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| Home > Publications database > Multi-center brain age prediction via dual-modality fusion convolutional network |
| Journal Article | FZJ-2025-01223 |
; ; ; ;
2025
Elsevier Science
Amsterdam [u.a.]
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Please use a persistent id in citations: doi:10.1016/j.media.2025.103455
Abstract: Accurate prediction of brain age is crucial for identifying deviations between typical individual brain development trajectories and neuropsychiatric disease progression. Although current research has made progress, the effective application of brain age prediction models to multi-center datasets, particularly those with small-sample sizes, remains a significant challenge that is yet to be addressed. To this end, we propose a multi-center data correction method, which employs a domain adaptation correction strategy with Wasserstein distance of optimal transport, along with maximum mean discrepancy to improve the generalizability of brain-age prediction models on small-sample datasets. Additionally, most of the existing brain age models based on neuroimage identify the task of predicting brain age as a regression or classification problem, which may affect the accuracy of the prediction. Therefore, we propose a brain dual-modality fused convolutional neural network model (BrainDCN) for brain age prediction, and optimize this model by introducing a joint loss function of mean absolute error and cross-entropy, which identifies the prediction of brain age as both a regression and classification task. Furthermore, to highlight age-related features, we construct weighting matrices and vectors from a single-center training set and apply them to multi-center datasets to weight important features. We validate the BrainDCN model on the CamCAN dataset and achieve the lowest average absolute error compared to state-of-the-art models, demonstrating its superiority. Notably, the joint loss function and weighted features can further improve the prediction accuracy. More importantly, our proposed multi-center correction method is tested on four neuroimaging datasets and achieves the lowest average absolute error compared to widely used correction methods, highlighting the superior performance of the method in cross-center data integration and analysis. Furthermore, the application to multi-center schizophrenia data shows a mean accelerated aging compared to normal controls. Thus, this research establishes a pivotal methodological foundation for multi-center brain age prediction studies, exhibiting considerable applicability in clinical contexts, which are predominantly characterized by small-sample datasets.
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