title: Why variability facilitates spinal learning.
abstract: Spinal Wistar Hannover rats trained to step bipedally on a treadmill with manual assistance of the hindlimbs have been shown to improve their stepping ability. Given the improvement in motor performance with practice and the ability of the spinal cord circuitry to learn to step more effectively when the mode of training allows variability, we examined why this intrinsic variability is an important factor. Intramuscular EMG electrodes were implanted to monitor and compare the patterns of activation of flexor (tibialis anterior) and extensor (soleus) muscles associated with a fixed-trajectory and assist-as-needed (AAN) step training paradigms in rats after a complete midthoracic (T8-T9) spinal cord transection. Both methods involved a robotic arm attached to each ankle of the rat to provide guidance during stepping. The fixed trajectory allowed little variance between steps, and the AAN provided guidance only when the ankle deviated a specified distance from the programmed trajectory. We hypothesized that an AAN paradigm would impose fewer disruptions of the control strategies intrinsic to the spinal locomotor circuitry compared with a fixed trajectory. Intrathecal injections of quipazine were given to each rat to facilitate stepping. Analysis confirmed that there were more corrections within a fixed-trajectory step cycle and consequently there was less coactivation of agonist and antagonist muscles during the AAN paradigm. These data suggest that some critical level of variation in the specific circuitry activated and the resulting kinematics reflect a fundamental feature of the neural control mechanisms even in a highly repetitive motor task.
authors: Department of Integrative Biology and Physiology, Brain Research Institute, University of California, Los Angeles, Los Angeles, California 90095, USA.
authors: Matthias D
authors: MD
authors: Ziegler
authors: Hui
authors: H
authors: Zhong
authors: Roland R
authors: RR
authors: Roy
authors: V Reggie
authors: VR
authors: Edgerton
mesh_terms: D000465:Algorithms; D000818:Animals; D004195:Disease Models, Animal; D004576:Electromyography / Q000379:methods; D005081:Exercise Therapy / Q000295:instrumentation / Q000379:methods*; D005260:Female; D007858:Learning / Q000502:physiology*; D009043:Motor Activity / Q000502:physiology; D009048:Motor Skills / Q000502:physiology; D018482:Muscle, Skeletal / Q000503:physiopathology; D011336:Probability; D011814:Quipazine / Q000627:therapeutic use; D051381:Rats; D017208:Rats, Wistar; D020127:Recovery of Function / Q000502:physiology*; D012371:Robotics / Q000379:methods; D017366:Serotonin Receptor Agonists / Q000627:therapeutic use; D013119:Spinal Cord Injuries / Q000188:drug therapy / Q000503:physiopathology* / Q000534:rehabilitation*
chemical_list: D017366:Serotonin Receptor Agonists; D011814:Quipazine