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.
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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].”
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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
Habes, M., Pomponio, R., Shou, H., Doshi, J., Mamourian, E., Erus, G., Nasrallah, I., Launer, L. J., Rashid, T., Bilgel, M., et al. (2021) · Alzheimer's & Dementia · doi:10.1002/alz.12178 · Cited by 173
Disentangling Heterogeneity in Alzheimer’s Disease and Related Dementias Using Data-Driven Methods
Habes, M., Grothe, M. J., Tunc, B., McMillan, C., Wolk, D. A., & Davatzikos, C. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.01.016 · Cited by 167
Detect and correct bias in multi-site neuroimaging datasets
Wachinger, C., Rieckmann, A., & Pölsterl, S. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101879 · Cited by 115
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
“A popular approach in neuroscience is to use statistical approaches to translate complex whole-brain multivariate patterns of aging into a single outcome (Luders et al., 2016), the so-called “brain age” (see Cole and Franke, 2017 for details).”
Cullen, N. C., Mälarstig, A. N., Stomrud, E., Hansson, O., & Mattsson-Carlgren, N. (2021) · Scientific Reports · doi:10.1038/s41598-021-81705-7 · Cited by 62
(2019) · doi:10.1016/j.nicl.2019.102159 · Cited by 65
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
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