SummaryThis project seeks to study different phases in a woman’s reproductive lifespan, linking data from different cross-sectional and longitudinal cohorts. NAKO will make an integral component of this data set. Using machine learning models applied to brain imaging data the project aims to characterize the brain anatomical and functional architecture of hormonal transition phases and other aspects relevant to women’s brain health, integrating environmental and biological data. In the long run, these insights target a contribution to better, more targeted mental health interventions in clinical care.

Keywords machine-learning reproductive-life-span womens-mental-health

InstitutionsUniversity Hospital of Tübingen