In a paper published in the August 2011 issue of Neuron, a team at the Stanford University, California, School of Engineering, led by Krishna Shenoy, PhD, associate professor in the departments of Electrical Engineering, Bioengineering, and Neurobiology, and Maneesh Sahani, PhD, visiting assistant professor in the department of electrical engineering, studied how the brain plans for and executes movements in reaction to a “go” signal.
“This research holds great promise in many areas of neuroscience, in particular human prostheses that can be controlled by the brain,” Shenoy said.
The team included graduate students Afsheen Afshar, Gopal Santhanam, and Byron Yu and postdoctoral researcher Stephen Ryu, MD.
The graduate students trained two rhesus monkeys to perform the task of touching a target on cue. The researchers then neurosurgically implanted on the surface of the monkeys’ brains a 4mm-square electronic chip arrayed with 100 tiny electrodes to monitor the neuron traffic.
The researchers concentrated on the area of the brain known as the dorsal pre-motor cortical area, which shows high levels of activity during the delay when arm movement planning takes place. Activity in this region varies depending upon the direction, distance, and speed of a pending movement.
Where most historical data had been limited to single neurons, the new technology allows researchers to monitor in real-time the activity of hundreds of individual neurons down to the millisecond. They can now account for reaction times in single events, something previously not possible.
What the researchers found is a departure from the way many scientists had theorized the process worked. The existing hypothesis, known as “rise-to-threshold,” held that in anticipation of a “go” cue, our brains begin to plan the motions necessary to satisfactorily complete the movement by increasing the activity of neurons. Neurons begin to fire, but not enough to cause the movement to take place. Upon the “go” signal, the brain accelerates this neural firing until it crosses a “threshold” initiating the motion. According to the theory, the longer the preparatory period a person has, the greater the neural activity will be and, thus, the faster the reaction time.
The Stanford team documented a process based less on the amount of activity and more on the trajectory of the neural activity through the brain. In graphs of neural activity prior to display of the target, the monkeys’ neural activity appears somewhat scattered. The moment a target is displayed, however, the neural activity concentrates in an activity region that the researchers dubbed the “optimal sub-space.”
The key to reaction time, the researchers found, is the relationship between where the neural activity is and its speed along the ideal trajectory just prior to the “go” cue. If the neural activity is closer to the final destination, then the reaction time will be shorter; if the neural activity is farther away, the reaction time will be longer.
From this new understanding, the researchers were able to shape a deeper understanding of the neural patterns and craft a model to predict reaction time based on the neural activity observed prior to movement.
Returning to the practical applications, Shenoy and Sahani point to improving neural prostheses as a practical application for this research.
“A fundamental understanding of planning and movement is a central question in building electronic interfaces that convert neural activity into signals that can control computer cursors and prosthetic arms,” Shenoy said. “These are also major areas of our research.”
Sahani added, “For most of us, reaction times usually don’t matter. But if you are an amputee hoping for a state-of-the-art prosthetic hand that you can control with your own brain, then understanding how the brain plans and executes motion is very important.”
The project was supported by the Collaborative Research in Computational Neuroscience (CRCNS) program, a joint initiative of the National Institutes of Health (NIH) and the National Science Foundation (NSF) to support partnerships between experimental and computational neuroscientists. Afshar was supported by the NIH Medical Scientist Training Program, and Shenoy is funded by an NIH Director’s Pioneer Award.
Editor’s note: This story has been adapted from materials provided by Stanford University.
