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Going Through the Motions
May 2007--Tucked away in a darkened room in the Traylor Building, a student stares intently at a screen, playing what appears to be a video game straight out of the 1970s. Over and over again, he maneuvers a green dot into a box using a joystick tethered to a flexible robotic arm, each success highlighted by a digitized beep. This student isn’t playing hooky, however. Rather, this game—specifically the simple, repetitive movement required of it—is part of the day’s work.
At the Laboratory of Computational Motor Control, headed by biomedical engineer Reza Shadmehr, video game-based experiments play a key part in the pursuit of understanding how the brain controls movement, allowing us to perform tricky motions like reaching for a glass of water without even looking, and making them seem simple.
“It’s really a miracle how our bodies move so seamlessly,” Shadmehr marvels. “Look at people recovering from brain damage, how they struggle to even pick up a spoon, and you begin to understand the intricacy of the communication between our brain and hands.”
To answer the fairly conceptual question of how we learn to move, Shadmehr has assembled a team of biologists, physicians, mathematicians and engineers. The brunt of the lab’s efforts involves training volunteers to complete various tasks using the specially designed robotic arm, ranging from simple back-and-forth motions to more intricate patterns. The arm’s resistance can be readily adjusted, and the speed, distance and direction of the trainees’ motions are recorded every 10 milliseconds.
Monitoring the subjects’ patterns has revealed how the brain adapts to new movements. For example, if a subject maneuvers the arm to the left for a few minutes then immediately switches to the right for a few minutes, these two actions wash each other out; when asked to move to the left a second time, he has to relearn the optimal trajectory. If the subject is given a break between the left and right motions, however, he relearns the left motion far quicker.
Observations like these reveal that motor skills share many traits with our declarative memories. While new movements can be learned extremely rapidly, spacing out a particular motion over time leads to better retention.
“Your teachers always tell you that it’s better to study for exams a little bit at a time instead of cramming the night before, because you’ll just forget the material once the exam is over,” says Shadmehr. “And that paradigm fits with how we learn motor skills as well.”
Another similarity lies in how movements and memories are built. By designing a specific repetitive task—like guiding a light into a box—and then deliberately altering one parameter, such as the arm resistance, during training, Shadmehr can see the resulting errors and the brain’s response on the next attempt. Calculating the different error responses shows that the brain combines single aspects of the movement—distance, weight of the object, etc.—to build a virtual model, much like you remember an event by piecing together individual components.
These robotic trials tell only part of the story, though, and the lab applies other disciplines to fill in the blanks. With tools like functional magnetic resonance imaging (fMRI), Shadmehr and his team have mapped out which brain regions respond to new movements and which ones process movement errors. He also has begun working with transcranial magnetic stimulation (TMS), a technique that briefly and safely disrupts information processing in a targeted brain region to assess how it affects the ability to learn and remember movements.
Shadmehr hopes these tools can help motor-impaired people such as stroke patients or individuals with Huntington’s disease regain control of their movements. If he knows which part of the brain has been damaged and what relearning hurdles have to be overcome, he can model effective rehabilitation programs.
And while great strides have been made, Shadmehr notes that brain science is still in a state of relative infancy. He’s now taking the next step, so to speak, having recently completed a new robotic device featuring two separate arms.
“We can now begin examining bimanual coordination,” he says. “Seeing how we adapt when each arm is undertaking a different task, and testing whether something learned with one hand can be transferred to the other.”
That means there are more movements to model, and more volunteers needed. So if you happen to be reading this over your morning coffee and wondering how you manage to lift the mug and take a sip without spilling a drop while reaching for a donut with your other hand, feel free to drop by Shadmehr’s lab.
Reza Shadmehr on motor learning