Biology subjects

Tian, Y. E.

Publications and source records attributed to Tian, Y. E..

3 recordsLinked to original sources

bioRxiv2025

Linking Oestradiol (E2) Timing and Tempo, Brain Development, and Mental Health in Adolescent Females

Background: Earlier timing and faster tempo of puberty have been associated with altered brain development and increased mental health symptoms in adolescents, particularly females. However, the role of oestradiol (E2) in these associations is unclear. Methods: Using longitudinal data from the US-based Adolescent Brain Cognitive Development Study SM (ABCD Study (R), we investigated whether, in females (N ~ 3k), E2 timing (at age 10) and tempo (rate of change from age 10 to 12) were prospectively associated with mental health symptoms at age 13 via structural brain development from age 10 to 12. Linear mixed-effects models and Bayesian mediation models were fitted to investigate the aims of the study. Results: Findings showed that E2 timing was not associated with mental health symptoms. However, earlier E2 timing was associated with a greater reduction in total cortical volume, total surface area, and surface area in the superior and middle temporal cortex over time. Further, a faster E2 tempo was associated with an increase in mental health symptoms, and this association was mediated by a faster reduction in total cortical volume and total surface area over time. Conclusion: Findings suggest that earlier E2 timing and faster E2 tempo contribute to accelerated development of gray matter structure in adolescent females, and for E2 tempo, such associated brain changes may partly contribute to increased mental health risk.

neuroscience↗

bioRxiv2024

Pubertal hormones and the early adolescent female brain: a multimodality brain MRI study

Puberty is a critical developmental process that is associated with changes in steroid hormone levels, which are believed to influence adolescent behaviour via their effects on the developing brain. So far, there are limited and inconsistent findings regarding the relationship between steroid hormones and brain structure and function in adolescent females, with many existing studies employing small sample sizes. Thus, in this study, we explored the association between oestradiol (E2), testosterone (Tes) and dehydroepiandrosterone (DHEA) and brain structure (gray matter volume, sulcal depth, cortical thickness and white matter microstructure) and function (resting-state connectivity, emotional n-back task-related function) in 3024 adolescent females (age 8.92 - 13.33 years, mean age (SD) = 10.37 (0.94) years) from the Adolescent Brain Cognitive DevelopmentSM (ABCD(R)) Study. We used elastic-net regression with cross- validation to investigate associations between hormones and brain phenotypes derived from multiple imaging modalities. We found that structural brain features, including cortical thickness, sulcal depth, and white matter microstructure, were among the most important features associated with hormones. E2 was most strongly associated with prefrontal and premotor regions involved in working memory and emotion processing, while Tes and DHEA were most strongly associated with parietal and occipital regions involved in visuospatial functioning. All three hormones were also associated with prefrontal, temporoparietal junction and insula cortices. Thus, using an advanced methodological approach, this study suggests both unique and overlapping neural correlates of pubertal hormones in adolescent females and sheds light on the mechanisms by which puberty influences adolescent development and behaviour.

neuroscience↗

bioRxiv2024

Benchmarking methods for mapping functional connectivity in the brain

The networked architecture of the brain promotes synchrony among neuronal populations and the emergence of coherent dynamics. These communication patterns can be comprehensively mapped using noninvasive functional imaging, resulting in functional connectivity (FC) networks. Despite its popularity, FC is a statistical construct and its operational definition is arbitrary. While most studies use zero-lag Pearsons correlations by default, there exist hundreds of pairwise interaction statistics in the broader scientific literature that can be used to estimate FC. How the organization of the FC matrix varies with the choice of pairwise statistic is a fundamental methodological question that affects all studies in this rapidly growing field. Here we comprehensively benchmark the topological and geometric organization, neurobiological associations, and cognitive-behavioral relevance of FC matrices computed using a large library of 239 pairwise interaction statistics. We comprehensively investigate how canonical features of FC networks vary with the choice of pairwise statistic, including (1) hub mapping, (2) weight-distance trade-offs, (3) structure-function coupling, (4) correspondence with other neurophysiological networks, (5) individual fingerprinting, and (6) brain-behavior prediction. We find substantial quantitative and qualitative variation across FC methods. Throughout, we observe that measures such as covariance (full correlation), precision (partial correlation) and distance display multiple desirable properties, including close correspondence with structural connectivity, the capacity to differentiate individuals and to predict individual differences in behavior. Using information flow decomposition, we find that differences among FC methods may arise from differential sensitivity to the underlying mechanisms of inter-regional communication, with some more sensitive to redundant and some to synergistic information flow. In summary, our report highlights the importance of tailoring a pairwise statistic to a specific neurophysiological mechanism and research question, providing a blueprint for future studies to optimize their choice of FC method.

neuroscience↗