Structural Connectivity Network in Human Brain in Relation to Transgenderism
Poster No:
1532
Submission Type:
Abstract Submission
Authors:
Ye Wu1, Yifei He1, Tao Zhou1, Xiaoming Liu2
Institutions:
1Nanjing University of Science and Technology, Nanjing, China, 2Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and, Wuhan, China
First Author:
Co-Author(s):
Xiaoming Liu
Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and
Wuhan, China
Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and
Wuhan, China
Introduction:
Gender identity and sexual orientation are among the most fascinating aspects of human biology[1]–[4]. However, the mechanisms that underlie them are still unknown. Both transgenderism and homosexuality are aspects of human biology that are believed to arise from different sexual differentiation of the brain, leading to differences in the structural network of the brain. The purpose of this study is to investigate the relationship between gender identity, sexual orientation, and structural white matter connections as measured by length. A hypothesis suggests that transgender individuals, regardless of their sexual orientation, may differ from cisgender individuals in terms of the transitivity of structural connections in the network involved in their body perception in the context of self.
Methods:
T1-weighted and pre-processed dMRI of 928 healthy subjects from the publicly accessible Amsterdam Open MRI Collection[5] were used in this study. The acquisition parameters of the images, as well as the pre-processing, are described in the original publications of each dataset.
The restricted diffusion was quantified using restricted diffusion imaging[6]. A deterministic fiber tracking algorithm[7] was used with augmented tracking strategies[8] to improve reproducibility. Shape analysis[8] was conducted to derive shape metrics for tractography. HCP-MMP[9] was used as the brain parcellation, and the connectivity matrix was calculated by using the mean length of the connecting tracks. The connectivity matrix and graph theoretical analysis were conducted using DSI Studio (http://dsi-studio.labsolver.org). The global structural connectivity was assessed with measures such as global efficiency, small-worldness index, and transitivity was investigated with the measure including betweenness centrality.
To test whether gender identity or sexual orientation correlated with either structural connectivity or its derived metrics, we conducted Bayesian correlation analyses[10]. All Bayesian tests used a non-informative prior and a medium-sized distribution (conjugate distributions on either side). The network metrics and sex distribution were compared using separate independent samples t-tests. Neuroimaging results are presented as the mean ± standard error of the mean. Statistical analyses were conducted using a 0.05 alpha level. All of the statistical analysis was implemented using JASP[11].
The restricted diffusion was quantified using restricted diffusion imaging[6]. A deterministic fiber tracking algorithm[7] was used with augmented tracking strategies[8] to improve reproducibility. Shape analysis[8] was conducted to derive shape metrics for tractography. HCP-MMP[9] was used as the brain parcellation, and the connectivity matrix was calculated by using the mean length of the connecting tracks. The connectivity matrix and graph theoretical analysis were conducted using DSI Studio (http://dsi-studio.labsolver.org). The global structural connectivity was assessed with measures such as global efficiency, small-worldness index, and transitivity was investigated with the measure including betweenness centrality.
To test whether gender identity or sexual orientation correlated with either structural connectivity or its derived metrics, we conducted Bayesian correlation analyses[10]. All Bayesian tests used a non-informative prior and a medium-sized distribution (conjugate distributions on either side). The network metrics and sex distribution were compared using separate independent samples t-tests. Neuroimaging results are presented as the mean ± standard error of the mean. Statistical analyses were conducted using a 0.05 alpha level. All of the statistical analysis was implemented using JASP[11].
Results:
The Bayesian correlation between transgenderism, which includes both gender identity and sexual orientation, and the transitivity of structural connectivity is depicted in Fig. 1. These results provide evidence that suggests a correlation between transgenderism and structural connectivity. Furthermore, the Bayes factor comparing the predictions of the two hypotheses shows that the data are ultra-high times more likely under the alternative hypothesis H+ than under H0.
Fig. 2 shows the results of independent samples t-tests with sex grouping on three network properties derived by structural connectivity. The results show that males have significantly higher values (p<.001) of transitivity, small worldness, and global efficiency when compared to females.
Fig. 3 explores the partial correlation between agreeableness personality (NEO-A) and the transitivity of structural connectivity while controlling for the effect of transgenderism. The results indicate a significant negative relationship (p<.001) between them. Additionally, a robustness analysis using a different value for the Cauchy scale parameter confirms the high robustness of this correlation to different prior specifications.
Fig. 2 shows the results of independent samples t-tests with sex grouping on three network properties derived by structural connectivity. The results show that males have significantly higher values (p<.001) of transitivity, small worldness, and global efficiency when compared to females.
Fig. 3 explores the partial correlation between agreeableness personality (NEO-A) and the transitivity of structural connectivity while controlling for the effect of transgenderism. The results indicate a significant negative relationship (p<.001) between them. Additionally, a robustness analysis using a different value for the Cauchy scale parameter confirms the high robustness of this correlation to different prior specifications.
Conclusions:
Using structural connectivity, the present study investigates whether and how brain networks are related to gender identity and sexual orientation, revealing sex-atypical brain network properties in transgender individuals. The present findings support the idea of a distinction and partial overlap between the brain network underlying sexual orientation and transgenderism.
Emotion, Motivation and Social Neuroscience:
Social Neuroscience Other 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Diffusion MRI Modeling and Analysis
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity
Novel Imaging Acquisition Methods:
Diffusion MRI
Keywords:
Computational Neuroscience
Social Interactions
Tractography
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
Provide references using author date format
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