Nat Commun: New study could help treat paralysed Parkinson's patients

February 3, 2021 // --- When drinking a cup of coffee or receiving or throwing a ball, our brains try to coordinate movements at no less than 27 joint angles in the arms and fingers.
exactly how the brain does this is the subject of debate between researchers.
now, led by Maryam Shanechi, an assistant professor of electrical and computer engineering at USC's Witby University, and the early career chair of Andrew and Erna Viterbi, researchers have discovered a signature dynamic brain pattern.
findings, now published in the journal Nature Communications, could be a catalyst for developing better brain-machine interfaces and improving treatment for paralysed patients.
(Photo: www.pixabay.com study aims to compare the scale of brain activity in space-time.
small-scale activity refers to the spikes of individual neurons or brain cells.
large-scale activity refers to local field potation (LFP) brain waves, which instead measure the polymerization activity of thousands of interacting individual neurons.
both may help with out-of-reach movements, but how do they do it? To answer this question, students in electrical engineering at Shanechi and Hamidreza Abbaspourazad have created a new machine learning algorithm to extract dynamic neural patterns that exist in both peak and LFP activities and to determine the interrelations between these patterns and their relationship to motion.
the study was conducted in collaboration with Bijan Pesaran, a professor of neuroscience at New York University, who conducted experiments to collect peaks and LFP brain activity in nature and use neurophysiological techniques in the field to master movement.
by applying the new algorithm to the collected data, they identified commonality and differences between peak and LFP activity.
they eventually discovered a common pattern that highly predicts motion.
, we found that this common multiscale pattern is actually primarily predicting motion compared to all other existing models, " said Shanechi, a recent economist.
other words, the team identified previously erred-detected patterns of brain activity associated with reaching out and grabbing motion, which provided them with possible neural signals.
interestingly, we found that this neural characteristic pattern is shared not only between spike and LFP signals, but also between different objects in which we move.
means shared patterns can help researchers understand how an individual's brain controls how to achieve and master movement.
, it also suggests that different people may have similar neurological characteristics when reaching for action.
of course, understanding what the brain is doing is only half the success.
turning brain activity into action is another matter entirely.
but Shanechi's model can do that.
she and her team were able to turn brain activity into exercise.
: "Our model not only found characteristic patterns in neural activity, but also predicted the movement of arms and fingers very accurately from these patterns.
" is particularly promising in developing brain-machine interfaces to restore movement in paralysed patients.
in addition to helping paralysed patients, Shanechi hopes the study will also help better understand the neural mechanisms of movement disorders, such as Parkinson's disease, to guide future treatments.
we hope that a better understanding of how the brain produces everyday movements will help us design better brain-machine interfaces that can help millions of people with disabilities with neurological impairments and diseases," he said.
" (Bioon.com) Source: Discovered brain pattern hass for treating paralyzed, Parkinson's Patients Original source: Hamidreza Abbaspourazad et al. Multiscale low-dimensional motor cortical state dynamics predict naturalistic reach-and-grasp behavior, Nature Communications (2021). DOI: 10.1038/s41467-020-20197-x