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Related Concept Videos

Pharmacodynamics in Geriatric Patients: Effects of Age01:27

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Age-related pharmacokinetic changes are extensively documented, but understanding age-related pharmacodynamic alterations is relatively limited. This knowledge gap can be partly attributed to the complexity of developing appropriate measures of drug responses compared to bioanalytical methods for determining drug concentrations.Most information regarding age-related differences in human pharmacodynamics originates from cross-sectional studies. However, these studies assume that observed mean...
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Intelligence is often thought to be linked to brain size, but the relationship is more complex than that. While brain size does correlate modestly with some abilities, like verbal skills, the connection is weaker for others, such as spatial reasoning. Other factors, like brain structure, also play crucial roles. For instance, despite Einstein's smaller-than-average brain, his parietal cortex, which is involved in spatial reasoning, was 15% wider, suggesting that neural density might matter...
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Related Experiment Video

Updated: Feb 21, 2026

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
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Sample composition alters associations between age and brain structure.

Kaja Z LeWinn1, Margaret A Sheridan2, Katherine M Keyes3

  • 1Department of Psychiatry, University of California, San Francisco, 401 Parnassus Ave., San Francisco, 94143, USA. kaja.lewinn@ucsf.edu.

Nature Communications
|October 14, 2017
PubMed
Summary
This summary is machine-generated.

Neuroimaging studies using representative samples reveal earlier brain maturation in children. Adjusting for sample composition significantly alters findings on brain development, highlighting the impact of population science in neuroimaging research.

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Area of Science:

  • Neuroimaging
  • Developmental Neuroscience
  • Population Science

Background:

  • Neuroimaging research often uses small, non-representative samples, potentially biasing results.
  • Incorporating population science principles is crucial for understanding brain development across diverse groups.

Purpose of the Study:

  • To investigate how sample composition affects age-related variations in brain structure.
  • To determine if weighting a sample to match U.S. Census demographics alters conclusions about brain maturation.

Main Methods:

  • Structural brain imaging data from 1162 children (aged 3-18) were analyzed.
  • Sample weights were applied to create a dataset representative of U.S. socioeconomic status, race/ethnicity, and sex.
  • Associations between age and brain structure (volume, thickness, surface area) were compared between weighted and unweighted samples.

Main Results:

  • The weighted sample demonstrated earlier maturation of cortical and sub-cortical brain structures compared to the unweighted sample.
  • Brain maturation patterns in the weighted sample more closely aligned with established developmental trajectories.
  • Non-representative sampling introduced bias, influencing the observed patterns of brain development.

Conclusions:

  • Sample composition significantly influences findings in neuroimaging studies of brain development.
  • Applying population-based weighting can provide more accurate insights into age-related brain maturation.
  • Future neuroimaging research should prioritize representative sampling to enhance the generalizability of findings.