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Journal Articles Cerebral Cortex Year : 2020

Predictive pattern classification can distinguish gender identity subtypes from behavior and brain imaging

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 cis-and transgender persons using a data-driven machine-learning strategy. Intrinsic functional connectivity andq uestionnaire data were obtained from cisgender (men/women) and transgender (transmen/transwomen) 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), providinga multi-level window into the conventional sex dichotomy. Our dimensional gender perspective captures four distinguish able brain phenotypes for gender identity, advocating a biologically grounded re-conceptualization 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.
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Dates and versions

hal-02467344 , version 1 (04-02-2020)

Identifiers

  • HAL Id : hal-02467344 , version 1

Cite

Benjamin Clemens, Birgit Derntl, Elke Smith, Jessica Junger, Josef Neulen, et al.. Predictive pattern classification can distinguish gender identity subtypes from behavior and brain imaging. Cerebral Cortex, 2020. ⟨hal-02467344⟩
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