Summary: Spatially organized recruitment of neural activity across the motor cortex carries information about planned movements.
Source: University of Chicago
Nicholas G. Hatsopoulos, PhD, Professor of Organismal Biology and Anatomy at the University of Chicago, has long focused on the organization of neural activity across the brain’s physical layout.
“Inside our heads, the brain is all crumpled up. If you flattened out the human cortex into 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 that space when organizing activity patterns, but aside from knowing that one patch of the brain controls the arm and another controls the leg, we’ve mostly ignored how the brain might use that spatial organization,” Hatsopoulos said.
In a new study published January 16 in Proceedings of the National Academy of Sciences, Hatsopoulos and colleagues present evidence that the motor cortex uses spatially organized, high-frequency propagating waves of neural activity to encode details about upcoming movements.
Propagating waves of neural activity have been observed across many cortical areas and are often linked to broad behavioral states such as sleep or wakefulness. This study is the first to demonstrate that the direction and spatial structure of these high-frequency waves across motor cortex carry specific information about a planned movement on a single-trial basis.
The authors recorded neural signals from multielectrode arrays implanted in the primary motor cortex of rhesus macaques performing a two-dimensional joystick reach task. They focused on the high-gamma band (200–400 Hz) of local field potential (LFP) envelopes because these signals provide rich information and are readily measurable across electrodes.
“We focused on the high frequency band signals given its rich information, ideal spatial reach and easiness of obtaining signal in every electrode,” said Wei Liang, the study’s first author and a graduate student in the Hatsopoulos lab.
Analyzing single-trial activity rather than trial averages, the team identified high-amplitude propagating wave patterns comprised of coordinated activity across hundreds of neurons. Crucially, the direction of these waves varied systematically with the direction the monkey intended to move the joystick.
“It’s like a series of dominoes falling,” Hatsopoulos explained. “All of the wave patterning we’ve seen in the past didn’t tell us what the animal was doing — it would just happen. This is very exciting because now we’re looking at this propagating wave pattern and shown that the direction the wave goes tells you something about what the animal is about to do.”
These findings shift the perspective on cortical function by emphasizing spatial organization. Rather than treating neural populations only as abstract ensembles, the study shows that mesoscopic spatiotemporal patterns across the cortical surface encode behaviorally relevant kinematic information.
Studying single movements posed a technical challenge because trial-by-trial recordings can be noisy. The team developed computational methods to denoise and extract meaningful propagating patterns without averaging away the very information they sought to preserve. That capability is important for practical applications such as brain-machine interfaces, which must decode intent in real time rather than from averaged data.
“If you average across trials, you miss information,” Hatsopoulos said. “If we want to implement this system as part of a brain‑machine interface, we can’t be averaging trials — your decoder has to do it on the fly, as the movement is happening, for the system to work effectively.”
Beyond basic science, the discovery that propagation direction and other spatiotemporal features add predictive power suggests new inputs for decoding algorithms. Incorporating mesoscopic spatial patterns alongside standard amplitude-based signals could enhance the accuracy and responsiveness of future neural prosthetics and brain‑computer interfaces.
Hatsopoulos emphasized the broader implication: “The spatial dimension has been mostly ignored thus far, but it’s a new angle we can use for understanding cortical function. When we try to understand the computations the cortex is doing, we should consider how the neurons are spatially laid out.”
Planned follow-up experiments will test whether similar propagating patterns appear during more complex sequences of movement and whether electrically induced wave-like stimulation can bias behavior. Those lines of investigation will probe how generalizable and causally influential spatiotemporal propagation is for motor control.
Funding: This research, titled “Propagating spatiotemporal activity patterns across macaque motor cortex carry kinematic information,” was supported by the National Institutes of Health (R01 NS111982). Additional authors include Karthikeyan Balasubramanian and Vasileios Papadourakis, both of the University of Chicago.
About this movement and neuroscience research news
Author: 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 macaque motor cortex carry kinematic information” by Wei Liang et al., PNAS
Abstract
Propagating spatiotemporal activity patterns across macaque motor cortex carry kinematic information
Propagating spatiotemporal neural patterns are widely evident across sensory, motor, and association cortical areas. However, it remains unclear whether any characteristics of neural propagation carry information about specific behavioral details.
Here, we provide the first evidence for a link between the direction of cortical propagation and specific behavioral features of an upcoming movement on a trial-by-trial basis.
We recorded local field potentials (LFPs) from multielectrode arrays implanted in the primary motor cortex of two rhesus macaque monkeys while they performed a 2D reach task. Propagating patterns were extracted from the information-rich high-gamma band (200 to 400 Hz) envelopes in the LFP amplitude.
We found that the exact direction of propagating patterns varied systematically according to initial movement direction, enabling kinematic predictions.
Furthermore, characteristics of these propagation patterns provided additional predictive capability beyond the LFP amplitude themselves, which suggests the value of including mesoscopic spatiotemporal characteristics when refining brain–machine interfaces.