The sexual brain, genes, and cognition: A machine‐predicted brain sex score explains individual differences in cognitive intelligence and genetic influence in young children

Abstract

Sex impacts the development of the brain and cognition differently across individuals. However, the literature on brain sex dimorphism in humans is mixed. We aim to investigate the biological underpinnings of the individual variability of sexual dimorphism in the brain and its impact on cognitive performance. To this end, we tested whether the individual difference in brain sex would be linked to that in cognitive performance that is influenced by genetic factors in prepubertal children (N = 9,658, ages 9-10 years old; the Adolescent Brain Cognitive Development study). To capture the interindividual variability of the brain, we estimated the probability of being male or female based on the brain morphometry and connectivity features using machine learning (herein called a brain sex score). The models accurately classified the biological sex with a test ROC-AUC of 93.32%. As a result, a greater brain sex score correlated significantly with greater intelligence (p fdr < .001, η 2 p = .011-.034; adjusted for covariates) and higher cognitive genome-wide polygenic scores (GPSs) (p fdr < .001, η 2 p < .005). Structural equation models revealed that the GPS-intelligence association was significantly modulated by the brain sex score, such that a brain with a higher maleness score (or a lower femaleness score) mediated a positive GPS effect on intelligence (indirect effects = .006-.009; p = .002-.022; sex-stratified analysis). The finding of the sex modulatory effect on the gene-brain-cognition relationship presents a likely biological pathway to the individual and sex differences in the brain and cognitive performance in preadolescence.

Sex, gender diversity, and brain structure in early adolescence

“…Our findings align with previous work involving the ABCD dataset where different types of machine learning techniques were implemented to probe potential discriminative sex differences in the early adolescent brain (Adeli et al, 2020 ; Brennan et al, 2021 ; Kim et al, 2021 , 2022 ). For example, deep learning identified the subcortex as the most sex‐discriminative area of the brain (Adeli et al, 2020 ).…”

“…In this regard, the landmark Adolescent Brain Cognitive Development℠ Study (ABCD Study®), the largest long‐term study of brain development in the United States (ABCD Study, 2022 ), enrolled over 11,800 9‐ and 10‐year‐old children to further study biological and social factors, like gender on brain development (Volkow et al, 2018 ). As such, several recent studies using ABCD data have reported significant neuroanatomical sex differences across numerous domains, including subcortical volume (Adeli et al, 2020 ), cortical thickness (Brennan et al, 2021 ; Tomasi & Volkow, 2023 ; Wiglesworth et al, 2023 ), gray matter density (Murray et al, 2022 ), white matter microstructure (Lawrence et al, 2023 ; Tomasi & Volkow, 2023 ), and differences in the association between brain structure and behavior (Chen et al, 2022 ; Kim et al, 2022 ). However, none of these previous studies on sex differences utilizing the ABCD study examined the potential role of gender.…”

Sex-differential cognitive performance on MCCB of youth with BD-II depression

“…In female patients with BD, estrogens can regulate BDNF concentrations of multiple brain regions such as the hippocampus, mitigate oxidative stress, and deactivate inflammatory response, which may be the underlying mechanism of better neurocognitive performance in female patients with BD [ 59 ]. Apart from that, the expression of gender disparity in cognitive function may be influenced by genetic factors [ 67 ]. A prior study investigating the sex-specific role of three BDNF single-nucleotide polymorphisms (SNPs) (rs6265, rs7103411, and rs7124442) in cognitive aging showed that there was only a relationship between rs6265 and processing speed in females and no correlation between BDNF SNPs and cognition in males [ 68 ].…”

Modeling brain sex in the limbic system as phenotype for female-prevalent mental disorders

“…Recently, multivariate approaches have been developed to study sex differences in the brain. Machine learning models that classify for sex based on brain structural or functional magnetic resonance images (MRI) yield class probabilities that can be used as an imaging-derived multivariate phenotype to study sex differences on a continuum from female- to male-like brains [ 26 – 29 ]. Conceptually similar approaches have already been used extensively to predict brain age [ 30 – 36 ], where machine learning models deliver a continuous phenotype reflecting apparent aging effects.…”