"Precision Medicine" and the Failed Search for Binary Brain Sex Differences to Address Gender Behavioral Health Disparities

Abstract

Human brain imaging took off in the 1980s and has since flooded the zone in the analysis of gender differences in behavior and mental health. Couched in the aims of "precision medicine," the vast majority of this research has taken a binary approach, dividing participants according to the M/F box at intake and asserting that the sex differences found in neuroimaging will lead to important advances for treating neuropsychiatric disorders. However, the actual findings from this 40-year project have not lived up to its promise, in part because of the over-binarization of sex and general ignorance of gender as a complex variable influencing human behavior and brain function. This paper reviews the history of failed claims about male-female brain difference in the modern era, illuminates the deep-pocketed incentives driving such research, and examines the limitations of this binary approach for understanding gender-related behavior and health disparities. It then considers more recent efforts to "break the binary" by using measures of "gender" in addition to "sex" as an independent variable in brain imaging studies. Given the multidimensional nature of gender-as identity, expression, roles and relations-this is challenging to implement, with initial efforts producing little of substance. Better approaches to addressing male-female disparities in brain health will require focusing on specific behaviors (e.g., anxiety, risk-taking, verbal memory, spatial navigation) and specific components of sex and gender (e.g., body size, hormone levels, gene expression, caregiver role, financial independence, discrimination) when seeking brain-behavior correlates in a diverse population.

Keywords: MRI; gender essentialism; machine learning; mental health; neuroimaging; neurosexism; sexual dimorphism.

FIGURE 1

Number of papers published each year that are captured by the search terms “human and brain and MRI and (sex or gender)” on the PubMed database from the US National Library of Medicine. Searched fields include article and journal titles, abstracts, keywords, and medical subject headings (MeSH terms).

FIGURE 2

Gender prevalence ratios for 10 major neurobehavioral disorders. Sources: autism (Loomes, Hull, and Mandy 2017); ADHD (Polanczyk et al. 2007); alcohol use disorder (Grant et al. 2004); dyslexia (Rutter et al. 2004); Parkinson disease (Zirra et al. 2023); dementia (Buckley et al. 2019); anxiety disorders (McLean et al. 2011); depression (Salk et al. 2017); multiple sclerosis (Briggs and Hill 2020); eating disorders (Hudson et al. 2007).