Volumetric Sex Differences in Hypothalamic Volume and Associated Subunits Revealed by Automated MRI Segmentation

Volumetric Sex Differences in Hypothalamic Volume and Associated Subunits Revealed by Automated MRI Segmentation | 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 Volumetric Sex Differences in Hypothalamic Volume and Associated Subunits Revealed by Automated MRI Segmentation Sadegh Ghaderi, Sana Mohammadi, Farzad Fatehi, Mahdi Mohammadi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7163918/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background and purpose: Sex differences in brain structure and function are well documented; however, the impact of these differences on the hypothalamic volume and associated subunits remains underexplored. This study aimed to investigate sex differences in hypothalamic volume and its subregions. Methods: Sixty-six healthy individuals (34 males) with a mean age of 49.42 ± 12.25 years underwent 3T imaging with a 64-channel head coil, using a 3D T1-weighted MPRAGE sequence. Automated segmentation of hypothalamic subregions, including the anterior-superior (a-sHyp), anterior-inferior (a-iHyp), superior tuberal (supTub), inferior tuberal (infTub), and posterior (posHyp), was performed to quantify the total volume and 10 subunits using a deep convolutional neural network validated by FreeSurfer v7.4.1, with total intracranial volume (TIV) normalization applied for individual head size variations. Results: Multivariate analysis of covariance (MANCOVA) revealed significant sexual dimorphism (Wilks’ Λ = 0.652, F(10,50) = 2.66, p = 0.011, partial η² = 0.35), with females exhibiting larger adjusted volumes across nearly all subunits. Age showed modest associations with right a-iHyp (p = 0.042), a-sHyp (p = 0.035), supTub (p = 0.049), and the whole right (p = 0.022). These subunits were positively correlated with age. The body mass index, education, handedness, and sex × handedness interactions were not significant (p > 0.05). Conclusions: Our findings suggest that females have larger hypothalamic volumes and that certain subunits exhibit sex-specific differences, emphasizing the importance of considering sex differences in neuroscientific research and clinical practice. Hypothalamus MRI Sex Segmentation Neuroendocrine Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Although the hypothalamus accounts for less than 1% of the brain volume (approximately 1.4 cm 3 ), it plays a crucial role in regulating various physiological processes in the human body because of its intricate anatomical and functional structure [ 1 , 2 ]. Located at the base of the brain, it consists of various nuclei that facilitate processes ranging from body temperature regulation and energy homeostasis to reproductive and social behaviors [ 3 , 4 ]. As the main coordinator of the neuroendocrine system, hypothalamic nuclei control vital activities, such as energy balance, thermoregulation, and emotional responses, through diverse neuronal populations [ 4 , 5 ]. Dysregulation of these processes is implicated in various conditions such as obesity, mood disorders, anxiety disorders, and sleep disorders. Thus, a deeper understanding of sex-specific variations in hypothalamic structure could hold significant clinical relevance for disorders with known neuroendocrine underpinnings [ 6 ]. Sex differences in the human brain have long been recognized, with males generally exhibiting larger total brain volumes than females from birth onwards [ 7 , 8 ]. The hypothalamus contains high concentrations of sex steroid hormone receptors and acts as a key regulator of neuroendocrine feedback loops that control sex hormone levels [ 9 , 10 ]. Thus, it may be particularly susceptible to the organizational and activational effects of sex hormones throughout development and across lifespan. However, it remains challenging to differentiate between innate biological factors and socio-environmental effects on sex differences in the central nervous system (CNS) [ 7 ]. Thus, sex hormones influence brain development throughout life. The hypothalamus, which contains high concentrations of estrogen and androgen receptors, may be particularly sensitive to its impact [ 3 ]. Advances in neuroimaging now enable the examination of anatomical structures, such as the hypothalamus and its subregions, in greater detail. Quantifying potential volumetric changes could reveal new biomarkers for better understanding disorders with known neuroendocrine disturbances [ 11 ]. Therefore, based on our current understanding and the available literature, no existing study has explicitly examined the potential influence of sex on the hypothalamic subunits using the Billot et al. approach. We investigated sex-specific differences in hypothalamic volume and associated subunits. Methods Ethics statement Before data collection, all participants with potentially identifiable images or information were informed about the study and provided written consent. The Ethics Committee of the National Institute for Medical Research Development (NIMAD) approved this study (Ethical Code: IR.NIMAD.REC.1396.319) in accordance with the Declaration of Helsinki. All experiments were conducted in compliance with applicable guidelines and regulations. Sample We used structural MRI data that had been previously published as part of an Iranian brain imaging database (IBID) for a neuropsychiatric study of healthy brains [ 12 ]. We included data from 34 males and 32 females who underwent MRI. Five categories of standard diagnostic assessments, including medical, mental health, cognitive, lifestyle, and MRI assessments, were conducted based on a previously published study [ 12 ]. To mitigate the impact of potential confounding variables, strict exclusion criteria were implemented during participant recruitment. Participants were screened for a history of neurological or psychiatric disorders, hormonal imbalances, or any medical condition known to affect brain structure or function. Specifically, individuals with a history of traumatic brain injury, stroke, epilepsy, tumors, endocrine disorders, or current use of medications that could affect hormonal levels were excluded from the study. Imaging MR Imaging MRI images were acquired using a Siemens 3.0 Tesla scanner (Prisma, 2016), specifically for research purposes, along with a 64-channel head coil. Our MRI protocols were selected to match the standards of international projects such as the UK Biobank or the ENIGMA consortium. For three-dimensional (3D) T1-weighted magnetization prepared rapid gradient echo (MPRAGE), we used the MRI protocol with the following parameters: TA = 4:12 min; TR = 1800 ms; TE = 3.53 ms; TI = 1100 ms; flip angle = 7 degrees; voxel size = 1.0×1.0×1.0 mm; multi-slice mode = sequential; FOV read = 256 mm; slices = 160; phase encoding direction = anterior > > posterior; matrix size = 256×256×160; averages = 1. Automated segmentation of the hypothalamus and associated subunits An automated tool based on a deep convolutional neural network (CNN) was used to parcellate the gray matter volumes (GMVs) of the subregions of the hypothalamus using volumetric MRI scans. This approach has been validated by Biliot et al. [ 13 ]. The 3D T1-weighted images underwent preprocessing using the FreeSurfer v7.4.1 'recon-all' pipeline ( https://surfer.nmr.mgh.harvard.edu/fswiki/recon-all ). Subsequently, the pre-processed structural data were segmented to isolate the entire hypothalamus and its nuclei, encompassing both the bilateral hypothalamus and five subunits ( https://surfer.nmr.mgh.harvard.edu/fswiki/HypothalamicSubunits ): anterior-superior (a-sHyp) [preoptic area and paraventricular nucleus (PVN)], anterior-inferior (a-iHyp) [suprachiasmatic nucleus (SCN) and supraoptic nucleus (SON)], superior tuberal (supTub) [dorsomedial nucleus, PVN, and lateral hypothalamus], inferior tuberal (infTub) [infundibular (or arcuate) nucleus, ventromedial nucleus, SON, lateral tuberal nucleus, and tuberomamillary nucleus (TMN)], and posterior (posHyp) [mamillary body (medial and lateral mamillary nuclei), lateral hypothalamus, and TMN] [ 13 ]. The observed differences in intracranial structure sizes between sexes were primarily explained by the total intracranial volume (TIV) [ 14 ]. TIV volume normalization [ 15 ] was performed for each participant using the proportion method based on Freesurfer's TIV output from the "aseg" atlas. This corrected for variations in individual TIV by calculating the volume in mm³ per TIV mm³ and multiplying by 10⁶. Consequently, before the statistical analysis, the hypothalamic and associated volumes (measured in mm³) for each participant were adjusted based on their estimated total TIV (mm³). Statistical analysis We assessed the normality of the variables using the Shapiro-Wilk test based on the sex group [ 16 ]. A Multivariate Analysis of Covariance (MANCOVA) was conducted to investigate the effects of sex and handedness on the hypothalamic volume and its associated subunits. Body mass index (BMI), age, and years of education were included as covariates to control for potential influences. Sex and handedness were treated as fixed factors, and the interaction between them was also examined. Multivariate significance was assessed using Wilks' lambda, with follow-up univariate ANOVAs conducted for each dependent variable to examine specific effects. Estimated Marginal Means were calculated for sex to further explore significant group differences. Partial eta squared (partial η²) was used as a measure of the effect size to quantify the proportion of variance in each dependent variable explained by the independent variables and covariates in the model. The homogeneity of the variance assumption was evaluated using Levene's test. In the bivariate analysis, correlations were used to examine the relationship between age and hypothalamic and associated volume. We set the significance level at p < 0.05, and performed all statistical analyses using the SPSS V.27.0 (IBM SPSS Statistics) software. The figures were created using GraphPad Prism 10 (GraphPad Software, San Diego, CA, USA). Results Overview of main results To ensure that the two groups were well-matched in terms of age, BMI, years of education, and handedness, we selected a sample from the IBID study [ 12 ], which comprised 66 participants (a convenience sample): 34 males (mean age ± standard deviation [SD]: 49.91 ± 12.67 years) and 32 females (mean age ± SD: 48.91 ± 11.81 years) (Table 1 ). Figure 1 illustrates the automated segmentation of various subcomponents of the hypothalamus. After performing TIV correction and normality testing for all continuous variables, we confirmed that all variables in each group followed a normal distribution. Table 1 Demographic data and other characteristics of the participants Female (N = 32) Male (N = 34) p-value a Age, years 48.91 (11.81) 49.91 (12.67) 0.74 Body mass index, kg/m 2 25.48 (4.00) 26.52 (3.43) 0.26 Education, years 15.59 (5.64) 16.68 (4.00) 0.39 Handedness, right% 26 (81.25%) 28 (82.35%) 0.909 a p < 0.05 Data presented as mean (SD) for continuous variables or n counts (%) for categorical variables. MANCOVA was conducted to assess sex differences in total hypothalamic volume and its subunits, with sex and handedness as fixed factors and BMI, age, and education as covariates. All volumetric measurements were corrected for TIV. The multivariate model evaluated using Wilks’ Lambda revealed a significant main effect of sex (Wilks’ Λ = 0.652, F(10,50) = 2.66, p = 0.011, partial η² = 0.35), indicating substantial sex-based differences across the hypothalamic subunits. No significant multivariate effects were observed for handedness (Wilks’ Λ = 0.921, F(10,50) = 0.43, p = 0.924), the sex × handedness interaction (Wilks’ Λ = 0.850, F(10,50) = 0.89, p = 0.553), or covariates BMI (Wilks’ Λ = 0.884, F(10,50) = 0.66, p = 0.758), age (Wilks’ Λ = 0.853, F(10,50) = 0.86, p = 0.575), or education (Wilks’ Λ = 0.858, F(10,50) = 0.83, p = 0.606). Levene’s Test of Equality of Error Variances indicated violations of homogeneity for several dependent variables: left posterior (p = 0.011), right posterior (p = 0.030), right infTub (p = 0.006), and whole right (p = 0.049). While these violations suggest heterogeneity in the residual variances for these subunits, the robustness of MANCOVA to such assumptions supports the validity of the findings. Comparison of hypothalamic volume and associated subunits between groups Univariate analyses confirmed significant sex effects across multiple subunits after adjusting for covariates (Table 2 ). The total hypothalamic volume (F(1,59) = 16.54, p < 0.001, partial η² = 0.22) and hemispheric aggregates (whole left: F(1,59) = 16.50, p < 0.001, partial η² = 0.22; whole right: F(1,59) = 14.37, p < 0.001, partial η² = 0.20) were significantly larger in females (Fig. 2 ). Females exhibited larger adjusted mean volumes compared to males in the left a-sHyp (F(1,59) = 5.50, p = 0.022, partial η² = 0.09), left posterior (F(1,59) = 11.44, p = 0.001, partial η² = 0.16), left infTub (F(1,59) = 16.33, p < 0.001, partial η² = 0.22), left supTub (F(1,59) = 7.52, p = 0.008, partial η² = 0.11), right a-sHyp (F(1,59) = 4.79, p = 0.033, partial η² = 0.08), right posterior (F(1,59) = 14.19, p < 0.001, partial η² = 0.19), right infTub (F(1,59) = 18.71, p < 0.001, partial η² = 0.24), and right supTub (F(1,59) = 4.44, p = 0.039, partial η² = 0.07) (Fig. 3 ). Table 2 Comparison of hypothalamic volume and associated subunits between females and males Non-adjusted Adjusted (Estimated Marginal Means) a Female Male Female Male p-value d partial η² Left Anterior-Inferior 12.71 (2.03) b 11.76 (2.57) 12.57 (0.56) c 11.90 (0.54) 0.398 0.012 Left Anterior-Superior 17.97 (3.21) 15.82 (2.76) 18.27 (0.70) 15.94 (0.69) 0.022 0.085 Left Posterior 86.65 (15.47) 74.17 (9.11) 88.42 (3.00) 74.07 (2.94) 0.001 0.162 Left Tuberal Inferior 108.08 (15.36) 89.21 (10.82) 107.54 (3.08) 89.93 (3.02) < 0.001 0.217 Left Tuberal Superior 82.61 (12.91) 72.76 (9.00) 82.87 (2.61) 72.74 (2.56) 0.008 0.113 Right Anterior-Inferior 12.93 (2.66) 11.99 (2.84) 12.63 (0.60) 12.02 (0.59) 0.472 0.009 Right Anterior-Superior 16.94 (4.11) 14.74 (2.23) 16.95 (0.74) 14.65 (0.73) 0.033 0.075 Right Posterior 86.07 (14.71) 75.71 (9.19) 89. 69 (2.77) 74.96 (2.71) < 0.001 0.194 Right Tuberal Inferior 99.45 (13.83) 83.04 (7.94) 98.83 (2.60) 83.22 (2.50) < 0.001 0.241 Right Tuberal Superior 83.93 (13.66) 74.95 (10.16) 83.48 (2.79) 75.16 (2.73) 0.039 0.070 Whole Left 308.02 (39.43) 263.72 (26.27) 309.67 (7.85) 264.58 (7.69) < 0.001 0.219 Whole Right 299.32 (41.75) 260.43 (24.50) 301.58 (7.75) 260.02 (7.59) < 0.001 0.196 Whole 607.34 (78.06) 524.15 (49.66) 611.24 (15.07) 524.60 (14.76) < 0.001 0.219 a Adjusted based on MANCOVA analysis (covariates appearing in the model are evaluated at the following values: BMI = 26.0141, Age = 49.42, Education = 16.15). b Data presented as mean (SD). Volume normalization with the proportion method (the volume in mm³ per TIV mm³ and multiplied by 10⁶). c Data presented as mean (SE). Volume normalization with the proportion method (the volume in mm³ per TIV mm³ and multiplied by 10⁶). d p < 0.05. Association of hypothalamic subunit volumes with covariates and fixed factors The covariate effects were limited to age, which showed univariate associations with right a-iHyp (F(1,59) = 4.32, p = 0.042, partial η² = 0.07), right a-sHyp (F(1,59) = 4.68, p = 0.035, partial η² = 0.07), right infTub (F(1,59) = 4.04, p = 0.049, partial η² = 0.06), and right (F(1,59) = 5.57, p = 0.022, partial η² = 0.09). The right a-iHyp (r = 0.344), right infTub (rho = 0.259), right whole (rho = 0.249), and right a-sHyp (rho = 0.244) were positively correlated with age (Fig. 4 ). BMI and education level did not significantly predict any volumetric measures (p > 0.05). Handedness did not exhibit a significant multivariate effect (p = 0.924), nor did the interaction between sex and handedness (p = 0.553) significantly influence the combined hypothalamic volumes. Handedness and sex × handedness interactions remained non-significant across all subunits (p > 0.05). Discussion The present study provides evidence of sexual dimorphism in the hypothalamic structure, revealing that females exhibit significantly larger volumes across most hypothalamic subunits and the entire hypothalamus than males, even after adjusting for TIV, BMI, age, and education. These findings align with the emerging literature on sex-based neuroanatomical differences but extend prior work by dissecting specific subunits of the hypothalamus and systematically controlling for potential confounders [ 17 – 20 ]. However, our results diverge from the recent large-scale findings of Xu et al. (2025) [ 21 ], which present a contrasting view on hypothalamic sexual dimorphism. In their analysis of two major population-based cohorts (UK Biobank and Life-Adult-Study), they reported that while males had larger absolute hypothalamic volumes, this difference was entirely accounted for by TIV, resulting in no significant sex difference in relative hypothalamic volume. This is in direct opposition to our finding of significantly larger TIV-corrected hypothalamic volumes in females. Furthermore, they observed a negative association between age and hypothalamic volume across the lifespan, whereas our data indicated a positive correlation in several right-sided subunits. These discrepancies may stem from several key methodological and demographic differences. The substantially larger sample size in the Xu et al. study (N > 50,000) provides greater statistical power and may reflect a more generalizable population trend compared to our more modest cohort (N = 66). Additionally, potential variations in TIV correction techniques, the specific automated segmentation algorithms employed, and underlying population characteristics (e.g., Iranian vs. European cohorts) could contribute to these divergent outcomes, underscoring the need for methodological standardization in future research. Notably, the multivariate model revealed that approximately 35% of the variance in the combined hypothalamic subunit volumes could be explained by sex, and subsequent univariate analyses confirmed this effect across multiple subregions. Together, the results underscore the hypothalamus as a sexually dimorphic structure, likely reflecting divergent neurodevelopmental trajectories or functional specializations tied to sex-specific physiological and endocrine demands [ 22 , 23 ]. These findings also resonate with animal studies highlighting sex differences in hypothalamic nuclei regulating reproduction, stress, and homeostasis, such as the sexually dimorphic nucleus and anteroventral periventricular nucleus, which are larger in females [ 24 – 31 ]. How do brain processes, hormonal activity, and behavior interact? One helpful way to understand this interaction is to consider the brain as part of the endocrine system [ 32 ]. The hypothalamus and pituitary gland regulate hormone production, and the brain acts as an endocrine organ [ 33 ]. Steroids and sex hormones can cross the blood-brain barrier and affect the central nervous system as well as other regions. Connections between hormone levels and alterations in the volume of particular subregions of the hypothalamus have been discovered in recent studies [ 34 – 36 ]. These dissimilarities are specifically evident in different cognitive states and between the sexes. Methodologically, automated segmentation of MRI data allows for detailed examination of structural alterations within specific hypothalamic nuclei, beyond gross regional assessments [ 13 ]. Notably, TIV correction helped account for inter-individual cranial variability unrelated to biological sex factors [ 15 ]. The findings of this study, based on volume normalization to TIV, have significantly contributed to the understanding of sex differences in the hypothalamic volume and its subunits. These results indicate that, contrary to the general trend of larger TIVs in males, females exhibit larger volumes in the hypothalamus and its specific subunits, which aligns with previous studies [ 37 ]. However, Makris et al. (2013) conducted a study using the parcellation method, which revealed that the total size of the hypothalamus in males was significantly larger than that in females [ 38 ]. This difference was relative to the size of the cerebrum and was found to be similar in both hemispheres. This inconsistency is particularly intriguing, given the role of the hypothalamus in regulating numerous physiological processes, including those related to sex hormones [ 11 ]. Therefore, future studies based on healthy participants and TIV normalization approaches should be conducted to confirm these findings. The hypothalamus is a crucial organ for human health and is responsible for sensing internal bodily states and responses via hormone production. It is linked to complex disorders and diseases, such as obesity [ 39 , 40 ], schizophrenia, bipolar spectrum disorders [ 41 – 43 ], dementia [ 44 ], and Prader-Willi syndrome (PWS) [ 45 ]. The developing hypothalamus may respond to suboptimal conditions by producing structural and functional changes that persist throughout the lifespan [ 24 ]. The larger hypothalamic volumes in females could be associated with the high density of estrogen and androgen receptors in these regions, suggesting a potential influence of sex hormones on hypothalamic development and structure [ 46 , 47 ]. Furthermore, the hypothalamus regulates the hypothalamic-pituitary-gonadal axis and shows high receptor expression for gonadal steroids, suggesting susceptibility to the organizational effects of circulating sex hormones in a sexually dimorphic manner [ 48 – 51 ]. Larger volumes in females could indicate adaptive responses to hormones, such as estrogen and progesterone, which fluctuate cyclically across their lifespans [ 52 – 54 ]. The absence of significant differences in the volumes of the left and right a-iHyp, encompassing the SCN and SON, between the sexes may indicate a more complex interplay of factors beyond hormonal influence, possibly involving genetic and environmental components or sexual orientation [ 51 , 55 , 56 ]. As the hormonal milieus changes with events such as menopause, neural structures can remodel in response [ 57 ]. The positive correlation between age and certain hypothalamic volumes raises questions regarding the effects of aging on hypothalamic structures and their functional implications [ 58 ]. These correlations suggest that as individuals age, there may be a sex-specific increase in hypothalamic volume, which could have consequences for neuroendocrine function [ 11 ]. Although age is typically associated with atrophy in many brain structures, some studies have reported region-specific increases or relative preservation in hypothalamic nuclei, possibly reflecting compensatory or adaptive mechanisms in neuroendocrine regulation [ 21 ]. Nevertheless, the absence of significant effects of BMI and years of education on hypothalamic volume aligns with prior findings, suggesting that in healthy individuals, the hypothalamus may be relatively resilient to variations in metabolic status or cognitive reserve proxies. This is particularly evident when measured using structural MRI techniques, and is consistent with reports linking obesity to hypothalamic microstructural changes rather than macrostructural alterations [ 40 ]. Previous studies have employed TIV adjustment and normalization techniques to account for head size variations in whole-brain and regional volumetric measures [ 14 , 15 , 59 – 62 ]. However, the optimal TIV adjustment method remains uncertain with inconsistent findings across studies. Furthermore, the accuracy of these methods when applied to specific brain substructures, such as the hypothalamic subunits, has not been extensively investigated. Univariate and multivariate sex differences in GMV were predominantly influenced by TIV disparities between males and females [ 60 ]. When the TIV variation was controlled for, these sex differences were significantly reduced. The choice of the TIV adjustment method can substantially impact the magnitude, direction, and replicability of the estimated sex differences in brain volume. Different studies have favored various approaches, such as the residual method [ 14 , 62 ], proportion method [ 14 , 15 ], or including TIV as a covariate in a linear model [ 63 ]. The limitations of the current TIV adjustment methods are compounded by the difficulty in defining functional subnuclei within the hypothalamus [ 13 ]. Given the lack of definitive guidance in the literature, we preferred a simple TIV volume normalization method for the hypothalamic volume and its associated subunits to minimize potential biases in our approach [ 15 ]. Despite promising findings, the results of this study must be interpreted within the context of several significant limitations. First, the relatively modest sample size, while sufficient to detect the primary effect of sex, limits the generalizability of our findings and increases the risk of both Type I and Type II errors. Such a sample size may not be robust enough to capture the full spectrum of population variance or reliably detect more subtle effects, such as interactions between sex and other variables. Second, our use of a convenience sample from the IBID may introduce potential selection bias, though we pursued efforts to ensure that the two groups were well-matched in terms of age, BMI, years of education, and handedness. The participants in this database may not be fully representative of the general population, potentially differing in unmeasured socioeconomic, health, or lifestyle factors that could influence brain structure, thereby limiting the external validity of our conclusions. A third major limitation is the absence of endocrine data. This study investigated a key neuroendocrine structure without measuring circulating sex hormone levels (e.g., estradiol, testosterone). Consequently, we cannot disentangle the lifelong organizational effects of sex from the transient activational effects of current hormone levels, which are known to fluctuate and influence brain plasticity. This omission prevents a deeper mechanistic understanding of the observed volumetric differences. Therefore, these specific subunit findings should be considered preliminary and require replication. Finally, as noted, the automated segmentation method groups nuclei by anatomical proximity rather than distinct function, which complicates the functional interpretation of volumetric changes [ 13 ]. The cross-sectional design also prevents us from inferring causality or tracking developmental changes over time. Future research should address these shortcomings by employing larger, more diverse cohorts, incorporating hormonal and genetic analyses, applying rigorous statistical corrections, and utilizing longitudinal designs to clarify the complex interplay between sex, age, and hypothalamic morphology. Billiot et al. employed the probabilistic output generated by a softmax layer to mitigate the influence of partial volume effects. This approach was used for volume calculations, offering a quantitative measure of the uncertainty associated with each label. Moreover, the Freesurfer pipeline lacks a dedicated method for segmenting the hypothalamus or its subregions; instead, it groups these structures within a broader anatomical region referred to as the ventral "ventral diencephalon (ventral DC)," which includes multiple other structures. To overcome this limitation, a segmentation protocol, developed and validated [ 13 ], was introduced to specifically address the segmentation of the hypothalamus and its subregions. Furthermore, FreeSurfer is known to exhibit variability in segmentation accuracy owing to differences in contrast across T1-weighted images. To address this issue and evaluate the reliability of the manual segmentation protocol, Billot et al. conducted inter- and intra-rater variability experiments. The robustness of the automated segmentation method trained on an internal dataset was also assessed using two additional datasets. This included an external dataset with diverse acquisition parameters and a heterogeneous ADNI dataset, ensuring the generalizability of the method across varying T1-weighted image characteristics. Conclusions Our study provides clear evidence of sexual dimorphism in the human hypothalamus. The primary finding is that females have significantly larger hypothalamic volumes than males, even after accounting for differences in body mass, age, and education. While handedness had no discernible effect, age was linked to volumetric changes in specific right-hemisphere subunits. These results highlight that sex is a major factor in shaping hypothalamic structure, with age playing a secondary, more localized role. Declarations Funding: The authors declare that no funds, grants, or other support was received during the preparation of this manuscript. Author Contributions: S.G. and S.M. contributed to the study's conception and design. Material preparation, data collection, and analysis were performed by S.G., S.M., M.M., M.S., and S.B. The first draft of the manuscript was written by S.G. and S.M., and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data Availability: This article contains all the data produced or analyzed during this investigation. Further inquiries should be forwarded to the corresponding author. Ethics declarations Ethics approval and consent to participate: The Ethics Committee of the National Institute for Medical Research Development (NIMAD) approved this study (Ethical Code: IR.NIMAD.REC.1396.319) in accordance with the Declaration of Helsinki. All individuals and/or their legal representatives gave written informed consent before entering the study. Consent to publish: Not Applicable. Competing Interests: The authors have no relevant financial or non-financial interests to disclose. 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Statistical adjustments for brain size in volumetric neuroimaging studies: Some practical implications in methods. Psychiatry Res. 2011;193:113–22. Opfer R, Krüger J, Spies L, Kitzler HH, Schippling S, Buchert R. Single-subject analysis of regional brain volumetric measures can be strongly influenced by the method for head size adjustment. Neuroradiology. 2022;64:2001–9. Malone IB, Leung KK, Clegg S, Barnes J, Whitwell JL, Ashburner J, et al. Accurate automatic estimation of total intracranial volume: A nuisance variable with less nuisance. NeuroImage. 2015;104:366–72. Additional Declarations No competing interests reported. Supplementary Files GraphicalAbstractImage.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 22 Oct, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviewers invited by journal 07 Oct, 2025 Editor invited by journal 08 Sep, 2025 Editor assigned by journal 31 Jul, 2025 Submission checks completed at journal 31 Jul, 2025 First submitted to journal 19 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":651817,"visible":true,"origin":"","legend":"\u003cp\u003eillustrates the automated segmentation of the hypothalamic subunits, including anterior-superior (a-sHyp), anterior-inferior (a-iHyp), superior tuberal (supTub), inferior tuberal (infTub), and posterior (posHyp). TIV, total intracranial volume.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7163918/v1/c8209ef31cad47dbfd49315f.jpg"},{"id":93917118,"identity":"2ec10db8-a5ef-4aa3-8b63-3bdf04ec8b3e","added_by":"auto","created_at":"2025-10-20 08:58:04","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":74198,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of bilateral and whole hypothalamic volumes between males and females. TIV, total intracranial volume. ***p-value \u0026lt;0.001\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7163918/v1/33a0f436532d41fe1b78cc99.jpg"},{"id":93914851,"identity":"876778b9-40da-4425-b12f-24ed94d263fd","added_by":"auto","created_at":"2025-10-20 08:42:04","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":216617,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of hypothalamic subunit volumes between groups. TIV, total intracranial volume. *p-value \u0026lt;0.05; **p-value \u0026lt;0.01; ***p-value \u0026lt;0.001\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7163918/v1/1c77ac4381f5f1269ad68690.jpg"},{"id":93914855,"identity":"e789a8ad-7770-47c8-927c-9bd9fc8752ff","added_by":"auto","created_at":"2025-10-20 08:42:04","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":358050,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between hypothalamic subunits (right a-iHyp, right a-sHyp, right supTub, and right whole) with age. TIV, total intracranial volume.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7163918/v1/9c7b8710fb253ad1fa449d0c.jpg"},{"id":93917443,"identity":"12d7d655-f94c-4d44-a427-317ca94bdc8f","added_by":"auto","created_at":"2025-10-20 09:06:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2086209,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7163918/v1/1622a37a-7b40-49ed-8b68-00cbe901c779.pdf"},{"id":93915935,"identity":"5160b2de-bf8d-4a4c-8781-cbad322f97ee","added_by":"auto","created_at":"2025-10-20 08:50:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":137774,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstractImage.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7163918/v1/6c74d734fa7ebd57254ac8c2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Volumetric Sex Differences in Hypothalamic Volume and Associated Subunits Revealed by Automated MRI Segmentation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlthough the hypothalamus accounts for less than 1% of the brain volume (approximately 1.4 cm\u003csup\u003e3\u003c/sup\u003e), it plays a crucial role in regulating various physiological processes in the human body because of its intricate anatomical and functional structure [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Located at the base of the brain, it consists of various nuclei that facilitate processes ranging from body temperature regulation and energy homeostasis to reproductive and social behaviors [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As the main coordinator of the neuroendocrine system, hypothalamic nuclei control vital activities, such as energy balance, thermoregulation, and emotional responses, through diverse neuronal populations [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Dysregulation of these processes is implicated in various conditions such as obesity, mood disorders, anxiety disorders, and sleep disorders. Thus, a deeper understanding of sex-specific variations in hypothalamic structure could hold significant clinical relevance for disorders with known neuroendocrine underpinnings [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSex differences in the human brain have long been recognized, with males generally exhibiting larger total brain volumes than females from birth onwards [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The hypothalamus contains high concentrations of sex steroid hormone receptors and acts as a key regulator of neuroendocrine feedback loops that control sex hormone levels [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Thus, it may be particularly susceptible to the organizational and activational effects of sex hormones throughout development and across lifespan. However, it remains challenging to differentiate between innate biological factors and socio-environmental effects on sex differences in the central nervous system (CNS) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Thus, sex hormones influence brain development throughout life. The hypothalamus, which contains high concentrations of estrogen and androgen receptors, may be particularly sensitive to its impact [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdvances in neuroimaging now enable the examination of anatomical structures, such as the hypothalamus and its subregions, in greater detail. Quantifying potential volumetric changes could reveal new biomarkers for better understanding disorders with known neuroendocrine disturbances [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, based on our current understanding and the available literature, no existing study has explicitly examined the potential influence of sex on the hypothalamic subunits using the Billot et al. approach. We investigated sex-specific differences in hypothalamic volume and associated subunits.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eEthics statement\u003c/b\u003e\u003c/p\u003e\u003cp\u003e Before data collection, all participants with potentially identifiable images or information were informed about the study and provided written consent. The Ethics Committee of the National Institute for Medical Research Development (NIMAD) approved this study (Ethical Code: IR.NIMAD.REC.1396.319) in accordance with the Declaration of Helsinki. All experiments were conducted in compliance with applicable guidelines and regulations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSample\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe used structural MRI data that had been previously published as part of an Iranian brain imaging database (IBID) for a neuropsychiatric study of healthy brains [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. We included data from 34 males and 32 females who underwent MRI. Five categories of standard diagnostic assessments, including medical, mental health, cognitive, lifestyle, and MRI assessments, were conducted based on a previously published study [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. To mitigate the impact of potential confounding variables, strict exclusion criteria were implemented during participant recruitment. Participants were screened for a history of neurological or psychiatric disorders, hormonal imbalances, or any medical condition known to affect brain structure or function. Specifically, individuals with a history of traumatic brain injury, stroke, epilepsy, tumors, endocrine disorders, or current use of medications that could affect hormonal levels were excluded from the study.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImaging\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eMR Imaging\u003c/em\u003e\u003c/p\u003e\u003cp\u003eMRI images were acquired using a Siemens 3.0 Tesla scanner (Prisma, 2016), specifically for research purposes, along with a 64-channel head coil. Our MRI protocols were selected to match the standards of international projects such as the UK Biobank or the ENIGMA consortium. For three-dimensional (3D) T1-weighted magnetization prepared rapid gradient echo (MPRAGE), we used the MRI protocol with the following parameters: TA = 4:12 min; TR = 1800 ms; TE = 3.53 ms; TI = 1100 ms; flip angle = 7 degrees; voxel size = 1.0×1.0×1.0 mm; multi-slice mode = sequential; FOV read = 256 mm; slices = 160; phase encoding direction = anterior \u0026gt; \u0026gt; posterior; matrix size = 256×256×160; averages = 1.\u003c/p\u003e\u003cp\u003e\u003cem\u003eAutomated segmentation of the hypothalamus and associated subunits\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAn automated tool based on a deep convolutional neural network (CNN) was used to parcellate the gray matter volumes (GMVs) of the subregions of the hypothalamus using volumetric MRI scans. This approach has been validated by Biliot et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe 3D T1-weighted images underwent preprocessing using the FreeSurfer v7.4.1 'recon-all' pipeline (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://surfer.nmr.mgh.harvard.edu/fswiki/recon-all\u003c/span\u003e\u003cspan address=\"https://surfer.nmr.mgh.harvard.edu/fswiki/recon-all\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Subsequently, the pre-processed structural data were segmented to isolate the entire hypothalamus and its nuclei, encompassing both the bilateral hypothalamus and five subunits (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://surfer.nmr.mgh.harvard.edu/fswiki/HypothalamicSubunits\u003c/span\u003e\u003cspan address=\"https://surfer.nmr.mgh.harvard.edu/fswiki/HypothalamicSubunits\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e): anterior-superior (a-sHyp) [preoptic area and paraventricular nucleus (PVN)], anterior-inferior (a-iHyp) [suprachiasmatic nucleus (SCN) and supraoptic nucleus (SON)], superior tuberal (supTub) [dorsomedial nucleus, PVN, and lateral hypothalamus], inferior tuberal (infTub) [infundibular (or arcuate) nucleus, ventromedial nucleus, SON, lateral tuberal nucleus, and tuberomamillary nucleus (TMN)], and posterior (posHyp) [mamillary body (medial and lateral mamillary nuclei), lateral hypothalamus, and TMN] [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe observed differences in intracranial structure sizes between sexes were primarily explained by the total intracranial volume (TIV) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. TIV volume normalization [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] was performed for each participant using the proportion method based on Freesurfer's TIV output from the \"aseg\" atlas. This corrected for variations in individual TIV by calculating the volume in mm³ per TIV mm³ and multiplying by 10⁶. Consequently, before the statistical analysis, the hypothalamic and associated volumes (measured in mm³) for each participant were adjusted based on their estimated total TIV (mm³).\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eWe assessed the normality of the variables using the Shapiro-Wilk test based on the sex group [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A Multivariate Analysis of Covariance (MANCOVA) was conducted to investigate the effects of sex and handedness on the hypothalamic volume and its associated subunits. Body mass index (BMI), age, and years of education were included as covariates to control for potential influences. Sex and handedness were treated as fixed factors, and the interaction between them was also examined. Multivariate significance was assessed using Wilks' lambda, with follow-up univariate ANOVAs conducted for each dependent variable to examine specific effects. Estimated Marginal Means were calculated for sex to further explore significant group differences. Partial eta squared (partial η²) was used as a measure of the effect size to quantify the proportion of variance in each dependent variable explained by the independent variables and covariates in the model. The homogeneity of the variance assumption was evaluated using Levene's test. In the bivariate analysis, correlations were used to examine the relationship between age and hypothalamic and associated volume. We set the significance level at p \u0026lt; 0.05, and performed all statistical analyses using the SPSS V.27.0 (IBM SPSS Statistics) software. The figures were created using GraphPad Prism 10 (GraphPad Software, San Diego, CA, USA).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eOverview of main results\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo ensure that the two groups were well-matched in terms of age, BMI, years of education, and handedness, we selected a sample from the IBID study [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], which comprised 66 participants (a convenience sample): 34 males (mean age\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation [SD]: 49.91\u0026thinsp;\u0026plusmn;\u0026thinsp;12.67 years) and 32 females (mean age\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: 48.91\u0026thinsp;\u0026plusmn;\u0026thinsp;11.81 years) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the automated segmentation of various subcomponents of the hypothalamus. After performing TIV correction and normality testing for all continuous variables, we confirmed that all variables in each group followed a normal distribution.\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\u003eDemographic data and other characteristics of the participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eFemale (N\u0026thinsp;=\u0026thinsp;32)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eMale (N\u0026thinsp;=\u0026thinsp;34)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e \u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAge, years\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48.91 (11.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.91 (12.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBody mass index, kg/m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.48 (4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.52 (3.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEducation, years\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.59 (5.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.68 (4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHandedness, right%\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (81.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (82.35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.909\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003cp\u003eData presented as mean (SD) for continuous variables or n counts (%) for categorical variables.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMANCOVA was conducted to assess sex differences in total hypothalamic volume and its subunits, with sex and handedness as fixed factors and BMI, age, and education as covariates. All volumetric measurements were corrected for TIV. The multivariate model evaluated using Wilks\u0026rsquo; Lambda revealed a significant main effect of sex (Wilks\u0026rsquo; Λ\u0026thinsp;=\u0026thinsp;0.652, F(10,50)\u0026thinsp;=\u0026thinsp;2.66, p\u0026thinsp;=\u0026thinsp;0.011, partial η\u0026sup2; = 0.35), indicating substantial sex-based differences across the hypothalamic subunits. No significant multivariate effects were observed for handedness (Wilks\u0026rsquo; Λ\u0026thinsp;=\u0026thinsp;0.921, F(10,50)\u0026thinsp;=\u0026thinsp;0.43, p\u0026thinsp;=\u0026thinsp;0.924), the sex \u0026times; handedness interaction (Wilks\u0026rsquo; Λ\u0026thinsp;=\u0026thinsp;0.850, F(10,50)\u0026thinsp;=\u0026thinsp;0.89, p\u0026thinsp;=\u0026thinsp;0.553), or covariates BMI (Wilks\u0026rsquo; Λ\u0026thinsp;=\u0026thinsp;0.884, F(10,50)\u0026thinsp;=\u0026thinsp;0.66, p\u0026thinsp;=\u0026thinsp;0.758), age (Wilks\u0026rsquo; Λ\u0026thinsp;=\u0026thinsp;0.853, F(10,50)\u0026thinsp;=\u0026thinsp;0.86, p\u0026thinsp;=\u0026thinsp;0.575), or education (Wilks\u0026rsquo; Λ\u0026thinsp;=\u0026thinsp;0.858, F(10,50)\u0026thinsp;=\u0026thinsp;0.83, p\u0026thinsp;=\u0026thinsp;0.606).\u003c/p\u003e\u003cp\u003eLevene\u0026rsquo;s Test of Equality of Error Variances indicated violations of homogeneity for several dependent variables: left posterior (p\u0026thinsp;=\u0026thinsp;0.011), right posterior (p\u0026thinsp;=\u0026thinsp;0.030), right infTub (p\u0026thinsp;=\u0026thinsp;0.006), and whole right (p\u0026thinsp;=\u0026thinsp;0.049). While these violations suggest heterogeneity in the residual variances for these subunits, the robustness of MANCOVA to such assumptions supports the validity of the findings.\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparison of hypothalamic volume and associated subunits between groups\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUnivariate analyses confirmed significant sex effects across multiple subunits after adjusting for covariates (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The total hypothalamic volume (F(1,59)\u0026thinsp;=\u0026thinsp;16.54, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, partial η\u0026sup2; = 0.22) and hemispheric aggregates (whole left: F(1,59)\u0026thinsp;=\u0026thinsp;16.50, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, partial η\u0026sup2; = 0.22; whole right: F(1,59)\u0026thinsp;=\u0026thinsp;14.37, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, partial η\u0026sup2; = 0.20) were significantly larger in females (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Females exhibited larger adjusted mean volumes compared to males in the left a-sHyp (F(1,59)\u0026thinsp;=\u0026thinsp;5.50, p\u0026thinsp;=\u0026thinsp;0.022, partial η\u0026sup2; = 0.09), left posterior (F(1,59)\u0026thinsp;=\u0026thinsp;11.44, p\u0026thinsp;=\u0026thinsp;0.001, partial η\u0026sup2; = 0.16), left infTub (F(1,59)\u0026thinsp;=\u0026thinsp;16.33, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, partial η\u0026sup2; = 0.22), left supTub (F(1,59)\u0026thinsp;=\u0026thinsp;7.52, p\u0026thinsp;=\u0026thinsp;0.008, partial η\u0026sup2; = 0.11), right a-sHyp (F(1,59)\u0026thinsp;=\u0026thinsp;4.79, p\u0026thinsp;=\u0026thinsp;0.033, partial η\u0026sup2; = 0.08), right posterior (F(1,59)\u0026thinsp;=\u0026thinsp;14.19, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, partial η\u0026sup2; = 0.19), right infTub (F(1,59)\u0026thinsp;=\u0026thinsp;18.71, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, partial η\u0026sup2; = 0.24), and right supTub (F(1,59)\u0026thinsp;=\u0026thinsp;4.44, p\u0026thinsp;=\u0026thinsp;0.039, partial η\u0026sup2; = 0.07) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\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\u003eComparison of hypothalamic volume and associated subunits between females and males\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eNon-adjusted\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003e\u003cem\u003eAdjusted (Estimated Marginal Means)\u003c/em\u003e \u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eMale\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eMale\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e \u003csup\u003e\u003cem\u003ed\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003epartial η\u0026sup2;\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLeft Anterior-Inferior\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.71 (2.03) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.76 (2.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.57 (0.56) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.90 (0.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLeft Anterior-Superior\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.97 (3.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.82 (2.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.27 (0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.94 (0.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLeft Posterior\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86.65 (15.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.17 (9.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.42 (3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74.07 (2.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLeft Tuberal Inferior\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e108.08 (15.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89.21 (10.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e107.54 (3.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89.93 (3.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLeft Tuberal Superior\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82.61 (12.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.76 (9.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82.87 (2.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72.74 (2.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRight Anterior-Inferior\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.93 (2.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.99 (2.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.63 (0.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.02 (0.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.472\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRight Anterior-Superior\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.94 (4.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.74 (2.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.95 (0.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.65 (0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRight Posterior\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86.07 (14.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.71 (9.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89. 69 (2.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74.96 (2.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRight Tuberal Inferior\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99.45 (13.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83.04 (7.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98.83 (2.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83.22 (2.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.241\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRight Tuberal Superior\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83.93 (13.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.95 (10.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83.48 (2.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75.16 (2.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.070\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eWhole Left\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e308.02 (39.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e263.72 (26.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e309.67 (7.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e264.58 (7.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eWhole Right\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e299.32 (41.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e260.43 (24.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e301.58 (7.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e260.02 (7.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.196\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eWhole\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e607.34 (78.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e524.15 (49.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e611.24 (15.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e524.60 (14.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Adjusted based on MANCOVA analysis (covariates appearing in the model are evaluated at the following values: BMI\u0026thinsp;=\u0026thinsp;26.0141, Age\u0026thinsp;=\u0026thinsp;49.42, Education\u0026thinsp;=\u0026thinsp;16.15).\u003c/p\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Data presented as mean (SD). Volume normalization with the proportion method (the volume in mm\u0026sup3; per TIV mm\u0026sup3; and multiplied by 10⁶).\u003c/p\u003e\u003cp\u003e\u003csup\u003ec\u003c/sup\u003e Data presented as mean (SE). Volume normalization with the proportion method (the volume in mm\u0026sup3; per TIV mm\u0026sup3; and multiplied by 10⁶).\u003c/p\u003e\u003cp\u003e\u003csup\u003ed\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssociation of hypothalamic subunit volumes with covariates and fixed factors\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe covariate effects were limited to age, which showed univariate associations with right a-iHyp (F(1,59)\u0026thinsp;=\u0026thinsp;4.32, p\u0026thinsp;=\u0026thinsp;0.042, partial η\u0026sup2; = 0.07), right a-sHyp (F(1,59)\u0026thinsp;=\u0026thinsp;4.68, p\u0026thinsp;=\u0026thinsp;0.035, partial η\u0026sup2; = 0.07), right infTub (F(1,59)\u0026thinsp;=\u0026thinsp;4.04, p\u0026thinsp;=\u0026thinsp;0.049, partial η\u0026sup2; = 0.06), and right (F(1,59)\u0026thinsp;=\u0026thinsp;5.57, p\u0026thinsp;=\u0026thinsp;0.022, partial η\u0026sup2; = 0.09). The right a-iHyp (r\u0026thinsp;=\u0026thinsp;0.344), right infTub (rho\u0026thinsp;=\u0026thinsp;0.259), right whole (rho\u0026thinsp;=\u0026thinsp;0.249), and right a-sHyp (rho\u0026thinsp;=\u0026thinsp;0.244) were positively correlated with age (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). BMI and education level did not significantly predict any volumetric measures (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eHandedness did not exhibit a significant multivariate effect (p\u0026thinsp;=\u0026thinsp;0.924), nor did the interaction between sex and handedness (p\u0026thinsp;=\u0026thinsp;0.553) significantly influence the combined hypothalamic volumes. Handedness and sex \u0026times; handedness interactions remained non-significant across all subunits (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study provides evidence of sexual dimorphism in the hypothalamic structure, revealing that females exhibit significantly larger volumes across most hypothalamic subunits and the entire hypothalamus than males, even after adjusting for TIV, BMI, age, and education. These findings align with the emerging literature on sex-based neuroanatomical differences but extend prior work by dissecting specific subunits of the hypothalamus and systematically controlling for potential confounders [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, our results diverge from the recent large-scale findings of Xu et al. (2025) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], which present a contrasting view on hypothalamic sexual dimorphism. In their analysis of two major population-based cohorts (UK Biobank and Life-Adult-Study), they reported that while males had larger absolute hypothalamic volumes, this difference was entirely accounted for by TIV, resulting in no significant sex difference in relative hypothalamic volume. This is in direct opposition to our finding of significantly larger TIV-corrected hypothalamic volumes in females. Furthermore, they observed a negative association between age and hypothalamic volume across the lifespan, whereas our data indicated a positive correlation in several right-sided subunits. These discrepancies may stem from several key methodological and demographic differences. The substantially larger sample size in the Xu et al. study (N\u0026thinsp;\u0026gt;\u0026thinsp;50,000) provides greater statistical power and may reflect a more generalizable population trend compared to our more modest cohort (N\u0026thinsp;=\u0026thinsp;66). Additionally, potential variations in TIV correction techniques, the specific automated segmentation algorithms employed, and underlying population characteristics (e.g., Iranian vs. European cohorts) could contribute to these divergent outcomes, underscoring the need for methodological standardization in future research.\u003c/p\u003e\u003cp\u003eNotably, the multivariate model revealed that approximately 35% of the variance in the combined hypothalamic subunit volumes could be explained by sex, and subsequent univariate analyses confirmed this effect across multiple subregions. Together, the results underscore the hypothalamus as a sexually dimorphic structure, likely reflecting divergent neurodevelopmental trajectories or functional specializations tied to sex-specific physiological and endocrine demands [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These findings also resonate with animal studies highlighting sex differences in hypothalamic nuclei regulating reproduction, stress, and homeostasis, such as the sexually dimorphic nucleus and anteroventral periventricular nucleus, which are larger in females [\u003cspan additionalcitationids=\"CR25 CR26 CR27 CR28 CR29 CR30\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHow do brain processes, hormonal activity, and behavior interact? One helpful way to understand this interaction is to consider the brain as part of the endocrine system [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The hypothalamus and pituitary gland regulate hormone production, and the brain acts as an endocrine organ [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Steroids and sex hormones can cross the blood-brain barrier and affect the central nervous system as well as other regions. Connections between hormone levels and alterations in the volume of particular subregions of the hypothalamus have been discovered in recent studies [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These dissimilarities are specifically evident in different cognitive states and between the sexes.\u003c/p\u003e\u003cp\u003eMethodologically, automated segmentation of MRI data allows for detailed examination of structural alterations within specific hypothalamic nuclei, beyond gross regional assessments [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Notably, TIV correction helped account for inter-individual cranial variability unrelated to biological sex factors [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The findings of this study, based on volume normalization to TIV, have significantly contributed to the understanding of sex differences in the hypothalamic volume and its subunits. These results indicate that, contrary to the general trend of larger TIVs in males, females exhibit larger volumes in the hypothalamus and its specific subunits, which aligns with previous studies [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, Makris et al. (2013) conducted a study using the parcellation method, which revealed that the total size of the hypothalamus in males was significantly larger than that in females [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. This difference was relative to the size of the cerebrum and was found to be similar in both hemispheres. This inconsistency is particularly intriguing, given the role of the hypothalamus in regulating numerous physiological processes, including those related to sex hormones [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, future studies based on healthy participants and TIV normalization approaches should be conducted to confirm these findings.\u003c/p\u003e\u003cp\u003eThe hypothalamus is a crucial organ for human health and is responsible for sensing internal bodily states and responses via hormone production. It is linked to complex disorders and diseases, such as obesity [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], schizophrenia, bipolar spectrum disorders [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], dementia [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], and Prader-Willi syndrome (PWS) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The developing hypothalamus may respond to suboptimal conditions by producing structural and functional changes that persist throughout the lifespan [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe larger hypothalamic volumes in females could be associated with the high density of estrogen and androgen receptors in these regions, suggesting a potential influence of sex hormones on hypothalamic development and structure [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Furthermore, the hypothalamus regulates the hypothalamic-pituitary-gonadal axis and shows high receptor expression for gonadal steroids, suggesting susceptibility to the organizational effects of circulating sex hormones in a sexually dimorphic manner [\u003cspan additionalcitationids=\"CR49 CR50\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Larger volumes in females could indicate adaptive responses to hormones, such as estrogen and progesterone, which fluctuate cyclically across their lifespans [\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe absence of significant differences in the volumes of the left and right a-iHyp, encompassing the SCN and SON, between the sexes may indicate a more complex interplay of factors beyond hormonal influence, possibly involving genetic and environmental components or sexual orientation [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. As the hormonal milieus changes with events such as menopause, neural structures can remodel in response [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe positive correlation between age and certain hypothalamic volumes raises questions regarding the effects of aging on hypothalamic structures and their functional implications [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. These correlations suggest that as individuals age, there may be a sex-specific increase in hypothalamic volume, which could have consequences for neuroendocrine function [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Although age is typically associated with atrophy in many brain structures, some studies have reported region-specific increases or relative preservation in hypothalamic nuclei, possibly reflecting compensatory or adaptive mechanisms in neuroendocrine regulation [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Nevertheless, the absence of significant effects of BMI and years of education on hypothalamic volume aligns with prior findings, suggesting that in healthy individuals, the hypothalamus may be relatively resilient to variations in metabolic status or cognitive reserve proxies. This is particularly evident when measured using structural MRI techniques, and is consistent with reports linking obesity to hypothalamic microstructural changes rather than macrostructural alterations [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrevious studies have employed TIV adjustment and normalization techniques to account for head size variations in whole-brain and regional volumetric measures [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR60 CR61\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. However, the optimal TIV adjustment method remains uncertain with inconsistent findings across studies. Furthermore, the accuracy of these methods when applied to specific brain substructures, such as the hypothalamic subunits, has not been extensively investigated. Univariate and multivariate sex differences in GMV were predominantly influenced by TIV disparities between males and females [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. When the TIV variation was controlled for, these sex differences were significantly reduced. The choice of the TIV adjustment method can substantially impact the magnitude, direction, and replicability of the estimated sex differences in brain volume. Different studies have favored various approaches, such as the residual method [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], proportion method [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], or including TIV as a covariate in a linear model [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. The limitations of the current TIV adjustment methods are compounded by the difficulty in defining functional subnuclei within the hypothalamus [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Given the lack of definitive guidance in the literature, we preferred a simple TIV volume normalization method for the hypothalamic volume and its associated subunits to minimize potential biases in our approach [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite promising findings, the results of this study must be interpreted within the context of several significant limitations. First, the relatively modest sample size, while sufficient to detect the primary effect of sex, limits the generalizability of our findings and increases the risk of both Type I and Type II errors. Such a sample size may not be robust enough to capture the full spectrum of population variance or reliably detect more subtle effects, such as interactions between sex and other variables. Second, our use of a convenience sample from the IBID may introduce potential selection bias, though we pursued efforts to ensure that the two groups were well-matched in terms of age, BMI, years of education, and handedness. The participants in this database may not be fully representative of the general population, potentially differing in unmeasured socioeconomic, health, or lifestyle factors that could influence brain structure, thereby limiting the external validity of our conclusions. A third major limitation is the absence of endocrine data. This study investigated a key neuroendocrine structure without measuring circulating sex hormone levels (e.g., estradiol, testosterone). Consequently, we cannot disentangle the lifelong organizational effects of sex from the transient activational effects of current hormone levels, which are known to fluctuate and influence brain plasticity. This omission prevents a deeper mechanistic understanding of the observed volumetric differences. Therefore, these specific subunit findings should be considered preliminary and require replication. Finally, as noted, the automated segmentation method groups nuclei by anatomical proximity rather than distinct function, which complicates the functional interpretation of volumetric changes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The cross-sectional design also prevents us from inferring causality or tracking developmental changes over time. Future research should address these shortcomings by employing larger, more diverse cohorts, incorporating hormonal and genetic analyses, applying rigorous statistical corrections, and utilizing longitudinal designs to clarify the complex interplay between sex, age, and hypothalamic morphology.\u003c/p\u003e\u003cp\u003eBilliot et al. employed the probabilistic output generated by a softmax layer to mitigate the influence of partial volume effects. This approach was used for volume calculations, offering a quantitative measure of the uncertainty associated with each label. Moreover, the Freesurfer pipeline lacks a dedicated method for segmenting the hypothalamus or its subregions; instead, it groups these structures within a broader anatomical region referred to as the ventral \"ventral diencephalon (ventral DC),\" which includes multiple other structures. To overcome this limitation, a segmentation protocol, developed and validated [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], was introduced to specifically address the segmentation of the hypothalamus and its subregions. Furthermore, FreeSurfer is known to exhibit variability in segmentation accuracy owing to differences in contrast across T1-weighted images. To address this issue and evaluate the reliability of the manual segmentation protocol, Billot et al. conducted inter- and intra-rater variability experiments. The robustness of the automated segmentation method trained on an internal dataset was also assessed using two additional datasets. This included an external dataset with diverse acquisition parameters and a heterogeneous ADNI dataset, ensuring the generalizability of the method across varying T1-weighted image characteristics.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study provides clear evidence of sexual dimorphism in the human hypothalamus. The primary finding is that females have significantly larger hypothalamic volumes than males, even after accounting for differences in body mass, age, and education. While handedness had no discernible effect, age was linked to volumetric changes in specific right-hemisphere subunits. These results highlight that sex is a major factor in shaping hypothalamic structure, with age playing a secondary, more localized role.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The authors declare that no funds, grants, or other support was received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e S.G. and S.M. contributed to the study\u0026apos;s conception and design. Material preparation, data collection, and analysis were performed by S.G., S.M., M.M., M.S., and S.B. The first draft of the manuscript was written by S.G. and S.M., and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eThis article contains all the data produced or analyzed during this investigation. Further inquiries should be forwarded to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe Ethics Committee of the National Institute for Medical Research Development (NIMAD) approved this study (Ethical Code: IR.NIMAD.REC.1396.319) in accordance with the Declaration of Helsinki. All individuals and/or their legal representatives gave written informed consent before entering the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish:\u003c/strong\u003e Not Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eWe extend our sincere gratitude to all participants for their invaluable contributions in enabling us to complete this research project successfully.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRasmussen JM, Wang Y, Graham AM, Fair DA, Posner J, O\u0026rsquo;Connor TG, et al. Segmenting hypothalamic subunits in human newborn magnetic resonance imaging data. Hum Brain Mapp. 2024;45:e26582.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSpindler M, Thiel CM. 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Elsevier; 2021. pp. 107\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRuggeri A, Nerland S, M\u0026oslash;rch-Johnsen L, J\u0026oslash;rgensen KN, Barth C, Wortinger LA et al. Hypothalamic Subunit Volumes in Schizophrenia and Bipolar Spectrum Disorders. Schizophr Bull. 2024;:sbad176.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBocchetta M, Gordon E, Manning E, Barnes J, Cash DM, Espak M, et al. Detailed volumetric analysis of the hypothalamus in behavioral variant frontotemporal dementia. J Neurol. 2015;262:2635\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrown SSG, Manning KE, Fletcher P, Holland A. In vivo neuroimaging evidence of hypothalamic alteration in Prader\u0026ndash;Willi syndrome. Brain Commun. 2022;4:fcac229.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePol HEH, Cohen-Kettenis PT, Van Haren NEM, Peper JS, Brans RGH, Cahn W et al. 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Effects of different intracranial volume correction methods on univariate sex differences in grey matter volume and multivariate sex prediction. Sci Rep. 2020;10:12953.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO\u0026rsquo;Brien LM, Ziegler DA, Deutsch CK, Frazier JA, Herbert MR, Locascio JJ. Statistical adjustments for brain size in volumetric neuroimaging studies: Some practical implications in methods. Psychiatry Res. 2011;193:113\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOpfer R, Kr\u0026uuml;ger J, Spies L, Kitzler HH, Schippling S, Buchert R. Single-subject analysis of regional brain volumetric measures can be strongly influenced by the method for head size adjustment. Neuroradiology. 2022;64:2001\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMalone IB, Leung KK, Clegg S, Barnes J, Whitwell JL, Ashburner J, et al. 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NeuroImage. 2015;104:366\u0026ndash;72.\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-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hypothalamus, MRI, Sex, Segmentation, Neuroendocrine","lastPublishedDoi":"10.21203/rs.3.rs-7163918/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7163918/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and purpose:\u003c/strong\u003e Sex differences in brain structure and function are well documented; however, the impact of these differences on the hypothalamic volume and associated subunits remains underexplored. This study aimed to investigate sex differences in hypothalamic volume and its subregions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Sixty-six healthy individuals (34 males) with a mean age of 49.42 ± 12.25 years underwent 3T imaging with a 64-channel head coil, using a 3D T1-weighted MPRAGE sequence. Automated segmentation of hypothalamic subregions, including the anterior-superior (a-sHyp), anterior-inferior (a-iHyp), superior tuberal (supTub), inferior tuberal (infTub), and posterior (posHyp), was performed to quantify the total volume and 10 subunits using a deep convolutional neural network validated by FreeSurfer v7.4.1, with total intracranial volume (TIV) normalization applied for individual head size variations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Multivariate analysis of covariance (MANCOVA) revealed significant sexual dimorphism (Wilks’ Λ = 0.652, F(10,50) = 2.66, p = 0.011, partial η² = 0.35), with females exhibiting larger adjusted volumes across nearly all subunits. Age showed modest associations with right a-iHyp (p = 0.042), a-sHyp (p = 0.035), supTub (p = 0.049), and the whole right (p = 0.022). These subunits were positively correlated with age. The body mass index, education, handedness, and sex × handedness interactions were not significant (p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Our findings suggest that females have larger hypothalamic volumes and that certain subunits exhibit sex-specific differences, emphasizing the importance of considering sex differences in neuroscientific research and clinical practice.\u003c/p\u003e","manuscriptTitle":"Volumetric Sex Differences in Hypothalamic Volume and Associated Subunits Revealed by Automated MRI Segmentation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-20 08:41:59","doi":"10.21203/rs.3.rs-7163918/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-10-22T10:59:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177100981351478700097778061522175940013","date":"2025-10-16T13:50:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-07T15:18:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-08T08:41:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-01T03:51:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-01T03:51:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2025-07-19T10:50:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d64a2867-a61d-4826-aaa7-baa40c4b6f35","owner":[],"postedDate":"October 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T08:41:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-20 08:41:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7163918","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7163918","identity":"rs-7163918","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}