BusinessDay 17 Dec 2017

Sunday 17 December 2017
C002D5556
SUNDAY
BD
45
Health&Science
Even brain images can be biased
...Study samples that are too rich and too well-educated may give a biased picture of brain development
BETHANY BROOKSHIRE
An astonishing number
of things that scientists
know about
brains and behavior
are based on small
groups of highly educated, mostly
white people between the ages
of 18 and 21. In other words, those
conclusions are based on college
students.
College students make a convenient
study population when
you’re a researcher at a university.
It makes for a biased sample, but
one that’s still useful for some
types of studies. It would be easy to
think that for studies of, say, how
the typical brain develops, a brain
is just a brain, no matter who’s
skull its resting in. A biased sample
shouldn’t really matter, right?
Wrong. Studies heavy in rich,
well-educated brains may provide
a picture of brain development
that’s inaccurate for the
American population at large, a
recent study found. The results
provide a strong argument for
scientists to pay more attention
to who, exactly, they’re studying
in their brain imaging experiments.
It’s “a solid piece of evidence
showing that those of us in neuroimaging
need to do a better
job thinking about our sample,
where it’s coming from and who
we can generalize our findings
to,” says Christopher Monk, who
studies psychology and neuroscience
at the University of Michigan
in Ann Arbor.
The new study is an example
of what happens when epidemiology
experiments — studies of
patterns in health and disease
— crash into studies of brain
imaging. “In epidemiology we
think about sample composition
a lot,” notes Kaja LeWinn, an
epidemiologist at the University
of California in San Francisco.
Who is in the study, where they
live and what they do is crucial to
finding out how disease patterns
spread and what contributes to
good health. But in conversations
with her colleagues in psychiatry
about brain imaging, LeWinn
realized they weren’t thinking
very much about whose brains
they were looking at. Particularly
when studying healthy populations,
she says, there was an idea
that “a brain is a brain is a brain.”
But that’s a dangerous assumption.
“The brain does not exist in a
vacuum, destined to follow some
predetermined developmental
pathway without any deviation,”
LeWinn says. “Quite the opposite,
our brains, especially in early
life, are exquisitely sensitive to
environmental cues, and these
cues shape how we develop.” She
wondered whether the sampling
used in brain imaging studies
might affect the results scientists
were seeing.
To find out, LeWinn and her
colleagues turned to the Pediatric
Imaging, Neurocognition
and Genetics — or PING — study.
“It’s probably the best study we
have of pediatric brain imaging,”
she says.
Conducted across eight cities
(including San Diego, New York
and Honolulu), the study included
more than 1,000 children
from ages of 3 to 20. It recorded
information about the children’s
genetics, mental development
and emotional function. And of
course, it contains lots of images
of their brains. The goal was to
gain a comprehensive set of data
on how children’s brain develop
over time.
The PING database is large,
well-organized and free for any
scientists to look at. LeWinn and
her colleagues examined the
dataset for the race, sex, parental
education and household income
of its participants.
The end sample of 1,162 brains
was a bit more diverse than the
U.S. population. According to the
2010 census, the U.S. population
is about 70 percent white, 14
percent black and 7.5 percent
Hispanic. By contrast, the racial
breakdown of the PING study
was 42 percent white, 10 percent
black and 24 percent Hispanic,
with a larger percentage of “other”
or mixed-race participants.
“It was more diverse. That’s
not common,” LeWinn says. This
could be because the study sites
were in large cities with diverse
populations, she notes.
The PING study participants
weren’t like the average American
in other ways as well. The
children were from richer households
than Americans in general,
and their parents were more
highly educated. While only 11
percent of Americans have a
post-college education, 35 percent
of the PING study’s children had
parents who had attended graduate
school.
So LeWinn and her colleagues
set out to make the data in the
PING study look more like the
data from the U.S. population as
a whole. They applied sample
weights to the brain imaging data,
giving more weight to the brains
of kids with poorer, less educated
families, and adding additional
weights to match the racial demographics
of the United States.
In the newly weighted data,
LeWinn and her group noticed
that children’s brains matured
more quickly. The cortex of the
brain reached a peak surface area
2.4 years earlier than the original
data would have suggested. Some
brains areas — such as the amygdala,
an area associated with
emotional processing — appeared
to reach maturity a full four
years faster. “Low socioeconomic
status is associated with faster
brain development, so that’s one
potential explanation,” LeWinn
notes. The group reported their
findings October 12 in Nature
Communications.
Unfortunately, this study can’t
tell scientists if children’s brains
actually are maturing faster than
we think they are. The weighted
sample isn’t a representation of
what average brain development
looks like in the United States.
Instead, it’s just closer to what it
might look like. “I would like to
see this replicated in an actual
sample of people who do represent
the population,” says Kate
Mills, a cognitive neuroscientist
at the University of Oregon in
Eugene.
But brain development wasn’t
the point. Instead, the point is to
show that when there’s a bias in
the sample of participants in a
brain imaging study, the data are
biased, too. Even a large sample
may not provide an accurate
picture of brain development — if
that sample has biases of its own.
It’s a strong argument for an
unbiased sample, no matter the
type of study. “It’s illustrating the
impact of sample composition on
these measures,” Mills says. “It’s
not something we can disregard
anymore.” She’s optimistic that
change is nigh. “The datasets being
collected now [in brain imaging
studies] are already taking this
more seriously.”
But it can be difficult to get
study volunteers who represent
a particular population. “A representative
sample is expensive and
challenging,” Monk notes. For his
own recent brain imaging work,
Monk has teamed up with a large
existing project to get a larger
sample, but even then, he says,
“it’s still questionable whether
or not the sample can be made
representative.” People may not
respond to the call. Volunteers
may not show up. But unless scientists
put in the extra legwork
to make sure those people are
accounted for, our picture of how
human brains work won’t apply
to everyone.