Sample groups in brain MR studies typically don’t reflect the broader U.S. population, according to new research from the University of California San Francisco.

The researchers are concerned that this may be providing neuroscientists with an incorrect understanding of normal brain development.

The team evaluated a publicly available dataset of brain MR scans from 1,162 children who took part in the Pediatric Imaging, Neurocognition and Genetics (PING) Study. They found that household education and income levels were much higher among the children in the dataset compared to the overall population.
The researchers noted that study investigators usually use this data without considering those demographic differences. They suggested using a weighted version of the dataset that reflects U.S. demographic characteristics such as sex, race, parental education and income.

The team created its own weighted version of the PING dataset and found that it generated a vastly different timeline of brain development during childhood and adolescence than the raw data. For instance, the unweighted data showed that brain regions reach peak size at 12.1 years but the weight data found it more likely occurs at 9.7 years.

"These findings give us pause, because they raise questions about existing knowledge of brain development in children, which is based almost entirely on non-representative samples,” Kaja LeWinn, lead author of the study, said in a statement. "These foundational studies inform our understanding not only of brain development, but also of mental health disorders like depression and autism.”

But in the real world, most cognitive neuroimaging studies are too small to apply the weighting method. This research adds to the ongoing debate in the neuroscience field over the use of small, biased study samples instead of investing in larger samples that represent target populations of interest.

"At a minimum, researchers doing these studies need to be very transparent about the demographic characteristics of their sample, and consider to whom it's reasonable to generalize their findings,” said LeWinn. “That way, when the community sees inconsistent findings across studies of the same research question, we can consider differences in sample composition as one potential explanation."

She did admit that it would be impractical and financially prohibitive to insist that all neuroimaging studies perfectly represent the U.S. population, but that large, multicenter studies should make it a priority.

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