Colored barriers on the screen created a simple maze. When the targets stopped jittering, the monkeys were trained to move to one or the other target by sweeping its fingertip through the maze until it touched one of the targets.
Image courtesy of the study authors and eLife.
Stanford University researchers said they have developed an algorithm that allowed them to record the moment-by-moment fluctuations in brain signals that occur when a laboratory monkey, making free choices, reaches its arm during an experiment. The findings result from experiments led by Krishna Shenoy, PhD, an electrical engineering professor whose Stanford lab focuses on movement control and neural prostheses, such as brain-controlled prosthetic and robotic arms.
“This basic neuroscience discovery will help create neural prostheses that can withhold moving a prosthetic arm until the user is certain of their decision, thereby averting premature or inopportune movements,” Shenoy said.
The experiments, described in a study published May 5 in the open-access journal eLife, were performed by neuroscientist Matthew Kaufman, PhD, while he was a graduate student in Shenoy’s lab.
Kaufman taught laboratory monkeys to perform a decision-making task. He then developed a technique to track the brain signals that occur during a single decision with split-second accuracy. This improvement on the “single-trial decoder” algorithm revealed the neural signals that occurred during a momentary hesitation or when the monkey changed its mind.
“The most critical result of our work here is that we can track a single decision and see how the monkey arrived there: whether he decided quickly, slowly, or changed his mind halfway through,” said Kaufman, who is now a postdoctoral scholar at Cold Spring Harbor Laboratory, New York.
The experiments involved monkeys that were trained to reach for either of two targets on a computer screen. It was often possible to reach either target, inviting a free choice. Sometimes, one target was blocked, resulting in a forced choice. Other times, the researchers would switch between these configurations while the monkey was deciding, encouraging a change of mind. The research focused on the time the monkey spent deliberating, before the actual movement began. During the experiments, 192 electrodes in each monkey’s motor and premotor cortex began measuring brain activity the moment that the targets appeared on screen. The measurements continued until the targets stopped “jittering” and the monkey began to move. The interval between the targets’ appearance and the monkey beginning to move marked the time of decision or, in some cases, hesitation.
Using his single-trial decoder algorithm, Kaufman could analyze moment-by-moment brain activity during each individual decision. In a sense, he was able to read the monkey’s mind during free choices, when each decision may be different. In previous experiments on decision-making, researchers have had monkeys perform many trials and average the readings they obtain to get summary statistics. But these older approaches do not allow researchers to identify unique or idiosyncratic events during any individual decision.
“We can now track single decisions with unprecedented precision,” Kaufman said. “We saw that the brain activity for a typical free choice looked just like it did for a forced choice. But a few of the free choices were different. Occasionally, [the monkey] was indecisive for a moment before he made any plan at all. About one time in eight, he made a plan quickly but spontaneously changed his mind a moment later.”
This deeper understanding of decision-making will help researchers to fine-tune the control algorithms of neural prostheses to enable people with paralysis to drive a brain-controlled prosthetic arm or guide a neurally activated cursor on a computer screen.
Editor’s note: This story was adapted from materials provided by Stanford University.
