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

Abstract

Background: 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. Methods: 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). Results: 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. Conclusions: 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.

Crystallized and fluid intelligence are predicted by microstructure of specific white‐matter tracts

“…In contradistinction, a positive association of the Fmn with the FI (Cam-CAN) has been reported by Kievit et al (2016). Even more complicated is the comparison with results of the UK Biobank study (Kievit et al, 2018) since the latent variable is defined with a set of measures that have been suggested to be difficult to interpret (Lyall et al, 2016). Even more complicated is the comparison with results of the UK Biobank study (Kievit et al, 2018) since the latent variable is defined with a set of measures that have been suggested to be difficult to interpret (Lyall et al, 2016).…”

“…Besides, findings from the same group (Kievit et al, 2018) showed a weak but significant negative association between age and fluid intelligence. As the authors state, the "suboptimal task design" restricted the exploration of cognitive interpretation.…”

“…The most upstream variables were the 10 major white-matter tract mTBFA, which were modeled as influencing only the two latent variables. Thus, the single-factor summarization of intelligence scales in Kievit et al (2016, Kievit et al, 2018 lead to a misallocation of variance of the intelligence measures that will affect the full model. were identified: reasoning, spatial ability, memory, PS, and vocabulary (Salthouse, 2004), which are very closely related but not identical to the four independent indexes of the WAIS-III: PO or Reasoning (PO), PS, VC, and WM.…”

“…This graph fulfills a Markov property that can be explained as follows: If variable A is connected to B and B to C, and there is no other direct or indirect path from A to C, B "totally mediates" the influence of A on C. In other words, B "screens off" A from C. Under appropriate conditions (Pearl, 2000), the resulting graphs allow some inference about mechanistic causal relations. This framework was leveraged by Kievit et al (2012Kievit et al ( , 2016 and Kievit, Fuhrmann, Borgeest, Simpson-Kent, and Henson (2018) to provide an SEM specification for the study of the relation of FA with intelligence, work that is worth summarizing in the next paragraph. A particularly useful type of SEM is the Multiple Indicator, Multiple Causes (MIMIC) introduced by Jöreskog and Goldberger (2006) in which latent variables are introduced as mediators between two sets of observed variables.…”

“…Using individualized average FA tract-based measures of whitematter microstructure as in Kievit et al (2018). Using individualized average FA tract-based measures of whitematter microstructure as in Kievit et al (2018).…”