High-frequency brain wave patterns in the motor cortex can predict upcoming movement

summary: Spatially regulated recruitment of neural activity across the motor cortex informs the details of planned movements.

Source: University of Chicago

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

“Inside our heads, the whole brain is packed together. If you flattened the human cortex into a single two-dimensional sheet of paper, it would cover two and a half square feet—about the size of four pieces of paper. You would think the brain would make use of all that space when organizing patterns of activity, but apart from Knowing that one piece of the brain controls the arm and another the leg, we have mostly ignored how the brain might use this spatial organization.”

Now in a new study published Jan. 16 in the Proceedings of the National Academy of Sciences, Hatsopoulos and his team found evidence that the brain does indeed use the spatial organization of high-frequency propagating waves of neural activity during movement.

Reproducible waves of neural activity have been shown to exist, but they are traditionally associated with the general behavioral state of the animal (such as alertness or sleep). This study is the first evidence that spatially organized recruitment of neural activity across the motor cortex can inform the details of striatal movement.

The team hopes the work will help show researchers and engineers how to decode movement information to build better brain-machine interfaces.

To conduct the study, the researchers recorded activity from multielectrode arrays implanted in the primary motor cortex of macaques while the monkeys performed a task that required them to move a joystick. Next, they looked for wave-like patterns of activity, specifically those with high amplitude.

“We focused on high-frequency band signals due to their rich information, perfect spatial reach and ease of signal acquisition at each electrode,” said Wei Liang, first author of the study and a graduate student in Hatsopoulos’ lab.

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

“It’s like a series of dominoes falling,” Hatsopoulos said. “All the wave patterns that 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 now we’re looking at this diffuse wave pattern and we’ve shown that the direction the wave is traveling tells you something about what the animal is about to do.” .

The findings provide a new way of looking at cortical function. “It shows that space is important,” said Hatsopoulos. “Instead of just looking at what groups of neurons are doing and paying attention to, we see that there is a spatial organized pattern that carries information. This is a completely different way of thinking about things.”

The research was made difficult by the fact that they were studying activity patterns from individual movements, rather than averaging recordings over repeated trials, which can be very noisy. The team was able to develop a computational method for cleaning the data to provide clarity about which signals are being recorded without losing important information.

This study is the first evidence that spatially organized recruitment of neural activity across the motor cortex can inform the details of striatal movement. The image is in the public domain

“If you average across trials, you miss out on information,” Hatsopoulos said. “If we want to implement this system as part of a brain-machine interface, we can’t average out the trials — your decoder has to do it instantly, as motion happens, for the system to work effectively.”

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

“The spatial dimension has been mostly 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 computations that the cerebral cortex is doing, we have to think about how neurons are arranged spatially.”

Future studies will examine whether similar wave patterns are seen in more complex movements, such as sequential movements rather than simple point-to-point reaching, and whether electrical stimulation of the brain in the form of a wave can bias monkey movement.

Funding: The study, “Diffusion of Spatiotemporal Activity Patterns Across the Macaque’s Motor Cortex Carrying Motor Information,” was supported by the National Institutes of Health (R01 NS111982). Additional authors include Karthikeyan Balasubramanianb and Vasileios Papadourakis from the University of Chicago.

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About this movement and neuroscience research news

author: Allison Caldwell
Source: University of Chicago
Contact: Allison Caldwell – University of Chicago
picture: The image is in the public domain

Original search: open access.
“Diffusion of spatiotemporal activity patterns across the motor cortex of macaques carries motor information” by Wei Liang et al. PNAS


Diffusion of spatiotemporal activity patterns across the macaque’s motor cortex results in the transmission of motor information

The spread of spatiotemporal neural patterns is widely apparent across sensory, motor, and associative cortical regions. However, it remains unclear whether any neurodegenerative properties carry information about specific behavioral details.

Here, we provide the first evidence of a link between cortical diffusion direction and specific behavioral features of a countercurrent movement on a trial-by-trial basis.

We recorded local field potentials (LFPs) from implanted multielectrode arrays in the primary motor cortex of two rhesus macaque monkeys while they were performing a two-dimensional reaching task. Propagation patterns were extracted from envelopes of the informative high gamma band (200 to 400 Hz) with LFP amplitude.

We found that the exact direction of the diffusion patterns varies systematically according to the initial motion direction, enabling kinetic predictions.

Furthermore, the characteristics of these diffusion patterns provided additional predictive power beyond that of the LFP itself, indicating the value of including averaged spatiotemporal characteristics in optimizing brain-machine interfaces.

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