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Related Concept Videos

Longitudinal Studies01:26

Longitudinal Studies

300
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Longitudinal Research02:20

Longitudinal Research

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Related Experiment Video

Updated: Nov 5, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

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Modeling sparse longitudinal data in early neurodevelopment.

Yaqing Chen1, Paromita Dubey2, Hans-Georg Müller1

  • 1Department of Statistics, University of California, Davis, Davis, CA, 95616, USA.

Neuroimage
|May 17, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to track brain development in children, creating growth charts for neuroanatomy and cognitive skills. This helps understand individual brain growth trajectories from infancy through early childhood.

Keywords:
Brain development percentilesBrain-for-age growth chartConcurrent regression modelingFunctional principal componentsIndividual developmental trajectory reconstructionWhole brain MRI

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Area of Science:

  • Neuroscience
  • Developmental Biology
  • Biostatistics

Background:

  • Early childhood brain development is rapid, influencing cognitive and motor skills.
  • Current methods lack developmental trajectories for comparing individual neuroanatomical growth over time.
  • Sparse longitudinal neuroimaging data pose challenges for subject-level dynamic analyses.

Purpose of the Study:

  • To investigate the relationship between neuroanatomical development and cognitive functioning in early childhood.
  • To develop age-varying growth percentiles for typical brain development using longitudinal data.
  • To establish a method for predicting individual brain development trajectories.

Main Methods:

  • Utilized the Principal Analysis through Conditional Expectation (PACE) for sparse longitudinal data.
  • Applied functional varying coefficient models to assess age-varying associations between brain structures and cognitive scores.
  • Employed local Fréchet regression to construct population-level growth percentiles.

Main Results:

  • Developed dynamic, age-varying growth percentiles for gray matter, white matter, and cerebrospinal fluid volumes.
  • Quantified the evolving associations between brain development and cognitive functioning across childhood.
  • Demonstrated the utility of PACE in predicting individual brain development trajectories.

Conclusions:

  • The PACE approach provides a robust framework for analyzing sparse longitudinal neuroimaging data.
  • Established novel growth percentiles enable normative comparisons of brain development in children.
  • This methodology advances our understanding of neurodevelopmental trajectories and their relation to cognitive skills.