While rehabilitation is moving towards an era of big data, we still lack a framework to analyze this data to improve outcomes. We took a stab at this, which we call a "Causal Framework for Precision Rehabilitation."

How does sensorimotor (S1/M1) cortex support adaptive motor control?  Come find out in our latest preprint, which spans the development of a full adult forelimb model + physics simulations, neural-modeling for control, complex 🐭behavior 🕹️, large-scale imaging, and of course @DeepLabCut and @CEBRA!  We hypothesized that S1 supports motor learning by computing prediction errors. To tackle this, we needed to understand what is being represented, and no studies have reported what forelimb S1 represents during learning in mice🧠🐭. Moreover, this requires modeling the body🦾: kinematics, torques, force, muscle activations, & proprioception (muscle spindles & GTOs).  After our 7 year journey, we have an answer: S1 & M1 represent muscle-level features. During learning, computational motifs map to functional types (like muscle-encoding), and neural dynamics in S1 change & encode sensorimotor prediction errors!  🧵👇