NOTE: This piece was originally submitted as an Op-Ed to the New York Times, but was passed over for publication. Presumably for being too critical of the current administration’s position on LGBTQ rights.
For matters regarding the Transgender community, most media outlets seem to be driven by the very tragedy of being Transgender. This has become a cyclical negative feedback loop regarding the narrative of our lives, and it is only being reinforced by the current U.S. Presidential administration.
As long as I have been aware of gender non-conforming individuals, I have always felt they were outsiders. But in the past two decades they stopped living in the shadows, and become emboldened to share their stories. To this end there are hundreds of thousands of others who have suffered with their own gender incongruity who have now found the strength to stop hiding and follow the path that allows them to live as the individual they’ve always felt they should have been.
Abstract The exact neurobiological underpinnings of gender identity (i.e., the subjective perception of oneself belonging to a certain gender) still remain unknown. Combining both resting-state functional connectivity and behavioral data, we examined gender identity in cisgender and transgender persons using a data-driven machine learning strategy. Intrinsic functional connectivity and questionnaire data were obtained from cisgender (men/women) and transgender (trans men/trans women) individuals. Machine learning algorithms reliably detected gender identity with high prediction accuracy in each of the four groups based on connectivity signatures alone. The four normative gender groups were classified with accuracies ranging from 48% to 62% (exceeding chance level at 25%). These connectivity-based classification accuracies exceeded those obtained from a widely established behavioral instrument for gender identity. Using canonical correlation analyses, functional brain measurements and questionnaire data were then integrated to delineate nine canonical vectors (i.e., brain-gender axes), providing a multilevel window into the conventional sex dichotomy. Our dimensional gender perspective captures four distinguishable brain phenotypes for gender identity, advocating a biologically grounded reconceptualization of gender dimorphism.We hope to pave the way towards objective, data-driven diagnostic markers for gender identity and transgender, taking into account neurobiological and behavioral differences in an integrative modeling approach.