The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Differences between chronological and brain age are related to education and self-reported physical activity
Open accessAbstract
This study investigated the relationship between education and physical activity and the difference between a physiological prediction of age and chronological age. Cortical and subcortical grey matter regional volumes were calculated from 331 healthy adults (range: 19-79 years). Multivariate analyses identified a covariance pattern of brain volumes best predicting chronological age (CA)(R2 = 47%). Individual expression of this brain pattern served as a physiologic measure of brain age (BA). The difference between CA and BA was predicted by education and self-report measures of physical activity. Education and the daily number of flights of stairs climbed were the only two significant predictors of decreased brain age. Effect sizes demonstrated that brain age decreased by 0.95 years for each year of education and by 0.58 years for one additional daily FOSC. Effects of education and FOSC on regional brain volume were largely driven by temporal and subcortical volumes. These results demonstrate that higher levels of education and daily FOSC are related to larger brain volume than predicted by chronological age which supports the utility of regional grey matter volume as a biomarker of healthy brain aging.
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Cites
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Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade
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Mapping Changes in the Human Cortex throughout the Span of Life
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Physical activity predicts gray matter volume in late adulthood: The Cardiovascular Health Study
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Neurobiological markers of exercise-related brain plasticity in older adults
Voss, M. W., Erickson, K. I., Prakash, R. S., Chaddock, L., Kim, J. S., Alves, H., Szabo, A., Phillips, S. M., Wójcicki, T. R., Mailey, E. L., et al. (2013) · Brain, Behavior, and Immunity · doi:10.1016/j.bbi.2012.10.021 · Cited by 418
Cited by
Cole, J. H., Poudel, R. P. K., Tsagkrasoulis, D., Caan, M. W. A., Steves, C., Spector, T. D., & Montana, G. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.059 · Cited by 872
“, 2016) and increased levels of education and physical exercise (Steffener et al., 2016).”
Cole, J. H., Ritchie, S. J., Bastin, M. E., Valdés Hernández, M. C., Muñoz Maniega, S., Royle, N., Corley, J., Pattie, A., Harris, S. E., Zhang, Q., et al. (2018) · Molecular Psychiatry · doi:10.1038/mp.2017.62 · Cited by 831
Prediction of brain age and cognitive age: quantifying brain and cognitive maintenance in aging
Anatürk, M., Kaufmann, T., Cole, J. H., Suri, S., Griffanti, L., Zsoldos, E., Filippini, N., Singh‐Manoux, A., Kivimäki, M., Westlye, L. T., et al. (2021) · Human Brain Mapping · doi:10.1002/hbm.25316 · Cited by 132
Han, L. K. M., Verhoeven, J. E., Tyrka, A. R., Penninx, B. W. J. H., Wolkowitz, O. M., Månsson, K. N. T., Lindqvist, D., Boks, M. P., Révész, D., Mellon, S. H., et al. (2019) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2019.04.004 · Cited by 104
“Higher physical activity levels are associated with lower brain age (Steffener et al., 2016), and “younger brains” are seen in those that learn to play an instrument (Rogenmoser et al.”
Longitudinal self-supervised learning
Zhao, Q., Liu, Z., Adeli, E., & Pohl, K. M. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102051 · Cited by 71
Metadata
- Authors
- Steffener, J., Habeck, C., O'Shea, D., Razlighi, Q., Bherer, L., & Stern, Y.
- Venue
- Neurobiology of Aging
- Publisher
- Elsevier BV
- Published
- 2016-04
- Volume
- 40
- Pages
- 138-144
- ISSN
- 0197-4580 (ISSN-L)
- OpenAlex
- W2255371896
- Cited by
- 235
- References
- 6
- FWCI
- 15.26
- Type
- journal article
- Language
- en
- Open access
- green
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- Steffener, J., Habeck, C., O'Shea, D., Razlighi, Q., Bherer, L., & Stern, Y. (2016). Differences between chronological and brain age are related to education and self-reported physical activity. Neurobiology of Aging. https://doi.org/10.1016/j.neurobiolaging.2016.01.014
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
