{"paper_id":"2ca1ecd3-7968-46e9-9fa4-7e83d5bbbacc","body_text":"The Circadian Mechanism May Contribute to Vestibular Migraine: A Case-Control Study Based on Resting-State Functional MRI (rs-fMRI) | 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 The Circadian Mechanism May Contribute to Vestibular Migraine: A Case-Control Study Based on Resting-State Functional MRI (rs-fMRI) Zhengxin Ni, Zhihui Fu, Xiaoxi Qian, Ping Ni, Huifeng Qian, Wei Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6538078/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Vestibular migraine (VM) pathophysiology remains unclear despite its circadian attack patterns. This study employed resting-state fMRI to characterize brain functional differences in VM patients with distinct diurnal attack rhythms. Methods: Forty-two VM patients (23 with early-morning attacks [VMm]; 19 without [VMnm]) and 19 age-/gender-matched healthy controls (HCs) underwent rs-fMRI. Group differences were analyzed using amplitude of low-frequency fluctuation (ALFF), functional connectivity (FC), graph theory metrics, and clinical correlations. Results: ALFF: Compared to HCs, VMm exhibited decreased ALFF in the left medial orbitofrontal cortex (Frontal_Med_Orb_L) and right medial superior frontal gyrus (Frontal_Sup_Medial_R). VMnm showed reduced ALFF in the right paracentral lobule. VMm had significantly lower orbitofrontal ALFF than VMnm (p<0.05). No group differences emerged in fALFF or ReHo. FC: VMm demonstrated impaired frontal-angular/cingulate connectivity versus HCs, including reduced FC between the right orbital middle frontal gyrus and angular gyrus, and between the right orbital inferior frontal gyrus and anterior cingulate cortex. VMnm exhibited disrupted limbic-temporal networks, with weakened connectivity between the right temporal pole/amygdala, putamen/parahippocampus, and hippocampus/amygdala. VMm showed lower putamen-parahippocampal FC than VMnm. Graph Theory: Global network efficiency (Eg) decreased in both VM groups versus HCs (p<0.05). VMm had reduced betweenness centrality in the left dorsolateral/medial superior frontal gyri and inferior temporal gyrus, while VMnm showed deficits only in the inferior temporal gyrus. Small-world properties (γ, λ, σ) and nodal metrics (degree centrality, clustering coefficient) showed no intergroup differences. Correlations: Left orbitofrontal ALFF negatively correlated with Morningness-Eveningness Questionnaire (MEQ) scores in both VM groups (r=-0.42, p=0.02). Disease duration showed positive but non-significant trends with ALFF values. Conclusions: VM subgroups exhibit distinct interictal functional abnormalities: VMm involves prefrontal dysregulation, while VMnm affects parietal-temporal-limbic integration. The left medial orbitofrontal cortex may serve as a hub regulating VM’s circadian attack rhythms.Reduced global network efficiency reflects impaired left-hemisphere information processing, independent of circadian mechanisms.Persistent fMRI abnormalities during attack-free periods suggest circadian influences on VM pathophysiology, highlighting potential chronotherapeutic targets for prevention and management. Vestibular migraine Circadian mechanism Resting-state functional MRI Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Vestibular migraine, the most common recurrent vertigo disease, has a long history. It was first proposed by Neuhauser et al. in 2004[ 1 ] and then unanimously recognized and updated by the International Headache Society and the International Classification Committee for Vestibular Disorders of the Barany Association in 2012[ 2 ]. Finally, in 2018, vestibular migraine was included in the appendix of the International Classification of Headache Disorders[ 3 ].The two steps in the diagnosis and recognition of the disease illustrate the disagreements among scholars regarding this condition, further emphasizing the need for more research to delve into its pathophysiological mechanisms.Currently, there is a lack of a recognized animal research model for vestibular migraine, and neuroimaging studies are needed to explore its structural and functional abnormalities[ 4 , 5 ]. In terms of structural imaging, Obermann M et al.[ 4 ] and Messina et al.[ 6 ] identified widespread cortical volume abnormalities in vestibular migraine patients through voxel-based morphometric studies. With respect to functional imaging, resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive imaging technique used to measure low-frequency fluctuations in intracranial Blood Oxygenation Level Dependent(BOLD) signals[ 7 ]. Currently, there is a lack of a recognized animal research model for vestibular migraine, and neuroimaging studies are needed to explore its structural and functional abnormalities[ 4 , 5 ]. Our preliminary research revealed that vestibular migraine attacks exhibit a circadian rhythm, with a significantly greater frequency and severity of attacks in the morning[ 8 ]. X. Wu and colleagues studied the circadian rhythm and functional magnetic resonance imaging of night-shift nurses, revealing notably lower sleep quality and melatonin levels in this population. Their findings also indicated decreased ReHo activation in the bilateral cerebellar hemispheres, coupled with increased ReHo in the bilateral occipital lobes and left parietal lobe. Compared with day-shift nurses, night-shift nurses presented greater FC from the hypothalamus to the right cingulate gyrus, right putamen, and vermis. Furthermore, activation of the right cerebellar hemisphere, left superior parietal gyrus, and right superior occipital gyrus was correlated with sleep quality scores.Furthermore, activation of the right cerebellar hemisphere was associated with melatonin levels, and higher sleepiness scores were correlated with stronger functional connectivity (FC) between the hypothalamus and the vermis. The decreased activity in the right cerebellum of night-shift nurses following circadian rhythm disruption was linked to poor sleep quality, suggesting possible involvement in sleep regulation mediated by the melatonin pathway. Additionally, resting-state FC indicated enhanced FC between the cerebellar vermis and the hypothalamus, and this enhancement was associated with increased daytime sleepiness[ 9 ]. As a central structure for vertigo, the cerebellum plays a crucial role in multisensory integration in vestibular migraine[ 6 ]. Abnormal brain network connections between the hypothalamus and cerebellar vermis in nurses with circadian rhythm disorders suggest that circadian rhythm may be involved in the pathophysiological mechanism of vertigo diseases. In this study, we divided patients with vestibular migraine during the interictal period into two groups: those with prominent morning attacks and those without morning attacks. We compared these groups with a healthy control group to investigate differences in resting-state functional magnetic resonance imaging. Additionally, we examined the potential relationships between clinical characteristics and functional MRI abnormalities in various subgroups of VM patients, conducting exploratory research on the possible circadian rhythm mechanisms underlying VM. Methods We collected data from 47 patients with vestibular migraine (VM) who visited the Department of Neurology Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine between October 1, 2022, and October 1, 2023. All patients were diagnosed jointly by two trained senior neurologists and an otolaryngologist. On the basis of previous scholarly methods for studying the circadian rhythm of migraine and referring to our prior research on the observation of vestibular migraine attack rhythms[8], the included VM patients were classified into different circadian rhythm types[10] through questioning. The question was\"Do at least 50% of your vertigo attacks (according to the aforementioned vestibular migraine attack criteria) occur during the following times: early morning (00:00-07:59) or not in the early morning (08:00-23:59)?\" All vestibular migraine patients were divided into an early morning attack group (VM attacks in the morning, VMm group, n=23) and a nonmorning attack group (VM attacks not in the morning, VMnm group, n=19). Additionally, we recruited a healthy control group (HC) consisting of 21 individuals matched for age and gender with the VMm and VMnm groups from our hospital's medical examination center and institutional staff. Among them, one patient was excluded because of a confirmed pituitary tumor on brain MRI, and one was lost to follow-up, resulting in a final total of 19 participants in the HC group.All the participants were right-handed. All participants provided written informed consent in accordance with the Declaration of Helsinki. These studies were approved by the Ethics Committee of Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine (Ethical Approval Number: 2019-030). Inclusion criteria (1) Patients with vestibular migraine and migraine without aura must have met the diagnostic criteria of the third edition of the International Classification of Headache Disorders (ICHD-III) from 2018[3]. The specific criteria are as follows: A. At least five vestibular symptoms and migraine attacks meeting criteria (C) and (D); B. Current or past history of migraine with or without aura; C. Moderate or severe vestibular symptoms lasting from 5 minutes to 72 hours; D. At least 50% of vestibular symptoms are accompanied by one of three migraine characteristics: two of the four headache features (unilateral, pulsating, moderate or severe headache that worsens with daily activities; photophobia and phonophobia; visual aura); E. Cannot be explained by other diseases in the 2018 ICHD-III diagnostic criteria or other vestibular diseases. (2) Age: 18-75 years; (3) Had the intellectual ability to complete psychometric measurements; (4) No vertigo, headache, or other disease episodes occurred within 3 days before or after cranial MR examination; (5) Signed informed consent. Exclusion criteria (1) Presence of chronic unstable diseases; (2) Organic changes in the central nervous system detected through neurological examination and imaging studies; (3) Subsequent discovery of secondary causes of vertigo during later diagnosis and treatment (VMm and VMnm groups); (4) Currently taking prophylactic medication for vestibular migraine or migraine (the VMm and VMnm groups); (5) Consumption of caffeine-containing beverages within 24 hours prior to undergoing cranial MR imaging; (6) Blindness, severe hepatic or renal dysfunction, or history of malignant disease; (7) Shift work or travel across two or more time zones within six weeks; (8) Patients with a history of medication use, including melatonin agonists, beta-blockers (Betaloc) that inhibit melatonin secretion, steroidal drugs, benzodiazepines, opioids, or immunosuppressants. Baseline characteristics of clinical data For all enrolled patients, data on gender, age, BMI, years of education, age of onset of vertigo, common triggers for vertigo attacks, manifestations of vertigo attacks[2], and sleep and emotion-related scales were collected. Insomnia Severity Index (ISI) The ISI is used to measure difficulty in maintaining sleep, satisfaction with current sleep patterns, interference with daily functioning, impairment of quality of life due to sleep problems, and the degree of concern caused by sleep issues through seven questions[11]. The total score ranges from 0 (no disability) to 28 (severe disability), with each item being scored from 0 (completely dissatisfied) to 4 (very satisfied). A cutoff score of 15 was used as the threshold for clinically significant insomnia, whereas scores between 8 and 14 were considered subthreshold insomnia. The Chinese version of the ISI has demonstrated high reliability and validity in assessing insomnia among the Chinese population[11]. Hamilton Anxiety Scale (HAMA-14) and Hamilton Depression Scale (HAMD-17) We utilized the 17-item HAMD (HAMD-17) and the 14-item Hamilton Anxiety Scale (HAMA-14) to evaluate the anxious and depressive emotional states of each patient[12, 13]. The HAMD-17 comprises five factor categories: anxiety/somatization (items 10, 11, 12, 13, 15, and 17), weight (item 16), cognitive disturbance (items 2, 3, and 9), retardation (items 1, 7, 8, and 14), and sleep disturbance (items 4, 5, and 6). The HAMA-14 includes two factor categories: physical anxiety (items 7, 8, 9, 10, 11, 12, and 13) and psychic anxiety (items 1, 2, 3, 4, 5, 6, and 14). Both scales exhibit good reliability and validity, making them widely used for assessing clinical mental states both domestically and internationally. Morningness-Eveningness Questionnaire Self-Assessment Scale (MEQ) Chronotype was assessed via the Morningness-Eveningness Questionnaire (MEQ) developed by Horne and Ostberg[14], which is the most widely used tool for identifying circadian rhythm preferences[15]. The questionnaire consists of 19 items with a scoring criterion that allows multiple choices, with each item scored between 4 and 5 points. The total score ranges from 16 to 86; scores of 41 and below are considered \"evening type,\" scores of 59 and above are considered \"morning type,\" and scores between 42 and 58 are considered \"intermediate type.\" Higher scores indicate a tendency toward morningness, whereas lower scores indicate a stronger preference for eveningness. According to the results, 49.8% of individuals preferred the morning type, whereas 5.6% preferred the evening type. Acquisition of BOLD-fMRI data The subjects were instructed to avoid eating, smoking, and consuming caffeine for four hours prior to the examination. All subjects underwent MR examination between 10 a.m. and 2 p.m. daily. MRI scans were performed via a SIGNA Architect 3.0T magnetic resonance scanner from GE Healthcare, equipped with an eight-channel phased-array head coil. The subjects' heads were immobilized with a sponge cushion to minimize motion artifacts. Initially, a routine cranial MRI scan was conducted to check for organic lesions, followed by 3D-TIWI and resting-state BOLD scans. The rs-fMRI scanning parameters were as follows: slice number = 43, repetition time (TR) = 2000 ms, echo time (TE) = 30 ms, matrix size = 64x64, flip angle = 90°, slice thickness = 3.2 mm, voxel size = 220 mm x 220 mm, and total scanning time = 8 minutes, yielding data from 240 time points. The sagittal 3D-TIWI scanning parameters included TR = 6.7 ms, TE = 2.9 ms, flip angle = 12°, matrix = 256x256, slice thickness = 1 mm, and slice number = 188. All enrolled patients reported no discomfort during the examination and were required to maintain a conscious state. Data preprocessing of resting-state fMRI Initially, the raw resting-state fMRI DICOM files were converted to NIfTI format via dcm2niigui software. Following this, the resting-state fMRI data were preprocessed with DPARSF software[16, 17], involving the specific steps outlined below: (1) Removal of the first 10 time points: Due to the instability of the magnetic resonance signal during the initial scanning phase and the patient's adaptation to the scanning environment, the data from the first 10 time points were discarded, leaving the data from the remaining 230 time points. (2) Slice timing correction: Within a single volume, 43 slices were acquired over one TR (i.e., 2 seconds), resulting in different actual time points for each slice. In this study, a GE 3.0T magnetic resonance scanner was used, with a scanning sequence that began with odd-numbered slices followed by even-numbered slices. Therefore, when the middle slice (the 43rd slice) was used as a reference, an interpolation method was employed to correct the signals of all slices to the same time point. (3) Realignment for head movement correction: During the scanning process, subjects may unavoidably move their heads. To address this, the images from the 230 time points were aligned, correcting for head movement. Subjects whose head movement exceeded 2.5 mm and 2.5° were excluded from the study. (4) Normalization: Considering variations in brain size, shape, and position among different subjects, all subject images were registered to a standard MNI space to ensure complete alignment of all the brains. The voxel size after normalization was set to [3 3 3]. (5) Smoothing: With a FWHM of [6 6 6], the values of all voxels were smoothed to the average signal of their surrounding voxels, reducing noise interference. (6) Detrending to remove linear drift: Over extended scanning periods, the BOLD signal can exhibit a general drift. Correcting for this drift ensures more accurate results. (7) Regression of nuisance covariates: Image signals were corrected using head movement data, white matter signals, and CSF signals as nuisance variables. (8) Filtering: High-frequency and low-frequency BOLD signals were filtered out, and signals within the 0.01-0.08 Hz range were retained. Quantitative analysis of resting-state fMRI eigenvalues Using DPARSF software, the preprocessed data were analyzed to obtain maps of the ALFF values, fALFF values, and ReHo values. The ALFF, which represents the amplitude of low-frequency fluctuations, represents the amplitude of low-frequency fluctuations in the BOLD signal. fALFF refers to the ratio of the amplitude of BOLD signals in the frequency range of 0.01-0.08 Hz to the amplitude of BOLD signals across all frequencies. ReHo, short for Regional Homogeneity, measures the consistency of the BOLD signal of a given voxel with its neighboring voxels. All three of these indicators can reflect the degree of brain activation. For each eigenvalue map, a two-sample t test was conducted between groups, and the FDR was used for multiple comparison correction. Functional connectivity analysis The brain was divided into 90 regions via the AAL90 atlas, where AAL represents Anatomical Automatic Labeling. The AAL parcellation was provided by the Montreal Neurological Institute (MNI). The synchronization of BOLD signals between each of the 90 brain regions was calculated via the functional connectivity feature of the DPARSF software. A functional connectivity matrix was constructed by computing the Pearson correlation coefficients between different brain regions. A stronger correlation between two brain regions indicates more pronounced functional connectivity. Graph Theory Analysis In this study, various topological properties of the brain network were computed via graph theory analysis to evaluate brain activity. The analysis employed the AAL atlas, which includes 90 cerebral ROIs. Initially, the brain was parcellated into 90 regions on the basis of the atlas template. The Pearson correlation coefficient of the time series between each pair of brain regions was calculated, generating a 90×90 correlation matrix for each participant. To eliminate weak and spurious connections in the functional network, a broader range of thresholds was applied, and topological metrics were computed at each threshold to obtain the area under the curve (AUC). In this study, the sparsity threshold ranged from 0.05 to 0.4 with a step size of 0.01, utilizing a binary network approach. Eight topological metrics were calculated via GRETNA software[18], namely, betweenness centrality, degree centrality, network efficiency, nodal clustering coefficient, nodal efficiency, nodal local efficiency, nodal shortest path length, and small-world property. A two-sample t test was subsequently conducted between groups, with Bonferroni correction applied for multiple comparisons. Statistical analysis Baseline data analysis: Gender, history of motion sickness, family history, type of vertigo, and triggers were expressed as counts and percentages and were analyzed via the chi-square test. Body mass index, years of education, age, age of onset, duration of illness, and attack frequency are expressed as the means ± standard deviations. For comparisons between two groups following a normal distribution, the t test was used, whereas Welch's test was applied when variances were unequal. For two groups that did not follow a normal distribution, the Mann-Whitney test was utilized. One-way ANOVA was conducted for three groups following a normal distribution, whereas the Kruskal-Wallis test was employed for three groups not adhering to a normal distribution. SPSS 22.0 was used for data analysis. Functional imaging analysis: Differences in ALFF, fALFF, and ReHo among the three groups were evaluated via covariance analysis and two-sample t tests in SPM12. NBS software (https://www.nitrc.org/projects/nbs) was employed to analyze differences in FC between groups (NBS correction), with age, gender, and years of education as covariates. Subsequently, ALFF, fALFF, and ReHo were subjected to multiple comparison corrections via the FDR method (p<0.05). Ultimately, ALFF survived after FDR correction. The results were visualized via xjView, DPABI, and MRIcron software. The ALFF values corresponding to the ROI for the VMm and VMnm groups were extracted and correlated with the clinical characteristics of the two groups, via Pearson correlation analysis. A p value of less than 0.05 was considered statistically significant. Results Demographic and clinical characteristics Among the 47 enrolled VM patients, one patient failed to complete the full sequence scan of cranial magnetic resonance imaging due to claustrophobia, one patient experienced acute vertigo during the cranial MR scan, and three patients were lost to follow-up. Ultimately, 42 patients were included in the study. On the basis of the diurnal rhythm tendency of vertigo attacks, these patients were divided into two groups: 23 in the VMm group and 19 in the VMnm group. Among the collected cases, the onset time of VMm group patients was before 8 a.m., whereas the onset time of VMnm group patients was nonmorning or had no diurnal rhythm tendency. Additionally, 19 individuals were included in the HC group. The patients’ demographic and clinical characteristics are presented in Table 1 . There were no significant differences in gender (p = 0.101), age (p = 0.085), body mass index (p = 0.390), or years of education (p = 0.068) among the three groups. There was no difference in the age of onset between the VMm group and the VMnm group (p = 0.220). Although the course of disease was shorter in the VMm groupc than in the VMnm group (p = 0.096), there was no difference in the incidence rate(IR)(p = 0.849). Both groups had higher rates of motion sickness history and family history than did the HC group. The rate of motion sickness history was greater in the VMm group than in the VMnm group (p = 0.043), but there was no difference in the rate of family history between the VMm and VMnm groups(p = 0.106). In terms of the types of vertigo attacks, the proportions of various types in the VMm group were as follows: spontaneous vertigo, 56.5%; vertigo induced by head movement,43.5%; positional vertigo, 39.1%; visual vertigo, 21.7%; and dizziness with nausea induced by head movement, 21.7%. Among those in the VMnm group, 31.6% had spontaneous vertigo, 63.2% had vertigo induced by head movement, 21.1% had positional vertigo, 21.2% had visual vertigo, and 42.2% had dizziness with nausea induced by head movement. The proportion of spontaneous vertigo was relatively greater in the VMm group, whereas the proportion of vertigo induced by head movement was relatively greater in the VMnm group. Regarding the triggers of vertigo attacks, the two most common triggers for both the VMm group and the VMnm group were staying up late (78.3%, 73.7%) and excessive fatigue (47.8%, 63.2%). Emotional fluctuations, menstruation, alcohol consumption, weather changes, and caffeine-containing foods intake accounted for relatively low proportions. In terms of the sleep and mood scales, there were no significant differences in the ISI, HAMA-14, or MEQ scores among the three groups (p = 0.524, 0.087, 0.830). There were differences in HAMD-17 scores among the three groups (P = 0.039), and the score in the VMm group was significantly greater than that in the HC group (p = 0.035). Table 1 Demographic and clinical characteristics VMm (n = 23) VMnm (n = 19) HC (n = 19) P -value Gender(M/F) 4/19 2/17 7/12 0.119 Age(years) 49.87 ± 14.28 52.26 ± 10.64 53.47 ± 13.26 0.654 BMI(kg/m 2 ) 24.30 ± 2.31 22.72 ± 2.75 23.67 ± 3.54 0.216 Education(years) 10.52 ± 4.85 9.68 ± 2.89 9.90 ± 4.19 0.933 Age of onset(years) 38.52 ± 15.03 37.16 ± 13.08 NA 0.22 Course of disease(years) 11.35 ± 12.01 16.53 ± 12.18 NA 0.096 History of motion sickness (N,%) 20,87.0% 11,57.9% 5,26.3% < 0.0001 Family history(N,%) 13,56.5% 6,31.6% 1,5.3% 0.002 Incidence rate(/month) 1.12 ± 2.08 1.00 ± 1.77 NA 0.849 Types of vertigo attacks (N, %) NA Spontaneous vertigo 13,56.5% 6,31.6% Vertigo induced by head movement 10,43.5% 12,63.2% Positional vertigo 9,39.1% 4/,21.1% Visual vertigo 5,21.7% 4/,21.2% Dizziness with nausea induced by head movement 5,21.7% 8/,42.2% Triggers of vertigo attacks(N, %) NA Staying up late 18,78.3% 14,73.7% Excessive fatigue 11,47.8% 12,63.2% Emotional fluctuations 5,21.7% 5,26.3% Menstruation 4,27.4% 2,10.5% Alcohol consumption 3,13.0% 0,0% Weather changes 0,0% 4,21.1% Caffeine-containing foods intake 0,0% 0,0% ISI 10.39 ± 5.77 9.05 ± 7.17 8.105 ± 6.70 0.524 HAMA-14 10.26 ± 6.70 9.42 ± 7.13 6.53 ± 6.77 0.087 HAMD-17 8.91 ± 5.76 7.53 ± 6.43 4.63 ± 4.90 0.039 MEQ 58.96 ± 8.49 60.42 ± 8.87 60.37 ± 9.38 0.830 BMI , body mass index; ISI , insomnia severity index; HAMA 14 ,Hamilton anxiety scale;Hamilton Anxiety Scale; HAMD-17 ,Hamilton Depression Scale; MEQ , Morningness-Eveningness Questionnaire Self-Assessment Scale. Results of ALFF, fALFF, and ReHo Compared with the HC group, the VMm group presented decreased ALFF values in the left frontal medial orbital region (Frontal_Med_Orb_L) (X=-6, Y = 63, Z=-15, FDR-corrected, p < 0.05) (Fig. 2 )and in the medial part of the right superior frontal gyrus (Frontal_Sup_Medial_R) (X = 6, Y = 57, Z = 33, FDR-corrected, p < 0.05) (Fig. 1 ). These findings suggest reduced spontaneous functional activity in these brain regions in the VMm group. Compared with the HCs, the VMnm group presented decreased ALFF values in the right paracentral lobule (Paracentral_Lobule_R) (X = 9, Y=-30, Z = 54, FDR-corrected, p < 0.05) (Fig. 1 ), indicating attenuated spontaneous functional activity in the right paracentral lobule region of the VMnm group. Compared with the VMnm group, the VMm group presented lower ALFF values in the left frontal medial orbital region (Frontal_Med_Orb_L) (X=-6, Y = 63, Z=-12, FDR-corrected, p < 0.05) (Fig. 1 ). This finding suggested that spontaneous functional activity in this left frontal brain region was more reduced in the VMm group than in the VMnm group.No significant differences were observed in fALFF (fractional amplitude of low-frequency fluctuations) or regional homogeneity (ReHo) among the groups. Results of whole-brain functional connectivity (FC) analysis Compared with the HC group, the VMm group presented decreased functional connectivity between the right middle frontal gyrus (orbital part) and the right middle frontal gyrus, between the left inferior frontal gyrus (opercular part) and the right angular gyrus, and between the right inferior frontal gyrus (orbital part) and the left anterior cingulate and paracingulate gyri (Fig. 2 ). Compared with the HC group, the VMnm group presented reduced functional connectivity between the right superior temporal gyrus and the right amygdala, between the right lenticular nucleus (putamen) and the parahippocampal gyrus, between the left fusiform gyrus and the right amygdala, between the right hippocampus and the right amygdala, and between the right parahippocampal gyrus and the right amygdala (Fig. 2 ). Furthermore, compared with the VMnm group, the VMm group demonstrated diminished functional connectivity between the left lenticular nucleus (putamen) and the left parahippocampal gyrus (Fig. 2 ). Results of Graph Theory Analysis We calculated eight primary topological metrics betweenness centrality, degree centrality, network efficiency, nodal clustering coefficient, nodal efficiency, nodal local efficiency, nodal shortest path length, and small-world properties. With respect to global efficiency (Eg), there was a statistically significant difference between the VMm group and the HC group (p = 0.0289), as well as between the VMnm group and the HC group (p = 0.0191). These findings indicate a significant reduction in global efficiency for patients with VMm and VMnm. However, no statistically significant difference was observed in global network efficiency between the VMm and VMnm groups (p = 0.1001) (Fig. 3 ), suggesting that there was no global difference in the efficiency of brain information transmission between the VMm and VMnm groups. In terms of nodal betweenness centrality, compared with the HCs, the VMm group presented decreased betweenness in three brain regions: the left superior frontal gyrus, dorsolateral gyrus, left superior frontal gyrus, medial gyrus, and left inferior temporal gyrus (Bonferroni corrected, p < 0.05). Similarly, compared with the HC group, the VMnm group presented decreased betweenness in the left inferior temporal gyrus (Bonferroni corrected, p < 0.05). No statistically significant difference in betweenness was observed between the VMm and VMnm groups (Bonferroni corrected, p > 0.05)(Fig. 3 ). With respect to the small-world networks, the σ values of the HC group, VMm group, and VMnm group were all significantly greater than 1, indicating that all three groups satisfied the criteria for a small-world network. Statistical analysis revealed no significant differences in the small-world network properties of the γ, λ, and σ values among the three groups(Fig. 3 ). For the remaining five metrics, degree centrality, nodal clustering coefficient, nodal efficiency, nodal local efficiency, and nodal shortest path length, no statistically significant differences were observed among the three groups(Fig. 3 ). Correlation analysis between ALFF values and clinical data Using the left frontal medial orbital region (Frontal Medial Orbital Left) as the region of interest (ROI), we extracted the amplitude of ALFF values for each participant. Correlation analysis was conducted between these ALFF values and clinically relevant continuous variables. Our findings revealed a negative correlation between ALFF values in VMm and the MEQ scores (r=-0.4679, p = 0.0244). Similarly, a negative correlation was observed between the ALFF values in VMnm and the MEQ scores (r=-0.5983, p = 0.0068). Both groups tended to be positively correlated with disease duration, but this correlation did not reach statistical significance (p > 0.1) (Fig. 4 ). Discussion The diagnostic criteria for vestibular migraine (VM) are primarily based on clinical history and constitute a diagnosis of exclusion, leading to both a high rate of accurate diagnosis and significant missed diagnoses[19]. As the pathophysiological mechanisms underlying VM remain incompletely understood, our preliminary study focused on circadian rhythm characteristics in migraine patients. Subsequent findings revealed that VM-associated vertigo episodes also exhibit distinct circadian patterns: 74.7% of VM attacks occurred before 12:00 noon, with a peak incidence between 07:00–07:59 and a trough period at 21:00–21:59[8]. On the basis of these observations, the present study conducted neuroimaging investigations to compare VM patients with morning-onset attacks (VMm) and nonmorning-onset attacks (VMnm). The aim is to identify structural or functional neuroimaging abnormalities, particularly in pathways potentially linked to circadian regulation. In our study, detailed demographic data were recorded for patients with vestibular migraine. Both VMm and VMnm groups had a higher prevalence of women, and their histories of motion sickness were significantly higher than those of the healthy control group, which is consistent with several previous studies[20-22]. However, our study found that the VMm group was significantly higher than the VMnm group. Previous research has found that the comorbidity rate of motion sickness in VM patients is higher than that in migraine patients, and both are higher than that in the healthy control group. This tells us about the susceptibility to motion sickness in migraine patients, especially those with vestibular migraine[23], which is widely recognized among VM patients[24]. Therefore, it is speculated that patients with vestibular migraine who have onset in the early morning in this study have a higher susceptibility to motion sickness. Most patients may experience more than one vestibular symptom during acute VM episodes[21]. In our study, patients in both VM groups also exhibited various types of vertigo listed in the VM diagnosis codes[3], and multiple types of vertigo often coexisted. The most common type of vertigo in the VMm group was spontaneous vertigo, while the most common type in the VMnm group was positional vertigo, which may be closely related to the time of onset and the patient's state. Regarding emotional scales, our data indicate that the depression scores of the VMm group were significantly higher than those of the control group, suggesting a higher comorbidity rate of emotional disorders in VM patients with early morning onset, which is basically consistent with previous research[25]. This finding plays an important role in the clinical assessment of comorbidities in vestibular migraine. Neuroimaging findings in VMm ( morning-onset vestibular migraine ): ALFF and FC abnormalities In this study, we identified distinct patterns of interictal functional abnormalities between morning-onset vestibular migraine (VMm) and nonmorning-onset VM (VMnm) groups, with differential deviations from healthy controls. The VMm group exhibited a reduced amplitude of ALFF in the left medial orbitofrontal region and right medial superior frontal gyrus. Whole-brain FC analysis across 90 regions revealed widespread frontal-dominant hypoconnectivity in VMm: diminished FC between the right orbital middle frontal gyrus and right middle frontal gyrus, left opercular inferior frontal gyrus and right angular gyrus, and right orbital inferior frontal gyrus with left anterior/posterior cingulate cortices. These findings collectively suggested persistent frontal lobe-related functional network disturbances in morning-onset VMm. These findings are consistent with previous neuroimaging studies demonstrating aberrant ALFF patterns in the prefrontal cortex of vestibular migraine (VM) patients, as documented by Wang et al., thereby reinforcing the critical role of frontal lobe dysregulation in VM pathophysiology[26]. The frontal lobe governs primarily voluntary motor functions, executive cognition, linguistic processing, and affective regulation. Notably, the frontoparietal network has been implicated in both cognitive control and top-down pain modulation[27], with evidence of interictal static FC alterations in right frontoparietal networks among migraineurs[28]. Emerging chronobiological insights reveal circadian influences on frontal circuitry: Seney et al. demonstrated robust circadian rhythmicity in dorsolateral prefrontal cortex gene expression[29], whereas Cronin et al. proposed that peripheral clock dysregulation in frontal cortices may drive circadian disruptions during early neurodegenerative processes[30]. Clinical correlations further underscore frontal‒circadian interplay. Hofstra et al. documented the nocturnal predominance (23:00--05:00) of frontal lobe seizures, temporally aligned with 6-12 hours postdim light melatonin onset (DLMO)[31]. Complementary fMRI evidence from Chen et al. links right orbital superior frontal gyrus ALFF elevations to diurnal mood variations in major depression patients[32]. These converging lines of evidence position the frontal lobe as a critical nexus for circadian pathophysiology, potentially explaining the morning predominance of VMm symptoms through chronobiological dysregulation within frontal networks. Neuroimaging Findings in VMnm (Non-Morning-Onset Vestibular Migraine): ALFF and FC Abnormalities Compared with VMm patients and healthy controls, the nonmorning-onset VM (VMnm) subgroup presented distinct functional alterations. ALFF analysis revealed reduced activity in the right paracentral lobule of the VMnm group. FC studies further demonstrated weakened interactions in temporolimbic networks: diminished FC between the right superior temporal gyrus (STG) and right amygdala; impaired connectivity between the right putamen and parahippocampal gyrus; reduced FC linking the left fusiform gyrus to the right amygdala; and attenuated connectivity between the right hippocampus and amygdala, as well as the right parahippocampal gyrus and amygdala. These findings suggest that VMnm is characterized by parietal hypoactivation and temporolimbic network dysfunction, which contrasts with the frontal-dominant abnormalities observed in VMm. X. Zhe et al. reported that in the VM group, the functional connectivity (FC) of the left thalamus was significantly weaker than that of two regions in the medial prefrontal cortex: two regions in the anterior cingulate cortex, the left superior/middle temporal gyrus, and the left temporal pole[33]. After the VM group was divided into two subgroups, we did not observe the same functional connectivity abnormalities as those reported in previous studies. We hypothesize that this discrepancy may be related to differences in the study populations or variations between the subgroups after grouping. However, a common finding across both studies is the involvement of frontal, limbic, and temporal regions. The temporal lobe is associated with multisensory integration and vestibular processing[4, 34], and structural abnormalities in this region have also been linked to multisensory integration, including visual, auditory, tactile, and vestibular processing[35, 36]. Obermann et al. reported reduced gray matter volume in the inferior temporal gyrus, middle temporal gyrus, superior temporal gyrus, middle cingulate gyrus, dorsolateral prefrontal cortex, insula, parietal lobe, and occipital cortex, suggesting that these structurally abnormal brain regions in VM patients are involved in multisensory vestibular control, pain processing, and central vestibular compensation[4]. Abnormalities in the parietal cortex have been linked to nociception and multisensory vestibular control. Additionally, VM-related studies have indicated the involvement of the parietal lobe in the modulation of pain perception and sensory integration dysfunction[37]. Our study also revealed decreased parietal lobe ALFF in the VMnm group, suggesting a possible mechanism related to this finding. The pathophysiological mechanisms of vestibular migraine are similar to those of migraine; therefore, we hypothesize that the observed functional connectivity abnormalities support the important role of multisensory integration abnormalities in VM. Abnormalities in Graph Theory Metrics of Patients with Vestibular Migraine Small-world architecture represents one of the primary organizational principles underlying the global topological characteristics of the complex human brain[38]. Global efficiency, local efficiency, and path length serve as metrics to gauge the integration of white matter (WM) networks, which are characterized by ease of communication[39]. Specifically, global efficiency assesses the efficacy of information transmission within WM networks, whereas local efficiency reflects the network's fault tolerance, indicating how efficiently first neighbors of a node can communicate when the node is removed[40]. Additionally, shorter paths signify reduced distances between brain regions during information transfer, revealing stronger integration among these regions[41]; conversely, longer paths imply weaker network integration. L. Dai and colleagues reported that migraine patients exhibit abnormal global network properties and local network topologies, distinguished by enhanced integration, efficiency, and swift information transmission[42]. These network disparities are broadly distributed across the occipital, temporal, and parietal regions[42]. In contrast to prior graph theory studies focusing on migraine, our study performed graph theory-based network analysis in VM, which demonstrated significantly reduced global efficiency in patients with VM (VMm and VMnm subgroups). Local network analysis further suggested reduced nodal betweenness centrality, particularly in the frontal and temporal lobes of the left cerebral hemisphere, implying diminished efficiency in brain network information transmission, predominantly affecting the left hemisphere. Consistent with our findings, C. Jiang et al.'s DTI-based study also revealed that diurnal variation in radial diffusivity in healthy individuals is predominantly observed in the left hemisphere[43]. Notably, analysis of other graph theory metrics yielded no significant results, suggesting a possible dissociation from the pathophysiological mechanisms underlying VM. In conclusion, these disparities may be attributed to potentially distinct pathophysiological mechanisms between vestibular migraine and migraine. Neuroimaging Evidence for Circadian Rhythm Mechanisms in VM VM is characterized by a circadian rhythm, with more frequent and severe vertigo attacks in the morning[8]. On the basis of the circadian rhythm mechanism of VM, we are the first to categorize VM into two groups for neuroimaging studies: the VMm group, with frequent morning onset, and the VMnm group, without morning onset. Our findings revealed significant differences between the two groups: (1) Compared with the VMnm group, the VMm group presented decreased ALFF values in the left medial orbital frontal cortex; (2) the functional connectivity between the left lenticular nucleus (putamen) and the left parahippocampal gyrus was weaker in the VMm group than in the VMnm group; and (3) there were no differences in global network efficiency or node betweenness centrality based on graph theory analysis. These results suggest the presence of objectively disrupted circadian rhythms in patients with vestibular migraine, providing neuroimaging evidence to support the rhythmic pattern observed in their vertigo attacks. Both the enrolled patients and the control group were right-handed, with the left hemisphere being the dominant hemisphere. Differences between the VMnm and VMm groups were observed in the left dominant hemisphere, suggesting a closer relationship between the structure of the left hemisphere and the circadian rhythm. On the one hand, compulsory drug use and drug-induced relapse are partially caused by changes in orbitofrontal cortex (OFC) function[44, 45]. Owing to the hypofunction of the orbitofrontal cortex, inhibitory mechanisms are impaired[46], leading to increased impulsivity and deterioration of reward and decision-making mechanisms[44, 47]. This condition has been observed in drug abusers and contributes to the chronic transformation of migraine patients, indicating the profound importance of the orbitofrontal cortex in the development of migraine. On the other hand, various regions within the medial frontal area are involved in functions such as attention, learning, and temporal organization of behavior[48, 49]. The central hub for circadian rhythm is located in the suprachiasmatic nucleus (SCN) of the hypothalamus. The SCN serves as a crucial central pacemaker that generates and coordinates the circadian rhythm of the organism, earning it the title of the master clock or circadian oscillator in mammals. The suprachiasmatic nucleus (SCN) is responsible for receiving and integrating information from different time sources, regulating the functions of other brain regions and organs, and coordinating the body to maintain a rhythm consistent with the external environment[50]. However, some circadian oscillators outside the SCN are directly controlled by the SCN pacemaker or receive indirect input from the SCN through other neural circuits or extrabrain structures[51]. Basic research indicates that the SCN projects to the medial frontal region through the paraventricular thalamic nucleus[52]. Catherine et al. reported abnormal clock phase activity after damage to the medial frontal cortex of mice, suggesting that the timing of behavior is determined by the interaction between the medial frontal cortex and the SCN[53]. Therefore, combined with our findings, it is speculated that the medial orbitofrontal cortex is likely a key region affecting the abnormal circadian rhythm presented in VM attacks. The parahippocampal region is most consistently associated with memory function[54], and as an important part of the vestibular center, it is closely related to human navigation[55, 56]. Reduced cerebral blood flow perfusion has been observed in the parahippocampal gyrus of shift workers with disrupted circadian rhythms[57]. In functional magnetic resonance imaging studies of delirium, a weakened functional connection between the SCN and the parahippocampal gyrus has been found in patients with delirium[58]. The parahippocampal gyrus may be involved in the functional network of circadian rhythms. The putamen, a key part of the basal ganglia and belonging to the pain network, is believed to contribute to pain processing and analgesia[59]. Abnormal network connections between the putamen and multiple sites, such as the cerebellum, in VM patients suggest that the putamen is involved in the integration of endogenous analgesic mechanisms[26]. In our study, reduced functional connectivity in the parahippocampal gyrus and putamen suggested a possible association with abnormal circadian rhythms and multisensory integration in vestibular migraine patients[37]. Through correlation analysis, our study further revealed a negative correlation between the ALFF values in the left medial orbital frontal region and the MEQ scores in both the VMm and VMnm groups. Patients who tended to have morning-type disease presented weaker ALFF values in the left medial orbital frontal region. The MEQ serves as a suitable variable for studying circadian rhythms, and H. Wang et al. identified distinct functional connectivity patterns across different chronotypes[60]. Although clinical research on the circadian rhythms of VM remains scarce, studies on migraine have shown a preference for headache onset time (TPHA), with earlier chronotypes observed in migraine patients with TPHAs[61]. G. Viticchi et al. reported that morning-type patients experienced lower migraine attack frequencies but had a longer disease duration[62]. Baksa D et al. classified migraine attacks on the basis of circadian rhythm time types and investigated task-based MRI, revealing increased activation in regions involved in emotion, self-reference (left posterior cingulate cortex, right precuneus), pain (including the left middle cingulate cortex, left posterior central gyrus, left superior marginal gyrus, and right Rolandic gyrus), and sensory processing (including the bilateral superior temporal gyrus and right Heschl's gyrus) in the evening-onset group[11]. Differences in peak attack times and interictal brain activity across circadian rhythms suggest heterogeneity among migraine patients[11]. Vestibular migraine, as a subtype of migraine, exhibits distinct imaging differences on the basis of onset time and a negative correlation with the MEQ, as observed in our clinical phenotypic study of VM[8]. These findings strongly suggest that circadian rhythms likely play a role in the pathophysiology of VM. Limitations Our study innovatively grouped vestibular migraine (VM) patients on the basis of their circadian rhythm for functional neuroimaging research. We discovered, for the first time, that the circadian rhythm may affect brain function in VM and may be involved in its pathophysiological mechanism. However, several limitations should be considered. First, grouping patients on the basis of their diurnal preference for VM attacks introduces subjective factors during patient interviews, potentially leading to biases in intra- and intergroup differences. Second, owing to research capacity constraints (the collection of volunteers coincided with the outbreak of the COVID-19 epidemic), our sample size was small. Increasing the sample size could reveal more differences within and between groups. Third, the suprachiasmatic nucleus (SCN) is the most crucial central clock, but there is currently no universally recognized neuroimaging evidence confirming the existence and function of circadian rhythm structures. Some studies have employed functional network connectivity seeded at the SCN[58, 63]. However, limited by the insufficient precision of current BOLD classic brain parcellation, such studies often rely on manually delineated regions of interest (ROIs), whose reliability is debatable. Therefore, we did not adopt this research method. In the future, better neuroimaging techniques may offer improved solutions. Finally, while the relationship between VM and the circadian rhythm has been a focal point of our research team, more in-depth basic, clinical, and imaging studies are needed for wider recognition. Conclusions (1) Abnormalities in brain function were observed in multiple distinct brain regions during the interictal period in both the early-morning onset and non-early-morning onset groups of VM patients. (2) The VMm group primarily presented abnormalities in the frontal lobe, whereas the VMnm group presented predominant abnormalities in the parietal lobe, temporal lobe, and limbic system. The left medial orbitofrontal cortex may be a key structure influencing the diurnal rhythm characteristics of VM attacks. (3) The efficiency of brain network information transmission decreased in VM patients, predominantly in the left hemisphere, but this was not related to circadian rhythm.(4) The presence of functional MRI abnormalities in VM patients with different circadian rhythm characteristics suggests that the circadian rhythm may be one of the pathophysiological mechanisms of VM. This finding points to a new direction for future VM prevention or acute treatment. Abbreviations rs-fMRI Resting-State Functional MRI VM Vestibular migraine ALFF amplitude of low-frequency fluctuation FC functional connectivity HC healthy controls BOLD Blood Oxygenation Level Dependent SCN suprachiasmatic nucleus Declarations Author Contribution WL conceived the study idea. XXQ and PN were responsible for data acquisition. ZXN analyzed the data. WL and ZXN took the lead in drafting and writing the manuscript. ZHF and HFQ were involved in revising the manuscript critically for important intellectual content. All authors have read and approved the final manuscript. Acknowledgement We extend our sincere gratitude to Dr.Zhenliang Xiong for his professional guidance in the imaging data analysis during this study References Neuhauser, H., & Lempert, T. (2004). Vertigo and dizziness related to migraine: A diagnostic challenge. Cephalalgia 24(2) 83–91. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6538078\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":451311521,\"identity\":\"6cc1f782-4ee2-40d4-b60d-b2c9d0b0e5c8\",\"order_by\":0,\"name\":\"Zhengxin Ni\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Radiology，Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Zhengxin\",\"middleName\":\"\",\"lastName\":\"Ni\",\"suffix\":\"\"},{\"id\":451311522,\"identity\":\"7aefc427-0e9b-4b76-9922-1d929bd8737a\",\"order_by\":1,\"name\":\"Zhihui Fu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Radiology，Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Zhihui\",\"middleName\":\"\",\"lastName\":\"Fu\",\"suffix\":\"\"},{\"id\":451311523,\"identity\":\"b25091fc-37a6-4ebc-ae4b-a3e1b0789b24\",\"order_by\":2,\"name\":\"Xiaoxi Qian\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Neurology，Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xiaoxi\",\"middleName\":\"\",\"lastName\":\"Qian\",\"suffix\":\"\"},{\"id\":451311524,\"identity\":\"6081779e-bba3-40fe-b2e2-4e8ae6c645a3\",\"order_by\":3,\"name\":\"Ping Ni\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Neurology，Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ping\",\"middleName\":\"\",\"lastName\":\"Ni\",\"suffix\":\"\"},{\"id\":451311525,\"identity\":\"abb86d57-7110-46c8-92ca-0bd80159b9d5\",\"order_by\":4,\"name\":\"Huifeng Qian\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Neurology，Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Huifeng\",\"middleName\":\"\",\"lastName\":\"Qian\",\"suffix\":\"\"},{\"id\":451311526,\"identity\":\"584dd97f-4dfb-4367-a931-eb514cc620c0\",\"order_by\":5,\"name\":\"Wei Liu\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACPghlw8PPzHzwAVFa2CBUmoxkO1uyASlaDtsYnOcxEyBOC3vzMYmfbcw8xocZzBgYamyiCWvhOZYm2dvGxmN2mCHtAcOxtNwGglokcsxu8LbxgLQcN2BsOEyEFvk3Zjf/tknwGDcztkkQp0WCx+w2b5sBjwEzMxuRWnjS0n/LnEvgkTjMxmyQQIxf+NkPHzZ8U/bfnr///McHH2psCGsBA0Zo7DAkEKUcDP4Qr3QUjIJRMApGIAAAVsk200l966oAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Department of Neurology，Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Wei\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-04-27 05:08:11\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6538078/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6538078/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":82161905,\"identity\":\"fb8bcb71-7f84-431d-9b93-f97fca74d5be\",\"added_by\":\"auto\",\"created_at\":\"2025-05-07 08:40:26\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":650852,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDifferences among three groups in amplitude of ALFF values.(A) The VMm group exhibited significantly reduced ALFF in the left medial orbitofrontal cortex (Frontal_Med_Orb_L) compared to healthy controls (HCs) (FDR-corrected, p\\u0026lt;0.05).(B) Decreased ALFF was observed in the right medial superior frontal gyrus (Frontal_Sup_Medial_R) of VMm patients relative to HCs (FDR-corrected, p\\u0026lt;0.05).(C) VMnm patients demonstrated diminished ALFF in the right paracentral lobule (Paracentral_Lobule_R) versus HCs (FDR-corrected, p\\u0026lt;0.05).(D) The VMm group showed lower ALFF values in the left medial orbitofrontal cortex compared to the VMnm group (FDR-corrected, p\\u0026lt;0.05).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6538078/v1/6bf54f4203ea2817b7f6755a.png\"},{\"id\":82163854,\"identity\":\"c53dbc47-2ce2-4b15-b413-7b9aedfb4d2b\",\"added_by\":\"auto\",\"created_at\":\"2025-05-07 08:56:26\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":286045,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDifferences among three groups in amplitude of FC values. The brain was divided into 90 regions via the AAL90 atlas, where AAL represents Anatomical Automatic Labeling.Blue lines indicate significantly reduced functional connectivity (FC) between specified brain regions in the vestibular migraine with morning attacks group (VMm) compared to healthy controls (HCs); Red lines represent decreased FC between identified regions in the vestibular migraine without morning attacks group (VMnm) versus HCs; Green lines denote enhanced FC between characteristic regions in the VMm group relative to the VMnm group.(FDR Corrected, p \\u0026lt; 0.05)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6538078/v1/3c8a71a27581ad610ee954f9.png\"},{\"id\":82161901,\"identity\":\"b1e3a3be-b992-4d9d-b25a-1b7ed88bee21\",\"added_by\":\"auto\",\"created_at\":\"2025-05-07 08:40:25\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":165568,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDifferences among three groups in graph theory analysis.(A)Global efficiency; (B)Nodal betweenness centrality(Bonferroni Corrected, p \\u0026lt; 0.05); (C) Small-world networks.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6538078/v1/ba4c448dbe7d7480468036ee.png\"},{\"id\":82162732,\"identity\":\"f7dc31ea-9db5-4ad5-9879-3669a5cf2caa\",\"added_by\":\"auto\",\"created_at\":\"2025-05-07 08:48:25\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":127325,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe ALFF value of each subject was extracted and the correlation analysis was performed with MEQ and disease course.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6538078/v1/3daf2042d60e4ae7faf930d0.png\"},{\"id\":83971231,\"identity\":\"39e3fbd6-a724-47a4-a731-b7a63fc0be46\",\"added_by\":\"auto\",\"created_at\":\"2025-06-05 08:02:28\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2057954,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6538078/v1/ef7fd03d-2c8e-4dd6-abf0-89f691cb17c8.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"The Circadian Mechanism May Contribute to Vestibular Migraine: A Case-Control Study Based on Resting-State Functional MRI (rs-fMRI)\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eVestibular migraine, the most common recurrent vertigo disease, has a long history. It was first proposed by Neuhauser et al. in 2004[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e] and then unanimously recognized and updated by the International Headache Society and the International Classification Committee for Vestibular Disorders of the Barany Association in 2012[\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Finally, in 2018, vestibular migraine was included in the appendix of the International Classification of Headache Disorders[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e].The two steps in the diagnosis and recognition of the disease illustrate the disagreements among scholars regarding this condition, further emphasizing the need for more research to delve into its pathophysiological mechanisms.Currently, there is a lack of a recognized animal research model for vestibular migraine, and neuroimaging studies are needed to explore its structural and functional abnormalities[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. In terms of structural imaging, Obermann M et al.[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e] and Messina et al.[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e] identified widespread cortical volume abnormalities in vestibular migraine patients through voxel-based morphometric studies. With respect to functional imaging, resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive imaging technique used to measure low-frequency fluctuations in intracranial Blood Oxygenation Level Dependent(BOLD) signals[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. Currently, there is a lack of a recognized animal research model for vestibular migraine, and neuroimaging studies are needed to explore its structural and functional abnormalities[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. Our preliminary research revealed that vestibular migraine attacks exhibit a circadian rhythm, with a significantly greater frequency and severity of attacks in the morning[\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. X. Wu and colleagues studied the circadian rhythm and functional magnetic resonance imaging of night-shift nurses, revealing notably lower sleep quality and melatonin levels in this population. Their findings also indicated decreased ReHo activation in the bilateral cerebellar hemispheres, coupled with increased ReHo in the bilateral occipital lobes and left parietal lobe. Compared with day-shift nurses, night-shift nurses presented greater FC from the hypothalamus to the right cingulate gyrus, right putamen, and vermis. Furthermore, activation of the right cerebellar hemisphere, left superior parietal gyrus, and right superior occipital gyrus was correlated with sleep quality scores.Furthermore, activation of the right cerebellar hemisphere was associated with melatonin levels, and higher sleepiness scores were correlated with stronger functional connectivity (FC) between the hypothalamus and the vermis. The decreased activity in the right cerebellum of night-shift nurses following circadian rhythm disruption was linked to poor sleep quality, suggesting possible involvement in sleep regulation mediated by the melatonin pathway. Additionally, resting-state FC indicated enhanced FC between the cerebellar vermis and the hypothalamus, and this enhancement was associated with increased daytime sleepiness[\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. As a central structure for vertigo, the cerebellum plays a crucial role in multisensory integration in vestibular migraine[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. Abnormal brain network connections between the hypothalamus and cerebellar vermis in nurses with circadian rhythm disorders suggest that circadian rhythm may be involved in the pathophysiological mechanism of vertigo diseases.\\u003c/p\\u003e \\u003cp\\u003eIn this study, we divided patients with vestibular migraine during the interictal period into two groups: those with prominent morning attacks and those without morning attacks. We compared these groups with a healthy control group to investigate differences in resting-state functional magnetic resonance imaging. Additionally, we examined the potential relationships between clinical characteristics and functional MRI abnormalities in various subgroups of VM patients, conducting exploratory research on the possible circadian rhythm mechanisms underlying VM.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003eWe collected data from 47 patients with vestibular migraine (VM) who visited the Department of Neurology Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine between October 1, 2022, and October 1, 2023. All patients were diagnosed jointly by two trained senior neurologists and an otolaryngologist. On the basis of previous scholarly methods for studying the circadian rhythm of migraine and referring to our prior research on the observation of vestibular migraine attack rhythms[8], the included VM patients were classified into different circadian rhythm types[10] through questioning. The question was\\u0026quot;Do at least 50% of your vertigo attacks (according to the aforementioned vestibular migraine attack criteria) occur during the following times: early morning (00:00-07:59) or not in the early morning (08:00-23:59)?\\u0026quot; All vestibular migraine patients were divided into an early morning attack group (VM attacks in the morning, VMm group, n=23) and a nonmorning attack group (VM attacks not in the morning, VMnm group, n=19).\\u003c/p\\u003e\\n\\u003cp\\u003eAdditionally, we recruited a healthy control group (HC) consisting of 21 individuals matched for age and gender with the VMm and VMnm groups from our hospital\\u0026apos;s medical examination center and institutional staff. Among them, one patient was excluded because of a confirmed pituitary tumor on brain MRI, and one was lost to follow-up, resulting in a final total of 19 participants in the HC group.All the participants were right-handed. All participants provided written informed consent in accordance with the Declaration of Helsinki. These studies were approved by the Ethics Committee of Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine (Ethical\\u0026nbsp;Approval\\u0026nbsp;Number: 2019-030).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eInclusion criteria\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e(1) Patients with vestibular migraine and migraine without aura must have met the diagnostic criteria of the third edition of the International Classification of Headache Disorders (ICHD-III) from 2018[3]. The specific criteria are as follows:\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eA. At least five vestibular symptoms and migraine attacks meeting criteria (C) and (D);\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eB. Current or past history of migraine with or without aura;\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eC. Moderate or severe vestibular symptoms lasting from 5 minutes to 72 hours;\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eD. At least 50% of vestibular symptoms are accompanied by one of three migraine characteristics: two of the four headache features (unilateral, pulsating, moderate or severe headache that worsens with daily activities; photophobia and phonophobia; visual aura);\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eE. Cannot be explained by other diseases in the 2018 ICHD-III diagnostic criteria or other vestibular diseases.\\u003c/p\\u003e\\n\\u003cp\\u003e(2) Age: 18-75 years;\\u003c/p\\u003e\\n\\u003cp\\u003e(3) Had the intellectual ability to complete psychometric measurements;\\u003c/p\\u003e\\n\\u003cp\\u003e(4) No vertigo, headache, or other disease episodes occurred within 3 days before or after cranial MR examination;\\u003c/p\\u003e\\n\\u003cp\\u003e(5) Signed informed consent.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eExclusion criteria\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e(1) Presence of chronic unstable diseases;\\u003c/p\\u003e\\n\\u003cp\\u003e(2) Organic changes in the central nervous system detected through neurological examination and imaging studies;\\u003c/p\\u003e\\n\\u003cp\\u003e(3) Subsequent discovery of secondary causes of vertigo during later diagnosis and treatment (VMm and VMnm groups);\\u003c/p\\u003e\\n\\u003cp\\u003e(4) Currently taking prophylactic medication for vestibular migraine or migraine (the VMm and VMnm groups);\\u003c/p\\u003e\\n\\u003cp\\u003e(5) Consumption of caffeine-containing beverages within 24 hours prior to undergoing cranial MR imaging;\\u003c/p\\u003e\\n\\u003cp\\u003e(6) Blindness, severe hepatic or renal dysfunction, or history of malignant disease;\\u003c/p\\u003e\\n\\u003cp\\u003e(7) Shift work or travel across two or more time zones within six weeks;\\u003c/p\\u003e\\n\\u003cp\\u003e(8) Patients with a history of medication use, including melatonin agonists, beta-blockers (Betaloc) that inhibit melatonin secretion, steroidal drugs, benzodiazepines, opioids, or immunosuppressants.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eBaseline characteristics of clinical data\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFor all enrolled patients, data on gender, age, BMI, years of education, age of onset of vertigo, common triggers for vertigo attacks, manifestations of vertigo attacks[2], and sleep and emotion-related scales were collected.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eInsomnia Severity Index (ISI)\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe ISI is used to measure difficulty in maintaining sleep, satisfaction with current sleep patterns, interference with daily functioning, impairment of quality of life due to sleep problems, and the degree of concern caused by sleep issues through seven questions[11]. The total score ranges from 0 (no disability) to 28 (severe disability), with each item being scored from 0 (completely dissatisfied) to 4 (very satisfied). A cutoff score of 15 was used as the threshold for clinically significant insomnia, whereas scores between 8 and 14 were considered subthreshold insomnia. The Chinese version of the ISI has demonstrated high reliability and validity in assessing insomnia among the Chinese population[11].\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eHamilton Anxiety Scale (HAMA-14) and Hamilton Depression Scale (HAMD-17)\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe utilized the 17-item HAMD (HAMD-17) and the 14-item Hamilton Anxiety Scale (HAMA-14) to evaluate the anxious and depressive emotional states of each patient[12, 13]. The HAMD-17 comprises five factor categories: anxiety/somatization (items 10, 11, 12, 13, 15, and 17), weight (item 16), cognitive disturbance (items 2, 3, and 9), retardation (items 1, 7, 8, and 14), and sleep disturbance (items 4, 5, and 6). The HAMA-14 includes two factor categories: physical anxiety (items 7, 8, 9, 10, 11, 12, and 13) and psychic anxiety (items 1, 2, 3, 4, 5, 6, and 14). Both scales exhibit good reliability and validity, making them widely used for assessing clinical mental states both domestically and internationally.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMorningness-Eveningness Questionnaire Self-Assessment Scale (MEQ)\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eChronotype was assessed via the Morningness-Eveningness Questionnaire (MEQ) developed by Horne and Ostberg[14], which is the most widely used tool for identifying circadian rhythm preferences[15]. The questionnaire consists of 19 items with a scoring criterion that allows multiple choices, with each item scored between 4 and 5 points. The total score ranges from 16 to 86; scores of 41 and below are considered \\u0026quot;evening type,\\u0026quot; scores of 59 and above are considered \\u0026quot;morning type,\\u0026quot; and scores between 42 and 58 are considered \\u0026quot;intermediate type.\\u0026quot; Higher scores indicate a tendency toward morningness, whereas lower scores indicate a stronger preference for eveningness. According to the results, 49.8% of individuals preferred the morning type, whereas 5.6% preferred the evening type.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcquisition of BOLD-fMRI\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003edata\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe subjects were instructed to avoid eating, smoking, and consuming caffeine for four hours prior to the examination. All subjects underwent MR examination between 10 a.m. and 2 p.m. daily. MRI scans were performed via a SIGNA Architect 3.0T magnetic resonance scanner from GE Healthcare, equipped with an eight-channel phased-array head coil. The subjects\\u0026apos; heads were immobilized with a sponge cushion to minimize motion artifacts. Initially, a routine cranial MRI scan was conducted to check for organic lesions, followed by 3D-TIWI and resting-state BOLD scans. The rs-fMRI scanning parameters were as follows: slice number = 43, repetition time (TR) = 2000 ms, echo time (TE) = 30 ms, matrix size = 64x64, flip angle = 90\\u0026deg;, slice thickness = 3.2 mm, voxel size = 220 mm x 220 mm, and total scanning time = 8 minutes, yielding data from 240 time points. The sagittal 3D-TIWI scanning parameters included TR = 6.7 ms, TE = 2.9 ms, flip angle = 12\\u0026deg;, matrix = 256x256, slice thickness = 1 mm, and slice number = 188. All enrolled patients reported no discomfort during the examination and were required to maintain a conscious state.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData preprocessing of resting-state fMRI\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eInitially, the raw resting-state fMRI DICOM files were converted to NIfTI format via dcm2niigui software. Following this, the resting-state fMRI data were preprocessed with DPARSF software[16, 17], involving the specific steps outlined below:\\u003c/p\\u003e\\n\\u003cp\\u003e(1) Removal of the first 10 time points: Due to the instability of the magnetic resonance signal during the initial scanning phase and the patient\\u0026apos;s adaptation to the scanning environment, the data from the first 10 time points were discarded, leaving the data from the remaining 230 time points.\\u003c/p\\u003e\\n\\u003cp\\u003e(2) Slice timing correction: Within a single volume, 43 slices were acquired over one TR (i.e., 2 seconds), resulting in different actual time points for each slice. In this study, a GE 3.0T magnetic resonance scanner was used, with a scanning sequence that began with odd-numbered slices followed by even-numbered slices. Therefore, when the middle slice (the 43rd slice) was used as a reference, an interpolation method was employed to correct the signals of all slices to the same time point.\\u003c/p\\u003e\\n\\u003cp\\u003e(3) Realignment for head movement correction: During the scanning process, subjects may unavoidably move their heads. To address this, the images from the 230 time points were aligned, correcting for head movement. Subjects whose head movement exceeded 2.5 mm and 2.5\\u0026deg; were excluded from the study.\\u003c/p\\u003e\\n\\u003cp\\u003e(4) Normalization: Considering variations in brain size, shape, and position among different subjects, all subject images were registered to a standard MNI space to ensure complete alignment of all the brains. The voxel size after normalization was set to [3 3 3].\\u003c/p\\u003e\\n\\u003cp\\u003e(5) Smoothing: With a FWHM of [6 6 6], the values of all voxels were smoothed to the average signal of their surrounding voxels, reducing noise interference.\\u003c/p\\u003e\\n\\u003cp\\u003e(6) Detrending to remove linear drift: Over extended scanning periods, the BOLD signal can exhibit a general drift. Correcting for this drift ensures more accurate results.\\u003c/p\\u003e\\n\\u003cp\\u003e(7) Regression of nuisance covariates: Image signals were corrected using head movement data, white matter signals, and CSF signals as nuisance variables.\\u003c/p\\u003e\\n\\u003cp\\u003e(8) Filtering: High-frequency and low-frequency BOLD signals were filtered out, and signals within the 0.01-0.08 Hz range were retained.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eQuantitative analysis of resting-state fMRI eigenvalues\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eUsing DPARSF software, the preprocessed data were analyzed to obtain maps of the ALFF values, fALFF values, and ReHo values. The ALFF, which represents the amplitude of low-frequency fluctuations, represents the amplitude of low-frequency fluctuations in the BOLD signal. fALFF refers to the ratio of the amplitude of BOLD signals in the frequency range of 0.01-0.08 Hz to the amplitude of BOLD signals across all frequencies. ReHo, short for Regional Homogeneity, measures the consistency of the BOLD signal of a given voxel with its neighboring voxels. All three of these indicators can reflect the degree of brain activation. For each eigenvalue map, a two-sample t test was conducted between groups, and the FDR was used for multiple comparison correction.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunctional connectivity analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe brain was divided into 90 regions via the AAL90 atlas, where AAL represents Anatomical Automatic Labeling. The AAL parcellation was provided by the Montreal Neurological Institute (MNI). The synchronization of BOLD signals between each of the 90 brain regions was calculated via the functional connectivity feature of the DPARSF software. A functional connectivity matrix was constructed by computing the Pearson correlation coefficients between different brain regions. A stronger correlation between two brain regions indicates more pronounced functional connectivity.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eGraph Theory Analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn this study, various topological properties of the brain network were computed via graph theory analysis to evaluate brain activity. The analysis employed the AAL atlas, which includes 90 cerebral ROIs. Initially, the brain was parcellated into 90 regions on the basis of the atlas template. The Pearson correlation coefficient of the time series between each pair of brain regions was calculated, generating a 90\\u0026times;90 correlation matrix for each participant. To eliminate weak and spurious connections in the functional network, a broader range of thresholds was applied, and topological metrics were computed at each threshold to obtain the area under the curve (AUC). In this study, the sparsity threshold ranged from 0.05 to 0.4 with a step size of 0.01, utilizing a binary network approach.\\u003c/p\\u003e\\n\\u003cp\\u003eEight topological metrics were calculated via GRETNA software[18], namely, betweenness centrality, degree centrality, network efficiency, nodal clustering coefficient, nodal efficiency, nodal local efficiency, nodal shortest path length, and small-world property. A two-sample t test was subsequently conducted between groups, with Bonferroni correction applied for multiple comparisons.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eStatistical analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBaseline data analysis: Gender, history of motion sickness, family history, type of vertigo, and triggers were expressed as counts and percentages and were analyzed via the chi-square test. Body mass index, years of education, age, age of onset, duration of illness, and attack frequency are expressed as the means \\u0026plusmn; standard deviations. For comparisons between two groups following a normal distribution, the t test was used, whereas Welch\\u0026apos;s test was applied when variances were unequal. For two groups that did not follow a normal distribution, the Mann-Whitney test was utilized. One-way ANOVA was conducted for three groups following a normal distribution, whereas the Kruskal-Wallis test was employed for three groups not adhering to a normal distribution. SPSS 22.0 was used for data analysis.\\u003c/p\\u003e\\n\\u003cp\\u003eFunctional imaging analysis: Differences in ALFF, fALFF, and ReHo among the three groups were evaluated via covariance analysis and two-sample t tests in SPM12. NBS software (https://www.nitrc.org/projects/nbs) was employed to analyze differences in FC between groups (NBS correction), with age, gender, and years of education as covariates. Subsequently, ALFF, fALFF, and ReHo were subjected to multiple comparison corrections via the FDR method (p\\u0026lt;0.05). Ultimately, ALFF survived after FDR correction. The results were visualized via xjView, DPABI, and MRIcron software. The ALFF values corresponding to the ROI for the VMm and VMnm groups were extracted and correlated with the clinical characteristics of the two groups, via Pearson correlation analysis. A p value of less than 0.05 was considered statistically significant.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eDemographic and clinical characteristics\\u003c/h2\\u003e\\n \\u003cp\\u003eAmong the 47 enrolled VM patients, one patient failed to complete the full sequence scan of cranial magnetic resonance imaging due to claustrophobia, one patient experienced acute vertigo during the cranial MR scan, and three patients were lost to follow-up. Ultimately, 42 patients were included in the study. On the basis of the diurnal rhythm tendency of vertigo attacks, these patients were divided into two groups: 23 in the VMm group and 19 in the VMnm group. Among the collected cases, the onset time of VMm group patients was before 8 a.m., whereas the onset time of VMnm group patients was nonmorning or had no diurnal rhythm tendency. Additionally, 19 individuals were included in the HC group. The patients\\u0026rsquo; demographic and clinical characteristics are presented in Table\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. There were no significant differences in gender (p\\u0026thinsp;=\\u0026thinsp;0.101), age (p\\u0026thinsp;=\\u0026thinsp;0.085), body mass index (p\\u0026thinsp;=\\u0026thinsp;0.390), or years of education (p\\u0026thinsp;=\\u0026thinsp;0.068) among the three groups.\\u003c/p\\u003e\\n \\u003cp\\u003eThere was no difference in the age of onset between the VMm group and the VMnm group (p\\u0026thinsp;=\\u0026thinsp;0.220). Although the course of disease was shorter in the VMm groupc than in the VMnm group (p\\u0026thinsp;=\\u0026thinsp;0.096), there was no difference in the incidence rate(IR)(p\\u0026thinsp;=\\u0026thinsp;0.849). Both groups had higher rates of motion sickness history and family history than did the HC group. The rate of motion sickness history was greater in the VMm group than in the VMnm group (p\\u0026thinsp;=\\u0026thinsp;0.043), but there was no difference in the rate of family history between the VMm and VMnm groups(p\\u0026thinsp;=\\u0026thinsp;0.106).\\u003c/p\\u003e\\n \\u003cp\\u003eIn terms of the types of vertigo attacks, the proportions of various types in the VMm group were as follows: spontaneous vertigo, 56.5%; vertigo induced by head movement,43.5%; positional vertigo, 39.1%; visual vertigo, 21.7%; and dizziness with nausea induced by head movement, 21.7%. Among those in the VMnm group, 31.6% had spontaneous vertigo, 63.2% had vertigo induced by head movement, 21.1% had positional vertigo, 21.2% had visual vertigo, and 42.2% had dizziness with nausea induced by head movement. The proportion of spontaneous vertigo was relatively greater in the VMm group, whereas the proportion of vertigo induced by head movement was relatively greater in the VMnm group.\\u003c/p\\u003e\\n \\u003cp\\u003eRegarding the triggers of vertigo attacks, the two most common triggers for both the VMm group and the VMnm group were staying up late (78.3%, 73.7%) and excessive fatigue (47.8%, 63.2%). Emotional fluctuations, menstruation, alcohol consumption, weather changes, and caffeine-containing foods intake accounted for relatively low proportions.\\u003c/p\\u003e\\n \\u003cp\\u003eIn terms of the sleep and mood scales, there were no significant differences in the ISI, HAMA-14, or MEQ scores among the three groups (p\\u0026thinsp;=\\u0026thinsp;0.524, 0.087, 0.830). There were differences in HAMD-17 scores among the three groups (P\\u0026thinsp;=\\u0026thinsp;0.039), and the score in the VMm group was significantly greater than that in the HC group (p\\u0026thinsp;=\\u0026thinsp;0.035).\\u003c/p\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u0026nbsp;\\u003ctable id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eDemographic and clinical characteristics\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003c/colgroup\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eVMm (n\\u0026thinsp;=\\u0026thinsp;23)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eVMnm (n\\u0026thinsp;=\\u0026thinsp;19)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHC (n\\u0026thinsp;=\\u0026thinsp;19)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e-value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGender(M/F)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4/19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2/17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7/12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.119\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAge(years)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e49.87\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;14.28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e52.26\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;10.64\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e53.47\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;13.26\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.654\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBMI(kg/m\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e24.30\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.31\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e22.72\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.75\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e23.67\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.54\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.216\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEducation(years)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e10.52\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.85\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9.68\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.89\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9.90\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.933\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAge of onset(years)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e38.52\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;15.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e37.16\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;13.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.22\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCourse of disease(years)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e11.35\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e16.53\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.096\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHistory of motion sickness\\u003c/p\\u003e\\n \\u003cp\\u003e(N,%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e20,87.0%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e11,57.9%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5,26.3%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFamily history(N,%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e13,56.5%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6,31.6%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1,5.3%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.002\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIncidence rate(/month)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.12\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.00\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.77\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.849\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTypes of vertigo attacks (N, %)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSpontaneous vertigo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e13,56.5%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6,31.6%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eVertigo induced by head movement\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e10,43.5%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e12,63.2%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePositional vertigo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9,39.1%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4/,21.1%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eVisual vertigo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5,21.7%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4/,21.2%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDizziness with nausea induced by head movement\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5,21.7%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8/,42.2%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTriggers of vertigo attacks(N, %)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStaying up late\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e18,78.3%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e14,73.7%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eExcessive fatigue\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e11,47.8%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e12,63.2%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEmotional fluctuations\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5,21.7%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5,26.3%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMenstruation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4,27.4%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2,10.5%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAlcohol consumption\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3,13.0%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0,0%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWeather changes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0,0%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4,21.1%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCaffeine-containing foods intake\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0,0%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0,0%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eISI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e10.39\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.77\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9.05\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8.105\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.70\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.524\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHAMA-14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e10.26\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.70\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9.42\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.53\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.77\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.087\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHAMD-17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8.91\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.76\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7.53\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.43\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.63\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.90\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.039\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMEQ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e58.96\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.49\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e60.42\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.87\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e60.37\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.38\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.830\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eBMI\\u003c/em\\u003e, body mass index; \\u003cem\\u003eISI\\u003c/em\\u003e, insomnia severity index; \\u003cem\\u003eHAMA 14\\u003c/em\\u003e,Hamilton anxiety scale;Hamilton Anxiety Scale; \\u003cem\\u003eHAMD-17\\u003c/em\\u003e,Hamilton Depression Scale; \\u003cem\\u003eMEQ\\u003c/em\\u003e, Morningness-Eveningness Questionnaire Self-Assessment Scale.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eResults of ALFF, fALFF, and ReHo\\u003c/h2\\u003e\\n \\u003cp\\u003eCompared with the HC group, the VMm group presented decreased ALFF values in the left frontal medial orbital region (Frontal_Med_Orb_L) (X=-6, Y\\u0026thinsp;=\\u0026thinsp;63, Z=-15, FDR-corrected, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e)and in the medial part of the right superior frontal gyrus (Frontal_Sup_Medial_R) (X\\u0026thinsp;=\\u0026thinsp;6, Y\\u0026thinsp;=\\u0026thinsp;57, Z\\u0026thinsp;=\\u0026thinsp;33, FDR-corrected, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). These findings suggest reduced spontaneous functional activity in these brain regions in the VMm group. Compared with the HCs, the VMnm group presented decreased ALFF values in the right paracentral lobule (Paracentral_Lobule_R) (X\\u0026thinsp;=\\u0026thinsp;9, Y=-30, Z\\u0026thinsp;=\\u0026thinsp;54, FDR-corrected, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e), indicating attenuated spontaneous functional activity in the right paracentral lobule region of the VMnm group. Compared with the VMnm group, the VMm group presented lower ALFF values in the left frontal medial orbital region (Frontal_Med_Orb_L) (X=-6, Y\\u0026thinsp;=\\u0026thinsp;63, Z=-12, FDR-corrected, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). This finding suggested that spontaneous functional activity in this left frontal brain region was more reduced in the VMm group than in the VMnm group.No significant differences were observed in fALFF (fractional amplitude of low-frequency fluctuations) or regional homogeneity (ReHo) among the groups.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eResults of whole-brain functional connectivity (FC) analysis\\u003c/h2\\u003e\\n \\u003cp\\u003eCompared with the HC group, the VMm group presented decreased functional connectivity between the right middle frontal gyrus (orbital part) and the right middle frontal gyrus, between the left inferior frontal gyrus (opercular part) and the right angular gyrus, and between the right inferior frontal gyrus (orbital part) and the left anterior cingulate and paracingulate gyri (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003cp\\u003eCompared with the HC group, the VMnm group presented reduced functional connectivity between the right superior temporal gyrus and the right amygdala, between the right lenticular nucleus (putamen) and the parahippocampal gyrus, between the left fusiform gyrus and the right amygdala, between the right hippocampus and the right amygdala, and between the right parahippocampal gyrus and the right amygdala (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003cp\\u003eFurthermore, compared with the VMnm group, the VMm group demonstrated diminished functional connectivity between the left lenticular nucleus (putamen) and the left parahippocampal gyrus (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eResults of Graph Theory Analysis\\u003c/h2\\u003e\\n \\u003cp\\u003eWe calculated eight primary topological metrics betweenness centrality, degree centrality, network efficiency, nodal clustering coefficient, nodal efficiency, nodal local efficiency, nodal shortest path length, and small-world properties.\\u003c/p\\u003e\\n \\u003cp\\u003eWith respect to global efficiency (Eg), there was a statistically significant difference between the VMm group and the HC group (p\\u0026thinsp;=\\u0026thinsp;0.0289), as well as between the VMnm group and the HC group (p\\u0026thinsp;=\\u0026thinsp;0.0191). These findings indicate a significant reduction in global efficiency for patients with VMm and VMnm. However, no statistically significant difference was observed in global network efficiency between the VMm and VMnm groups (p\\u0026thinsp;=\\u0026thinsp;0.1001) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e), suggesting that there was no global difference in the efficiency of brain information transmission between the VMm and VMnm groups.\\u003c/p\\u003e\\n \\u003cp\\u003eIn terms of nodal betweenness centrality, compared with the HCs, the VMm group presented decreased betweenness in three brain regions: the left superior frontal gyrus, dorsolateral gyrus, left superior frontal gyrus, medial gyrus, and left inferior temporal gyrus (Bonferroni corrected, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Similarly, compared with the HC group, the VMnm group presented decreased betweenness in the left inferior temporal gyrus (Bonferroni corrected, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). No statistically significant difference in betweenness was observed between the VMm and VMnm groups (Bonferroni corrected, p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05)(Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003cp\\u003eWith respect to the small-world networks, the \\u0026sigma; values of the HC group, VMm group, and VMnm group were all significantly greater than 1, indicating that all three groups satisfied the criteria for a small-world network. Statistical analysis revealed no significant differences in the small-world network properties of the \\u0026gamma;, \\u0026lambda;, and \\u0026sigma; values among the three groups(Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003cp\\u003eFor the remaining five metrics, degree centrality, nodal clustering coefficient, nodal efficiency, nodal local efficiency, and nodal shortest path length, no statistically significant differences were observed among the three groups(Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eCorrelation analysis between ALFF values and clinical data\\u003c/h2\\u003e\\n \\u003cp\\u003eUsing the left frontal medial orbital region (Frontal Medial Orbital Left) as the region of interest (ROI), we extracted the amplitude of ALFF values for each participant. Correlation analysis was conducted between these ALFF values and clinically relevant continuous variables. Our findings revealed a negative correlation between ALFF values in VMm and the MEQ scores (r=-0.4679, p\\u0026thinsp;=\\u0026thinsp;0.0244). Similarly, a negative correlation was observed between the ALFF values in VMnm and the MEQ scores (r=-0.5983, p\\u0026thinsp;=\\u0026thinsp;0.0068). Both groups tended to be positively correlated with disease duration, but this correlation did not reach statistical significance (p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.1) (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThe diagnostic criteria for vestibular migraine (VM) are primarily based on clinical history and constitute a diagnosis of exclusion, leading to both a high rate of accurate diagnosis and significant missed diagnoses[19]. As the pathophysiological mechanisms underlying VM remain incompletely understood, our preliminary study focused on circadian rhythm characteristics in migraine patients. Subsequent findings revealed that VM-associated vertigo episodes also exhibit distinct circadian patterns: 74.7% of VM attacks occurred before 12:00 noon, with a peak incidence between 07:00\\u0026ndash;07:59 and a trough period at 21:00\\u0026ndash;21:59[8]. On the basis of these observations, the present study conducted neuroimaging investigations to compare VM patients with morning-onset attacks (VMm) and nonmorning-onset attacks (VMnm). The aim is to identify structural or functional neuroimaging abnormalities, particularly in pathways potentially linked to circadian regulation.\\u003c/p\\u003e\\n\\u003cp\\u003eIn our study, detailed demographic data were recorded for patients with vestibular migraine. Both VMm and VMnm groups had a higher prevalence of women, and their histories of motion sickness were significantly higher than those of the healthy control group, which is consistent with several previous studies[20-22]. However, our study found that the VMm group was significantly higher than the VMnm group. Previous research has found that the comorbidity rate of motion sickness in VM patients is higher than that in migraine patients, and both are higher than that in the healthy control group. This tells us about the susceptibility to motion sickness in migraine patients, especially those with vestibular migraine[23], which is widely recognized among VM patients[24]. Therefore, it is speculated that patients with vestibular migraine who have onset in the early morning in this study have a higher susceptibility to motion sickness. Most patients may experience more than one vestibular symptom during acute VM episodes[21]. In our study, patients in both VM groups also exhibited various types of vertigo listed in the VM diagnosis codes[3], and multiple types of vertigo often coexisted. The most common type of vertigo in the VMm group was spontaneous vertigo, while the most common type in the VMnm group was positional vertigo, which may be closely related to the time of onset and the patient\\u0026apos;s state. Regarding emotional scales, our data indicate that the depression scores of the VMm group were significantly higher than those of the control group, suggesting a higher comorbidity rate of emotional disorders in VM patients with early morning onset, which is basically consistent with previous research[25]. This finding plays an important role in the clinical assessment of comorbidities in vestibular migraine.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eNeuroimaging\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003efindings\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;in VMm (\\u003c/strong\\u003e\\u003cstrong\\u003emorning-onset vestibular migraine\\u003c/strong\\u003e\\u003cstrong\\u003e): ALFF and FC\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003eabnormalities\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn this study, we identified distinct patterns of interictal functional abnormalities between morning-onset vestibular migraine (VMm) and nonmorning-onset VM (VMnm) groups, with differential deviations from healthy controls. The VMm group exhibited a reduced amplitude of ALFF in the left medial orbitofrontal region and right medial superior frontal gyrus. Whole-brain FC analysis across 90 regions revealed widespread frontal-dominant hypoconnectivity in VMm: diminished FC between the right orbital middle frontal gyrus and right middle frontal gyrus, left opercular inferior frontal gyrus and right angular gyrus, and right orbital inferior frontal gyrus with left anterior/posterior cingulate cortices. These findings collectively suggested persistent frontal lobe-related functional network disturbances in morning-onset VMm. These findings are consistent with previous neuroimaging studies demonstrating aberrant ALFF patterns in the prefrontal cortex of vestibular migraine (VM) patients, as documented by Wang et al., thereby reinforcing the critical role of frontal lobe dysregulation in VM pathophysiology[26].\\u003c/p\\u003e\\n\\u003cp\\u003eThe frontal lobe governs primarily voluntary motor functions, executive cognition, linguistic processing, and affective regulation. Notably, the frontoparietal network has been implicated in both cognitive control and top-down pain modulation[27], with evidence of interictal static FC alterations in right frontoparietal networks among migraineurs[28]. Emerging chronobiological insights reveal circadian influences on frontal circuitry: Seney et al. demonstrated robust circadian rhythmicity in dorsolateral prefrontal cortex gene expression[29], whereas Cronin et al. proposed that peripheral clock dysregulation in frontal cortices may drive circadian disruptions during early neurodegenerative processes[30]. Clinical correlations further underscore frontal‒circadian interplay. Hofstra et al. documented the nocturnal predominance (23:00--05:00) of frontal lobe seizures, temporally aligned with 6-12 hours postdim light melatonin onset (DLMO)[31]. Complementary fMRI evidence from Chen et al. links right orbital superior frontal gyrus ALFF elevations to diurnal mood variations in major depression patients[32].\\u003c/p\\u003e\\n\\u003cp\\u003eThese converging lines of evidence position the frontal lobe as a critical nexus for circadian pathophysiology, potentially explaining the morning predominance of VMm symptoms through chronobiological dysregulation within frontal networks.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eNeuroimaging Findings in VMnm (Non-Morning-Onset Vestibular Migraine): ALFF and FC Abnormalities\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eCompared with VMm patients and healthy controls, the nonmorning-onset VM (VMnm) subgroup presented distinct functional alterations. ALFF analysis revealed reduced activity in the right paracentral lobule of the VMnm group. FC studies further demonstrated weakened interactions in temporolimbic networks: diminished FC between the right superior temporal gyrus (STG) and right amygdala; impaired connectivity between the right putamen and parahippocampal gyrus; reduced FC linking the left fusiform gyrus to the right amygdala; and attenuated connectivity between the right hippocampus and amygdala, as well as the right parahippocampal gyrus and amygdala. These findings suggest that VMnm is characterized by parietal hypoactivation and temporolimbic network dysfunction, which contrasts with the frontal-dominant abnormalities observed in VMm.\\u003c/p\\u003e\\n\\u003cp\\u003eX. Zhe et al. reported that in the VM group, the functional connectivity (FC) of the left thalamus was significantly weaker than that of two regions in the medial prefrontal cortex: two regions in the anterior cingulate cortex, the left superior/middle temporal gyrus, and the left temporal pole[33]. After the VM group was divided into two subgroups, we did not observe the same functional connectivity abnormalities as those reported in previous studies. We hypothesize that this discrepancy may be related to differences in the study populations or variations between the subgroups after grouping. However, a common finding across both studies is the involvement of frontal, limbic, and temporal regions. The temporal lobe is associated with multisensory integration and vestibular processing[4, 34], and structural abnormalities in this region have also been linked to multisensory integration, including visual, auditory, tactile, and vestibular processing[35, 36].\\u003c/p\\u003e\\n\\u003cp\\u003eObermann et al. reported reduced gray matter volume in the inferior temporal gyrus, middle temporal gyrus, superior temporal gyrus, middle cingulate gyrus, dorsolateral prefrontal cortex, insula, parietal lobe, and occipital cortex, suggesting that these structurally abnormal brain regions in VM patients are involved in multisensory vestibular control, pain processing, and central vestibular compensation[4]. Abnormalities in the parietal cortex have been linked to nociception and multisensory vestibular control. Additionally, VM-related studies have indicated the involvement of the parietal lobe in the modulation of pain perception and sensory integration dysfunction[37]. Our study also revealed decreased parietal lobe ALFF in the VMnm group, suggesting a possible mechanism related to this finding. The pathophysiological mechanisms of vestibular migraine are similar to those of migraine; therefore, we hypothesize that the observed functional connectivity abnormalities support the important role of multisensory integration abnormalities in VM.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAbnormalities in Graph Theory Metrics of Patients with Vestibular Migraine\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSmall-world architecture represents one of the primary organizational principles underlying the global topological characteristics of the complex human brain[38]. Global efficiency, local efficiency, and path length serve as metrics to gauge the integration of white matter (WM) networks, which are characterized by ease of communication[39]. Specifically, global efficiency assesses the efficacy of information transmission within WM networks, whereas local efficiency reflects the network\\u0026apos;s fault tolerance, indicating how efficiently first neighbors of a node can communicate when the node is removed[40]. Additionally, shorter paths signify reduced distances between brain regions during information transfer, revealing stronger integration among these regions[41]; conversely, longer paths imply weaker network integration.\\u003c/p\\u003e\\n\\u003cp\\u003eL. Dai and colleagues reported that migraine patients exhibit abnormal global network properties and local network topologies, distinguished by enhanced integration, efficiency, and swift information transmission[42]. These network disparities are broadly distributed across the occipital, temporal, and parietal regions[42]. In contrast to prior graph theory studies focusing on migraine, our study performed graph theory-based network analysis in VM, which demonstrated significantly reduced global efficiency in patients with VM (VMm and VMnm subgroups). Local network analysis further suggested reduced nodal betweenness centrality, particularly in the frontal and temporal lobes of the left cerebral hemisphere, implying diminished efficiency in brain network information transmission, predominantly affecting the left hemisphere. Consistent with our findings, C. Jiang et al.\\u0026apos;s DTI-based study also revealed that diurnal variation in radial diffusivity in healthy individuals is predominantly observed in the left hemisphere[43]. Notably, analysis of other graph theory metrics yielded no significant results, suggesting a possible dissociation from the pathophysiological mechanisms underlying VM. In conclusion, these disparities may be attributed to potentially distinct pathophysiological mechanisms between vestibular migraine and migraine.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eNeuroimaging Evidence for Circadian Rhythm Mechanisms in VM\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eVM is characterized by a circadian rhythm, with more frequent and severe vertigo attacks in the morning[8]. On the basis of the circadian rhythm mechanism of VM, we are the first to categorize VM into two groups for neuroimaging studies: the VMm group, with frequent morning onset, and the VMnm group, without morning onset. Our findings revealed significant differences between the two groups: (1) Compared with the VMnm group, the VMm group presented decreased ALFF values in the left medial orbital frontal cortex; (2) the functional connectivity between the left lenticular nucleus (putamen) and the left parahippocampal gyrus was weaker in the VMm group than in the VMnm group; and (3) there were no differences in global network efficiency or node betweenness centrality based on graph theory analysis. These results suggest the presence of objectively disrupted circadian rhythms in patients with vestibular migraine, providing neuroimaging evidence to support the rhythmic pattern observed in their vertigo attacks.\\u003c/p\\u003e\\n\\u003cp\\u003eBoth the enrolled patients and the control group were right-handed, with the left hemisphere being the dominant hemisphere. Differences between the VMnm and VMm groups were observed in the left dominant hemisphere, suggesting a closer relationship between the structure of the left hemisphere and the circadian rhythm. On the one hand, compulsory drug use and drug-induced relapse are partially caused by changes in orbitofrontal cortex (OFC) function[44, 45]. Owing to the hypofunction of the orbitofrontal cortex, inhibitory mechanisms are impaired[46], leading to increased impulsivity and deterioration of reward and decision-making mechanisms[44, 47]. This condition has been observed in drug abusers and contributes to the chronic transformation of migraine patients, indicating the profound importance of the orbitofrontal cortex in the development of migraine. On the other hand, various regions within the medial frontal area are involved in functions such as attention, learning, and temporal organization of behavior[48, 49]. The central hub for circadian rhythm is located in the suprachiasmatic nucleus (SCN) of the hypothalamus. The SCN serves as a crucial central pacemaker that generates and coordinates the circadian rhythm of the organism, earning it the title of the master clock or circadian oscillator in mammals. The suprachiasmatic nucleus (SCN) is responsible for receiving and integrating information from different time sources, regulating the functions of other brain regions and organs, and coordinating the body to maintain a rhythm consistent with the external environment[50]. However, some circadian oscillators outside the SCN are directly controlled by the SCN pacemaker or receive indirect input from the SCN through other neural circuits or extrabrain structures[51]. Basic research indicates that the SCN projects to the medial frontal region through the paraventricular thalamic nucleus[52]. Catherine et al. reported abnormal clock phase activity after damage to the medial frontal cortex of mice, suggesting that the timing of behavior is determined by the interaction between the medial frontal cortex and the SCN[53]. Therefore, combined with our findings, it is speculated that the medial orbitofrontal cortex is likely a key region affecting the abnormal circadian rhythm presented in VM attacks.\\u003c/p\\u003e\\n\\u003cp\\u003eThe parahippocampal region is most consistently associated with memory function[54], and as an important part of the vestibular center, it is closely related to human navigation[55, 56]. Reduced cerebral blood flow perfusion has been observed in the parahippocampal gyrus of shift workers with disrupted circadian rhythms[57]. In functional magnetic resonance imaging studies of delirium, a weakened functional connection between the SCN and the parahippocampal gyrus has been found in patients with delirium[58]. The parahippocampal gyrus may be involved in the functional network of circadian rhythms. The putamen, a key part of the basal ganglia and belonging to the pain network, is believed to contribute to pain processing and analgesia[59]. Abnormal network connections between the putamen and multiple sites, such as the cerebellum, in VM patients suggest that the putamen is involved in the integration of endogenous analgesic mechanisms[26]. In our study, reduced functional connectivity in the parahippocampal gyrus and putamen suggested a possible association with abnormal circadian rhythms and multisensory integration in vestibular migraine patients[37].\\u003c/p\\u003e\\n\\u003cp\\u003eThrough correlation analysis, our study further revealed a negative correlation between the ALFF values in the left medial orbital frontal region and the MEQ scores in both the VMm and VMnm groups. Patients who tended to have morning-type disease presented weaker ALFF values in the left medial orbital frontal region. The MEQ serves as a suitable variable for studying circadian rhythms, and H. Wang et al. identified distinct functional connectivity patterns across different chronotypes[60]. Although clinical research on the circadian rhythms of VM remains scarce, studies on migraine have shown a preference for headache onset time (TPHA), with earlier chronotypes observed in migraine patients with TPHAs[61]. G. Viticchi et al. reported that morning-type patients experienced lower migraine attack frequencies but had a longer disease duration[62]. Baksa D et al. classified migraine attacks on the basis of circadian rhythm time types and investigated task-based MRI, revealing increased activation in regions involved in emotion, self-reference (left posterior cingulate cortex, right precuneus), pain (including the left middle cingulate cortex, left posterior central gyrus, left superior marginal gyrus, and right Rolandic gyrus), and sensory processing (including the bilateral superior temporal gyrus and right Heschl\\u0026apos;s gyrus) in the evening-onset group[11]. Differences in peak attack times and interictal brain activity across circadian rhythms suggest heterogeneity among migraine patients[11]. Vestibular migraine, as a subtype of migraine, exhibits distinct imaging differences on the basis of onset time and a negative correlation with the MEQ, as observed in our clinical phenotypic study of VM[8]. These findings strongly suggest that circadian rhythms likely play a role in the pathophysiology of VM.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLimitations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eOur study innovatively grouped vestibular migraine (VM) patients on the basis of their circadian rhythm for functional neuroimaging research. We discovered, for the first time, that the circadian rhythm may affect brain function in VM and may be involved in its pathophysiological mechanism. However, several limitations should be considered. First, grouping patients on the basis of their diurnal preference for VM attacks introduces subjective factors during patient interviews, potentially leading to biases in intra- and intergroup differences. Second, owing to research capacity constraints (the collection of volunteers coincided with the outbreak of the COVID-19 epidemic), our sample size was small. Increasing the sample size could reveal more differences within and between groups. Third, the suprachiasmatic nucleus (SCN) is the most crucial central clock, but there is currently no universally recognized neuroimaging evidence confirming the existence and function of circadian rhythm structures. Some studies have employed functional network connectivity seeded at the SCN[58, 63]. However, limited by the insufficient precision of current BOLD classic brain parcellation, such studies often rely on manually delineated regions of interest (ROIs), whose reliability is debatable. Therefore, we did not adopt this research method. In the future, better neuroimaging techniques may offer improved solutions. Finally, while the relationship between VM and the circadian rhythm has been a focal point of our research team, more in-depth basic, clinical, and imaging studies are needed for wider recognition.\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003e(1) Abnormalities in brain function were observed in multiple distinct brain regions during the interictal period in both the early-morning onset and non-early-morning onset groups of VM patients. (2) The VMm group primarily presented abnormalities in the frontal lobe, whereas the VMnm group presented predominant abnormalities in the parietal lobe, temporal lobe, and limbic system. The left medial orbitofrontal cortex may be a key structure influencing the diurnal rhythm characteristics of VM attacks. (3) The efficiency of brain network information transmission decreased in VM patients, predominantly in the left hemisphere, but this was not related to circadian rhythm.(4) The presence of functional MRI abnormalities in VM patients with different circadian rhythm characteristics suggests that the circadian rhythm may be one of the pathophysiological mechanisms of VM. This finding points to a new direction for future VM prevention or acute treatment.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003ers-fMRI \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;Resting-State Functional MRI\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eVM \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; Vestibular migraine\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eALFF \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;amplitude of low-frequency fluctuation\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eFC \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;functional connectivity\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eHC \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;healthy controls\\u003c/p\\u003e\\n\\u003cp\\u003eBOLD \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; Blood Oxygenation Level Dependent\\u003c/p\\u003e\\n\\u003cp\\u003eSCN \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;suprachiasmatic nucleus\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eWL conceived the study idea. XXQ and PN were responsible for data acquisition. ZXN analyzed the data. WL and ZXN took the lead in drafting and writing the manuscript. ZHF and HFQ were involved in revising the manuscript critically for important intellectual content. All authors have read and approved the final manuscript.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgement\\u003c/h2\\u003e\\u003cp\\u003eWe extend our sincere gratitude to Dr.Zhenliang Xiong for his professional guidance in the imaging data analysis during this study\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eNeuhauser, H., \\u0026amp; Lempert, T. (2004). Vertigo and dizziness related to migraine: A diagnostic challenge. Cephalalgia 24(2) 83\\u0026ndash;91.\\u003c/li\\u003e\\n \\u003cli\\u003eLempert, T., Olesen, J., Furman, J., Waterston, J., Seemungal, B., Carey, J., Bisdorff, A., Versino, M., Evers, S., \\u0026amp; Newman-Toker, D. (2012). Vestibular migraine: Diagnostic criteria. 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Zhurnal Nevrologii i Psikhiatrii imeni S.S. Korsakova 117(6. Vyp. 2) 11\\u0026ndash;15.\\u003c/li\\u003e\\n \\u003cli\\u003eHufner, K., Strupp, M., Smith, P., Brandt, T., \\u0026amp; Jahn, K. (2011). Spatial separation of visual and vestibular processing in the human hippocampal formation. Annals of the New York Academy of Sciences 1233 177\\u0026ndash;186.\\u003c/li\\u003e\\n \\u003cli\\u003ePark, Y. K., Kim, J. H., Choi, S. J., Kim, S. T., \\u0026amp; Joo, E. Y. (2019). Altered regional cerebral blood flow associated with mood and sleep in shift workers: Cerebral perfusion magnetic resonance imaging study. Journal of Clinical Neurology 15(4) 438\\u0026ndash;447.\\u003c/li\\u003e\\n \\u003cli\\u003eKyeong, S., Choi, S. H., Eun Shin, J., Lee, W. S., Yang, K. H., Chung, T. S., \\u0026amp; Kim, J. J. (2017). Functional connectivity of the circadian clock and neural substrates of sleep-wake disturbance in delirium. 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Chronobiology International 36(11) 1528\\u0026ndash;1536.\\u003c/li\\u003e\\n \\u003cli\\u003eViticchi, G., Falsetti, L., Paolucci, M., Altamura, C., Buratti, L., Salvemini, S., Brunelli, N., Bartolini, M., Vernieri, F., \\u0026amp; Silvestrini, M. (2019). Influence of chronotype on migraine characteristics. Neurological Sciences 40(9) 1841\\u0026ndash;1848.\\u003c/li\\u003e\\n \\u003cli\\u003eMcGlashan, E. M., Poudel, G. R., Vidafar, P., Drummond, S. P. A., \\u0026amp; Cain, S. W. (2018). Imaging individual differences in the response of the human suprachiasmatic area to light. Frontiers in Neurology 9 1022.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Vestibular migraine, Circadian mechanism, Resting-state functional MRI\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6538078/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6538078/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground: \\u003c/strong\\u003eVestibular migraine (VM) pathophysiology remains unclear despite its circadian attack patterns. This study employed resting-state fMRI to characterize brain functional differences in VM patients with distinct diurnal attack rhythms.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods: \\u003c/strong\\u003eForty-two VM patients (23 with early-morning attacks [VMm]; 19 without [VMnm]) and 19 age-/gender-matched healthy controls (HCs) underwent rs-fMRI. Group differences were analyzed using amplitude of low-frequency fluctuation (ALFF), functional connectivity (FC), graph theory metrics, and clinical correlations.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults: \\u003c/strong\\u003eALFF: Compared to HCs, VMm exhibited decreased ALFF in the left medial orbitofrontal cortex (Frontal_Med_Orb_L) and right medial superior frontal gyrus (Frontal_Sup_Medial_R). VMnm showed reduced ALFF in the right paracentral lobule. VMm had significantly lower orbitofrontal ALFF than VMnm (p\\u0026lt;0.05). No group differences emerged in fALFF or ReHo. FC: VMm demonstrated impaired frontal-angular/cingulate connectivity versus HCs, including reduced FC between the right orbital middle frontal gyrus and angular gyrus, and between the right orbital inferior frontal gyrus and anterior cingulate cortex. VMnm exhibited disrupted limbic-temporal networks, with weakened connectivity between the right temporal pole/amygdala, putamen/parahippocampus, and hippocampus/amygdala. VMm showed lower putamen-parahippocampal FC than VMnm. Graph Theory: Global network efficiency (Eg) decreased in both VM groups versus HCs (p\\u0026lt;0.05). VMm had reduced betweenness centrality in the left dorsolateral/medial superior frontal gyri and inferior temporal gyrus, while VMnm showed deficits only in the inferior temporal gyrus. Small-world properties (γ, λ, σ) and nodal metrics (degree centrality, clustering coefficient) showed no intergroup differences. Correlations: Left orbitofrontal ALFF negatively correlated with Morningness-Eveningness Questionnaire (MEQ) scores in both VM groups (r=-0.42, p=0.02). Disease duration showed positive but non-significant trends with ALFF values.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusions: \\u003c/strong\\u003eVM subgroups exhibit distinct interictal functional abnormalities: VMm involves prefrontal dysregulation, while VMnm affects parietal-temporal-limbic integration. The left medial orbitofrontal cortex may serve as a hub regulating VM’s circadian attack rhythms.Reduced global network efficiency reflects impaired left-hemisphere information processing, independent of circadian mechanisms.Persistent fMRI abnormalities during attack-free periods suggest circadian influences on VM pathophysiology, highlighting potential chronotherapeutic targets for prevention and management.\\u003c/p\\u003e\",\"manuscriptTitle\":\"The Circadian Mechanism May Contribute to Vestibular Migraine: A Case-Control Study Based on Resting-State Functional MRI (rs-fMRI)\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-05-07 08:40:21\",\"doi\":\"10.21203/rs.3.rs-6538078/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"b5b2a44b-3337-4b17-bbe2-117a4df21ee9\",\"owner\":[],\"postedDate\":\"May 7th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-06-05T07:54:18+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-05-07 08:40:21\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6538078\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6538078\",\"identity\":\"rs-6538078\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}