Health Medicine Researchers successfully heal rats' broken spines A new study paves the way toward developing a treatment for spinal cord injuries. Breakthroughs, discoveries, and DIY tips sent every weekday. There is currently no way to completely reverse the damage and paralysis that results from a spinal cord injury. One of the biggest obstacles is that neurons die and can't regrow across the damage. Now, researchers have developed a biomedical structure that has given impressive functional recovery to lab rats with severed spinal cords.

Autonomic Dysreflexia (AD) is a potentially life-threatening condition characterized by sudden, severe blood pressure (BP) spikes in individuals with spinal cord injury (SCI). Early, accurate detection is essential to prevent cardiovascular complications, yet current monitoring methods are either invasive or rely on subjective symptom reporting, limiting applicability in daily file. This study presents a non-invasive, explainable machine learning framework for detecting AD using multimodal wearable sensors. Data were collected from 27 individuals with chronic SCI during urodynamic studies, including electrocardiography (ECG), photoplethysmography (PPG), bioimpedance (BioZ), temperature, respiratory rate (RR), and heart rate (HR), across three commercial devices. Objective AD labels were derived from synchronized cuff-based BP measurements. Following signal preprocessing and feature extraction, BorutaSHAP was used for robust feature selection, and SHAP values for explainability. We trained modality- and device-specific weak learners and aggregated them using a stacked ensemble meta-model. Cross-validation was stratified by participants to ensure generalizability. HR- and ECG-derived features were identified as the most informative, particularly those capturing rhythm morphology and variability. The Nearest Centroid ensemble yielded the highest performance (Macro F1 = 0.77+/-0.03), significantly outperforming baseline models. Among modalities, HR achieved the highest area under the curve (AUC = 0.93), followed by ECG (0.88) and PPG (0.86). RR and temperature features contributed less to overall accuracy, consistent with missing data and low specificity. The model proved robust to sensor dropout and aligned well with clinical AD events. These results represent an important step toward personalized, real-time monitoring for individuals with SCI.

Maintaining an active lifestyle is vital for quality of life, yet challenging for wheelchair users. For instance, powered wheelchairs face increasing risks of obesity and deconditioning due to inactivity. Conversely, manual wheelchair users, who propel the wheelchair by pushing the wheelchair's handrims, often face upper extremity injuries from repetitive motions. These challenges underscore the need for a mobility system that promotes activity while minimizing injury risk. Maintaining optimal exertion during wheelchair use enhances health benefits and engagement, yet the variations in individual physiological responses complicate exertion optimization. To address this, we introduce PulseRide, a novel wheelchair system that provides personalized assistance based on each user's physiological responses, helping them maintain their physical exertion goals. Unlike conventional assistive systems focused on obstacle avoidance and navigation, PulseRide integrates real-time physiological data-such as heart rate and ECG-with wheelchair speed to deliver adaptive assistance. Using a human-in-the-loop reinforcement learning approach with Deep Q-Network algorithm (DQN), the system adjusts push assistance to keep users within a moderate activity range without under- or over-exertion. We conducted preliminary tests with 10 users on various terrains, including carpet and slate, to assess PulseRide's effectiveness. Our findings show that, for individual users, PulseRide maintains heart rates within the moderate activity zone as much as 71.7 percent longer than manual wheelchairs. Among all users, we observed an average reduction in muscle contractions of 41.86 percent, delaying fatigue onset and enhancing overall comfort and engagement. These results indicate that PulseRide offers a healthier, adaptive mobility solution, bridging the gap between passive and physically taxing mobility options.

Multiple brain-computer interface (BCI) projects are currently underway, but BrainGate is one of the first aimed at motor restoration in users affected by neurodegenerative disorders and spinal cord injuries. Researchers have spent years working through the device's clinical trial phases, but their most recent breakthrough isn't focused on physical accomplishments. Instead, the latest achievements could pave the way for people with disabilities to more easily utilize complex computer software, communicate with loved ones, work remotely, and even make music. According to a study published by BrainGate engineers on January 20 in the journal Nature Medicine, a volunteer with quadriplegia can now maintain unprecedented control over a virtual object using their surgically implanted BrainGate BCI device. To demonstrate the ability, the patient guided a virtual rotocopter through hoops in a digital obstacle course by simply thinking about moving the fingers on one of their hands.

Focused ultrasound (FUS) therapy is a promising tool for optimally targeted treatment of spinal cord injuries (SCI), offering submillimeter precision to enhance blood flow at injury sites while minimizing impact on surrounding tissues. However, its efficacy is highly sensitive to the placement of the ultrasound source, as the spinal cord's complex geometry and acoustic heterogeneity distort and attenuate the FUS signal. Current approaches rely on computer simulations to solve the governing wave propagation equations and compute patient-specific pressure maps using ultrasound images of the spinal cord anatomy. While accurate, these high-fidelity simulations are computationally intensive, taking up to hours to complete parameter sweeps, which is impractical for real-time surgical decision-making. To address this bottleneck, we propose a convolutional deep operator network (DeepONet) to rapidly predict FUS pressure fields in patient spinal cords. Unlike conventional neural networks, DeepONets are well equipped to approximate the solution operator of the parametric partial differential equations (PDEs) that govern the behavior of FUS waves with varying initial and boundary conditions (i.e., new transducer locations or spinal cord geometries) without requiring extensive simulations. Trained on simulated pressure maps across diverse patient anatomies, this surrogate model achieves real-time predictions with only a 2% loss on the test set, significantly accelerating the modeling of nonlinear physical systems in heterogeneous domains. By facilitating rapid parameter sweeps in surgical settings, this work provides a crucial step toward precise and individualized solutions in neurosurgical treatments.

Zapping the brain has allowed partially paralysed patients to walk again in a'major milestone' for wheelchair users. Deep brain stimulation has been found to improve walking and promote recovery in two people with a spinal cord injury. The surgical procedure involves implanting electrodes into the brain to produce electrical impulses. These can be easily switched'on' and'off'. Traditionally, it has been used to treat movement disorders like Parkinson's by targeting areas of the brain responsible for motor control.

While deep learning has catalyzed breakthroughs across numerous domains, its broader adoption in clinical settings is inhibited by the costly and time-intensive nature of data acquisition and annotation. To further facilitate medical machine learning, we present an ultrasound dataset of 10,223 Brightness-mode (B-mode) images consisting of sagittal slices of porcine spinal cords (N=25) before and after a contusion injury. We additionally benchmark the performance metrics of several state-of-the-art object detection algorithms to localize the site of injury and semantic segmentation models to label the anatomy for comparison and creation of task-specific architectures. Finally, we evaluate the zero-shot generalization capabilities of the segmentation models on human ultrasound spinal cord images to determine whether training on our porcine dataset is sufficient for accurately interpreting human data. Our results show that the YOLOv8 detection model outperforms all evaluated models for injury localization, achieving a mean Average Precision (mAP50-95) score of 0.606. Segmentation metrics indicate that the DeepLabv3 segmentation model achieves the highest accuracy on unseen porcine anatomy, with a Mean Dice score of 0.587, while SAMed achieves the highest Mean Dice score generalizing to human anatomy (0.445). To the best of our knowledge, this is the largest annotated dataset of spinal cord ultrasound images made publicly available to researchers and medical professionals, as well as the first public report of object detection and segmentation architectures to assess anatomical markers in the spinal cord for methodology development and clinical applications.

A trip to a Pennsylvania lake turned into a tragedy for one man who was left paralyzed after running into the water for a swim. Noland Arbaugh, 29, recalls being hit on the side of the head by another person, leaving him unable to move his body from the shoulders down when he woke up face down in the lake. The 2016 accident led him on a journey to become Neuralink's patient zero this year, which saw him receive a brain implant that lets him control computers and other devices. 'I was a little worried it wouldn't work because [that could happen] with the first of anything, but I wanted to be the first to test all of that out,' he said in an interview on The Kim Komando Show. 'If anyone was going to go through it, to experience the downsides, I wanted to take that on as much as possible to help people after me.'

Neuralink has successfully implanted in a second patient its device designed to give paralyzed patients the ability to use digital devices by thinking alone, according to the startup's owner Elon Musk. Neuralink is in the process of testing its device, which is intended to help people with spinal cord injuries. The device has allowed the first patient to play video games, browse the internet, post on social media and move a cursor on his laptop. Musk, in comments made during a podcast released late on Friday that ran more than eight hours, gave few details about the second participant beyond saying the person had a spinal cord injury similar to the first patient, who was paralyzed in a diving accident. Musk said 400 of the implant's electrodes on the second patient's brain are working.

Spinal cord injury (SCI) is a devastating incidence leading to permanent paralysis and loss of sensory-motor functions potentially resulting in the formation of lesions within the spinal cord. Imaging biomarkers obtained from magnetic resonance imaging (MRI) scans can predict the functional recovery of individuals with SCI and help choose the optimal treatment strategy. Currently, most studies employ manual quantification of these MRI-derived biomarkers, which is a subjective and tedious task. In this work, we propose (i) a universal tool for the automatic segmentation of intramedullary SCI lesions, dubbed SCIsegV2, and (ii) a method to automatically compute the width of the tissue bridges from the segmented lesion. Tissue bridges represent the spared spinal tissue adjacent to the lesion, which is associated with functional recovery in SCI patients. The tool was trained and validated on a heterogeneous dataset from 7 sites comprising patients from different SCI phases (acute, sub-acute, and chronic) and etiologies (traumatic SCI, ischemic SCI, and degenerative cervical myelopathy). Tissue bridges quantified automatically did not significantly differ from those computed manually, suggesting that the proposed automatic tool can be used to derive relevant MRI biomarkers. SCIsegV2 and the automatic tissue bridges computation are open-source and available in Spinal Cord Toolbox (v6.4 and above) via the sct_deepseg -task seg_sc_lesion_t2w_sci and sct_analyze_lesion functions, respectively. Keywords: Spinal Cord Injury Segmentation MRI Deep Learning Tissue Bridges these authors contributed equally to this work joint senior authors arXiv:2407.17265v1