Limbic structural markers of language delay in late-preterm children by NeuroQuant and machine learning

Language development relies on distributed neural systems that include temporal and limbic–striatal circuits, yet the neuroanatomical substrates of language delay in late-preterm children remain incompletely characterized. This study investigated structural brain differences associated with language delay in late-preterm children using quantitative automated volumetry at term-equivalent age and an interpretable analytical approach to identify neuroanatomical correlates of later language outcomes. In this retrospective cohort study, late-preterm children with language delay (n = 31) and without language delay (n = 120) were included. T1-weighted MRI scans acquired at term-equivalent age were analyzed using NeuroQuant. Exploratory feature selection and prioritization were performed to identify volumetric features associated with language delay, followed by multivariable logistic regression analyses adjusting for relevant clinical covariates. Compared with controls, language delay was associated with increased left amygdalar volume and decreased hippocampal volume. Receptive language delay was associated with reduced right nucleus accumbens volume, whereas expressive language delay was associated with increased left amygdalar volume and reduced hippocampal volume. These findings indicate that distinct limbic and striatal brain structures are differentially associated with receptive and expressive language domains in late-preterm children. Quantitative automated volumetry may help characterize limbic–striatal neuroanatomical patterns related to language outcomes and generate testable hypotheses for future longitudinal neurodevelopmental studies.