"Conceptually, this error-clamp is analogous to a voltage-clamp, commonly used in electrophysiology to measure how ions move through a neuron's membrane when it fires," explains lead author Gary C. Sing, a graduate student at SEAS. "The general idea is that devising an experimental method to clamp and control the key variable in an experiment can allow for greater insight into the underlying physiology."
Analysis of the data extracted by the error-clamp technique led to the creation of a computational model that identifies a set of vectors that characterize the principal components of motor adaptation in the state space of physical motion. While such analysis is commonplace in systems engineeringfor example, in evaluating how a bridge might react to high winds or earthquakesthe method has only been recently applied to how motor output evolves.
"We observed that the initial stages of motor learning are often quick but non-specific, whereas later stages of learning are slower and more precise," says Sing. "Further, we saw that some physical patterns of movement are learned more quickly than others."
By understanding what types of motor adaptations are easier to learn, the researchers hope to design rehabilitation activities that will encourage patients to use an affected limb more.
"In stroke rehabilitation, patients who make a greater effort to use their impaired limbs can achieve better outcomes," says Smith. "However, there is often a vicious cycle, as a patient is far less likely to use an impaired limb if his or her other limb is fine. This pattern slows recovery and leads to greater impairment of the affected limb."
Smith and his colleagues are beginning studies with stroke patients to determine whether training them with such optimized patterns will, in fact, improve their rate of motor learning and speed up recovery.
|Contact: Michael Patrick Rutter|