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Developmental cognitive neuroscience using latent change score models: A tutorial and applications.

Rogier A Kievit1, Andreas M Brandmaier2, Gabriel Ziegler3

  • 1Max Planck Centre for Computational Psychiatry and Ageing Research, London/Berlin; MRC Cognition and Brain Sciences Unit University of Cambridge, Cambridge, 15 Chaucer Rd, Cambridge CB2 7EF.

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Summary
This summary is machine-generated.

Latent change score (LCS) models analyze individual differences in developmental neuroscience. These models reveal correlated brain and behavior changes in cognitive training and sex differences in adolescent cortical thinning.

Keywords:
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Area of Science:

  • Developmental neuroscience
  • Longitudinal data analysis
  • Individual differences research

Background:

  • Assessing individual differences in change over time is crucial for developmental neuroscience.
  • Current literature relies heavily on cross-sectional comparisons, which cannot directly represent developmental change.
  • Latent change score (LCS) models offer a statistical framework to analyze longitudinal data for developmental insights.

Purpose of the Study:

  • To advocate for and illustrate the use of latent change score (LCS) models in longitudinal developmental neuroscience research.
  • To demonstrate the flexibility of LCS models in addressing key developmental questions, even with limited data points.
  • To provide practical resources for researchers to adopt LCS models.

Main Methods:

  • Latent change score (LCS) modeling applied to longitudinal samples.
  • Analysis of two empirical examples: a cognitive training study (COGITO) and an adolescent development cohort (NSPN).
  • Provision of analysis code and primers for SEM software (lavaan and Ωnyx).

Main Results:

  • In a cognitive training study (COGITO), correlated changes in brain and behavior were observed.
  • In an adolescent cohort (NSPN), greater variability in cortical thinning was found in males compared to females.
  • LCS models effectively analyze developmental trajectories and individual differences.

Conclusions:

  • Latent change score (LCS) models are a powerful statistical framework for analyzing individual differences in developmental neuroscience.
  • LCS models can reveal complex developmental processes in brain and behavior using longitudinal data.
  • The study provides tools and examples to encourage the adoption of LCS models in the field.