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Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers

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Predicting Age Using Neuroimaging : Innovative Brain Ageing Biomarkers. / Cole, James H.; Franke, Katja.

In: Trends in Neurosciences, Vol. 40, No. 12, 01.12.2017, p. 681-690.

Research output: Contribution to journalReview articlepeer-review

Harvard

Cole, JH & Franke, K 2017, 'Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers', Trends in Neurosciences, vol. 40, no. 12, pp. 681-690. https://doi.org/10.1016/j.tins.2017.10.001

APA

Cole, J. H., & Franke, K. (2017). Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers. Trends in Neurosciences, 40(12), 681-690. https://doi.org/10.1016/j.tins.2017.10.001

Vancouver

Cole JH, Franke K. Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers. Trends in Neurosciences. 2017 Dec 1;40(12):681-690. https://doi.org/10.1016/j.tins.2017.10.001

Author

Cole, James H. ; Franke, Katja. / Predicting Age Using Neuroimaging : Innovative Brain Ageing Biomarkers. In: Trends in Neurosciences. 2017 ; Vol. 40, No. 12. pp. 681-690.

Bibtex Download

@article{9c039dee65b544a889223223ef4e294c,
title = "Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers",
abstract = "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 {\textquoteleft}brain age{\textquoteright} 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 {\textquoteleft}older{\textquoteright}-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 {\textquoteleft}deep learning{\textquoteright} methods. Brain age can be predicted in individuals based on neuroimaging data using machine learning approaches to model trajectories of healthy brain ageing. The predicted brain age for a new individual can differ from his or her chronological age; this difference appears to reflect advanced or delayed brain ageing. Brain age has been shown to relate to cognitive ageing and multiple aspects of physiological ageing and to predict the risk of neurodegenerative diseases and mortality in older adults. Various diseases, including HIV, schizophrenia, and diabetes, have been shown to make the brain appear older. Further, brain age is being used to identify possible protective or deleterious factors for brain health as people age. Brain age is being actively developed to combine multiple measures of brain structure and function, capturing increasing amounts of detail on the ageing brain.",
keywords = "ageing biomarker, brain ageing, brain diseases, machine learning, neuroimaging",
author = "Cole, {James H.} and Katja Franke",
year = "2017",
month = dec,
day = "1",
doi = "10.1016/j.tins.2017.10.001",
language = "English",
volume = "40",
pages = "681--690",
journal = "Trends in Neurosciences",
issn = "0166-2236",
publisher = "Elsevier Limited",
number = "12",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Predicting Age Using Neuroimaging

T2 - Innovative Brain Ageing Biomarkers

AU - Cole, James H.

AU - Franke, Katja

PY - 2017/12/1

Y1 - 2017/12/1

N2 - 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. Brain age can be predicted in individuals based on neuroimaging data using machine learning approaches to model trajectories of healthy brain ageing. The predicted brain age for a new individual can differ from his or her chronological age; this difference appears to reflect advanced or delayed brain ageing. Brain age has been shown to relate to cognitive ageing and multiple aspects of physiological ageing and to predict the risk of neurodegenerative diseases and mortality in older adults. Various diseases, including HIV, schizophrenia, and diabetes, have been shown to make the brain appear older. Further, brain age is being used to identify possible protective or deleterious factors for brain health as people age. Brain age is being actively developed to combine multiple measures of brain structure and function, capturing increasing amounts of detail on the ageing brain.

AB - 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. Brain age can be predicted in individuals based on neuroimaging data using machine learning approaches to model trajectories of healthy brain ageing. The predicted brain age for a new individual can differ from his or her chronological age; this difference appears to reflect advanced or delayed brain ageing. Brain age has been shown to relate to cognitive ageing and multiple aspects of physiological ageing and to predict the risk of neurodegenerative diseases and mortality in older adults. Various diseases, including HIV, schizophrenia, and diabetes, have been shown to make the brain appear older. Further, brain age is being used to identify possible protective or deleterious factors for brain health as people age. Brain age is being actively developed to combine multiple measures of brain structure and function, capturing increasing amounts of detail on the ageing brain.

KW - ageing biomarker

KW - brain ageing

KW - brain diseases

KW - machine learning

KW - neuroimaging

UR - http://www.scopus.com/inward/record.url?scp=85034704875&partnerID=8YFLogxK

U2 - 10.1016/j.tins.2017.10.001

DO - 10.1016/j.tins.2017.10.001

M3 - Review article

AN - SCOPUS:85034704875

VL - 40

SP - 681

EP - 690

JO - Trends in Neurosciences

JF - Trends in Neurosciences

SN - 0166-2236

IS - 12

ER -

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