Endocrine variability in large-scale brain networks.
Project status: Ongoing…
Endocrine-mediated pubertal brain network development: Bridging datasets with machine learning
Puberty is a critical period for neuroendocrine development characterized by substantial neuroplasticity and changing sex hormone levels, which are poorly understood in humans. Innovative analytic strategies are needed for integrating data from multiple approaches (i.e., large- and small-scale datasets with sparse and dense longitudinal sampling) to better characterize the contributions of long-term pubertal changes, and more short- term monthly variability in hormones as it pertains to structure and function of the developing brain. The proposed study will apply machine learning to assess hormone-related brain structure and functional connectivity, the roles of sex hormones in microstructure-function coupling across the brain, and the impact of hormone variability and sampling design on identified neuroendocrine phenomena. This work is funded by the NIH BRAIN Initiative (K99MH135075-01; PI: Bottenhorn).
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Dense Investigation of Variability in Affect (DIVA)
The rise of large neuroimaging datasets and multi-dataset mega-analyses brings the power to study interindividual differences in brain structure and function on a heretofore unseen scale. However, unknown and poorly characterized intra-individual variability continues to undermine the detection of robust brain-behavior associations and, ultimately, our understanding of the brain on the whole. Women’s and reproductive health underlie variability in more than half of the population, but have long been overlooked in the study of both inter- and intra-individual differences in the brain. To this end, the Dense Investigation of Variability in Affect (DIVA) Study was designed to study intra-individual variability in the brain and behavior across the menstrual cycle in a small cohort of premenopausal female participants. The DIVA Study acquired weekly actigraphy, self-report, biospecimen, and both functional and structural magnetic resonance imaging data with concurrent peripheral physiological recordings. These data facilitate the study of several common sources of variability in the brain and behavior: the menstrual cycle and ovarian hormones, sleep, stress, exercise, and exogenous sources of hemodynamic variability.