Maternal risk in the development of postpartum depression: A longitudinal neurobiological study from pregnancy to delayed postpartum

• Applicants: Professorin Dr. Natalya Chechko; Dr. Susanne Meinert
• Subject Area: Biological Psychiatry
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 564164998

Postpartum depression (PPD) poses a significant challenge for affected families. However, despite its profound impact on mothers and infants, the causes of PPD remain inadequately understood. Therefore, this bi-centric study (Münster and Aachen) aims to examine a cohort of n=130 pregnant women at increased risk for PPD through in-depth deep phenotyping and longitudinally follow them for 6 months thereafter. This entails combining multimodal neuroimaging (both structural and functional), hormonal fluctuations, neuropsychological test performance, and comprehensive psychological characterization of risk and protective factors. Enrollment occurs during the third trimester of pregnancy, where participants undergo detailed deep phenotyping (previous [mental] illnesses, risk/protective factors, pregnancy complications, neuropsychological testing, biomaterial sampling). Subsequently, these women are systematically followed longitudinally: 10 days (T1), 6 weeks (T2), and 6 months (T3) post-delivery, where recurring symptoms and mental illnesses are assessed again, biomaterial is collected, and a multimodal imaging battery is performed. The study aims to: 1. Investigate the dynamics of structural (DTI) and functional (rsfMRI) neurobiological networks during the postpartum phase and their association with the development of PPD. These neurobiological trajectories will be linked to hormonal fluctuations. 2. Conduct a risk assessment and develop predictive models for PPD diagnosis based on in-depth phenotypic data, biomaterial collection, and neurobiological trajectories. Our analysis plan utilizes Linear Mixed Effect Models to modulate individual neurobiological trajectories. Structural neuroimaging (WP1) will examine changes in white matter microstructure and network efficiency, providing insights into brain connectivity patterns. Functional neuroimaging (WP2) will complement this by examining resting-state connectivity and investigating brain function during the postpartum phase. Finally, machine learning methods (WP3) will be employed to develop predictive models for PPD diagnosis by integrating biological markers, psychopathological data, and hormonal fluctuations. By comprehensively analyzing various aspects of postpartum adaptation, our study aims to provide a nuanced understanding of PPD. Ultimately, we aim to improve our understanding of the mechanisms of PPD and raise awareness for early detection and intervention strategies for vulnerable individuals.

• DFG Programme: Research Grants