Abstract
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 Ωnyx).
Highlights
When thinking about any repeated measures analysis it is best to ask first, what is your model for change? (McArdle, 2009, p. 579)Developmental cognitive neuroscience is concerned with how cognitive and neural processes change during development, and how they interact to give rise to a rich and rapidly fluctuating profile of cognitive, emotional and behavioural changes
Developmental mismatch theory (Ahmed et al, 2015; Mills et al, 2014; Steinberg, 2008; van den Bos and Eppinger, 2016) suggests that a key explanation of risk taking behaviour in adolescence is the delayed development of brain regions associated with cognitive control compared to regions associated with mediating emotional responses
We describe a specific subtype of longitudinal Structural equation modelling (SEM) known as the Latent Change Score Models (LCS, 2 it should be noted that SEM software packages can vary in their default model specifications, so users should always be aware of these modelling assumptions sometimes called Latent Difference Score models) (McArdle and Hamagami, 2001b; McArdle and Nesselroade, 1994)
Summary
When thinking about any repeated measures analysis it is best to ask first, what is your model for change? (McArdle, 2009, p. 579). Developmental mismatch theory (Ahmed et al, 2015; Mills et al, 2014; Steinberg, 2008; van den Bos and Eppinger, 2016) suggests that a key explanation of risk taking behaviour in adolescence is the delayed development of brain regions associated with cognitive control (e.g. the frontal lobe) compared to regions associated with mediating emotional responses (e.g. the amygdala) This too posits a clear brain-behaviour dynamic, where a mismatch between maturation in executive brain regions compared to emotion systems is hypothesized to affect the likelihood of certain (mal) adaptive behaviours. Bengtsson et al (2005) found that degree and intensity of piano practice in childhood and adolescence correlated with regionally specific differences in white matter structure, and that this effect was more pronounced in developmental windows in which maturation was ongoing This was interpreted as evidence of training-induced plasticity, suggesting that behavioural modifications (i.e. prolonged practice) preceded, and caused, measurable changes in white matter structure..
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