Even simple movements create waves in the brain
Abstract: A simple movement like pressing a button can send waves of activity across neurons that span the entire brain, a new study reports.
Source: University of Oregon
Even a simple movement like pressing a button sends waves of activity through networks of neurons that stretch throughout the brain, new research from the University of Oregon shows.
This discovery highlights how complex the human brain is, challenging the simplistic textbook picture of different areas of the brain dedicated to specific functions.
“It’s really well known that the primary motor cortex controls the output of movement,” said Alex Rockhill, a graduate student in the lab of human physiology professor Nicki Swann. “But movement is much more than this area of the brain.”
Rockhill is the first author of the new paper from the laboratory, published in December in Journal of Neural Engineering.
Swann and her team are studying brain networks in humans through a collaboration with doctors and researchers at Oregon Health & Science University. The OHSU team is using a technique called intracranial EEG to determine where seizures may begin in patients with treatment-resistant epilepsy. They surgically implant an array of electrodes into patients’ brains to pinpoint when and where a seizure is occurring and potentially remove the affected area of the brain.
Intracranial EEG can also provide valuable insight into other brain activity. It’s the “gold standard” technique, Swann said. But that’s something researchers rarely have access to, because implanting electrodes is such an intensive process. Participants in Swann’s study agreed to let her team study their brains while they were already attached to electrodes for the seizure study.
Swann and her colleagues gave research participants a simple movement-related task: pressing a button. They recorded the activity of thousands of neurons in the brain while the participants performed the task. They then tested whether they could train the computer to identify whether certain patterns of brain activity were captured while the participant was at rest or moving.
In certain parts of the brain, the signals were obvious. These were areas previously associated with movement, where most neurons are likely focused on that behavior. But the researchers also found that brain signals predict movement throughout the brain, including areas not specifically dedicated to it.
In many parts of the brain, “we can predict with greater than chance accuracy whether that data was during movement or not during movement,” Swann said.
“We found that there is a spectrum of brain areas, from primary motor areas where you can decode that a person is moving 100 percent of the time, to other areas that can be decoded 75 percent of the time,” Rockhill added.
In some areas that aren’t specialized for movement, “some of the neurons might be activated, but they might be overpowered by neurons that aren’t related to movement,” he said.
Their findings complement a study published in 2019 in the journal Naturein which other researchers showed similar long-range brain networks associated with movement in mice.
“That work showed that movement is everywhere in the brain, and our work shows that this is true in humans as well,” Swann said.
The phenomenon is probably not limited to movement. Other systems, such as vision and touch, are also likely to span more of the brain than previously thought.
Now the team is working to develop new tasks that involve different types of movements, to see how they appear in the brain. And they plan to continue developing the collaboration with OHSU, bringing more researchers into the project and gaining a deeper understanding of the intricacies of the brain.
“There are a lot of opportunities now that we have this new collaboration,” Swann said. “We are truly fortunate to have the opportunity to collect such exciting data working with the OHSU team and their amazing patients.”
About this neuroscience research news
Author: Laurel Hammers
Source: University of Oregon
Contact: Laurel Hamers – University of Oregon
Picture: The image is in the public domain
Original research: Closed access.
“Stereo-EEG recordings extend the known distributions of movement-related canonical oscillations” by Alexander P Rockhill et al. Journal of Neural Engineering
Stereo-EEG recordings extend the known distributions of movement-related canonical oscillations
Goal. Previous electrophysiological studies have characterized movement-related canonical oscillatory patterns mainly from recordings of the primary sensorimotor cortex. There has been less work attempting to decode movement from electrophysiological recordings from a wider range of brain regions such as those sampled by stereoelectroencephalography (sEEG), particularly in humans. We aimed to identify and characterize distinct movement-related oscillations in a relatively broad sample of human brain regions and if they extend beyond brain regions previously associated with movement.
Access. We used a linear support vector machine to decode time-frequency spectrograms time-locked to motion, and we validated our results by testing cluster permutation and joint spatial pattern decoding.
Main results. We were able to accurately classify sEEG spectrograms during the keypress movement task with respect to the intertrial interval. Specifically, we found these previously described patterns: beta (13-30 Hz) desynchronization, beta synchronization (bounce), alpha modulation (8-15 Hz) before movement, wide-band gamma (60-90 Hz) increase after movement, and a potential associated with an event. These oscillatory patterns have recently been observed in a wide range of brain regions accessible by sEEG that are not accessible by other electrophysiological recording methods. For example, the presence of beta desynchronization in the frontal lobe was more widespread than previously described, extending beyond the primary and secondary motor cortex.
Importance. Our classification revealed prominent time-frequency patterns that were also observed in previous studies using noninvasive electroencephalography and electrocorticography, but here we identified these patterns in brain regions not yet associated with movement. This provides new evidence for the anatomical extent of the putative motor network systems that exhibit each of these oscillatory patterns.