Journal of magnetic resonance imaging : JMRI · 2026
Infant Brain Age Estimation With T1w/T2w Ratio MRI: A Myelination-Aware Deep Learning Approach.
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Park Hyeryn, Choi Young Hun, Gho Sung-Min
Аннотация
BACKGROUND: Brain age estimation provides a noninvasive MRI biomarker of neurodevelopment. In infancy, rapid regionally ordered myelination reflects brain maturation, yet early-life brain age estimation remains underexplored, particularly with myelination-sensitive MRI and biologically informed modeling.
PURPOSE: To develop and evaluate a biologically informed deep learning framework for infant brain age estimation using T1w/T2w ratio MRI.
STUDY TYPE: Retrospective.
POPULATION: Internal cohort: 629 infants aged 0-24 months (626 with age-appropriate myelination, train/validation/test = 376/125/125), 3 with myelin-related developmental abnormalities for qualitative review. External cohort: 10 healthy infants aged 0-15 months (5 females, 5 males).
FIELD STRENGTH/SEQUENCE: Internal: 3T; 3D gradient-echo or 2D spin-echo T1w, and 2D turbo spin-echo T2w. External: 3T; 3D gradient-echo T1w and 2D turbo spin-echo T2w.
ASSESSMENT: 3D convolutional neural networks were trained with T1w, T2w, and T1w/T2w ratio inputs using manually defined biological age labels from visual myelination assessment. The model incorporated multi-task learning for age regression, white matter segmentation, and image reconstruction.
STATISTICAL TESTS: Performance was evaluated using five-fold cross-validation with repeated random splits. Metrics included mean absolute error, root mean squared error, R 2 , and Pearson and Spearman correlations. Modality differences were tested using one-way ANOVA, t -tests, and Mann-Whitney U , with Cohen's d and 95% confidence intervals. In the external cohort, absolute prediction errors were compared using the Wilcoxon signed-rank test. Statistical significance was defined as p < 0.05 .
RESULTS: T1w/T2w ratio models achieved the best overall performance (MAE: 1.489 ± 0.302 months; r = 0.966 ± 0.012), compared with T1w (2.055 ± 0.944; 0.933 ± 0.061), T2w (1.794 ± 0.434; 0.947 ± 0.023), T1w+T2w (1.546 ± 0.291; 0.960 ± 0.013), and T1w+T2w+RI (1.498 ± 0.313; 0.963 ± 0.012). Modality effects were significant for MAE, RMSE, R 2 , r , but not for ρ ( p = 0.250 ). Auxiliary-task and multi-scale modeling numerically improved performance (MAE, 1.203 months; r = 0.979). External validation showed the lowest error for the RI-based model (MAE, 1.16 months), and Grad-CAM highlighted myelination-relevant white matter.
DATA CONCLUSION: T1w/T2w ratio MRI combined with biologically informed deep learning enabled accurate and interpretable infant brain age estimation. This framework showed promising cross-scanner performance and may support MRI-based assessment of early brain maturation.
EVIDENCE LEVEL: 3.
TECHNICAL EFFICACY: 2.
PURPOSE: To develop and evaluate a biologically informed deep learning framework for infant brain age estimation using T1w/T2w ratio MRI.
STUDY TYPE: Retrospective.
POPULATION: Internal cohort: 629 infants aged 0-24 months (626 with age-appropriate myelination, train/validation/test = 376/125/125), 3 with myelin-related developmental abnormalities for qualitative review. External cohort: 10 healthy infants aged 0-15 months (5 females, 5 males).
FIELD STRENGTH/SEQUENCE: Internal: 3T; 3D gradient-echo or 2D spin-echo T1w, and 2D turbo spin-echo T2w. External: 3T; 3D gradient-echo T1w and 2D turbo spin-echo T2w.
ASSESSMENT: 3D convolutional neural networks were trained with T1w, T2w, and T1w/T2w ratio inputs using manually defined biological age labels from visual myelination assessment. The model incorporated multi-task learning for age regression, white matter segmentation, and image reconstruction.
STATISTICAL TESTS: Performance was evaluated using five-fold cross-validation with repeated random splits. Metrics included mean absolute error, root mean squared error, R 2 , and Pearson and Spearman correlations. Modality differences were tested using one-way ANOVA, t -tests, and Mann-Whitney U , with Cohen's d and 95% confidence intervals. In the external cohort, absolute prediction errors were compared using the Wilcoxon signed-rank test. Statistical significance was defined as p < 0.05 .
RESULTS: T1w/T2w ratio models achieved the best overall performance (MAE: 1.489 ± 0.302 months; r = 0.966 ± 0.012), compared with T1w (2.055 ± 0.944; 0.933 ± 0.061), T2w (1.794 ± 0.434; 0.947 ± 0.023), T1w+T2w (1.546 ± 0.291; 0.960 ± 0.013), and T1w+T2w+RI (1.498 ± 0.313; 0.963 ± 0.012). Modality effects were significant for MAE, RMSE, R 2 , r , but not for ρ ( p = 0.250 ). Auxiliary-task and multi-scale modeling numerically improved performance (MAE, 1.203 months; r = 0.979). External validation showed the lowest error for the RI-based model (MAE, 1.16 months), and Grad-CAM highlighted myelination-relevant white matter.
DATA CONCLUSION: T1w/T2w ratio MRI combined with biologically informed deep learning enabled accurate and interpretable infant brain age estimation. This framework showed promising cross-scanner performance and may support MRI-based assessment of early brain maturation.
EVIDENCE LEVEL: 3.
TECHNICAL EFFICACY: 2.