New Technique Developed At UCSD For Deciphering Brain Recordings Can Capture

From: sid myers (gloopencrotum_at_hotmail.com)
Date: 07/18/04 

Date: 17 Jul 2004 17:13:08 -0700

Source: University of California
http://ucsdnews.ucsd.edu/newsrel/science/sneweegs.asp

New Technique Developed At UCSD For Deciphering Brain Recordings Can Capture
Thinking As It Happens

By Sherry Seethaler

A team led by University of California San Diego neurobiologists has
developed a new approach to interpreting brain electroencephalograms, or
EEGs, that provides an unprecedented view of thought in action and has the
potential to advance our understanding of disorders like epilepsy and
autism.

The new information processing and visualization methods that make it
possible to follow activation in different areas of the brain dynamically
are detailed in a paper featured on the cover of the June 15 issue of the
journal Public Library of Science Biology (plos.org) The significance of the
advance is that thought processes occur on the order of
milliseconds-thousandths of a second-but current brain imaging techniques,
such as functional Magnetic Resonance Imaging and traditional EEGs, are
averaged over seconds. This provides a "blurry" picture of how the neural
circuits in the brain are activated, just as a picture of waves breaking on
the shore would be a blur if it were created from the average of multiple
snapshots.

"Our paper is the culmination of eight years of work to find a new way to
parse EEG data and identify the individual signals coming from different
areas of the brain," says lead author Scott Makeig, a research scientist in
UCSD's Swartz Center for Computational Neuroscience of the Institute for
Neural Computation. "This much more comprehensive view of brain dynamics was
only made possible by exploiting recent advances in mathematics and
increases in computing power. We expect many clinical applications to flow
from the method and have begun collaborations to study patients with
epilepsy and autism."

To take an EEG, recording electrodes-small metal disks-are attached to the
scalp. These electrodes can detect the tiny electrical impulses nerve cells
in the brain send to communicate with each other. However, interpreting the
pattern of electrical activity recorded by the electrodes is complicated
because each scalp electrode indiscriminately sums all of the electrical
signals it detects from the brain and non-brain sources, like muscles in the
scalp and the eyes.

"The challenge of interpreting an EEG is that you have a composite of
signals from all over the brain and you need to find out what sources
actually contributed to the pattern," explains Makeig. "It is a bit like
listening in on a cocktail party and trying to isolate the sound of each
voice. We found that it is possible, using a mathematical technique called
Independent Component Analysis, to separate each signal or "voice" in the
brain by just treating the voices as separate sources of information, but
without other prior knowledge about each voice."

Independent component analysis, or ICA, looks at the distinctiveness of
activity in each patch of the brain's cortex. It uses this information to
determine the location of the patch and separate out the signals from
non-brain sources. Because ICA can distinguish signals that are active at
the same time, it makes it possible to identify the electrical signals in
the brain that correspond to the brain telling the muscles to take an
action -which in the paper was deciding whether or not to press a button in
response to an image flashed on a computer screen-and to separate this
signal from the signals the brain uses to evaluate the consequences of that
action.

According to Makeig, UCSD was a leader in developing the earlier methods of
interpreting EEGs forty years ago. "The new, more general 'ICA' method
continues this tradition of UCSD excellence in cognitive electrophysiology
research," he says.

The coauthors on the paper, in addition to Makeig, include Arnaud Delorme
and Tzyy-Ping Jung, Swartz Center for Computational Neuroscience; Marissa
Westerfield and Jeanne Townsend, UCSD's Department of Neurosciences; Eric
Courchesne, Children's Hospital Research Center and UCSD's Department of
Neurosciences; and Terrence Sejnowski, UCSD professor of biology and Howard
Hughes Medical Institute professor at the Swartz Center for Computational
Neuroscience and the Salk Institute for Biological Studies. The study was
funded by the Swartz Foundation, the National Institutes of Health and the
Howard Hughes Medical Institute.

Software for performing the EEG analysis is openly available at no cost at
http://www.sccn.ucsd.edu/eeglab.

Media Contact: Sherry Seethaler (858) 534-4656
Comment: Scott Makeig (858) 458-1927

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