Effects of NMES pulse width and intensity on muscle mechanical output and oxygen extraction in able-bodied and paraplegic individuals.

PURPOSE: Neuromuscular Electrical Stimulation (NMES) is commonly used in neuromuscular rehabilitation protocols, and its parameters selection substantially affects the characteristics of muscle activation. Here, we investigated the effects of short pulse width (200 µs) and higher intensity (short-high) NMES or long pulse width (1000 µs) and lower intensity (long-low) NMES on muscle mechanical output and fractional oxygen extraction. Muscle contractions were elicited with 100 Hz stimulation frequency, and the initial torque output was matched by adjusting stimulation intensity. METHODS: Fourteen able-bodied and six spinal cord-injured (SCI) individuals participated in the study. The NMES protocol (75 isometric contractions, 1-s on-3-s off) targeting the knee extensors was performed with long-low or short-high NMES applied over the midline between anterior superior iliac spine and patella protrusion in two different days. Muscle work was estimated by torque-time integral, contractile properties by rate of torque development and half-relaxation time, and vastus lateralis fractional oxygen extraction was assessed by Near-Infrared Spectroscopy (NIRS). RESULTS: Torque-time integral elicited by the two NMES paradigms was similar throughout the stimulation protocol, with differences ranging between 1.4% (p = 0.877; able-bodied, mid-part of the protocol) and 9.9% (p = 0.147; SCI, mid-part of the protocol). Contractile properties were also comparable in the two NMES paradigms. However, long-low NMES resulted in higher fractional oxygen extraction in able-bodied (+ 36%; p = 0.006). CONCLUSION: Long-low and short-high NMES recruited quadriceps femoris motor units that demonstrated similar contractile and fatigability properties. However, long-low NMES conceivably resulted in the preferential recruitment of vastus lateralis muscle fibers as detected by NIRS.

Epidural stimulation for cardiovascular function increases lower limb lean mass in individuals with chronic motor complete spinal cord injury.

NEW FINDINGS: What is the central question of this study? Spinal cord injury results in paralysis and deleterious neuromuscular and autonomic adaptations. Lumbosacral epidural stimulation can modulate motor and/or autonomic functions. Does long-term epidural stimulation for normalizing cardiovascular function affect leg muscle properties? What is the main finding and its importance? Leg lean mass increased after long-term epidural stimulation for cardiovascular function, which was applied in the sitting position and did not activate the leg muscles. Leg muscle strength and fatigue resistance, assessed in a subgroup of individuals, also increased. These adaptations might support interventions for motor recovery and warrant further mechanistic investigation. ABSTRACT: Chronic motor complete spinal cord injury (SCI) results in paralysis and deleterious neuromuscular and autonomic adaptations. Paralysed muscles demonstrate atrophy, loss of force and increased fatigability. Also, SCI-induced autonomic impairment results in persistently low resting blood pressure and heart rate, among other features. We previously reported that spinal cord epidural stimulation (scES) optimized for cardiovascular (CV) function (CV-scES), which is applied in sitting position and does not activate the leg muscles, can maintain systolic blood pressure within a normotensive range during quiet sitting and during orthostatic stress. In the present study, dual-energy X-ray absorptiometry collected from six individuals with chronic clinically motor complete SCI demonstrated that 88 ± 11 sessions of CV-scES (7 days week-1 ; 2 h day-1 in four individuals and 5 h day-1 in two individuals) over a period of ∼6 months significantly increased lower limb lean mass (by 0.67 ± 0.39 kg or 9.4 ± 8.1%; P < 0.001). Additionally, muscle strength and fatigability data elicited by neuromuscular electrical stimulation in three of these individuals demonstrated a general increase (57 ± 117%) in maximal torque output (between 2 and 44 N m in 14 of the 17 muscle groups tested overall) and torque-time integral during intermittent, fatiguing contractions (63 ± 71%; between 7 and 230% in 16 of the 17 muscle groups tested overall). In contrast, whole-body mass and composition did not change significantly. In conclusion, long-term use of CV-scES can have a significant impact on lower limb muscle properties after chronic motor complete SCI.

Correction: Novel stochastic framework for automatic segmentation of human thigh MRI volumes and its applications in spinal cord injured individuals.

[This corrects the article DOI: 10.1371/journal.pone.0216487.].

Novel stochastic framework for automatic segmentation of human thigh MRI volumes and its applications in spinal cord injured individuals.

Severe spinal cord injury (SCI) leads to skeletal muscle atrophy and adipose tissue infiltration in the skeletal muscle, which can result in compromised muscle mechanical output and lead to health-related complications. In this study, we developed a novel automatic 3-D approach for volumetric segmentation and quantitative assessment of thigh Magnetic Resonance Imaging (MRI) volumes in individuals with chronic SCI as well as non-disabled individuals. In this framework, subcutaneous adipose tissue, inter-muscular adipose tissue and total muscle tissue are segmented using linear combination of discrete Gaussians algorithm. Also, three thigh muscle groups were segmented utilizing the proposed 3-D Joint Markov Gibbs Random Field model that integrates first order appearance model, spatial information, and shape model to localize the muscle groups. The accuracy of the automatic segmentation method was tested both on SCI (N = 16) and on non-disabled (N = 14) individuals, showing an overall 0.93±0.06 accuracy for adipose tissue and muscle compartments segmentation based on Dice Similarity Coefficient. The proposed framework for muscle compartment segmentation showed an overall higher accuracy compared to ANTs and STAPLE, two previously validated atlas-based segmentation methods. Also, the framework proposed in this study showed similar Dice accuracy and better Hausdorff distance measure to that obtained using DeepMedic Convolutional Neural Network structure, a well-known deep learning network for 3-D medical image segmentation. The automatic segmentation method proposed in this study can provide fast and accurate quantification of adipose and muscle tissues, which have important health and functional implications in the SCI population.