Even simple movements create ripples across the brain
summary: A new study reports that a simple movement such as pressing a button can send waves of activity through neurons covering the entire brain.
Source: University of Oregon
New University of Oregon research shows that even a simple movement like pressing a button sends waves of activity through networks of neurons stretching across the brain.
The discovery highlights the complexity of the human brain, challenging the textbook’s simplistic picture of distinct brain regions assigned to specific functions.
“It’s really well known that the primary motor cortex controls movement production,” said Alex Rockhill, graduate student in Nikki Swan’s Human Physiology Laboratory. “But there is much more to movement than this brain region.”
Rockhill is the first author of a new paper from the lab, published in December in Journal of Neural Engineering.
Swan and her team are studying human brain networks thanks to a collaboration with physicians and researchers from 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 precisely when and where a seizure occurs, potentially removing the affected brain area.
An intracranial EEG can also provide insight into other brain activity as well. It’s the “gold standard” technology, Swan said. But researchers rarely have access to it, because implanting electrodes is an intensive process. Participants in Swann’s study agreed to let her team study their brains while they were already connected to electrodes to study seizures.
Swan and her colleagues gave study participants a simple movement task: pressing a button. They recorded the activity of thousands of neurons throughout the brain while the participants performed the task. Next, they tested if they could train a computer to determine whether certain patterns of brain activity were picked up at rest or in motion.
The signals were evident in specific areas of the brain. Those were regions previously associated with movement, where most neurons are likely to focus on this behavior. But the researchers also found brain signals that predict movement throughout the brain, including in regions not specifically designated for it.
In many parts of the brain, “we can predict with greater accuracy than chance whether or not that data came during movement,” Swan said.
“We found a spectrum of brain regions, from basic motor regions where you can decode that a person is moving 100 percent of the time, to other regions that can be decoded 75 percent of the time,” Rockhill added.
In some areas that are not specialized for movement, “some neurons may be firing, but they may be overwhelmed by neurons that are not related to movement,” he said.
Their findings complement a study published in 2019 in the journal naturewhere other researchers have shown similar long-distance brain networks associated with movement in mice.
“That paper showed that movement is ubiquitous in the brain, and our paper showed that this is true in humans as well,” Swan said.
Perhaps the phenomenon is not limited to movement either. It’s also possible that other systems, such as vision and touch, span more parts of the brain than previously expected.
The team is now developing new tasks that involve different types of movements, to see how these manifest in the brain. They plan to continue growing the collaboration with OHSU, getting more researchers involved in the project and gaining a deeper understanding of the complexities of the brain.
“There’s a lot of opportunity now that we have this new collaboration,” Swan 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 Hammers – University of Oregon
picture: The image is in the public domain
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“Stereo-EEG recordings expand known distributions of canonical motion-related oscillationsBy Alexander P Rockhill et al. Journal of Neural Engineering
Stereo-EEG recordings expand known distributions of canonical motion-related oscillations
objective. Previous electrophysiological research has characterized canonical oscillatory patterns associated with movement predominantly from recordings of 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 stereotactic electroencephalography (sEEG), particularly in humans. We aimed to identify and characterize different oscillations associated with movement across a relatively wide sample of brain regions in humans, and if they extend beyond brain regions previously associated with movement.
Approaching. We used a linear support vector machine to decode motion-restricted temporal-frequency spectral patterns, and validated our results with a cluster permutation test and co-spatial pattern decoding.
Key findings. We were able to accurately classify sEEG spectroscopy during the keystroke movement task versus the time interval between trials. Specifically, we found these previously described patterns: beta desynchronization (13–30 Hz), beta synchronization (rebound), alpha modulation before motion (8–15 Hz), broadband gamma increase after motion (60–90 Hz) and potential associated with the event. These oscillatory patterns have recently been observed in a wide range of brain regions accessible with sEEG that are not accessible with other electrophysiological recording methods. For example, the presence of beta desynchronization in the frontal lobe was more prevalent than previously described, extending beyond the primary and secondary motor cortices.
indication. Our classification revealed prominent temporal frequency patterns also observed in previous studies using non-invasive EEG and EEG, but here we identified these patterns in brain regions not yet associated with movement. This provides new evidence for the anatomical extent of a system of putative kinematic networks that display each of these oscillatory modes.