ML algorithms during decoding directional movements, using a dataset of microelectrode recordings from a macaque during a directional joystick task, and also an open-source dataset. We thus quantified how swSNN-SLSTM performed compared to existing ML models: an unscented Kalman filter, LSTM-based ANN, and membrane-based SNN techniques. Result. The proposed swSNN-SLSTM outperforms both the unscented Kalman filter, the LSTM-based ANN, and the membrane based SNN technique. This shows that incorporating SLSTM can better capture long-term dependencies within neural data. Also, our proposed swSNN-SLSTM algorithm shows promise in reducing power consumption and lowering heat dissipation in implanted BMIs. [ABSTRACT FROM AUTHOR]