Replication of Previous Findings? Comparing Gray Matter Volumes in Transgender Individuals with Gender Incongruence and Cisgender Individuals

Pain mechanisms in the transgender individual: a review

Specific Aim

Provide an overview of the literature addressing major areas pertinent to pain in transgender persons and to identify areas of primary relevance for future research.

Methods

A team of scholars that have previously published on different areas of related research met periodically though zoom conferencing between April 2021 and February 2023 to discuss relevant literature with the goal of providing an overview on the incidence, phenotype, and mechanisms of pain in transgender patients. Review sections were written after gathering information from systematic literature searches of published or publicly available electronic literature to be compiled for publication as part of a topical series on gender and pain in the Frontiers in Pain Research.

Results

While transgender individuals represent a significant and increasingly visible component of the population, many researchers and clinicians are not well informed about the diversity in gender identity, physiology, hormonal status, and gender-affirming medical procedures utilized by transgender and other gender diverse patients. Transgender and cisgender people present with many of the same medical concerns, but research and treatment of these medical needs must reflect an appreciation of how differences in sex, gender, gender-affirming medical procedures, and minoritized status impact pain.

Conclusions

While significant advances have occurred in our appreciation of pain, the review indicates the need to support more targeted research on treatment and prevention of pain in transgender individuals. This is particularly relevant both for gender-affirming medical interventions and related medical care. Of particular importance is the need for large long-term follow-up studies to ascertain best practices for such procedures. A multi-disciplinary approach with personalized interventions is of particular importance to move forward.

Accurate sex prediction of cisgender and transgender individuals without brain size bias

The increasing use of machine learning approaches on neuroimaging data comes with the important concern of confounding variables which might lead to biased predictions and in turn spurious conclusions about the relationship between the features and the target. A prominent example is the brain size difference between women and men. This difference in total intracranial volume (TIV) can cause bias when employing machine learning approaches for the investigation of sex differences in brain morphology. A TIV-biased model will not capture qualitative sex differences in brain organization but rather learn to classify an individual's sex based on brain size differences, thus leading to spurious and misleading conclusions, for example when comparing brain morphology between cisgender- and transgender individuals. In this study, TIV bias in sex classification models applied to cis- and transgender individuals was systematically investigated by controlling for TIV either through featurewise confound removal or by matching the training samples for TIV. Our results provide strong evidence that models not biased by TIV can classify the sex of both cis- and transgender individuals with high accuracy, highlighting the importance of appropriate modeling to avoid bias in automated decision making.

Gender Diversity and Brain Morphology Among Adolescents

Importance

Gender-diverse youths have higher rates of mental health problems compared with the general population, as shown in both clinical and nonclinical populations. Brain correlates of gender diversity, however, have been reported only among youths with gender dysphoria or in transgender individuals.

Objective

To examine brain morphologic correlates of gender diversity among adolescents from a general pediatric population who were assigned male or female at birth, separately.

Design, Setting, and Participants

This cross-sectional study was embedded in Generation R, a multiethnic population-based study conducted in Rotterdam, the Netherlands. Adolescents who were born between April 1, 2002, and January 31, 2006, and had information on self-reported or parent-reported gender diversity and structural neuroimaging at ages 13 to 15 years were included. Data analysis was performed from April 1 to July 31, 2022.

Exposures

Gender-diverse experiences among adolescents were measured with selected items from the Achenbach System of Empirically Based Assessment forms and the Gender Identity/Gender Dysphoria Questionnaire for Adolescents and Adults, as reported by adolescents and/or their parents.

Main Outcomes and Measures

High-resolution structural neuroimaging data were collected using a 3-T magnetic resonance imaging scanner (at a single site). We used linear regression models to examine differences in global brain volumetric measures between adolescents who reported gender diversity and those who did not.

Results

This study included 2165 participants, with a mean (SD) age of 13.8 (0.6) years at scanning. A total of 1159 participants (53.5%) were assigned female at birth and 1006 (46.5%) were assigned male at birth. With regard to maternal country of origin, 1217 mothers (57.6%) were from the Netherlands and 896 (42.4%) were from outside the Netherlands. Adolescents who reported gender diversity did not differ in global brain volumetric measures from adolescents who did not report gender diversity. In whole-brain, vertexwise analyses among adolescents assigned male at birth, thicker cortices in the left inferior temporal gyrus were observed among youths who reported gender diversity compared with those who did not. No associations were observed between gender diversity and surface area in vertexwise analyses.

Conclusions and Relevance

The findings of this cross-sectional study suggest that global brain volumetric measures did not differ between adolescents who reported gender diversity and those who did not. However, these findings further suggest that gender diversity in the general population correlates with specific brain morphologic features in the inferior temporal gyrus among youths who are assigned male at birth. Replication of these findings is necessary to elucidate the potential neurobiological basis of gender diversity in the general population. Future longitudinal studies should also investigate the directionality of these associations.

Accurate machine learning prediction of sexual orientation based on brain morphology and intrinsic functional connectivity

BACKGROUND

Sexual orientation in humans represents a multilevel construct that is grounded in both neurobiological and environmental factors.

OBJECTIVE

Here, we bring to bear a machine learning approach to predict sexual orientation from gray matter volumes (GMVs) or resting-state functional connectivity (RSFC) in a cohort of 45 heterosexual and 41 homosexual participants.

METHODS

In both brain assessments, we used penalized logistic regression models and nonparametric permutation.

RESULTS

We found an average accuracy of 62% (±6.72) for predicting sexual orientation based on GMV and an average predictive accuracy of 92% (±9.89) using RSFC. Regions in the precentral gyrus, precuneus and the prefrontal cortex were significantly informative for distinguishing heterosexual from homosexual participants in both the GMV and RSFC settings.

CONCLUSIONS

These results indicate that, aside from self-reports, RSFC offers neurobiological information valuable for highly accurate prediction of sexual orientation. We demonstrate for the first time that sexual orientation is reflected in specific patterns of RSFC, which enable personalized, brain-based predictions of this highly complex human trait. While these results are preliminary, our neurobiologically based prediction framework illustrates the great value and potential of RSFC for revealing biologically meaningful and generalizable predictive patterns in the human brain.