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Trends Neurosci. 2017 Dec;40(12):681-690. doi: 10.1016/j.tins.2017.10.001. Epub 2017 Oct 23.

Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers.

Author information

1
Computational, Cognitive & Clinical Neuroimaging Laboratory, Division of Brain Sciences, Department of Medicine, Imperial College London, London, UK. Electronic address: james.cole@imperial.ac.uk.
2
Structural Brain Mapping Group, University Hospital Jena, Jena, Germany. Electronic address: katja.franke@uni-jena.de.

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 '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.

KEYWORDS:

ageing biomarker; brain ageing; brain diseases; machine learning; neuroimaging

PMID:
29074032
DOI:
10.1016/j.tins.2017.10.001
[Indexed for MEDLINE]
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