Functional electrical stimulation therapy for upper extremity rehabilitation following spinal cord injury: a pilot study

Repertoire of timescales in uni - and transmodal regions mediate working memory capacity

Working memory (WM) describes the dynamic process of maintenance and manipulation of information over a certain time delay. Neuronally, WM recruits a distributed network of cortical regions like the visual and dorsolateral prefrontal cortex as well as the subcortical hippocampus. How the input dynamics and subsequent neural dynamics impact WM remains unclear though. To answer this question, we combined the analysis of behavioral WM capacity with measuring neural dynamics through task-related power spectrum changes, e.g., median frequency (MF) in functional magnetic resonance imaging (fMRI). We show that the processing of the input dynamics, e.g., the task structure's specific timescale, leads to changes in the unimodal visual cortex's corresponding timescale which also relates to working memory capacity. While the more transmodal hippocampus relates to working memory capacity through its balance across multiple timescales or frequencies. In conclusion, we here show the relevance of both input dynamics and different neural timescales for WM capacity in uni - and transmodal regions like visual cortex and hippocampus for the subject's WM performance.

Neuromodulation to guide circuit reorganization with regenerative therapies in upper extremity rehabilitation following cervical spinal cord injury

Spinal cord injury (SCI) is a profoundly debilitating condition with no effective treatment to date. The complex response of the central nervous system (CNS) to injury and its limited regeneration capacity pose bold challenges for restoring function. Cervical SCIs are the most prevalent and regaining hand function is a top priority for individuals living with cervical SCI. A promising avenue for addressing this challenge arises from the emerging field of regenerative rehabilitation, which combines regenerative biology with physical medicine approaches. The hypothesis for optimizing gains in upper extremity function centers on the integration of targeted neurorehabilitation with novel cell- and stem cell-based therapies. However, the precise roles and synergistic effects of these components remain poorly understood, given the intricate nature of SCI and the diversity of regenerative approaches. This perspective article sheds light on the current state of regenerative rehabilitation for cervical SCI. Notably, preclinical research has yet to fully incorporate rehabilitation protocols that mimic current clinical practices, which often rely on neuromodulation strategies to activate spared circuits below the injury level. Therefore, it becomes imperative to comprehensively investigate the combined effects of neuromodulation and regenerative medicine strategies in animal models before translating these therapies to individuals with SCI. In cases of severe upper extremity paralysis, the advent of neuromodulation strategies, such as corticospinal tract (CST) and spinal cord stimulation, holds promise as the next frontier in enhancing the effectiveness of cell- and stem cell-based therapies. Future preclinical studies should explore this convergence of neuromodulation and regenerative approaches to unlock new possibilities for upper extremity treatment after SCI.

A computational model of surface electromyography signal alterations after spinal cord injury

Objective. Spinal cord injury (SCI) can cause significant impairment and disability with an impact on the quality of life for individuals with SCI and their caregivers. Surface electromyography (sEMG) is a sensitive and non-invasive technique to measure muscle activity and has demonstrated great potential in capturing neuromuscular changes resulting from SCI. The mechanisms of the sEMG signal characteristic changes due to SCI are multi-faceted and difficult to studyin vivo. In this study, we utilized well-established computational models to characterize changes in sEMG signal after SCI and identify sEMG features that are sensitive and specific to different aspects of the SCI.Approach. Starting from existing models for motor neuron pool organization and motor unit action potential generation for healthy neuromuscular systems, we implemented scenarios to model damages to upper motor neurons, lower motor neurons, and the number of muscle fibers within each motor unit. After simulating sEMG signals from each scenario, we extracted time and frequency domain features and investigated the impact of SCI disruptions on sEMG features using the Kendall Rank Correlation analysis.Main results. The commonly used amplitude-based sEMG features (such as mean absolute values and root mean square) cannot differentiate between injury scenarios, but a broader set of features (including autoregression and cepstrum coefficients) provides greater specificity to the type of damage present.Significance. We introduce a novel approach to mechanistically relate sEMG features (often underused in SCI research) to different types of neuromuscular alterations that may occur after SCI. This work contributes to the further understanding and utilization of sEMG in clinical applications, which will ultimately improve patient outcomes after SCI.