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Shortcut to: http://dukemednews.org/news/article.php?id=8320
New Technique Scans Electrical "Brainscape"






 keywords :  Brain Imaging, Neuroscience
date :  12/7/2004
media contact :  Dennis Meredith , (919) 681-8054 or (919) 417-6581
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DURHAM, N.C. -- Using hairlike microelectrodes and computer analysis,
neurobiologists at Duke University Medical Center have demonstrated that
they can see the detailed instant-to-instant electrical "brainscape" of
neural activity across a living brain.

In their study on rats, they demonstrated that they could distinguish in
unprecedented detail the patterns of brain activity -- including
fleeting changes in communication among brain structures -- in awake
animals, as they fall sleep and as they transition among different sleep
stages.

The study is important, not only for its insight into the sleep process,
but because neurobiologists have strong evidence that memory
consolidation occurs during sleep, said the researchers.

More generally, they believe that their new analytical technique will
enable unprecedented insights into function of both the healthy brain
and those afflicted with neurological disease. Such insights could lead
to new understanding and treatment if diseases including epilepsy,
Alzheimer's disease and schizophrenia, they said.

Led by neurobiologist Dr. Miguel Nicolelis, M.D., Ph.D., the researchers
published their findings in the December 8, 2004, Journal of
Neuroscience. Nicolelis is professor of neurobiology and co-director of
Duke's Center for Neuroengineering. Other co-authors were Damien
Gervasoni, Shih-Chieh Lin, Sidarta Ribeiro, Ernesto Soares and Janaina
Pantoja. The research was sponsored by the National Institutes of
Health.

In their studies, Nicolelis and his colleagues implanted the
microelectrodes, smaller than the diameter of a human hair, into regions
of the brain responsible for a range of functions -- including sensory
processing, motor function and memory formation. They then recorded and
analyzed the electrical signals from the rats as the animals went
through several days of sleep-wake cycling. Their analysis could detect
activity patterns that marked waking, deep "slow wave" sleep and
so-called "rapid-eye movement" sleep.

Importantly, said Nicolelis, their analysis could distinguish the
fleeting changes in the brain as the animals transitioned from one sleep
state from the other.

"We can actually predict such changes, because at that moment, these
different structures fire together for a few hundred milliseconds to
create a synchronous pattern of firing that is a signature of the change
from the previous state to the next," said Nicolelis. A millisecond is
one thousandth of a second.

"It's almost like two computers exchanging information over a modem, and
they get synchronized in the process," he said.

"Our analysis revealed significant functional insights into sleep," said
Nicolelis. "For example, we found that there are only a few
physiologically possible transitions from state to state -- just as in
chemistry there are only certain chemical reactions that are possible."
For example, he said, the data distinguished the elusive transition
called "intermediate sleep" between slow wave sleep and
rapid-eye-movement sleep.

Importantly, said Nicolelis, the transitions they observed were the same
from one animal to another, "suggesting that we have arrived at a major
basic principle of how the brain actually operates."

The technology and analysis the researchers used is an extension of that
used to enable monkeys to control a robot arm using only their brain
signals, which Nicolelis and his colleagues reported in 2003.

"Now, however, we are recording broader brain signals -- hundreds,
perhaps thousands," said Nicolelis. "By filtering and analyzing them, we
can actually measure the global dynamic activity that tells us what
behavioral states the animals are going through.

"Such capability is broadly important because it is the first
physiological measurement that can reveal the global behavior of the
brain, including the broad coordination of so many areas."

In contrast, said Nicolelis, magnetic resonance imaging and positron
emission tomography -- the most widely used brain-scanning techniques --
can give only limited time-resolution of brain activity. Also, they give
only indirect indications of brain activity by measuring blood flow as
an indicator of activity.

According to Nicolelis, their detailed studies of brain activity --
including a previous study reported in the June 25, 2004, issue of
Science, reveal that the brain is not the passive, unchanging computer
postulated by most current theory. Rather, he said, it is a dynamic,
constantly adapting organ. In the Science paper, the researchers
reported that the brain response of a rat to tactile stimulus to its
whiskers changed according to whether the animal was actively performing
a task or passively receiving input.

"Our studies suggest that perception is not just a process of analyzing
an incoming signal, which is what most textbooks teach and most
scientists believe," said Nicolelis. "Rather, perception depends on the
internal state of the brain at that given moment of time, and what
behavior the animal is using to sample the environment. With this new
technique we can monitor such states as attention and expectation and
how they modulate how the animal processes that incoming information."

According to Nicolelis, the new results "support a global theory of
brain function that holds that all these processes are extremely
dynamic. And now with this analytic technique we can measure these
dynamics. It gives us a new language of how to describe continuous brain
function.

"One of the Holy Grails of neurobiology has been the neural 'code' by
which the brain processes information. Now we can say that there is no
such thing as a single neural code, because the code is continuously
changing according to the internal state of the brain, and according to
the strategy the animal selects to search the environment."

Also, said Nicolelis, such analyses will influence neurobiology to
advance beyond the current theory that the single neuron is the basic
computational unit of the brain. "A single neuron is too noisy to act as
a reliable unit of neuronal function," he said. "But an ensemble of
neurons resolves that noise and makes neuronal output stable."

The technique will be important for studying both the different states
of the healthy brain and the pathology of neurological disorders, said
Nicolelis.

"For example, we can analyze brain activity in sleep-deprived animals,
those in the process of learning or transgenic animals with changes in
their brain circuitry," he said. "We can quantify the differences in the
global dynamics of such brains from those of normal animals.

"Also, there exist many mouse models of neurological disorders, and we
can use this technique to explore how the brain functions in those
models. For example, we can understand the global dynamic structure of
the brain in Parkinson's disease, Alzheimer's disease or schizophrenia.
Knowing the details of what goes wrong in such disorders is a critical
step in treating them," said Nicolelis.

contact sources : Miguel Nicolelis , (919) 684-4580
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