Cohort protocol: risk assessment of maternal inflammation and early brain development in infants and young children
Xianghui Huang1,2, Cuimin Su3, Ying Lin1,2
1Fujian Key Laboratory of Neonatal Diseases, Xiamen, China.
Insights
Maternal inflammation may impact infant brain development. This study uses AI to create a risk assessment model for early identification and intervention, improving long-term cognitive outcomes.
Area of Science:
- Neuroscience
- Developmental Biology
- Artificial Intelligence
Background:
- Infancy and early childhood are critical periods for brain development, influencing future intelligence and health.
- Maternal inflammation is a potential environmental factor affecting infant brain development through various mechanisms.
- Early identification and intervention are crucial for preventing brain development disorders.
Purpose of the Study:
- To evaluate the risk of maternal inflammation on early infant brain development.
- To understand the mechanisms linking maternal inflammation to infant brain development.
- To develop an early risk assessment model for infant brain development.
Main Methods:
- Recruited 360 pregnant women and their offspring for the Xiamen Children's Brain Development Cohort.
- Collected data on maternal exposures, child environmental exposures, behavior, and neuroimaging up to age 3.
- Utilized deep learning AI to model the interaction of gene-image-environment-behavior factors.
Main Results:
- Developed an AI-based early risk assessment model for infant brain development.
- The model considers developmental trajectories of brain structure, function, and connections.
- The model integrates multi-factor interactions including "gene-image-environment-behavior".
Conclusions:
- The AI model can improve early identification of infant brain development issues.
- Precise early intervention can enhance cognitive learning and performance across lifespan.
- This approach aids in preventing developmental disorders and optimizing child development.
Introduction:
Infancy 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 Analysis:
Between 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.
Conclusion:
In 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 Registration:
https://www.clinicaltrials.gov/; identifier [NCT05040542].
