Brain age prediction and deviations from normative trajectories in the neonatal connectome

Abstract

Structural and functional connectomes undergo rapid changes during the third trimester and the first month of postnatal life. Despite progress, our understanding of the developmental trajectories of the connectome in the perinatal period remains incomplete. Brain age prediction uses machine learning to estimate the brain's maturity relative to normative data. The difference between the individual's predicted and chronological age-or brain age gap (BAG)-represents the deviation from these normative trajectories. Here, we assess brain age prediction and BAGs using structural and functional connectomes for infants in the first month of life. We use resting-state fMRI and DTI data from 611 infants (174 preterm; 437 term) from the Developing Human Connectome Project (dHCP) and connectome-based predictive modeling to predict postmenstrual age (PMA). Structural and functional connectomes accurately predict PMA for term and preterm infants. Predicted ages from each modality are correlated. At the network level, nearly all canonical brain networks-even putatively later developing ones-generate accurate PMA prediction. Additionally, BAGs are associated with perinatal exposures and toddler behavioral outcomes. Overall, our results underscore the importance of normative modeling and deviations from these models during the perinatal period.

Fig. 1. Overview of study design.

Predictive models for infants’ postmenstrual ages were built using structural and functional connectomes separately for term and preterm infants. Brain age gaps (BAGs) were calculated as the difference between predicted and actual postmenstrual age. Structural and functional BAGs were associated with perinatal effects and later cognitive behaviors. Image was created in BioRender. Sun, H. (2024)BioRender.com/m43x607.

Fig. 2. Structural and functional connectomes predict postmenstrual age (PMA) in term and preterm infants.

a PMA was accurately predicted using structural connectomes for term (blue dots, Pearson’s correlation: r = 0.73, p = 7.39e-74; two-sided) and preterm infants (red dots, Pearson’s correlation: r = 0.67, p = 5.04e-24; two-sided). b PMA was accurately predicted using function connectomes for term (Pearson’s correlation: r = 0.42, p = 3.57e-20; two-sided) and preterm infants (Pearson’s correlation: r = 0.55, p = 6.37e-15; two-sided). Structural (c) and functional (d) connections predicting term and preterm infants’ PMAs. Each heatmap shows the number of edges between each pair of canonical networks during feature selection that were positively (purple) or negatively (green) correlated with postmentural age. VS visual, SM somatomotor, DA dorsal attention, VA ventral attention, LM limbic, FP frontoparietal, DM default mode, SC subcortical. Source data are provided as a Source Data file.

Fig. 3. Brain network age prediction.

a Structure and (b) Function canonical brain networks age predictions. Within-network connections for multiple networks successfully predicted postmenstrual age in term (blue lines) and preterm (red line) infants. Solid lines indicate significant two-sided Pearson’s correlation at p < 0.05, FDR-corrected, while dashed lines indicate non-significant predictions. Correlations between predicted ages based on within-network connections for term (c) and preterm (d) infants. Heatmaps show the correlation between predicted PMA from within-network connections. The upper triangle shows the correlation between functional ages. The lower triangle shows correlations between structural ages. The diagonal shows the correlations between structural and functional age for a network (p < 0.05; Pearson’s correlation, two-sided; n.s. not significant, box crossed: age not predictable from the within-network connections). VI visual, SM somatomotor, DA dorsal attention, VA ventral attention, LM limbic, FP frontoparietal, DM default mode, SC subcortical. Source data are provided as a Source Data file.

Fig. 4. Brain age gaps (BAGs) were associated with maternal effects and toddler behaviors.

Maternal mental health, physical health, demographics, and substance use correlate with BAGs for term and preterm infants, controlling for the infant’s postmenstrual age. The BAGs also correlate with several later behaviors in toddlerhood, controlling for the infant’s postmenstrual age. The p-values of two-sided Pearson’s correlation are FDR-corrected. Dashed lines associations were not significant after FDR correction, Solid lines associations were significant after FDR correction. BSID the Bayley Scales of Infant and Toddler Development, CBCL the Child Behavior Checklist, ECBQ the Early Childhood Behavior Questionnaire, Q-CHAT the Quantitative Checklist for Autism in Toddlers. Source data are provided as a Source Data file.