New findings raise questions about reliability of fMRI as gauge of
neural activity
By Laura Sanders
December 19th, 2009; Vol.176 #13 (p. 16)
The 18-inch-long Atlantic salmon lay perfectly still for its brain
scan. Emotional pictures —a triumphant young girl just out of a
somersault, a distressed waiter who had just dropped a plate — flashed
in front of the fish as a scientist read the standard instruction
script aloud. The hulking machine clunked and whirred, capturing
minute changes in the salmon's brain as it assessed the images.
Millions of data points capturing the fluctuations in brain activity
streamed into a powerful computer, which performed herculean number
crunching, sorting out which data to pay attention to and which to
ignore.
By the end of the experiment, neuroscientist Craig Bennett and his
colleagues at Dartmouth College could clearly discern in the scan of
the salmon's brain a beautiful, red-hot area of activity that lit up
during emotional scenes.
An Atlantic salmon that responded to human emotions would have been an
astounding discovery, guaranteeing publication in a top-tier journal
and a life of scientific glory for the researchers. Except for one
thing. The fish was dead.
The scanning technique used on the salmon — called functional magnetic
resonance imaging — allows scientists to view the innards of a working
brain, presumably reading the ebbs and flows of activity that underlie
almost everything the brain does. Over the last two decades, fMRI has
transformed neuroscience, enabling experiments that researchers once
could only dream of. With fMRI, scientists claim to have found the
brain regions responsible for musical ability, schadenfreude,
Coca-Cola or Pepsi preference, fairness and even tennis skill, among
many other highly publicized conclusions.
But many scientists say that serious issues have been neglected during
fMRI's meteoric rise in popularity. Drawing conclusions from an fMRI
experiment requires complex analyses relying on chains of assumptions.
When subjected to critical scrutiny, inferences from such analyses and
many of the assumptions don't always hold true. Consequently, some
experts allege, many results claimed from fMRI studies are simply dead
wrong.
"It's a dirty little secret in our field that many of the published
findings are unlikely to replicate," says neuro scientist Nancy
Kanwisher of MIT.
A reanalysis of the salmon's post mortem brain, using a statistical
check to prevent random results from accidentally seeming significant,
showed no red-hot regions at all, Bennett, now at the University of
California, Santa Barbara, and colleagues report in a paper submitted
to Human Brain Mapping. In other words, the whole brain was as cold as
a dead fish.
Less dramatic studies have also called attention to flawed statistical
methods in fMRI studies. Some such methods, in fact, practically
guarantee that researchers will seem to find exactly what they're
looking for in the tangle of fMRI data. Other new research raises
questions about one of the most basic assumptions of fMRI — that blood
flow is a sign of increased neural activity. At least in some
situations, the link between blood flow and nerve action appears to be
absent. Still other papers point out insufficient attention to
insidious pitfalls in interpreting the complex enigmatic relationship
between an active brain region and an emotion or task.
Make no mistake: fMRI is a powerful tool allowing neuroscientists to
elucidate some of the brain's deepest secrets. It "provides you a
different window into how mental processes work in the brain that we
wouldn't have had without it," says Russell Poldrack of the University
of Texas at Austin.
But like any powerful tool, fMRI must be used with caution. "All
methods have shortcomings — conclusions they support and conclusions
they don't support," Kanwisher says. "Neuroimaging is no exception."
BOLD assumptions
fMRI machines use powerful magnets, radio transmitters and detectors
to peer into the brain. First, strong magnets align protons in the
body with a magnetic field. Next, a radio pulse knocks protons out of
that alignment. A detector then measures how long it takes for the
protons to recover and emit telltale amounts of energy. Such energy
signatures act as beacons, revealing the locations of protons
ensconced in specific molecules.
fMRI is designed to tell researchers which brain regions are active —
the areas where nerve cells are abuzz with electrical signals.
Scientists have known for a long time how to record these electrical
communiqués with electrodes, which can sit on the scalp or be
implanted in brain tissue. Yet electrodes outside the skull can't
precisely pinpoint active regions deep within the brain, and
implanting electrodes in the brain comes with risks. fMRI, on the
other hand, offers a nonintrusive way to measure neuron activity,
requiring nothing more of the subject than an ability to lie in a big
tube for a while.
But fMRI doesn't actually measure electrical signals. Instead, the
most common fMRI method, BOLD (for blood oxygen level–dependent),
relies on tiny changes in oxygenated blood as a proxy for brain
activity. The assumption is that when neurons are working hard, they
need more energy, brought to them by fresh, oxygen-rich blood. Protons
in oxygen-laden hemoglobin molecules, whisked along in blood, respond
to magnetic fields differently than protons in oxygen-depleted blood.
Detecting these different signatures allows researchers to follow the
oxygenated blood to track brain activity — presumably.
"There's still some mystery," Bennett says. "There are still some
things we don't understand about the coupling between neural activity
and the BOLD signal that we're measuring in fMRI."
Researchers use BOLD because it's the best approximation to neural
activity that fMRI offers. And for the most part, it works. But a
study published in January in Nature reported that the link between
blood flow and neural activity is not always so clear. In their
experiments, Aniruddha Das and Yevgeniy Sirotin, both of Columbia
University, found that in monkeys some blood changes in the brain had
nothing to do with localized neuron firing.
Das and Sirotin used electrodes to measure neuronal activity at the
same time and place as blood flow in monkeys who were looking at an
appearing and disappearing dot. As expected, when vision neurons
detected the dot and fired, blood rushed into the scrutinized brain
region. But surprisingly, at times when the dot never appeared and the
neurons remained silent, the researchers also saw a dramatic change in
blood flow. This unprompted change in blood flow occurred when the
monkeys were anticipating the dot, the researchers found. The
imperfect correlations between blood flow and neural firing can
confound BOLD signals and muddle the resulting conclusions about brain
activity.
Mass action
Another fMRI difficulty arises from its view-from-the-top scale.
Predicting a single neuron's activity from fMRI is like trying to tell
which way an ant on the ground is crawling from the top of the
Washington Monument, without binoculars. The smallest single unit
measured by BOLD fMRI, called a voxel, is often a few millimeters on
each side, dwarfing the size of individual neurons. Each voxel — a
mashup of volume and pixel — holds around 5.5 million neurons,
calculates Nikos Logothetis of the Max Planck Institute for Biological
Cybernetics in Tübingen, Germany. Assuming that the millions of
neurons in a voxel perform identically is like assuming every single
ant on the National Mall crawls north at noon.
"fMRI is a measure of mass action," Logothetis says. "You almost have
to be a professional moron to think you're saying something profound
about the neural mechanisms. You're nowhere close to explaining what's
happening, but you have a nice framework, an excellent starting
point." BOLD signals could reflect many different events, he says. For
instance, some neurons send signals that stop other neurons from
firing, so increased activity of these dampening neurons could
actually lead to an overall decrease in neuron activity.
Kanwisher points out that words such as "activity" and "response,"
mainstays of fMRI paper titles, are intentionally vague. Pinning down
the details from such a zoomed-out view, she says, is impossible.
"What exactly are the neurons doing in there? Is one inhibiting the
other? Are there action potentials? Is there synaptic activity? Well,
we have no idea," she says. "It would be nice to know what the neurons
are doing, but we don't with this method. And that's life."
Inadvertent mischief
After BOLD signals have been measured and the patient has been
released from the machine, researchers must sort the red-hot voxels
from the dead fish. Statistics for dealing with these gigantic data
sets are so complex that some researchers outsource the analyses to
professional number crunchers. Choosing criteria to catch real and
informative brain changes, and guarding against spurious results, is
one of the most important parts of an fMRI experiment, and also one of
the most opaque.
"It's hellishly complicated, this data analysis," says Hal Pashler, a
psychologist at the University of California, San Diego. "And that
creates great opportunity for inadvertent mischief."
Making millions, often billions, of comparisons can skew the numbers
enough to make random fluctuations seem interesting, as with the dead
salmon. The point of the salmon study, Bennett says, was to point out
how easy it is to get bogus results without the appropriate checks.
Bennett and colleagues have written an editorial to appear in Social
Cognitive and Affective Neuroscience that argues for strong measures
to protect against false alarms. Another group takes the counterpoint
position, arguing that these protections shouldn't be so strong that
the real results are tossed too, like a significant baby with the
statistical bathwater.
One of the messiest aspects of fMRI analysis is choosing which part of
the brain to scrutinize. Some studies have dealt with this problem by
selecting defined anatomical regions in advance. Often, though,
researchers don't know where to focus, instead relying on statistics
to tell them which voxels in the entire brain are worth a closer look.
In a paper originally titled "Voodoo correlations in social
neuro science" in the May issue of Perspectives on Psychological
Science, Edward Vul of MIT, Pashler and colleagues called out 28 fMRI
papers (of 53 analyzed) for committing the statistical sin of
"nonindependence." In nonindependent analyses, the hypothesis in
question is not an innocent bystander, but in fact distorts the
experiment's outcome. In other words, the answer is influenced by how
the question is asked.
One version of this error occurs when researchers define interesting
voxels with one set of criteria — say, those that show a large change
when a person is scared — and then use those same voxels to test the
strength of the link between voxel and fear. Not surprisingly, the
correlation will be big. "If you have many voxels to choose from, and
you choose the largest ones, they'll be large," Vul says.
In a paper in the May Nature Neuro science, Nikolaus Kriegeskorte of
the Medical Research Council in Cambridge, England, and colleagues
call the non-independence issue the error that "beautifies" results.
"It tends to clean things up at the expense of a veritable
representation of the data," Kriegeskorte says.
Digging through the methods sections of fMRI papers published in 2008
in Nature, Science, Nature Neuroscience, Neuron and the Journal of
Neuroscience turned up some sort of nonindependence error in 42
percent, Kriegeskorte and colleagues report in their paper. Authors
"do very complicated analyses, and they don't realize that they're
actually walking in a very big circle, logically," Kriegeskorte says.
Kanwisher, who just cowrote a book chapter with Vul about the
nonindependence error, says that researchers can lean too heavily on
"fancy" math. "Statistics should support common sense," she says. "If
the math is so complicated that you don't understand it, do something
else."
The problem with blobology
An issue that particularly irks some researchers has little to do with
statistical confounders in fMRI, but rather with what the red-hot
blobs in the brain images actually mean. Just because a brain region
important for a particular feeling is active does not mean a person
must be feeling that feeling. It's like concluding that a crying baby
must be hungry. True, a hungry baby does cry, but a crying baby might
be tired, feverish, frightened or wet while still well-fed.
Likewise, studies have found that a brain structure called the insula
is active when a person is judging fairness. But if a scan shows the
insula to be active, the person is not necessarily contemplating
fairness; studies have found that the insula also responds to pain,
tastes, interoceptive awareness, speech and memory.
In most cases, the brain does not rely on straightforward
relationships, with a specific part of the brain responsible for one
and only one task, making these reverse inferences risky, Poldrack
points out.
"Researchers often assume that there are one-to-one relations between
brain areas and mental functions," he says. "But we don't actually
know if that is true, and there are many reasons to think that it's
not." Inferring complex human emotions from the activity of a single
brain region is not something that should be done casually, as it is
often is, he says.
Sometimes, reverse inference is warranted, though, as long as it is
done with care. "There's nothing wrong with saying there's a brain
region for x," Kanwisher says. "It just takes many years to establish
that. And like all other results, you establish it, and it can still
crash if somebody presents a new piece of data that argues against
it."
Marco Iacoboni of the University of California, Los Angeles and
colleagues drew heat from fellow neuroscientists for a New York Times
op-ed in November 2007 in which the team claimed to have ascertained
the emotional states of undecided voters as they were presented with
pictures of candidates. For instance, the researchers concluded that
activity in the anterior cingulate cortex meant that subjects were
"battling unacknowledged impulses to like Mrs. Clinton." Poldrack and
16 other neuroscientists quickly wrote their own editorial, saying
that the original article's claims had gone too far.
Iacoboni counters that reverse inference has a valuable place in
research, as long as readers realize that it is a probabilistic
measure. "A little bit of reverse inference, to me, is almost
necessary," he says.
Careful language and restrained conclusions may solve some of the
issues swirling around fMRI interpretations, but a more serious
challenge comes from fMRI's noise. Random fluctuations masquerading as
bona fide results are insidious, but the best way to flush them out is
simple: Do the experiment again and see if the results hold up. This
built-in reality check is time-consuming and expensive, Kanwisher
says, but it's the best line of defense against spurious results.
A paper published April 15 in NeuroImage clearly illustrates the
perils of one-off experiments. In an fMRI experiment, Bradley
Schlaggar of Washington University in St. Louis and colleagues found
differences in 13 brain regions between men and women during a
language task. To see how robust these results were, the researchers
scrambled the groups to create random mixes of men and women. Any
differences found between these mixed-up groups could be chalked up to
noise or unknown factors, the researchers reasoned. The team found 14
"significant" different regions between the scrambled groups,
undermining the original finding and rendering the experiment
uninterpretable.
"The upshot of the paper is really a cautionary one," Schlaggar says.
"It's easy and common to find some group differences at some
statistical threshold. So go ahead and do the study again."
In many ways, fMRI has earned its reputation as a powerful
neuroscience tool. In the laboratories of capable, thoughtful
researchers, the challenges, exceptions and assumptions that plague
fMRI can be overcome. Its promise to decode the human brain is real.
fMRI "is a great success story of modern science, and I think
historically it will definitely be viewed as that," Kriegeskorte says.
"Overwhelmingly it is a very, very positive thing."
But the singing of fMRI's praises ought to be accompanied by a chorus
of caveats. fMRI cannot read minds nor is it bogus neophrenology, as
Logothetis pointed out in Nature in 2008. Rather, fMRI's true
capabilities fall somewhere between those extremes. Ultimately,
understanding the limitations of neuro imaging, instead of ignoring
them, may propel scientists toward a deeper understanding of the
brain.
没有评论:
发表评论