AUTHOR=Liu Ying , Nie Binbin , Wang Yituo , He Fang , Ma Qiaozhi , Han Tao , Mao Guangjuan , Liu Jiqiang , Zu Haiping , Mu Xuetao , Wu Bing 

TITLE=Correlation of abnormal brain changes with perinatal factors in very preterm infants based on diffusion tensor imaging

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 17 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1137559

DOI=10.3389/fnins.2023.1137559

ISSN=1662-453X

ABSTRACT=Background: It remains unclear whether very preterm (VP) infants exhibit similar levels of brain structure and function compared to the full-term (FT) infants. Additionally, the relationship between these potential differences and specific perinatal factors has not been fully characterized.
Objective: The aim of this study is to investigate whether there are differences in brain white matter microstructure and network connectivity between VP and FT infants at term-equivalent age (TEA), and to examine the potential association of these differences with perinatal factors. 
Methods: Eighty-three infants were prospectively selected for this study, including 43 VP infants (GA: 27-32 weeks) and 40 FT infants (GA: 37-44 weeks). At TEA, all infants underwent both conventional MRI and diffusion tensor imaging (DTI). White matter with significant differences in anisotropy fraction (FA) and mean diffusivity (MD) images between the VP and FT groups were identified using tract-based spatial statistics (TBSS). Fibers were tracked between each pair of regions in the individual space, using the automated anatomical labeling (AAL) atlas. Then structural brain network was constructed, where the connection between every node pair was defined by the number of fibers. Network-based statistics (NBS) was used to examine differences in brain network connectivity between the very preterm (VP) and full-term (FT) groups. Additionally, we conducted multivariate linear regression to investigate potential correlations between fiber bundle numbers and network metrics  (global efficiency, local efficiency and small-worldness) and perinatal factors. 
Results: Significant differences in FA were observed between the VP and FT groups in several regions. The observed differences were found to be significantly associated with perinatal factors such as bronchopulmonary dysplasia, Apgar score, gestational hypertension, and infection. Significant differences in network connectivity between the VP and FT groups. The results of linear regression showed significant correlations between maternal years of education, weight, Apgar score and gestational age at birth and VP group network metrics.
Conclusions: The findings of this study shed light on the influence of perinatal factors on VP infants’ brain development. These results can inform clinical interventions and treatments to improve the outcomes of preterm infants.