Programming the next generation of prenatal programming of stress research: A review and suggestions for the future of the field

Abstract

In this article, I highlight core ideas, empirical findings, and advances in the study of how stress during pregnancy may prenatally program child neurodevelopmental, psychopathological, and health outcomes, emphasizing reviews, metanalyses, and recent contributions of conceptual and empirical work. The article offers a perspective on the history of this area of science, the underrecognized contributions of influential scholars from diverse fields of study, what we know from the evidence to date, the persistent challenges in sorting through what is left to learn, and suggestions for future research. I include sections focused on promoting resilience, pregnancy interventions that demonstrate positive effects across two generations, and the translational implications of the accruing data for practice and policy, highlighting opportunities for integrating across a range of fields and sectors. In the concluding sections, I discuss lessons learned from conducting this work and provide a closing summary of progress and future directions. The goal of this writing was to provide a viewpoint on some ways that emerging intergenerational transmission scholars might responsibly contribute to the future of the field of developmental psychopathology.

Integrating data science and neuroscience in developmental psychopathology: Formative examples and future directions

“…The multiple perspectives published in this special issue honoring the lifetime contributions of Dr Cicchetti beautifully illustrate ways to continue progress. This exciting future work includes investigating prenatal and intergenerational influences on development (Beeghly, 2024;Bush, 2024), advancing the frontiers of resilience research (Masten, 2024), growing interdisciplinary training (Gotlib et al, 2024), expanding participant representation and inclusivity (Tyrell et al, 2024), and informing the program and policy choices of governmental and non-governmental organizations (Scott et al, 2024). Here, we contribute our perspective that data science and machine learning techniques are valuable tools for the advancement of developmental psychopathology research.…”