**Developmental cognitive neuroscience using Latent Change Score models: A tutorial and applications**
![Dual change score data][1]
Rogier A. Kievit, Andreas M. Brandmaierc, Gabriel Ziegler, Anne-Laura van Harmelen, Susanne de Mooij, Michael Moutoussisa, Ian Goodyer, Ed Bullmoree, Peter Jonese, Peter Fonagy, the NSPN Consortium, Ulman Lindenberger & Raymond J. Dolan
This code base provides the syntax, datafiles and .xml files to simulate and fit five different Latent Change Score Models. Please have a look at the README.pdf for a step by step instruction on how to use and/or modify these files.
Abstract
Assessing and analysing individual differences in change over time is of central scientific importance to developmental neuroscience. However, the extant literature is based largely on cross-sectional comparisons, which reflect a variety of influences and cannot directly represent change. We advocate using latent change score (LCS) models in longitudinal samples as a statistical framework to tease apart the complex processes underlying lifespan development in brain and behaviour using longitudinal data. LCS models provide a flexible framework that naturally accommodates key developmental questions as model parameters and can even be used, with some limitations, in cases with only two measurement occasions. We illustrate the use of LCS models with two empirical examples. In a lifespan cognitive training study (COGITO, N=204, two waves) we observe correlated change in brain and behaviour in the context of a high-intensity training intervention. In an adolescent development cohort (NSPN, N=176, two waves) we find greater variability in cortical thinning in males than in females. To facilitate the adoption of LCS by the developmental community, we provide analysis code that can be adapted by other researchers and basic primers in two freely available SEM software packages (Lavaan and Ωnyx).
[1]: https://mfr.osf.io/export?url=https://osf.io/dwgg5/?action=download&direct&mode=render&initialWidth=684&childId=mfrIframe&format=600x600.jpeg
