Hypothalamic Subregion Alterations in Short Stature Children and Relations to Growth-Regulated Hormone Level and Cognitive Changes

Hypothalamic Subregion Alterations in Short Stature Children and Relations to Growth-Regulated Hormone Level and Cognitive Changes | 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 Hypothalamic Subregion Alterations in Short Stature Children and Relations to Growth-Regulated Hormone Level and Cognitive Changes Xiaojun Chen, Xiafei zhan, Yi Lu, Jiangfeng Pan, Zhihan Yan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5047218/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Rationale and Objectives: To delve into the volume alterations of the hypothalamus subregions among short stature children, identify the relations to growth-regulated hormone level and cognitive changes. Materials and Methods: Structural magnetic resonance imaging (MRI) was obtained from 79 children with diagnosed with growth hormone deficiency (GHD), 89 children with idiopathic short stature (ISS). Levels of IGF-1, IGFBP-3, ACTH and cortisol were measured and the growth hormone stimulation test was used to documented the GH level. The Wechsler Intelligence Scale test was used to assessment cognitive changes. Based on an automated hypothalamus segmentation tool, the hypothalamus and its subregions were segmented. Volumes of the hypothalamic subregions were compared between the two groups. Correlational analysis was used to assess the relationship between morphometric alterations with hormone levels and cognitive changes. Results: Posterior subregion (PS) and tubular inferior subregion (TIS) volumes were significant smaller in GHD children compared with ISS children. However, GHD children got bigger volume in whole hypothalamus. In the whole cohort, the intelligent scores of Processing Speed Index (PSI) and Cognitive Proficiency Index (CPI) were positively correlated with the volume of PS, WH (whole hypothalamus) respectively. In GHD group, the volume of TIS was positively correlated with the level of IGFBP-3. Additionally, the volume of PS was negatively correlated with the levels of peak GH and GH levels recorded at intervals of 30 min, 90 min. Conclusion: Analyzing hypothalamus subregions could improve understanding of GHD pathophysiology and may serve as non-invasive imaging biomarkers, potentially leading to new therapeutic strategies. Hypothalamus Subregion Deep Learning Automated Segmentation Tool Growth Hormone Deficiency Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The hypothalamus, occupying merely 4 milliliters within the vast expanse of a total brain volume ranging from approximately 1.1 to 1.2 liters[ 1 ]. It is regulated by the pituitary gland through the secretion of various stimulating and inhibiting hypothalamic factors[ 2 ]. And, the hormones from hypothalamic-pituitary axis secrete into the blood system and exert their effects. Undeniably, hypothalamic-pituitary axis emerges as a pivotal master regulator orchestrating growth, reproduction, and the maintenance of homeostasis[ 1 ]. Growth hormone (GH) is released from anterior pituitary gland, and controlled by growth hormone-releasing hormone (GHRH) which released by hypothalamus. This hormone plays a cardinal role in growth and development[ 3 ]. In children, a decline in growth hormone levels (growth hormone deficiency, GHD) precipitates more than just the manifestation of liner growth, characterized by short stature, it also casts a wide shadow over neurological health. This hormonal insufficiency has been implicated in alterations to brain connectivity and activity patterns, impacting the maturation and structural integrity of cerebral regions. Such changes can further extend to cognitive functions and behavioral traits, potentially leading to multifaceted developmental challenges[ 4 – 6 ]. Moreover, a comprehensive longitudinal study[ 7 ] has revealed that treatment with recombinant human growth hormone (rhGH) exerts a beneficial influence on the growth, cognitive capabilities, and behavioral aspects of children afflicted with short stature. Notably, rhGH therapy has been shown to normalize spontaneous brain activity, which is a crucial component of healthy neurological function. In terms of brain structure, GHD children exhibit reduced volumes in specific cerebral structures, including the splenium of the corpus callosum, left globus pallidum, thalamus and hippocampus[ 8 ]. On the other hand, a significant body of research has also concentrated on elucidating the alterations within pituitary or hypothalamus -pituitary axis resulting from GHD in children[ 9 , 10 ], but little focus on the hypothalamus alone, despite the pivotal role of this region in regulating the secretion of growth hormone. A possible reason for this might be attributed to the region's uniquely complex, delicate anatomical structure, but holding a variety of roles[ 11 ]. The hypothalamus, a constituent of the diencephalon situated beneath the thalamus, is intricately organized into roughly a dozen nuclei—though this number can vary depending on the classification criteria employed[ 12 ]. Each nucleus harbors distinct neuroendocrine functions and houses specialized cellular assemblies[ 13 ]. Given that various diseases selectively target specific hypothalamic nuclei, the study of these subregions holds significant potential for advancing our comprehension of disease-specific pathologies. However, despite small volume, the hypothalamus contains even tinier subregions. Moreover, compounding this issue, the delineation of hypothalamic substructures on magnetic resonance (MR) scans is notoriously complex due to their close proximity, similar tissue characteristics and the lack of image contrast in its vicinity. As a result, the accurate segmentation of these subregions remains a significant obstacle, impeding progress in understanding the pathophysiological mechanisms by which diseases exert their effects on the hypothalamus. Following recent advances in deep machine learning[ 14 ], a publicly available automated segmentation tool for the whole hypothalamus and its subregions from T1-weighted MRI scans was created[ 13 ]. This tool is based on a deep convolutional neural network, enables not only precise and robust segmentation, but also volumetric measurement of the hypothalamus and its subregions. Currently, there have been some studies utilizing this tool to investigate the impacts of various diseases on the hypothalamus subregions, and have yielded credible results. For instance, compared to individuals with a normal weight, using this automated segmentation tool, patients with anorexia nervosa and obesity were found to exhibit distinct alterations in the volumes of specific hypothalamic subregions[ 15 ], which promoting the understanding of the underlying mechanisms. Additionally, this tool also helps in deepening our understanding of the hypothalamus' role in aggression, which may facilitate the creation of targeted interventions and treatments[ 16 ]. The hypothalamus is involved in the secretion of growth hormone, and may also be subject to the effects of growth hormone deficiency. Then, in the present study, we aim to delve into the alterations of the hypothalamus and its constituent subregions in terms of volume among children short stature, based on the fully automated hypothalamus segmentation tool, as well as to explore the relationship between these changes and the regulatory hormones involved in growth hormone modulation, as well as cognitive changes. We believe that analyzing hypothalamic subregions may provide a further understanding of the pathophysiology of short stature children. 2. Materials and methods 2.1 Participants The prospective study received approval from the Institutional Ethics Committee (Clinical trial number: LCKY-2020-58). Written informed consent was secured from the parents or legal guardians of all participants. Children were recruited from the Pediatric Endocrinology Department, aged 8–12. Following height assessment, if their height was less than two standard deviations below the mean for age, sex, and race, and these children also presented an annual growth rate of < 5 cm [ 5 ], participants were diagnosed as exhibiting short stature. Those with short stature subsequently underwent growth hormone stimulation test to differentiate between GHD and ISS subgroups. The exclusion criteria were as follow: (1) children received growth hormone provocation test, intelligence scale test or growth hormone therapy prior to fMRI scanning; (2) abnormal findings on cerebral MRI;(3) artefacts (e.g. metal artefacts or motion artefacts) affected image quality and data post-processing;(4) Intrauterine distress, dystocia, premature delivery, etc.;(5) critical illness such as congenital heart disease, congenital abnormalities of bone development, abnormal thyroid hormone axis, abnormal chromosomes, neuropsychiatric disorders, other endocrinopathies, etc. ;(6) left-handedness.[ 17 ] 2.2 Growth hormone level measurement The growth hormone stimulation test remains the gold standard for clinical differential diagnosis of short children with GHD or ISS. Children were instructed to undergo an overnight fast of 8 to 12 hours before the test, and L-dopa or clonidine was used in the test. However, ongoing debates continue globally concerning the exact number of tests required and the interpretation of GH peak levels[ 18 ].Commonly, in China, GH levels were measured at intervals of 0, 30, 45, 60, and 90 minutes. Participants were then categorized as GHD or ISS based on peak GH response. A peak GH ≥ 10 ng/mL indicated ISS, while<10ng/ml defined GHD[ 19 , 20 ]. Specifically, a peak between 5–10 ng/mL suggested partial GHD, and < 5 ng/mL indicated complete GH deficiency. Levels of IGF-1, IGFBP-3, ACTH and cortisol were measured in the same time. 2.3 Wechsler Intelligence Scale test All children underwent the Wechsler Intelligence Scale 4th edition (WISC-IV) test. This cognitive test included four subscales containing Verbal Comprehension Index (VCI), Perceptual Reasoning Index (PRI), Working Memory Index (WMI) and Processing Speed Index (PSI). The total score of the four subscales was called Full-Scale Intelligence Quotient (IQ). In addition, the score of PSI and WMI is also known as Cognitive Proficiency Index (CPI), the score of PRI and VCI known as General Ability Index (GAI)[ 21 ]. 2.4 Image acquisition T1-weighted high-resolution anatomical images were acquired with a 3.0-T MR system (3.0 T Discovery MR750, GE Healthcare), using an 8-channel head coil: repetition time = 7.68ms, echo time = 3.43ms, flip angle = 12°, field of view = 256 mm 2 , slice thickness = 1 mm, layer spacing=1 mm, and voxel size = 1 mm × 1 mm × 1 mm. 2.5 Image processing The study workflow is shown in Fig. 1 . High-resolution T1-weighted anatomical images underwent pre-processing through the standard FreeSurfer-7.2 recon-all pipeline ( https://surfer.nmr.mgh.harvard.edu/ )[ 16 ]. Subsequently, the pre-processed T1 images were subjected to an automated hypothalamus segmentation process utilizing a publicly accessible pipeline ( https://github.com/BBillot/hypothalamus_seg ), which is built upon the FreeSurfer-7.2 framework[ 13 ]. This pipeline employs a sophisticated deep convolutional neural network (DCNN) to facilitate the segmentation of the entire hypothalamus and its ten constituent subregions—five on each side—as delineated from T1-weighted MRI scans, anatomically. The application of this pipeline yielded volumetric data encompassing both the entirety of the hypothalamus and the individual volumes of each subregion. The aforementioned subregions are defined by the following hypothalamic nuclei[ 13 , 15 ]: Anterior-Inferior Subregion (AIS): suprachiasmatic nucleus; supraoptic nucleus (SON) Anterior-superior Subregion (ASS): preoptic area; paraventricular nucleus (PVN) Posterior Subregion (PS): mamillary body (including medial and lateral mamillary nuclei); lateral hypothalamus; tuberomamillary nucleus (TMN) Tubular Inferior Subregion (TIS): infundibular (or arcuate) nucleus; ventromedial nucleus; SON; lateral tubular nucleus; TMN Tubular Superior Subregion (TSS): dorsomedial nucleus; PVN; lateral hypothalamus The total brain volume (TBV) and total intra-cranial volume (TIV) were generated by SPM12/CAT12 ( https://www.fil.ion.ucl.ac.uk/spm/software/spm12/ ) to be used as covariates of no interest in further analyses. TBV, encompassing the volume of all intracranial tissues including grey matter, white matter, cerebrospinal fluid, etc., is a crucial metric for investigating cerebral development. TIV focused on the space within the cranial cavity rather than just the brain tissue itself, which is often used as a covariate in neuroimaging studies to account for individual differences in head size when analyzing brain structures. 2.6 Statistical analysis R Studio( https://posit.co/ ) was used for statistical analysis of clinical and volumetric measures between the groups. Initially, the Shapiro-Wilk test was employed to evaluate the normalcy of data distribution within each group. Should the data adhere to a normal distribution, the independent samples t-test will be conducted, with outcomes being reported as mean ± standard deviation. Conversely, in cases where data deviate from normal distribution, the non-parametric Mann-Whitney U test was utilized, and results were described using the median along with the 25th to 75th percentile interquartile range. Gender difference was analyzed using the Chi-squared test (Male/Female). p < 0.05 was considered significant. According to previous report[ 22 ], in the developing pediatric brain, the hypothalamus volumes of males were larger than females. Moreover, there is an imbalance in the development of brain tissues and variations in skull size. Then, age, sex, TIV and TBV were controlled as covariates[ 15 ]. Additionally, the right and left hypothalamus volumes existed notable asymmetries. Therefore, the average of left and right hypothalamus was calculated for measurement in this study. Pituitary gland height was measured by an experienced radiologist from T1 coronal reconstruction images which clearly showed the maximum cross section of the pituitary gland. Finally, One-way analysis of covariance (ANCOVA) was employed to investigate potential differences between the GHD and ISS groups. When testing the association between volumes and hormone levels of GH at every interval, levels of IGF-1, IGFBP-3, ACTH and cortisol, Spearman's ρ partial correlation was used, adjusting for sex, age, TIV and TBV. Firstly, residuals for the volumes and hormone levels were calculated by controlling for the effects of age, sex, TBV and TIV. Subsequently, Spearman correlation analyses were conducted based on these residuals. Each morphometric analysis was Bonferroni corrected; a corrected p < 0.05 was considered significant. The forementioned statistical method was also used to analysis the correlation between WISC-IV test scores and the volumes of hypothalamus subregions. 3. Results 3.1 Demographic and clinical features Data was from our three previous fMRI researches. As shown in Table 1 , totally 168 children were included after exclusion, including 79 children with GHD, 89 children with ISS, matched with age and sex. No statistically significant differences were observed in gender ( P = 0.904), age ( P = 0.514), height ( P = 0.756) or weight ( P = 0.477) across the groups. Additionally, significant group differences were found in GH levels recorded in throughout GH provocation test as basal GH level ( P = 0.014), GH level at 30 min ( P = 0.000), at 45 min ( P = 0.000), at 60 min ( P = 0.000), at 90 min ( P = 0.000) and peak GH level ( P = 0.000) between the GHD and ISS groups. Otherwise, the levels of IGF-1, IGFBP-3, ACTH and cortisol had no statistical deficits during the groups. The scores of WISC-IV test had no statistical deficits between GHD and ISS group either, shown in Fig. 2 . Table 1 Demographic and clinical characteristics GHD ISS P -value Number 79 89 - Gender (F/M) 33/46 38/51 0.904 b Age(years) 9.51(7.88–10.5) 9.23(8.10–11.00) 0.514 c Height (m) 1.23 ± 0.09 1.23 ± 0.09 0.756 a Weight (kg) 22.30 (20.00-27.40) 23.00(20.00-25.50) 0.477 c Basal GH level(ng/ml) 0.41 (0.20–1.31) 0.72(0.29–3.05) 0.014 *c GH level at 30min(ng/ml) 5.05(2.47–6.74) 9.86(6.91-12.00) 0.000 ***c GH level at 45min(ng/ml) 4.86 (2.28–6.77) 11.60 (7.77–16.50) 0.000 ***c GH level at 60min(ng/ml) 3.52 (1.54–5.39) 10.07 (5.71–14.60) 0.000 ***c GH level at 90min(ng/ml) 4.08 (1.86–6.66) 8.97 (5.62-13.00) 0.000 ***c Peak GH level(ng/ml) 7.12 (5.22–8.33) 14.80 (12.00-18.30) 0.000 ***c IGF-1(ng/ml) 147 (114–183) 144 (108–194) 0.792 c IGFBP-3 (µg/ml) 3.58 ± 0.98 3.73 ± 0.89 0.316 a ACTH (pg/ml) 14.7(11.1–22.0) 14.9 (11.7–21.3) 0.980 c Cortisol (µg/dl) 9.10 (6.74–11.80) 8.8 (6.6–12.7) 0.932 c a, independent two-sample t-test, data are presented as mean ± standard deviation; b, chi-square test; c, Mann-Whitney U test. B and c data are presented as median (first and third quartile). * P <0.05, *** P < 0.001 Abbreviations: GH, Growth Hormone; GHD, Growth Hormone Deficiency; ISS, Idiopathic Short Stature; IGF-1, Insulin-like Growth Factor-1; IGFBP-3, Insulin-like Growth Factor Binding Protein-3; ACTH, Adrenocorticotropic Hormone. 3.2 Group comparisons of hypothalamic volumes One-way analysis of covariance (ANCOVA) was employed to investigate volumes of hypothalamus subregions and pituitary gland height. We found that PS ( P < 0.05) and TIS ( P < 0.05) volumes were significant smaller in GHD children compared with ISS children. However, GHD children got bigger volume in whole hypothalamus ( P < 0.05). Moreover, there were no significant difference in volume of AIS, ASS, TSS and pituitary gland height. As shown in Table 2 . Table 2 Average volume of hypothalamus subregions and pituitary gland height Region GHD ISS F Pr (> F) AIS 17.70 (0.43) 17.94 (0.40) 0.07 0.789 ASS 22.34 (0.44) 21.43 (0.41) 0.81 0.370 PS 114.62 (1.61) 115.60 (1.52) 4.58 0.034 * TIS 123.41 (1.12) 126.75 (1.06) 4.02 0.047 * TSS 112.76 (1.17) 113.10 (1.11) 1.23 0.269 WH 787.67 (7.72) 785.36 (7.37) 5.48 0.020 * PGH 3.68 (0.10) 3.79 (0.11) 0.15 0.703 Data are presented as mean (standard error of the mean). * P <0.05. One-way analysis of covariance (ANCOVA) was employed to investigate potential differences in average volume of hypothalamus subregions and pituitary gland height between the GHD and ISS groups, adjusting age, sex, total intracranial volume (TIV) and total brain volume (TBV). Abbreviations: GHD, Growth Hormone Deficiency; ISS, Idiopathic Short Stature. AIS, Anterior-Inferior Subregion; ASS, Anterior-Superior Subregion; PS, Posterior Subregion; TIS, Tubular-Inferior Subregion; TSS, Tubular-Superior Subregion; WH, Whole Hypothalamus; PGH, Pituitary Gland Height. 3.3 Correlational analysis In the whole cohort, showed in Fig. 3 , the score of PSI and CPI in WISC-IV test were positively correlated with the volume of PS respectively (ρ = 0.229, P -adjusted = 0.020; ρ = 0.207, P -adjusted = 0.049). Additionally, the score of PSI and CPI in WISC-IV test were also positively correlated with the volume of WH (ρ = 0.258, P -adjusted = 0.005; ρ = 0.250, P -adjusted = 0.008). Whereas, we found no significant correlation within GHD group or ISS group. In GHD group, the volume of TIS was positively correlated with the level of IGFBP-3(ρ = 0.324, P -adjusted = 0.04). Additionally, the volume of PS was negatively correlated with the levels of peak GH(ρ=-0.348, P -adjusted = 0.018) and GH levels recorded at intervals of 30 min(ρ=-0.324, P -adjusted = 0.038), 90 min(ρ=-0.360, P -adjusted = 0.012). These results forementioned were showed in Fig. 4 . However, no correlation was observed between hypothalamus subregion volumes and clinical hormone level in the ISS group. 4 Discussion To our best knowledge, no previous investigation assessing the alterations in the volumes of hypothalamic subregions among children with short stature. In our research, we utilized a deep learning-based tool capable of segmenting the hypothalamus and its constituent subregions with remarkable precision. Our findings revealed morphological differences within the hypothalamus when comparing children with GHD to those with ISS. Furthermore, we discerned the associations between the volume of the subregions and growth-regulated hormone level, as well as cognitive changes. In this study, we chose children diagnosed with ISS as the control cohort, rather than employing a group comprised of children who exhibit average height norms. This choice is pivotal in ensuring that any observed discrepancies in hypothalamic volumes or correlations can be attributed specifically to the absence or insufficiency of growth hormone, as opposed to merely the condition of being short in stature. The majority of studies investigating the consequences of GHD also included children ISS as controls, exploring directions such as brain structure and function, cognitive abilities, and pituitary radiomics[ 8 , 20 , 23 ]. Meanwhile, some studies also chose healthy controls with normal height for analysis. These studies often feature relatively small sample sizes[ 4 , 6 , 24 ], and comparing GHD children with normally growing children, as opposed to ISS subjects, may yield greater likelihoods of detecting differences. This is because, besides the absence or reduction of growth hormone acting as a variable, height itself may influence the data outcomes at the same time. When conducting comparisons between the hypothalamic subregions of the GHD and ISS groups, adjustments for multiple comparisons were not performed. In fact, the hypothalamus regions are exceedingly small in volume, furthermore, each of which encompasses not just a single nucleus[ 25 ]. Then, These subregions carry out a multitude of neuroendocrine functions and subject to the modulatory influence of more than one hormone[ 26 , 27 ]. Thus, this was the first research focus on the hypothalamus subregions alterations about GHD, more likely be exploratory in nature. Potentially interesting trends or relationships deserve greater attention, as they may reveal novel insights or previously unexplored connections[ 28 ]. In our results, we found PS and TIS volumes were significant smaller in children with GHD than ISS children. Typically, smaller volumes may indicate less developed neural networks or fewer neurons. As mentioned above, TIS contains arcuate nucleus, ventromedial nucleus, supraoptic nucleus, lateral tubular nucleus and tuberomamillary nucleus (TMN), and PS contains mamillary body, lateral hypothalamus and TMN. Among these nucleuses, it has become clear that arcuate nucleus (ARC) is an important endocrine regulatory center that contains neurons producing growth hormone-releasing hormone (GHRH) and growth hormone release-inhibiting hormone (GHIH), also expresses growth hormone receptors (GHR) which receive regulation signals from GH secreted by the adenohypophysis[ 29 ]. Meanwhile, in GHD group, the volume of TIS is positively correlated with the level of IGFBP-3. In children with GHD, the regulatory mechanisms of the hypothalamic-pituitary-growth hormone axis may be more complex, modulating IGF-1 and IGFBP-3 levels, which in turn indirectly affect GH secretion. This is crucial for normal growth, development, and metabolism. The remaining hormones, including the nucleus in PS, do not directly participate in the regulation of GH secretion; however, they may indirectly influence GH secretion through physiological levels, metabolism, or stress responses[ 26 , 30 – 32 ]. Moreover, according to correlational results, in GHD group, the volume of PS was negatively correlated with several levels of GH. This negative correlation may suggest that children with GHD have potentially activated an underlying compensatory mechanism. PS may involve in the inhibitory control of GH secretion. When the volume of this region decreases, the inhibitory effect on growth hormone secretion is weakened, leading to elevated GH levels. It's worth noting that such research findings require further validation and confirmation. Taking together, we hypothesized that the potential pathophysiological mechanism of GHD may involve morphological and functional abnormalities in special subregions of the hypothalamus which directly or indirectly regulate GH secretion, and may further serve as imaging biomarkers for the differential diagnosis of GHD or as one of the indicators for treatment efficacy. However, the whole hypothalamus volume of GHD group is bigger than ISS group. According to previous research, the development of the hypothalamus is complex. In the pediatric period, the hypothalamus is right-lateralized, get logarithmic trend lines as a function of age, and also exist sexual dimorphism[ 22 ]. This finding in our research could be due to multiple factors in addition to GHD, such as normal growth and developmental rhythms and other concurrent factors. Therefore, studying subregions of the hypothalamus in children is more meaningful than studying the hypothalamus as a whole. In summary, our findings may suggest an alteration in hypothalamic structure and provide direction for studies on the hypothalamus in children. According to previous studies, the hypothalamus serves not only as a regulatory center for hormones but also participates in cognitive functions[ 33 ]. In this study, we found short stature children existed positive correlation between the intelligent scores of PSI, CPI with the volume of PS, WH, respectively. The anatomical structure of the hypothalamus may be related to cognitive function, with a larger hypothalamic volume potentially correlating with higher data processing scores. This positive correlation might suggest that the hypothalamus is involved in more complex cognitive processes within the nervous system. However, the specific neural mechanisms underlying this relationship require further investigation. Our study has several limitations. Firstly, the sample size is an issue. The study data were sourced from our three previous brain function databases containing 3D T1-weighted images, with a limited amount of data. Fortunately, the database is constantly updated, and multicenter research has commenced to include more participants in future studies. In future studies, we hope to conduct age-stratified group analyses and investigate gender differences. We plan to include more children with GHD, dividing them into partial GHD and complete GHD for further research (a peak level 5 ng/ml indicated partial deficiency, and a peak level < 5 ng/ml indicated complete deficiency[ 5 ]). Secondly, only structural data were acquired in this study. Although the hypothalamic volume is sufficiently large to serve as a seed point for functional analysis, the subregions of the hypothalamus are still too small. Currently, we have not found methods to perform functional analyses based on hypothalamic subregions. We will prospectively explore methods for functional analysis of the nervous system, focusing on microstructural functional analysis. Thirdly, in this study, children of normal height were not included for analysis, as there is no need to perform growth hormone stimulation test on children without short stature. In subsequent research, we will also explore differences research directions among the three groups of children in terms of cognitive or intellectual development. Finally, this study only measured the height of the pituitary gland and did not conduct volume measurements. The reason is that currently there are no automated tools for segmenting the pituitary gland, and manual delineation on magnetic resonance images is time-consuming, especially for thin-slice 3D-T1 images. In subsequent studies, we will focus on automatic segmentation techniques for the pituitary gland, and then utilize other novel analytical methods, such as radiomics or deep learning, for hypothalamic-pituitary axis research. 5. Conclusion In conclusion, our study emphasizes the volume alterations of the hypothalamus subregions in children with GHD compared to ISS children. Analyzing hypothalamic subregions may provide a better understanding of the pathophysiology of short stature children, further serve as imaging biomarkers for the differential diagnosis of GHD instead of invasive examination, as well as for cognitive assessment. However, further studies in a larger sample size and different research directions are needed to validate the specific relationship between hypothalamic structure and neuroendocrine function, with the aim of developing new therapeutic approaches. Abbreviations GHD Growth Hormone Deficiency ISS Idiopathic Short Stature. AIS,Anterior-Inferior Subregion ASS Anterior-Superior Subregion PS Posterior Subregion TIS Tubular-Inferior Subregion TSS Tubular-Superior Subregion WH Whole Hypothalamus PGH Pituitary Gland Height Declarations Ethics approval and consent to participate The prospective study received approval from the Institutional Ethics Committee. Written informed consent was secured from the parents or legal guardians of all participants. Consent for publication Not applicable. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This work was supported by grants from the National Natural Science Foundation of China (No. 82071902 and No. 82100952), Jinhua Science and Technology (NO.2023-4-090). Funding This work was supported by grants from the National Natural Science Foundation of China (No. 82071902 and No. 82100952), Jinhua Science and Technology (NO.2023-4-090). Author Contribution XJ and XF designed the study; XJ performed the statistical analysis and visualization and wrote the paper; LY verified all the underlying data; XJ, JF and ZH contributed to the interpretation of results and revision of the manuscript. All authors read and approved the final version. Acknowledgement The authors extend their deepest appreciation to the children volunteers and their families. The authors also thank the Pediatric Endocrinology Department for providing short stature children and thank foundations for supporting our study. Availability of data and material Not applicable. References Müller HL, Tauber M, Lawson EA, et al. Hypothalamic syndrome. Nat Rev Dis Primers. 2022;8(1):24. Bereket A, Kiess W, Lustig RH, et al. Hypothalamic obesity in children. Obes Rev. 2012;13(9):780–98. Gray SM, Bartell PA, Staniar WB. High glycemic and insulinemic responses to meals affect plasma growth hormone secretory characteristics in Quarter Horse weanlings. Domest Anim Endocrinol. 2013;44(4):165–75. Zhang F, Hua B, Wang M, Wang T, Ding Z, Ding JR. Regional homogeneity abnormalities of resting state brain activities in children with growth hormone deficiency. Sci Rep. 2021;11(1):334. Hu Y, Liu X, Chen X, et al. Differences in the functional connectivity density of the brain between individuals with growth hormone deficiency and idiopathic short stature. Psychoneuroendocrinology. 2019;103:67–75. Ding JR, Liu Y, Chen Q et al. Frequency Dependent Changes of Regional Homogeneity in Children with Growth Hormone Deficiency. Neuroscience. 2023: S0306-4522(23)00277-4 [pii]. Shen L, Lin X, Wang C et al. Longitudinal unraveling: The impact of recombinant human growth hormone on spontaneous brain activity in children with short stature-A resting-state fMRI study. J Neuroradiol. 2023: S0150-9861(23)00250-X [pii]. Webb EA, O'Reilly MA, Clayden JD, et al. Effect of growth hormone deficiency on brain structure, motor function and cognition. Brain. 2012;135(Pt 1):216–27. Degerblad M, Brismar K, Rähn T, Thorén M. The hypothalamus-pituitary function after pituitary stereotactic radiosurgery: evaluation of growth hormone deficiency. J Intern Med. 2003;253(4):454–62. Shu K, Wang K, Zhang R et al. Pituitary MRI Radiomics Improves Diagnostic Performance of Growth Hormone Deficiency in Children Short Stature: A Multicenter Radiomics Study. Acad Radiol. 2024: S1076-6332(24)00293-9 [pii]. Gouveia FV, Hamani C, Fonoff ET, et al. Amygdala and Hypothalamus: Historical Overview With Focus on Aggression. Neurosurgery. 2019;85(1):11–30. Baroncini M, Jissendi P, Balland E, et al. MRI atlas of the human hypothalamus. NeuroImage. 2012;59(1):168–80. Billot B, Bocchetta M, Todd E, Dalca AV, Rohrer JD, Iglesias JE. Automated segmentation of the hypothalamus and associated subunits in brain MRI. NeuroImage. 2020;223:117287. Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ. Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions. J Digit Imaging. 2017;30(4):449–59. Alzaid H, Simon JJ, Brugnara G, Vollmuth P, Bendszus M, Friederich HC. Hypothalamic subregion alterations in anorexia nervosa and obesity: Association with appetite-regulating hormone levels. Int J Eat Disord. 2024;57(3):581–92. Bell C, Rokicki J, Tesli N et al. Hypothalamic subunit volumes and relations to violence and psychopathy in male offenders with or without a psychotic disorder. Eur Arch Psychiatry Clin Neurosci. 2024. Tang J, Xia Y, Liu N, et al. Growth hormone deficiency interferes with dynamic brain networks in short children. Psychoneuroendocrinology. 2022;142:105786. Partenope C, Galazzi E, Albanese A, Bellone S, Rabbone I, Persani L. Sex steroid priming in short stature children unresponsive to GH stimulation tests: Why, who, when and how. Front Endocrinol (Lausanne). 2022;13:1072271. Dror N, Oren L, Pantanowitz M, Eliakim A, Nemet D. The Wingate anaerobic test cannot be used for the evaluation of growth hormone secretion in children with short stature. J Sport Health Sci. 2017;6(4):443–6. Cong M, Qiu S, Li R, Sun H, Cong L, Hou Z. Development of a predictive model of growth hormone deficiency and idiopathic short stature in children. Exp Ther Med. 2021;21(5):494. Yang P, Cheng CP, Chang CL, Liu TL, Hsu HY, Yen CF. Wechsler Intelligence Scale for Children 4th edition-Chinese version index scores in Taiwanese children with attention-deficit/hyperactivity disorder. Psychiatry Clin Neurosci. 2013;67(2):83–91. Isıklar S, Turan Ozdemir S, Ozkaya G, Ozpar R. Hypothalamic volume and asymmetry in the pediatric population: a retrospective MRI study. Brain Struct Funct. 2022;227(7):2489–501. Zhang Z, Wang Y, Gao Y, et al. Morphological changes of the cerebral cortex between children with isolated growth hormone deficiency and idiopathic short stature. Brain Res. 2020;1748:147081. Zhou Z, Luo Y, Gao X, et al. Alterations in brain structure and function associated with pediatric growth hormone deficiency: A multi-modal magnetic resonance imaging study. Front Neurosci. 2022;16:1043857. Mehay D, Silberman Y, Arnold AC. The Arcuate Nucleus of the Hypothalamus and Metabolic Regulation: An Emerging Role for Renin-Angiotensin Pathways. Int J Mol Sci. 2021;22(13):7050. Khodai T, Luckman SM. Ventromedial Nucleus of the Hypothalamus Neurons Under the Magnifying Glass. Endocrinology. 2021;162(10):bqab141. McClellan KM, Parker KL, Tobet S. Development of the ventromedial nucleus of the hypothalamus. Front Neuroendocrinol. 2006;27(2):193–209. Armstrong RA. When to use the Bonferroni correction. Ophthalmic Physiol Opt. 2014;34(5):502–8. de Lima J, Debarba LK, Rupp AC, et al. ARC(GHR) Neurons Regulate Muscle Glucose Uptake. Cells. 2021;10(5):1093. Lopes-Azevedo S, Fortaleza E, Busnardo C, et al. The Supraoptic Nucleus of the Hypothalamus Modulates Autonomic, Neuroendocrine, and Behavioral Responses to Acute Restraint Stress in Rats. Neuroendocrinology. 2020;110(1–2):10–22. Naganuma F, Nakamura T, Kuroyanagi H, et al. Chemogenetic modulation of histaminergic neurons in the tuberomamillary nucleus alters territorial aggression and wakefulness. Sci Rep. 2021;11(1):17935. Ozturk A, Yousem DM, Mahmood A, El Sayed S. Prevalence of asymmetry of mamillary body and fornix size on MR imaging. AJNR Am J Neuroradiol. 2008;29(2):384–7. Liu X, Guo Z, Cheng J, Wei F, Zhang J. Abnormal white matter integrity of anterior-inferior hypothalamus in mild cognitive impairment patients with depression symptoms. J Affect Disord. 2024;362:225–9. Additional Declarations No competing interests reported. Supplementary Files supplementarymateral.docx Cite Share Download PDF Status: Posted Version 1 posted 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5047218","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":358000417,"identity":"fa785c1c-cdbb-4408-9f94-b6ed0c75c74c","order_by":0,"name":"Xiaojun Chen","email":"","orcid":"","institution":"The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojun","middleName":"","lastName":"Chen","suffix":""},{"id":358000418,"identity":"47d9fff8-9bbd-46db-ac4a-f0973c9e6fb2","order_by":1,"name":"Xiafei zhan","email":"","orcid":"","institution":"Jinhua Municipal Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiafei","middleName":"","lastName":"zhan","suffix":""},{"id":358000419,"identity":"c552be8f-15e6-47fd-bc3b-65ae4eac615b","order_by":2,"name":"Yi Lu","email":"","orcid":"","institution":"The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Lu","suffix":""},{"id":358000420,"identity":"566167c7-4a15-4519-93df-853ef12a5081","order_by":3,"name":"Jiangfeng Pan","email":"","orcid":"","institution":"Jinhua Municipal Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiangfeng","middleName":"","lastName":"Pan","suffix":""},{"id":358000421,"identity":"93ed447e-3ad3-47d5-a458-68ba93db7c2d","order_by":4,"name":"Zhihan Yan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYDACCcY2CIO9B0zx8BGvhecMA8MBIMVGWAsDVI1EDlgLA0Et8rOb2x783HFY3lzy7cHHH3PsZNgYmB8+uoFHC+Ocg+2GvWcOG+6cnZdscHBbMtBhbMbGOXi0MEsktknwth1m3HA7x0zi4DZmoBYeNml8WtiAWiT/th2233DzDEhLPWEtPEAt0kBbEjfc4AFpOUxYiwRIi2xbevKGMznGBme3HedhYybgF/kZ6c8k37ZZ2244fsbwQeW2ant+9uaHj/FpgYJmJDYzYeUgUEecslEwCkbBKBiZAADwV0eFgf8A/gAAAABJRU5ErkJggg==","orcid":"","institution":"The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zhihan","middleName":"","lastName":"Yan","suffix":""}],"badges":[],"createdAt":"2024-09-07 05:36:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5047218/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5047218/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68653593,"identity":"3f4b2068-40fc-4425-9ea3-abfb7f930dac","added_by":"auto","created_at":"2024-11-10 13:44:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1299311,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow of the study. High-resolution T1-weighted images were segmented by an automated hypothalamus segmentation tool. The entire hypothalamus and its ten constituent subregions (five on each side) were delineated and each volume was got. Statistical analysis of group comparisons and correlational analysis were employed.\u003c/p\u003e\n\u003cp\u003eAbbreviations: GHD, Growth Hormone Deficiency; ISS, Idiopathic Short Stature. AIS, Anterior-Inferior Subregion; ASS, Anterior-Superior Subregion; PS, Posterior Subregion; TIS, Tubular-Inferior Subregion; TSS, Tubular-Superior Subregion; WH, Whole Hypothalamus; PGH, Pituitary Gland Height\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5047218/v1/2be911f694c378cce2b2c4d6.jpg"},{"id":68654029,"identity":"d90fe530-29e5-47f9-8908-a96863f7af4e","added_by":"auto","created_at":"2024-11-10 13:52:14","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1244393,"visible":true,"origin":"","legend":"\u003cp\u003eThe scores of WISC-IV test had no statistical deficits between GHD and ISS group.\u003c/p\u003e\n\u003cp\u003eAbbreviations: GHD, Growth Hormone Deficiency; ISS, Idiopathic Short Stature; VCI, Verbal Comprehension index; PRI, Perceptual Reasoning Index; WMI, Working Memory Index; PSI, Processing Speed Index; GAI, General Ability Index; CPI, Cognitive Proficiency Index; IQ, Full-Scale Intelligence Quotient.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5047218/v1/ebae7372ce5f9da0840df1f7.jpg"},{"id":68653594,"identity":"1595155f-d58e-44e5-8f7a-bd97486325e6","added_by":"auto","created_at":"2024-11-10 13:44:14","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":474003,"visible":true,"origin":"","legend":"\u003cp\u003eIn the whole cohort, the score of PSI and CPI were positively correlated with the volume of PS respectively. The score of PSI and CPI were also positively correlated with the volume of WH.\u003c/p\u003e\n\u003cp\u003eAbbreviations: PSI, Processing Speed Index; CPI, Cognitive Proficiency Index; PS, posterior subregion; WH, whole hypothalamus.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5047218/v1/22f91009bd9b93b5abf9442f.jpg"},{"id":68654028,"identity":"255dc383-426f-4031-82da-c45f7fe78191","added_by":"auto","created_at":"2024-11-10 13:52:14","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":658074,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelational analysis in GHD group. The volume of TIS was positively correlated with the level of IGFBP-3. Additionally, the volume of PS was negatively correlated with the levels of peak GH and GH levels recorded at intervals of 30 min, 90 min.\u003c/p\u003e\n\u003cp\u003eAbbreviations: GHD, Growth Hormone Deficiency; GH, Growth Hormone; TI, Tubular-Inferior; PS, Posterior Subregion.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5047218/v1/d51c76a1b5e30440fa233af7.jpg"},{"id":69026701,"identity":"b042e521-1ebc-4193-9a49-67750354151c","added_by":"auto","created_at":"2024-11-14 17:16:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4197412,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5047218/v1/428cd1ef-91a1-460a-94e7-b3e644aa3bc9.pdf"},{"id":68653592,"identity":"d429a045-219d-4435-897e-8822e44b16a3","added_by":"auto","created_at":"2024-11-10 13:44:14","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":16915,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymateral.docx","url":"https://assets-eu.researchsquare.com/files/rs-5047218/v1/08e6f1f1f19a967a3527c224.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hypothalamic Subregion Alterations in Short Stature Children and Relations to Growth-Regulated Hormone Level and Cognitive Changes","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe hypothalamus, occupying merely 4 milliliters within the vast expanse of a total brain volume ranging from approximately 1.1 to 1.2 liters[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is regulated by the pituitary gland through the secretion of various stimulating and inhibiting hypothalamic factors[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. And, the hormones from hypothalamic-pituitary axis secrete into the blood system and exert their effects. Undeniably, hypothalamic-pituitary axis emerges as a pivotal master regulator orchestrating growth, reproduction, and the maintenance of homeostasis[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Growth hormone (GH) is released from anterior pituitary gland, and controlled by growth hormone-releasing hormone (GHRH) which released by hypothalamus. This hormone plays a cardinal role in growth and development[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn children, a decline in growth hormone levels (growth hormone deficiency, GHD) precipitates more than just the manifestation of liner growth, characterized by short stature, it also casts a wide shadow over neurological health. This hormonal insufficiency has been implicated in alterations to brain connectivity and activity patterns, impacting the maturation and structural integrity of cerebral regions. Such changes can further extend to cognitive functions and behavioral traits, potentially leading to multifaceted developmental challenges[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Moreover, a comprehensive longitudinal study[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] has revealed that treatment with recombinant human growth hormone (rhGH) exerts a beneficial influence on the growth, cognitive capabilities, and behavioral aspects of children afflicted with short stature. Notably, rhGH therapy has been shown to normalize spontaneous brain activity, which is a crucial component of healthy neurological function. In terms of brain structure, GHD children exhibit reduced volumes in specific cerebral structures, including the splenium of the corpus callosum, left globus pallidum, thalamus and hippocampus[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. On the other hand, a significant body of research has also concentrated on elucidating the alterations within pituitary or hypothalamus -pituitary axis resulting from GHD in children[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], but little focus on the hypothalamus alone, despite the pivotal role of this region in regulating the secretion of growth hormone. A possible reason for this might be attributed to the region's uniquely complex, delicate anatomical structure, but holding a variety of roles[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe hypothalamus, a constituent of the diencephalon situated beneath the thalamus, is intricately organized into roughly a dozen nuclei\u0026mdash;though this number can vary depending on the classification criteria employed[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Each nucleus harbors distinct neuroendocrine functions and houses specialized cellular assemblies[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Given that various diseases selectively target specific hypothalamic nuclei, the study of these subregions holds significant potential for advancing our comprehension of disease-specific pathologies. However, despite small volume, the hypothalamus contains even tinier subregions. Moreover, compounding this issue, the delineation of hypothalamic substructures on magnetic resonance (MR) scans is notoriously complex due to their close proximity, similar tissue characteristics and the lack of image contrast in its vicinity. As a result, the accurate segmentation of these subregions remains a significant obstacle, impeding progress in understanding the pathophysiological mechanisms by which diseases exert their effects on the hypothalamus.\u003c/p\u003e \u003cp\u003eFollowing recent advances in deep machine learning[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], a publicly available automated segmentation tool for the whole hypothalamus and its subregions from T1-weighted MRI scans was created[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This tool is based on a deep convolutional neural network, enables not only precise and robust segmentation, but also volumetric measurement of the hypothalamus and its subregions. Currently, there have been some studies utilizing this tool to investigate the impacts of various diseases on the hypothalamus subregions, and have yielded credible results. For instance, compared to individuals with a normal weight, using this automated segmentation tool, patients with anorexia nervosa and obesity were found to exhibit distinct alterations in the volumes of specific hypothalamic subregions[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], which promoting the understanding of the underlying mechanisms. Additionally, this tool also helps in deepening our understanding of the hypothalamus' role in aggression, which may facilitate the creation of targeted interventions and treatments[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The hypothalamus is involved in the secretion of growth hormone, and may also be subject to the effects of growth hormone deficiency.\u003c/p\u003e \u003cp\u003eThen, in the present study, we aim to delve into the alterations of the hypothalamus and its constituent subregions in terms of volume among children short stature, based on the fully automated hypothalamus segmentation tool, as well as to explore the relationship between these changes and the regulatory hormones involved in growth hormone modulation, as well as cognitive changes. We believe that analyzing hypothalamic subregions may provide a further understanding of the pathophysiology of short stature children.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003e The prospective study received approval from the Institutional Ethics Committee (Clinical trial number: LCKY-2020-58). Written informed consent was secured from the parents or legal guardians of all participants.\u003c/p\u003e \u003cp\u003eChildren were recruited from the Pediatric Endocrinology Department, aged 8\u0026ndash;12. Following height assessment, if their height was less than two standard deviations below the mean for age, sex, and race, and these children also presented an annual growth rate of \u0026lt;\u0026thinsp;5 cm [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], participants were diagnosed as exhibiting short stature. Those with short stature subsequently underwent growth hormone stimulation test to differentiate between GHD and ISS subgroups.\u003c/p\u003e \u003cp\u003eThe exclusion criteria were as follow: (1) children received growth hormone provocation test, intelligence scale test or growth hormone therapy prior to fMRI scanning; (2) abnormal findings on cerebral MRI;(3) artefacts (e.g. metal artefacts or motion artefacts) affected image quality and data post-processing;(4) Intrauterine distress, dystocia, premature delivery, etc.;(5) critical illness such as congenital heart disease, congenital abnormalities of bone development, abnormal thyroid hormone axis, abnormal chromosomes, neuropsychiatric disorders, other endocrinopathies, etc. ;(6) left-handedness.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Growth hormone level measurement\u003c/h2\u003e \u003cp\u003eThe growth hormone stimulation test remains the gold standard for clinical differential diagnosis of short children with GHD or ISS. Children were instructed to undergo an overnight fast of 8 to 12 hours before the test, and L-dopa or clonidine was used in the test. However, ongoing debates continue globally concerning the exact number of tests required and the interpretation of GH peak levels[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].Commonly, in China, GH levels were measured at intervals of 0, 30, 45, 60, and 90 minutes. Participants were then categorized as GHD or ISS based on peak GH response. A peak GH\u0026thinsp;\u0026ge;\u0026thinsp;10 ng/mL indicated ISS, while\u0026lt;10ng/ml defined GHD[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Specifically, a peak between 5\u0026ndash;10 ng/mL suggested partial GHD, and \u0026lt;\u0026thinsp;5 ng/mL indicated complete GH deficiency. Levels of IGF-1, IGFBP-3, ACTH and cortisol were measured in the same time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Wechsler Intelligence Scale test\u003c/h2\u003e \u003cp\u003eAll children underwent the Wechsler Intelligence Scale 4th edition (WISC-IV) test. This cognitive test included four subscales containing Verbal Comprehension Index (VCI), Perceptual Reasoning Index (PRI), Working Memory Index (WMI) and Processing Speed Index (PSI). The total score of the four subscales was called Full-Scale Intelligence Quotient (IQ). In addition, the score of PSI and WMI is also known as Cognitive Proficiency Index (CPI), the score of PRI and VCI known as General Ability Index (GAI)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Image acquisition\u003c/h2\u003e \u003cp\u003eT1-weighted high-resolution anatomical images were acquired with a 3.0-T MR system (3.0 T Discovery MR750, GE Healthcare), using an 8-channel head coil: repetition time\u0026thinsp;=\u0026thinsp;7.68ms, echo time\u0026thinsp;=\u0026thinsp;3.43ms, flip angle\u0026thinsp;=\u0026thinsp;12\u0026deg;, field of view\u0026thinsp;=\u0026thinsp;256 mm\u003csup\u003e2\u003c/sup\u003e, slice thickness\u0026thinsp;=\u0026thinsp;1 mm, layer spacing=1 mm, and voxel size\u0026thinsp;=\u0026thinsp;1 mm \u0026times; 1 mm \u0026times; 1 mm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Image processing\u003c/h2\u003e \u003cp\u003eThe study workflow is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. High-resolution T1-weighted anatomical images underwent pre-processing through the standard FreeSurfer-7.2 recon-all pipeline (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://surfer.nmr.mgh.harvard.edu/\u003c/span\u003e\u003cspan address=\"https://surfer.nmr.mgh.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Subsequently, the pre-processed T1 images were subjected to an automated hypothalamus segmentation process utilizing a publicly accessible pipeline (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/BBillot/hypothalamus_seg\u003c/span\u003e\u003cspan address=\"https://github.com/BBillot/hypothalamus_seg\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which is built upon the FreeSurfer-7.2 framework[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This pipeline employs a sophisticated deep convolutional neural network (DCNN) to facilitate the segmentation of the entire hypothalamus and its ten constituent subregions\u0026mdash;five on each side\u0026mdash;as delineated from T1-weighted MRI scans, anatomically. The application of this pipeline yielded volumetric data encompassing both the entirety of the hypothalamus and the individual volumes of each subregion. The aforementioned subregions are defined by the following hypothalamic nuclei[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAnterior-Inferior Subregion (AIS): suprachiasmatic nucleus; supraoptic nucleus (SON)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAnterior-superior Subregion (ASS): preoptic area; paraventricular nucleus (PVN)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePosterior Subregion (PS): mamillary body (including medial and lateral mamillary nuclei); lateral hypothalamus; tuberomamillary nucleus (TMN)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTubular Inferior Subregion (TIS): infundibular (or arcuate) nucleus; ventromedial nucleus; SON; lateral tubular nucleus; TMN\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTubular Superior Subregion (TSS): dorsomedial nucleus; PVN; lateral hypothalamus\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe total brain volume (TBV) and total intra-cranial volume (TIV) were generated by SPM12/CAT12 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fil.ion.ucl.ac.uk/spm/software/spm12/\u003c/span\u003e\u003cspan address=\"https://www.fil.ion.ucl.ac.uk/spm/software/spm12/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to be used as covariates of no interest in further analyses. TBV, encompassing the volume of all intracranial tissues including grey matter, white matter, cerebrospinal fluid, etc., is a crucial metric for investigating cerebral development. TIV focused on the space within the cranial cavity rather than just the brain tissue itself, which is often used as a covariate in neuroimaging studies to account for individual differences in head size when analyzing brain structures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eR Studio(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://posit.co/\u003c/span\u003e\u003cspan address=\"https://posit.co/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for statistical analysis of clinical and volumetric measures between the groups.\u003c/p\u003e \u003cp\u003eInitially, the Shapiro-Wilk test was employed to evaluate the normalcy of data distribution within each group. Should the data adhere to a normal distribution, the independent samples t-test will be conducted, with outcomes being reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Conversely, in cases where data deviate from normal distribution, the non-parametric Mann-Whitney U test was utilized, and results were described using the median along with the 25th to 75th percentile interquartile range. Gender difference was analyzed using the Chi-squared test (Male/Female). \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e \u003cp\u003eAccording to previous report[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], in the developing pediatric brain, the hypothalamus volumes of males were larger than females. Moreover, there is an imbalance in the development of brain tissues and variations in skull size. Then, age, sex, TIV and TBV were controlled as covariates[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, the right and left hypothalamus volumes existed notable asymmetries. Therefore, the average of left and right hypothalamus was calculated for measurement in this study. Pituitary gland height was measured by an experienced radiologist from T1 coronal reconstruction images which clearly showed the maximum cross section of the pituitary gland. Finally, One-way analysis of covariance (ANCOVA) was employed to investigate potential differences between the GHD and ISS groups.\u003c/p\u003e \u003cp\u003eWhen testing the association between volumes and hormone levels of GH at every interval, levels of IGF-1, IGFBP-3, ACTH and cortisol, Spearman's ρ partial correlation was used, adjusting for sex, age, TIV and TBV. Firstly, residuals for the volumes and hormone levels were calculated by controlling for the effects of age, sex, TBV and TIV. Subsequently, Spearman correlation analyses were conducted based on these residuals. Each morphometric analysis was Bonferroni corrected; a corrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant. The forementioned statistical method was also used to analysis the correlation between WISC-IV test scores and the volumes of hypothalamus subregions.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Demographic and clinical features\u003c/h2\u003e \u003cp\u003eData was from our three previous fMRI researches. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, totally 168 children were included after exclusion, including 79 children with GHD, 89 children with ISS, matched with age and sex. No statistically significant differences were observed in gender (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.904), age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.514), height (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.756) or weight (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.477) across the groups. Additionally, significant group differences were found in GH levels recorded in throughout GH provocation test as basal GH level (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014), GH level at 30 min (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000), at 45 min (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000), at 60 min (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000), at 90 min (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000) and peak GH level (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000) between the GHD and ISS groups. Otherwise, the levels of IGF-1, IGFBP-3, ACTH and cortisol had no statistical deficits during the groups. The scores of WISC-IV test had no statistical deficits between GHD and ISS group either, shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics\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\u003eGHD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eISS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (F/M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33/46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38/51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.904\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.51(7.88\u0026ndash;10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.23(8.10\u0026ndash;11.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.514\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.756\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.30 (20.00-27.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.00(20.00-25.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.477\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasal GH level(ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.41 (0.20\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72(0.29\u0026ndash;3.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003csup\u003e*c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGH level at 30min(ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.05(2.47\u0026ndash;6.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.86(6.91-12.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003csup\u003e***c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGH level at 45min(ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.86 (2.28\u0026ndash;6.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.60 (7.77\u0026ndash;16.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003csup\u003e***c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGH level at 60min(ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.52 (1.54\u0026ndash;5.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.07 (5.71\u0026ndash;14.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003csup\u003e***c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGH level at 90min(ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.08 (1.86\u0026ndash;6.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.97 (5.62-13.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003csup\u003e***c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak GH level(ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.12 (5.22\u0026ndash;8.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.80 (12.00-18.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003csup\u003e***c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIGF-1(ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147 (114\u0026ndash;183)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144 (108\u0026ndash;194)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.792\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIGFBP-3 (\u0026micro;g/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.316\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACTH (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.7(11.1\u0026ndash;22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.9 (11.7\u0026ndash;21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.980\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCortisol (\u0026micro;g/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.10 (6.74\u0026ndash;11.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.8 (6.6\u0026ndash;12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.932\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003ea, independent two-sample t-test, data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation; b, chi-square test; c, Mann-Whitney U test. B and c data are presented as median (first and third quartile).\u003c/p\u003e \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003eAbbreviations: GH, Growth Hormone; GHD, Growth Hormone Deficiency; ISS, Idiopathic Short Stature; IGF-1, Insulin-like Growth Factor-1; IGFBP-3, Insulin-like Growth Factor Binding Protein-3; ACTH, Adrenocorticotropic Hormone.\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 \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Group comparisons of hypothalamic volumes\u003c/h2\u003e \u003cp\u003eOne-way analysis of covariance (ANCOVA) was employed to investigate volumes of hypothalamus subregions and pituitary gland height. We found that PS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and TIS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) volumes were significant smaller in GHD children compared with ISS children. However, GHD children got bigger volume in whole hypothalamus (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Moreover, there were no significant difference in volume of AIS, ASS, TSS and pituitary gland height. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\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\u003eAverage volume of hypothalamus subregions and pituitary gland height\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGHD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eISS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePr (\u0026gt;\u0026thinsp;F)\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\u003eAIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.70 (0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.94 (0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.34 (0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.43 (0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114.62 (1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115.60 (1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123.41 (1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126.75 (1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112.76 (1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113.10 (1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e787.67 (7.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e785.36 (7.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.68 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.79 (0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eData are presented as mean (standard error of the mean). \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e \u003cp\u003eOne-way analysis of covariance (ANCOVA) was employed to investigate potential differences in average volume of hypothalamus subregions and pituitary gland height between the GHD and ISS groups, adjusting age, sex, total intracranial volume (TIV) and total brain volume (TBV).\u003c/p\u003e \u003cp\u003eAbbreviations: GHD, Growth Hormone Deficiency; ISS, Idiopathic Short Stature. AIS, Anterior-Inferior Subregion; ASS, Anterior-Superior Subregion; PS, Posterior Subregion; TIS, Tubular-Inferior Subregion; TSS, Tubular-Superior Subregion; WH, Whole Hypothalamus; PGH, Pituitary Gland Height.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Correlational analysis\u003c/h2\u003e \u003cp\u003eIn the whole cohort, showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the score of PSI and CPI in WISC-IV test were positively correlated with the volume of PS respectively (ρ\u0026thinsp;=\u0026thinsp;0.229,\u003cem\u003eP\u003c/em\u003e-adjusted\u0026thinsp;=\u0026thinsp;0.020; ρ\u0026thinsp;=\u0026thinsp;0.207, \u003cem\u003eP\u003c/em\u003e-adjusted\u0026thinsp;=\u0026thinsp;0.049). Additionally, the score of PSI and CPI in WISC-IV test were also positively correlated with the volume of WH (ρ\u0026thinsp;=\u0026thinsp;0.258, \u003cem\u003eP\u003c/em\u003e-adjusted\u0026thinsp;=\u0026thinsp;0.005; ρ\u0026thinsp;=\u0026thinsp;0.250, \u003cem\u003eP\u003c/em\u003e-adjusted\u0026thinsp;=\u0026thinsp;0.008). Whereas, we found no significant correlation within GHD group or ISS group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn GHD group, the volume of TIS was positively correlated with the level of IGFBP-3(ρ\u0026thinsp;=\u0026thinsp;0.324, \u003cem\u003eP\u003c/em\u003e-adjusted\u0026thinsp;=\u0026thinsp;0.04). Additionally, the volume of PS was negatively correlated with the levels of peak GH(ρ=-0.348, \u003cem\u003eP\u003c/em\u003e-adjusted\u0026thinsp;=\u0026thinsp;0.018) and GH levels recorded at intervals of 30 min(ρ=-0.324, \u003cem\u003eP\u003c/em\u003e-adjusted\u0026thinsp;=\u0026thinsp;0.038), 90 min(ρ=-0.360, \u003cem\u003eP\u003c/em\u003e-adjusted\u0026thinsp;=\u0026thinsp;0.012). These results forementioned were showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. However, no correlation was observed between hypothalamus subregion volumes and clinical hormone level in the ISS group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eTo our best knowledge, no previous investigation assessing the alterations in the volumes of hypothalamic subregions among children with short stature. In our research, we utilized a deep learning-based tool capable of segmenting the hypothalamus and its constituent subregions with remarkable precision. Our findings revealed morphological differences within the hypothalamus when comparing children with GHD to those with ISS. Furthermore, we discerned the associations between the volume of the subregions and growth-regulated hormone level, as well as cognitive changes.\u003c/p\u003e \u003cp\u003eIn this study, we chose children diagnosed with ISS as the control cohort, rather than employing a group comprised of children who exhibit average height norms. This choice is pivotal in ensuring that any observed discrepancies in hypothalamic volumes or correlations can be attributed specifically to the absence or insufficiency of growth hormone, as opposed to merely the condition of being short in stature. The majority of studies investigating the consequences of GHD also included children ISS as controls, exploring directions such as brain structure and function, cognitive abilities, and pituitary radiomics[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Meanwhile, some studies also chose healthy controls with normal height for analysis. These studies often feature relatively small sample sizes[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and comparing GHD children with normally growing children, as opposed to ISS subjects, may yield greater likelihoods of detecting differences. This is because, besides the absence or reduction of growth hormone acting as a variable, height itself may influence the data outcomes at the same time.\u003c/p\u003e \u003cp\u003eWhen conducting comparisons between the hypothalamic subregions of the GHD and ISS groups, adjustments for multiple comparisons were not performed. In fact, the hypothalamus regions are exceedingly small in volume, furthermore, each of which encompasses not just a single nucleus[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Then, These subregions carry out a multitude of neuroendocrine functions and subject to the modulatory influence of more than one hormone[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Thus, this was the first research focus on the hypothalamus subregions alterations about GHD, more likely be exploratory in nature. Potentially interesting trends or relationships deserve greater attention, as they may reveal novel insights or previously unexplored connections[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our results, we found PS and TIS volumes were significant smaller in children with GHD than ISS children. Typically, smaller volumes may indicate less developed neural networks or fewer neurons. As mentioned above, TIS contains arcuate nucleus, ventromedial nucleus, supraoptic nucleus, lateral tubular nucleus and tuberomamillary nucleus (TMN), and PS contains mamillary body, lateral hypothalamus and TMN. Among these nucleuses, it has become clear that arcuate nucleus (ARC) is an important endocrine regulatory center that contains neurons producing growth hormone-releasing hormone (GHRH) and growth hormone release-inhibiting hormone (GHIH), also expresses growth hormone receptors (GHR) which receive regulation signals from GH secreted by the adenohypophysis[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Meanwhile, in GHD group, the volume of TIS is positively correlated with the level of IGFBP-3. In children with GHD, the regulatory mechanisms of the hypothalamic-pituitary-growth hormone axis may be more complex, modulating IGF-1 and IGFBP-3 levels, which in turn indirectly affect GH secretion. This is crucial for normal growth, development, and metabolism. The remaining hormones, including the nucleus in PS, do not directly participate in the regulation of GH secretion; however, they may indirectly influence GH secretion through physiological levels, metabolism, or stress responses[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Moreover, according to correlational results, in GHD group, the volume of PS was negatively correlated with several levels of GH. This negative correlation may suggest that children with GHD have potentially activated an underlying compensatory mechanism. PS may involve in the inhibitory control of GH secretion. When the volume of this region decreases, the inhibitory effect on growth hormone secretion is weakened, leading to elevated GH levels. It's worth noting that such research findings require further validation and confirmation. Taking together, we hypothesized that the potential pathophysiological mechanism of GHD may involve morphological and functional abnormalities in special subregions of the hypothalamus which directly or indirectly regulate GH secretion, and may further serve as imaging biomarkers for the differential diagnosis of GHD or as one of the indicators for treatment efficacy.\u003c/p\u003e \u003cp\u003eHowever, the whole hypothalamus volume of GHD group is bigger than ISS group. According to previous research, the development of the hypothalamus is complex. In the pediatric period, the hypothalamus is right-lateralized, get logarithmic trend lines as a function of age, and also exist sexual dimorphism[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This finding in our research could be due to multiple factors in addition to GHD, such as normal growth and developmental rhythms and other concurrent factors. Therefore, studying subregions of the hypothalamus in children is more meaningful than studying the hypothalamus as a whole. In summary, our findings may suggest an alteration in hypothalamic structure and provide direction for studies on the hypothalamus in children.\u003c/p\u003e \u003cp\u003eAccording to previous studies, the hypothalamus serves not only as a regulatory center for hormones but also participates in cognitive functions[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In this study, we found short stature children existed positive correlation between the intelligent scores of PSI, CPI with the volume of PS, WH, respectively. The anatomical structure of the hypothalamus may be related to cognitive function, with a larger hypothalamic volume potentially correlating with higher data processing scores. This positive correlation might suggest that the hypothalamus is involved in more complex cognitive processes within the nervous system. However, the specific neural mechanisms underlying this relationship require further investigation.\u003c/p\u003e \u003cp\u003eOur study has several limitations. Firstly, the sample size is an issue. The study data were sourced from our three previous brain function databases containing 3D T1-weighted images, with a limited amount of data. Fortunately, the database is constantly updated, and multicenter research has commenced to include more participants in future studies. In future studies, we hope to conduct age-stratified group analyses and investigate gender differences. We plan to include more children with GHD, dividing them into partial GHD and complete GHD for further research (a peak level\u0026thinsp;\u0026lt;\u0026thinsp;10 ng/ml and \u0026gt;\u0026thinsp;5 ng/ml indicated partial deficiency, and a peak level\u0026thinsp;\u0026lt;\u0026thinsp;5 ng/ml indicated complete deficiency[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]). Secondly, only structural data were acquired in this study. Although the hypothalamic volume is sufficiently large to serve as a seed point for functional analysis, the subregions of the hypothalamus are still too small. Currently, we have not found methods to perform functional analyses based on hypothalamic subregions. We will prospectively explore methods for functional analysis of the nervous system, focusing on microstructural functional analysis. Thirdly, in this study, children of normal height were not included for analysis, as there is no need to perform growth hormone stimulation test on children without short stature. In subsequent research, we will also explore differences research directions among the three groups of children in terms of cognitive or intellectual development. Finally, this study only measured the height of the pituitary gland and did not conduct volume measurements. The reason is that currently there are no automated tools for segmenting the pituitary gland, and manual delineation on magnetic resonance images is time-consuming, especially for thin-slice 3D-T1 images. In subsequent studies, we will focus on automatic segmentation techniques for the pituitary gland, and then utilize other novel analytical methods, such as radiomics or deep learning, for hypothalamic-pituitary axis research.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, our study emphasizes the volume alterations of the hypothalamus subregions in children with GHD compared to ISS children. Analyzing hypothalamic subregions may provide a better understanding of the pathophysiology of short stature children, further serve as imaging biomarkers for the differential diagnosis of GHD instead of invasive examination, as well as for cognitive assessment. However, further studies in a larger sample size and different research directions are needed to validate the specific relationship between hypothalamic structure and neuroendocrine function, with the aim of developing new therapeutic approaches.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGHD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGrowth Hormone Deficiency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eISS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIdiopathic Short Stature. AIS,Anterior-Inferior Subregion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnterior-Superior Subregion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePosterior Subregion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTubular-Inferior Subregion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTubular-Superior Subregion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWhole Hypothalamus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePGH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePituitary Gland Height\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThe prospective study received approval from the Institutional Ethics Committee. Written informed consent was secured from the parents or legal guardians of all participants.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by grants from the National Natural Science Foundation of China (No. 82071902 and No. 82100952), Jinhua Science and Technology (NO.2023-4-090).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by grants from the National Natural Science Foundation of China (No. 82071902 and No. 82100952), Jinhua Science and Technology (NO.2023-4-090).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXJ and XF designed the study; XJ performed the statistical analysis and visualization and wrote the paper; LY verified all the underlying data; XJ, JF and ZH contributed to the interpretation of results and revision of the manuscript. All authors read and approved the final version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors extend their deepest appreciation to the children volunteers and their families. The authors also thank the Pediatric Endocrinology Department for providing short stature children and thank foundations for supporting our study.\u003c/p\u003e\u003ch2\u003eAvailability of data and material\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eM\u0026uuml;ller HL, Tauber M, Lawson EA, et al. Hypothalamic syndrome. Nat Rev Dis Primers. 2022;8(1):24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBereket A, Kiess W, Lustig RH, et al. Hypothalamic obesity in children. Obes Rev. 2012;13(9):780\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGray SM, Bartell PA, Staniar WB. High glycemic and insulinemic responses to meals affect plasma growth hormone secretory characteristics in Quarter Horse weanlings. Domest Anim Endocrinol. 2013;44(4):165\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang F, Hua B, Wang M, Wang T, Ding Z, Ding JR. Regional homogeneity abnormalities of resting state brain activities in children with growth hormone deficiency. Sci Rep. 2021;11(1):334.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu Y, Liu X, Chen X, et al. Differences in the functional connectivity density of the brain between individuals with growth hormone deficiency and idiopathic short stature. Psychoneuroendocrinology. 2019;103:67\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing JR, Liu Y, Chen Q et al. Frequency Dependent Changes of Regional Homogeneity in Children with Growth Hormone Deficiency. Neuroscience. 2023: S0306-4522(23)00277-4 [pii].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen L, Lin X, Wang C et al. Longitudinal unraveling: The impact of recombinant human growth hormone on spontaneous brain activity in children with short stature-A resting-state fMRI study. J Neuroradiol. 2023: S0150-9861(23)00250-X [pii].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWebb EA, O'Reilly MA, Clayden JD, et al. Effect of growth hormone deficiency on brain structure, motor function and cognition. Brain. 2012;135(Pt 1):216\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDegerblad M, Brismar K, R\u0026auml;hn T, Thor\u0026eacute;n M. The hypothalamus-pituitary function after pituitary stereotactic radiosurgery: evaluation of growth hormone deficiency. J Intern Med. 2003;253(4):454\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShu K, Wang K, Zhang R et al. Pituitary MRI Radiomics Improves Diagnostic Performance of Growth Hormone Deficiency in Children Short Stature: A Multicenter Radiomics Study. Acad Radiol. 2024: S1076-6332(24)00293-9 [pii].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGouveia FV, Hamani C, Fonoff ET, et al. Amygdala and Hypothalamus: Historical Overview With Focus on Aggression. Neurosurgery. 2019;85(1):11\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaroncini M, Jissendi P, Balland E, et al. MRI atlas of the human hypothalamus. NeuroImage. 2012;59(1):168\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBillot B, Bocchetta M, Todd E, Dalca AV, Rohrer JD, Iglesias JE. Automated segmentation of the hypothalamus and associated subunits in brain MRI. NeuroImage. 2020;223:117287.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ. Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions. J Digit Imaging. 2017;30(4):449\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlzaid H, Simon JJ, Brugnara G, Vollmuth P, Bendszus M, Friederich HC. Hypothalamic subregion alterations in anorexia nervosa and obesity: Association with appetite-regulating hormone levels. Int J Eat Disord. 2024;57(3):581\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBell C, Rokicki J, Tesli N et al. Hypothalamic subunit volumes and relations to violence and psychopathy in male offenders with or without a psychotic disorder. Eur Arch Psychiatry Clin Neurosci. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang J, Xia Y, Liu N, et al. Growth hormone deficiency interferes with dynamic brain networks in short children. Psychoneuroendocrinology. 2022;142:105786.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePartenope C, Galazzi E, Albanese A, Bellone S, Rabbone I, Persani L. Sex steroid priming in short stature children unresponsive to GH stimulation tests: Why, who, when and how. Front Endocrinol (Lausanne). 2022;13:1072271.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDror N, Oren L, Pantanowitz M, Eliakim A, Nemet D. The Wingate anaerobic test cannot be used for the evaluation of growth hormone secretion in children with short stature. J Sport Health Sci. 2017;6(4):443\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCong M, Qiu S, Li R, Sun H, Cong L, Hou Z. Development of a predictive model of growth hormone deficiency and idiopathic short stature in children. Exp Ther Med. 2021;21(5):494.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang P, Cheng CP, Chang CL, Liu TL, Hsu HY, Yen CF. Wechsler Intelligence Scale for Children 4th edition-Chinese version index scores in Taiwanese children with attention-deficit/hyperactivity disorder. Psychiatry Clin Neurosci. 2013;67(2):83\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIsıklar S, Turan Ozdemir S, Ozkaya G, Ozpar R. Hypothalamic volume and asymmetry in the pediatric population: a retrospective MRI study. Brain Struct Funct. 2022;227(7):2489\u0026ndash;501.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z, Wang Y, Gao Y, et al. Morphological changes of the cerebral cortex between children with isolated growth hormone deficiency and idiopathic short stature. Brain Res. 2020;1748:147081.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Z, Luo Y, Gao X, et al. Alterations in brain structure and function associated with pediatric growth hormone deficiency: A multi-modal magnetic resonance imaging study. Front Neurosci. 2022;16:1043857.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehay D, Silberman Y, Arnold AC. The Arcuate Nucleus of the Hypothalamus and Metabolic Regulation: An Emerging Role for Renin-Angiotensin Pathways. Int J Mol Sci. 2021;22(13):7050.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhodai T, Luckman SM. Ventromedial Nucleus of the Hypothalamus Neurons Under the Magnifying Glass. Endocrinology. 2021;162(10):bqab141.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcClellan KM, Parker KL, Tobet S. Development of the ventromedial nucleus of the hypothalamus. Front Neuroendocrinol. 2006;27(2):193\u0026ndash;209.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArmstrong RA. When to use the Bonferroni correction. Ophthalmic Physiol Opt. 2014;34(5):502\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Lima J, Debarba LK, Rupp AC, et al. ARC(GHR) Neurons Regulate Muscle Glucose Uptake. Cells. 2021;10(5):1093.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopes-Azevedo S, Fortaleza E, Busnardo C, et al. The Supraoptic Nucleus of the Hypothalamus Modulates Autonomic, Neuroendocrine, and Behavioral Responses to Acute Restraint Stress in Rats. Neuroendocrinology. 2020;110(1\u0026ndash;2):10\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaganuma F, Nakamura T, Kuroyanagi H, et al. Chemogenetic modulation of histaminergic neurons in the tuberomamillary nucleus alters territorial aggression and wakefulness. Sci Rep. 2021;11(1):17935.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOzturk A, Yousem DM, Mahmood A, El Sayed S. Prevalence of asymmetry of mamillary body and fornix size on MR imaging. AJNR Am J Neuroradiol. 2008;29(2):384\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, Guo Z, Cheng J, Wei F, Zhang J. Abnormal white matter integrity of anterior-inferior hypothalamus in mild cognitive impairment patients with depression symptoms. J Affect Disord. 2024;362:225\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hypothalamus Subregion, Deep Learning, Automated Segmentation Tool, Growth Hormone Deficiency","lastPublishedDoi":"10.21203/rs.3.rs-5047218/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5047218/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRationale and Objectives: To delve into the volume alterations of the hypothalamus subregions among short stature children, identify the relations to growth-regulated hormone level and cognitive changes.\u003c/p\u003e\n\u003cp\u003eMaterials and Methods: Structural magnetic resonance imaging (MRI) was obtained from 79 children with diagnosed with growth hormone deficiency (GHD), 89 children with idiopathic short stature (ISS). Levels of IGF-1, IGFBP-3, ACTH and cortisol were measured and the growth hormone stimulation test was used to documented the GH level. The Wechsler Intelligence Scale test was used to assessment cognitive changes. Based on an automated hypothalamus segmentation tool, the hypothalamus and its subregions were segmented. Volumes of the hypothalamic subregions were compared between the two groups. Correlational analysis was used to assess the relationship between morphometric alterations with hormone levels and cognitive changes.\u003c/p\u003e\n\u003cp\u003eResults: Posterior subregion (PS) and tubular inferior subregion (TIS) volumes were significant smaller in GHD children compared with ISS children. However, GHD children got bigger volume in whole hypothalamus. In the whole cohort, the intelligent scores of Processing Speed Index (PSI) and Cognitive Proficiency Index (CPI) were positively correlated with the volume of PS, WH (whole hypothalamus) respectively. In GHD group, the volume of TIS was positively correlated with the level of IGFBP-3. Additionally, the volume of PS was negatively correlated with the levels of peak GH and GH levels recorded at intervals of 30 min, 90 min.\u003c/p\u003e\n\u003cp\u003eConclusion: Analyzing hypothalamus subregions could improve understanding of GHD pathophysiology and may serve as non-invasive imaging biomarkers, potentially leading to new therapeutic strategies.\u003c/p\u003e","manuscriptTitle":"Hypothalamic Subregion Alterations in Short Stature Children and Relations to Growth-Regulated Hormone Level and Cognitive Changes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-10 13:44:09","doi":"10.21203/rs.3.rs-5047218/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ff9b40fd-e2e7-4fa1-add3-b65e5d082753","owner":[],"postedDate":"November 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-14T17:08:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-10 13:44:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5047218","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5047218","identity":"rs-5047218","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}