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The Neural Determinants of Age-Related Changes in Fluid Intelligence: A Pre-Registered, Longitudinal Analysis in UK Biobank


The Neural Determinants of Age-Related Changes in Fluid Intelligence: A Pre-Registered, Longitudinal Analysis in UK Biobank

Rogier A Kievit et al. Wellcome Open Res.


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.

Keywords: Aging; Biobank; cognitive aging; fluid intelligence; grey matter; individual differences; structural equation modelling; white matter.

Conflict of interest statement

No competing interests were disclosed.


Figure 1.
Figure 1.. Top: Linear relation between age and fluid intelligence sumscores at Time 1 (some jitter added for visibility).
Bottom: A random subsample of raw fluid intelligence scores across testing occasions.
Figure 2.
Figure 2.. Intelligence intercept scores (top) and age at first testing occasion (bottom) as a function of the number of measurement occasions (one, two or three).
Individuals who took part in all three waves were slightly older, and scored slightly higher on the fluid intelligence task.
Figure 3.
Figure 3.. Latent growth curve model for fluid intelligence sum scores across 3 occasions.
Plot shows beta/standard errors. gf = fluid intelligence. T=timepoint. icept= intercept.
Figure 4.
Figure 4.. Sample size adjusted Bayesian Information Criterion (BIC), left, and entropy (right) for 1–5 classes in a growth mixture model approach.
Dashed line indicates commonly accepted entropy criterion for good separation.
Figure 5.
Figure 5.. Multiple Indicator, Multiple Causes (MIMIC) model of fluid intelligence and white matter tracts showing 5 significant predictors, jointly predicting 1.3% of the variance in gf.
Plot shows beta/standard errors. Non-significant tracts and tract covariances were estimated but are omitted for clarity. gF= fluid intelligence. icept= intercept. medial_lemn: medial lemniscus; PTR: posterior thalamic radiation; Unc: uncinate fasciculus; IFOF: inferior fronto-occipital fasciculus; forc_maj: forceps major.
Figure 6.
Figure 6.. Multiple Indicator, Multiple Causes (MIMIC) Latent Growth (LGM) model for fluid intelligence and grey matter, jointly predicting 4.5% of the variance.
All paths shown are beta/standard errors. Non-significant tracts and tract covariances were estimated but are omitted for clarity.
Figure 7.
Figure 7.. Final Multiple Indicator, Multiple Causes (MIMIC) Latent Growth (LGM) of fluid intelligence and neural determinants (grey and white matter).
The strongest predictions are the frontal pole grey matter volume and the posterior thalamic radiations, both such that greater volume and greater Fractional Anisotropy (FA) were associated with better scores. All paths shown are beta/standard errors. Non-significant tracts and region covariances were estimated but are omitted for clarity.

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