Associations Between Anterior Hypothalamic Subunits and ADHD and Autistic Traits Revealed by Deep Learning MRI Segmentation

Associations Between Anterior Hypothalamic Subunits and ADHD and Autistic Traits Revealed by Deep Learning MRI Segmentation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Associations Between Anterior Hypothalamic Subunits and ADHD and Autistic Traits Revealed by Deep Learning MRI Segmentation Uk-Su Choi, Sachiko Kiyama, Yul-Wan Sung, Masaki Fukunaga, Seiji Ogawa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8737027/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract The hypothalamus is a central regulator of neuroendocrine function and social behavior, yet its internal organization has remained difficult to examine in vivo in human neurodevelopmental research. Based on neuroendocrine models, we hypothesized that anterior hypothalamic subunits enriched in magnocellular neurosecretory nuclei—approximating the paraventricular (PVN) and supraoptic (SON) regions—form a structurally coherent axis linked to dimensional traits associated with autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). To test this hypothesis, we applied a convolutional neural network–based automated segmentation framework to high-resolution T1-weighted MRI data from a non-clinical adult sample (n = 23), enabling volumetric quantification of ten hypothalamic subunits. After adjustment for biological sex, age, and total intracranial volume, PVN- and SON-associated volumes showed a significant positive structural covariance (r = 0.51, p = 0.022), consistent with a coherent anterior magnocellular-rich network. Reduced SON-associated volume was significantly associated with higher autistic traits (Autism-Spectrum Quotient: r = − 0.45, p = 0.046) and greater ADHD symptom severity (CAARS: r = − 0.59, p = 0.006). Bootstrapped path analyses (5,000 iterations) further supported this network organization, revealing a robust association between PVN- and SON-associated volumes (β = 0.51, p = 0.013) and a significant indirect association between PVN-associated volume and ADHD symptoms via SON-associated volume (β = −0.29, p = 0.006), with a marginal indirect association observed for autistic traits. Together, these findings provide in vivo evidence linking anterior hypothalamic structure to dimensional neurodevelopmental traits and highlight a potential neuroendocrine pathway relevant to psychiatric vulnerability. Hypothalamus Oxytocin system Autism spectrum traits ADHD symptoms Structural MRI Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The hypothalamus comprises less than 1% of total brain volume, yet exerts disproportionate influence over survival-critical functions, including homeostasis, circadian regulation, and social behavior (Adamantidis & de Lecea, 2023 ; Chen et al., 2025 ; Mei et al., 2023 ). Within this compact and highly specialized structure, the paraventricular nucleus (PVN) and supraoptic nucleus (SON) form the core of the magnocellular neurosecretory system (MNS), a key neuroendocrine pathway responsible for the synthesis and systemic release of oxytocin (OXT) and arginine vasopressin (AVP) (Silverman & Zimmerman, 1983 ; Qin et al., 2018 ; Møller et al., 2018 ). Although the PVN contains a heterogeneous neuronal population—including parvocellular neurons that regulate stress responses via the hypothalamic–pituitary–adrenal axis—the SON is composed almost exclusively of magnocellular neurons projecting to the posterior pituitary, underscoring its specialized neurosecretory role (Caldwell et al., 2008 ). Accumulating evidence from human and animal research implicates the OXT and AVP systems in social bonding, empathy, emotional regulation, and stress responsivity, forming a central component of the so-called “social brain” (Feldman, 2012 ; Tolomeo et al., 2020 ). Disruption of these neuropeptide systems has therefore been proposed as a plausible pathophysiological mechanism underlying Autism Spectrum Disorder (ASD). Consistent with this framework, clinical trials of intranasal oxytocin administration have reported variable but promising effects on social functioning in subsets of individuals with ASD, suggesting that hypo-oxytocinergic states may characterize specific phenotypes within the autism spectrum (Anagnostou et al., 2014 ; Parker et al., 2017 ; Yamasue et al., 2020 ). Beyond ASD, increasing evidence suggests that hypothalamic neuroendocrine mechanisms may also be relevant to Attention-Deficit/Hyperactivity Disorder (ADHD), a condition that frequently co-occurs with ASD (Canals et al., 2024 ). While dopaminergic dysregulation remains central to prevailing models of ADHD, oxytocin has been shown to modulate reward processing and motivational salience through interactions with dopaminergic neurons in the nucleus accumbens (Peris et al., 2017 ). These findings raise the possibility that hypothalamic integrity may play a previously underappreciated role in attentional regulation and impulse control, linking neuroendocrine function to dimensional neurodevelopmental traits that span diagnostic categories. Despite strong theoretical and experimental support, direct investigation of hypothalamic structure in human neurodevelopmental research has long been constrained by a fundamental “resolution gap.” Standard clinical MRI lacks sufficient contrast to delineate individual hypothalamic nuclei, forcing many human studies to rely on peripheral neuropeptide measurements, which may not accurately reflect central neuroendocrine organization or function (Billot et al., 2020 ). Recent advances in deep learning–based neuroimaging, however, have begun to overcome this limitation. In particular, convolutional neural network–based automated segmentation frameworks trained on histological atlases now permit reliable in vivo quantification of anatomically defined hypothalamic subunits using conventional T1-weighted MRI (Billot et al., 2020 ; Estrada et al., 2023 ). Leveraging these methodological advances, the present study tested the hypothesis that anterior hypothalamic subunits enriched in magnocellular neurosecretory nuclei—approximating the paraventricular (PVN) and supraoptic (SON) regions—form a structurally coherent anterior axis associated with dimensional traits of ADHD and ASD in a non-clinical adult population (n = 23). By integrating automated hypothalamic volumetry with behavioral measures, this work aims to provide an anatomically grounded framework for investigating hypothalamic contributions to neurodevelopmental variation in humans. 2. Materials and Methods 2.1. Participants and Ethical Considerations The study sample comprised twenty-three healthy adult volunteers (n = 23; 11 females, 12 males) recruited from the local community in Sendai, Japan. Participants had a mean age of 21.87 years (SD = 1.96). Recruitment was conducted through community advertisements designed to capture a broad range of behavioral phenotypes within the non-clinical population. Participants were screened to exclude any history of diagnosed neurological disorders, major psychiatric conditions (e.g., schizophrenia or bipolar disorder), or active substance abuse. In addition, all participants were free from medical conditions contraindicating MRI (e.g., pregnancy, implanted metallic devices, or claustrophobia), as assessed by pre-screening interviews. This recruitment strategy was intended to characterize subclinical variability in neurodevelopmental traits, consistent with a dimensional approach to psychiatric research. Written informed consent was obtained from all participants, and all procedures were conducted in accordance with the Declaration of Helsinki. The study protocol was approved by the Institutional Review Board of Tohoku Fukushi University (approval No: [RS190607]). 2.2. Behavioral Assessment: ADHD and ASD Traits To quantify dimensional neurodevelopmental traits of interest, participants completed two standardized Japanese self-report questionnaires assessing ADHD symptoms and autistic traits. Attention-Deficit/Hyperactivity Disorder (ADHD) traits were assessed using the Conners’ Adult ADHD Rating Scale (CAARS) (Conners et al., 1999 ; Takeda et al., 2017 ). The CAARS evaluates the frequency and severity of current ADHD symptoms in adults and provides a total score as well as subscale scores for inattention and hyperactivity/impulsivity. In the present study, the total CAARS score was used as the primary behavioral index of attentional regulation. Autistic traits were assessed using the Autism-Spectrum Quotient (AQ), a 50-item self-report measure designed to quantify the degree to which adults exhibit traits associated with the autism spectrum (Baron-Cohen et al., 2001 ; Wakabayashi et al., 2006 ). The AQ comprises five subdomains—social skill, attention switching, attention to detail, communication, and imagination—with total scores ranging from 0 to 50, where higher scores indicate greater autistic traits. Statistical treatment of behavioral scores. Given the potential overlap between ASD and ADHD traits, we calculated a pairwise Variance Inflation Factor (VIF) between AQ and ADHD total scores (VIF = 1.66), indicating that while correlated, these variables were statistically distinct enough for separate analyses. Furthermore, to control for potential confounding effects of biological sex and age, behavioral scores were residualized using linear regression prior to correlation and path analyses with hypothalamic volumes. 2.3. MRI Data Acquisition and Parameters Structural neuroimaging data were acquired using a 3.0 Tesla MRI scanner (Siemens MAGNETOM Skyra fit, Siemens Healthineers, Erlangen, Germany) equipped with a 20-channel head coil. For all participants (n = 23), high-resolution T1-weighted images were obtained using a magnetization-prepared rapid gradient-echo (MPRAGE) sequence with the following parameters: repetition time (TR) = 2300 ms; echo time (TE) = 2.98 ms; inversion time (TI) = 900 ms; flip angle = 9°; voxel size = 1 × 1 × 1 mm³ isotropic; field of view (FOV) = 256 × 256 mm; 192 slices; and total acquisition time = 5 min 20 s. Images were acquired in the sagittal plane. To ensure adequate image quality for automated segmentation of small hypothalamic subunits, all scans underwent strict quality control procedures. Structural images were visually inspected for motion artifacts, susceptibility-related distortions, and overall signal-to-noise ratio. No datasets were excluded due to insufficient image quality, resulting in a final sample of 23 high-quality structural scans for subsequent volumetric analyses. 2.4. Image Preprocessing and the Segmentation Pipeline Hypothalamic segmentation was performed using the pre-trained hypothalamus_seg tool (Billot et al., 2020 ). The pre-trained convolutional neural network model was applied without modification. T1-weighted images were processed using the tool’s default pipeline, which includes resampling to approximately 1.0 mm isotropic resolution when necessary and intensity normalization by linearly mapping the 0.5th and 99.5th intensity percentiles to 0 and 1, respectively. The pipeline generated bilateral labels for anatomically defined hypothalamic subunits, which were subsequently used for volumetric analyses. Posterior hypothalamic subunits were included as a negative-control region in exploratory analyses to assess anatomical specificity. 2.5. Hypothalamic Subunit Definitions The automated segmentation tool parcellates the hypothalamus into five anatomically defined subunits per hemisphere, yielding a total of ten subunits across the brain. These subunits are delineated based on established anatomical landmarks and are designed to approximate underlying functional hypothalamic nuclei. Specifically, the anterior–inferior subunit primarily encompasses the supraoptic nucleus (SON) and the suprachiasmatic nucleus; the anterior–superior subunit primarily encompasses the paraventricular nucleus (PVN) and the preoptic area; the tuberal–inferior subunit includes the infundibular (arcuate) and ventromedial nuclei; the tuberal–superior subunit includes the dorsomedial nucleus; and the posterior subunit contains the mammillary bodies and the posterior hypothalamic nucleus. For all hypothalamic subunits, volumetric measures were derived using soft volumes, calculated as the sum of voxel-wise posterior probabilities across each subunit. This probabilistic approach accounts for uncertainty at subunit boundaries and provides increased sensitivity to subtle volumetric variation compared with binary segmentation masks, which is particularly advantageous when analyzing small hypothalamic structures. For the primary analyses, we focused on PVN- and SON-associated subunits.. To assess anatomical specificity, hypothalamic subunits were further grouped into an anterior, magnocellular-rich system (SON- and PVN-associated subunits) and a posterior hypothalamic region encompassing the mammillary bodies. The posterior subunit was included as a negative-control region in statistical analyses to evaluate whether observed brain–behavior associations were preferentially related to the anterior neurosecretory system rather than reflecting global hypothalamic volumetric variation. 2.6. Statistical Analysis and Bootstrapped Path Analysis All statistical analyses were performed in Python (version 3.10), using the statsmodels library for regression-based residualization and scipy.stats for correlation analyses. Covariate handling. To minimize potential confounding effects of demographic factors and global brain size, hypothalamic subunit volumes and behavioral scores were adjusted for biological sex, age, and total intracranial volume (TIV) using linear regression. The resulting residuals were used for all subsequent partial correlation and path analyses unless otherwise specified. Total intracranial volume (TIV; cm³) was estimated from intracranial masks generated using the deepbet (Fisch et al., 2024 ) applied to T1-weighted images. Partial correlation analyses. Associations between hypothalamic subunit volumes and behavioral measures were assessed using partial correlation analyses based on residualized variables. Statistical significance was set at p < 0.05 (two-tailed). Primary and exploratory analyses. Guided by an a priori magnocellular hypothesis, primary analyses focused on PVN- and SON-associated subunits. Associations involving other hypothalamic subunits were examined to assess anatomical specificity. As no nominally significant associations were observed for these regions, they were not included in subsequent path analyses. Bootstrapped path analysis. To evaluate the hypothesized anterior magnocellular axis linking PVN volume to behavioral measures via SON volume, a path model was constructed. Given the modest sample size (n = 23), nonparametric bootstrap resampling with 5,000 iterations was employed to estimate indirect effects. Statistical significance was determined based on bootstrap-derived 95% percentile confidence intervals. 3. Results 3.1. Descriptive Statistics and Quality Control The final sample consisted of 23 healthy adults (12 males, 11 females) with a mean age of 21.87 years (SD = 1.96). Behavioral measures showed approximately symmetric distributions for both Autism-Spectrum Quotient (AQ) scores (mean = 20.26, SD = 6.65) and ADHD symptom scores (mean = 50.39, SD = 13.33). Automated segmentation successfully delineated hypothalamic subunits in all participants. Visual inspection of segmentation outputs confirmed appropriate localization of the anterior–inferior (SON-associated) and anterior–superior (PVN-associated) subunits relative to the optic chiasm and the third ventricle (Fig. 1 ; Supplementary Fig. 1). Mean volumes of the target subunits were consistent with established anatomical ranges (left PVN: 23.26 ± 4.80 mm³; left SON: 16.70 ± 5.75 mm³; Fig. 2 ). No significant hemispheric differences in subunit volumes were observed at the group level (all p > 0.05). 3.2. Structural Covariance of the Anterior Magnocellular System Consistent with the hypothesized anterior magnocellular axis, partial correlation analysis controlling for biological sex, age, and total intracranial volume (TIV) revealed a significant positive association between left PVN- and left SON-associated subunit volumes ( r = 0.51, p = 0.022; Fig. 3 ). This association was not attributable to demographic factors or global brain size. A comparable, though not statistically significant, association was observed in the right hemisphere ( r = 0.43, p = 0.059), suggesting a trend toward bilateral structural coherence of the magnocellular-rich anterior hypothalamic system. 3.3. Hypothalamic Volume–Behavior Associations Primary analyses examined associations between left PVN- and SON-associated subunit volumes and dimensional measures of autistic traits (AQ) and ADHD symptoms, guided by the magnocellular hypothesis. Partial correlations were computed using residuals adjusted for biological sex, age, and TIV. Right-hemisphere associations were examined exploratorily and did not reach statistical significance (right SON–AQ: r = − 0.27, p = 0.255; right SON–ADHD: r = − 0.16, p = 0.493; right PVN–AQ: r = − 0.25, p = 0.290; right PVN–ADHD: r = − 0.01, p = 0.952). 3.3.1. SON-Associated Subunits and Behavioral Traits Left SON-associated subunit volume showed a significant inverse association with AQ scores ( r = − 0.45, p = 0.046; Fig. 4 ), indicating that smaller SON-associated volumes were related to higher levels of autistic-like traits. A stronger inverse association was observed between left SON-associated volume and ADHD symptom severity ( r = − 0.59, p = 0.006; Fig. 4 ). In contrast, left PVN-associated subunit volume was not significantly associated with either AQ ( r = − 0.30, p = 0.199) or ADHD symptom scores ( r = − 0.22, p = 0.346). Exploratory analyses of other hypothalamic subunits did not reveal any nominally significant associations and were therefore not pursued further or included in subsequent modeling. 3.3.2. Anatomical Specificity (Negative Control) To assess anatomical specificity, posterior hypothalamic subunit volumes encompassing the mammillary bodies were examined as a negative-control region. No significant associations were observed between left mammillary body volume and AQ ( r = − 0.05, p = 0.826) or ADHD symptom scores ( r = − 0.23, p = 0.322; Supplementary Fig. 2). Importantly, the associations between left SON-associated volume and behavioral measures remained significant when posterior hypothalamic volume was included as an additional covariate (AQ: r = − 0.49, p = 0.035; ADHD: r = − 0.56, p = 0.013), supporting the specificity of these effects to anterior magnocellular-rich subunits. 3.4. Bootstrapped Path Analysis To further examine network-level relationships, bootstrapped path analyses were conducted to test the hypothesized anterior magnocellular axis linking PVN-associated volume to behavioral traits via SON-associated volume. All paths were estimated using residuals adjusted for biological sex, age, and TIV, with 5,000 bootstrap iterations used to estimate indirect effects (Fig. 4 ). 3.4.1. Model A: Autistic Traits The path from left PVN-associated volume to left SON-associated volume was significant (β = 0.51, p = 0.013, 95% CI [0.257, 0.725]). The path from left SON-associated volume to AQ scores was not significant (β = −0.36, p = 0.089, 95% CI [− 0.644, 0.004]). The indirect association between PVN-associated volume and AQ scores via SON-associated volume approached, but did not reach, statistical significance (β_ind = − 0.19, p = 0.052, 95% CI [− 0.354, 0.003]). No direct association between PVN-associated volume and AQ scores was observed. 3.4.2. Model B: ADHD Symptoms In contrast, a significant indirect association between PVN-associated volume and ADHD symptoms was observed via SON-associated volume. The PVN–SON path was significant (β = 0.51, p = 0.013, 95% CI [0.257, 0.725]), as was the SON–ADHD path (β = −0.57, p = 0.005, 95% CI [− 0.786, − 0.202]). The resulting indirect effect was significant (β_ind = − 0.29, p = 0.006, 95% CI [− 0.498, − 0.074]). No direct association between PVN-associated volume and ADHD symptom scores was observed. 4. Discussion 4.1. Structural Coherence of the Anterior Magnocellular System The present study provides in vivo evidence for structural coherence between anterior hypothalamic subunits approximating the paraventricular nucleus (PVN) and supraoptic nucleus (SON). The significant positive association between PVN- and SON-associated volumes is consistent with the known anatomical and developmental organization of the hypothalamo–neurohypophyseal system, in which these regions form a tightly coupled neuroendocrine network. This finding aligns with prior work emphasizing functional differentiation within this system, whereby PVN-associated regions integrate diverse afferent inputs, while SON-associated regions are specialized for neurosecretory output (Brown et al., 2013 ; Son et al., 2022 ; Brown, 2016 ; Wang et al., 2022 ). Importantly, our path analyses indicate that SON-associated volume occupies a proximal position linking anterior hypothalamic structure to behavioral variation, whereas PVN-associated volume contributes indirectly through its structural relationship with the SON. This pattern is compatible with a hierarchical network organization in which variability in downstream magnocellular-rich regions is more directly related to behavioral traits than upstream integrative regions (Son et al., 2022 ). Such an organization is consistent with models proposing that the functional capacity of neurosecretory output regions may represent a limiting factor in neuroendocrine regulation. The anatomical specificity of this network was supported by negative-control analyses, which revealed no associations between posterior hypothalamic (mammillary) volumes and behavioral measures. This finding suggests that the observed associations are not attributable to generalized hypothalamic or global brain volume effects, but rather reflect regionally specific variation within anterior magnocellular-rich subunits. Furthermore, by employing sex-adjusted residuals, we have ensured that these structural-behavioral links are independent of baseline differences in brain volume, providing a refined biological marker (Sanfilipo et al., 2004 ; Ma et al., 2019 ; van Eijk et al., 2020 ). 4.2. Anterior Hypothalamic Structure and Autistic Traits We observed a significant inverse association between SON-associated volume and autistic traits, as measured by the Autism-Spectrum Quotient. This finding is consistent with theoretical frameworks implicating hypothalamic neuropeptide systems in social cognition and stress regulation (Quattrocki & Friston, 2014 ; Lukas & Neumann, 2013 ; Zhang et al., 2017 ). While oxytocin and vasopressin have been proposed as key mediators of social behavior, the present study extends this literature by providing structural evidence that variation in anterior hypothalamic subunits is related to subclinical autistic traits in humans. Recent research in animal models further suggests that oxytocin may influence social deficits, potentially through mechanisms such as oligodendrocyte differentiation (Wen et al., 2025 ). However, the mediation pathway linking PVN-associated volume to autistic traits via SON-associated volume did not reach conventional statistical significance. This suggests that, although the direction of effects is consistent with the hypothesized anterior magnocellular network, the association with autistic traits may be weaker or more heterogeneous than that observed for ADHD-related symptoms. These findings underscore the importance of interpreting structural–behavioral associations within a dimensional and transdiagnostic framework, particularly given the known overlap between autistic and attentional traits. 4.3. ADHD Symptoms and Neuroendocrine Network Organization The most robust behavioral association observed in this study was the inverse relationship between SON-associated volume and ADHD symptom severity. This finding expands prevailing neurobiological models of ADHD, which have traditionally emphasized dopaminergic fronto-striatal circuits (Faraone & Biederman, 1998 ; Biederman, 2005 ; Klein et al., 2019 ), by implicating anterior hypothalamic structure as an additional contributor to attentional and impulse-regulation processes. Recent evidence also highlights that oxytocin levels may correlate negatively with impulsivity and inattention, suggesting a hypo-oxytocinergic component in ADHD pathophysiology (Sasaki et al., 2015 ; Demirci et al., 2016 ; Levi-Shachar et al., 2020 ). Animal and human studies have demonstrated extensive interactions between hypothalamic neuropeptide systems and mesolimbic dopamine pathways, suggesting a plausible mechanism by which variation in SON-associated structure could influence attentional regulation (Hung et al., 2017 ; Melis et al., 2007 ; Kohli et al., 2019 ). Within this framework, reduced SON-associated volume may reflect diminished capacity of neuroendocrine modulation of reward and salience processing, contributing to increased impulsivity and attentional instability. We propose that a robust SON volume reflects a high-capacity system that may exerts a "stabilizing" modulatory influence on dopaminergic firing (Borland et al., 2019 ). Importantly, this association was specific to anterior hypothalamic subunits and was not observed in posterior regions, supporting its anatomical specificity. 4.4. Structural Covariance as an Intermediate Phenotype Increasingly, neuroimaging research conceptualizes coordinated structural variation across brain regions as an intermediate phenotype linking neurodevelopmental processes to behavioral outcomes (Bethlehem et al., 2017 ; King, D. J. & Wood, A. G, 2020). In this context, the present findings identify a specific anterior hypothalamic structural network associated with dimensional ADHD and ASD traits, extending prior large-scale studies that have reported subcortical anatomical variation across neurodevelopmental conditions (Boedhoe et al., 2020 ; Opel et al., 2020 ). 4.5. Lateralization and Future Directions The predominance of left-sided associations observed in this study is consistent with prior work on hemispheric specialization related to social and communicative processing (Pobric et al., 2016 ; Kühn-Popp et al., 2016 ). However, given the modest sample size and the presence of trend-level associations in the right hemisphere, conclusions regarding lateralization should be considered preliminary. Future studies employing larger samples and higher-resolution imaging will be essential to clarify hemispheric asymmetries within hypothalamic networks. Several directions for future research emerge from these findings. High-field (7T) MRI could provide improved delineation of magnocellular and parvocellular subdivisions within anterior hypothalamic regions. Combining structural imaging with dynamic neuroendocrine assays during behavioral tasks would further elucidate structure–function relationships. Finally, longitudinal studies are needed to determine whether variation in SON-associated volume represents a stable developmental trait or reflects experience-dependent neuroplasticity. 5. Conclusion This study provides novel in vivo evidence linking anterior hypothalamic subunit structure to dimensional traits of ADHD and ASD in a non-clinical population. Using automated deep learning–based segmentation of high-resolution MRI, we identified a coherent structural relationship between PVN- and SON-associated subunits and demonstrated that variation in SON-associated volume is systematically related to social and attentional traits. The specificity of these associations to anterior magnocellular-rich regions, and their absence in posterior hypothalamic subunits, underscores the relevance of hypothalamic network organization in neurodevelopmental variation. Together, these findings highlight the potential of hypothalamic subunit volumetry as an anatomically grounded approach for advancing precision psychiatry. Declarations Ethics approval This study was approved by the Institutional Review Board of Tohoku Fukushi University (approval No: [RS190607]) and was conducted in accordance with the Declaration of Helsinki. Consent to participate Written informed consent to participate in the study was obtained from all participants. Participants were informed about the purpose of the study, the procedures involved, potential risks, and their right to withdraw at any time without any negative consequences. Consent to publish Written informed consent to publish anonymized data derived from this study (Consent to Participate and Consent to Publish) was obtained from all participants as part of the formal consent process. Declaration of competing interest The authors declare no competing interests. Funding This study was supported by JSPS KAKENHI Grant Number 25K03427 and Cooperative Study Program (25NIPS607) of National Institute for Physiological Sciences. Author Contribution **U.C.** : Conceptualization, Data curation, Formal Analysis, Methodology, Resources, Writing – original draft, Writing – review & editing. **S.K** .: Conceptualization, Acquiring behavioral data, Formal Analysis, Writing-review & editing. **Y.S** : Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Software, Project administration, Validation, Visualization, Writing – original draft, Writing – review & editing. **M.F.** : Formal Analysis, Investigation, Writing-review & editing. **S.O.** : Conceptualization, Data curation, Formal Analysis, Funding, Investigation, Project administration, Resources, Supervision, Writing – review & editing. Data Availability The datasets generated and/or analysed during the current study are not publicly available due to ethical and privacy restrictions related to participant confidentiality but are available from the corresponding author on reasonable request. References Adamantidis AR, de Lecea L. 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Hum Brain Mapp. 2019;40(5):1507–27. van Eijk L, Hansell NK, Strike LT, Couvy-Duchesne B, de Zubicaray GI, Thompson PM, McMahon KL, Zietsch BP, Wright MJ. Region-specific sex differences in the hippocampus. NeuroImage. 2020;215:116781. Quattrocki E, Friston K. Autism, oxytocin and interoception. Neurosci Biobehav Rev. 2014;47:410–30. Lukas M, Neumann ID. Oxytocin and vasopressin in rodent behaviors related to social dysfunctions in autism spectrum disorders. Behav Brain Res. 2013;251:85–94. Zhang R, Xu XJ, Zhang HF, Han SP, Han JS. (2017). The role of the oxytocin/arginine vasopressin system in animal models of autism spectrum disorder. Advances in Anatomy, Embryology, and Cell Biology, 224, 135–158. Wen J, et al. Oxytocin enhances oligodendrocyte development and improves social deficits in autistic rats. Frontiers in Neuroscience; 2025. Faraone SV, Biederman J. Neurobiology of attention-deficit hyperactivity disorder. Biol Psychiatry. 1998;44(10):951–8. Biederman J. Attention-deficit/hyperactivity disorder: A selective overview. Biol Psychiatry. 2005;57(11):1215–20. Klein MO, Battagello DS, Cardoso AR, Hauser DN, Bittencourt JC, Correa RG. Dopamine: Functions, signaling, and association with neurological diseases. Cell Mol Neurobiol. 2019;39(1):31–59. Sasaki T, Hashimoto K, Oda Y, Ishima T, Kurata T, Takahashi J, Iyo M. Decreased levels of serum oxytocin in pediatric patients with attention deficit/hyperactivity disorder. Psychiatry Res. 2015;228(3):746–53. Demirci E, Özmen S, Öztop DB. Relationship between impulsivity and serum oxytocin in male children and adolescents with attention-deficit and hyperactivity disorder: A preliminary study. Noro Psikiyatri Arsivi. 2016;53(4):291–5. Levi-Shachar O, Gvirts HZ, Goldwin Y, Bloch Y, Shamay-Tsoory S, Zagoory-Sharon O, Feldman R, Maoz H. The effect of methylphenidate on social cognition and oxytocin in children with attention deficit hyperactivity disorder. Neuropsychopharmacology. 2020;45(2):367–73. Hung LW, Neuner S, Polepalli JS, Beier KT, Wright M, Walsh JJ, Lewis EM, Luo L, Deisseroth K, Dölen G, Malenka RC. Gating of social reward by oxytocin in the ventral tegmental area. Science. 2017;357(6358):1406–11. Melis MR, Melis T, Cocco C, Succu S, Sanna F, Pillolla G, Boi A, Ferri GL, Argiolas A. Oxytocin injected into the ventral tegmental area induces penile erection and increases extracellular dopamine in the nucleus accumbens and paraventricular nucleus of the hypothalamus of male rats. Eur J Neurosci. 2007;26(4):1026–35. Kohli S, King MV, Williams S, Edwards A, Ballard TM, Steward LJ, Alberati D, Fone KC F. Oxytocin attenuates phencyclidine hyperactivity and increases social interaction and nucleus accumbens dopamine release in rats. Neuropsychopharmacology. 2019;44(2):295–305. Borland JM, Rilling JK, Frantz KJ, Albers HE. Sex-dependent regulation of social reward by oxytocin: An inverted U hypothesis. Neuropsychopharmacology. 2019;44(1):97–110. Bethlehem RA, Romero-Garcia R, Mak E, Bullmore ET, Baron-Cohen S. Structural covariance networks in children with autism or ADHD. Cereb Cortex. 2017;27(8):4267–76. King DJ, Wood AG. Clinically feasible brain morphometric similarity network construction approaches with restricted magnetic resonance imaging acquisitions. Netw Neurosci. 2020;4(1):274–91. Boedhoe PSW, van Rooij D, Hoogman M, Twisk JWR, Schmaal L, Abe Y, van den Heuvel OA. Subcortical brain volume, regional cortical thickness, and cortical surface area across disorders: Findings from the ENIGMA ADHD, ASD, and OCD Working Groups. Am J Psychiatry. 2020;177(9):834–43. Opel N, Goltermann J, Hermesdorf M, Berger K, Baune BT, Dannlowski U. Cross-disorder analysis of brain structural abnormalities in six major psychiatric disorders: A secondary analysis of mega- and meta-analytical findings from the ENIGMA Consortium. Biol Psychiatry. 2020;88(9):678–86. Pobric G, Lambon Ralph MA, Zahn R. Hemispheric specialization within the superior anterior temporal cortex for social and nonsocial concepts. J Cogn Neurosci. 2016;28(3):351–60. Kühn-Popp N, Kristen S, Paulus M, Meinhardt J, Sodian B. Left hemisphere EEG coherence in infancy predicts infant declarative pointing and preschool epistemic language. Soc Neurosci. 2016;11(1):49–59. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials2601131.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Mar, 2026 Reviews received at journal 05 Mar, 2026 Reviews received at journal 03 Mar, 2026 Reviews received at journal 28 Feb, 2026 Reviewers agreed at journal 21 Feb, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviewers invited by journal 17 Feb, 2026 Editor invited by journal 14 Feb, 2026 Editor assigned by journal 12 Feb, 2026 Submission checks completed at journal 12 Feb, 2026 First submitted to journal 12 Feb, 2026 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. 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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-8737027","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593602420,"identity":"ccc42c40-1ffd-42f4-a667-736136b93137","order_by":0,"name":"Uk-Su Choi","email":"","orcid":"","institution":"Daegu-Gyeongbuk Medical Innovation Foundation","correspondingAuthor":false,"prefix":"","firstName":"Uk-Su","middleName":"","lastName":"Choi","suffix":""},{"id":593602421,"identity":"5b84054e-96ad-4a8c-9004-fdd069d6af21","order_by":1,"name":"Sachiko Kiyama","email":"","orcid":"","institution":"Tohoku University","correspondingAuthor":false,"prefix":"","firstName":"Sachiko","middleName":"","lastName":"Kiyama","suffix":""},{"id":593602422,"identity":"6075b84d-dd34-40e7-a02a-a9bab39c4649","order_by":2,"name":"Yul-Wan Sung","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYLCCDwxyYPrAAwYGfogQD34djDMSjCFaEhgMJBuI0cLMA9XCgNCCB8jPyD382vaHQWKD2OGHQFv+SBgcYH74gUHmDk4tBjfy0qxzEoBapNMMQA4DamEzlmDgeYZbi0SOmXFOwp/E/bcTwFrqDA4wmAH9chiPw4BaLMC2pH+A2sL+Da8Whhs5xo8ZwFpyYA7jwW+LwZk3Zow9aQbGQC0FBxIMjCUkD/MUSyTg8Yt8e47xhx82BrJAh23+8KFCToLvePvGDx97cIcYELBJIFkKxMxAnNhzAJ8W5g9YBH/g1TIKRsEoGAUjCwAAWltR5Bsd0aUAAAAASUVORK5CYII=","orcid":"","institution":"Kansei Fukushi Research Institute, Tohoku Fukushi University","correspondingAuthor":true,"prefix":"","firstName":"Yul-Wan","middleName":"","lastName":"Sung","suffix":""},{"id":593602423,"identity":"06d413eb-7935-4e87-8a91-e8525a1a91b4","order_by":3,"name":"Masaki Fukunaga","email":"","orcid":"","institution":"National Institute of Physiological Sciences","correspondingAuthor":false,"prefix":"","firstName":"Masaki","middleName":"","lastName":"Fukunaga","suffix":""},{"id":593602424,"identity":"cb063095-a919-4330-b18b-4335131605da","order_by":4,"name":"Seiji Ogawa","email":"","orcid":"","institution":"Kansei Fukushi Research Institute, Tohoku Fukushi University","correspondingAuthor":false,"prefix":"","firstName":"Seiji","middleName":"","lastName":"Ogawa","suffix":""}],"badges":[],"createdAt":"2026-01-30 04:38:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8737027/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8737027/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103177459,"identity":"c6d53f99-d0b2-4836-b7ae-12482725d7d3","added_by":"auto","created_at":"2026-02-22 16:51:18","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":331679,"visible":true,"origin":"","legend":"\u003cp\u003eAutomated segmentation of hypothalamic subunits on a representative T1-weighted structural MRI image. (a) Sagittal view, (b) coronal view, (c) axial view, and (d) three-dimensional rendering depicting the anterior–inferior (supraoptic nucleus–associated; yellow) and anterior–superior (paraventricular nucleus–associated; blue) subunits in relation to the optic chiasm and third ventricle.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8737027/v1/1072e4f21fa0811bfb5cdc2b.jpeg"},{"id":103505268,"identity":"b5fe0eb1-2fd5-4e39-b3a7-bb3d2b1d5629","added_by":"auto","created_at":"2026-02-26 13:29:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":201432,"visible":true,"origin":"","legend":"\u003cp\u003eDistributions of hypothalamic subunit volumes. Group-level mean volumes and inter-individual variability are presented for left and right paraventricular nucleus– and supraoptic nucleus–associated subunits, demonstrating anatomically plausible ranges and no evidence of lateralized volume differences (ns, not significant).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8737027/v1/0af02c591e7543b4545f4e45.png"},{"id":103177456,"identity":"9c390d60-f0f7-4d4a-8456-5d059de6d9a8","added_by":"auto","created_at":"2026-02-22 16:51:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":150962,"visible":true,"origin":"","legend":"\u003cp\u003eStructural covariance between anterior hypothalamic subunits and associations with neurodevelopmental traits. (a) Partial correlation between left paraventricular nucleus– and supraoptic nucleus–associated volumes, indicating significant positive structural coupling consistent with an anterior magnocellular axis. (b) Association between left supraoptic nucleus–associated volume and Autism-Spectrum Quotient scores. (c) Association between left supraoptic nucleus–associated volume and ADHD symptom scores. All correlations are adjusted for sex, age, and total intracranial volume. *p \u0026lt; 0.05, **p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8737027/v1/7cc2c8b47b7233e02ad1f3a8.png"},{"id":103177457,"identity":"36406fdf-2fcd-439f-af93-42fe9a881d2f","added_by":"auto","created_at":"2026-02-22 16:51:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51717,"visible":true,"origin":"","legend":"\u003cp\u003ePath analytic models linking anterior hypothalamic structure to ASD traits and ADHD symptoms. Bootstrapped structural equation models (5,000 iterations) depict positive coupling between paraventricular nucleus– and supraoptic nucleus–associated volumes and show an indirect association between paraventricular nucleus–associated volume and ASD/ADHD measures via supraoptic nucleus–associated volume, after adjustment for sex, age, and total intracranial volume. Non-significant paths are labeled as ns; *p \u0026lt; 0.05, **p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8737027/v1/c2109677a44a9450e5840c8c.png"},{"id":103509619,"identity":"fbc6f45d-6e9c-42fe-8150-37ccc894b7a0","added_by":"auto","created_at":"2026-02-26 14:00:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1672805,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8737027/v1/94c382af-e489-4011-9101-71568ee030e5.pdf"},{"id":103177458,"identity":"13912677-080e-4d36-b8e4-e8c4ca560ab7","added_by":"auto","created_at":"2026-02-22 16:51:18","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1429005,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials2601131.docx","url":"https://assets-eu.researchsquare.com/files/rs-8737027/v1/c948facb2c5c676289332494.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations Between Anterior Hypothalamic Subunits and ADHD and Autistic Traits Revealed by Deep Learning MRI Segmentation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe hypothalamus comprises less than 1% of total brain volume, yet exerts disproportionate influence over survival-critical functions, including homeostasis, circadian regulation, and social behavior (Adamantidis \u0026amp; de Lecea, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mei et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Within this compact and highly specialized structure, the paraventricular nucleus (PVN) and supraoptic nucleus (SON) form the core of the magnocellular neurosecretory system (MNS), a key neuroendocrine pathway responsible for the synthesis and systemic release of oxytocin (OXT) and arginine vasopressin (AVP) (Silverman \u0026amp; Zimmerman, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Qin et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; M\u0026oslash;ller et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although the PVN contains a heterogeneous neuronal population\u0026mdash;including parvocellular neurons that regulate stress responses via the hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal axis\u0026mdash;the SON is composed almost exclusively of magnocellular neurons projecting to the posterior pituitary, underscoring its specialized neurosecretory role (Caldwell et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccumulating evidence from human and animal research implicates the OXT and AVP systems in social bonding, empathy, emotional regulation, and stress responsivity, forming a central component of the so-called \u0026ldquo;social brain\u0026rdquo; (Feldman, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tolomeo et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Disruption of these neuropeptide systems has therefore been proposed as a plausible pathophysiological mechanism underlying Autism Spectrum Disorder (ASD). Consistent with this framework, clinical trials of intranasal oxytocin administration have reported variable but promising effects on social functioning in subsets of individuals with ASD, suggesting that hypo-oxytocinergic states may characterize specific phenotypes within the autism spectrum (Anagnostou et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Parker et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yamasue et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond ASD, increasing evidence suggests that hypothalamic neuroendocrine mechanisms may also be relevant to Attention-Deficit/Hyperactivity Disorder (ADHD), a condition that frequently co-occurs with ASD (Canals et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While dopaminergic dysregulation remains central to prevailing models of ADHD, oxytocin has been shown to modulate reward processing and motivational salience through interactions with dopaminergic neurons in the nucleus accumbens (Peris et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These findings raise the possibility that hypothalamic integrity may play a previously underappreciated role in attentional regulation and impulse control, linking neuroendocrine function to dimensional neurodevelopmental traits that span diagnostic categories.\u003c/p\u003e \u003cp\u003eDespite strong theoretical and experimental support, direct investigation of hypothalamic structure in human neurodevelopmental research has long been constrained by a fundamental \u0026ldquo;resolution gap.\u0026rdquo; Standard clinical MRI lacks sufficient contrast to delineate individual hypothalamic nuclei, forcing many human studies to rely on peripheral neuropeptide measurements, which may not accurately reflect central neuroendocrine organization or function (Billot et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Recent advances in deep learning\u0026ndash;based neuroimaging, however, have begun to overcome this limitation. In particular, convolutional neural network\u0026ndash;based automated segmentation frameworks trained on histological atlases now permit reliable in vivo quantification of anatomically defined hypothalamic subunits using conventional T1-weighted MRI (Billot et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Estrada et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLeveraging these methodological advances, the present study tested the hypothesis that anterior hypothalamic subunits enriched in magnocellular neurosecretory nuclei\u0026mdash;approximating the paraventricular (PVN) and supraoptic (SON) regions\u0026mdash;form a structurally coherent anterior axis associated with dimensional traits of ADHD and ASD in a non-clinical adult population (n\u0026thinsp;=\u0026thinsp;23). By integrating automated hypothalamic volumetry with behavioral measures, this work aims to provide an anatomically grounded framework for investigating hypothalamic contributions to neurodevelopmental variation in humans.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Participants and Ethical Considerations\u003c/h2\u003e \u003cp\u003eThe study sample comprised twenty-three healthy adult volunteers (n\u0026thinsp;=\u0026thinsp;23; 11 females, 12 males) recruited from the local community in Sendai, Japan. Participants had a mean age of 21.87 years (SD\u0026thinsp;=\u0026thinsp;1.96). Recruitment was conducted through community advertisements designed to capture a broad range of behavioral phenotypes within the non-clinical population. Participants were screened to exclude any history of diagnosed neurological disorders, major psychiatric conditions (e.g., schizophrenia or bipolar disorder), or active substance abuse. In addition, all participants were free from medical conditions contraindicating MRI (e.g., pregnancy, implanted metallic devices, or claustrophobia), as assessed by pre-screening interviews. This recruitment strategy was intended to characterize subclinical variability in neurodevelopmental traits, consistent with a dimensional approach to psychiatric research. Written informed consent was obtained from all participants, and all procedures were conducted in accordance with the Declaration of Helsinki. The study protocol was approved by the Institutional Review Board of Tohoku Fukushi University (approval No: [RS190607]).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Behavioral Assessment: ADHD and ASD Traits\u003c/h2\u003e \u003cp\u003eTo quantify dimensional neurodevelopmental traits of interest, participants completed two standardized Japanese self-report questionnaires assessing ADHD symptoms and autistic traits.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAttention-Deficit/Hyperactivity Disorder (ADHD) traits\u003c/b\u003e were assessed using the Conners\u0026rsquo; Adult ADHD Rating Scale (CAARS) (Conners et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Takeda et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The CAARS evaluates the frequency and severity of current ADHD symptoms in adults and provides a total score as well as subscale scores for inattention and hyperactivity/impulsivity. In the present study, the total CAARS score was used as the primary behavioral index of attentional regulation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAutistic traits\u003c/b\u003e were assessed using the Autism-Spectrum Quotient (AQ), a 50-item self-report measure designed to quantify the degree to which adults exhibit traits associated with the autism spectrum (Baron-Cohen et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Wakabayashi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The AQ comprises five subdomains\u0026mdash;social skill, attention switching, attention to detail, communication, and imagination\u0026mdash;with total scores ranging from 0 to 50, where higher scores indicate greater autistic traits.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical treatment of behavioral scores.\u003c/b\u003e Given the potential overlap between ASD and ADHD traits, we calculated a pairwise Variance Inflation Factor (VIF) between AQ and ADHD total scores (VIF\u0026thinsp;=\u0026thinsp;1.66), indicating that while correlated, these variables were statistically distinct enough for separate analyses. Furthermore, to control for potential confounding effects of biological sex and age, behavioral scores were residualized using linear regression prior to correlation and path analyses with hypothalamic volumes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. MRI Data Acquisition and Parameters\u003c/h2\u003e \u003cp\u003eStructural neuroimaging data were acquired using a 3.0 Tesla MRI scanner (Siemens MAGNETOM Skyra fit, Siemens Healthineers, Erlangen, Germany) equipped with a 20-channel head coil. For all participants (n\u0026thinsp;=\u0026thinsp;23), high-resolution T1-weighted images were obtained using a magnetization-prepared rapid gradient-echo (MPRAGE) sequence with the following parameters: repetition time (TR)\u0026thinsp;=\u0026thinsp;2300 ms; echo time (TE)\u0026thinsp;=\u0026thinsp;2.98 ms; inversion time (TI)\u0026thinsp;=\u0026thinsp;900 ms; flip angle\u0026thinsp;=\u0026thinsp;9\u0026deg;; voxel size\u0026thinsp;=\u0026thinsp;1 \u0026times; 1 \u0026times; 1 mm\u0026sup3; isotropic; field of view (FOV)\u0026thinsp;=\u0026thinsp;256 \u0026times; 256 mm; 192 slices; and total acquisition time\u0026thinsp;=\u0026thinsp;5 min 20 s. Images were acquired in the sagittal plane.\u003c/p\u003e \u003cp\u003eTo ensure adequate image quality for automated segmentation of small hypothalamic subunits, all scans underwent strict quality control procedures. Structural images were visually inspected for motion artifacts, susceptibility-related distortions, and overall signal-to-noise ratio. No datasets were excluded due to insufficient image quality, resulting in a final sample of 23 high-quality structural scans for subsequent volumetric analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Image Preprocessing and the Segmentation Pipeline\u003c/h2\u003e \u003cp\u003eHypothalamic segmentation was performed using the pre-trained hypothalamus_seg tool (Billot et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The pre-trained convolutional neural network model was applied without modification. T1-weighted images were processed using the tool\u0026rsquo;s default pipeline, which includes resampling to approximately 1.0 mm isotropic resolution when necessary and intensity normalization by linearly mapping the 0.5th and 99.5th intensity percentiles to 0 and 1, respectively.\u003c/p\u003e \u003cp\u003eThe pipeline generated bilateral labels for anatomically defined hypothalamic subunits, which were subsequently used for volumetric analyses. Posterior hypothalamic subunits were included as a negative-control region in exploratory analyses to assess anatomical specificity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Hypothalamic Subunit Definitions\u003c/h2\u003e \u003cp\u003eThe automated segmentation tool parcellates the hypothalamus into five anatomically defined subunits per hemisphere, yielding a total of ten subunits across the brain. These subunits are delineated based on established anatomical landmarks and are designed to approximate underlying functional hypothalamic nuclei. Specifically, the anterior\u0026ndash;inferior subunit primarily encompasses the supraoptic nucleus (SON) and the suprachiasmatic nucleus; the anterior\u0026ndash;superior subunit primarily encompasses the paraventricular nucleus (PVN) and the preoptic area; the tuberal\u0026ndash;inferior subunit includes the infundibular (arcuate) and ventromedial nuclei; the tuberal\u0026ndash;superior subunit includes the dorsomedial nucleus; and the posterior subunit contains the mammillary bodies and the posterior hypothalamic nucleus.\u003c/p\u003e \u003cp\u003eFor all hypothalamic subunits, volumetric measures were derived using soft volumes, calculated as the sum of voxel-wise posterior probabilities across each subunit. This probabilistic approach accounts for uncertainty at subunit boundaries and provides increased sensitivity to subtle volumetric variation compared with binary segmentation masks, which is particularly advantageous when analyzing small hypothalamic structures. For the primary analyses, we focused on PVN- and SON-associated subunits..\u003c/p\u003e \u003cp\u003eTo assess anatomical specificity, hypothalamic subunits were further grouped into an anterior, magnocellular-rich system (SON- and PVN-associated subunits) and a posterior hypothalamic region encompassing the mammillary bodies. The posterior subunit was included as a negative-control region in statistical analyses to evaluate whether observed brain\u0026ndash;behavior associations were preferentially related to the anterior neurosecretory system rather than reflecting global hypothalamic volumetric variation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Statistical Analysis and Bootstrapped Path Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed in Python (version 3.10), using the \u003cem\u003estatsmodels\u003c/em\u003e library for regression-based residualization and \u003cem\u003escipy.stats\u003c/em\u003e for correlation analyses.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCovariate handling.\u003c/b\u003e To minimize potential confounding effects of demographic factors and global brain size, hypothalamic subunit volumes and behavioral scores were adjusted for biological sex, age, and total intracranial volume (TIV) using linear regression. The resulting residuals were used for all subsequent partial correlation and path analyses unless otherwise specified. Total intracranial volume (TIV; cm\u0026sup3;) was estimated from intracranial masks generated using the \u003cem\u003edeepbet\u003c/em\u003e (Fisch et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) applied to T1-weighted images.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePartial correlation analyses.\u003c/b\u003e Associations between hypothalamic subunit volumes and behavioral measures were assessed using partial correlation analyses based on residualized variables. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePrimary and exploratory analyses.\u003c/b\u003e Guided by an a priori magnocellular hypothesis, primary analyses focused on PVN- and SON-associated subunits. Associations involving other hypothalamic subunits were examined to assess anatomical specificity. As no nominally significant associations were observed for these regions, they were not included in subsequent path analyses.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBootstrapped path analysis.\u003c/b\u003e To evaluate the hypothesized anterior magnocellular axis linking PVN volume to behavioral measures via SON volume, a path model was constructed. Given the modest sample size (n\u0026thinsp;=\u0026thinsp;23), nonparametric bootstrap resampling with 5,000 iterations was employed to estimate indirect effects. Statistical significance was determined based on bootstrap-derived 95% percentile confidence intervals.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Descriptive Statistics and Quality Control\u003c/h2\u003e \u003cp\u003eThe final sample consisted of 23 healthy adults (12 males, 11 females) with a mean age of 21.87 years (SD\u0026thinsp;=\u0026thinsp;1.96). Behavioral measures showed approximately symmetric distributions for both Autism-Spectrum Quotient (AQ) scores (mean\u0026thinsp;=\u0026thinsp;20.26, SD\u0026thinsp;=\u0026thinsp;6.65) and ADHD symptom scores (mean\u0026thinsp;=\u0026thinsp;50.39, SD\u0026thinsp;=\u0026thinsp;13.33).\u003c/p\u003e \u003cp\u003eAutomated segmentation successfully delineated hypothalamic subunits in all participants. Visual inspection of segmentation outputs confirmed appropriate localization of the anterior\u0026ndash;inferior (SON-associated) and anterior\u0026ndash;superior (PVN-associated) subunits relative to the optic chiasm and the third ventricle (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Fig.\u0026nbsp;1). Mean volumes of the target subunits were consistent with established anatomical ranges (left PVN: 23.26\u0026thinsp;\u0026plusmn;\u0026thinsp;4.80 mm\u0026sup3;; left SON: 16.70\u0026thinsp;\u0026plusmn;\u0026thinsp;5.75 mm\u0026sup3;; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). No significant hemispheric differences in subunit volumes were observed at the group level (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Structural Covariance of the Anterior Magnocellular System\u003c/h2\u003e \u003cp\u003eConsistent with the hypothesized anterior magnocellular axis, partial correlation analysis controlling for biological sex, age, and total intracranial volume (TIV) revealed a significant positive association between left PVN- and left SON-associated subunit volumes (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.51, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This association was not attributable to demographic factors or global brain size.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA comparable, though not statistically significant, association was observed in the right hemisphere (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.059), suggesting a trend toward bilateral structural coherence of the magnocellular-rich anterior hypothalamic system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Hypothalamic Volume\u0026ndash;Behavior Associations\u003c/h2\u003e \u003cp\u003ePrimary analyses examined associations between left PVN- and SON-associated subunit volumes and dimensional measures of autistic traits (AQ) and ADHD symptoms, guided by the magnocellular hypothesis. Partial correlations were computed using residuals adjusted for biological sex, age, and TIV. Right-hemisphere associations were examined exploratorily and did not reach statistical significance (right SON\u0026ndash;AQ: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.27, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.255; right SON\u0026ndash;ADHD: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.493; right PVN\u0026ndash;AQ: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.25, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.290; right PVN\u0026ndash;ADHD: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.952).\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. SON-Associated Subunits and Behavioral Traits\u003c/h2\u003e \u003cp\u003eLeft SON-associated subunit volume showed a significant inverse association with AQ scores (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), indicating that smaller SON-associated volumes were related to higher levels of autistic-like traits. A stronger inverse association was observed between left SON-associated volume and ADHD symptom severity (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.59, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, left PVN-associated subunit volume was not significantly associated with either AQ (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.30, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.199) or ADHD symptom scores (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.22, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.346).\u003c/p\u003e \u003cp\u003eExploratory analyses of other hypothalamic subunits did not reveal any nominally significant associations and were therefore not pursued further or included in subsequent modeling.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Anatomical Specificity (Negative Control)\u003c/h2\u003e \u003cp\u003eTo assess anatomical specificity, posterior hypothalamic subunit volumes encompassing the mammillary bodies were examined as a negative-control region. No significant associations were observed between left mammillary body volume and AQ (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.826) or ADHD symptom scores (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.322; Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eImportantly, the associations between left SON-associated volume and behavioral measures remained significant when posterior hypothalamic volume was included as an additional covariate (AQ: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.49, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035; ADHD: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.56, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), supporting the specificity of these effects to anterior magnocellular-rich subunits.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Bootstrapped Path Analysis\u003c/h2\u003e \u003cp\u003eTo further examine network-level relationships, bootstrapped path analyses were conducted to test the hypothesized anterior magnocellular axis linking PVN-associated volume to behavioral traits via SON-associated volume. All paths were estimated using residuals adjusted for biological sex, age, and TIV, with 5,000 bootstrap iterations used to estimate indirect effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1. Model A: Autistic Traits\u003c/h2\u003e \u003cp\u003eThe path from left PVN-associated volume to left SON-associated volume was significant (β\u0026thinsp;=\u0026thinsp;0.51, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013, 95% CI [0.257, 0.725]). The path from left SON-associated volume to AQ scores was not significant (β = \u0026minus;0.36, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.089, 95% CI [\u0026minus;\u0026thinsp;0.644, 0.004]). The indirect association between PVN-associated volume and AQ scores via SON-associated volume approached, but did not reach, statistical significance (β_ind\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.052, 95% CI [\u0026minus;\u0026thinsp;0.354, 0.003]). No direct association between PVN-associated volume and AQ scores was observed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2. Model B: ADHD Symptoms\u003c/h2\u003e \u003cp\u003eIn contrast, a significant indirect association between PVN-associated volume and ADHD symptoms was observed via SON-associated volume. The PVN\u0026ndash;SON path was significant (β\u0026thinsp;=\u0026thinsp;0.51, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013, 95% CI [0.257, 0.725]), as was the SON\u0026ndash;ADHD path (β = \u0026minus;0.57, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005, 95% CI [\u0026minus;\u0026thinsp;0.786, \u0026minus;\u0026thinsp;0.202]). The resulting indirect effect was significant (β_ind\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.29, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006, 95% CI [\u0026minus;\u0026thinsp;0.498, \u0026minus;\u0026thinsp;0.074]). No direct association between PVN-associated volume and ADHD symptom scores was observed.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Structural Coherence of the Anterior Magnocellular System\u003c/h2\u003e \u003cp\u003eThe present study provides in vivo evidence for structural coherence between anterior hypothalamic subunits approximating the paraventricular nucleus (PVN) and supraoptic nucleus (SON). The significant positive association between PVN- and SON-associated volumes is consistent with the known anatomical and developmental organization of the hypothalamo\u0026ndash;neurohypophyseal system, in which these regions form a tightly coupled neuroendocrine network. This finding aligns with prior work emphasizing functional differentiation within this system, whereby PVN-associated regions integrate diverse afferent inputs, while SON-associated regions are specialized for neurosecretory output (Brown et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Son et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Brown, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImportantly, our path analyses indicate that SON-associated volume occupies a proximal position linking anterior hypothalamic structure to behavioral variation, whereas PVN-associated volume contributes indirectly through its structural relationship with the SON. This pattern is compatible with a hierarchical network organization in which variability in downstream magnocellular-rich regions is more directly related to behavioral traits than upstream integrative regions (Son et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Such an organization is consistent with models proposing that the functional capacity of neurosecretory output regions may represent a limiting factor in neuroendocrine regulation.\u003c/p\u003e \u003cp\u003eThe anatomical specificity of this network was supported by negative-control analyses, which revealed no associations between posterior hypothalamic (mammillary) volumes and behavioral measures. This finding suggests that the observed associations are not attributable to generalized hypothalamic or global brain volume effects, but rather reflect regionally specific variation within anterior magnocellular-rich subunits. Furthermore, by employing sex-adjusted residuals, we have ensured that these structural-behavioral links are independent of baseline differences in brain volume, providing a refined biological marker (Sanfilipo et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; van Eijk et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Anterior Hypothalamic Structure and Autistic Traits\u003c/h2\u003e \u003cp\u003eWe observed a significant inverse association between SON-associated volume and autistic traits, as measured by the Autism-Spectrum Quotient. This finding is consistent with theoretical frameworks implicating hypothalamic neuropeptide systems in social cognition and stress regulation (Quattrocki \u0026amp; Friston, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lukas \u0026amp; Neumann, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). While oxytocin and vasopressin have been proposed as key mediators of social behavior, the present study extends this literature by providing structural evidence that variation in anterior hypothalamic subunits is related to subclinical autistic traits in humans. Recent research in animal models further suggests that oxytocin may influence social deficits, potentially through mechanisms such as oligodendrocyte differentiation (Wen et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, the mediation pathway linking PVN-associated volume to autistic traits via SON-associated volume did not reach conventional statistical significance. This suggests that, although the direction of effects is consistent with the hypothesized anterior magnocellular network, the association with autistic traits may be weaker or more heterogeneous than that observed for ADHD-related symptoms. These findings underscore the importance of interpreting structural\u0026ndash;behavioral associations within a dimensional and transdiagnostic framework, particularly given the known overlap between autistic and attentional traits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.3. ADHD Symptoms and Neuroendocrine Network Organization\u003c/h2\u003e \u003cp\u003eThe most robust behavioral association observed in this study was the inverse relationship between SON-associated volume and ADHD symptom severity. This finding expands prevailing neurobiological models of ADHD, which have traditionally emphasized dopaminergic fronto-striatal circuits (Faraone \u0026amp; Biederman, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Biederman, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Klein et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), by implicating anterior hypothalamic structure as an additional contributor to attentional and impulse-regulation processes. Recent evidence also highlights that oxytocin levels may correlate negatively with impulsivity and inattention, suggesting a hypo-oxytocinergic component in ADHD pathophysiology (Sasaki et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Demirci et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Levi-Shachar et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnimal and human studies have demonstrated extensive interactions between hypothalamic neuropeptide systems and mesolimbic dopamine pathways, suggesting a plausible mechanism by which variation in SON-associated structure could influence attentional regulation (Hung et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Melis et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Kohli et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Within this framework, reduced SON-associated volume may reflect diminished capacity of neuroendocrine modulation of reward and salience processing, contributing to increased impulsivity and attentional instability. We propose that a robust SON volume reflects a high-capacity system that may exerts a \"stabilizing\" modulatory influence on dopaminergic firing (Borland et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Importantly, this association was specific to anterior hypothalamic subunits and was not observed in posterior regions, supporting its anatomical specificity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Structural Covariance as an Intermediate Phenotype\u003c/h2\u003e \u003cp\u003eIncreasingly, neuroimaging research conceptualizes coordinated structural variation across brain regions as an intermediate phenotype linking neurodevelopmental processes to behavioral outcomes (Bethlehem et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; King, D. J. \u0026amp; Wood, A. G, 2020). In this context, the present findings identify a specific anterior hypothalamic structural network associated with dimensional ADHD and ASD traits, extending prior large-scale studies that have reported subcortical anatomical variation across neurodevelopmental conditions (Boedhoe et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Opel et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Lateralization and Future Directions\u003c/h2\u003e \u003cp\u003eThe predominance of left-sided associations observed in this study is consistent with prior work on hemispheric specialization related to social and communicative processing (Pobric et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; K\u0026uuml;hn-Popp et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, given the modest sample size and the presence of trend-level associations in the right hemisphere, conclusions regarding lateralization should be considered preliminary. Future studies employing larger samples and higher-resolution imaging will be essential to clarify hemispheric asymmetries within hypothalamic networks.\u003c/p\u003e \u003cp\u003eSeveral directions for future research emerge from these findings. High-field (7T) MRI could provide improved delineation of magnocellular and parvocellular subdivisions within anterior hypothalamic regions. Combining structural imaging with dynamic neuroendocrine assays during behavioral tasks would further elucidate structure\u0026ndash;function relationships. Finally, longitudinal studies are needed to determine whether variation in SON-associated volume represents a stable developmental trait or reflects experience-dependent neuroplasticity.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study provides novel in vivo evidence linking anterior hypothalamic subunit structure to dimensional traits of ADHD and ASD in a non-clinical population. Using automated deep learning\u0026ndash;based segmentation of high-resolution MRI, we identified a coherent structural relationship between PVN- and SON-associated subunits and demonstrated that variation in SON-associated volume is systematically related to social and attentional traits. The specificity of these associations to anterior magnocellular-rich regions, and their absence in posterior hypothalamic subunits, underscores the relevance of hypothalamic network organization in neurodevelopmental variation. Together, these findings highlight the potential of hypothalamic subunit volumetry as an anatomically grounded approach for advancing precision psychiatry.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval\u003c/strong\u003e \u003cp\u003e This study was approved by the Institutional Review Board of Tohoku Fukushi University (approval No: [RS190607]) and was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e \u003cp\u003e Written informed consent to participate in the study was obtained from all participants. Participants were informed about the purpose of the study, the procedures involved, potential risks, and their right to withdraw at any time without any negative consequences.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to publish\u003c/strong\u003e \u003cp\u003e Written informed consent to publish anonymized data derived from this study (Consent to Participate and Consent to Publish) was obtained from all participants as part of the formal consent process.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by JSPS KAKENHI Grant Number 25K03427 and Cooperative Study Program (25NIPS607) of National Institute for Physiological Sciences.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e**U.C.** : Conceptualization, Data curation, Formal Analysis, Methodology, Resources, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. **S.K** .: Conceptualization, Acquiring behavioral data, Formal Analysis, Writing-review \u0026amp; editing. **Y.S** : Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Software, Project administration, Validation, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. **M.F.** : Formal Analysis, Investigation, Writing-review \u0026amp; editing. **S.O.** : Conceptualization, Data curation, Formal Analysis, Funding, Investigation, Project administration, Resources, Supervision, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to ethical and privacy restrictions related to participant confidentiality but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdamantidis AR, de Lecea L. 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Soc Neurosci. 2016;11(1):49\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-mental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dimh","sideBox":"Learn more about [Discover Mental Health](https://www.springer.com/44192)","snPcode":"","submissionUrl":"","title":"Discover Mental Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hypothalamus, Oxytocin system, Autism spectrum traits, ADHD symptoms, Structural MRI","lastPublishedDoi":"10.21203/rs.3.rs-8737027/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8737027/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe hypothalamus is a central regulator of neuroendocrine function and social behavior, yet its internal organization has remained difficult to examine in vivo in human neurodevelopmental research. Based on neuroendocrine models, we hypothesized that anterior hypothalamic subunits enriched in magnocellular neurosecretory nuclei\u0026mdash;approximating the paraventricular (PVN) and supraoptic (SON) regions\u0026mdash;form a structurally coherent axis linked to dimensional traits associated with autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD).\u003c/p\u003e \u003cp\u003eTo test this hypothesis, we applied a convolutional neural network\u0026ndash;based automated segmentation framework to high-resolution T1-weighted MRI data from a non-clinical adult sample (n\u0026thinsp;=\u0026thinsp;23), enabling volumetric quantification of ten hypothalamic subunits. After adjustment for biological sex, age, and total intracranial volume, PVN- and SON-associated volumes showed a significant positive structural covariance (r\u0026thinsp;=\u0026thinsp;0.51, p\u0026thinsp;=\u0026thinsp;0.022), consistent with a coherent anterior magnocellular-rich network. Reduced SON-associated volume was significantly associated with higher autistic traits (Autism-Spectrum Quotient: r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.45, p\u0026thinsp;=\u0026thinsp;0.046) and greater ADHD symptom severity (CAARS: r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.59, p\u0026thinsp;=\u0026thinsp;0.006).\u003c/p\u003e \u003cp\u003eBootstrapped path analyses (5,000 iterations) further supported this network organization, revealing a robust association between PVN- and SON-associated volumes (β\u0026thinsp;=\u0026thinsp;0.51, p\u0026thinsp;=\u0026thinsp;0.013) and a significant indirect association between PVN-associated volume and ADHD symptoms via SON-associated volume (β = \u0026minus;0.29, p\u0026thinsp;=\u0026thinsp;0.006), with a marginal indirect association observed for autistic traits.\u003c/p\u003e \u003cp\u003eTogether, these findings provide in vivo evidence linking anterior hypothalamic structure to dimensional neurodevelopmental traits and highlight a potential neuroendocrine pathway relevant to psychiatric vulnerability.\u003c/p\u003e","manuscriptTitle":"Associations Between Anterior Hypothalamic Subunits and ADHD and Autistic Traits Revealed by Deep Learning MRI Segmentation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-22 16:51:13","doi":"10.21203/rs.3.rs-8737027/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-11T07:17:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-05T18:08:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-03T09:40:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-01T03:00:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11976598366327428717992321703693535262","date":"2026-02-22T02:52:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126821441018596853020172406568668242469","date":"2026-02-20T02:30:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129800393165191081095824022819926203163","date":"2026-02-18T12:31:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315714927585094474073056897221799206021","date":"2026-02-18T08:27:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-18T03:16:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-14T23:06:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-12T11:33:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-12T05:42:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Mental Health","date":"2026-02-12T05:37:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-mental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dimh","sideBox":"Learn more about [Discover Mental Health](https://www.springer.com/44192)","snPcode":"","submissionUrl":"","title":"Discover Mental Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"48dca090-5122-4a56-84a3-243daf3e21ad","owner":[],"postedDate":"February 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T17:53:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-22 16:51:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8737027","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8737027","identity":"rs-8737027","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}