The neural determinants of age-related changes in fluid intelligence: a pre-registered, longitudinal analysis in UK Biobank

Abstract

Fluid intelligence declines with advancing age, starting in early adulthood. Within-subject declines in fluid intelligence are highly correlated with contemporaneous declines in the ability to live and function independently. To support healthy aging, the mechanisms underlying these declines need to be better understood. In this pre-registered analysis, we applied latent growth curve modelling to investigate the neural determinants of longitudinal changes in fluid intelligence across three time points in 185,317 individuals (N=9,719 two waves, N=870 three waves) from the UK Biobank (age range: 39-73 years).We found a weak but significant effect of cross-sectional age on the mean fluid intelligence score, such that older individuals scored slightly lower. However, the mean longitudinal slope was positive, rather than negative, suggesting improvement across testing occasions. Despite the considerable sample size, the slope variance was non-significant, suggesting no reliable individual differences in change over time. This null-result is likely due to the nature of the cognitive test used. In a subset of individuals, we found that white matter microstructure (N=8839, as indexed by fractional anisotropy) and grey-matter volume (N=9931) in pre-defined regions-of-interest accounted for complementary and unique variance in mean fluid intelligence scores. The strongest effects were such that higher grey matter volume in the frontal pole and greater white matter microstructure in the posterior thalamic radiations were associated with higher fluid intelligence scores. In a large preregistered analysis, we demonstrate a weak but significant negative association between age and fluid intelligence. However, we did not observe plausible longitudinal patterns, instead observing a weak increase across testing occasions, and no significant individual differences in rates of change, likely due to the suboptimal task design. Finally, we find support for our preregistered expectation that white- and grey matter make separate contributions to individual differences in fluid intelligence beyond age.

Bridging Brain and Cognition: A Multilayer Network Analysis of Brain Structural Covariance and General Intelligence in a Developmental Sample of Struggling Learners

“…FreeSurfer morphology output statistics were computed for each ROI, and also included cortical thickness and surface area (see Supplementary Materials Figures S5 and S6 for analyses involving these two metrics). Based on a recent meta-analysis on functional and structural correlates of intelligence ( Basten et al 2015 ), as well as a previous longitudinal analysis of the UK Biobank sample (see Kievit et al 2018 ), we included a subset of 10 cortical volume regions in this study: caudal anterior cingulate (CAC), caudal middle frontal gyrus (CMF), frontal pole (FP), medial orbitofrontal cortex (MOF), rostral anterior cingulate gyrus (RAC), rostral middle frontal gyrus (RMF), superior frontal gyrus (SFG), superior temporal gyrus (STG), supramarginal gyrus (SMG), and transverse temporal gyrus (TTG).…”

“…Emerging theoretical proposals emphasize the role of networks of brain areas in producing intelligent behavior (e.g., Parieto-Frontal Integration Theory (P-FIT), Jung and Haier ( 2007 ) and The Network Neuroscience Theory of Human Intelligence, Barbey 2018 ) rather than individual regions-of-interest (ROIs) in isolation (e.g., primarily the prefrontal cortex). As stated above, we selected 10 grey matter and 10 white matter ROIs based upon combined evidence from a recent meta-analysis ( Basten et al 2015 ) on associations between functional and structural ROIs and cognitive ability, that further extended the P-FIT theory, but also more recent work performed in two large cohorts, one in longitudinal analysis of the UK Biobank sample (grey matter, Kievit et al 2018 ) and another in the same (cross-sectional) developmental cohort, although with a smaller behavioral sample size (cognitive data, N = 551; white matter, N = 165, same fractional anisotropy data as the present study; no grey matter data used), as that studied here (see, Simpson-Kent et al 2020 ). See Figure 1 for illustrations of ROIs analyzed in this study.…”

Structural brain imaging correlates of general intelligence in UK Biobank

“…Another study using an earlier release of UK Biobank imaging data examined the association between Verbal-Numerical Reasoning and brain size, reporting a correlation of r = 0.19 ( N = 13,608; Nave, Jung, Linnér, Kable, & Koellinger, 2019). In addition, analyses of regional white and 10 grey matter measures have been reported with respect to Verbal-Numerical Reasoning in an earlier UK Biobank release; however, the authors of that study cited several reasons to doubt that this test, in isolation, is a valid indicator of fluid cognitive ability (Kievit, Fuhrmann, Borgeest, Simpson-Kent, & Henson, 2018; see also Hagenaars et al, 2016).…”

Novel age-dependent cortico-subcortical morphologic interactions predict fluid intelligence: A multi-cohort geometric deep learning study

“…Gf increases rapidly from birth through late adolescence, when it reaches a plateau which is sustained through the third decade of life, followed by a slow decay over the remaining lifespan ( 75 ). This trajectory parallels that of grey matter pruning in the cortex, which is much more pronounced in latency-aged children (e.g., ABCD cohort) relative to young adults (HCP cohort).…”