Relationships between altered brain activity, childhood trauma, neurotransmitter, and genetic traits in Chinese early adulthood with gender dysphoria

Relationships between altered brain activity, childhood trauma, neurotransmitter, and genetic traits in Chinese early adulthood with gender dysphoria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Relationships between altered brain activity, childhood trauma, neurotransmitter, and genetic traits in Chinese early adulthood with gender dysphoria Ruoyi Chen, Guanmao Chen, Shilin Sun, Pan Chen, Zixuan Guo, Mengqi Liao, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8978712/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background Although early-life stress and trauma may influence cortical development and increase vulnerability to gender dysphoria (GD), the relationship between brain function and childhood traumatic experiences in GD remains unexplored. This study aims to investigate the neurobiological mechanisms underlying GD by examining functional brain alterations and their associations with childhood trauma, neurotransmitter systems, and gene expression patterns. Methods This study recruited 44 transgender women (TW), 40 cisgender male, and 42 cisgender female controls. Participants were assessed using the Childhood Trauma Questionnaire (CTQ) and Gender Identity/Gender Dysphoria Questionnaire for Adolescents and Adults (GIDYQ-AA), while resting-state fMRI data were analyzed for amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) measures. The transcriptional data were sourced from the Allen Human Brain Atlas. The JuSpace toolbox was used to investigate the atlas-based nuclear imaging-derived neurotransmitter maps. Results Compared to cisgender controls, TW showed decreased ALFF in the posterior cingulate cortex (PCC) but increased ALFF in the cerebellar lobule IX. TW exhibited decreased fALFF in the right PCC compared to cisgender women. TW also reported higher CTQ total and subscale scores. Notably, CTQ total and its subscale scores [emotional abuse and emotional neglect (EN)] were positively correlated with increased ALFF in the PCC. Multivariate regression analysis further revealed that interactions between fALFF value of the right PCC × CTQ total and fALFF value of the right PCC × EN independently predicted GIDYQ-AA scores in TW. The abnormal brain activity in TW was linked to genes enriched in mRNA catabolism and synaptic function, and was correlated with dopaminergic and serotonergic neurotransmission. Conclusions TW involves the PCC hypofunction and the cerebellar hyperactivation, synergistically modulated by childhood trauma, especially emotional neglect. These neural alterations are linked to gene regulatory and neurotransmitter pathways, revealing a multilevel neurophysiological framework of TW. Gender dysphoria amplitude of low frequency fluctuation childhood trauma gene expression neurotransmitter Figures Figure 1 Figure 2 Figure 3 Introduction Gender dysphoria (GD) is a psychiatric condition defined by a significant discrepancy between an individual's assigned sex at birth and their experienced or self-identified gender, accompanied by clinically notable distress [ 1 ] . In the past two decades, the number of clinical visits to GD has steadily increased, and the proportion of transgender women (TW) is higher than that of transgender men (TM) [2; 3] . Cisgender is used to refer to a gender identity that matches the sex assigned at birth [ 4 ] . Compared to their cisgender peers, individuals with GD carry a heavier burden of mental health challenges, including higher rates of substance abuse and suicide attempts [ 5 ] . A neurobiological theory proposes that gender identity is established by structural and functional brain differences shaped during prenatal development by hormonal, genetic, and epigenetic mechanisms [6; 7] . Despite existing theoretical frameworks, the neurobiological mechanisms underlying GD remain elusive, and the pathogenesis of this condition within the Chinese population remains entirely unexplored. Previous neuroimaging studies have documented structural brain alterations in individuals with GD. Previous findings indicate that voxel-based morphometry (VBM) has revealed increased grey matter volume in the right posterior cingulate cortex in the adolescent TW compared to cisgender women (CW), and reduced bilateral cerebellar grey matter volume in TW compared to cisgender men (CM) [8; 9] . However, functional investigations, particularly those examining intrinsic brain activity using resting-state fMRI (rs-fMRI), remain underexplored in this population. Rs-fMRI is a crucial, sophisticated, non-invasive neuroimaging tool for examining changes in brain function [ 10 ] . Previous seed-based resting-state functional connectivity (rs-FC) studies have demonstrated that TW exhibit altered FC between the ventral tegmental area and cingulate cortex, and between the posterior cingulate cortex (PCC) and dorsolateral prefrontal cortex (DLPFC), compared to cisgender control [11; 12] . Studies relying on seed-based FC analysis may overlook information from brain regions not selected as regions of interest (ROIs), and these omitted areas could play a significant role in the pathophysiology of GD [13; 14] . Amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) are whole-brain, hypothesis-free methods for measuring intrinsic brain activity [ 15 ] . Recently, ALFF/fALFF have emerged as dependable and effective neuroimaging biomarkers for quantifying resting-state local brain activity in vivo in individuals with various neuropsychiatric disorders [13; 16; 17] . However, the application of these methods in the TW patients remains unexplored. Childhood trauma refers to the experience of severe abuse, neglect, or exploitation during childhood, which adversely affects an individual's health, developmental processes, and psychological well-being [ 18 ] . Individuals experiencing GD frequently endure significantly elevated psychological distress due to stigma, prejudice, and discrimination, leading to a hostile and stressful social atmosphere [ 19 ] . Childhood trauma, particularly emotional neglect and abuse, represents a significantly higher burden among individuals with GD compared to cisgender controls [20; 21] . However, the specific link between such childhood trauma and alterations in brain function in GD remains unclear. To date, the relationship between childhood trauma, alterations in brain functional activity, and the severity of TW remains unexplored. Investigating the spatial link between regional brain features and neurotransmitter systems can bridge neuroimaging and nuclear imaging data to advance our understanding of disease mechanisms [ 22 ] . Dysregulation of key neurotransmitters is frequently associated with a range of neuropsychiatric disorders, including major depression [22; 23] , schizophrenia [22; 24] , and bipolar disorder [22; 25] . Developments in neuroimaging transcriptomes facilitate the formation of spatial correlations between microscale disorder-associated whole brain gene expression data from the Allen Human Brain Atlas (AHBA) and macroscale neuroimaging [ 26 ] . As an emerging field, neuroimaging transcriptomics is focused on identifying genes with spatial profiles of regional expression and tracking anatomical variation in specific neuroimaging biomarkers [ 27 ] . Nonetheless, the abnormal neurotransmitter and gene expression in the GD brain has not been reported. In this study, the whole-brain ALFF/fALFF in TW and all cisgender controls were calculated. The Childhood trauma experiences and clinical scale were assessed. We also examined the spatial correlations of ALFF/fALFF in TW with patterns of gene expression and neurotransmitter systems. We hypothesize that: i) Individuals with TW will exhibit abnormal ALFF/fALFF values compared to cisgender controls. ii) There is a correlation among ALFF/fALFF abnormality, childhood trauma and severity of TW. iii) The observed alterations in brain function are expected to exhibit spatial correlations with specific gene expression patterns and neurotransmitter systems in TW. Methods and Materials Participants TW patients were recruited from the First Affiliated Hospital of Jinan University, Guangzhou, China. Each patient met the criteria for GD as outlined in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (known as DSM-V). All patients were aged 18–55. Exclusion criteria included individuals with mental health disorders, neurological issues, intellectual impairments, disorders of sexual development, and alcohol or substance misuse. CM and CW participants were recruited via local advertisements. They were absence of GD and aged 18–55. Exclusion from the cisgender group was based on the presence of psychiatric disorders, a family history of such disorders, neurological issues, disorders of sexual development, or alcohol/substance abuse. During the last three months, the CW groups were not permitted to use hormonal contraceptives. The study excluded participants who had been treated with hormonal sex steroids apart from contraceptives. Approval for the study was granted by the ethics committee at the First Affiliated Hospital of Jinan University in Guangzhou, China. The study participants signed written informed consent forms after being given detailed information about the research both in writing and verbally. All participants were confirmed by experienced clinical psychiatrists and gynecologists to be capable of consenting to the study. Assessment of Mood Symptoms The Gender Identity/Gender Dysphoria Questionnaire for Adults and Adolescents (GIDYQ-AA) comprises 27 items, with responses ranging from 1 ("always") to 5 ("never"). Of these, 1, 13 and 27 were scored in reverse [ 28 ] . The total score of the GIDYQ-AA was derived by calculating the mean of all 27 items, with lower scores indicating higher levels of GD [ 29 ] . This scale is a self-rating scale. Participants were instructed to complete the questionnaires based on their personal experiences and genuine feelings, following standardized guidance provided by the researchers. Recently, the GIDYQ-AA has recently been validated as a psychometrically robust instrument, demonstrating strong reliability and validity, which has facilitated its broad application in international clinical studies on GD [30; 31] . Furthermore, every participant provided their sexual orientation using a Kinsey scale, which spans from 0 (exclusively opposite sex preference) to 6 (exclusively same sex preference). In particular, 0 indicates "entirely heterosexual"; 1 and 2 indicate "predominantly heterosexual"; 3 indicates "equally heterosexual and homosexual"; 4 and 5 indicate "predominantly homosexual"; 6 indicates "entirely homosexual". The Chinese version of the Childhood Trauma Questionnaire (CTQ) is widely used in childhood adversity research and is valued for its strong psychometric properties, including reliability, validity, and multidimensional assessment capability [ 32 ] . The scale comprises 3 validity items and 25 clinical items designed to evaluate five dimensions of maltreatment: physical abuse (PA), physical neglect (PN), emotional abuse (EA), emotional neglect (EN), and sexual abuse (SA) [ 33 ] . Every CTQ item is evaluated on a 5-point scale, with responses ranging from (never true) to (very often true) [34; 35] . Hamilton Depression Scale (HDRS-24), and Hamilton Anxiety Scale (HAMA) were used to assess mood. MRI Data Acquisition Data were collected using a GE Discovery MR750 3.0T system equipped with an 8-channel phased-array head coil (Supplemental Materials). Data processing The preprocessing of rs-fMRI data was conducted using DPABI (Ver 3.0) ( http://rfmri.org/dpabi ). The following procedures are part of the pipeline: removal of the first 10 time points, time layer correction, head movement correction, normalization, detrend, regression (head movement, white matter and cerebrospinal fluid signals). We also omitted images from the rs-fMRI analysis if head movement was more than 2.0 mm in any plane or if rotation was greater than 2.0°on any axis. The time series of each voxel were converted to the frequency domain using the fast Fourier transform (FFT). The amplitude spectrum was derived for each voxel by computing the square root of the power spectrum. The mean amplitude within the low-frequency range (0.01–0.1 Hz) was then calculated to define the ALFF [ 16 ] . For fALFF analysis, the power spectrum was also estimated via FFT. The fALFF value was computed as the ratio of the power within the low-frequency band (0.01–0.1 Hz) to that of the entire frequency range, thus representing the relative contribution of low-frequency oscillations to the total signal power. Notably, we applied z-standardization within the gray matter mask, and both ALFF and fALFF maps were smoothed using a Gaussian kernel of 6 mm full width at half maximum (FWHM). Brain gene expression data processing The AHBA database ( http://www.brain-map.org ) provided brain gene expression profiles for six deceased human donors [ 26 ] , featuring normalized microarray expression data for over 20,000 genes measured in 3,702 different brain tissue samples [ 36 ] . For the processing of brain gene expression data, the specific processing steps have been provided in previous publications [ 37 ] . Thus, the brain function metrics were processed to produce a final sample×gene matrix of 1137×10,028. Then, we defined a sphere of 3 mm radius centered on the MNI coordinates of this sample and extracted the mean t -value of voxels within the sphere from differential brain maps of cisgender controls and TW. Transcriptome-neuroimaging spatial association analysis Partial least squares (PLS) regression analysis was used to explore the relationship between transcriptional patterns and differences between groups in the main profiles [ 38 ] . The PLS component explains the permutation of variance between independent and dependent variables according to the weighted gene expression values, and its principal component provides an optimal low-dimensional view of the covariance within the high-dimensional data matrix [ 39 ] . The adjusted gene weights may indicate their individual contribution to the PLS regression component. The BrainSMASH toolkit was used to validate the correlation results of our brain and transcriptional profiles [ 40 ] . We might have created 5000 different maps to maintain the spatial autocorrelation of alterations in brain function. Ultimately, we determined the null distribution of the correlation between gene expression and alternative brain maps, which we then compared with our actual findings. Gene enrichment analysis We used the Gorilla tool ( http://cbl-gorilla.cs.technion.ac.il/ ) to perform gene enrichment analysis, covering both descending and ascending sequences, to pinpoint enriched Gene Ontology concepts [ 41 ] . The assessment included the ontology categories of biological process (BPs), molecular function (MFs), and cellular component (CCs). The importance of enrichment was assessed using the Benjamini-Hochberg method with a false discovery rate (FDR) correction, applying a q -value threshold of less than 0.05. Neurotransmitter distribution map The distribution of neurotransmitters across various brain regions was extracted using the JuSpace toolbox (version 1.5, accessible at https://github.com/juryxy/JuSpace , with default settings and computing option 3) [ 42 ] . Specifically, we examined the correlation between brain activity changes and the following neurotransmitters [ 42 ] : 1) Serotonin includes: 5-hydroxytryptamine 5-HT1a subtype (5HT1a), 5-hydroxytryptamine receptor 1b subtype (5HT1b), 5-hydroxytryptamine receptor 2a subtype (5HT2a), serotonin transporter (SERT); 2) Dopamine includes: Dopamine D1 (D1), dopamine D2 (D2), dopamine transporter (DAT), 6-fluoro-(18F)-L-3-4-dihydroxyphenylalanine (FDOPA); 3) Gamma-aminobutyric acid type a (GABAa). For all analyses, exact p-values were calculated using 10,000 permutations, and FDR correction was applied to account for the number of tests in each group comparison [ 42 ] . Statistical Analysis SPSS Statistics 22 software was used to analyze the data. The goodness-of-fit test was used to measure the normal distribution of all indicators. Data that did not conform to the normal distribution were expressed as median (quartile), and Mann-Whitney U test was used. Measurement data conforming to normal distribution were represented as mean ± SD and analyzed using the independent sample t test and one-way analysis of variance. Count data were expressed as frequency or percentage, and the χ 2 test was used. One-way analysis of variance (ANOVA) was performed to compare clinical scale scores and whole-brain ALFF/fALFF values across the three groups. For ALFF/fALFF analyses, multiple comparisons were corrected using the Gaussian Random Field (GRF) method with a voxel-level threshold of p < 0.005 and cluster-level threshold of p < 0.05 to identify regions exhibiting significant differences. Post hoc tests for both clinical scales and significant brain regions were conducted using Bonferroni correction, with a significance level set at p < 0.05. In the correlation analysis, partial correlation analysis was further performed on the clinical scale and the ALFF/fALFF index of brain function that had significant changes in the comparison between groups to explore the relationship between these factors. Age and years of education were included as nuisance covariates, and partial correlation analyses were performed separately for the TW and cisgender controls groups. After adjusting for potential confounders (age, sex, and years of education), multiple linear regression models were used to analyze the relationships among CTQ, clinical scale scores, and ALFF/fALFF values. The GIDYQ-AA score was specified as the dependent variable, while the CTQ total and subscale scores, as well as ALFF/fALFF values, were entered as independent variables in the regression model. The level of significance was set at p < 0.05. Results Participants A total of 44 TW patients (mean age = 23.66 years ± 6.46 years) were enrolled, while 40 CM (mean age = 24.05 years ± 5.83 years) and 42 CW (mean age = 22.43 years ± 2.77 years) participants were enrolled in the control group. No significant differences were observed in age among the three groups ( p > 0.05), but the education level of the patients was lower than that of the cisgender control group. One-way ANOVA revealed significant differences in GIDYQ-AA scores, CTQ total scores, and its subscale scores across the three groups. Post hoc comparisons with Bonferroni correction indicated that the TW group scored significantly higher on the GIDYQ-AA than the cisgender control groups. Conversely, the TW group demonstrated significantly lower scores on the CTQ total scale, as well as on the EN, EA, PN, and PA subscales, compared to the cisgender control groups. Additionally, the TW group reported significantly lower SA scores than the CM group. The demographic and clinical data of all participants were presented in Table 1 . Table 1 Demographics and clinical scales between TW, CM, and CW groups. Number of participants TW CM CW t/x 2 /F p 44 40 42 Age (years) 23.66 (6.46) 24.05 (5.83) 22.43 (2.77) 1.064 0.348 a Education (years) 14.09 (2.32) b 15.38 (2.45) 15.93 (2.39) 6.736 0.002 a,** GIDYQ-AA scores 2.14 (0.33) c 4.85 (0.20) 4.69 (0.24) 999.578 < 0.001 a,*** HAMD scores 6.71 (4.52) d 0.16 (0.59) 0.22 (0.52) 79.678 < 0.001 a,*** HAMA scores 6.00 (4.71) d 0.13 (0.47) 0.12 (0.40) 60.251 < 0.001 a,*** CTQ total scores 49.53 (14.87) d 34.00 (6.61) 34.36 (6.49) 32.042 < 0.001 a,*** PA scores 8.11 (3.61) d 5.65 (1.24) 5.73 (1.13) 15.308 < 0.001 a,*** PN scores 10.05 (3.87) d 6.78 (2.22) 7.18 (2.09) 16.491 < 0.001 a,*** EA scores 10.87 (4.70) d 6.62 (2.04) 6.76 (1.61) 25.269 < 0.001 a,*** EN scores 14.34 (4.61) d 9.68 (3.69) 9.21 (2.82) 24.074 < 0.001 a,*** SA scores 6.16 (2.56) e 5.27 (0.54) 5.41 (0.76) 3.805 0.025 a,* Mean (with standard deviations in parentheses) are reported unless otherwise noted. TW, transgender women; CM, cisgender men; CW, cisgender women; GIDYQ-AA, The gender identity/gender dysphoria questionnaire for adolescents and adults; HAMD, Hamilton Depression Rating Scale; HAMA, Hamilton Anxiety Rating Scale; PA, physical abuse; PN, physical neglect; EA, emotional abuse; EN, emotional neglect; SA, sexual abuse. * , p < 0.05; ** , p < 0.01; *** , p < 0.001. a The p values were obtained by one-way ANOVA. b Lower in TW compared to CM ( p = 0.045) and CW ( p = 0.002). c Lower in TW compared to CM ( p < 0.001) and CW ( p < 0.001). d Higher in TW compared to CM ( p < 0.001) and CW ( p < 0.001). e Higher in TW compared to CM ( p < 0.001) and CW ( p < 0.001). ALFF/ fALFF differences between TW, CM, and CW The ALFF values of the bilateral cerebellar lobule IX and the bilateral PCC were significantly different among the three groups by one-way ANOVA. Post hoc analysis revealed an increase in ALFF values in the bilateral cerebellar lobule IX in the TW group compared with the CM and CW groups. In addition, ALFF in the bilateral PCC was reduced in the TW group compared with the CM and CW groups. No significant differences in ALFF values were observed in the bilateral cerebellar lobule IX and the bilateral PCC between CW and CM groups (Fig. 1 a, Fig. 1 b and Table 2 ). There were significant differences in fALFF values of the right PCC among the three groups by one-way ANOVA. Post hoc analyses showed reduced fALFF values for the right PCC in the TW and CM groups compared with the CW group (Fig. 1 c and Table 2 ). Table 2 The significant ALFF and fALFF among the TW, CM, and CW groups. One-way ANOVA (voxel p value < 0.005; cluster p value < 0.05, GRF corrected) Post-hoc analysis (Bonferroni correction, p < 0.05) Significant regions Voxels BA MNI F Comparisons p X Y Z ALFF bilateral PCC 88 18 12 -48 30 17.247 TW < CM TW < CW 0.004 CM TW > CW < 0.001 0.007 fALFF right PCC 57 18 12 -48 30 22.437 TW < CW CM < CW < 0.001 < 0.001 ALFF, amplitude of low-frequency fluctuations; fALFF, fractional amplitude of low-frequency fluctuations; PCC, posterior cingulate cortex; TW, transgender women; CM, cisgender men; CW, cisgender women; GRF, Gaussian random field; BA, Brodmann Area; MNI, Montreal SNeurological coordinate. Correlation Analysis The GIDYQ-AA score was positively correlated with the ALFF values of the bilateral PCC in TW ( r = 0.368, p = 0.025) (Fig. 2 a). CTQ total scores were negatively correlated with the ALFF values of the bilateral PCC in TW ( r = -0.340, p = 0.034) (Fig. 2 b). The EA score in CTQ was negatively correlated with the ALFF value of the bilateral PCC in TW ( r = -0.329, p = 0.041) (Fig. 2 c). The EN score in the CTQ was negatively correlated with the ALFF value of the bilateral PCC in TW ( r = -0.356, p = 0.026) (Fig. 2 d). The EN score in the CTQ was negatively correlated with the fALFF value of the right PCC in TW ( r = -0.332, p = 0.039) (Fig. 2 e). Multiple regression analysis showed that CTQ total score × fALFF value of right PCC ( β = -0.07, t = -3.33, p = 0.003), EN score × fALFF value of the right PCC ( β = -0.18, t = -3.15, p = 0.004) were the independent predictors of GIDYQ-AA score in TW patients. No significant correlations or interactions were observed between the mood scales and ALFF/fALFF values in either CM or CW groups. Gene enrichment analysis The transcriptome-neuroimaging spatial correlation analysis showed that the first component of partial least squares (PLS-1) regression analysis explained 16.11% of the variance of ALFF bias in TW ( r = 0.305, p < 0.001). The outcomes of the gene functional enrichment analyses are presented in Table 3 . The enriched genes of functional changes in TW were associated with the nuclear-transcribed mRNA catabolic process, nonsense-mediated decay and SRP-dependent co-translational protein targeting to the membrane in BPs (Fig. 3 a); The enriched genes were uniquely associated with synapses in CCs (Fig. 3 b). Table 3 Functional enrichment results of the genes related to brain changes in TW and cisgender controls Category ID Name P value q value (FDR-BH correction) GO: Biological Process GO:0006695 cholesterol biosynthetic process 9.11E-06 1.33E-01 GO: Biological Process GO:1902653 secondary alcohol biosynthetic process 9.11E-06 6.64E-02 GO: Molecular Function GO:0005179 hormone activity 6.92E-06 3.02E-02 TW, transgender women; GO, gene ontology; FDR-BH, Benjamini and Hochberg false discovery. Neurotransmitter analysis The ALFF differences between TW and cisgender controls were significantly correlated with 5HT1b (rho = -0.43, p < 0.001), 5HT2a (rho = -0.20, p < 0.001), SERT (rho = − 0.27, p < 0.001) in serotonin and D1 (rho = − 0.42, p < 0.001), D2 (rho = − 0.33, p 0.05). Discussion This study represents the first investigation into the complex associations among GD severity, childhood trauma, and resting-state brain function in Chinese TW. Utilizing ALFF/fALFF analyses, it reveals specific neural alterations and provides new insights into the neurodevelopmental mechanisms linking early adverse experiences to altered brain activity. The main results of this study are summarized as follows: i) Compared with cisgender controls, the TW group had increased ALFF values in cerebellar lobule IX and decreased ALFF/fALFF values in the PCC. ii) The GIDYQ-AA scale of TW patients was positively correlated with the ALFF value of the bilateral PCC. The CTQ total score and its subscore of TW patients were negatively correlated with the ALFF/fALFF value in TW. CTQ scores and fALFF in the right PCC regions together impact the GIDYQ-AA score in the TW. iii) Genes enriched with altered functions in TW were associated with the nuclear transcribed mRNA, nonsense-mediated decay and SRP-dependent cotranslational protein targeting to membrane in BPs. Conversely, these enriched genes were linked to synapse-associated functions in CCs. Differences in ALFF between TW and cisgender controls were significantly correlated with serotonin and dopamine transmitters. The PCC is one of the most metabolically active regions of the brain and a central part of the default mode network (DMN). It is related to self-perception and self-referential thinking, and also has the function of regulating attention concentration and episodic memory retrieval [43; 44] . Our study found the lower ALFF values in the bilateral PCC in the TW group than that in the cisgender control group. The fALFF values of the right PCC in TW and CM groups were lower than those in CW group. And the positive correlation between GIDYQ-AA scores and ALFF values in the bilateral PCC in TW group. Previous volumetric (GVM) studies in healthy populations have found that the overall grey matter volume of the PCC is larger in females than in males [ 45 ] . Simultaneously, a meta-analysis found that the density of neural functional activation in this region during emotional processing is also higher in females [ 46 ] . A diffusion tensor imaging (DTI) study revealed differences in fractional anisotropy (FA) within the cingulum bundle of TW compared to CW, suggesting a feminization of this white matter tract in TW [ 47 ] . The prior VBM studies identified reduced gray matter volume in the PCC among TW relative to cisgender controls, indicating that a larger posterior midline structure might facilitate heightened sensitivity to self-referential processing by altering visual self-perception in TW [9; 48] . A rs-FC analysis within the DMN revealed that TW exhibited reduced FC between the PCC and the precuneus compared to cisgender controls, reflecting that altered self-body perception may influence brain network organization in this population [ 49 ] . A task-based fMRI study on face recognition observed activation in the PCC, indicating its involvement in self-referential processing, while also suggesting that the bodily sexual dimorphism in TW may contribute to their distress [ 50 ] . Therefore, the PCC is involved in self-referential processing in individuals with TW and may represent a neuropathological basis for the experience of incongruence between gender identity and biological sex. Our study found that the TW group exhibited significantly lower scores on both the CTQ total and its subscales compared to the cisgender control groups. We found significantly negative correlations between CTQ score (total, EA, and EN) and ALFF in the bilateral PCC were identified in TW group. And EN subscore of the CTQ was negatively correlated with fALFF values in the right PCC among TW individuals. In addition, our findings indicated that fALFF values in the PCC collectively influence the severity of GD, together with the CTQ total score and the EN subscale. Abuse, which refers to criminal behavior involving harm or threats of harm, is associated with alterations in regions that support emotional processing; Neglect refers to acts of omission involving deprivation and is associated with changes in supporting cognitive processing [51; 52] . A cognitive reappraisal task study using the International Affective Picture System (IAPS) found that individuals with a history of childhood trauma showed activation in the PCC during the task, suggesting that early trauma impairs self-referential cognitive reappraisal capacity [ 53 ] . Our results did not reveal a significant correlation between CTQ scores and GD severity, speculating that the influence of childhood trauma on gender dysphoria may not follow a simple linear relationship. In conclusion, the findings speculate that CTQ and altered the PCC activity interact to shape the neurobiological mechanisms underlying GD, collectively driving its development and clinical presentation. From a clinical perspective, these findings highlight childhood trauma as an important component of an early intervention strategy to alleviate the psychological and emotional difficulties of TW individuals. We observed a significant elevation in ALFF within the cerebellar lobule IX of TW group. The cerebellum is not only involved in self-referential processing and executive function, but also associated with gender identity [8; 54; 55; 56] . VBM studies have demonstrated alterations in cerebellar gray matter volume in TW compared to cisgender controls [9; 57] . During a cognitively demanding task involving sexual dimorphism, TW exhibited a distinct cerebellar activation pattern compared to cisgender male controls, suggesting a tendency toward a female-typical processing mode in this region [ 58 ] . A previous rs-fMRI study demonstrated significantly enhanced FC in the right cerebellum within the visual network in transgender girls compared to both cisgender control, revealing a unique GD-specific cerebellar connectivity pattern and suggesting potential alterations in visual system processing related to body perception in individuals with GD [ 59 ] . Collectively, the cerebellum is involved in visual body perception, and its alterations point to a role in the neurobiological basis of gender identity development. Gene enrichment analysis revealed that genes associated with altered neural function in TW were enriched in several key BPs, including nuclear transcribed mRNA catabolic process, nonsense-mediated decay (NMD) and signal recognition particle (SRP)-dependent cotranslational protein targeting to membrane. In terms of CCs, this gene set was uniquely enriched for synaptic components. Furthermore, the abnormal functional activities in TW show a spatial overlap with the distribution of key neurotransmitter systems, including the serotonergic and dopaminergic pathways. NMD is an important cellular mechanism that helps regulate gene expression in nerve cells. Dysfunction of NMD can lead to neurodevelopmental disorders. [60; 61] . The loss of core NMD factors, such as UPF2, can directly impairs the development of the male reproductive system [62; 63] . SRP-dependent co-translational protein targeting to the membrane is the primary translation product of mRNA [ 64 ] . Earlier researches shown that m6A modification and miRNA activity work together to precisely control sexual development by influencing how key sex-determining genes are expressed [65; 66] . A study in mice found that the production and development of new nerve cells, which is guided by gene activity in interplay between hormones and sex chromosomes, plays a role in how biological sex differences are established [ 67 ] . Previous basic research using Caenorhabditis elegans has shown that sexual differentiation is linked to synaptic pruning, which plays a critical role in shaping neural circuits into distinct gender phenotypes [ 68 ] . The serotonergic system contributes to brain sexual differentiation in teleosts via its specific expression within the preoptic-hypothalamic region of the hypothalamic-pituitary axis [ 69 ] . Previous reviews have indicated that sex-specific dopaminergic alterations during critical periods of brain development play a critical role in the sexual differentiation of neural circuits [70; 71] . Synaptic and mRNA-related genes, along with serotonin and dopamine receptor neurotransmission, contribute to altered brain functional activity in TW, implicating their role in the neurobiology of gender identity. Limitations Our study has several limitations. Firstly, the sample sizes of the TW groups were relatively small, which may limit the statistical power and robustness of the findings. Future studies with larger cohorts are needed to validate and replicate these results. Secondly, as this investigation focused exclusively on Chinese individuals with GD, the generalizability of the results to other cultural and demographic populations may be constrained. Thirdly, due to its cross-sectional design, this study possesses an inherent limitation, as it cannot establish causality and can only be used to generate hypotheses. Finally, the AHBA's gene expression data, obtained from a limited sample of six postmortem brains, may constrain the generalizability of findings due to inherent inter-individual variability and differences in donor characteristics, potentially introducing confounding factors that are challenging to fully address. Conclusions In conclusion, we found that TW have altered neural activity in the PCC and cerebellum. Interactions between childhood trauma and reduced PCC function collectively contribute to the severity of GD. In addition, the presence of unique neuromodulation patterns in TW is further supported by the disorder of genetic and neurotransmitter systems. Declarations Conflict of Interest The authors declare that they have no competing interests. Author Contribution Ying Wang designed the study; Ruoyi Chen, Guanmao Chen, Shilin Sun and Ying Wang contributed to data sources and study selection; Ruoyi Chen, Guanmao Chen, Shilin Sun, Pan Chen, Zixuan Guo, Mengqi Liao, Chao Chen, Xinyue Tang, Zhangzhang Qi, Jing Li, Xinquan Wu, Xiaoying Zhang, Junjun Liang and Shuming Zhong contributed to data acquisition; Ruoyi Chen, Guanmao Chen and Shilin Sun contributed to data analysis; Ruoyi Chen and Guanmao Chen wrote the manuscript; Ruoyi Chen, Guanmao Chen, and Ying Wang revised the manuscript. All authors contributed and approved the final manuscript. Acknowledgement The study was supported by grants from the National Natural Science Foundation of China (82502506 and 82472057); Guangdong Basic and Applied Basic Research Foundation, China (2023A1515110582 and 2024A1515220106); China Postdoctoral Science Foundation (2023M741381). 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14:14:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8978712/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8978712/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105259615,"identity":"7d88bfaa-96d2-4ef9-8d75-f4402967aef9","added_by":"auto","created_at":"2026-03-24 05:52:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1124656,"visible":true,"origin":"","legend":"\u003cp\u003eGroup differences in ALFF/fALFF among TW, CM, and CW, with post-hoc comparisons showing significant regional variations. ALFF, amplitude of low-frequency fluctuation; fALFF, fractional amplitude of low-frequency fluctuation; TW, transgender women; CM, cisgender men; CW, cisgender women; PCC, posterior cingulate cortex.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8978712/v1/bf0f388cc55821b4d7ddfc0d.png"},{"id":105564778,"identity":"b93a5474-595c-43b4-9cb7-7682691b95e1","added_by":"auto","created_at":"2026-03-27 12:50:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":328589,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations of ALFF/fALFF with CTQ total and subscale scores, and with GIDYQ-AA scores. GIDYQ-AA, Gender Identity/Gender Dysphoria Questionnaire for Adolescents and Adults; CTQ, Childhood Trauma Questionnaire; ALFF, amplitude of low-frequency fluctuation; fALFF, fractional amplitude of low-frequency fluctuation; PCC, posterior cingulate cortex.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8978712/v1/df07e3c85cab28a4755143d1.png"},{"id":105564240,"identity":"e5466d88-3fa1-4a03-83d4-339aaf9daf76","added_by":"auto","created_at":"2026-03-27 12:49:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":684536,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial enrichment analysis of TW-related functional alterations (color-coded by enrichment p-value) showing correlated brain changes and neurotransmitter distribution patterns in patients versus cisgender controls. (a) BPs, biological process, (b) CCs, cellular component, (c) The PLS-1 identified a profile of genes that were positively correlated with ALFF differences. (d) Spatial correlations between Spatial correlations between ALFF alterations and neurotransmitter distribution maps in GD compared to cisgender controls. 5HT1a, 5-hydroxytryptamine receptor subtype 1a; 5HT1b, 5-hydroxytryptamine subtype 1b; 5HT2a, 5-hydroxytryptamine subtype 2a; D1, dopamine D1; D2, dopamine D2; DAT, dopamine transporter; FDOPA, fluorodopa; GABAa, gamma-aminobutric acid type a; SERT, serotonin transporter; TW, transgender women; ALFF, amplitude of low-frequency fluctuation; PLS-1, first component of partial least squares component; *, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01; ***, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8978712/v1/5653b9053ffb6fd7a38183a2.png"},{"id":105569118,"identity":"2082c054-7edb-4670-8546-cb9d21f1a887","added_by":"auto","created_at":"2026-03-27 13:11:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3004266,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8978712/v1/b34eb053-25ce-4d58-8529-6ec16209e095.pdf"},{"id":105259618,"identity":"4a3198d7-1460-44f1-bac5-ab5fb86898f5","added_by":"auto","created_at":"2026-03-24 05:52:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12766,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8978712/v1/90a5f50fbfafc68434c7bb38.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationships between altered brain activity, childhood trauma, neurotransmitter, and genetic traits in Chinese early adulthood with gender dysphoria","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGender dysphoria (GD) is a psychiatric condition defined by a significant discrepancy between an individual's assigned sex at birth and their experienced or self-identified gender, accompanied by clinically notable distress\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. In the past two decades, the number of clinical visits to GD has steadily increased, and the proportion of transgender women (TW) is higher than that of transgender men (TM)\u003csup\u003e[2; 3]\u003c/sup\u003e. Cisgender is used to refer to a gender identity that matches the sex assigned at birth\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Compared to their cisgender peers, individuals with GD carry a heavier burden of mental health challenges, including higher rates of substance abuse and suicide attempts\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. A neurobiological theory proposes that gender identity is established by structural and functional brain differences shaped during prenatal development by hormonal, genetic, and epigenetic mechanisms\u003csup\u003e[6; 7]\u003c/sup\u003e. Despite existing theoretical frameworks, the neurobiological mechanisms underlying GD remain elusive, and the pathogenesis of this condition within the Chinese population remains entirely unexplored.\u003c/p\u003e \u003cp\u003ePrevious neuroimaging studies have documented structural brain alterations in individuals with GD. Previous findings indicate that voxel-based morphometry (VBM) has revealed increased grey matter volume in the right posterior cingulate cortex in the adolescent TW compared to cisgender women (CW), and reduced bilateral cerebellar grey matter volume in TW compared to cisgender men (CM)\u003csup\u003e[8; 9]\u003c/sup\u003e. However, functional investigations, particularly those examining intrinsic brain activity using resting-state fMRI (rs-fMRI), remain underexplored in this population. Rs-fMRI is a crucial, sophisticated, non-invasive neuroimaging tool for examining changes in brain function\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Previous seed-based resting-state functional connectivity (rs-FC) studies have demonstrated that TW exhibit altered FC between the ventral tegmental area and cingulate cortex, and between the posterior cingulate cortex (PCC) and dorsolateral prefrontal cortex (DLPFC), compared to cisgender control\u003csup\u003e[11; 12]\u003c/sup\u003e. Studies relying on seed-based FC analysis may overlook information from brain regions not selected as regions of interest (ROIs), and these omitted areas could play a significant role in the pathophysiology of GD\u003csup\u003e[13; 14]\u003c/sup\u003e. Amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) are whole-brain, hypothesis-free methods for measuring intrinsic brain activity\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Recently, ALFF/fALFF have emerged as dependable and effective neuroimaging biomarkers for quantifying resting-state local brain activity in vivo in individuals with various neuropsychiatric disorders\u003csup\u003e[13; 16; 17]\u003c/sup\u003e. However, the application of these methods in the TW patients remains unexplored.\u003c/p\u003e \u003cp\u003eChildhood trauma refers to the experience of severe abuse, neglect, or exploitation during childhood, which adversely affects an individual's health, developmental processes, and psychological well-being\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Individuals experiencing GD frequently endure significantly elevated psychological distress due to stigma, prejudice, and discrimination, leading to a hostile and stressful social atmosphere\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Childhood trauma, particularly emotional neglect and abuse, represents a significantly higher burden among individuals with GD compared to cisgender controls\u003csup\u003e[20; 21]\u003c/sup\u003e. However, the specific link between such childhood trauma and alterations in brain function in GD remains unclear. To date, the relationship between childhood trauma, alterations in brain functional activity, and the severity of TW remains unexplored.\u003c/p\u003e \u003cp\u003eInvestigating the spatial link between regional brain features and neurotransmitter systems can bridge neuroimaging and nuclear imaging data to advance our understanding of disease mechanisms\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Dysregulation of key neurotransmitters is frequently associated with a range of neuropsychiatric disorders, including major depression\u003csup\u003e[22; 23]\u003c/sup\u003e, schizophrenia\u003csup\u003e[22; 24]\u003c/sup\u003e, and bipolar disorder\u003csup\u003e[22; 25]\u003c/sup\u003e. Developments in neuroimaging transcriptomes facilitate the formation of spatial correlations between microscale disorder-associated whole brain gene expression data from the Allen Human Brain Atlas (AHBA) and macroscale neuroimaging\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. As an emerging field, neuroimaging transcriptomics is focused on identifying genes with spatial profiles of regional expression and tracking anatomical variation in specific neuroimaging biomarkers\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Nonetheless, the abnormal neurotransmitter and gene expression in the GD brain has not been reported.\u003c/p\u003e \u003cp\u003eIn this study, the whole-brain ALFF/fALFF in TW and all cisgender controls were calculated. The Childhood trauma experiences and clinical scale were assessed. We also examined the spatial correlations of ALFF/fALFF in TW with patterns of gene expression and neurotransmitter systems. We hypothesize that: i) Individuals with TW will exhibit abnormal ALFF/fALFF values compared to cisgender controls. ii) There is a correlation among ALFF/fALFF abnormality, childhood trauma and severity of TW. iii) The observed alterations in brain function are expected to exhibit spatial correlations with specific gene expression patterns and neurotransmitter systems in TW.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eTW patients were recruited from the First Affiliated Hospital of Jinan University, Guangzhou, China. Each patient met the criteria for GD as outlined in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (known as DSM-V). All patients were aged 18\u0026ndash;55. Exclusion criteria included individuals with mental health disorders, neurological issues, intellectual impairments, disorders of sexual development, and alcohol or substance misuse.\u003c/p\u003e \u003cp\u003eCM and CW participants were recruited via local advertisements. They were absence of GD and aged 18\u0026ndash;55. Exclusion from the cisgender group was based on the presence of psychiatric disorders, a family history of such disorders, neurological issues, disorders of sexual development, or alcohol/substance abuse. During the last three months, the CW groups were not permitted to use hormonal contraceptives. The study excluded participants who had been treated with hormonal sex steroids apart from contraceptives.\u003c/p\u003e \u003cp\u003e Approval for the study was granted by the ethics committee at the First Affiliated Hospital of Jinan University in Guangzhou, China. The study participants signed written informed consent forms after being given detailed information about the research both in writing and verbally. All participants were confirmed by experienced clinical psychiatrists and gynecologists to be capable of consenting to the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment of Mood Symptoms\u003c/h3\u003e\n\u003cp\u003eThe Gender Identity/Gender Dysphoria Questionnaire for Adults and Adolescents (GIDYQ-AA) comprises 27 items, with responses ranging from 1 (\"always\") to 5 (\"never\"). Of these, 1, 13 and 27 were scored in reverse\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. The total score of the GIDYQ-AA was derived by calculating the mean of all 27 items, with lower scores indicating higher levels of GD\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. This scale is a self-rating scale. Participants were instructed to complete the questionnaires based on their personal experiences and genuine feelings, following standardized guidance provided by the researchers. Recently, the GIDYQ-AA has recently been validated as a psychometrically robust instrument, demonstrating strong reliability and validity, which has facilitated its broad application in international clinical studies on GD\u003csup\u003e[30; 31]\u003c/sup\u003e. Furthermore, every participant provided their sexual orientation using a Kinsey scale, which spans from 0 (exclusively opposite sex preference) to 6 (exclusively same sex preference). In particular, 0 indicates \"entirely heterosexual\"; 1 and 2 indicate \"predominantly heterosexual\"; 3 indicates \"equally heterosexual and homosexual\"; 4 and 5 indicate \"predominantly homosexual\"; 6 indicates \"entirely homosexual\". The Chinese version of the Childhood Trauma Questionnaire (CTQ) is widely used in childhood adversity research and is valued for its strong psychometric properties, including reliability, validity, and multidimensional assessment capability\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. The scale comprises 3 validity items and 25 clinical items designed to evaluate five dimensions of maltreatment: physical abuse (PA), physical neglect (PN), emotional abuse (EA), emotional neglect (EN), and sexual abuse (SA)\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Every CTQ item is evaluated on a 5-point scale, with responses ranging from (never true) to (very often true)\u003csup\u003e[34; 35]\u003c/sup\u003e. Hamilton Depression Scale (HDRS-24), and Hamilton Anxiety Scale (HAMA) were used to assess mood.\u003c/p\u003e\n\u003ch3\u003eMRI Data Acquisition\u003c/h3\u003e\n\u003cp\u003eData were collected using a GE Discovery MR750 3.0T system equipped with an 8-channel phased-array head coil (Supplemental Materials).\u003c/p\u003e\n\u003ch3\u003eData processing\u003c/h3\u003e\n\u003cp\u003eThe preprocessing of rs-fMRI data was conducted using DPABI (Ver 3.0) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rfmri.org/dpabi\u003c/span\u003e\u003cspan address=\"http://rfmri.org/dpabi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The following procedures are part of the pipeline: removal of the first 10 time points, time layer correction, head movement correction, normalization, detrend, regression (head movement, white matter and cerebrospinal fluid signals). We also omitted images from the rs-fMRI analysis if head movement was more than 2.0 mm in any plane or if rotation was greater than 2.0\u0026deg;on any axis. The time series of each voxel were converted to the frequency domain using the fast Fourier transform (FFT). The amplitude spectrum was derived for each voxel by computing the square root of the power spectrum. The mean amplitude within the low-frequency range (0.01\u0026ndash;0.1 Hz) was then calculated to define the ALFF\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. For fALFF analysis, the power spectrum was also estimated via FFT. The fALFF value was computed as the ratio of the power within the low-frequency band (0.01\u0026ndash;0.1 Hz) to that of the entire frequency range, thus representing the relative contribution of low-frequency oscillations to the total signal power. Notably, we applied z-standardization within the gray matter mask, and both ALFF and fALFF maps were smoothed using a Gaussian kernel of 6 mm full width at half maximum (FWHM).\u003c/p\u003e\n\u003ch3\u003eBrain gene expression data processing\u003c/h3\u003e\n\u003cp\u003eThe AHBA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.brain-map.org\u003c/span\u003e\u003cspan address=\"http://www.brain-map.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) provided brain gene expression profiles for six deceased human donors\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e, featuring normalized microarray expression data for over 20,000 genes measured in 3,702 different brain tissue samples\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. For the processing of brain gene expression data, the specific processing steps have been provided in previous publications\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Thus, the brain function metrics were processed to produce a final sample\u0026times;gene matrix of 1137\u0026times;10,028. Then, we defined a sphere of 3 mm radius centered on the MNI coordinates of this sample and extracted the mean \u003cem\u003et\u003c/em\u003e-value of voxels within the sphere from differential brain maps of cisgender controls and TW.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptome-neuroimaging spatial association analysis\u003c/h2\u003e \u003cp\u003ePartial least squares (PLS) regression analysis was used to explore the relationship between transcriptional patterns and differences between groups in the main profiles\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. The PLS component explains the permutation of variance between independent and dependent variables according to the weighted gene expression values, and its principal component provides an optimal low-dimensional view of the covariance within the high-dimensional data matrix\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. The adjusted gene weights may indicate their individual contribution to the PLS regression component. The BrainSMASH toolkit was used to validate the correlation results of our brain and transcriptional profiles\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. We might have created 5000 different maps to maintain the spatial autocorrelation of alterations in brain function. Ultimately, we determined the null distribution of the correlation between gene expression and alternative brain maps, which we then compared with our actual findings.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGene enrichment analysis\u003c/h3\u003e\n\u003cp\u003eWe used the Gorilla tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cbl-gorilla.cs.technion.ac.il/\u003c/span\u003e\u003cspan address=\"http://cbl-gorilla.cs.technion.ac.il/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to perform gene enrichment analysis, covering both descending and ascending sequences, to pinpoint enriched Gene Ontology concepts\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. The assessment included the ontology categories of biological process (BPs), molecular function (MFs), and cellular component (CCs). The importance of enrichment was assessed using the Benjamini-Hochberg method with a false discovery rate (FDR) correction, applying a \u003cem\u003eq\u003c/em\u003e-value threshold of less than 0.05.\u003c/p\u003e\n\u003ch3\u003eNeurotransmitter distribution map\u003c/h3\u003e\n\u003cp\u003eThe distribution of neurotransmitters across various brain regions was extracted using the JuSpace toolbox (version 1.5, accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/juryxy/JuSpace\u003c/span\u003e\u003cspan address=\"https://github.com/juryxy/JuSpace\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, with default settings and computing option 3)\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Specifically, we examined the correlation between brain activity changes and the following neurotransmitters\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e: 1) Serotonin includes: 5-hydroxytryptamine 5-HT1a subtype (5HT1a), 5-hydroxytryptamine receptor 1b subtype (5HT1b), 5-hydroxytryptamine receptor 2a subtype (5HT2a), serotonin transporter (SERT); 2) Dopamine includes: Dopamine D1 (D1), dopamine D2 (D2), dopamine transporter (DAT), 6-fluoro-(18F)-L-3-4-dihydroxyphenylalanine (FDOPA); 3) Gamma-aminobutyric acid type a (GABAa). For all analyses, exact p-values were calculated using 10,000 permutations, and FDR correction was applied to account for the number of tests in each group comparison\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eSPSS Statistics 22 software was used to analyze the data. The goodness-of-fit test was used to measure the normal distribution of all indicators. Data that did not conform to the normal distribution were expressed as median (quartile), and Mann-Whitney U test was used. Measurement data conforming to normal distribution were represented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and analyzed using the independent sample t test and one-way analysis of variance. Count data were expressed as frequency or percentage, and the χ\u003csup\u003e2\u003c/sup\u003e test was used. One-way analysis of variance (ANOVA) was performed to compare clinical scale scores and whole-brain ALFF/fALFF values across the three groups. For ALFF/fALFF analyses, multiple comparisons were corrected using the Gaussian Random Field (GRF) method with a voxel-level threshold of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.005 and cluster-level threshold of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 to identify regions exhibiting significant differences. Post hoc tests for both clinical scales and significant brain regions were conducted using Bonferroni correction, with a significance level set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eIn the correlation analysis, partial correlation analysis was further performed on the clinical scale and the ALFF/fALFF index of brain function that had significant changes in the comparison between groups to explore the relationship between these factors. Age and years of education were included as nuisance covariates, and partial correlation analyses were performed separately for the TW and cisgender controls groups.\u003c/p\u003e \u003cp\u003eAfter adjusting for potential confounders (age, sex, and years of education), multiple linear regression models were used to analyze the relationships among CTQ, clinical scale scores, and ALFF/fALFF values. The GIDYQ-AA score was specified as the dependent variable, while the CTQ total and subscale scores, as well as ALFF/fALFF values, were entered as independent variables in the regression model. The level of significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eA total of 44 TW patients (mean age\u0026thinsp;=\u0026thinsp;23.66 years\u0026thinsp;\u0026plusmn;\u0026thinsp;6.46 years) were enrolled, while 40 CM (mean age\u0026thinsp;=\u0026thinsp;24.05 years\u0026thinsp;\u0026plusmn;\u0026thinsp;5.83 years) and 42 CW (mean age\u0026thinsp;=\u0026thinsp;22.43 years\u0026thinsp;\u0026plusmn;\u0026thinsp;2.77 years) participants were enrolled in the control group. No significant differences were observed in age among the three groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), but the education level of the patients was lower than that of the cisgender control group. One-way ANOVA revealed significant differences in GIDYQ-AA scores, CTQ total scores, and its subscale scores across the three groups. Post hoc comparisons with Bonferroni correction indicated that the TW group scored significantly higher on the GIDYQ-AA than the cisgender control groups. Conversely, the TW group demonstrated significantly lower scores on the CTQ total scale, as well as on the EN, EA, PN, and PA subscales, compared to the cisgender control groups. Additionally, the TW group reported significantly lower SA scores than the CM group. The demographic and clinical data of all participants were presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographics and clinical scales between TW, CM, and CW groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of participants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et/x\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/F\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.66 (6.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.05 (5.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.43 (2.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.348\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.09 (2.32)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.38 (2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.93 (2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003csup\u003ea,**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIDYQ-AA scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.14 (0.33)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.85 (0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.69 (0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e999.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ea,***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAMD scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.71 (4.52)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16 (0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.22 (0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e79.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ea,***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAMA scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.00 (4.71)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13 (0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12 (0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ea,***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTQ total scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.53 (14.87)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.00 (6.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.36 (6.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ea,***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePA scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.11 (3.61)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.65 (1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.73 (1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ea,***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePN scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.05 (3.87)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.78 (2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.18 (2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ea,***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEA scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.87 (4.70)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.62 (2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.76 (1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ea,***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEN scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.34 (4.61)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.68 (3.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.21 (2.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ea,***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSA scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.16 (2.56)\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.27 (0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.41 (0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003csup\u003ea,*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eMean (with standard deviations in parentheses) are reported unless otherwise noted. TW, transgender women; CM, cisgender men; CW, cisgender women; GIDYQ-AA, The gender identity/gender dysphoria questionnaire for adolescents and adults; HAMD, Hamilton Depression Rating Scale; HAMA, Hamilton Anxiety Rating Scale; PA, physical abuse; PN, physical neglect; EA, emotional abuse; EN, emotional neglect; SA, sexual abuse. \u003csup\u003e*\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; \u003csup\u003e**\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u003csup\u003e***\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e The \u003cem\u003ep\u003c/em\u003e values were obtained by one-way ANOVA.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003eb\u003c/sup\u003e Lower in TW compared to CM (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045) and CW (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ec\u003c/sup\u003e Lower in TW compared to CM (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CW (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ed\u003c/sup\u003e Higher in TW compared to CM (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CW (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ee\u003c/sup\u003e Higher in TW compared to CM (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CW (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eALFF/ fALFF differences between TW, CM, and CW\u003c/h2\u003e \u003cp\u003eThe ALFF values of the bilateral cerebellar lobule IX and the bilateral PCC were significantly different among the three groups by one-way ANOVA. Post hoc analysis revealed an increase in ALFF values in the bilateral cerebellar lobule IX in the TW group compared with the CM and CW groups. In addition, ALFF in the bilateral PCC was reduced in the TW group compared with the CM and CW groups. No significant differences in ALFF values were observed in the bilateral cerebellar lobule IX and the bilateral PCC between CW and CM groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). There were significant differences in fALFF values of the right PCC among the three groups by one-way ANOVA. Post hoc analyses showed reduced fALFF values for the right PCC in the TW and CM groups compared with the CW group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe significant ALFF and fALFF among the TW, CM, and CW groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eOne-way ANOVA (voxel \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.005; cluster \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, GRF corrected)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003ePost-hoc analysis (Bonferroni correction, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignificant regions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVoxels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eMNI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eComparisons\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALFF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebilateral PCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTW\u0026thinsp;\u0026lt;\u0026thinsp;CM\u003c/p\u003e \u003cp\u003eTW\u0026thinsp;\u0026lt;\u0026thinsp;CW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebilateral cerebellar lobule IX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTW\u0026thinsp;\u0026gt;\u0026thinsp;CM\u003c/p\u003e \u003cp\u003eTW\u0026thinsp;\u0026gt;\u0026thinsp;CW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003efALFF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eright PCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTW\u0026thinsp;\u0026lt;\u0026thinsp;CW\u003c/p\u003e \u003cp\u003eCM\u0026thinsp;\u0026lt;\u0026thinsp;CW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eALFF, amplitude of low-frequency fluctuations; fALFF, fractional amplitude of low-frequency fluctuations; PCC, posterior cingulate cortex; TW, transgender women; CM, cisgender men; CW, cisgender women; GRF, Gaussian random field; BA, Brodmann Area; MNI, Montreal SNeurological coordinate.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Analysis\u003c/h2\u003e \u003cp\u003eThe GIDYQ-AA score was positively correlated with the ALFF values of the bilateral PCC in TW (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.368, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). CTQ total scores were negatively correlated with the ALFF values of the bilateral PCC in TW (\u003cem\u003er\u003c/em\u003e = -0.340, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The EA score in CTQ was negatively correlated with the ALFF value of the bilateral PCC in TW (\u003cem\u003er\u003c/em\u003e = -0.329, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The EN score in the CTQ was negatively correlated with the ALFF value of the bilateral PCC in TW (\u003cem\u003er\u003c/em\u003e = -0.356, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). The EN score in the CTQ was negatively correlated with the fALFF value of the right PCC in TW (\u003cem\u003er\u003c/em\u003e = -0.332, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMultiple regression analysis showed that CTQ total score \u0026times; fALFF value of right PCC (\u003cem\u003eβ\u003c/em\u003e = -0.07, \u003cem\u003et\u003c/em\u003e = -3.33, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), EN score \u0026times; fALFF value of the right PCC (\u003cem\u003eβ\u003c/em\u003e = -0.18, \u003cem\u003et\u003c/em\u003e = -3.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) were the independent predictors of GIDYQ-AA score in TW patients. No significant correlations or interactions were observed between the mood scales and ALFF/fALFF values in either CM or CW groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGene enrichment analysis\u003c/h2\u003e \u003cp\u003eThe transcriptome-neuroimaging spatial correlation analysis showed that the first component of partial least squares (PLS-1) regression analysis explained 16.11% of the variance of ALFF bias in TW (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.305, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The outcomes of the gene functional enrichment analyses are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The enriched genes of functional changes in TW were associated with the nuclear-transcribed mRNA catabolic process, nonsense-mediated decay and SRP-dependent co-translational protein targeting to the membrane in BPs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea); The enriched genes were uniquely associated with synapses in CCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFunctional enrichment results of the genes related to brain changes in TW and cisgender controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eq value (FDR-BH correction)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO: Biological Process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0006695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echolesterol biosynthetic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.11E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO: Biological Process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:1902653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esecondary alcohol biosynthetic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.11E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.64E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO: Molecular Function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0005179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehormone activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.92E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.02E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eTW, transgender women; GO, gene ontology; FDR-BH, Benjamini and Hochberg false discovery.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eNeurotransmitter analysis\u003c/h2\u003e \u003cp\u003eThe ALFF differences between TW and cisgender controls were significantly correlated with 5HT1b (rho = -0.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 5HT2a (rho = -0.20, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SERT (rho\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.27, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in serotonin and D1 (rho\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), D2 (rho\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.33, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and FDOPA (rho\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.25, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) in dopamine (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). DAT and GABA showed no correlation with the ALFF values in the group-differentiated brain regions (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study represents the first investigation into the complex associations among GD severity, childhood trauma, and resting-state brain function in Chinese TW. Utilizing ALFF/fALFF analyses, it reveals specific neural alterations and provides new insights into the neurodevelopmental mechanisms linking early adverse experiences to altered brain activity. The main results of this study are summarized as follows: i) Compared with cisgender controls, the TW group had increased ALFF values in cerebellar lobule IX and decreased ALFF/fALFF values in the PCC. ii) The GIDYQ-AA scale of TW patients was positively correlated with the ALFF value of the bilateral PCC. The CTQ total score and its subscore of TW patients were negatively correlated with the ALFF/fALFF value in TW. CTQ scores and fALFF in the right PCC regions together impact the GIDYQ-AA score in the TW. iii) Genes enriched with altered functions in TW were associated with the nuclear transcribed mRNA, nonsense-mediated decay and SRP-dependent cotranslational protein targeting to membrane in BPs. Conversely, these enriched genes were linked to synapse-associated functions in CCs. Differences in ALFF between TW and cisgender controls were significantly correlated with serotonin and dopamine transmitters.\u003c/p\u003e \u003cp\u003eThe PCC is one of the most metabolically active regions of the brain and a central part of the default mode network (DMN). It is related to self-perception and self-referential thinking, and also has the function of regulating attention concentration and episodic memory retrieval\u003csup\u003e[43; 44]\u003c/sup\u003e. Our study found the lower ALFF values in the bilateral PCC in the TW group than that in the cisgender control group. The fALFF values of the right PCC in TW and CM groups were lower than those in CW group. And the positive correlation between GIDYQ-AA scores and ALFF values in the bilateral PCC in TW group. Previous volumetric (GVM) studies in healthy populations have found that the overall grey matter volume of the PCC is larger in females than in males\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. Simultaneously, a meta-analysis found that the density of neural functional activation in this region during emotional processing is also higher in females\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. A diffusion tensor imaging (DTI) study revealed differences in fractional anisotropy (FA) within the cingulum bundle of TW compared to CW, suggesting a feminization of this white matter tract in TW\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. The prior VBM studies identified reduced gray matter volume in the PCC among TW relative to cisgender controls, indicating that a larger posterior midline structure might facilitate heightened sensitivity to self-referential processing by altering visual self-perception in TW\u003csup\u003e[9; 48]\u003c/sup\u003e. A rs-FC analysis within the DMN revealed that TW exhibited reduced FC between the PCC and the precuneus compared to cisgender controls, reflecting that altered self-body perception may influence brain network organization in this population\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. A task-based fMRI study on face recognition observed activation in the PCC, indicating its involvement in self-referential processing, while also suggesting that the bodily sexual dimorphism in TW may contribute to their distress\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. Therefore, the PCC is involved in self-referential processing in individuals with TW and may represent a neuropathological basis for the experience of incongruence between gender identity and biological sex.\u003c/p\u003e \u003cp\u003eOur study found that the TW group exhibited significantly lower scores on both the CTQ total and its subscales compared to the cisgender control groups. We found significantly negative correlations between CTQ score (total, EA, and EN) and ALFF in the bilateral PCC were identified in TW group. And EN subscore of the CTQ was negatively correlated with fALFF values in the right PCC among TW individuals. In addition, our findings indicated that fALFF values in the PCC collectively influence the severity of GD, together with the CTQ total score and the EN subscale. Abuse, which refers to criminal behavior involving harm or threats of harm, is associated with alterations in regions that support emotional processing; Neglect refers to acts of omission involving deprivation and is associated with changes in supporting cognitive processing\u003csup\u003e[51; 52]\u003c/sup\u003e. A cognitive reappraisal task study using the International Affective Picture System (IAPS) found that individuals with a history of childhood trauma showed activation in the PCC during the task, suggesting that early trauma impairs self-referential cognitive reappraisal capacity\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e. Our results did not reveal a significant correlation between CTQ scores and GD severity, speculating that the influence of childhood trauma on gender dysphoria may not follow a simple linear relationship. In conclusion, the findings speculate that CTQ and altered the PCC activity interact to shape the neurobiological mechanisms underlying GD, collectively driving its development and clinical presentation. From a clinical perspective, these findings highlight childhood trauma as an important component of an early intervention strategy to alleviate the psychological and emotional difficulties of TW individuals.\u003c/p\u003e \u003cp\u003eWe observed a significant elevation in ALFF within the cerebellar lobule IX of TW group. The cerebellum is not only involved in self-referential processing and executive function, but also associated with gender identity\u003csup\u003e[8; 54; 55; 56]\u003c/sup\u003e. VBM studies have demonstrated alterations in cerebellar gray matter volume in TW compared to cisgender controls\u003csup\u003e[9; 57]\u003c/sup\u003e. During a cognitively demanding task involving sexual dimorphism, TW exhibited a distinct cerebellar activation pattern compared to cisgender male controls, suggesting a tendency toward a female-typical processing mode in this region\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e. A previous rs-fMRI study demonstrated significantly enhanced FC in the right cerebellum within the visual network in transgender girls compared to both cisgender control, revealing a unique GD-specific cerebellar connectivity pattern and suggesting potential alterations in visual system processing related to body perception in individuals with GD\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e. Collectively, the cerebellum is involved in visual body perception, and its alterations point to a role in the neurobiological basis of gender identity development.\u003c/p\u003e \u003cp\u003eGene enrichment analysis revealed that genes associated with altered neural function in TW were enriched in several key BPs, including nuclear transcribed mRNA catabolic process, nonsense-mediated decay (NMD) and signal recognition particle (SRP)-dependent cotranslational protein targeting to membrane. In terms of CCs, this gene set was uniquely enriched for synaptic components. Furthermore, the abnormal functional activities in TW show a spatial overlap with the distribution of key neurotransmitter systems, including the serotonergic and dopaminergic pathways. NMD is an important cellular mechanism that helps regulate gene expression in nerve cells. Dysfunction of NMD can lead to neurodevelopmental disorders.\u003csup\u003e[60; 61]\u003c/sup\u003e. The loss of core NMD factors, such as UPF2, can directly impairs the development of the male reproductive system\u003csup\u003e[62; 63]\u003c/sup\u003e. SRP-dependent co-translational protein targeting to the membrane is the primary translation product of mRNA\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e]\u003c/sup\u003e. Earlier researches shown that m6A modification and miRNA activity work together to precisely control sexual development by influencing how key sex-determining genes are expressed\u003csup\u003e[65; 66]\u003c/sup\u003e. A study in mice found that the production and development of new nerve cells, which is guided by gene activity in interplay between hormones and sex chromosomes, plays a role in how biological sex differences are established\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e]\u003c/sup\u003e. Previous basic research using Caenorhabditis elegans has shown that sexual differentiation is linked to synaptic pruning, which plays a critical role in shaping neural circuits into distinct gender phenotypes\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e]\u003c/sup\u003e. The serotonergic system contributes to brain sexual differentiation in teleosts via its specific expression within the preoptic-hypothalamic region of the hypothalamic-pituitary axis\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e]\u003c/sup\u003e. Previous reviews have indicated that sex-specific dopaminergic alterations during critical periods of brain development play a critical role in the sexual differentiation of neural circuits\u003csup\u003e[70; 71]\u003c/sup\u003e. Synaptic and mRNA-related genes, along with serotonin and dopamine receptor neurotransmission, contribute to altered brain functional activity in TW, implicating their role in the neurobiology of gender identity.\u003c/p\u003e "},{"header":"Limitations","content":"\u003cp\u003eOur study has several limitations. Firstly, the sample sizes of the TW groups were relatively small, which may limit the statistical power and robustness of the findings. Future studies with larger cohorts are needed to validate and replicate these results. Secondly, as this investigation focused exclusively on Chinese individuals with GD, the generalizability of the results to other cultural and demographic populations may be constrained. Thirdly, due to its cross-sectional design, this study possesses an inherent limitation, as it cannot establish causality and can only be used to generate hypotheses. Finally, the AHBA's gene expression data, obtained from a limited sample of six postmortem brains, may constrain the generalizability of findings due to inherent inter-individual variability and differences in donor characteristics, potentially introducing confounding factors that are challenging to fully address.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, we found that TW have altered neural activity in the PCC and cerebellum. Interactions between childhood trauma and reduced PCC function collectively contribute to the severity of GD. In addition, the presence of unique neuromodulation patterns in TW is further supported by the disorder of genetic and neurotransmitter systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYing Wang designed the study; Ruoyi Chen, Guanmao Chen, Shilin Sun and Ying Wang contributed to data sources and study selection; Ruoyi Chen, Guanmao Chen, Shilin Sun, Pan Chen, Zixuan Guo, Mengqi Liao, Chao Chen, Xinyue Tang, Zhangzhang Qi, Jing Li, Xinquan Wu, Xiaoying Zhang, Junjun Liang and Shuming Zhong contributed to data acquisition; Ruoyi Chen, Guanmao Chen and Shilin Sun contributed to data analysis; Ruoyi Chen and Guanmao Chen wrote the manuscript; Ruoyi Chen, Guanmao Chen, and Ying Wang revised the manuscript. All authors contributed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe study was supported by grants from the National Natural Science Foundation of China (82502506 and 82472057); Guangdong Basic and Applied Basic Research Foundation, China (2023A1515110582 and 2024A1515220106); China Postdoctoral Science Foundation (2023M741381). The funding organizations play no further role in study design, data collection, analysis and interpretation and paper writing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data will be made available upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmerican Psychiatric Association. 5th ed. APA; Washington D. Diagnostic and statistical manual of mental disorders.[J].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuyper L, Wijsen C. 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Curr Opin Neurobiol. 2010;20(4):424\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gender dysphoria, amplitude of low frequency fluctuation, childhood trauma, gene expression, neurotransmitter","lastPublishedDoi":"10.21203/rs.3.rs-8978712/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8978712/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlthough early-life stress and trauma may influence cortical development and increase vulnerability to gender dysphoria (GD), the relationship between brain function and childhood traumatic experiences in GD remains unexplored. This study aims to investigate the neurobiological mechanisms underlying GD by examining functional brain alterations and their associations with childhood trauma, neurotransmitter systems, and gene expression patterns.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study recruited 44 transgender women (TW), 40 cisgender male, and 42 cisgender female controls. Participants were assessed using the Childhood Trauma Questionnaire (CTQ) and Gender Identity/Gender Dysphoria Questionnaire for Adolescents and Adults (GIDYQ-AA), while resting-state fMRI data were analyzed for amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) measures. The transcriptional data were sourced from the Allen Human Brain Atlas. The JuSpace toolbox was used to investigate the atlas-based nuclear imaging-derived neurotransmitter maps.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCompared to cisgender controls, TW showed decreased ALFF in the posterior cingulate cortex (PCC) but increased ALFF in the cerebellar lobule IX. TW exhibited decreased fALFF in the right PCC compared to cisgender women. TW also reported higher CTQ total and subscale scores. Notably, CTQ total and its subscale scores [emotional abuse and emotional neglect (EN)] were positively correlated with increased ALFF in the PCC. Multivariate regression analysis further revealed that interactions between fALFF value of the right PCC \u0026times; CTQ total and fALFF value of the right PCC \u0026times; EN independently predicted GIDYQ-AA scores in TW. The abnormal brain activity in TW was linked to genes enriched in mRNA catabolism and synaptic function, and was correlated with dopaminergic and serotonergic neurotransmission.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eTW involves the PCC hypofunction and the cerebellar hyperactivation, synergistically modulated by childhood trauma, especially emotional neglect. These neural alterations are linked to gene regulatory and neurotransmitter pathways, revealing a multilevel neurophysiological framework of TW.\u003c/p\u003e","manuscriptTitle":"Relationships between altered brain activity, childhood trauma, neurotransmitter, and genetic traits in Chinese early adulthood with gender dysphoria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 05:52:00","doi":"10.21203/rs.3.rs-8978712/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-22T19:35:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-14T18:10:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-06T05:00:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248155193090468886891564734859182106883","date":"2026-03-24T06:48:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108349345733647833999817324109101940393","date":"2026-03-18T22:56:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-18T14:51:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-03T19:26:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-27T10:04:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-27T09:30:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medicine","date":"2026-02-26T13:58:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"79f8b159-339f-48d8-9fc5-e5939096e4d8","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T19:38:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 05:52:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8978712","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8978712","identity":"rs-8978712","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}