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 amara
Swaab and Edith Kaan are about to read my “mind.” Kaan gently
fits an elastic, electrode-studded cap
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| Mindreading:
Meredith, left, is "capped" by postdoc fellow Kaan to measure
brain-wave responses. |
down over my scalp and fastens it with a chinstrap, also taping electrodes
to my face.
Through a hole in each electrode, Swaab, an assistant
professor of psychology, carefully squirts a bit of conductive gel
onto my skin to ensure a good electrical contact. Such contact between
electrode and scalp is essential, they tell me, if the infinitesimal
signals from my brain are to be detected accurately by the bank of
amplifiers and the computer that crowd the small room. They also caution
me not to move my eyes, blink, yawn, or talk during the testing, since
even the interference from fidgeting facial muscles would swamp the
faint neural signals.
Kaan, a postdoctoral fellow, installs me in a comfortable
chair in the small testing room that is padded with waffled, sound-absorbent
foam, and which I am told is shielded from radio-frequency interference.
There, I am instructed to sit motionless during the test, only my
brain sparking away on the sentence recognition that is my task. Those
sentences will appear word-by-word on the computer screen before me,
which is covered with cardboard except for a small word-sized rectangle.
My job is easy. Just press the button Kaan places in my
hand when I detect something wrong with a sentence. But Swaab and
Kaan’s job is incredibly hard—analyzing immense masses of
data from dozens of subjects for clues about how the brain processes
language. I tense, my thumb poised to jab at the button the instant
I see a wrong sentence. My writer’s pride is at stake, since
I’m supposed to have an eye, or rather a brain, for sentence
errors. The sentences begin to flash onto the screen.
“Kevin put the pill in his mouth and Mario the money
in his wallet.” (No press.)
“Alice looked at the raincoat beside the umbrella
that were rather old.” (Press!)
“Most rich people have a very nice villa with a swimming
pool.” (No press.)
“The dentist checked the address beside the tooth
that he was going to extract.” (Press!)
“The woman behind the counter recommends the map
that are very detailed.” (Press!)
MAGNETIC ATTRACTIONS
Most of the researchers in the Center for Cognitive Neuroscience
were attracted to Duke by high magnetic fields—more accurately,
the powerful, precise, functional Magnetic Resonance Imaging
(fMRI) machines of the new Brain Imaging and Analysis Center.
The analytical technique of fMRI works by using
magnetic pulses that produce telltale changes in the molecules
within brain tissues that are already under a powerful-but-harmless
static magnetic field. Since even subtle differences in brain
tissues cause them to react distinctively under the magnetic
fields, the technique allows high-resolution mapping of the
brain’s regions. Specifically, fMRI can map increased blood
flow to a brain region, which is triggered by increased activity
of the brain cells, called neurons, in that region.
Duke’s cognitive neuroscientists, as well as
other brain researchers in the BIAC and throughout Duke Medical
Center, use the technique of “event-related fMRI,”
in which they ask subjects to perform a mental task and map
their brains using “magnetic snapshots” every second
or so. This fMRI mapping technique has evolved only very recently,
says BIAC director Gregory McCarthy.
“I got started in it in about 1992,” he
says, “and what’s happened between now and then is
tremendous improvement in the instrumentation. While you can
get a decent fMRI signal from practically any high-quality clinical
MRI scanner, the kind of equipment we have here at Duke far
surpasses that.”
Duke’s two research fMRI machines can generate
fields of 1.5 Tesla or 4 Tesla, a Tesla being 10,000 times the
strength of Earth’s magnetic field. The former machine,
which generates fields comparable to a commercial MRI scanner,
allows researchers to “see” the blood flow through
the brain’s vessels, like a satellite image that can see
the freeways of a city.
Using the more powerful 4 Tesla machine, McCarthy
and his colleagues are pushing the limits of fMRI. By achieving
highly stable magnetic fields and inventing new techniques for
producing the pulses and analyzing the resulting signals, they
hope to map blood flow changes around groups of neurons, like
an aerial photograph that can see an individual neighborhood.
This finer resolution will reveal even greater details of the
living brain at work.
“Up until now all of the tremendous progress
in neurobiology has taken place in animal models with invasive
staining techniques,” says McCarthy. “But what we
want to do now is observe in detail what happens in the living
human brain, both the normal and the abnormal.”
Thus, he says, not only are the BIAC machines used
in basic research but also clinically by Duke neurosurgeons
to map their patients’ brains—for example, to plan
surgeries to remove tumors and avoid damaging critical adjacent
brain structures. Still other neuroscientists are using fMRI
to map the brains of sufferers of Alzheimer’s disease,
schizophrenia, autism, and elderly depression, both to understand
those disorders better and even to assess the effectiveness
of drug treatments.
“However, such achievements are possible only
because the BIAC is a multidisciplinary center that includes
not only people with expertise in the neurosciences, but also
in engineering, physics, biophysics, and statistics,” emphasizes
McCarthy. “The center couldn’t have fit into a single
department and still draw on all these different strengths of
the university.” |
Next, the two scientists ask me to listen to a series
of tones, most identical, pressing the button to count the occasional
ones that are higher than the rest. It’s a control task, they
explain, to distinguish my language perception from my brain’s
general ability to perceive sound.
Once I’m finished, Swaab and Kaan show me the jagged
traces of my brain waves during the language tests, pointing to telltale
peaks that marked the very instant I recognized a sentence’s
syntactic or grammatical error.
They had eavesdropped on my brain by recording my brain
waves, and then computer-averaging the signals to extract “event-related
potentials” (ERPs) to the words. This analysis allows them to
pinpoint in time with amazing accuracy when an event happens in the
brain. If the scientists had wanted to know where brain activity was
occurring, they could have had me carry out my sentence-recognizing
while inserted into one of Duke’s powerful “functional Magnetic
Resonance Imaging” (fMRI) machines in Duke Medical Center’s
new Brain Imaging and Analysis Center. These machines use powerful-but-harmless
magnetic pulses to map the brain, pinpointing active brain regions.
These two techniques constitute the high-tech foundation
for research by the cadre of young scientists recruited to the new
Center for Cognitive Neuroscience (CCN) to tackle what until recently
has been considered an impossible ambition—understanding how
the hundred billion or so neurons in the human brain somehow produce
the mental abilities that constitute our mind. Until now, says the
center’s director, Ron Mangun, these abilities—language,
memory, attention, consciousness, and emotion—were mysterious
components of the “black box” that is the brain. That black
box had been probed from two different directions, says Mangun. Using
behavioral experiments, cognitive psychologists developed overall
theories about the mechanisms in the black-box brain; and neurobiologists
had disassembled the black box to tease apart the finest details of
the brain’s wiring. Now, he insists, it’s possible to bridge
the intellectual gulf between the two approaches.
“We’ve come through thirty years of growth in
knowledge about the brain, but our knowledge is still in its infancy,”
says Mangun. “Just as the cognitive psychologist’s theories
stop at the black box, it’s not enough for the neurobiologists
to stop at saying, ‘Well, we know that neurons are connected
and they squirt out chemicals and they communicate electrically and
they form circuits and they form systems, and somehow that produces
behavior.’ There’s no reason to wait another thirty years
before we start asking interesting questions about the mental life
of the human mind and how it’s really organized. So, cognitive
neuroscience meetings are places where the psychologists and the neurobiologists
can come together to convey their part of the story and to join in
developing new cognitive-neurobiological models of the mind.”
The new center represents just such an arena, where faculty
from both campus and medical center—neurobiologists, neurologists,
psychologists, philosophers, engineers, and computer scientists—can
find common intellectual ground on which they can cultivate a new
understanding of the mind. And mapping this common ground—as
translators, educators, collaborators, experimentalists, and theoreticians—are
the young scientists whom Mangun has recruited, and whom he has dubbed
Duke’s “Mind Trust.”
Mangun says Duke’s initiative in cognitive neuroscience
and cognitive neuro-imaging is the largest ongoing program of development
in cognitive neuroscience in the country. “And it’s putting
Duke on the map in that area, along with Harvard, Princeton, M.I.T.,
and Caltech.” Mangun himself exemplifies this handpicked cadre,
having been lured in 1999 from the University of California at Davis,
where he headed the psychology department’s Perception and Cognition
Area.
The scientific mysteries that the faculty are tackling
illustrate the potential for cognitive neuroscientists to explain
the mind, as well as the daunting research challenges they face. They
seek to understand how the human brain enables us to understand language,
pay attention, grasp numbers, and store emotion-laden memories.
Swaab, in whose laboratory I suffered the assaults of
ungrammatical sentences, explores how the brain understands language.
Besides testing normal subjects, she uses ERP to eavesdrop on the
brains of aphasic patients, whose brain damage may be so severe that
it prevents them from understanding or using language normally. Those
studies are suggesting a very different underlying handicap. “Traditionally,
most researchers in aphasia have thought of aphasic people as having
lost the linguistic information, or the representations responsible
for understanding meaning or structure of sentences,” she says.
Cognitive neuroscientists have uncovered evidence that such patients
may retain some linguistic ability, but that this understanding is
somehow “imprisoned” in a malfunctioning brain. The challenge,
says Swaab, is somehow penetrating the walls of that prison. “A
major problem in testing aphasic patients is asking them to perform
a task, because one of their problems is that they have difficulty
understanding language. That made me think of another way of testing
them: If you can’t ask the patient, you can ask their brain what
they still understand of normal language.”
So Swaab and Kaan were using ERP to “ask my brain”
what I was understanding when I sat in that small room watching those
sentences flash past. Such studies are leading Swaab to believe that
subtle problems in processing language information may be the root
of language comprehension problems in aphasic patients.
“We as normal language-users usually don’t think
about it, but language is actually a very complex but also a very
rapid process,” she says. Her studies aim to distinguish the
meaning-related elements of understanding language from those that
deal with processing. “We’d like to see whether these aphasic
patients have lost the relevant information, or maybe there is a problem
in the processes that access this information or use it in real time.”
Just as Swaab had set me the task of recognizing sentences,
Elizabeth Brannon has set children, monkeys, and even pigeons the
task of perceiving numbers, with startling results. Much to the surprise
of anybody who struggled through math in school, she has found that
numerical thinking appears to be built in to our brains through the
pressure of evolution.
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| LaBar:his
experiments explore the brain, not as an information processor
but as an "emotion processor" |
In 1998, while at Columbia University, Brannon and her
colleagues reported that two rhesus monkeys named Rosencrantz and
MacDuff showed that they could compare groups of objects—up to
nine—and figure out which group had fewer. The study, which involved
teaching the monkeys to use a computer touch screen to select images
of groups of objects in numerical order, convinced Brannon that numerical
thinking was built in to the brain.
“You can imagine that a monkey chased up a tree and
surrounded by a group of wild dogs would need to keep track of where
they were and how many were there,” says Brannon. “So, if
some of the dogs left, they would need to know if one were still lurking
behind a bush.”
What’s more, when Brannon tested humans using the
same system, she found that their reaction times in judging the numerical
pictures were very similar to the monkeys’, suggesting that both
species use a common and ancient “math mechanism.”
At Duke, Brannon has launched the Cognitive Development
Laboratory to study numerical thinking in children. With her “fun-and-games”
approach to studying two-year-olds, she and her colleagues have come
up with some seriously fascinating findings. In her experiments, she
shows a child two trays holding various-sized boxes and asks the child
to pick the tray with the greater number of boxes. A correct choice
wins the child brightly colored stickers.
“Previous studies of two-year-olds have shown that
they don’t understand the meaning of the number words or how
to count,” says Brannon. “Their performance was not as impressive
as the monkeys’. But over a large number of trials we found when
first shown that the larger number always contained the stickers,
they reliably chose the tray with the larger number.” Now, Brannon
is tracing numerical thinking farther back in development by presenting
infants with a given quantity of objects and after accustoming them
to that number of objects, changing the quantity. By measuring how
long the infants stare at the new quantity, she can determine whether
they are recognizing a difference in number. “We’re generally
finding they are looking longer at the new number,” she says.
“So, that does suggest an innate understanding of quantity.”
In some of her latest work, Brannon has traced numerical
ability farther back in
evolution, discovering that pigeons can apparently subtract. In her
experiments, she presented pigeons with keys to peck to get a food
reward after a number of light flashes. One key always yielded food
after fewer flashes than the other; and of course, the hungry pigeons
preferred the key that required them to wait for fewer flashes. Next,
Brandon changed the experiment’s rules so that the pigeons had
to do arithmetic to decide which key would yield a reward after the
fewer number of flashes. The pigeons quickly adjusted. “We don’t
know exactly how the pigeons are doing it, but they’re solving
the task in a way that’s hard to explain other than arguing that
they’re subtracting.”
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