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

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  • 1Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, 52074 Aachen, Germany.

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|January 31, 2020
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Summary
This summary is machine-generated.

Neuroimaging reveals distinct brain connectivity patterns differentiate gender identity in cisgender and transgender individuals. This research offers a biologically grounded approach to understanding gender dimorphism.

Keywords:
fMRIgender identitymachine learningresting-state functional connectivitytransgender

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Area of Science:

  • Neuroscience
  • Genetics
  • Psychology

Background:

  • The neurobiological basis of gender identity remains largely unknown.
  • Previous research has not fully elucidated the brain mechanisms underlying gender identity in diverse populations.

Purpose of the Study:

  • To investigate the neurobiological underpinnings of gender identity using resting-state functional connectivity and machine learning.
  • To explore gender identity in cisgender and transgender individuals through a data-driven approach.
  • To establish objective, data-driven diagnostic markers for gender identity.

Main Methods:

  • Acquired intrinsic functional connectivity and questionnaire data from cisgender (men/women) and transgender (trans men/trans women) individuals.
  • Employed machine learning algorithms to classify gender identity based on brain connectivity signatures.
  • Utilized canonical correlation analyses to integrate functional brain measurements and behavioral data.

Main Results:

  • Machine learning accurately classified gender identity across four groups (cisgender men, cisgender women, trans men, trans women) with accuracies from 48% to 62%, exceeding chance levels.
  • Connectivity-based classification surpassed the accuracy of a widely used behavioral gender identity instrument.
  • Identified nine canonical vectors representing brain-gender axes and four distinct brain phenotypes for gender identity.

Conclusions:

  • Functional brain connectivity patterns can reliably differentiate gender identity.
  • Findings advocate for a biologically grounded reconceptualization of gender dimorphism, moving beyond a strict binary.
  • This study paves the way for objective, neurobiologically informed diagnostic markers for gender identity and transgender experiences.