Data‐driven subtyping reveals heterogeneous functional brain development trajectories in preterm infants

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Abstract
Brain development in preterm infants shows marked heterogeneity, often obscured by group‐level analyses. Between the group‐level and the individual difference, subgroup can model the heterogeneity of early developmental trajectories. To characterize this, we analyzed longitudinal functional connectome data from 90 preterm infants (scanned at birth and term‐equivalent age) and 521 full‐term controls from the developing Human Connectome Project. A machine learning model predicted individual brain‐age gap (BAG), quantifying maturational deviation. Clustering of longitudinal BAG trajectories revealed two distinct preterm subgroups with divergent developmental pathways. These subgroups exhibited significantly different functional network architectures and, at 19‐month follow‐up, distinct behavioral outcomes in cognitive, language, and motor domains. Our findings establish that early preterm brain maturation follows identifiable, heterogeneous trajectories, providing a data‐driven framework for early risk stratification.
Funding Information
  • National Key Research and Development Program of China (2023YFF1204300)
  • National Natural Science Foundation of China (82202249)

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