New Technique Developed At UCSD
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.
of the brain with colored spheres indicating clusters of
Photo Credit: Scott Makeig
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.
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
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