Predicting age from cortical structure across the lifespan
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 es...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2018
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| Online Access: | https://eprints.nottingham.ac.uk/49119/ |
| _version_ | 1848797926021660672 |
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| author | Madan, Christopher R. Kensinger, Elizabeth A. |
| author_facet | Madan, Christopher R. Kensinger, Elizabeth A. |
| author_sort | Madan, Christopher R. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:11:38Z |
| format | Article |
| id | nottingham-49119 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:11:38Z |
| publishDate | 2018 |
| publisher | Wiley |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-491192019-02-12T04:30:11Z https://eprints.nottingham.ac.uk/49119/ Predicting age from cortical structure across the lifespan Madan, Christopher R. Kensinger, Elizabeth A. 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. Wiley 2018-03-03 Article PeerReviewed application/pdf en https://eprints.nottingham.ac.uk/49119/1/jurojin_final_sm.pdf 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 http://onlinelibrary.wiley.com/doi/10.1111/ejn.13835/abstract? doi:10.1111/ejn.13835 doi:10.1111/ejn.13835 |
| spellingShingle | Madan, Christopher R. Kensinger, Elizabeth A. Predicting age from cortical structure across the lifespan |
| title | Predicting age from cortical structure across the lifespan |
| title_full | Predicting age from cortical structure across the lifespan |
| title_fullStr | Predicting age from cortical structure across the lifespan |
| title_full_unstemmed | Predicting age from cortical structure across the lifespan |
| title_short | Predicting age from cortical structure across the lifespan |
| title_sort | predicting age from cortical structure across the lifespan |
| url | https://eprints.nottingham.ac.uk/49119/ https://eprints.nottingham.ac.uk/49119/ https://eprints.nottingham.ac.uk/49119/ |