Quantifying Age-Related Changes in Brain and Behavior: A Longitudinal versus Cross-Sectional Approach

Abstract

Cross-sectional versus longitudinal comparisons of age-related change have often revealed differing results. In the current study, we employed within-subject task-based fMRI to investigate changes in voxel-based activations and behavioral performance across the lifespan in the Reference Ability Neural Network (RANN) cohort, at both baseline and 5-year follow-up.We analyzed fMRI data from between 127 and 159 participants (20-80 years), on a battery of tests relating to each of four cognitive reference abilities (RAs). We applied a Gaussian age kernel to capture continuous change across the lifespan using a 5-year sliding window centered on each age in our participant sample, with a subsequent division into young, middle, and old age brackets. This method was applied separately to both cross-sectional approximations of change and real longitudinal changes adopting a comparative approach. We then focused on longitudinal measurements of neural change to identify regions expressing peak changes and fluctuations of sign change across our sample. Our results revealed several regions expressing divergence between cross-sectional and longitudinal measurements in each domain and age bracket; behavioral comparisons between measurements showed differences in change curves for all four domains, with processing speed displaying the steepest declines. In the longitudinal change measurement, we found lack of support for age-related frontal increases across analyses types, instead finding more posterior regions displaying peak increases in activation, particularly in the old age bracket. Our findings encourage greater focus on longitudinal measurements of age-related changes, which display appreciable differences from cross-sectional approximations. 2 Significance StatementKnowledge of the aging process is mostly informed by cross-sectional studies. The fewer studies that have looked at longitudinal aging trajectories display variable consensus with cross-sectional findings. The current study provides a direct comparison between cross-sectional and longitudinal measurements of change in both neural activation and behavioral performance across several cognitive domains, providing insight into similarities versus discrepancies. Furthermore, it adopts a method of analysis used in the MRI 4D atlas literature to quantify continuous change across the lifespan through construction of neural activation "templates" that are generated from age-weighted averaging across the entire sample. Longitudinal measurements of change could then further be probed for characteristics such as peaks and change fluctuations, enabling a better understanding of true age-related changes.

Effect of acupuncture on brain regions modulation of mild cognitive impairment: A meta-analysis of functional magnetic resonance imaging studies

“…In contrast, only one of the original studies we included was an RCT, and most were cross-sectional studies. Currently, the application of MRI modalities has shown promising results in cross-sectional studies of neurodegenerative diseases (Argiris et al, 2021). However, in studies related to cognitive function, cross-sectional showed differences in outcomes between crosssectional and longitudinal, as aptly demonstrated by the results of the meta-analysis in this study (Salthouse, 2019).…”

Musical development during adolescence: Perceptual skills, cognitive resources, and musical training

“…34 Longitudinal data allow for different types of inference and give rise to different developmental curves compared to cross-sectional data, as has been shown in studies comparing longitudinal and age-stratified cross-sectional data. [35][36][37] The systematic review in part I summarizes empirical results from longitudinal studies on musical skills and abilities in the published literature. In part II, data on musical constructs made available through the MCS are modeled with a focus on the interplay of musical abilities and musical engagement.…”

“…Finally, obtaining true longitudinal data from the same individuals through repeated testing is different from cross‐sectional data that is stratified by age 34 . Longitudinal data allow for different types of inference and give rise to different developmental curves compared to cross‐sectional data, as has been shown in studies comparing longitudinal and age‐stratified cross‐sectional data 35–37 …”

“… 34 Longitudinal data allow for different types of inference and give rise to different developmental curves compared to cross‐sectional data, as has been shown in studies comparing longitudinal and age‐stratified cross‐sectional data. 35 , 36 , 37 …”