Mapping human brain charts cross-sectionally and longitudinally

Abstract

Brain scans acquired across large, age-diverse cohorts have facilitated recent progress in establishing normative brain aging charts. Here, we ask the critical question of whether cross-sectional estimates of age-related brain trajectories resemble those directly measured from longitudinal data. We show that age-related brain changes inferred from cross-sectionally mapped brain charts can substantially underestimate actual changes measured longitudinally. We further find that brain aging trajectories vary markedly between individuals and are difficult to predict with population-level age trends estimated cross-sectionally. Prediction errors relate modestly to neuroimaging confounds and lifestyle factors. Our findings provide explicit evidence for the importance of longitudinal measurements in ascertaining brain development and aging trajectories.

Using normative models pre-trained on cross-sectional data to evaluate intra-individual longitudinal changes in neuroimaging data

“…After averaging these scores across all subjects, the z-diff score of no region was statistically significant from zero (after FDR correction). However, as pointed out by a recent work [13] studying the effect of cross-sectional normative models on longitudinal predictions, the cross-sectionally derived population centiles by design lack information about longitudinal dynamics. Consequently, what may appear as a population-level trajectory does not necessarily align with individual subjects’ actual trajectories.…”

“…Normative modelling is a relatively new area of research and thus, despite its potential, longitudinal normative models have not been systematically explored [6, 13]. Indeed, virtually all large-scale normative models released to date are estimated on cross-sectional data [6, 14] and a recent report [13] has provided empirical data to suggest that such cross-sectional models may underestimate the variance in longitudinal data [13]. However, from a theoretical perspective, it is very important to recognise that cross-sectional models describe group-level population variation across the lifespan, where such group level centiles are interpolated smoothly across time.…”

“…Normative modelling is a relatively new area of research and thus, despite its potential, longitudinal normative models have not been systematically explored [6, 13]. Indeed, virtually all large-scale normative models released to date are estimated on cross-sectional data [6, 14] and a recent report [13] has provided empirical data to suggest that such cross-sectional models may underestimate the variance in longitudinal data [13].…”

Individual-level deviations from normative brain morphology in violence, psychosis, and psychopathy

“…An inherent limitation in all cross-sectional neuroimaging studies is the inability to distinguish between specific disease mechanisms and the effects of early-life influences and developmental trajectories. Cross-sectional normative modeling has shown lower individual level prediction, propensity than longitudinal models (Di Biase et al, 2023), which should also be employed in future studies of violence and psychosis. However, it is worth noting that our study encompassed a broad age range providing comprehensive coverage across a significant fraction of the lifespan.…”

Brain asymmetries from mid- to late life and hemispheric brain age

“…This is also shown in the present study by stronger age-effects for specific regional asymmetries compared to asymmetries in hemispheric averages. Age-MRI metric relationships depend, however, on the selected metric, the sample, and the sampling (cross-sectional or longitudinal) 52, 53 . For example, previous evidence from T 1 -weighted MRI indicates no differences in GM volume between hemispheres 54 , but hemispheric differences of cortical thickness and surface area across ageing 4, 9 .…”