Transgender individuals  show brain structural alterations that differ from their biological sex as well as their perceived gender. To substantiate evidence that the brain structure of transgender individuals differs from male and female, we use a combined multivariate and univariate approach. Gray matter segments resulting from voxel-based morphometry preprocessing of N = 1753 cisgender (CG) healthy participants were used to train (N=1402) and validate (20 \% hold-out  N = 351) a support vector machine classifying the biological sex. As a second validation, we classified N = 1104 patients with depression. A third validation was performed using the matched CG sample of the transgender women ( TW) application sample . Subsequently, the classifier was applied to N = 25 TW . Finally, we compared brain volumes of CG-men, women and TW pre /post treatment ( CHT ) in a univariate analysis controlling for sexual orientation, age and total brain volume. The application of our biological sex classifier to the transgender sample resulted in a significantly lower true positive rate (  TPR-male = 56.0 \%). The TPR did not differ between CG-individuals with (TPR-male = 86.9 \%) and without depression (TPR-male = 88.5 \%). The univariate analysis of the transgender application sample revealed that TW pre /post treatment show brain structural differences from CG-women and CG-men in the putamen and insula, as well as the whole-brain analysis. Our results support the hypothesis that brain structure in TW differs from brain structure of their biological sex (male) as well as their perceived gender (female). This finding substantiates evidence that transgender individuals show specific brain structural alterations leading to a different pattern of brain structure than CG individuals .
Transgender individuals (TIs) show brain structural alterations that differ from their biological sex as well as their perceived gender. To substantiate evidence that the brain structure of TIs differs from male and female, we use a combined multivariate and univariate approach. Gray matter segments resulting from voxel-based morphometry preprocessing of N = 1753 cisgender (CG) healthy participants were used to train (N=1402) and validate (20 \% hold-out ; N = 351) a support-vector machine classifying the biological sex. As a second validation, we classified N = 1104 patients with depression. A third validation was performed using the matched CG sample of the transgender women ( TWs) application-sample . Subsequently, the classifier was applied to N = 26 TWs . Finally, we compared brain volumes of CG-men, women and TW-pre /post treatment ( cross-sex hormone treatment ) in a univariate analysis controlling for sexual orientation, age and total brain volume. The application of our biological sex classifier to the transgender sample resulted in a significantly lower true positive rate ( TPR) ( TPR-male = 56.0 \%). The TPR did not differ between CG-individuals with (TPR-male = 86.9 \%) and without depression (TPR-male = 88.5 \%). The univariate analysis of the transgender application-sample revealed that TW-pre /post treatment show brain structural differences from CG-women and CG-men in the putamen and insula, as well as the whole-brain analysis. Our results support the hypothesis that brain structure in TW differs from brain structure of their biological sex (male) as well as their perceived gender (female). This finding substantiates evidence that TIs show specific brain structural alterations leading to a different pattern of brain structure than CG-individuals .
A
(TIs)
R
transgender individuals
TIs
A
;
R
support vector
support-vector
R
TW) application sample
TWs) application-sample
R
25 TW
26 TWs
R
TW pre
TW-pre
R
CHT
cross-sex hormone treatment
A
TPR) (
R
application sample revealed that TW pre
application-sample revealed that TW-pre
R
transgender individuals
TIs
R
CG individuals
CG-individuals