Any scientist who studies groups of people knows that the characteristics of the “sample” — the group of people selected for the study — can profoundly impact the study’s findings. To produce the most accurate findings, a study group ought to be as similar as possible to the people in the larger population you want to say something about.
A new UC San Francisco-led study shows that failure to follow this basic principle of population science — a common complaint about research in the cognitive sciences — can profoundly skew the results of brain imaging studies, leading to errors that may be throwing off neuroscientists’ understanding of normal brain development.
“Much of what we know about how the brain develops comes from samples that don’t look like the broader U.S. at all,” said Kaja LeWinn, ScD, an epidemiologist and assistant professor of psychiatry at UCSF, member of the UCSF Weill Institute for Neurosciences, and lead author of the new study. “We would never try to understand the burden of other health conditions, like cardiovascular disease, in a sample with much higher socioeconomic status than the U.S. population as a whole, for instance.”
In recent years, social scientists have drawn attention to the over-representation of so-called W.E.I.R.D (White, Educated, Industrial, Rich, Developed) individuals in cognitive science experiments, but LeWinn says her team’s new study — published online October 12, 2017 in Nature Communications — is the first to look directly at exactly how these sampling practices impact the research results of neuroimaging studies.
The research team — which included Margaret A. Sheridan, PhD, of the University of North Carolina at Chapel Hill; Katherine M. Keyes, PhD, and Ava Hamilton of Columbia University; and Katie A. McLaughlin, PhD, of the University of Washington — found that applying the sampling approaches frequently used in brain imaging studies to a large dataset of pediatric MRI images significantly…