BabyPy: a brain-age model for infancy, childhood and adolescence
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| Publicat a: | bioRxiv (Feb 19, 2025) |
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| Autor principal: | |
| Altres autors: | , , , , , , , , |
| Publicat: |
Cold Spring Harbor Laboratory Press
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| Matèries: | |
| Accés en línia: | Citation/Abstract Full text outside of ProQuest |
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| Resum: | Intro: Brain-age models quantify biological ageing by predicting a person's age from neuroimaging data. In early life, brain-age can reflect underlying biological maturity (or immaturity), providing a candidate predictor of typical neurodevelopment versus deviation. Although widely used in adult research, the use of brain-age in early development has been limited due to data availability, heterogeneity and restricted model accessibility. Here, we introduce BabyPy, a shareable brain-age model for individuals aged 0-17 years that achieves accurate predictions despite substantial variability in site, scanner, and preprocessing pipelines. Methods: We trained BabyPy on 4,021 structural T1-weighted MRI scans from multi-site datasets (ages 0-17 years). An external test set of 1,143 scans (ages 0-16 years) was used for validation. Coarse neuroimaging features - grey matter volume (GMV), white matter volume (WMV), and subcortical grey matter volume (sGMV) - along with sex, were the model inputs. An ensemble machine learning approach combined Extra Trees Regression, Support Vector Machine, and Multilayer Perceptron base models. Performance was evaluated via 5-fold cross-validation and external testing. Results: The ensemble meta-model explained 80% of the variance in age (training set, MAE = 1.55 years) and 46% of the variance in the external test set (MAE = 1.72 years). Conclusion: BabyPy is a shareable framework that estimates brainage across a broad developmental range, removing the need for separate age-specific models. Despite limitations due to data heterogeneity, it demonstrates robust predictive performance and supports cross-study comparisons. Future improvements in data harmonisation will further enhance the utility of generic brain-age models like BabyPy.Competing Interest StatementAll authors declare no competing interests except: JS, RAIB and AA-B hold shares in and JS and RAIB are directors of Centile Bioscience; JHC is shareholder/advisor for BrainKey and Claritas HealthTech.Footnotes* Minor adjustments with text and figures. Addition of a graphical abstract. |
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| ISSN: | 2692-8205 |
| DOI: | 10.1101/2025.02.05.636598 |
| Font: | Biological Science Database |