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).

It is well established that brain structures and cognitive functions change across the life span. A long-standing hypothesis called “age differentiation” additionally posits that the relations between cognitive functions also change with age. To date, however, evidence for age-related differentiation is mixed, and no study has examined differentiation of the relationship between brain and cognition. Here we use multigroup structural equation models (SEMs) and SEM trees to study differences within and between brain and cognition across the adult life span (18–88 years) in a large (N > 646, closely matched across sexes), population-derived sample of healthy human adults from the Cambridge Centre for Ageing and Neuroscience (www.cam-can.org). After factor analyses of gray matter volume (from T1- and T2-weighted MRI) and white matter organization (fractional anisotropy from diffusion-weighted MRI), we found evidence for the differentiation of gray and white matter, such that the covariance between brain factors decreased with age. However, we found no evidence for age differentiation among fluid intelligence, language, and memory, suggesting a relatively stable covariance pattern among cognitive factors. Finally, we observed a specific pattern of age differentiation between brain and cognitive factors, such that a white matter factor, which loaded most strongly on the hippocampal cingulum, became less correlated with memory performance in later life. These patterns are compatible with the reorganization of cognitive functions in the face of neural decline, and/or with the emergence of specific subpopulations in old age.SIGNIFICANCE STATEMENT The theory of age differentiation posits age-related changes in the relationships among cognitive domains, either weakening (differentiation) or strengthening (dedifferentiation), but evidence for this hypothesis is mixed. Using age-varying covariance models in a large cross-sectional adult life span sample, we found age-related reductions in the covariance among both brain measures (neural differentiation), but no covariance change among cognitive factors of fluid intelligence, language, and memory. We also observed evidence of uncoupling (differentiation) between a white matter factor and cognitive factors in older age, most strongly for memory. Together, our findings support age-related differentiation as a complex, multifaceted pattern that differs for brain and cognition, and discuss several mechanisms that might explain the changing relationship between brain and cognition.

The ability to monitor and adjust our performance is crucial for adaptive behaviour, a key component of human cognitive control. One widely studied metric of this behaviour is post-error slowing (PES), the finding that humans tend to slow down their performance after making an error. This study is a first attempt at generalizing the effect of PES to an online adaptive learning environment where children practise mathematics and language skills. This population was of particular interest since the major development of error processing occurs during childhood. Eight million response patterns were collected from 150,000 users aged 5 to 13 years old for 6 months, across 23 different learning activities. PES could be observed in most learning activities and greater PES was associated with greater post-error accuracy.PES also varied as a function of several variables. At the task level, PES was greater when there was less time pressure, when errors were slower, and in learning activities focusing on mathematical rather than language skills. At the individual level, students who chose the most difficult level to practise and had higher skill ability also showed greater PES. Finally, non-linear developmental differences in error processing were found, where the PES magnitude increased from 6 to 9-years-old and decreased from 9 to 13. This study shows that PES underlies adaptive behaviour in an educational context for primary school students.

No abstract

Molenaar's manifesto on psychology as idiographic science (Molenaar, 2004) brought the N = 1 times series perspective firmly to the attention of developmental scientists. The rich intraindividual variation in complex developmental processes requires the study of these processes at the level of the individual. Yet, the idiographic approach is all but easy in practical research. One major limitation is the collection of short interval times series of high quality data on developmental processes. In this paper, we present a novel measurement approach to this problem. We developed an online practice and monitoring system which is now used by thousands of Dutch primary school children on a daily or weekly basis, providing a new window on cognitive development. We will introduce the origin of this new instrument, called Math Garden, explain its setup, and present and discuss ways to analyze children's individual developmental pathways.