So who's going to take this and make a hypothesis about how to use this information to help stroke patients recover?
http://engineering.stanford.edu/news/different-drummer-stanford-engineers-discover-neural-rhythms-drive-physical-movement
The neurons that control movement are not a predictable bunch.
Scientists working to decode how such neurons convey information to
muscles have been stymied when trying to establish a one-to-one
relationship between a neuron’s behavior and factors such as muscle
activity or movement velocity.
In
an article published online June 3rd by the journal Nature,
a team of electrical engineers and neuroscientists working at Stanford
University propose a new theory of the brain activity behind arm
movements. Their theory is a significant departure from existing
understanding and helps to explain, in relatively simple and elegant
terms, some of the more perplexing aspects of the activity of neurons in
the motor cortex.
In their paper, electrical engineering
Associate Professor Krishna Shenoy
and post-doctoral researchers Mark Churchland, now a professor at
Columbia, and John Cunningham of Cambridge University, now a professor
at Washington University in Saint Louis, have shown that the brain
activity controlling arm movement does not encode external spatial
information – such as direction, distance, and speed – but is instead
rhythmic in nature.
Understanding the brain
Neuroscientists have long known that the neurons responsible for
vision encode specific, external-world information – the parameters of
sight. It had been theorized and widely suggested that motor cortex
neurons function similarly, conveying specifics of movement such as
direction, distance and speed, in the same way the visual cortex records
color, intensity and form.
“Visual neurons encode things in the world. They are a map, a
representation,” said Churchland, who is first author of the paper.
“It’s not a leap to imagine that neurons in the motor cortex should
behave like neurons in the visual cortex, relating in a faithful way to
external parameters, but things aren’t so concrete for movement.”
Scientists have disagreed about which movement parameters are being
represented by individual neurons. They could not look at a particular
neuron firing in the motor cortex and determine with confidence what
information it was encoding.
“Many experiments have sought such lawfulness and yet none have found
it. Our findings indicate an alternative principle is at play,” said
co-first author Cunningham.
“Our main finding is that the motor cortex is a flexible pattern
generator, and sends rhythmic signals down the spinal cord,” said
Churchland.
19th century mathematician Joseph Fourier showed that two
rhythms could be summed to produce a third rhythm. Researchers at
Stanford have shown that such mathematics are at play in the brain
activity that produces arm movements.
Engine of movement
To employ an automotive analogy, the motor cortex is not the steering
wheel, odometer or speedometer representing external-world information.
It is more like an engine, comprised of parts whose activities appear
complicated in isolation, but which cooperate in a lawful way as a whole
to generate motion.
“If you saw a piston or a spark plug by itself, would you be able to
explain how it makes a car move?” asked Cunningham rhetorically.
“Motor-cortex neurons are like that, too, understandable only in the
context of the whole.”
In monitoring electrical brain activity of motor-cortex neurons,
researchers found that they typically exhibit a brief oscillatory
response. These responses are not independent from neuron to neuron.
Instead, the entire neural population oscillates as one in a beautiful
and lawfully coordinated way.
The electrical signal that drives a given movement is therefore an
amalgam – a summation – of the rhythms of all the motor neurons firing
at a given moment.
“Under this new way of looking at things, the inscrutable becomes
predictable,” said Churchland. “Each neuron behaves like a player in a
band. When the rhythms of all the players are summed over the whole
band, a cascade of fluid and accurate motion results.”
In
a series of striking graphs, the Stanford team plotted the signals from
individual neurons in the motor cortex as monkeys completed a series of
reaches. The reaching motions are shown by the starburst patterns at
the top left of each graph. The neuronal patterns are then plotted atop
one another for the entire series of reaches, clearly establishing the
rhythmic nature of the brain activity.
Precedents in nature
In the new model, a few relatively simple rhythms explain neural features that had confounded science earlier.
“Many of the most-baffling aspects of motor-cortex neurons seem
natural and straightforward in light of this model,” said Cunnigham.
The team studied non-rhythmic reaching movements, which made the
presence of rhythmic neural activity a surprise even though, the team
notes, rhythmic neural activity has a long precedence in nature. Such
rhythms are present in the swimming motion of leeches and the gait of a
walking monkey, for instance.
“The brain has had an evolutionary goal to drive movements that help
us survive. The primary motor cortex is key to these functions. The
patterns of activity it displays presumably derive from evolutionarily
older rhythmic motions such as swimming and walking. Rhythm is a basic
building block of movement,” explained Churchland.
Reaching for the grail
To test their hypothesis, the engineers studied the brain activity of
monkeys reaching to touch a target. According to the researchers,
experiments show this ‘underlying rhythm’ strategy works very well to
explain both brain and muscle activity. In their reaching studies, the
pattern of shoulder-muscle behavior could always be described by the sum
of two underlying rhythms.
“Say you’re throwing a ball. Beneath it all is a pattern. Maybe your
shoulder muscle contracts, relaxes slightly, contracts again, and then
relaxes completely, all in short order,” explained Churchland.
The researchers say that although the activity may not be exactly
rhythmic, it can be created by adding together two or three other
rhythms. The team asserts that this may be how the brain solves the
problem of creating patterns of movement.
"This surprised us a bit. In decidedly arrhythmic movements, there were these unmistakable patterns,” said Churchland.
"This research builds on a strong theoretical framework and adds to
growing evidence that rhythmic activity is important for many
fundamental brain functions," said Yuan Liu of the National Institute of
Neurological Disorders and Stroke, NIH. "Further research in this area
may help us devise more effective technology for controlling prosthetic
limbs." Liu is the co-lead of the NIH-NSF Collaborative Research in
Computational Neuroscience program.
"In trying to find the basic response properties of the motor cortex,
Dr. Shenoy and his colleagues are searching for the holy grail of
neuroscience," said Dr. Daofen Chen, Program Director, Systems and
Cognitive Neuroscience at the National Institute of Neurological
Disorders and Stroke at the National Institutes of Health. "They
consistently tackle important but tough questions in thought-provoking
ways and in ambitious proposals. NIH is proud to support this kind of
pioneering and transformative research."
Accordingly, the seemingly complex system that is the motor cortex
can now be at least partially understood in more straightforward terms.
"The motor cortex is an engine of movement that obeys lawful dynamics,” said Shenoy.
Stanford post-doctoral fellow Matthew Kaufman, bioengineering PhD
student and medical science training program student Paul Nuyujukian,
electrical engineering graduate student Justin Foster, and electrical
engineering consulting assistant professor and Palo Alto Medical
Foundation neurosurgeon Stephen Ryu were also authors on this paper.
The work of the various co-authors was supported in part by the
National Institutes of Health, the Burroughs Wellcome Fund Career Awards
in the Biomedical Sciences (BWF-CABS) and an Engineering and Physical
Sciences Research Council grant. The work of Krishna Shenoy was
supported in part by: NIH Director’s Pioneer Award (1DP1OD006409), NIH
NINDS EUREKA Award (R01-NS066311), NIH NINDS BRP (R01-NS064318), NIH
NINDS CRCNS (R01-NS054283), DARPA REPAIR, and BWF-CABS.