Multimodal Graphical Network Analysis of Small-for-Gestational-Age in Preterm Infants: Integrating Neonatal Brain...

Area of Science:

• Neuroscience
• Developmental Biology
• Medical Imaging

Background:

• Infants born Small-for-gestational-age (SGA) are at increased risk for cognitive and language impairments.
• The underlying neurobiological mechanisms for these deficits are not well understood.
• Identifying reliable biomarkers is crucial for early detection and intervention.

Purpose of the Study:

• To identify multimodal neuroimaging biomarkers associated with fetal growth restriction.
• To characterize network-level associations between SGA and neurodevelopmental vulnerability.
• To utilize a data-driven graph-based framework for comprehensive analysis.

Main Methods:

• Prospective cohort study of 186 preterm infants.
• Analysis of near-term brain MRI (T2-weighted and diffusion tensor imaging) and Bayley-III assessments.
• Graph-based network modeling (Graphical Lasso) to identify associations between neuroimaging features and birthweight Z-scores.

Main Results:

• Eight neuroanatomical correlates of birthweight Z-scores were identified, including increased CSF volume and altered white matter diffusivity (ILFL, IFOF).
• Cerebrospinal fluid volume and left inferior longitudinal fasciculus (ILFL) diffusivity independently predicted SGA status.
• Trends suggested associations between ILFL diffusivity, PCC centrality, and language delay in SGA infants.

Conclusions:

• Multimodal MRI and graph-based modeling reveal latent neurobiological markers for SGA.
• Identified biomarkers offer potential for early risk stratification in SGA infants.
• Findings support individualized intervention strategies for neurodevelopmental vulnerabilities in SGA.