Cessez d'utiliser cette plateforme comme l'un des principaux porte-parole des communications officielles en France. Il existe des alternatives bien mieux modérées et régulées, et il est même possible ...

L’eurodéputé explique, dans un entretien au « Monde », que la gauche est désormais composée d’un pôle autour de La France insoumise et d’un autre qui s’est formé lors des européennes et qu’il entend s...

Le député de la Somme, qui plaide pour un départage ouvert à tous les partis de gauche, se juge le mieux placé pour opérer un «franc changement de cap» et battre l’extrême droite.

Mice are key model organisms in neuroscience and motor systems physiology. Fine motor control tasks performed by mice have become widely used in assaying neural and biophysical motor system mechanisms. Although fine motor tasks provide useful insights into behaviors which require complex multi-joint motor control, there is no previously developed physiological biomechanical model of the adult mouse forelimb available for estimating kinematics nor muscle activity or kinetics during behaviors. Here, we developed a musculoskeletal model based on high-resolution imaging of the mouse forelimb that includes muscles spanning the neck, trunk, shoulder, and limbs. Physics-based optimal control simulations of the forelimb model were used to estimate in vivo muscle activity present when constrained to the tracked kinematics during reaching movements. The activity of a subset of muscles was recorded and used to assess the accuracy of the muscle patterning in simulation. We found that the synthesized muscle patterning in the forelimb model had a strong resemblance to empirical muscle patterning, suggesting that our model has utility in providing a realistic set of estimated muscle excitations over time when given a kinematic template. The strength of the similarity between empirical muscle activity and optimal control predictions increases as mice performance improves throughout learning of the reaching task. Our computational tools are available as open-source in the OpenSim physics and modeling platform. Our model can enhance research into limb control across broad research topics and can inform analyses of motor learning, muscle synergies, neural patterning, and behavioral research that would otherwise be inaccessible.

YouTube video by Michael Black