Effect sizes for brain-based predictive models of cognition across the lifespan

Abstract

Brain-based predictive models offer a promising approach to understanding individual differences in cognitive functioning across the lifespan. This review synthesizes current literature on brain-based cognitive predictions using large-scale datasets, examining how methodological choices and data-related factors impact model performance. We find that brain-based predictive models demonstrate relatively consistent, albeit wide ranging performance across the lifespan, explaining approximately 10-40% of variance in cognitive abilities. Key findings include: (1) models perform better when predicting general cognitive ability compared to specific functions; (2) crystallized abilities are more predictable than fluid abilities; (3) task-based functional connectivity generally outperforms resting-state approaches in younger populations, while structural measures become superior predictors in older adults; (4) multimodal approaches using hierarchical stacked models optimize prediction accuracy; and (5) elastic net and ridge regression provide optimal balance between predictive performance and computational efficiency. In summary, we propose methodological advances to improve the precision and utility of brain-based cognitive predictions. This review synthesizes literature on brain-based cognitive predictions using large-scale datasets, examining how methodological choices and data-related factors impact model performance. Key advances are proposed to improve the precision and utility of brain-based cognitive predictions.