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High-frequency brainwave patterns in motor cortex can predict next move

Summary: The spatially organized recruitment of neural activity throughout the motor cortex informs the details of planned movements.

Source: University of Chicago

Nicholas G. Hatsopoulos, PhD, professor of biology and organismal anatomy at the University of Chicago, has long been interested in space. Specifically, the physical space occupied by the brain.

“Inside our heads, the brain is all mashed up. If you were to flatten the human cortex onto a single 2D sheet, it would cover two and a half square feet of space – roughly the size of four pieces of paper. You would think that the brain would take advantage of all this space when organizing patterns of activity, but other than knowing that one part of the brain controls the arm and another part controls the leg, we mostly ignore how the brain can use this spatial organization. .”

Now, in a new study published Jan. 16 in Annals of the National Academy of Sciences, Hatsopoulos and his team found evidence that the brain actually uses the spatial organization of waves to propagate high-frequency neuronal activity during movement.

The presence of propagating waves of neuronal activity has been 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 across 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 multiple electrode arrays implanted in the primary motor cortex of monkeys as the monkeys performed a task that required them to move a joystick. Next, they looked for wave-like patterns of activity, specifically those of high amplitude.

“We focused on the high-frequency band signals because of their wealth of information, ideal spatial range, and ease of obtaining a signal from all electrodes,” said Wei Liang, first author of the study and a graduate student in Hatsopoulos’ lab.

They found that these propagating waves, made up of the activity of hundreds of neurons, traveled in different directions across the cortical surface based on which direction the monkey pushed the joystick.

“It’s like a series of dominoes falling,” said Hatsopoulos. “All the wave patterns we’ve seen in the past didn’t tell us what the animal was doing, it just happened. That’s really exciting because we’re now looking at this wave propagation pattern and we’ve shown that the direction of the wave says something about what the animal is about to do.”

The results provide a new way of looking at cortical function. “It shows that space matters,” said Hatsopoulos. “Instead of just looking at what populations of neurons do and care about, we’re seeing that there’s a spatially organized pattern that carries information. This is a very different way of thinking about things.”

The research 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 on the recorded signals without losing important information.

It shows a brain
This study is the first evidence that spatially organized recruitment of neuronal activity across the motor cortex can inform details of a planned movement. The image is in the public domain

“If you average the attempts, you lose information,” said Hatsopoulos. “If we want to implement this system as part of a brain-machine interface, we cannot average the tests – your decoder needs to do this in real time, as motion is happening, for the system to work effectively.”

Knowing that these waves contain information about movement opens the door to a new dimension of understanding how the brain moves the body, which may, in turn, provide additional information for the computational systems that will drive the brain-machine interfaces of the future.

“The spatial dimension has been ignored until now, but it is a new angle that we can use to understand cortical function,” said Hatsopoulos. “When we try to understand the calculations that the cortex is doing, we must consider how neurons are spatially arranged.”

Future studies will examine whether similar wave patterns are seen in more complicated movements, such as sequential movements as opposed to simple point-to-point reaching, and whether wave-like electrical stimulation of the brain can influence the monkey’s movement.

Financing: The study, “Propagation of Spatiotemporal Activity Patterns Across Monkey Motor Cortex Carries Kinematic Information,” was supported by the National Institutes of Health (R01 NS111982). Additional authors include Karthikeyan Balasubramanianb and Vasileios Papadourakis of the University of Chicago.

See too

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About this movement and news from neuroscience research

Author: Alison Caldwell
Source: University of Chicago
Contact: Alison Caldwell – University of Chicago
Image: The image is in the public domain

Original search: Free access.
“The propagation of spatiotemporal activity patterns through the monkey motor cortex carries kinematic information” by Wei Liang et al. PNAS


Summary

The propagation of spatiotemporal activity patterns through the monkey motor cortex carries kinematic information

The propagation of spatiotemporal neural patterns is largely evident in sensory, motor, and associational cortical areas. However, it remains unclear whether any features of neural propagation carry information about specific behavioral details.

Here, we provide the first evidence of a link between the direction of cortical propagation and specific behavioral characteristics of an upcoming move on a trial-by-trial basis.

We recorded local field potentials (LFPs) from multiple electrode arrays implanted in the primary motor cortex of two rhesus macaques while performing a 2D reaching task. The propagation patterns were extracted from the envelopes of the high gamma band (200 to 400 Hz) rich in information on the amplitude of the LFP.

We found that the exact direction of propagation patterns varied systematically according to the initial direction of movement, allowing for kinematic predictions.

Furthermore, the features of these propagation patterns provided additional predictive capability beyond the LFP amplitude itself, which suggests the value of including mesoscopic spatiotemporal features in the refinement of brain-machine interfaces.

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