Volume of the hypothalamus and its subunits in patients with episodic migraine without aura during interictal periods

Volume of the hypothalamus and its subunits in patients with episodic migraine without aura during interictal periods | 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 Volume of the hypothalamus and its subunits in patients with episodic migraine without aura during interictal periods Irene Giardina, Antonio Di Renzo, Davide Chiffi, Francesca Giovannini, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6663261/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Aug, 2025 Read the published version in The Journal of Headache and Pain → Version 1 posted 9 You are reading this latest preprint version Abstract Background The hypothalamus is thought to play a crucial role in the recurrence of migraine attacks, exhibiting activity prior to the onset of a migraine attack. Nonetheless, our comprehension of the roles of its subunits, especially during the interictal phase, remains limited. This study investigated hypothalamic volumetric differences between individuals with episodic migraine and healthy controls, with scans conducted during the interictal phase, free from the effects of preventative medications. Methods We analyzed hypothalamic volumes in 30 patients with episodic migraine without aura (MO), scanned during interictal periods and not on preventative medication, and in 30 healthy controls (HCs) matched for age and sex. Volumetric segmentation was performed of both hypothalamic subunits (anterior-inferior, anterior-superior, posterior, tubular inferior, and tubular superior) and the entire hypothalamus using magnetic resonance imaging (MRI) with T1-weighted sequences. General linear models were employed to evaluate volumetric differences after controlling for age, sex, and total brain volume. Results The volumes of hypothalamic subunits and overall hypothalamus volumes exhibited no statistically significant differences between HCs and MO patients (p > 0.05). No associations were found between the clinical characteristics of MO and the total hypothalamic volume or its subunits. Discussion Our findings indicate that volumetric alterations in the hypothalamus and its subunits do not contribute to the interictal susceptibility to recurrent migraine attacks. Freesurfer segmentation hypothalamus MRI migraine interictal Figures Figure 1 Introduction In recent years, although migraine was traditionally seen as a functional condition of the brain, many structural and morpho-functional cerebral anomalies have been identified during all phases of migraine [ 1 ]. Significant emphasis was placed on the hypothalamus, due to its activation observed during the premonitory phase (24–48 hours prior to onset) of a migraine attack, and its active connectivity with the brainstem during the attack itself [ 2 – 4 ]. Apart from this peri-ictal involvement, the hypothalamus's morpho-functionality during the interictal pain-free phase is not completely elucidated. Prior investigations into brain functional connectivity (FC) during resting state MRI between migraine attacks revealed either no anomalies in hypothalamic FC [ 3 ] or significantly altered FC with the locus coeruleus, caudate, parahippocampus, cerebellum, temporal pole, and lingual and orbitofrontal cortices when compared to control subjects [ 5 , 6 ]. Temporal variations in hypothalamic-cortical interactions have been characterized by a seed-based functional MRI correlation methodology in 23 interictal migraine patients and controls, longitudinally monitored for a duration of up to 4.5 years. Disrupted hypothalamic connection was noted with various brain regions previously associated with pain processing and migraine pathogenesis, including the cerebellum, parahippocampal gyrus, lingual gyrus, and orbitofrontal cortex [ 6 ]. Furthermore, an increased headache effect at follow-up was associated with diminished resting state functional connectivity of the hypothalamic-orbitofrontal gyrus at baseline, but a reduced frequency of migraine attacks connected with enhanced resting state functional connectivity in the same region [ 6 ]. Driven by the necessity to elucidate the microstructural anatomical foundation of interictal functional anomalies in migraine sufferers, we utilised diffusion tensor imaging (DTI) analysis on 3T MRI data from patients with migraine without aura during the interictal phase, revealing abnormal proton diffusivity in both the anterior and posterior regions of the hypothalamus bilaterally [ 7 ]. Utilizing voxel-based morphometry, a study identified a reduction in volume across the entire hypothalamus, particularly in its posterior region, among 18 patients with migraine without aura compared to 21 controls, which was significantly negatively correlated with headache frequency [ 8 ]. The potential role of interictal morpho-functional hypothalamic anomalies as a neuroanatomical substrate predisposing to the start of the prodromal phase and subsequent initiation of an attack is still being investigated. Moreover, it remains ambiguous which particular subregion of the hypothalamus is most actually implicated in the interictal migraine predisposition process. We employed a fully validated automated tool utilising a deep convolutional neural network to segment the entire hypothalamus and its subregions from T1-weighted MRI scans [ 9 ] in a cohort of episodic migraine without aura patients during the interictal phase, devoid of the effects of prophylactic treatments, in comparison to healthy controls. We examined the hypotheses that the volume of the hypothalamus, particularly its posterior subregions, may differ between patients in the interictal phase and healthy individuals, and that clinical characteristics of migraine may correlate with the macrostructure of the hypothalamus. Methods Participants A total of 60 participants were prospectively enrolled. Thirty patients (Table 1 ) diagnosed with episodic migraine without aura (MO) were recruited from the Headache Centres of Rome and Latina. Patients were diagnosed based on the criteria established by the International Classification of Headache Disorders (ICHD III) [ 10 ]. Patients received MRI scans during the interictal phase, characterized by a minimum absence of migraine attacks for three days before and following the MRI. Patients experienced unilateral or bilateral migraine headaches, but did not have persistent pain on the same side. We excluded patients who were receiving preventive treatment in the preceding three months. Other primary or secondary headache types have been excluded via clinical and/or instrumental evaluation, if necessary. Symptomatic therapy was prohibited on the day of the scan. Table 1 Clinical and demographic characteristics of healthy controls (HCs) and patients with migraine without aura scanned during migraine-free intervals (MO). Data are presented as mean ± standard deviation (SD). HCs (n = 30) MO (n = 30) Women (n) 23 25 Age (years) 28.40 ± 8.36 32.17 ± 10.89 Attack frequency/month (n) 5.7 ± 3.0 Attack duration (hours) 33.7 ± 20.0 Days from the last migraine attack (n) 7.3 ± 2.8 MIDAS 20.7 ± 16.8 HIT-6 61.2 ± 7.6 ASC-12 4.3 ± 4.1 We enlisted 30 healthy controls (HCs) of comparable age and sex from among medical students and healthcare professionals for comparative analysis. They displayed no evident medical conditions, personal or familial history of primary headaches or epilepsy, nor regular substance use. Female participants were consistently scanned at mid-cycle. All scanning sessions occurred in the afternoon (4:00–7:00 p.m.). All recruited subjects abstained from sleep deprivation and alcohol use on the day before to the scans. Caffeinated drinks were prohibited on the day of the scan. Additional exclusion criteria for both HCs and MO were the presence of brain lesions on structural magnetic resonance imaging. All participants received a detailed explanation of the study and gave written informed permission. The ethical review board of the Faculty of Medicine at the University of Rome, Italy, sanctioned the experiment (N° 0295/2023). MRI protocol MRI protocol All subjects had a 3T MRI scan utilizing a standard head coil. MRI data was acquired via a Siemens Magnetom Vida 3T scanner equipped with a 32-channel head coil. High-resolution T1-weighted sagittal magnetization-prepared rapid gradient echo (MPRAGE) images were obtained from all subjects (TR: 2300 ms, TE: 2.25 ms, 208 sagittal slices, voxel dimensions 0.8 x 0.8 x 0.8 mm³, base resolution 320, field of view 256 mm, slice thickness 0.8 mm). Imaging data processing: Hypothalamus subunits analysis Hypothalamic segmentation was conducted via Hypothalamus_seg ( https://github.com/BBillot/hypothalamus_seg ), a specialized software developed for the extraction and analysis of hypothalamic characteristics from MRI images. Initially, each participant's 3D T1-weighted scan was resampled to an isotropic voxel of 1mm³ to enhance the segmentation procedure. The hypothalamic structure was partitioned into right and left subunits: anterior-inferior, anterior-superior, posterior, tubular inferior, and tubular superior [ 9 ]; their volumes were calculated and recorded for each participant in the group (Fig. 1 ). Volumetric and morphological differences were evaluated, and statistical comparisons between the two groups were conducted using SPSS (v). A general linear model (GLM) was constructed, utilizing the nuclei volumes of each participant's group as the dependent variable, while age and total intracranial volume (TIV) served as covariates, with gender and group as factors, to assess any variations between groups. A new GLM was developed to evaluate the correlation between the volume of each hypothalamus subunit (dependent variable) and each clinical variable (independent variable), while adjusting for age and TIV (covariates) and including gender as a factor. The Anderson-Darling and/or Kolmogorov-Smirnov tests were conducted for each model's covariate and dependent variable to evaluate normality distributions. The TIV of each participant was computed using Freesurfer (version 7.4.1) [ 11 – 24 ]. P-values were set up at 0.025 for the volume differences between groups of the entire hypothalamus (left and right) and its subunits at p < 0.005, adjusted for multiple comparisons by the Bonferroni method. Finally, p-values were adjusted to a 0.05 false discovery rate (Benjamini Hochberg) to account for multiple comparisons arising from the number of regions of interest and clinical factors assessed (N = 7). Additionally, other clinical factors were evaluated, including the frequency of attacks, duration of attacks, pain severity, HIT-6, MIDAS, ASC-12, and days elapsed since the last attack. Results Table 2 presents the volumes of the entire hypothalamus and its subdivisions for both the right and left hemispheres. No statistically significant differences (p unc > 0.05) were seen between patients and healthy controls in the study of the whole hypothalamus and its subdivisions (Table 2 ). Table 2 Mean volumes (mm³) ± standard deviations and interquartile ranges (25th–75th percentile) of hypothalamic subunits and total right and left hypothalamus in patients with episodic migraine without aura (MO) and healthy controls (HCs). Results from the general linear inferential statistical models (GLMs) are reported in the table. Right Left HCs MO Inferential statistics HCs MO Inferential statistics Anterior-inferior 16.88 ± 3.44 (13.73–19.64) 16.37 ± 4.62 (13.22–18.37) F = 0.39; p = 0.53 19.09 ± 3.12 (16.97–21.58) 17.95 ± 3.91 (15.28–20.50) F = 1.86; p = 0.18 Anterior-superior 25.63 ± 5.75 (20.78–30.04) 23.82 ± 4.14 (20.42–26.85) F = 1.38; p = 0.24 24.54 ± 4.32 (21.13–28.41) 23.41 ± 4.69 (18.96–26.63) F = 0.35; p = 0.56 Posterior 131.27 ± 18.11 (115.93–145.08) 128.73 ± 18.85 (114.04–143.52) F = 0.37; p = 0.55 123.40 ± 15.40 (108.97–134.96) 123.57 ± 17.02 (107.44–134.78) F = 0.73; p = 0.40 Tubular inferior 139.23 ± 19.47 (127.31–153.65) 134.49 ± 15.61 (123.55–141.46) F = 0.19; p = 0.67 148.60 ± 17.86 (132.63–161.210) 146.88 ± 17.99 (132.46–159.53) F = 0.70; p = 0.41 Tubular superior 120.02 ± 18.58 (108.63–130.16) 115.03 ± 12.80 (108.10–126.64) F = 0.15; p = 0.70 124.94 ± 17.32 (110.60–133.59) 115.95 ± 11.67 (107.93–127.03) F = 1.77; p = 0.19 Whole hypothalamus 433.04 ± 49.33 (398.17–465.56) 418.44 ± 41.88 (406.75–451.65) F = 0.00; p = 0.95 440.58 ± 43.60 (403.56–468.36) 426.97 ± 42.89 (398.60–453.49) F = 0.01; p = 0.92 No statistically significant correlations were observed (Table 3 ) between the total volume of the hypothalamus (p unc > 0.05); however, its subunits exhibited significant correlations with clinical factors (p unc 0.05). Table 3 Linear relationship between clinical variables and left and right hypothalamic volumes (F; p); * indicates p unc <0.05 N° Attacks Attacks duration (h) Severity of Pain HIT-6 MIDAS ASC-12 Days since the last attack Anterior-inferior (L/R) (0.03; 0.87) (2.21; 0.15) (0.55; 0.47) (0.06; 0.81) (0.46; 0.50) (0.10; 0.75) (0.02; 0.90) (0.02; 0.89) (1.37; 0.25) (0.61; 0.44) (0.03; 0.86) (1.57; 0.22) (0.04; 0.85) (0.09; 0.77) Anterior-superior (L/R) (0.00; 0.95) (0.31; 0.58) (3.94; 0.06) (0.82; 0.37) (0.53; 0.47) (1.81; 0.19) (0.54; 0.47) (0.14; 0.71) (0.01; 0.92) (0.02; 0.89) (4.41; 0.05) (4.14; 0.05) (0.12; 0.73) (0.38; 0.54) Posterior (L/R) (0.70; 0.41) (0.73; 0.40) (1.36; 0.25) (0.03; 0.86) (1.41; 0.25) (0.02; 0.88) (0.13; 0.72) (1.52; 0.23) (0.15; 0.70) (0.58; 0.45) (0.58; 0.45) (0.77; 0.39) (0.04; 0.84) (0.36; 0.55) Tubular inferior (L/R) (0.62; 0.44) (4.45; 0.05)* (0.00; 0.95) (1.38; 0.25) (6.71; 0.02)* (0.10; 0.75) (0.63; 0.43) (0.05; 0.83) (6.79; 0.02)* (0.40; 0.53) (0.36; 0.56) (2.37; 0.14) (0.10; 0.76) (0.28; 0.60) Tubular superior (L/R) (0.46; 0.51) (0.64; 0.43) (1.40; 0.25) (1.30; 0.27) (0.28; 0.60) (0.40; 0.53) (0.31; 0.59) (0.01; 0.94) (0.03; 0.88) (0.00; 0.99) (1.63; 0.21) (4.78; 0.04)* (0.01; 0.94) (1.26; 0.27) Whole hypothalamus (L/R) (2.39; 0.14) (2.61; 0.12) (1.90; 0.18) (0.85; 0.37) (0.45; 0.51) (0.02; 0.90) (0.63; 0.44) (0.35; 0.56) (0.22; 0.64) (0.43; 0.52) (2.88; 0.10) (4.00; 0.06) (0.10; 0.76) (0.20; 0.66) Discussion Our data indicate that, in patients with migraine without aura examined during the interictal phase, the total hypothalamus and its anterior-inferior, anterior-superior, posterior, tubular inferior, and tubular superior subdivisions have volumes similar to those of healthy controls. Furthermore, we did not detect a correlation between grey matter macrostructure and the clinical characteristics of migraine. Our results are inconsistent with those of Chen et al. [ 8 ], who documented a considerable volumetric loss of the whole hypothalamus, particularly in its posterior region, in a small cohort of interictal patients with migraine without aura compared to healthy controls. They also noted a negative link between hypothalamic volume and the monthly frequency of headaches, indicating a more adaptable involvement of the hypothalamus concerning the recurrence of attacks. Messina et al. [ 6 ] conducted a longitudinal investigation on a cohort of patients, showing that hypothalamic-cortical connection fluctuates over time in relation to clinical characteristics; specifically, increased attack frequency correlates with diminished resting state functional connectivity between the hypothalamus and the lingual gyrus. The observation that our patients exhibit a mean attack frequency greater than that reported in the two prior studies (mean of 3 attacks per month compared to our 5 attacks per month) and the lack of correlation between volume and clinical features do not suggest negative results stemming from a selection bias among patients. A potential source of bias is that in both prior studies, patients were free from attacks for a minimum of three days following their last migraine episode and prior to the scan; however, in these studies the occurrence of an attack in the days following the scan was not monitored, indicating that patients may have been recorded during the premonitory phase (24–48 hours prior) of an attack [ 6 , 8 ]. Longitudinal investigations have demonstrated functional activity of the hypothalamus up to 48 hours prior to a migraine episode and its hyperconnectivity with the brainstem during the attack [ 2 – 4 ]. Additional investigations have recorded an elevation in infra-slow oscillatory activity, functional connectivity, and regional homogeneity [ 3 ], alongside atypical metabolic activity on PET scans of the hypothalamus [ 25 ], specifically during the premonitory phase of migraine. This evidence indicates a distinct role of the hypothalamus in the preparation to a migraine attack and may partially elucidate the negative results of our investigation.. By verifying that no migraine attacks occurred either before or after the MRI session, we ensured that data were acquired entirely outside the premonitory phase, during which metabolic and functional changes in the hypothalamus are known to occur. Furthermore, none of the prior investigations conducted hypothalamic segmentation into its subunits. This study demonstrates that even with the subsegmentation of the hypothalamus into its subregions, patients with MO exhibit no volumetric alterations during the interictal phase, regardless of the distance from the most recent migraine attack or other clinical characteristics. In a previous study involving interictal migraine sufferers who underwent DTI analysis, allowing in-vivo visualisation of the bilateral white matter architecture of the entire posterior/anterior hypothalamic regions of interest, we showed considerably increased diffusivity measures compared to healthy controls [ 7 ]. We interpreted this diffusivity pattern as suggestive of a modest reduction in cellularity (neuronal and glial cells) and/or a disruption in the directional organisation of highly anisotropic myelinated fibres connecting individual hypothalamic nuclei, alongside an elevated cell density [ 26 ]. Nonetheless, our current study conducted on a comparable cohort of patients found no macrostructural abnormalities in the entire hypothalamus and its subregions; thus, we infer that microstructural abnormalities do not necessarily correlate with macrostructural volumetric anomalies. Comparable patterns of white matter integrity deficits, without macrostructural damage, have been previously identified in other neurological illnesses, including cluster headache, schizophrenia, and restless legs syndrome [ 27 – 29 ]. Like all studies, ours must be considered in the context of certain limitations. Firstly, we did not evaluate the same individuals at various stages of their migraine cycle, specifically the pre-ictal, ictal, and post-ictal phases. The second point is that we did not monitor patients longitudinally to see whether spontaneous variations or those produced by preventative interventions could elicit plastic alterations in the hypothalamus macrostructure. A potential limitation is the limited number of patients included in the study, which may hinder the rapid generalisability of the data, even if our sample size exceeds that of other studies. Conclusions In summary, based on our volumetric results and prior microstructural and functional studies, we conclude that the hypothalamus of migraine patients between attacks does not exhibit significant alterations in grey matter density detectable by high-field MRI. Abbreviations DTI: diffusion tensor imaging; FC: functional connectivity, FDR: false discovery rate, GLM: general linear model, MRI: magnetic resonance imaging, HCs: healthy controls, MO: migraine without aura, TIV: total intracranial volume. Declarations Ethics approval and consent to participate All the participants provided written informed consent to participate in the study, which was approved by the ethical review board of the Faculty of Medicine at the University of Rome, Italy (N° 0295/2023). Consent for publication Not applicable Availability of Data Material The informed consent signed by all participants in this study did not include a provision stating that individual raw data can be made publicly accessible. Therefore, in agreement with the Italian data protection law, individual de-identified participant raw data cannot be shared publicly. Researchers meeting the criteria for access to confidential data may access the data upon request, involving the documentation of data access. Competing interests The authors declare that they have no competing interests. Funding The authors did not receive funding for the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. Authors’ contributions: IG, ADR, and GC made substantial contributions to protocol development, interpretation of data as well as in drafting the manuscript. MA, DC, FCar, and VDP were implied in the interpretation of data as well as in drafting the manuscript; GG, GS, CA, and FCas contributed to participant enrolment and recording. ADR and FCar were implied in data processing, analysis, and statistics. Acknowledgment The contribution of the IRCCS - Fondazione Bietti in this paper was supported by the Italian Ministry of Health and Fondazione Roma. References Coppola G, Parisi V, Di Renzo A, Pierelli F (2020) Cortical pain processing in migraine. J Neural Transm 127:551–566. https://doi.org/10.1007/s00702-019-02089-7 Schulte LH, Menz MM, Haaker J, May A (2020) The migraineur’s brain networks: Continuous resting state fMRI over 30 days. Cephalalgia. 10.1177/0333102420951465 Meylakh N, Marciszewski KK, Di Pietro F et al (2018) Deep in the brain: Changes in subcortical function immediately preceding a migraine attack. Hum Brain Mapp 39:2651–2663. https://doi.org/10.1002/hbm.24030 Meylakh N, Marciszewski KK, Di Pietro F et al (2020) Altered regional cerebral blood flow and hypothalamic connectivity immediately prior to a migraine headache. Cephalalgia Int J Headache 40:448–460. https://doi.org/10.1177/0333102420911623 Moulton E, Becerra L, Johnson A et al (2014) Altered hypothalamic functional connectivity with autonomic circuits and the locus coeruleus in migraine. PLoS ONE 9:10 Messina R, Rocca MA, Valsasina P et al (2022) Clinical correlates of hypothalamic functional changes in migraine patients. Cephalalgia Int J Headache 42:279–290. https://doi.org/10.1177/03331024211046618 Porcaro C, Di Renzo A, Tinelli E et al (2021) Hypothalamic structural integrity and temporal complexity of cortical information processing at rest in migraine without aura patients between attacks. Sci Rep 11:1–11. https://doi.org/10.1038/s41598-021-98213-3 Chen Z, Chen X, Liu M et al (2019) Volume of hypothalamus as a diagnostic biomarker of chronic migraine. Front Neurol 10:606. https://doi.org/10.3389/fneur.2019.00606 Billot B, Bocchetta M, Todd E et al (2020) Automated segmentation of the hypothalamus and associated subunits in brain MRI. NeuroImage 223:117287. https://doi.org/10.1016/j.neuroimage.2020.117287 (2018) Headache Classification Committee of the International Headache Society (IHS) The International Classification of Headache Disorders, 3rd edition. Cephalalgia 38:1–211. https://doi.org/10.1177/0333102417738202 Chong CD, Aguilar M, Schwedt TJ (2020) Altered Hypothalamic Region Covariance in Migraine and Cluster Headache: A Structural MRI Study. Headache 60:553–563. https://doi.org/10.1111/head.13742 Dale AM, Sereno MI (1993) Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach. J Cogn Neurosci 5:162–176. https://doi.org/10.1162/jocn.1993.5.2.162 Fischl B, Salat DH, Busa E et al (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33:341–355. https://doi.org/10.1016/s0896-6273(02)00569-x Desikan RS, Ségonne F, Fischl B et al (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31:968–980. https://doi.org/10.1016/j.neuroimage.2006.01.021 Jovicich J, Czanner S, Greve D et al (2006) Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data. NeuroImage 30:436–443. https://doi.org/10.1016/j.neuroimage.2005.09.046 Reuter M, Schmansky NJ, Rosas HD, Fischl B (2012) Within-subject template estimation for unbiased longitudinal image analysis. NeuroImage 61:1402–1418. https://doi.org/10.1016/j.neuroimage.2012.02.084 Reuter M, Rosas HD, Fischl B (2010) Highly accurate inverse consistent registration: a robust approach. NeuroImage 53:1181–1196. https://doi.org/10.1016/j.neuroimage.2010.07.020 Ségonne F, Dale AM, Busa E et al (2004) A hybrid approach to the skull stripping problem in MRI. NeuroImage 22:1060–1075. https://doi.org/10.1016/j.neuroimage.2004.03.032 Han X, Jovicich J, Salat D et al (2006) Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer. NeuroImage 32:180–194. https://doi.org/10.1016/j.neuroimage.2006.02.051 Fischl B, Salat DH, van der Kouwe AJW et al (2004) Sequence-independent segmentation of magnetic resonance images. NeuroImage 23 Suppl 1S69–84. https://doi.org/10.1016/j.neuroimage.2004.07.016 Fischl B, Sereno MI, Dale AM (1999) Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. NeuroImage 9:195–207. https://doi.org/10.1006/nimg.1998.0396 Fischl B, Sereno MI, Tootell RB, Dale AM (1999) High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp 8:272–284. https://doi.org/10.1002/(sici)1097-0193(1999)8:43.0.co;2-4 Fischl B, Liu A, Dale AM (2001) Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE Trans Med Imaging 20:70–80. https://doi.org/10.1109/42.906426 Fischl B, Dale AM (2000) Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A 97:11050–11055. https://doi.org/10.1073/pnas.200033797 Maniyar F, Sprenger T, Monteith T et al (2014) Brain activations in the premonitory phase of nitroglycerin-triggered migraine attacks. Brain J Neurol 137:232–241 Mandl RC, Schnack HG, Zwiers MP et al (2008) Functional diffusion tensor imaging: measuring task-related fractional anisotropy changes in the human brain along white matter tracts. PLoS ONE 3:10 Kraguljac NV, Anthony T, Skidmore FM et al (2019) Micro- and Macrostructural White Matter Integrity in Never-treated and Currently Unmedicated Patients with Schizophrenia and Effects of Short Term Antipsychotic Treatment. Biol Psychiatry Cogn Neurosci Neuroimaging 4:462–471. https://doi.org/10.1016/j.bpsc.2019.01.002 Belke M, Heverhagen JT, Keil B et al (2015) DTI and VBM reveal white matter changes without associated gray matter changes in patients with idiopathic restless legs syndrome. Brain Behav 5:e00327. https://doi.org/10.1002/brb3.327 Abagnale C, Di Renzo A, Giuliani G et al (2025) MRI-based analysis of the microstructure of the thalamus and hypothalamus and functional connectivity between cortical networks in episodic cluster headache. J Headache Pain 26:12. https://doi.org/10.1186/s10194-024-01920-1 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Aug, 2025 Read the published version in The Journal of Headache and Pain → Version 1 posted Editorial decision: Revision requested 21 May, 2025 Reviews received at journal 21 May, 2025 Reviewers agreed at journal 20 May, 2025 Reviews received at journal 18 May, 2025 Reviewers agreed at journal 15 May, 2025 Reviewers invited by journal 14 May, 2025 Editor assigned by journal 14 May, 2025 Submission checks completed at journal 14 May, 2025 First submitted to journal 14 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6663261","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":458282293,"identity":"4ad09ff1-858d-4cbc-9f8d-bfcfd54746c2","order_by":0,"name":"Irene Giardina","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIie3RsQqCQBjA8QvBW45eILEnCA4EIwx6lZMglwKhF3Bz8QF6EqeGT1oFaROC6AicWlrCoKFPbAvPxob7w4d4+uMOJUSn+8NMHCDEYDh45c3aICLiN2KKDzGQqEybgcP454ZGRLXNkNAjPPeeNaX5YxSGZ3sSZxGRtepgLMySKmCzZJOOdnzruLmvPhgSAQwOjAMSxoWfwg8kezWkuFUtKWQfofh+Q8q12ZKybxf8vAcLAiSV4yFx3FJGIFbdZBzH1/sNvAUvlvLEXsJ2i0DKet5N8Cfy70VQAIxe1M91Op1O9wYlNVHTLAsZ0AAAAABJRU5ErkJggg==","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":true,"prefix":"","firstName":"Irene","middleName":"","lastName":"Giardina","suffix":""},{"id":458282294,"identity":"273bfff3-0872-462e-bc60-de3939441262","order_by":1,"name":"Antonio Di Renzo","email":"","orcid":"","institution":"Fondazione G.B. Bietti","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"Di","lastName":"Renzo","suffix":""},{"id":458282295,"identity":"20f6ae42-3f1b-4828-b5a4-f41bc6eff44a","order_by":2,"name":"Davide Chiffi","email":"","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Davide","middleName":"","lastName":"Chiffi","suffix":""},{"id":458282296,"identity":"a27dc63d-0c04-4308-884b-0055300f18dd","order_by":3,"name":"Francesca Giovannini","email":"","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Francesca","middleName":"","lastName":"Giovannini","suffix":""},{"id":458282297,"identity":"1079090f-9a15-4f6e-8d05-80734ec765ec","order_by":4,"name":"Giada Giuliani","email":"","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Giada","middleName":"","lastName":"Giuliani","suffix":""},{"id":458282298,"identity":"fb12bc88-2d76-46e4-9cad-af0bd2e55235","order_by":5,"name":"Gabriele Sebastianelli","email":"","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Gabriele","middleName":"","lastName":"Sebastianelli","suffix":""},{"id":458282299,"identity":"629779ab-1a4a-4a96-8427-7f708744417b","order_by":6,"name":"Francesco Casillo","email":"","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Francesco","middleName":"","lastName":"Casillo","suffix":""},{"id":458282300,"identity":"7ba2f258-d5c5-4bd7-9c1f-276b3e7ea784","order_by":7,"name":"Chiara Abagnale","email":"","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Chiara","middleName":"","lastName":"Abagnale","suffix":""},{"id":458282301,"identity":"1645dc2f-8765-4549-8085-a966fd33441d","order_by":8,"name":"Marta Altieri","email":"","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Marta","middleName":"","lastName":"Altieri","suffix":""},{"id":458282302,"identity":"c834dfc8-5f75-4dfc-a37b-e4d328282592","order_by":9,"name":"Vittorio Di Piero","email":"","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Vittorio","middleName":"Di","lastName":"Piero","suffix":""},{"id":458282303,"identity":"2103b5e2-ea74-41af-b0d3-f3ea4da82263","order_by":10,"name":"Gianluca Coppola","email":"","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Gianluca","middleName":"","lastName":"Coppola","suffix":""},{"id":458282304,"identity":"6059ac9b-3356-4f67-9e9e-a8e260c55f71","order_by":11,"name":"Francesca Caramia","email":"","orcid":"","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Francesca","middleName":"","lastName":"Caramia","suffix":""}],"badges":[],"createdAt":"2025-05-14 10:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6663261/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6663261/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s10194-025-02118-9","type":"published","date":"2025-08-04T15:57:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83157232,"identity":"eff75218-27de-4d65-ac68-f5e32061f3f0","added_by":"auto","created_at":"2025-05-20 14:52:56","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1074905,"visible":true,"origin":"","legend":"\u003cp\u003eExample of a segmented hypothalamus with appropriate labels.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6663261/v1/e344146c8519a95ddad30f4e.jpeg"},{"id":88814159,"identity":"8ecbba2b-5699-48b1-930a-acc6b6e2ee06","added_by":"auto","created_at":"2025-08-11 16:07:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1736758,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6663261/v1/c03a6c69-c19c-4e2d-afda-df9d4295c5f2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Volume of the hypothalamus and its subunits in patients with episodic migraine without aura during interictal periods","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, although migraine was traditionally seen as a functional condition of the brain, many structural and morpho-functional cerebral anomalies have been identified during all phases of migraine [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Significant emphasis was placed on the hypothalamus, due to its activation observed during the premonitory phase (24\u0026ndash;48 hours prior to onset) of a migraine attack, and its active connectivity with the brainstem during the attack itself [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Apart from this peri-ictal involvement, the hypothalamus's morpho-functionality during the interictal pain-free phase is not completely elucidated. Prior investigations into brain functional connectivity (FC) during resting state MRI between migraine attacks revealed either no anomalies in hypothalamic FC [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] or significantly altered FC with the locus coeruleus, caudate, parahippocampus, cerebellum, temporal pole, and lingual and orbitofrontal cortices when compared to control subjects [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTemporal variations in hypothalamic-cortical interactions have been characterized by a seed-based functional MRI correlation methodology in 23 interictal migraine patients and controls, longitudinally monitored for a duration of up to 4.5 years. Disrupted hypothalamic connection was noted with various brain regions previously associated with pain processing and migraine pathogenesis, including the cerebellum, parahippocampal gyrus, lingual gyrus, and orbitofrontal cortex [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, an increased headache effect at follow-up was associated with diminished resting state functional connectivity of the hypothalamic-orbitofrontal gyrus at baseline, but a reduced frequency of migraine attacks connected with enhanced resting state functional connectivity in the same region [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDriven by the necessity to elucidate the microstructural anatomical foundation of interictal functional anomalies in migraine sufferers, we utilised diffusion tensor imaging (DTI) analysis on 3T MRI data from patients with migraine without aura during the interictal phase, revealing abnormal proton diffusivity in both the anterior and posterior regions of the hypothalamus bilaterally [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Utilizing voxel-based morphometry, a study identified a reduction in volume across the entire hypothalamus, particularly in its posterior region, among 18 patients with migraine without aura compared to 21 controls, which was significantly negatively correlated with headache frequency [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The potential role of interictal morpho-functional hypothalamic anomalies as a neuroanatomical substrate predisposing to the start of the prodromal phase and subsequent initiation of an attack is still being investigated. Moreover, it remains ambiguous which particular subregion of the hypothalamus is most actually implicated in the interictal migraine predisposition process.\u003c/p\u003e \u003cp\u003eWe employed a fully validated automated tool utilising a deep convolutional neural network to segment the entire hypothalamus and its subregions from T1-weighted MRI scans [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] in a cohort of episodic migraine without aura patients during the interictal phase, devoid of the effects of prophylactic treatments, in comparison to healthy controls. We examined the hypotheses that the volume of the hypothalamus, particularly its posterior subregions, may differ between patients in the interictal phase and healthy individuals, and that clinical characteristics of migraine may correlate with the macrostructure of the hypothalamus.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eA total of 60 participants were prospectively enrolled. Thirty patients (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) diagnosed with episodic migraine without aura (MO) were recruited from the Headache Centres of Rome and Latina. Patients were diagnosed based on the criteria established by the International Classification of Headache Disorders (ICHD III) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Patients received MRI scans during the interictal phase, characterized by a minimum absence of migraine attacks for three days before and following the MRI. Patients experienced unilateral or bilateral migraine headaches, but did not have persistent pain on the same side. We excluded patients who were receiving preventive treatment in the preceding three months. Other primary or secondary headache types have been excluded via clinical and/or instrumental evaluation, if necessary. Symptomatic therapy was prohibited on the day of the scan.\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\u003eClinical and demographic characteristics of healthy controls (HCs) and patients with migraine without aura scanned during migraine-free intervals (MO). Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCs\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMO\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\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\u003e28.40\u0026thinsp;\u0026plusmn;\u0026thinsp;8.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.17\u0026thinsp;\u0026plusmn;\u0026thinsp;10.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttack frequency/month (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttack duration (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.7\u0026thinsp;\u0026plusmn;\u0026thinsp;20.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays from the last migraine attack (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIDAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.7\u0026thinsp;\u0026plusmn;\u0026thinsp;16.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIT-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASC-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\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\u003eWe enlisted 30 healthy controls (HCs) of comparable age and sex from among medical students and healthcare professionals for comparative analysis. They displayed no evident medical conditions, personal or familial history of primary headaches or epilepsy, nor regular substance use.\u003c/p\u003e \u003cp\u003eFemale participants were consistently scanned at mid-cycle. All scanning sessions occurred in the afternoon (4:00\u0026ndash;7:00 p.m.). All recruited subjects abstained from sleep deprivation and alcohol use on the day before to the scans. Caffeinated drinks were prohibited on the day of the scan. Additional exclusion criteria for both HCs and MO were the presence of brain lesions on structural magnetic resonance imaging.\u003c/p\u003e \u003cp\u003eAll participants received a detailed explanation of the study and gave written informed permission. The ethical review board of the Faculty of Medicine at the University of Rome, Italy, sanctioned the experiment (N\u0026deg; 0295/2023).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMRI protocol\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eMRI protocol\u003c/div\u003e \u003cp\u003eAll subjects had a 3T MRI scan utilizing a standard head coil. MRI data was acquired via a Siemens Magnetom Vida 3T scanner equipped with a 32-channel head coil. High-resolution T1-weighted sagittal magnetization-prepared rapid gradient echo (MPRAGE) images were obtained from all subjects (TR: 2300 ms, TE: 2.25 ms, 208 sagittal slices, voxel dimensions 0.8 x 0.8 x 0.8 mm\u0026sup3;, base resolution 320, field of view 256 mm, slice thickness 0.8 mm).\u003c/p\u003e\n\u003ch3\u003eImaging data processing: Hypothalamus subunits analysis\u003c/h3\u003e\n\u003cp\u003eHypothalamic segmentation was conducted via Hypothalamus_seg (\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), a specialized software developed for the extraction and analysis of hypothalamic characteristics from MRI images. Initially, each participant's 3D T1-weighted scan was resampled to an isotropic voxel of 1mm\u0026sup3; to enhance the segmentation procedure.\u003c/p\u003e \u003cp\u003eThe hypothalamic structure was partitioned into right and left subunits: anterior-inferior, anterior-superior, posterior, tubular inferior, and tubular superior [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]; their volumes were calculated and recorded for each participant in the group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVolumetric and morphological differences were evaluated, and statistical comparisons between the two groups were conducted using SPSS (v).\u003c/p\u003e \u003cp\u003eA general linear model (GLM) was constructed, utilizing the nuclei volumes of each participant's group as the dependent variable, while age and total intracranial volume (TIV) served as covariates, with gender and group as factors, to assess any variations between groups.\u003c/p\u003e \u003cp\u003eA new GLM was developed to evaluate the correlation between the volume of each hypothalamus subunit (dependent variable) and each clinical variable (independent variable), while adjusting for age and TIV (covariates) and including gender as a factor.\u003c/p\u003e \u003cp\u003eThe Anderson-Darling and/or Kolmogorov-Smirnov tests were conducted for each model's covariate and dependent variable to evaluate normality distributions.\u003c/p\u003e \u003cp\u003eThe TIV of each participant was computed using Freesurfer (version 7.4.1) [\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eP-values were set up at 0.025 for the volume differences between groups of the entire hypothalamus (left and right) and its subunits at p\u0026thinsp;\u0026lt;\u0026thinsp;0.005, adjusted for multiple comparisons by the Bonferroni method.\u003c/p\u003e \u003cp\u003eFinally, p-values were adjusted to a 0.05 false discovery rate (Benjamini Hochberg) to account for multiple comparisons arising from the number of regions of interest and clinical factors assessed (N\u0026thinsp;=\u0026thinsp;7).\u003c/p\u003e \u003cp\u003eAdditionally, other clinical factors were evaluated, including the frequency of attacks, duration of attacks, pain severity, HIT-6, MIDAS, ASC-12, and days elapsed since the last attack.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the volumes of the entire hypothalamus and its subdivisions for both the right and left hemispheres. No statistically significant differences (p\u003csub\u003eunc\u003c/sub\u003e \u0026gt; 0.05) were seen between patients and healthy controls in the study of the whole hypothalamus and its subdivisions (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\u003eMean volumes (mm\u0026sup3;)\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations and interquartile ranges (25th\u0026ndash;75th percentile) of hypothalamic subunits and total right and left hypothalamus in patients with episodic migraine without aura (MO) and healthy controls (HCs). Results from the general linear inferential statistical models (GLMs) are reported in the table.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eLeft\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInferential statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInferential statistics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnterior-inferior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.88\u0026thinsp;\u0026plusmn;\u0026thinsp;3.44\u003c/p\u003e \u003cp\u003e(13.73\u0026ndash;19.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.37\u0026thinsp;\u0026plusmn;\u0026thinsp;4.62\u003c/p\u003e \u003cp\u003e(13.22\u0026ndash;18.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.39; p\u0026thinsp;=\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.09\u0026thinsp;\u0026plusmn;\u0026thinsp;3.12\u003c/p\u003e \u003cp\u003e(16.97\u0026ndash;21.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.95\u0026thinsp;\u0026plusmn;\u0026thinsp;3.91\u003c/p\u003e \u003cp\u003e(15.28\u0026ndash;20.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;1.86; p\u0026thinsp;=\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnterior-superior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.63\u0026thinsp;\u0026plusmn;\u0026thinsp;5.75\u003c/p\u003e \u003cp\u003e(20.78\u0026ndash;30.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.82\u0026thinsp;\u0026plusmn;\u0026thinsp;4.14\u003c/p\u003e \u003cp\u003e(20.42\u0026ndash;26.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;1.38; p\u0026thinsp;=\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.54\u0026thinsp;\u0026plusmn;\u0026thinsp;4.32\u003c/p\u003e \u003cp\u003e(21.13\u0026ndash;28.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.41\u0026thinsp;\u0026plusmn;\u0026thinsp;4.69\u003c/p\u003e \u003cp\u003e(18.96\u0026ndash;26.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.35; p\u0026thinsp;=\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePosterior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131.27\u0026thinsp;\u0026plusmn;\u0026thinsp;18.11\u003c/p\u003e \u003cp\u003e(115.93\u0026ndash;145.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128.73\u0026thinsp;\u0026plusmn;\u0026thinsp;18.85\u003c/p\u003e \u003cp\u003e(114.04\u0026ndash;143.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.37; p\u0026thinsp;=\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e123.40\u0026thinsp;\u0026plusmn;\u0026thinsp;15.40\u003c/p\u003e \u003cp\u003e(108.97\u0026ndash;134.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e123.57\u0026thinsp;\u0026plusmn;\u0026thinsp;17.02\u003c/p\u003e \u003cp\u003e(107.44\u0026ndash;134.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.73; p\u0026thinsp;=\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTubular inferior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139.23\u0026thinsp;\u0026plusmn;\u0026thinsp;19.47\u003c/p\u003e \u003cp\u003e(127.31\u0026ndash;153.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134.49\u0026thinsp;\u0026plusmn;\u0026thinsp;15.61\u003c/p\u003e \u003cp\u003e(123.55\u0026ndash;141.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.19; p\u0026thinsp;=\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e148.60\u0026thinsp;\u0026plusmn;\u0026thinsp;17.86\u003c/p\u003e \u003cp\u003e(132.63\u0026ndash;161.210)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e146.88\u0026thinsp;\u0026plusmn;\u0026thinsp;17.99\u003c/p\u003e \u003cp\u003e(132.46\u0026ndash;159.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.70; p\u0026thinsp;=\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTubular superior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120.02\u0026thinsp;\u0026plusmn;\u0026thinsp;18.58\u003c/p\u003e \u003cp\u003e(108.63\u0026ndash;130.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115.03\u0026thinsp;\u0026plusmn;\u0026thinsp;12.80\u003c/p\u003e \u003cp\u003e(108.10\u0026ndash;126.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.15; p\u0026thinsp;=\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e124.94\u0026thinsp;\u0026plusmn;\u0026thinsp;17.32\u003c/p\u003e \u003cp\u003e(110.60\u0026ndash;133.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e115.95\u0026thinsp;\u0026plusmn;\u0026thinsp;11.67\u003c/p\u003e \u003cp\u003e(107.93\u0026ndash;127.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;1.77; p\u0026thinsp;=\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole hypothalamus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e433.04\u0026thinsp;\u0026plusmn;\u0026thinsp;49.33\u003c/p\u003e \u003cp\u003e(398.17\u0026ndash;465.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e418.44\u0026thinsp;\u0026plusmn;\u0026thinsp;41.88\u003c/p\u003e \u003cp\u003e(406.75\u0026ndash;451.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.00; p\u0026thinsp;=\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e440.58\u0026thinsp;\u0026plusmn;\u0026thinsp;43.60\u003c/p\u003e \u003cp\u003e(403.56\u0026ndash;468.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e426.97\u0026thinsp;\u0026plusmn;\u0026thinsp;42.89\u003c/p\u003e \u003cp\u003e(398.60\u0026ndash;453.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.01; p\u0026thinsp;=\u0026thinsp;0.92\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\u003eNo statistically significant correlations were observed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) between the total volume of the hypothalamus (p\u003csub\u003eunc\u003c/sub\u003e \u0026gt; 0.05); however, its subunits exhibited significant correlations with clinical factors (p\u003csub\u003eunc\u003c/sub\u003e \u0026lt; 0.05), which did not persist after adjustment for multiple comparisons (p\u003csub\u003eadj\u003c/sub\u003e \u0026gt; 0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear relationship between clinical variables and left and right hypothalamic volumes (F; p); * indicates p\u003csub\u003eunc\u003c/sub\u003e\u0026lt;0.05\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\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\u003eN\u0026deg; Attacks\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAttacks duration (h)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSeverity of Pain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHIT-6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMIDAS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eASC-12\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDays since the last attack\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnterior-inferior\u003c/p\u003e \u003cp\u003e(L/R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.03; 0.87)\u003c/p\u003e \u003cp\u003e(2.21; 0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.55; 0.47)\u003c/p\u003e \u003cp\u003e(0.06; 0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.46; 0.50)\u003c/p\u003e \u003cp\u003e(0.10; 0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.02; 0.90)\u003c/p\u003e \u003cp\u003e(0.02; 0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.37; 0.25)\u003c/p\u003e \u003cp\u003e(0.61; 0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.03; 0.86)\u003c/p\u003e \u003cp\u003e(1.57; 0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.04; 0.85)\u003c/p\u003e \u003cp\u003e(0.09; 0.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnterior-superior (L/R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.00; 0.95)\u003c/p\u003e \u003cp\u003e(0.31; 0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.94; 0.06)\u003c/p\u003e \u003cp\u003e(0.82; 0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.53; 0.47)\u003c/p\u003e \u003cp\u003e(1.81; 0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.54; 0.47)\u003c/p\u003e \u003cp\u003e(0.14; 0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.01; 0.92)\u003c/p\u003e \u003cp\u003e(0.02; 0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(4.41; 0.05)\u003c/p\u003e \u003cp\u003e(4.14; 0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.12; 0.73)\u003c/p\u003e \u003cp\u003e(0.38; 0.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePosterior\u003c/p\u003e \u003cp\u003e(L/R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.70; 0.41)\u003c/p\u003e \u003cp\u003e(0.73; 0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.36; 0.25)\u003c/p\u003e \u003cp\u003e(0.03; 0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.41; 0.25)\u003c/p\u003e \u003cp\u003e(0.02; 0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.13; 0.72)\u003c/p\u003e \u003cp\u003e(1.52; 0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.15; 0.70)\u003c/p\u003e \u003cp\u003e(0.58; 0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.58; 0.45)\u003c/p\u003e \u003cp\u003e(0.77; 0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.04; 0.84)\u003c/p\u003e \u003cp\u003e(0.36; 0.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTubular inferior\u003c/p\u003e \u003cp\u003e(L/R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.62; 0.44)\u003c/p\u003e \u003cp\u003e(4.45; 0.05)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00; 0.95)\u003c/p\u003e \u003cp\u003e(1.38; 0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(6.71; 0.02)*\u003c/p\u003e \u003cp\u003e(0.10; 0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.63; 0.43)\u003c/p\u003e \u003cp\u003e(0.05; 0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(6.79; 0.02)*\u003c/p\u003e \u003cp\u003e(0.40; 0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.36; 0.56)\u003c/p\u003e \u003cp\u003e(2.37; 0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.10; 0.76)\u003c/p\u003e \u003cp\u003e(0.28; 0.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTubular superior\u003c/p\u003e \u003cp\u003e(L/R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.46; 0.51)\u003c/p\u003e \u003cp\u003e(0.64; 0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.40; 0.25)\u003c/p\u003e \u003cp\u003e(1.30; 0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.28; 0.60)\u003c/p\u003e \u003cp\u003e(0.40; 0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.31; 0.59)\u003c/p\u003e \u003cp\u003e(0.01; 0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.03; 0.88)\u003c/p\u003e \u003cp\u003e(0.00; 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.63; 0.21)\u003c/p\u003e \u003cp\u003e(4.78; 0.04)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.01; 0.94)\u003c/p\u003e \u003cp\u003e(1.26; 0.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole hypothalamus (L/R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.39; 0.14)\u003c/p\u003e \u003cp\u003e(2.61; 0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.90; 0.18)\u003c/p\u003e \u003cp\u003e(0.85; 0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.45; 0.51)\u003c/p\u003e \u003cp\u003e(0.02; 0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.63; 0.44)\u003c/p\u003e \u003cp\u003e(0.35; 0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.22; 0.64)\u003c/p\u003e \u003cp\u003e(0.43; 0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.88; 0.10)\u003c/p\u003e \u003cp\u003e(4.00; 0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.10; 0.76)\u003c/p\u003e \u003cp\u003e(0.20; 0.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur data indicate that, in patients with migraine without aura examined during the interictal phase, the total hypothalamus and its anterior-inferior, anterior-superior, posterior, tubular inferior, and tubular superior subdivisions have volumes similar to those of healthy controls. Furthermore, we did not detect a correlation between grey matter macrostructure and the clinical characteristics of migraine.\u003c/p\u003e \u003cp\u003eOur results are inconsistent with those of Chen et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], who documented a considerable volumetric loss of the whole hypothalamus, particularly in its posterior region, in a small cohort of interictal patients with migraine without aura compared to healthy controls. They also noted a negative link between hypothalamic volume and the monthly frequency of headaches, indicating a more adaptable involvement of the hypothalamus concerning the recurrence of attacks. Messina et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] conducted a longitudinal investigation on a cohort of patients, showing that hypothalamic-cortical connection fluctuates over time in relation to clinical characteristics; specifically, increased attack frequency correlates with diminished resting state functional connectivity between the hypothalamus and the lingual gyrus. The observation that our patients exhibit a mean attack frequency greater than that reported in the two prior studies (mean of 3 attacks per month compared to our 5 attacks per month) and the lack of correlation between volume and clinical features do not suggest negative results stemming from a selection bias among patients. A potential source of bias is that in both prior studies, patients were free from attacks for a minimum of three days following their last migraine episode and prior to the scan; however, in these studies the occurrence of an attack in the days following the scan was not monitored, indicating that patients may have been recorded during the premonitory phase (24\u0026ndash;48 hours prior) of an attack [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLongitudinal investigations have demonstrated functional activity of the hypothalamus up to 48 hours prior to a migraine episode and its hyperconnectivity with the brainstem during the attack [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Additional investigations have recorded an elevation in infra-slow oscillatory activity, functional connectivity, and regional homogeneity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], alongside atypical metabolic activity on PET scans of the hypothalamus [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], specifically during the premonitory phase of migraine. This evidence indicates a distinct role of the hypothalamus in the preparation to a migraine attack and may partially elucidate the negative results of our investigation.. By verifying that no migraine attacks occurred either before or after the MRI session, we ensured that data were acquired entirely outside the premonitory phase, during which metabolic and functional changes in the hypothalamus are known to occur. Furthermore, none of the prior investigations conducted hypothalamic segmentation into its subunits. This study demonstrates that even with the subsegmentation of the hypothalamus into its subregions, patients with MO exhibit no volumetric alterations during the interictal phase, regardless of the distance from the most recent migraine attack or other clinical characteristics.\u003c/p\u003e \u003cp\u003eIn a previous study involving interictal migraine sufferers who underwent DTI analysis, allowing in-vivo visualisation of the bilateral white matter architecture of the entire posterior/anterior hypothalamic regions of interest, we showed considerably increased diffusivity measures compared to healthy controls [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. We interpreted this diffusivity pattern as suggestive of a modest reduction in cellularity (neuronal and glial cells) and/or a disruption in the directional organisation of highly anisotropic myelinated fibres connecting individual hypothalamic nuclei, alongside an elevated cell density [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Nonetheless, our current study conducted on a comparable cohort of patients found no macrostructural abnormalities in the entire hypothalamus and its subregions; thus, we infer that microstructural abnormalities do not necessarily correlate with macrostructural volumetric anomalies. Comparable patterns of white matter integrity deficits, without macrostructural damage, have been previously identified in other neurological illnesses, including cluster headache, schizophrenia, and restless legs syndrome [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLike all studies, ours must be considered in the context of certain limitations. Firstly, we did not evaluate the same individuals at various stages of their migraine cycle, specifically the pre-ictal, ictal, and post-ictal phases. The second point is that we did not monitor patients longitudinally to see whether spontaneous variations or those produced by preventative interventions could elicit plastic alterations in the hypothalamus macrostructure. A potential limitation is the limited number of patients included in the study, which may hinder the rapid generalisability of the data, even if our sample size exceeds that of other studies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, based on our volumetric results and prior microstructural and functional studies, we conclude that the hypothalamus of migraine patients between attacks does not exhibit significant alterations in grey matter density detectable by high-field MRI.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eDTI: diffusion tensor imaging; FC: functional connectivity, FDR: false discovery rate, GLM: general linear model, MRI: magnetic resonance imaging, HCs: healthy controls, MO: migraine without aura, TIV: total intracranial volume.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the participants provided written informed consent to participate in the study, which was approved by the ethical review board of the Faculty of Medicine at the University of Rome, Italy (N° 0295/2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data Material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe informed consent signed by all participants in this study did not include a provision stating that individual raw data can be made publicly accessible. Therefore, in agreement with the Italian data protection law, individual de-identified participant raw data cannot be shared publicly. Researchers meeting the criteria for access to confidential data may access the data upon request, involving the documentation of data access.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not receive funding for the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIG, ADR, and GC made substantial contributions to protocol development, interpretation of data as well as in drafting the manuscript. MA, DC, FCar, and VDP were implied in the interpretation of data as well as in drafting the manuscript; GG, GS, CA, and FCas contributed to participant enrolment and recording. ADR and FCar were implied in data processing, analysis, and statistics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe contribution of the IRCCS - Fondazione Bietti in this paper was supported by the Italian Ministry of Health and Fondazione Roma.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCoppola G, Parisi V, Di Renzo A, Pierelli F (2020) Cortical pain processing in migraine. J Neural Transm 127:551\u0026ndash;566. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00702-019-02089-7\u003c/span\u003e\u003cspan address=\"10.1007/s00702-019-02089-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchulte LH, Menz MM, Haaker J, May A (2020) The migraineur\u0026rsquo;s brain networks: Continuous resting state fMRI over 30 days. Cephalalgia. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/0333102420951465\u003c/span\u003e\u003cspan address=\"10.1177/0333102420951465\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeylakh N, Marciszewski KK, Di Pietro F et al (2018) Deep in the brain: Changes in subcortical function immediately preceding a migraine attack. Hum Brain Mapp 39:2651\u0026ndash;2663. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/hbm.24030\u003c/span\u003e\u003cspan address=\"10.1002/hbm.24030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeylakh N, Marciszewski KK, Di Pietro F et al (2020) Altered regional cerebral blood flow and hypothalamic connectivity immediately prior to a migraine headache. Cephalalgia Int J Headache 40:448\u0026ndash;460. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0333102420911623\u003c/span\u003e\u003cspan address=\"10.1177/0333102420911623\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoulton E, Becerra L, Johnson A et al (2014) Altered hypothalamic functional connectivity with autonomic circuits and the locus coeruleus in migraine. PLoS ONE 9:10\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMessina R, Rocca MA, Valsasina P et al (2022) Clinical correlates of hypothalamic functional changes in migraine patients. Cephalalgia Int J Headache 42:279\u0026ndash;290. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/03331024211046618\u003c/span\u003e\u003cspan address=\"10.1177/03331024211046618\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePorcaro C, Di Renzo A, Tinelli E et al (2021) Hypothalamic structural integrity and temporal complexity of cortical information processing at rest in migraine without aura patients between attacks. Sci Rep 11:1\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-021-98213-3\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-98213-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Z, Chen X, Liu M et al (2019) Volume of hypothalamus as a diagnostic biomarker of chronic migraine. Front Neurol 10:606. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fneur.2019.00606\u003c/span\u003e\u003cspan address=\"10.3389/fneur.2019.00606\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBillot B, Bocchetta M, Todd E et al (2020) Automated segmentation of the hypothalamus and associated subunits in brain MRI. NeuroImage 223:117287. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2020.117287\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2020.117287\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e(2018) Headache Classification Committee of the International Headache Society (IHS) The International Classification of Headache Disorders, 3rd edition. Cephalalgia 38:1\u0026ndash;211. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0333102417738202\u003c/span\u003e\u003cspan address=\"10.1177/0333102417738202\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChong CD, Aguilar M, Schwedt TJ (2020) Altered Hypothalamic Region Covariance in Migraine and Cluster Headache: A Structural MRI Study. Headache 60:553\u0026ndash;563. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/head.13742\u003c/span\u003e\u003cspan address=\"10.1111/head.13742\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDale AM, Sereno MI (1993) Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach. J Cogn Neurosci 5:162\u0026ndash;176. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1162/jocn.1993.5.2.162\u003c/span\u003e\u003cspan address=\"10.1162/jocn.1993.5.2.162\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFischl B, Salat DH, Busa E et al (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33:341\u0026ndash;355. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s0896-6273(02)00569-x\u003c/span\u003e\u003cspan address=\"10.1016/s0896-6273(02)00569-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesikan RS, S\u0026eacute;gonne F, Fischl B et al (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31:968\u0026ndash;980. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2006.01.021\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2006.01.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJovicich J, Czanner S, Greve D et al (2006) Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data. NeuroImage 30:436\u0026ndash;443. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2005.09.046\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2005.09.046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReuter M, Schmansky NJ, Rosas HD, Fischl B (2012) Within-subject template estimation for unbiased longitudinal image analysis. NeuroImage 61:1402\u0026ndash;1418. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2012.02.084\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2012.02.084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReuter M, Rosas HD, Fischl B (2010) Highly accurate inverse consistent registration: a robust approach. NeuroImage 53:1181\u0026ndash;1196. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2010.07.020\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2010.07.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026eacute;gonne F, Dale AM, Busa E et al (2004) A hybrid approach to the skull stripping problem in MRI. NeuroImage 22:1060\u0026ndash;1075. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2004.03.032\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2004.03.032\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan X, Jovicich J, Salat D et al (2006) Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer. NeuroImage 32:180\u0026ndash;194. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2006.02.051\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2006.02.051\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFischl B, Salat DH, van der Kouwe AJW et al (2004) Sequence-independent segmentation of magnetic resonance images. NeuroImage 23 Suppl 1S69\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2004.07.016\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2004.07.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFischl B, Sereno MI, Dale AM (1999) Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. NeuroImage 9:195\u0026ndash;207. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1006/nimg.1998.0396\u003c/span\u003e\u003cspan address=\"10.1006/nimg.1998.0396\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFischl B, Sereno MI, Tootell RB, Dale AM (1999) High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp 8:272\u0026ndash;284. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/(sici)1097-0193(1999)8:4\u0026lt;272::aid-hbm10\u0026gt;3.0.co;2-4\u003c/span\u003e\u003cspan address=\"10.1002/(sici)1097-0193(1999)8:4%3C272::aid-hbm10%3E3.0.co;2-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFischl B, Liu A, Dale AM (2001) Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE Trans Med Imaging 20:70\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/42.906426\u003c/span\u003e\u003cspan address=\"10.1109/42.906426\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFischl B, Dale AM (2000) Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A 97:11050\u0026ndash;11055. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.200033797\u003c/span\u003e\u003cspan address=\"10.1073/pnas.200033797\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManiyar F, Sprenger T, Monteith T et al (2014) Brain activations in the premonitory phase of nitroglycerin-triggered migraine attacks. Brain J Neurol 137:232\u0026ndash;241\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMandl RC, Schnack HG, Zwiers MP et al (2008) Functional diffusion tensor imaging: measuring task-related fractional anisotropy changes in the human brain along white matter tracts. PLoS ONE 3:10\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKraguljac NV, Anthony T, Skidmore FM et al (2019) Micro- and Macrostructural White Matter Integrity in Never-treated and Currently Unmedicated Patients with Schizophrenia and Effects of Short Term Antipsychotic Treatment. Biol Psychiatry Cogn Neurosci Neuroimaging 4:462\u0026ndash;471. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bpsc.2019.01.002\u003c/span\u003e\u003cspan address=\"10.1016/j.bpsc.2019.01.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelke M, Heverhagen JT, Keil B et al (2015) DTI and VBM reveal white matter changes without associated gray matter changes in patients with idiopathic restless legs syndrome. Brain Behav 5:e00327. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/brb3.327\u003c/span\u003e\u003cspan address=\"10.1002/brb3.327\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbagnale C, Di Renzo A, Giuliani G et al (2025) MRI-based analysis of the microstructure of the thalamus and hypothalamus and functional connectivity between cortical networks in episodic cluster headache. J Headache Pain 26:12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s10194-024-01920-1\u003c/span\u003e\u003cspan address=\"10.1186/s10194-024-01920-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-journal-of-headache-and-pain","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tjhp","sideBox":"Learn more about [The Journal of Headache and Pain](https://thejournalofheadacheandpain.biomedcentral.com/)","snPcode":"10194","submissionUrl":"https://submission.nature.com/new-submission/10194/3","title":"The Journal of Headache and Pain","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Freesurfer, segmentation, hypothalamus, MRI, migraine, interictal","lastPublishedDoi":"10.21203/rs.3.rs-6663261/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6663261/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe hypothalamus is thought to play a crucial role in the recurrence of migraine attacks, exhibiting activity prior to the onset of a migraine attack. Nonetheless, our comprehension of the roles of its subunits, especially during the interictal phase, remains limited. This study investigated hypothalamic volumetric differences between individuals with episodic migraine and healthy controls, with scans conducted during the interictal phase, free from the effects of preventative medications.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed hypothalamic volumes in 30 patients with episodic migraine without aura (MO), scanned during interictal periods and not on preventative medication, and in 30 healthy controls (HCs) matched for age and sex. Volumetric segmentation was performed of both hypothalamic subunits (anterior-inferior, anterior-superior, posterior, tubular inferior, and tubular superior) and the entire hypothalamus using magnetic resonance imaging (MRI) with T1-weighted sequences. General linear models were employed to evaluate volumetric differences after controlling for age, sex, and total brain volume.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe volumes of hypothalamic subunits and overall hypothalamus volumes exhibited no statistically significant differences between HCs and MO patients (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). No associations were found between the clinical characteristics of MO and the total hypothalamic volume or its subunits.\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e \u003cp\u003eOur findings indicate that volumetric alterations in the hypothalamus and its subunits do not contribute to the interictal susceptibility to recurrent migraine attacks.\u003c/p\u003e","manuscriptTitle":"Volume of the hypothalamus and its subunits in patients with episodic migraine without aura during interictal periods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-20 14:52:52","doi":"10.21203/rs.3.rs-6663261/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-21T14:20:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-21T13:44:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260690340608475570004273818004400310601","date":"2025-05-20T07:21:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-18T16:52:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16246863927700457473260176599486767495","date":"2025-05-15T22:03:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-14T23:49:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-14T23:39:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-14T23:23:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Journal of Headache and Pain","date":"2025-05-14T10:35:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-journal-of-headache-and-pain","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tjhp","sideBox":"Learn more about [The Journal of Headache and Pain](https://thejournalofheadacheandpain.biomedcentral.com/)","snPcode":"10194","submissionUrl":"https://submission.nature.com/new-submission/10194/3","title":"The Journal of Headache and Pain","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9fb55cc0-0f9a-44ad-8767-17696f4d1125","owner":[],"postedDate":"May 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-11T16:01:35+00:00","versionOfRecord":{"articleIdentity":"rs-6663261","link":"https://doi.org/10.1186/s10194-025-02118-9","journal":{"identity":"the-journal-of-headache-and-pain","isVorOnly":false,"title":"The Journal of Headache and Pain"},"publishedOn":"2025-08-04 15:57:30","publishedOnDateReadable":"August 4th, 2025"},"versionCreatedAt":"2025-05-20 14:52:52","video":"","vorDoi":"10.1186/s10194-025-02118-9","vorDoiUrl":"https://doi.org/10.1186/s10194-025-02118-9","workflowStages":[]},"version":"v1","identity":"rs-6663261","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6663261","identity":"rs-6663261","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}