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 . ISSN 1460-9568 (In Press)

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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
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Psychology
Identification Number: 10.1111/ejn.13835
Depositing User: Madan, Christopher
Date Deposited: 16 Jan 2018 08:45
Last Modified: 05 Feb 2018 22:31

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