Prediction of brain age and cognitive age: Quantifying brain and cognitive maintenance in aging

Abstract

The concept of brain maintenance refers to the preservation of brain integrity in older age, while cognitive reserve refers to the capacity to maintain cognition in the presence of neurodegeneration or aging‐related brain changes. While both mechanisms are thought to contribute to individual differences in cognitive function among older adults, there is currently no “gold standard” for measuring these constructs. Using machine‐learning methods, we estimated brain and cognitive age based on deviations from normative aging patterns in the Whitehall II MRI substudy cohort (N = 537, age range = 60.34–82.76), and tested the degree of correspondence between these constructs, as well as their associations with premorbid IQ, education, and lifestyle trajectories. In line with established literature highlighting IQ as a proxy for cognitive reserve, higher premorbid IQ was linked to lower cognitive age independent of brain age. No strong evidence was found for associations between brain or cognitive age and lifestyle trajectories from midlife to late life based on latent class growth analyses. However, post hoc analyses revealed a relationship between cumulative lifestyle measures and brain age independent of cognitive age. In conclusion, we present a novel approach to characterizing brain and cognitive maintenance in aging, which may be useful for future studies seeking to identify factors that contribute to brain preservation and cognitive reserve mechanisms in older age.

A history of previous childbirths is linked to women's white matter brain age in midlife and older age

“…However, the exact neurobiological underpinnings of diffusion metrics cannot be directly inferred, and although we utilised advanced diffusion modelling which is sensitive to biophysical tissue properties (Jelescu & Budde, 2017 ), the biological substrates underlying these metrics remain to be elucidated by future studies. In addition, controlling for the effect of extracellular water or indices of hydration (Jones & Cercignani, 2010 ) as well as including measures of WM hyper‐intensities (Anatürk et al, 2020 ; Habes et al, 2016 ) could potentially provide more accurate models of WM ageing.…”

Population modeling with machine learning can enhance measures of mental health

“…This may suggest that the cues for neuroticism conveyed by brain imaging were already present in sociodemographic predictors, hinting at common causes. Of note, in the specific context of aging, the empirical distinction between brain age and cognitive age (age predicted from cognitive and behavioral data) is reflecting a similar intuition that different inputs can yield complementary proxies of the same target [ 66 ].…”

Examining the Role of the Noradrenergic Locus Coeruleus for Predicting Attention and Brain Maintenance in Healthy Old Age and Disease: An MRI Structural Study for the Alzheimer’s Disease Neuroimaging Initiative

“…In the current study, putative indices of cognitive reserve, education and occupational status were strongly related to each other but not associated with BrainPAD scores, subcortical ROI volumes or attention performance. Although previous findings [ 159 ] have reported an association between higher education and younger brain age, more recent research has not yielded such a relationship [ 160 ]. Alternatively, it is plausible that factors such as education and occupation are more directly related to actual reserve built through developmental plasticity in the early stages of life, and indexed by total intracranial volume (TIV), and are therefore more indirectly related to brain maintenance in the later stages of life [ 8 ].…”