New light on how the brain plans for and executes movements in reaction to a "go" signal has been shed by a team of scientists led by Indian-origin electrical engineer.
"This research holds great promise in many areas of neuroscience, in particular human prostheses that can be controlled by the brain," said Krishna Shenoy, who led the study with Maneesh Sahani, both electrical engineers at the Stanford School of Engineering.
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 simply 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 a preparatory period one has, the greater the neural activity will be and, thus, the faster the reaction time.
But the Stanford team was able to document 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 study 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."
"We can watch as the pattern of neural activity gets focused in a specific region at the moment the target appears, and then when the 'go' cue is given, the activity moves again, ending with the successful touching of the target," explained Shenoy.
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 farther away, then 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.
"Our model allows us to predict with four times greater accuracy what the reaction time of any single arm motion is going to be based on the neural activity observed prior to movement," added Sahani.
The findings were recently published in the journal Neuron.