ArticlePediatric radiology2026
Myelination-attention-empowered deep learning model improved brain age prediction in children below 2 years of age.
Article in Pediatric radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Infant Brain Age Estimation With T1w/T2w Ratio MRI: A Myelination-Aware Deep Learning Approach.Journal of magnetic resonance imaging : JMRI · 2026Article
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Authors and funding
7 authors.
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Abstract
backgroundMyelination is a key biomarker of healthy brain maturation, and its disruption can signal neurodevelopmental disorders.
objectiveThe study aimed to enhance the accuracy and interpretability of brain age prediction in early infancy by incorporating the biological process of myelination as an attention mechanism into deep learning models. MATERIALS AND
methodsA fully automated deep learning framework, called myelination-attention-empowered model (MAENet), was developed through retrospective analysis of structural magnetic resonance imaging (sMRI) data from 603 participants who met the inclusion criteria, aged 0-2 years, collected in a local hospital between July 2017 and June 2024. The MAENet consisted of four modules: a multiscale information fusion channel (MSIF-channel) on the T2WI brain image, a myelination-empowered feature extraction channel (MEFE-channel) on an automated and standardized segmentation of the white matter image, a communication mechanism that enabled inter-channel information flow and enhanced the MSIF-channel's sensitivity to myelination-related features, and a myelination-attention mechanism that dynamically emphasized myelination-sensitive regions.
resultsThe proposed MAENet model exhibited superior performance over multiple deep learning models, including ResNet-50, VGG, Inception, SFCN, Skewed, FiA-Net, and TSAN. The mean absolute error (MAE) between the predicted brain age and chronological age was significantly reduced by 18%-41% in the subgroup of 0-1-year-old infants, 25%-37% in the subgroup of 1-2-year-old infants, and 18%-40% in the whole group of 0-2-year-old infants in the experimental comparison (P < 0.05). The brain regions attended to by the MAENet model were visualized and consistent with the well-known developmental trajectories of white matter myelination in early infancy.
conclusionThe MAENet model demonstrated a significant improvement in brain age prediction accuracy in 0-2-year-olds by effectively leveraging the developmental process of myelination.
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