Search

Epidural spinal cord stimulation has been reported to partially restore volitional movement and autonomic functions after motor and sensory-complete spinal cord injury (SCI). Modern spinal cord stimulation platforms offer significant flexibility in spatial and temporal parameters of stimulation delivered. Heterogeneity in SCI and injury-related symptoms necessitate stimulation personalization to maximally restore functions. However, the large multi-dimensional stimulation space makes exhaustive tests impossible. In this paper, we present a Bayesian optimization strategy for identifying personalized optimal stimulation patterns based on the participant's expressed preference for stimulation settings. We present companion validation protocols for investigating the credibility of learned preference models. The results obtained for five participants in the E-STAND spinal cord stimulation clinical trial are reported. Personalized preference models produced by the proposed learning and optimization algorithm show that there is more similarity in optimal frequency than in pulse width across participants. Across five participants, the average model prediction accuracy is 71.5% in internal cross-validation and 65.6% in prospective validation. Statistical tests of both validation studies show that the ability of the preference models to correctly predict unseen preference data is significantly greater than chance. The personalized preference models are also shown to be significantly correlated with motor task performance across participants. We show that several aspects in participants' quality of life has been improved over the course of the trial. Overall, the results indicate that the Bayesian preference optimization algorithm could assist clinicians in the systematic programming of individualized therapeutic stimulation settings and improve the therapeutic outcomes.

Multiple studies have corroborated restored volitional motor control after motor-complete spinal cord injury (SCI) through the use of spinal cord stimulation (SCS/eSCS) but rigorous quantitative descriptions have been lacking. Using a structured surface electromyogram based (sEMG) task with and without SCS during the Epidural Stimulation After Neurological Damage (ESTAND) study in participants with chronic, motor and sensory complete SCI, we investigated muscle activity complexity and muscle synergies to better characterize neuro-muscular control.In addition, competition exists between the task and neural origin hypotheses underlying muscle synergies, and this analysis in humans with motor and sensory complete chronic injury provided an opportunity to test these hypotheses. Muscle activity complexity was computed with Higuchi Fractal Dimensional analysis (HFD), and muscle synergies were estimated using non-negative matrix factorization (NNMF) in six participants with AIS A chronic SCI. We found that the complexity of muscle activity is immediately reduced with SCS in the SCI participants. We also found that over the follow-up sessions, the muscle synergy structure of the SCI participants became more defined, and the number of synergies decreased over time, indicating improved coordination between the muscle groups. Lastly, we found that the muscle synergies were restored with SCS, supporting the neural hypothesis of muscle synergies. We conclude that SCS restores muscle movements and muscle synergies that are distinct from healthy, able-bodied controls.

Injuries from Less-lethal Weapons during the George Floyd Protests In this analysis involving patients treated in Minnesota during protests after George Floyd’s death, 89 patients were identified w...

Objective Traumatic brain injuries (TBIs) are largely underdiagnosed and may have persistent refractory consequences. Current assessments for acute TBI are limited to physical examination and imaging. Biomarkers such as glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase L1 (UCH-L1), and S100 calcium-binding protein B (S100B) have shown predictive value as indicators of TBI and potential screening tools. Methods In total, 37 controls and 118 unique trauma subjects who received a clinically ordered head computed tomography (CT) in the emergency department of a level 1 trauma center were evaluated. Blood samples collected at 0–8 hours (initial) and 12–32 hours (delayed) postinjury were analyzed for GFAP, UCH-L1, and S100B concentrations. These were then compared in CT-negative and CT-positive subjects. Results Median GFAP, UCH-L1, and S100B concentrations were greater in CT-positive subjects at both timepoints compared with CT-negative subjects. In addition, median UCH-L1 and S100B concentrations were lower at the delayed timepoint, whereas median GFAP concentrations were increased. As predictors of a positive CT of the head, GFAP outperformed UCH-L1 and S100B at both timepoints (initial: 0.89 sensitivity, 0.62 specificity; delayed: 0.94 sensitivity, 0.67 specificity). GFAP alone also outperformed all possible combinations of biomarkers. Conclusions GFAP, UCH-L1, and S100B demonstrated utility for rapid prediction of a CT-positive TBI within 0–8 hours of injury. GFAP exhibited the greatest predictive power at 12–32 hours. Furthermore, these results suggest that GFAP alone has greater utility for predicting a positive CT of the head than UCH-L1, S100B, or any combination of the 3.