The combination of voluntary effort and functional electrical stimulation (ES) appears to have a greater potential to induce plasticity in the motor cortex than either electrical stimulation or voluntary training alone. However, it is not clear whether the motor commands from the central nervous system, the afferent input from peripheral organs, or both, are indispensable to induce the facilitative effects on cortical excitability. To clarify whether voluntary motor commands enhance corticospinal tract (CoST) excitability during neuromuscular ES, without producing voluntary muscular contraction (VMC), we examined the effect of a combination of motor imagery (MI) and electrical muscular stimulation on CoST excitability using transcranial magnetic stimulation (TMS).

Background and objectives: There are no reports on articular stress distribution during walking based on any computed tomography (CT)-finite element model (CT-FEM). This study aimed to develop a calculation model of the load response (LR) phase, the most burdensome phase on the knee, during walking using the finite element method of quantitative CT images. Materials and Methods: The right knee of a 43-year-old man who had no history of osteoarthritis or surgeries of the knee was examined. An image of the knee was obtained using CT and the extension position image was converted to the flexion angle image in the LR phase. The bone was composed of heterogeneous materials. The ligaments were made of truss elements; therefore, they do not generate strain during expansion or contraction and do not affect the reaction force or pressure. The construction of the knee joint included material properties of the ligament, cartilage, and meniscus. The extensor and flexor muscles were calculated and set as the muscle exercise tension around the knee joint. Ground reaction force was vertically applied to suppress the rotation of the knee, and the thigh was restrained. Results: An FEM was constructed using a motion analyzer, floor reaction force meter, and muscle tractive force calculation. In a normal knee, the equivalent stress and joint contact reaction force in the LR phase were distributed over a wide area on the inner upper surface of the femur and tibia. Conclusions: We developed a calculation model in the LR phase of the knee joint during walking using a CT-FEM. Methods to evaluate the heteromorphic risk, mechanisms of transformation, prevention of knee osteoarthritis, and treatment may be developed using this model.

This study aimed to evaluate the mechanism of progression involved in knee osteoarthritis (OA). We used the computed tomography-based finite element method (CT-FEM) of quantitative X-ray CT imaging to calculate and create a model of the load response phase, wherein the greatest burden is placed on the knee joint while walking. Weight gain was simulated by asking a male individual with a normal gait to carry sandbags on both shoulders. We developed a CT-FEM model that incorporated walking characteristics of individuals. Upon simulating changes owing to a weight gain of approximately 20%, the equivalent stress increased extensively in both medial and lower leg aspects of the femur and increased medio-posteriorly by approximately 230%. As the varus angle increased, stress on the surface of the femoral cartilage did not change significantly. However, the equivalent stress on the surface of the subchondral femur was distributed over a wider area, increasing by approximately 170% in the medio-posterior direction. The range of equivalent stress affecting the lower-leg end of the knee joint widened, and stress on the posterior medial side also increased significantly. It was reconfirmed that weight gain and varus enhancement increase knee-joint stress and cause the progression of OA.

Muscle activity changes quantitatively and temporally during the motor learning process. However, the association between variability in muscle electrical activity and the learning and performance of dexterous hand movements is not well understood. Therefore, we undertook this study to investigate the relationships between temporal and quantitative variabilities in muscle activity and the learning of motor skills. Thirty-eight healthy participants performed 30 trials of a task that measured the time taken to rotate two cork balls 20 times using their non-dominant hand. The electromyographic (EMG) activities of the abductor pollicis brevis (APB), first dorsal interosseous, and extensor digitorum (ED) muscles were recorded. Temporal and quantitative variabilities in the EMG activity were evaluated by calculating the coefficient of variation of the duration and area of EMG activation. As motor learning proceeded, the task was completed more quickly and the EMG variability decreased. For all three muscles, significant correlations were observed between individual participants’ ball rotation time and EMG variability. Furthermore, significant positive correlations were observed between improvement in ball rotation time and reduction in EMG variability for the APB and ED muscles. These novel findings provide important insights regarding the relationships between temporal and quantitative variabilities in muscle activity and the learning of fine motor skills.