Developmental cognitive neuroscience using latent change score models : a tutorial and applications
Developmental cognitive neuroscience, 2018, Vol. 33, pp. 99-117
KIEVIT, Rogier A., BRANDMAIER, Andreas M., ZIEGLER, Gabriel, VAN HARMELEN, Anne-Laura, DE MOOIJ, Susanne M. M., MOUTOUSSIS, Michael, GOODYER, Ian M., BULLMORE, Ed, JONES, Peter, FONAGY, Peter, LINDENBERGER, Ulman, DOLAN, Raymond J., Developmental cognitive neuroscience using latent change score models : a tutorial and applications, Developmental cognitive neuroscience, 2018, Vol. 33, pp. 99-117 - https://hdl.handle.net/1814/59912
Retrieved from Cadmus, EUI Research Repository
Assessing and analysing individual differences in change over time is of central scientific importance to developmental neuroscience. However, the 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 (N = 32 imaging) on 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 Omega nyx).
Available online: 22 November 2017
Cadmus permanent link: https://hdl.handle.net/1814/59912
Full-text via DOI: 10.1016/j.dcn.2017.11.007
ISSN: 1878-9293; 1878-9307
Keyword(s): Latent change scores Longitudinal modelling Development Individual differences Structural equation modelling Adolescence Structural equation models White-matter microstructure Growth curve models Covariance structure-analysis Likelihood ratio test Measurement invariance Longitudinal data Old-age Individual-differences Factorial invariance
Sponsorship and Funder information:Sir Henry Wellcome Trust [107392/Z/15/Z]UK Medical Research Council Programme [MC-A060-5PR61]Wellcome Trust [095844/Z/11/Z]European Union's Horizon 2020 research and innovation programme 
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