Abstract
Despite calls to incorporate population science into neuroimaging research, most studies recruit small, non-representative samples. Here, we examine whether sample composition influences age-related variation in global measurements of gray matter volume, thickness, and surface area. We apply sample weights to structural brain imaging data from a community-based sample of children aged 3–18 (N = 1162) to create a “weighted sample” that approximates the distribution of socioeconomic status, race/ethnicity, and sex in the U.S. Census. We compare associations between age and brain structure in this weighted sample to estimates from the original sample with no sample weights applied (i.e., unweighted). Compared to the unweighted sample, we observe earlier maturation of cortical and sub-cortical structures, and patterns of brain maturation that better reflect known developmental trajectories in the weighted sample. Our empirical demonstration of bias introduced by non-representative sampling in this neuroimaging cohort suggests that sample composition may influence understanding of fundamental neural processes.
Highlights
Despite calls to incorporate population science into neuroimaging research, most studies recruit small, non-representative samples
This work suggests that sample composition may influence a study’s conclusions when the association between the independent and dependent variable differs between those selected into the study and those who are eligible from the target population but not included[6, 7]
This study represents a considerable advance toward representative sampling in cognitive neuroscience, participants were from a single geographic location and had higher levels of socioeconomic status (SES) than in the U.S population overall, indicating that this sample does not fully represent the U.S While sample composition has become a growing area of focus in neuroimaging research[1, 2], to date there are no neuroimaging studies based on a representative sample of the U.S population
Summary
Despite calls to incorporate population science into neuroimaging research, most studies recruit small, non-representative samples. This work suggests that sample composition may influence a study’s conclusions when the association between the independent and dependent variable (e.g., age and brain structure) differs between those selected into the study and those who are eligible from the target population but not included[6, 7]. Such a scenario is likely to occur when study participants do not represent the target population in characteristics known to influence neural structure or function, for example, socioeconomic status (SES)[8]. This study represents a considerable advance toward representative sampling in cognitive neuroscience, participants were from a single geographic location and had higher levels of SES than in the U.S population overall, indicating that this sample does not fully represent the U.S While sample composition has become a growing area of focus in neuroimaging research[1, 2], to date there are no neuroimaging studies based on a representative sample of the U.S population
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