Predictive Pattern Classification Can Distinguish Gender Identity Subtypes from Behavior and Brain Imaging.

Clicks: 373
ID: 88257
2020
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Ranked #3 of 107 articles by views in cerebral cortex (new york, ny : 1991)

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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.
Reference Key
clemens2020predictivecerebral Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Clemens, Benjamin;Derntl, Birgit;Smith, Elke;Junger, Jessica;Neulen, Josef;Mingoia, Gianluca;Schneider, Frank;Abel, Ted;Bzdok, Danilo;Habel, Ute;
Journal cerebral cortex (new york, ny : 1991)
Year 2020
DOI
bhz272
URL
Keywords
Machine learning fmri transgender gender identity resting-state functional connectivity

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