ArticleNeurobiology of aging2020
Optimized prediction of cognition based on brain morphometry across the adult life span.
Article in Neurobiology of aging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- Multimodal MRI prediction of cognitive functioning across the lifespan: separating between-person differences from within-person changes.GeroScience · 2026Article
- Neuroimaging Correlates of the NIH-Toolbox-Driven Cognitive Metrics in Children.Journal of integrative neuroscience · 2024Article
- Differential predictability of cognitive profiles from brain structure in older males and females.GeroScience · 2024Article
- Prediction of cognitive performance differences in older age from multimodal neuroimaging data.GeroScience · 2024Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
We mapped out the combined and unique contributions of 5 different biomarkers for 2 cognitive outcomes in cognitively healthy adults. Beside associations of biomarkers with cognition in the full experimental sample, we focused on how well any such associations would persist in held-out data. Three hundred thirty-five cognitively normal participants, 20-80 years older, were included in the study. Z-scores were computed for fluid reasoning and vocabulary. The following imaging data were included: regional brain volume, regional thickness, fractional anisotropy of white-matter tracts, volumes of select deep gray-matter regions, and global white-matter hyperintensity. Volume accounted for most of the variance in both cognitive domains. In out-of-sample data, fluid reasoning was best predicted by volumes, but vocabulary by the combination of all modalities. Although the predictive utility was better overall for older participants, the information gleaned relative to null models was less for older participants. An optimized set of brain biomarkers can thus predict cognition in out-of-sample data, to various degrees, for both fluid and crystallized intelligence.
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Registered trials
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