Neuroimaging and genetic correlates of cognitive ability and cognitive development in adolescence

Abstract

Adolescence is marked by changes in cognitive abilities and in several MRI-based measures of brain structure. This study took an individual-differences approach to help understand adolescent cognitive development in a large-sample longitudinal cohort, the IMAGEN study (initial n = 2,316). We used a latent change score model to assess the associations between levels and changes in the brain’s grey-matter regions and latent general cognitive ability between ages 14 and 19 years. As expected, higher cognitive ability was correlated with higher cortical volume and larger surface area, with more ambiguous results for cortical thickness. Higher-ability participants at age 14 tended to have accelerated subsequent cortical thinning, as well as cortical volume loss. There was no statistically significant link between changes in cognitive ability and changes in the brain measures we used. We also attempted to predict levels and changes in the brain and in cognitive ability using a polygenic score for genetic variants linked to educational attainment: the score was modestly associated with the baseline measures, but did not predict the trajectory of change in any measure to a statistically significant degree. Age-14 cortical volume and surface area—though not cortical thickness—mediated a portion (9-10%) of the association between the polygenic score and age-19 cognitive ability. These findings demonstrate how large-sample data can shed light on the links between brain and cognitive ability in this important phase of the lifespan.

Associations between brain imaging and polygenic scores of mental health and educational attainment in children aged 9-11

“…Psychiatric disorders are highly polygenic and are thus likely to have distributed effects on the brain (Birnbaum and Weinberger, 2017;Insel, 2010;Ripke et al, 2020), possibly observed in multiple neuroimaging modalities that are sensitive to different tissue properties (Lazari et al, 2021;Natu et al, 2019;Ripke et al, 2020;Rokicki et al, 2021). Besides, brain maturation during late childhood and adolescence is highly spatially heterogeneous and dynamic, covering increases in surface area, apparent cortical thinning or white matter microstructural changes that possibly reflect synaptic remodeling and myelination (Norbom et al, 2021;Ritchie et al, 2019;Westlye et al, 2010;Zhou et al, 2015). Multimodal neuroimaging can aid the mechanistic interpretation of neuroimaging results (Natu et al, 2019) and increase certainty and sensitivity to detect associations between brain imaging and other traits.…”

In search of better practice in executive functions assessment: Methodological issues and potential solutions.

“…Furthermore, SEM techniques are a powerful statistical tool for individual differences research in the fields of working memory (Engle et al, 1999; Kane & Engle, 2002; Ørskov et al, 2021; Rey-Mermet et al, 2019; Shah & Miyake, 1996) and executive functions (Arán Filippetti & Richaud, 2017; Friedman et al, 2008; Schmidt et al, 2017; van der Sluis et al, 2007; Spencer et al, 2020). It comes thus as no surprise that in the last decade many studies have used SEM techniques to investigate executive functions development, their structure, as well as their neural organization (Alfonso & Lonigan, 2021; Brydges et al, 2014; Cirino et al, 2018; Huizinga et al, 2006; Lambek & Shevlin, 2011; Lee et al, 2013; Lerner & Lonigan, 2014; Monette et al, 2015; Montroy et al, 2019; Ritchie et al, 2019; Rose et al, 2012; Usai et al, 2014; Wiebe et al, 2011; Willoughby et al, 2012; Xu et al, 2013). For example, latent variable studies suggest that executive functions develop and differentiate from a rather unitary structure to a multidimensional structure throughout childhood and adolescence.…”