Introduction: Several studies have investigated the specific neural correlates of trans people, highlighting mixed results. This study aimed to compare the presence of specific functional connectivity and differences in cognitive profile and hormone levels in trans men diagnosed with gender dysphoria (GD), and a homogeneous group of cisgender men and cisgender women. Methods: A total of 42 participants (19 trans men, 11 cisgender men, and 12 cisgender women) underwent a resting state fMRI and were measured for blood levels of testosterone, estradiol, and progesterone. A neuropsychological battery evaluated executive functions, attention, visual-perceptual ability, verbal fluency, manual preference, and general intelligence. Results: Trans men showed weaker functional connectivity in the precentral gyrus, subcallosal cortex, paracingulate gyrus, temporal pole, and cingulate gyrus than cisgender men (p < 0.01). Trans men performed worse than cisgender men in verbal and visuospatial working memory but similarly to cisgender women (p < 0.05). In trans men, functional connectivity of the precentral gyrus correlated positively with testosterone (r = 0.459, p = 0.064) and negatively with estradiol (r = −0.654, p = 0.004) and progesterone blood levels (r = −0.475, p = 0.054). The cluster involving the subcallosal cortex showed a positive correlation with testosterone (r = 0.718, p = 0.001), and a negative correlation with estradiol (r = −0.602, p = 0.011). The functional connectivity from a cluster involving the paracingulate gyrus showed a positive correlation with testosterone (r = 0.592, p = 0.012). Conclusions: This study highlights the importance of overpassing the binary model by underlining the presence of neural pathways that could represent the peculiarity of the neural profile of people with GD.

The increasing prevalence of suicidal ideation among university students, especially those enrolled in medical and healthcare courses, necessitates innovative approaches for early detection and prevention. The southern part of Italy presents a distinct cultural, social, and economic context and is under‐represented in research. This study leverages the capabilities of machine learning methods and explainable artificial intelligence (XAI) to address this challenge, developing a predictive model for suicidal thoughts within a large sample of university students in Sicily, the southernmost and largest Italian region. The XGBoost algorithm was implemented for model training on survey data from 3278 students, including sociodemographic factors, academic performance metrics, and mental health indicators such as the Depression, Anxiety, and Stress Scale short version (DASS‐21) and the Eating Attitude Test short version (EAT‐26). The optimized XGBoost achieved promising performance, with AUROC ranging from 0.719 to 0.808 and balanced accuracy between 0.666 and 0.780 across internal and external test sets. Probability calibration was generally reasonable (Brier 0.151–0.185; ECE 0.035–0.120), supporting the reliability of predicted risk estimates. Performance in general varied by degree program, suggesting cohort‐specific characteristics. Our findings underscore the potential of machine learning models to identify at‐risk students and XAI for understating model prediction, emphasizing the importance of early intervention and targeted mental health support in university settings. The model′s performance and explainability highlight the potential in developing a student‐centric and self‐assessment framework while helping to mitigate potential ethical concerns associated with predictive mental health tools.