The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers
Open accessAbstract
The brain changes as we age and these changes are associated with functional deterioration and neurodegenerative disease. It is vital that we better understand individual differences in the brain ageing process; hence, techniques for making individualised predictions of brain ageing have been developed. We present evidence supporting the use of neuroimaging-based 'brain age' as a biomarker of an individual's brain health. Increasingly, research is showing how brain disease or poor physical health negatively impacts brain age. Importantly, recent evidence shows that having an 'older'-appearing brain relates to advanced physiological and cognitive ageing and the risk of mortality. We discuss controversies surrounding brain age and highlight emerging trends such as the use of multimodality neuroimaging and the employment of 'deep learning' methods.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2013) · Cell · doi:10.1016/j.cell.2013.05.039 · Cited by 14,982
Vos, T., Flaxman, A. D., Naghavi, M., Lozano, R., Michaud, C., Ezzati, M., Shibuya, K., Salomon, J. A., Abdalla, S., Aboyans, V., et al. (2012) · The Lancet · doi:10.1016/s0140-6736(12)61729-2 · Cited by 8,291
Mitochondrial Dysfunction and Oxidative Stress in Neurodegenerative Diseases
Lin, M. T., & Beal, M. F. (2006) · Nature · doi:10.1038/nature05292 · Cited by 6,455
A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains
Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N. A., Friston, K. J., & Frackowiak, R. S. J. (2001) · NeuroImage · doi:10.1006/nimg.2001.0786 · Cited by 4,561
Gait speed and survival in older adults.
Studenski, S. (2011) · JAMA · doi:10.1001/jama.2010.1923 · Cited by 4,558
Insights into the ageing mind: a view from cognitive neuroscience
Hedden, T., & Gabrieli, J. D. E. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1323 · Cited by 2,223
Longitudinal Magnetic Resonance Imaging Studies of Older Adults: A Shrinking Brain
Resnick, S. M., Pham, D. L., Kraut, M. A., Zonderman, A. B., & Davatzikos, C. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-08-03295.2003 · Cited by 1,425
Progressive multiple sclerosis: pathology and pathogenesis
Lassmann, H., van Horssen, J., & Mahad, D. (2012) · Nature Reviews Neurology · doi:10.1038/nrneurol.2012.168 · Cited by 1,033
Inflammation after trauma: Microglial activation and traumatic brain injury
Ramlackhansingh, A. F., Brooks, D. J., Greenwood, R. J., Bose, S. K., Turkheimer, F. E., Kinnunen, K. M., Gentleman, S., Heckemann, R. A., Gunanayagam, K., Gelosa, G., et al. (2011) · Annals of Neurology · doi:10.1002/ana.22455 · Cited by 999
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 · cited 2×
“In particular, deep learning methods show [344_TD$DIFF]considerable promise [9].”
Self-citationBlalock, E. M., Chen, K.-C., Sharrow, K., Herman, J. P., Porter, N. M., Foster, T. C., & Landfield, P. W. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-09-03807.2003 · Cited by 570
Biological Aging Is No Longer an Unsolved Problem
HAYFLICK, L. (2007) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1395.001 · Cited by 418
The epigenetics of aging and neurodegeneration
Lardenoije, R., Iatrou, A., Kenis, G., Kompotis, K., Steinbusch, H. W. M., Mastroeni, D., Coleman, P., Lemere, C. A., Hof, P. R., van den Hove, D. L. A., et al. (2015) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2015.05.002 · Cited by 381
Neuroanatomical Assessment of Biological Maturity
Brown, T. T., Kuperman, J. M., Chung, Y., Erhart, M., McCabe, C., Hagler, D. J., Venkatraman, V. K., Akshoomoff, N., Amaral, D. G., Bloss, C. S., et al. (2012) · Current Biology · doi:10.1016/j.cub.2012.07.002 · Cited by 332
Imaging Patterns of Brain Development and their Relationship to Cognition
Erus, G., Battapady, H., Satterthwaite, T. D., Hakonarson, H., Gur, R. E., Davatzikos, C., & Gur, R. C. (2015) · Cerebral Cortex · doi:10.1093/cercor/bht425 · Cited by 226 · cited 2×
“, brainpredicted age difference) relates to cognitive performance, ageing fitness and, subsequent survival [13,15,20,40,42,47] strongly supports the idea that, by quantifying this error, clinically and biologically meaningful insights can be derived.”
Advanced BrainAGE in older adults with type 2 diabetes mellitus
Franke, K., Gaser, C., Manor, B., & Novak, V. (2013) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2013.00090 · Cited by 221
Self-citation
Cited by
Hallmarks of Brain Aging: Adaptive and Pathological Modification by Metabolic States
Mattson, M. P., & Arumugam, T. V. (2018) · Cell Metabolism · doi:10.1016/j.cmet.2018.05.011 · Cited by 1,242
Establishment of Best Practices for Evidence for Prediction: A Review
Poldrack, R. A., Huckins, G., & Varoquaux, G. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.3671 · Cited by 738
Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality
Tian, Y. E., Cropley, V., Maier, A. B., Lautenschlager, N. T., Breakspear, M., & Zalesky, A. (2023) · Nature Medicine · doi:10.1038/s41591-023-02296-6 · Cited by 618
Conceptualizing mental disorders as deviations from normative functioning
Marquand, A. F., Kia, S. M., Zabihi, M., Wolfers, T., Buitelaar, J. K., & Beckmann, C. F. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0441-1 · Cited by 433
Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer's disease
Wang, X., Huang, W., Su, L., Xing, Y., Jessen, F., Sun, Y., Shu, N., & Han, Y. (2020) · Molecular Neurodegeneration · doi:10.1186/s13024-020-00395-3 · Cited by 265
Metadata
- Authors
- Cole, J. H., & Franke, K.
- Venue
- Trends in Neurosciences
- Publisher
- Elsevier BV
- Published
- 2017-12
- Volume
- 40
- Issue
- 12
- Pages
- 681-690
- ISSN
- 0166-2236 (ISSN-L)
- OpenAlex
- W2766141193
- Cited by
- 903
- References
- 16
- FWCI
- 7.50
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
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.
