Influence of sex and gender on the stress response: going beyond the binary framework for improving personalized healthcare
The exclusion of females from research studies was a critical issue that harmed their health and the advance of science and medicine. In order to correct this situation, funding agencies started requesting the inclusion of women and the consideration of sex as a biological variable, which despite being necessary, also led to an excessive focus on the search for sex differences within a rigid binary framework. Moreover, when these results are not properly communicated to the general public, they may lead to essentialist interpretations of innate differences between man and woman and serve to perpetuate sexist stereotypes.
In this project we aim to recruit 140 healthy controls and explore sex-related variables that could be more useful for research than the current binary approach of sex differences; analyze their association with participants’ stress and neuroimaging measures; and explore the impact of a functional magnetic resonance imaging (fMRI) gender stereotype threat task on both resting-state network dynamics and stress.
With this project we expect to (1) advance sex-related stress research going beyond the binary framework; (2) develop specific guidelines and instruments for a more appropriate study of sex-related variables; and (3) improve personalized healthcare, by being able to detect more specific risk factors for mental health conditions, and implementing prevention strategies to mitigate the effect of those sex-related variables that are context-dependent, and thus, susceptible to change.
Main Project Outcomes
S. Queirós, “Right ventricular segmentation in multi-view cardiac MRI using a unified U-net model”, in E. Puyol Antón et al. (eds) Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge. STACOM 2021. Lecture Notes in Computer Science, vol 13131, pp. 287-295, Springer, Cham, 2022.
“Best Paper Award in the M&Ms-2 Challenge”, by M&Ms2 Challenge organizers and the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society.