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BabyPy: a brain-age model for infancy, childhood and adolescence

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Опубліковано в::bioRxiv (Feb 19, 2025)
Автор: Biondo, Francesca
Інші автори: O'muircheartaigh, Jonathan, Richard Ai Bethlehem, Seidlitz, Jakob, Alexander-Bloch, Aaron, Elison, Jed, D'sa, Viren, Deoni, Sean Cl, Bruchhage, Muriel Mk, Cole, James H
Опубліковано:
Cold Spring Harbor Laboratory Press
Предмети:
Neuroimaging
Substantia grisea
Medical imaging
Age
Children
Regression analysis
Aging
Substantia alba
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Короткий огляд: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.
ISSN:2692-8205
DOI:10.1101/2025.02.05.636598
Джерело:Biological Science Database