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www.sciencemag.org SCIENCE VOL 340 5 APRIL 2013 21
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“From the sky, I saw something like a bronze
statue, a big bronze statue,” the drowsy man
told neuroscientists as he lay inside an MRI
machine that moments earlier had been
recording his brain activity as he dozed.
“The bronze statue existed on a small hill.
Below the hill, there were houses, streets,
and trees in an ordinary way.”
This surreal recounting of a just-
interrupted dream—along with nearly 500
more descriptions gathered from the man
and two other research subjects in a similar
fashion—is the basis for an ambitious
new study reported online this week in
Science by a research team at the ATR
Computational Neuroscience Labo-
ratories in Kyoto, Japan (http://scim.
ag/THorikawa). Utilizing these dream
reports, the authors have, for the fi rst
time, successfully predicted images
seen in sleep based exclusively on
MRI scans of brain activity. Although
researchers warn that we’re still far
from having a machine that can fully
read our dreams, Robert Stickgold,
a neuroscientist at Harvard Medical
School in Boston who studies dreams,
describes the work as “stunning in its
detail and success.”
Not long ago, the idea that you
could “decode” what people were
seeing, thinking, or dreaming about
based on brain activity alone was
“Star Trek, at best,” Stickgold says.
Over the past decade, however, ATR
neuroscientist Yukiyasu Kamitani has
been developing computer algorithms
aimed at doing just that. In a 2005 paper in
Nature Neuroscience, for example, he and
colleagues described creating a computer
program that can learn to associate the brain
activity patterns recorded during functional
magnetic resonance imaging (fMRI) with
specifi c visual stimuli, in order to accurately
predict at which of eight orientations of a
grid a subject is looking.
To determine whether a similar approach
could detect what sleeping people see,
Kamitani and his colleagues recently
recruited three volunteers to lie in MRI
machines for 3-hour sessions over the course
of 10 days. The loud banging caused by the
vibration of an MRI machine’s metal coils
isn’t conducive to slumber, but the volunteers
wore headphones and found a way to doze.
As each one drifted off, the researchers mon-
itored the volunteer’s brain activity with the
fMRI machine, which measures blood fl ow
in the brain and is thought to refl ect neuro-
nal activity. They also used an EEG machine
to track the brain’s overall electrical activity.
Rather than waiting more than an hour
for the people to enter rapid eye movement
(REM) sleep, which is marked by long, often
bizarre narrative dreams and temporary
paralysis, the researchers took advantage of
the frequent hallucinations that occur dur-
ing the onset of sleep, called stage 1. People
often don’t know that they are asleep during
this stage, Stickgold says. (This is the same
stage in which you might try to take the
remote control away from a snoring com-
panion and be grumpily refused, he adds.)
Once the three volunteers became accus-
tomed to napping in the fMRI machines,
they would fall easily into a light slumber,
Kamitani says. Every 6 or 7 minutes, just as
one began to drop into a deeper sleep, the
researchers would see a ripple of EEG activ-
ity suggesting the likely presence of hallu-
cinations. The researchers woke subjects,
asked them to report anything they had seen,
then told them to go back to sleep. After
gathering roughly 200 of these reports from
each subject, Kamitani’s team extracted
frequently repeated visual elements, such
as “tree” or “man,” from the reports and
grouped them into approximately 20 broad
categories tailored to each participant. In
one category, for example, “ice pick,” “key,”
and “plunger,” were all grouped under the
category “implement.”
To train a computer program to associate
broad categories of images seen in dreams
with specifi c patterns of brain activity in
the visual cortex, the researchers gathered
photos on the Internet that corresponded to
each category and recorded the three vol-
unteers’ fMRI activity as they viewed the
images. Like other machine-learning tech-
nologies that can learn to identify hand-
writing based on subtle differences in
penmanship, the program weeded out
nonvisual brain activity that occurred
during sleep and soon learned to asso-
ciate specifi c brain activity “signa-
tures” with different types of images,
Kamitani says.
When the researchers applied the
newly trained programs to a second
round of dream-reporting by the same
trio, coupled with fMRI monitoring,
they were able to predict what the
people had seen with 60% accuracy—
a far higher rate than can be attributed
to chance, Kamitani says.
“This is probably the fi rst real dem-
onstration of the brain basis of dream
content,” Stickgold says, describing
the results as “incredibly robust.” One
exciting possibility, he says, is that the
technique might reveal not only what
we remember in our dreams, but also
what we forget or fail to notice.
Technically, the images that the
people in this study describe should
not be called “dreams” at all, cautions Allan
Hobson, a psychiatrist at Harvard Medical
School who studies dreams. Instead, they are
“hypnagogic hallucinations” with a differ-
ent underlying physiology from the classic
dreams that occur in REM sleep, he contends.
Researchers still fi nd REM and stage 1
sleep mysterious, maintains ATR neuro-
scientist Masako Tamaki, a study co-author.
“They may not be that different.” She hopes
that the technique will be applied in clini-
cal sleep research and to help people who
experience bad dreams. At a minimum, “it’s
nice to hear that what people report seeing
when they’re asleep is at least somewhat
accurate,” Stickgold says. “Up until this
moment, there were no grounds on which to
say we don’t just make up our dreams when
we wake up.”
–EMILY UNDERWOOD
Dream catcher. Researchers collected reports of dreamed images
from people awoken after sleeping in an MRI machine.
fMRI
scans
Awake
Sleep Stage 1
EEG
Awakening
bookbuilding
carcharacter
commoditycomputer screen
coveringdwelling
electronic equipmentfemale
foodfurniture
malemercantile establishment
pointregion
representationstreet
Awakening index
50 100 150
Verbal
Report
How to Build a Dream-Reading Machine
N E U R O S C I E N C E
Published by AAAS
on
Janu
ary
18, 2
014
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