Infant Brain Age Estimation With T1w/T2w Ratio MRI: A Myelination-Aware Deep Learning Approach

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.

To develop and evaluate a biologically informed deep learning framework for infant brain age estimation using T1w/T2w ratio MRI.

Retrospective.

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).

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.

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.

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 $p<0.05$ .

T1w/T2w ratio models achieved the best overall performance (MAE: 1.489  $\pm$ 0.302 months; $r$  = 0.966  $\pm$  0.012), compared with T1w (2.055  $\pm$  0.944; 0.933  $\pm$  0.061), T2w (1.794  $\pm$  0.434; 0.947  $\pm$  0.023), T1w+T2w (1.546  $\pm$  0.291; 0.960  $\pm$  0.013), and T1w+T2w+RI (1.498  $\pm$  0.313; 0.963 $\pm$ 0.012). Modality effects were significant for MAE, RMSE, $R^2$ , $r$ , but not for $\rho$ ( $p=0.250$ ). 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.

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.

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