Overview: Spatially organized recruitment of neural activity in the motor cortex informs details about planned movements.
Source: University of Chicago
Nicholas G. Hatsopoulos, PhD, professor of Organic Biology and Anatomy at the University of Chicago, has long had an interest in space. Specifically, the physical space occupied by the brain.
“In our head, the brain is all crumpled up. If you were to flatten the human cortex into a single 2D sheet, it would take up two and a half square feet of space — about the size of four pieces of paper. You’d think the brain would take advantage of all that space in organizing patterns of activity, but beyond the knowledge that one part of the brain controls the arm and another the leg, we’ve largely ignored how the brain could do that spatial organization. to use. .”
Now in a new study published Jan. 16 in Proceedings of the National Academy of Sciences, Hatsopoulos and his team found evidence that the brain does indeed make use of the spatial organization of high-frequency propagating waves of neuronal activity during movement.
The presence of propagating waves of neuronal activity is well established, but they are traditionally associated with an animal’s general behavioral state (such as awake or asleep). This study is the first evidence that spatially organized recruitment of neuronal activity in the motor cortex can inform details of a planned movement.
The team hopes the work will help inform how researchers and engineers decode motor information to build better brain-machine interfaces.
To conduct the study, the researchers recorded the activity of multi-electrode arrays implanted in the primary motor cortex of macaque monkeys as the monkeys performed a task that involved moving a joystick. They then looked for wave-like activity patterns, particularly those with high amplitude.
“We focused on the high-frequency band signals because of the rich information, ideal spatial range, and ease of obtaining signals in each electrode,” said Wei Liang, first author of the study and a graduate student at Hatsopoulos University. lab.
They found that these propagating waves, made up of the activity of hundreds of neurons, traveled in different directions across the cortical surface depending on which direction the monkey pushed the joystick.
“It’s like a series of falling dominoes,” Hatsopoulos said. “All the wave patterns we’ve seen in the past didn’t tell us what the animal was doing, it was just going to happen. This is very exciting because we are now looking at this propagating wave pattern and showing that the direction the wave is going says something about what the animal is about to do.”
The results provide a new way to look at cortical function. “This shows that space matters,” Hatsopoulos said. “Instead of just looking at what populations of neurons do and care about, we see that there are spatially organized patterns that hold information. This is a very different way of thinking about things.”
The study was challenging due to the fact that they were studying the activity patterns of individual movements, rather than averaging the recordings over repeated trials, which can be quite noisy. The team was able to develop a computational method to clean up the data to provide clarity about the recorded signals without losing important information.
“If you average trials, you miss information,” Hatsopoulos said. “If we want to implement this system as part of a brain-machine interface, we can’t do averaging — your decoder has to do it during the motion, while the motion is happening for the system to work effectively.”
Knowing that these waves carry information about movement opens the door to a new dimension of understanding how the brain moves the body, which in turn can provide additional information for the computer systems that will power the brain-machine interfaces of the future.
“The spatial dimension has been largely ignored until now, but it’s a new angle we can use to understand cortical function,” Hatsopoulos said. “If we’re trying to understand the computations that the cortex does, we need to consider how the neurons are spatially arranged.”
Future studies will investigate whether similar wave patterns are seen in more complicated movements, such as sequential movements as opposed to simple point-to-point reaching, and whether or not wave-like electrical stimulation of the brain can affect the monkey’s movement.
financing: The study, “Propagating spatiotemporal activity patterns across macaque motor cortex carry kinematic information,” was supported by the National Institutes of Health (R01 NS111982). Other authors include Karthikeyan Balasubramanianb and Vasileios Papadourakis of the University of Chicago.
About this movement and neuroscience research news
Writer: Alison Caldwell
Source: University of Chicago
Contact: Alison Caldwell – University of Chicago
Image: The image is in the public domain
Original research: Open access.
“Propagating spatiotemporal activity patterns across the macaque motor cortex carries kinematic information” by Wei Liang et al. PNAS
Propagation of spatiotemporal activity patterns across the motor cortex of macaque monkeys carry kinematic information
Spatiotemporal neural patterns that propagate are widely apparent in sensory, motor, and associative cortical regions. However, it remains unclear whether features of neural reproduction contain information about specific behavioral details.
Here we provide the first evidence for an association between the direction of cortical propagation and specific behavioral features of an upcoming trial-based movement.
We recorded local field potentials (LFPs) from multi-electrode arrays implanted in the primary motor cortex of two rhesus monkeys while performing a 2D range task. Propagation patterns were extracted from the high gamma band (200 to 400 Hz) information-rich envelopes in the LFP amplitude.
We found that the exact direction of propagation patterns varied systematically according to the initial direction of motion, allowing for kinematic predictions.
In addition, features of these propagation patterns provided additional predictive power beyond LFP amplitude itself, suggesting the value of incorporating mesoscopic spatiotemporal features in refining brain-machine interfaces.