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Biology subjects

van Harmelen, A.-L.

Publications and source records attributed to van Harmelen, A.-L..

2 recordsLinked to original sources

Positive memory specificity reduces adolescent vulnerability to depression

Depression is the leading cause of ill health and disability worldwide1. A known risk factor of depression is exposure to early life stress2. Such early stress exposure has been proposed to sensitise the maturing psychophysiological stress system to later life stress3. Activating positive memories dampens acute stress responses with resultant lower cortisol response and improved mood in humans4 and reduced depression-like behaviour in mice5. It is unknown whether recalling positive memories similarly reduces adolescent vulnerability to depression. Here we used path modelling to examine the effects of positive autobiographical memory specificity on later morning cortisol and negative self-cognitions during low mood in adolescents at risk for depression due to early life stress (n = 427, age: 14 years)6. We found that experimentally assessed positive but not negative memory specificity was associated with lower morning cortisol and less negative self-cognitions during low mood one year later. Moderated mediation analyses demonstrated that positive memory specificity reduced later depressive symptoms through lowering negative self-cognitions in response to negative life events reported in the one-year interval. Positive memory specificity actively dampened the negative effect of stressors over time, thereby operating as a resilience factor reducing the risk of subsequent depression7. These findings suggest that developing methods to improve positive memory specificity in at-risk adolescents may counteract vulnerability to depression.

neuroscience

Developmental cognitive neuroscience using Latent Change Score models: A tutorial and applications

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).\n\nHighlightsO_LIWe describe Latent change score modelling as a flexible statistical tool\nC_LIO_LIKey developmental questions can be readily formalized using LCS models\nC_LIO_LIWe provide accessible open source code and software examples to fit LCS models\nC_LIO_LIWhite matter structural change is negatively correlated with processing speed gains\nC_LIO_LIFrontal lobe thinning in adolescence is more variable in males than females\nC_LI

neuroscience