AUTHOR=Huang Xianghui , Su Cuimin , Lin Ying , Zhou Tianyi , Ye Ruming , Li Dan , Liu Miaoshuang , Wu Guanhong , Li Wanting , Xie Namei , Deng Xiaofang , Zhu Nanxi , Lin Shaohong , Li Qin , Yan Kai , Zhuang Deyi 

TITLE=Cohort protocol: risk assessment of maternal inflammation and early brain development in infants and young children based on multi-source data modeling

JOURNAL=Frontiers in Public Health

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1530285

DOI=10.3389/fpubh.2025.1530285

ISSN=2296-2565

ABSTRACT=IntroductionInfancy and early childhood are the key stage for the rapid development of brain structure and function, and brain development at this stage has a profound impact on the future intelligence, behavior and health of individuals. A growing body of research suggests that maternal inflammation, as a potential environmental factor, may affect brain development in infants and young children through a variety of mechanisms. Therefore, it is of great significance to evaluate the risk of maternal inflammation to early brain development in infants and young children based on multi-source data modeling to understand the mechanism of early development and prevent brain development disorders.Methods and analysisBetween December 2021 and May 2024, 360 pairs of pregnant women and their offspring were recruited into the Xiamen Children's Brain Development Cohort. Pregnant women's exposure during pregnancy was collected through standardized and structured questionnaires and medical records. All children were followed up to 3 years of age. We administered questionnaires, behavioral assessments, and performed neuroimaging. Environmental exposures during infancy and early childhood were collected. Children's cognitive, emotional, and linguistic development was evaluated, and blood samples were obtained for whole-exome sequencing and exposure-related biomarker analysis.ConclusionIn this study, we used deep learning artificial intelligence to construct an early risk assessment model for infant brain development based on the developmental trajectory and developmental results of early brain structure, function, and connections under the complex interaction of “gene-image-environment-behavior” multi-factors, which can improve the early identification and precise intervention of problems in this period, and improve infants cognitive learning and work performance in childhood, adolescence and even adulthood.Clinical trial registrationhttps://www.clinicaltrials.gov/; identifier [NCT05040542].