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

Benjamin Clemens; Birgit Derntl; Elke Smith; Jessica Junger; Josef Neulen; Gianluca Mingoia; Frank Schneider; Ted Abel; Danilo Bzdok; Ute Habel

doi:10.1093/cercor/bhz272

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

Journal article

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

Benjamin Clemens, Birgit Derntl, Elke Smith, Jessica Junger, Josef Neulen, Gianluca Mingoia, Frank Schneider, Ted Abel, Danilo Bzdok and Ute Habel

Cerebral cortex (New York, N.Y. 1991), Vol.30(5), pp.2755-2765

05/14/2020

DOI: 10.1093/cercor/bhz272

PMID: 31999324

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Abstract

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.

Machine Learning

resting-state functional connectivity

fMRI

gender identity

transgender

Details

Title: Subtitle: Predictive Pattern Classification Can Distinguish Gender Identity Subtypes from Behavior and Brain Imaging
Creators: Benjamin Clemens - Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, 52074 Aachen, Germany
Birgit Derntl - Department of Psychiatry and Psychotherapy, University of Tübingen, 72076 Tübingen, Germany
Elke Smith - Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, 52074 Aachen, Germany
Jessica Junger - Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, 52074 Aachen, Germany
Josef Neulen - Department of Gynecological Endocrinology and Reproductive Medicine, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany
Gianluca Mingoia - Interdisciplinary Center for Clinical Research (IZKF), RWTH Aachen University, Faculty of Medicine, Pauwelsstrasse 30, 52074 Aachen, Germany
Frank Schneider - Institute of Neuroscience and Medicine: JARA-Institute Brain Structure Function Relationship (INM 10), Wilhelm-Johnen-Straße 52425 Jülich, Germany
Ted Abel - Department of Biology, University of Pennsylvania, 433 South University Avenue, Philadelphia, PA 19104, United States
Danilo Bzdok - Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, 52074 Aachen, Germany
Ute Habel - Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, 52074 Aachen, Germany
Resource Type: Journal article
Publication Details: Cerebral cortex (New York, N.Y. 1991), Vol.30(5), pp.2755-2765
DOI: 10.1093/cercor/bhz272
PMID: 31999324
NLM abbreviation: Cereb Cortex
ISSN: 1047-3211
eISSN: 1460-2199
Publisher: Oxford University Press
Grant note: DOI: 10.13039/501100001659, name: German Research Foundation, award: HA 3202/7-1, IRTG 1328, DFG, BZ2/2-1, BZ2/3-1, BZ2/4-1; name: International Research Training Group, award: IRTG2150; name: Start program 34/13 and the Brain Imaging Facility of the Interdisciplinary Centre for Clinical Research of the Faculty of Medicine; DOI: 10.13039/501100007210, name: RWTH Aachen University; name: Amazon AWS Research, award: 2016, 2017; name: German National Merit Foundation; DOI: 10.13039/501100010794, name: Faculty of Medicine, award: 126/16; name: Exploratory Research Space, award: OPSF449; DOI: 10.13039/501100007210, name: RWTH Aachen
Language: English
Date published: 05/14/2020
Academic Unit: Molecular Physiology and Biophysics; Psychiatry; Psychological and Brain Sciences; Iowa Neuroscience Institute; Neuroscience and Pharmacology; Biochemistry and Molecular Biology
Record Identifier: 9984070570402771

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