By training a group of human subjects to operate a
robot-controlled joystick, Johns Hopkins researchers have
shown that the slower the brain "learns" to control certain
muscle movements, the more likely it is to remember the
lesson over the long haul. The results, the investigators
say, could alter rehabilitation approaches for people who
have lost motor abilities to brain injuries like
strokes.
In a report on the work in the May 23 issue of PLoS
Biology, the researchers built on their observations
that some parts of the brain learn — and forget
— fast, while others learn more slowly and more
lastingly. Both types of learning are critical.
"We believe our work is the first to show that motor
learning involves different time scales and implies that
the best strategy in rehabilitating a stroke patient should
focus on slow learning because slow-learned motor skills
will be maintained longer," said the report's senior
author, Reza
Shadmehr, a professor of
biomedical
engineering in the Institute of Basic Biomedical
Sciences at Johns Hopkins.
Neuroscientists long have thought that two things are
required for mastering such muscle control: time and error.
Time refers to the need to "sleep on it," for the brain to
somehow process and "remember" how to carefully control
muscles. As for error, it's thought that mistakes help the
brain and muscles fine-tune fine movements. The requirement
for time and error explains why repetition of simple
movements day after day is used routinely in rehabilitating
partially paralyzed stroke patients and those with other
brain injuries.
To test the need for time in mastering muscle control,
the research team designed a simple short task. Fourteen
healthy human subjects were asked to hold onto a
robot-controlled joystick and keep it from moving as the
robot driver pushed repeatedly — in quick pulses
— to one side. The joystick then pushed repeatedly in
the opposite direction, and again the subjects were asked
to keep the joystick centered.
The research team found that after all this pushing in
different directions, the subjects still were inclined to
push the joystick in the first direction, even when the
joystick was perfectly centered and not moving. Somehow the
brain and muscles in the arm had "learned" this simple
movement over the course of the experiment, which took only
a few minutes, according to the researchers, showing that
sleep is not required for learning such simple
movements.
The robot-controlled joystick used in these
experiments can measure precisely how hard and in what
direction it's being pushed by the hand holding it. Using
computer programs, the researchers then were able to apply
mathematical equations to these measurements and calculate
predictions of how the brain might be "learning" these
simple movements.
For example, by taking into account the number of
repetitions it took for the subjects to push the joystick
in the first direction to keep it centered and how long it
took for the subjects to "forget" how hard to push the
joystick, the predictions suggest that the brain learns
muscle control using at least two different steps.
First, the computer programs were able to tease out
that the brain picked up the control task quickly but
actually forgot the task quickly as well. But, at the same
time, the brain also was learning the same task more
slowly, and that process was responsible for the subjects'
being able to "remember" the initial joystick-pushing
movement.
"Rehab is about training, and you want to be able to
train the slow-learning system to be successful," Shadmehr
said.
As a next step, the team is interested in uncovering
which parts of the brain are responsible for slow learning.
They hope that teasing this system apart will not only
improve the understanding of brain function but also tailor
therapy strategies to target slow learning and increase
recovery of muscle control after brain injuries.
The researchers were funded by the National Institute
of Neurologic Disorders and Stroke, a branch of the
National Institutes of Health.
Authors on the paper are Maurice Smith, Ali Ghazizadeh
and Shadmehr, all of Johns Hopkins.