ArticleNature communications2026
Precision estimates of longitudinal brain aging capture unexpected individual differences in one year.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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
3 citing papers in PubMed.
- Practical considerations for dense longitudinal neuroimaging: Ten simple rules for frequent scanning over short time periods.Developmental cognitive neuroscience · 2026Article
- Individualized mapping of functional brain networks in older adulthood.Imaging neuroscience (Cambridge, Mass.)Article
- Detecting short-interval longitudinal cortical atrophy in neurodegenerative dementias via cluster scanning: A proof of concept.Imaging neuroscience (Cambridge, Mass.)Article
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10 authors.
Funding
Abstract
Longitudinal studies are required to measure individual differences in human brain aging, but are challenging over short intervals due to measurement error. Using cluster scanning, an approach that reduces error by densely repeating rapid structural scans, we assess brain aging in individuals across three timepoints in one year. Cluster scanning substantially improves the precision of individualized estimates, revealing previously undetectable individual differences in brain change. In just one year, we detect expected differences in the rates of brain aging between younger and older individuals, as well as differences between cognitively unimpaired and impaired individuals. Cognitively unimpaired older individuals variably reveal relative brain maintenance, unexpectedly rapid decline, and asymmetrical changes. We observe these atypical brain aging trajectories across structures and verify them in independent within-individual test-retest data. Cluster scanning promises to advance our understanding of the marked heterogeneity in brain aging by affording better short-term tracking of individual variability in structural change.
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