Cerebral Morphometric Markers and Molecular Profiles in Pregnant Women: A Cross-Sectional Study

International Journal of Psychological and Brain Sciences, Journal Year: 2025, Volume and Issue: 10(1), P. 29 - 36

Published: Feb. 21, 2025

Pregnancy induces a range of hormonal and physiological changes also affect the brain. Yet specific cerebral morphometric markers their associated molecular profiles throughout pregnancy remain poorly understood. In this study, we investigated in 23 pregnant women using T1-weighted MRI scans, with progression quantified by post-menstrual age (PMA). We performed whole-brain regression analysis to examine how gray matter volume (GMV) was influenced PMA, further explored these integrating GMV findings JuSpace toolbox. Our revealed that PMA increased, there significant reduction left medial frontal gyrus (MFG) GMV, suggesting structural brain progression. Spatial correlation analyses did not reveal any associations between neurotransmitter distribution observed changes. Gene enrichment pointed an important shift: protein binding most significantly enriched term during pregnancy. This suggests mechanisms related may play crucial role neurobiological adaptations conclusion, our provide new insights into is alterations both structure profiles. The decreased MFG functions contribute understanding neural biological underlying These offer foundation for future research maternal health long-term effects on function.

Language: Английский

Connectomics modeling of regional networks of white-matter fractional anisotropy to predict the severity of young adult drinking

Quantitative Imaging in Medicine and Surgery, Journal Year: 2025, Volume and Issue: 15(3), P. 2405 - 2419

Published: March 1, 2025

Alcohol use impacts brain structure, including white matter integrity, which can be quantified by fractional anisotropy (FA) in diffusion tensor imaging (DTI). This study explored the relationship between severity of alcohol consumption and FA changes, its sex differences, young adults, using data from Human Connectome Project. We analyzed DTI 949 participants (491 females) used principal component analysis (PCA) 15 drinking metrics to quantify severity. Connectome-based predictive modeling (CPM) was employed predict network values a matrix 116×116 regions. Mediation analyses were conducted explore interrelationships among networks identified CPM, severity, rule-breaking behavior. Significant correlations found values. Both men women showed significant negative connectivity (men: r=0.15, P=0.001; women: r=0.30, P<0.001). Sex differences observed regions contributing predictions. revealed inter-relationships features, The connectomics consumption, incorporating pathways, identify differences. approach provides new clues biological basis abuse evaluates how these interact broader for understanding misuse comorbidities.

Language: Английский