Overview: A simple movement like pressing a button can send ripples of activity across neurons that span the entire brain, a new study reports.
Source: University of Oregon
Even a simple movement, such as pressing a button, sends ripples of activity through networks of neurons that span the brain, according to new research from the University of Oregon.
The finding highlights how complex the human brain is, challenging the simplified textbook view of different brain regions devoted to specific functions.
“It’s very well known that the primary motor cortex controls movement output,” said Alex Rockhill, a graduate student in the lab of human physiology professor Nicki Swann. “But movement is much more than just this one brain region.”
Rockhill is the first author of a new paper from the lab, to be published in December Journal of Neural Engineering.
Swann and her team are studying brain networks in humans through a collaboration with physicians and researchers at Oregon Health & Science University. The OHSU team uses a technique called intracranial EEG to determine where seizures may start in patients with treatment-resistant epilepsy. They surgically implant a series of electrodes into patients’ brains to pinpoint exactly 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 a “gold standard” technique, Swann said. But researchers rarely have access to them because implanting the electrodes is such an intensive process. Participants in Swann’s study have agreed to let her team study their brains while they are already hooked up to electrodes for the epileptic study.
Swann and her colleagues gave study participants a simple motion-related task: press a button. They recorded the activity of thousands of neurons in the brain as the participants performed the task. They then tested whether they could train a computer to determine whether certain patterns of brain activity were captured while the participant was resting or moving.
In certain parts of the brain, the signals were clear. Those were areas previously associated with movement, where most of the neurons are probably focused on that behavior. But the researchers also found brain signals that predict movement throughout the brain, including in areas not specifically devoted to it.
In many parts of the brain, “we can predict with greater than likely accuracy whether that data came from during movement or not during movement,” Swann said.
“We found that there is a spectrum of brain regions, from primary motor areas where you can decode that the person is moving 100 percent of the time, to other areas that can be decoded 75 percent of the time,” Rockhill added.
In some of the areas that aren’t specialized for movement, “some neurons might fire, but they can be overwhelmed 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 far-reaching brain networks associated with movement in mice.
“That paper showed that there’s movement all over the brain, and our paper shows that’s the case in humans as well,” Swann said.
The phenomenon is probably not limited to movement either. Other systems, such as sight and touch, also likely extend through more of the brain than previously believed.
Now the team is working on developing new tasks involving different types of movements to see how they appear in the brain. And they plan to continue expanding the collaboration with OHSU, bringing more researchers into the project and gaining a better understanding of the intricacies of the brain.
“There are a lot of opportunities now that we have this new partnership,” Swann said. “We’re really lucky to have the opportunity to collect such exciting data by collaborating with the OHSU team and their incredible patients.”
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About this neuroscience research news
Writer: Laurel Hammers
Source: University of Oregon
Contact: Laurel Hamers – University of Oregon
Image: The image is in the public domain
Original research: Closed access.
“Stereo-EEG recordings extend known distributions of canonical motion-related oscillations” by Alexander P. Rockhill et al. Journal of Neural Engineering
Abstract
Stereo EEG recordings extend known distributions of canonical motion-related oscillations
Objectively. Previous electrophysiology research has characterized canonical oscillatory patterns associated with movement, mostly from recordings from the primary sensorimotor cortex. Less work has attempted to decode movement based on electrophysiological recordings from a wider range of brain regions, such as those sampled by stereoelectroencephalography (sEEG), especially in humans. We aimed to identify and characterize distinct motion-related oscillations in a relatively broad sample of brain regions in humans and if they extended beyond brain regions previously associated with motion.
Approach. We used a linear support vector machine to decode time-frequency spectrograms time-bound to motion, and we validated our results with cluster permutation testing and common spatial pattern decoding.
Main results. We were able to accurately classify sEEG spectrograms during a keypress task relative to the interval between trials. Specifically, we found these previously described patterns: beta (13-30 Hz) desynchronization, beta-synchronization (rebound), pre-motion alpha (8-15 Hz) modulation, a post-motion broadband gamma (60-90 Hz) increase and an event-related potential. These oscillatory patterns were newly observed in a wide range of brain regions accessible with sEEG that are inaccessible with other electrophysiological recording methods. For example, the presence of beta desynchronization in the frontal lobe was more widespread than previously described and extended beyond the primary and secondary motor cortices.
Meaning. Our classification revealed prominent time-frequency patterns also observed in previous studies using non-invasive electroencephalography and electrocorticography, but here we identified these patterns in brain regions not yet associated with movement. This provides new evidence for the system’s anatomical size of putative motor networks that exhibit each of these oscillatory patterns.