Predicting age from cortical structure across the lifespan

Madan, Christopher R. and Kensinger, Elizabeth A. (2018) Predicting age from cortical structure across the lifespan. European Journal of Neuroscience, 47 (5). pp. 399-416. ISSN 1460-9568

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Abstract

Despite inter-individual differences in cortical structure, cross-sectional and longitudinal studies have demonstrated a large degree of population-level consistency in age-related differences in brain morphology. The present study assessed how accurately an individual’s age could be predicted by estimates of cortical morphology, comparing a variety of structural measures, including thickness, gyrification, and fractal dimensionality. Structural measures were calculated across up to seven different parcellation approaches, ranging from 1 region to 1000 regions. The age-prediction framework was trained using morphological measures obtained from T1-weighted MRI volumes collected from multiple sites, yielding a training dataset of 1056 healthy adults, aged 18-97. Age predictions were calculated using a machine-learning approach that incorporated non-linear differences over the lifespan. In two independent, held-out test samples, age predictions had a median error of 6-7 years. Age predictions were best when using a combination of cortical metrics, both thickness and fractal dimensionality. Overall, the results reveal that age-related differences in brain structure are systematic enough to enable reliable age prediction based on metrics of cortical morphology.

Item Type: Article
Additional Information: This is the peer reviewed version of the following article: Madan, C. R. and Kensinger, E. A. (2018), Predicting age from cortical structure across the lifespan. Eur J Neurosci, 47: 399–416, which has been published in final form at doi:10.1111/ejn.13835. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Psychology
Identification Number: https://doi.org/10.1111/ejn.13835
Depositing User: Madan, Christopher
Date Deposited: 16 Jan 2018 08:45
Last Modified: 06 Mar 2018 01:27
URI: http://eprints.nottingham.ac.uk/id/eprint/49119

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