Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers.

By James H. Cole, Katja Franke. Trends in Neurosciences, 2017

848 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Pediatrics, Perinatology and Child Health, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/be63ed9378fabded6ab4a0e4413e6b987d917ece

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Citations in the corpus

Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan (2019, background): “…a model that summarizes volumetric measures across multiple ROIs as an index that captures the process of typical brain aging, and which has received increasing attention in the literature (Cole and Franke, 2017; Habes et al., 2016; Dosenbach et al., 2010; Erus et al., 2015; Franke et al., 2010).”
MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide. (2020, methodology): “The construct of MRI-derived brain age has been widely adopted by the neuroscience community as an informative biomarker of brain health at the individual level (Franke et al., 2010; Cole and Franke, 2017; Cole et al., 2017a, 2019).”
Hallmarks of Brain Aging: Adaptive and Pathological Modification by Metabolic States (2018, background): “While there is inter-individual variability in the rate of brain atrophy during aging, it has been suggested that brain imaging data can be used to establish a ‘‘biological age’’ of one’s brain (Cole and Franke, 2017).”
Brain Age from the Electroencephalogram of Sleep (2018, background): “Using magnetic resonance images (MRI), chronological age can be predicted with mean absolute deviation of 5 years in healthy participants (Franke et al., 2010).”
Lifespan brain age prediction based on multiple EEG oscillatory features and sparse group lasso (2025, unknown): “Magnetic resonance imaging (MRI) has been widely used to model brain age (BA) prediction based on anatomical changes (Franke et al., 2010; Vergun et al., 2013; Cole and Franke, 2017; Amoroso et al., 2019; Mouches et al., 2022; Poloni and Ferrari, 2022).”