Abnormalities in Resting-State EEG Microstates are a Vulnerability Marker of Migraine

Abnormalities in Resting-State EEG Microstates are a Vulnerability Marker of Migraine | 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 Abnormalities in Resting-State EEG Microstates are a Vulnerability Marker of Migraine Yansong Li, Guoliang Chen, Jing Lv, Zhao Dong, Rongfei Wang, Min Su, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1023439/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Apr, 2022 Read the published version in The Journal of Headache and Pain → Version 1 posted 6 You are reading this latest preprint version Abstract Background: Resting-state EEG microstates are thought to reflect brief activations of several interacting components of resting-state brain networks. Surprisingly, we still know little about the role of these microstates in migraine. In the present study, we attempted to address this issue by examining EEG microstates in patients with migraine without aura (MwoA) during the interictal period and comparing them with those of a group of healthy controls (HC). Methods: Resting-state EEG was recorded in 61 MwoA patients (50 females) and 66 HC (50 females). Microstate parameters were compared between the two groups. We computed four widely identified canonical microstate classes A-D. Results: Microstate classes B and D displayed higher time coverage and occurrence in the MwoA patient group than in the HC group, while microstate class C exhibited significantly lower time coverage and occurence in the MwoA patient group. Meanwhile, the mean duration of microstate class C was significantly shorter in the MwoA patient group than in the HC group. Moreover, among the MwoA patient group, the duration of microstate class C correlated negatively with clinical measures of headache‐related disability as assessed by the six-item Headache Impact Test (HIT-6). Finally, microstate syntax analysis showed significant differences in transition probabilities between the two groups, primarily involving microstate classes B, C and D. Conclusions: By exploring EEG microstate characteristics at baseline we were able to explore the neurobiological mechanisms underlying altered cortical excitability and aberrant sensory, affective and cognitive processing, thus deepening our understanding of migraine pathophysiology. Anesthesiology & Pain Medicine Neurology Microstate Resting-state Migraine MwoA EEG Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Migraine is characterized by recurrent headache attacks which exert a significant impact on the daily lives of sufferers ( 1 , 2 ). Although our understanding of the exact pathogenetic mechanisms behind migraine remain incomplete, it is now widely accepted that migraine, at its core, represents a complex brain network disorder ( 3 , 4 ). Over the last decade, we have witnessed remarkable progress in understanding the causes of migraine. Such progress can, to a large degree, be ascribed to an increased effort into examining the neural function of the migraine brain. Among others, measuring neural function with electrophysiological methods (i.e., Electroencephalogram, EEG) has been demonstrated to be an effective approach in describing migraine pathophysiology ( 5 – 8 ). Within this domain, findings regarding migraine pathophysiology were primarily derived from task-oriented EEG studies that sought to determine electrophysiological activity associated with the performance of an explicit task ( 5 ). Such studies have provided compelling evidence showing that migraine is associated with a state of functional cortical disexcitability, usually reflected by abnormal cortical evoked responses, across sensory, affective and cognitive processes ( 9 – 11 ). Despite these encouraging results, this type of task-oriented EEG is unable to fully capture all aspects of migraine pathophysiology. Migraine-related neural abnormalities at rest represent a clear example of the limitations of such a method ( 12 ). For this reason, over the past decade we have witnessed a surge in interest in the use of resting-state EEG, in which migraineurs refrain explicit activity and usually keep their eyes closed, to evaluate neural abnormalities that cannot be identified using task-oriented EEG ( 13 ). The majority of previous resting-state EEG studies have focused on dynamic changes in spectral patterns and functional connectivity networks in migraineurs ( 14 – 20 ). This conventional resting-state EEG analysis relied primarily on the examination of power- or oscillation-related variation in different frequency bands that integrate brain activity over seconds. Although these studies have revealed some new insights into migraine-related neural abnormalities, such a traditional analysis is not capable of detecting spatial and temporal properties of resting-state brain networks that occur on shorter time scales (e.g., within fractions of seconds). Compared to the traditional resting-state EEG analysis, by using multichannel EEG on a sub-second time scale, EEG microstate analysis offers the promise of capturing the spatiotemporal dynamics of several components of resting-state brain networks at the whole-brain level ( 21 , 22 ). EEG microstates are often referred to as global patterns of spatial configurations of electric potentials that dynamically evolve over time in an organized manner ( 23 ). Two key properties of EEG microstates have consistently been identified across studies ( 24 ). First, although there are a large number of possible spatial configurations, they can typically be classified into four canonical classes, labeled A, B, C and D. These four classes typically explain 65–84% of total topographic variance ( 22 ). Second, a single configuration is a brief period (about 60–120 ms) in which its spatial configuration remains dominant and quasi-stable before rapidly transitioning to another configuration. These periods of quasi-stability of a single configuration are thus called “microstates”. These key properties may serve EEG microstates well in the detection of alterations in rapid, dynamic activity in large-scale resting state brain networks in neuropsychiatric disorders. Over the past decade, an increasing number of resting-state EEG studies have demonstrated the potential utility of a set of EEG microstate parameters (duration, occurrence, time coverage and syntax) in detecting neurophysiological changes underlying certain neuropsychiatric disorders ( 21 , 22 ). To date, previous research has identified alterations in certain microstate parameters in neuropsychiatric conditions such as schizophrenia ( 25 ), major depressive disorder ( 26 ), panic disorder ( 27 ), autism spectrum disorder ( 28 , 29 ), Alzheimer's disease ( 30 ) and Parkinson’s disease ( 31 ). Overall, previous work clearly suggests an intriguing relationship between features of certain EEG microstates and the neurophysiological basis of these neuropsychiatric disorders. These findings can in turn provide us with novel insights into the pathophysiological mechanisms underlying such disorders. Surprisingly, our current understanding of the characteristics of resting-state EEG microstates in migraineurs is still lacking. To this end, we evaluated EEG microstates in MwoA patients during the interictal period, the period between seizures, in comparison with a group of HC participants. Based on previous brain imaging studies reporting aberrant resting-state brain networks associated with migraine ( 32 – 34 ), we expected to observe changes in certain microstate parameters in the MwoA patient group compared to the HC group. Methods And Materials Participants We based the participant recruitment procedure on our recent study ( 10 ). The patient group comprised of 61 MwoA patients (age =32.79 ± 0.88, 50 females), all of whom had received diagnoses by trained neurologists (Z.D. and S.Y.) as well as neuropsychologists (G.C. and J.L.). Each patient kept a headache diary and completed structured questionnaires on demographics, headache profile, medical history, and medication use. The headache profile examined migraine history (years), the frequency (amount per month) and duration (days per month) of headaches, the severity of migraines, and included the six-item Headache Impact Test (HIT)-6, Hamilton Anxiety Scale (HAMA) and Hamilton Depression Scale (HAMD). The inclusion criteria for MwoA patients were: 1) fulfilling the diagnosed criteria for migraine according to the International Classification of Headache Disorders, 3rd edition (ICHD-3); 2) a history of at least two years of migraine and at least one migraine episode per month and 3) the period, during the resting state EEG recording, between the last migraine or headache and the next one (the interictal period). Moreover, individuals were to be excluded according to the following criteria: 1) neurological diseases (i.e., epilepsy, cerebral infarction, encephalitis, neuromuscular disorders); 2) mental retardation; 3) a current or past history of substance dependence; 4) receiving prophylactic anti-migraine therapy; 5) depressive and anxiety disorders (scores more than 7 points in HAMA and HAMD). We also employed an age- and sex-matched healthy control group consisting of 66 healthy volunteers (age = 31.44 ± 0.57, 50 females), none of whom reported any personal or family history of psychiatric or neurological disorders. This was confirmed by both a self-reported past history and a psychiatric examination of present mental state using the DSM-IV criteria of axis I. None of the female participants from either group took any oral contraceptives for at least 1 week prior to involvement in this study. All participants in the two groups were right-handed and signed informed consent forms. The study protocol was approved by the Ethics Committee of the Chinese PLA General Hospital. Demographic and clinical characteristics are described in Table 1 . Table 1 Demographic and clinical characteristics of the study sample. MwoA patients (n = 61) HC (n = 66) Group comparison (M ± SD) (M ± SD) Age, years 32.79 ± 6.83 31.44 ± 4.63 t(125) = -1.31, p = 0.20 Gender (F/M) (50/11) (50/16) χ 2 = 0.41, p = 0.52 Education, years 15.76 ± 3.16 15.87 ± 3.08 t(125) = -0.20, p = 0.84 BMI [kg/m 2 ] 21.56 ± 3.02 21.81 ± 2.74 t(125) = 0.50, p = 0.62 MoCA 28.18 ± 1.12 28.50 ± 0.90 t(125) = 1.78, p = 0.08 Duration of migraine, days per month 4.38 ± 3.12 History of migraine, years 11.38 ± 6.47 Migraine frequency, times per month 3.15 ± 1.74 Severity of headache (VAS scale) 7.72 ± 1.55 HIT-6 65.85 ± 6.91 VAS, visual analog scale, with 0 indicating no pain and 10 worst possible pain; BMI, body mass index;MoCA, the Montreal Cognitive Assessment; HIT-6, the six-item Headache Impact Test; M, mean; SD, standard deviation; HC, healthy controls; MwoA, migraine without aura. EEG data acquisition and preprocessing We employed an EEG data recording procedure similar to that described in our previous studies ( 10 , 35 , 36 ). Resting-state EEG data were recorded (SynAmps amplifier, NeuroScan) with a quick cap carrying 64 Ag/AgCl electrodes placed at standard locations covering the whole scalp (the extended international 10–20 system). The reference electrode was attached to the right mastoid (M2), and the ground electrode was placed on the forehead. The vertical electrooculogram (VEOG) was recorded with electrodes placed above and below the left eye. The horizontal electrooculogram (HEOG) was recorded using electrodes placed beside the two eyes. Impedance was kept below 5 kΩ. Electrophysiological data were continuously recorded with a bandwidth 0.05–100 Hz and sampled at a rate of 1000 Hz. All participants were asked to keep their eyes closed and to relax throughout the recording period (4 min). Offline EEG data were down-sampled to 500 Hz and preprocessed using EEGLAB 2021.0 ( 37 ). Preprocessing analysis was consistent with the procedure reported in previous work ( 29 , 38 , 39 ). Specifically, the raw EEG data were filtered with a bandpass of 0.5 – 70 Hz and a notch (50 Hz) filter. Upon visual inspection, epochs with artefacts caused by movement or poor signal were detected and removed manually. Independent component analysis (ICA) was then used to remove eye movements-, muscular- and bad channel-related artifacts. Subsequently, data were divided into 2s duration segments. Finally, data were re-referenced to common average reference. EEG microstate analysis Consistent with recent work ( 38 ), we performed microstate analysis using the Microstate Analysis plug-in (Version 1.1) for EEGLAB ( 37 ). This adhered to well-established standard procedures reported in previous studies ( 40 , 41 ). Briefly, preprocessed EEG data were digitally filtered with a band pass of 2–20 Hz. Then, we computed the Global Field Power (GFP) for each participant, which represents the overall potential variance across all electrodes at each sample in time. Given that EEG scalp topographies around the peaks of the GFP remain stable, we only extracted and submitted topographies at the momentary peaks of GFP to subsequent analysis. Four classes of microstate topography have previously been found to optimally account for EEG data variance and have frequently been adopted in the existing research on neuropsychiatric disorders ( 22 ). We thus computed four microstate class topographies in the present study using a modified version of the K-mean clustering algorithm ( 42 ). Microstate class topographies for each group were calculated separately using a permutation algorithm that minimized common variance across participants ( 43 ). We labeled the four microstate classes as A, B, C or D according to their similarities to the microstate class topographies reported in previous work ( 21 ). For each participant, four microstate parameters were computed for each class: mean duration (ms) (the average time that a given microstate was continuously present), occurrence (the mean number of a given microstate per second), time coverage (%) (the percentage of total analysis time spent in a given microstate) and syntax (the transition from each of the four microstate classes to any other microstate classes). Statistical analysis We used a non-parametric chi-square test to assess group differences in gender ratio. Independent sample t-tests were employed to examine between-group differences in age, years of education and body mass index (BMI). To assess between-group differences in microstate parameters (mean duration, occurrence and time coverage), we ran three separate mixed analyses of variance (ANOVA), with group as a between-participants factor (MwoA patients versus HC) and microstate class (A versus B versus C versus D) as a within-participants factor. Regarding microstate syntax analysis, we followed a procedure described in previous work ( 26 , 39 , 44 , 45 ), in which we computed the percentage of transitions from one microstate class to another. We achieved this by calculating the relative observed occurrence of transitions from one microstate class to all other classes. After normalization, we obtained the percentage for each possible transition for every participant in this study. Subsequently, while Bonferroni correcting for multiple comparisons, we performed a two-sample t-test on each pair of microstate class transitions to examine whether there were significant between-group differences in the transition probabilities. Finally, for microstates showing significant between-group differences, we performed Pearson’s correlations to assess the relationships between microstate parameters and clinical measures. All data were analyzed using R (version 4.1.0). Statistical comparisons were made at p-values of p < .05, with the Greenhouse–Geisser correction when violations of sphericity occurred. Results Microstate maps The topographies of 4 dominant microstate classes strongly resembled those reported in previous work ( 21 , 22 , 39 , 44 ). These four microstate classes explained more than 77% of the global variance in each group (79.58% in the MwoA patient group and 77.45% in the HC group) (Figure 1 ). Therefore, we were able to categorize them as microstate classes A, B, C, and D. Mean duration (ms) A mixed ANOVA did not reveal a significant main effect of either group (F(1, 125) = 1.72, p = 0.19) or microstate class (F(3, 375) = 1.33, p = 0.26). However, we found a significant group × microstate class interaction (F(3, 375) = 15.49, p < .001). An analysis of simple effects revealed that the mean duration of microstate class C was shorter in the MwoA patient group than in the HC group (p < .001) (Table 2 , Figure 2 A). Table 2 MwoA patients vs. healthy controls for all microstate parameters and for each microstate class. Microstate MwoA patients (M ± SD) HC (M ± SD) Mean duration (ms) Class A 65.13 ± 10.94 68.06 ± 8.50 Class B 64.99 ± 8.23 64.74 ± 9.67 Class C 61.44 ± 8.70 69.45 ± 11.52 Class D 63.61 ± 10.03 67.25 ± 9.68 Occurrence (/s) Class A 3.86 ± 0.62 3.83 ± 0.70 Class B 3.99 ± 0.67 3.72 ± 0.62 Class C 3.70 ± 0.65 4.04 ± 0.51 Class D 4.18 ± 0.62 3.76 ± 0.55 Time coverage (%) Class A 24.55 ± 5.18 25.51 ± 4.81 Class B 25.41 ± 4.11 23.64 ± 4.52 Class C 22.41 ± 4.62 27.30 ± 4.82 Class D 27.62 ± 5.15 23.56 ± 4.45 M, mean; SD, standard deviation;HC, healthy controls; MwoA, migraine without aura Occurrence (Times/S) Using a mixed ANOVA, we found no significant main effects of either group (F(1, 125) = 1.65, p = 0.20) or microstate class (F(3, 375) = 1.64, p = 0.18). However, there was a significant interaction between group and microstate class (F(3, 375) = 12.58, p < .001). An analysis of simple effects revealed that microstate classes B (p < .05) and D (p < .001) were significantly more frequent in the MwoA patient group than in the HC group, while the microstate class C was significantly less frequent in the MwoA patient group than in the HC group (p < .01) (Table 2 , Figure 2 B). Time Coverage (%) A mixed ANOVA failed to reveal significant main effects for either group (F(1, 125) = 1.02, p = 0.32) or microstate class (F(3, 375) = 0.85, p = 0.46). However, we did find a significant interaction between group and microstate class (F(3, 375) = 15.84, p < .001). An analysis of simple effects revealed that microstate classes B (p < .05) and D (p < .001) covered significantly more time in the MwoA patient group than in the HC group, while microstate class C covered significantly less time in the MwoA patient group than in the HC group (p < .001) (Table 2 , Figure 2 C). Microstate syntax The percentage of observed transitions from one microstate class to all other classes in the MwoA patient and HC groups is shown in Table 3 . We performed t tests to examine observed transition probabilities between groups and found that, compared to the HC group, the MwoA patient group showed a bias toward making fewer transition from one microstate to another. Specifically, we observed this bias between the following microstates: A to C (t(125) = 4.81, p < .001); from B to C (t(125) = 3.39, p < .05); from C to A (t(125) = 5.23, p < .001) and from C to B (t(125) = 3.54, p < .01) (Figure 3 ). In contrast, we found that the MwoA patient group showed a bias toward making more transitions from B to D (t(125) = -4.69, p < .001) and from D to B (t(125) = -5.64, p < .001) than the HC group (Figure 3 ). Table 3 Percentage of transitions from one microstate class to all other classes in MwoA patients and healthy controls. Transition MwoA patients (M ± SD) HC (M ± SD) A to B 7.92 ± 1.74 7.42 ± 1.79 A to C 7.00 ± 1.82 8.58 ± 1.87 A to D 8.67 ± 1.81 7.92 ± 2.04 B to A 8.06 ± 1.76 7.33 ± 1.78 B to C 7.38 ± 1.84 8.54 ± 1.97 B to D 8.88 ± 2.14 7.29 ± 1.65 C to A 6.99 ± 1.74 8.71 ± 1.93 C to B 7.30 ± 1.84 8.51 ± 1.98 C to D 8.24 ± 1.70 8.29 ± 1.78 D to A 8.53 ± 1.89 7.88 ± 2.05 D to B 9.14 ± 2.17 7.34 ± 1.36 D to C 8.15 ± 1.79 8.36 ± 1.92 M, mean; SD, standard deviation; HC, healthy controls; MwoA, migraine without aura Correlation between microstate parameters and clinical measures Our correlation analysis only revealed a significantly negative correlation between the mean duration of microstate class C and HIT-6 scores in the MwoA patient group (r = -0.27, p < .05) (Figure 4 ). Discussion The present study demonstrate the presence of abnormalities in resting-state EEG microstates among MwoA patients. Overall, MwoA patients exhibited divergent temporal microstate profiles compared to those in the HC group. Moreover, the MwoA patient group, relative to the HC group, displayed multiple distinct microstate transition probabilities, which primarily involved microstate classes B, C and D. These findings Here, we will discuss how these findings may help to shed light on migraine pathophysiology. Microstate class B and the visual network (VN) We found a significant change in this microstate class B (time coverage and occurrence) in the MwoA patient group compared to the HC group. Microstate class B has been shown to be linked to activities in the VN encompassing the bilateral lateral extrastriate visual areas ( 46 ). A clear change in this microstate class may thus represent underlying structural abnormalities in this area in MwoA patients. This argument is supported by previous anatomical imaging studies reporting anatomical alterations in the VN (e.g., increased cortical thickness) in MwoA patients ( 47 ). Consistent with the notion that brain areas showing structural abnormalities in migraineurs also show functional alterations, structural alterations in the VN may provide an explanation for the functional changes that we observe in microstate class B. Meanwhile, such a change in microstate class B may also indicate functional alterations of the VN in MwoA patients at baseline. That is, these results reflect activity occurring default functional states that are independent of any task performance. Occurrence and time coverage of a particular microstate class have usually been interpreted to reflect the tendency of its underlying cortical and subcortical sources to be activated as well as the corresponding relative time coverage of such underlying neural activities ( 22 ). It is thus reasonable to speculate that an increase in these parameters of microstate class B at rest in MwoA patients may indicate an enhanced likelihood of neural activation of the VN in response to visual events. This speculation is indeed supported by previous functional studies in migraineurs which have converged to reveal a functional impairment in the form of visual cortex hyperexcitability in migraineurs ( 48 – 50 ). In this sense, our observation of increased microstate class B activity at rest may provide a potential neurobiological mechanism to explain the visual disturbances and visual hyperactivity in the visual system of MwoA patients. It should be noted that patients in the present study all suffered from migraines without aura. The effect observed in these patients adds to a growing literature showing that visual hyperactivity in the visual system can also be found in MwoA patients ( 51 – 53 ). In spite of some ongoing controversy ( 54 ) and their differing clinical symptoms ( 55 ), our finding provides adds support to the viewpoint that similar pathogenic mechanisms may be shared among all migraine patients, both with visual aura (usually with coexisting visual disturbances) and without aura. Microstate class C and the salience network (SN) In contrast to microstate class B, we found a significant decrease in microstate class (mean duration, occurrence and time coverage) in the MwoA patient group compared to the HC group. This microstate class has been related to the salience network (SN) focusing mainly on the dorsal anterior cingulate cortex (dACC) and anterior insula (AI) ( 46 ). Decreased microstate class C may be associated with the structural abnormalities in the SN that have been reported by previous anatomical imaging studies ( 56 ). These three microstate parameters have been interpreted to reflect three complents: the average length of time a given microstate class remains stable; the tendency of its underlying cortical and subcortical sources to be activated; and the corresponding relative time coverage of such underlying neural activities ( 22 ). Thus, a decrease in these three parameters in microstate class C may suggest a functional impairment of the SN in MwoA patients at baseline. This would be consistent with previous resting-state fMRI studies showing reduced intrinsic connectivity within the SN in these patients ( 57 ). Regarding the functional significance of the SN, several views have emerged to provide a possible explanation of its activity during resting state. A prevailing view points to the well-established role of the SN in interceptive awareness and sensory processing of salient events ( 58 , 59 ). This view emphasizes its role in detecting and filtering salient stimuli and in coordinating other brain networks (e.g., central-executive network (CEN)) to guide behavior. In addition to this view, recent studies have begun to identify a specific role of this network in inhibitory control ( 60 , 61 ). Despite some controversy, one line of evidence supports the notion that the SN integrates salient information that is subsequently used by the CEN, including the inferior frontal cortex (IFC), for recruiting inhibition ( 61 , 62 ). This implies an indirect involvement of salience processing in inhibitory control. Therefore, we can speculate that dysfunctional SN may lead to an aberrant assignment of salience to sensory stimuli. This is then improperly, or incompletely, processed by the CEN and ultimately causes reduced involvement of the CEN in recruiting inhibition in MwoA patients. From this perspective, it is possible that the aberrant role of salience processing in inhibitory control in MwoA patients is related to a hypervigilance to salient events (e.g., ongoing pain and sensory stimuli), which are common triggers for migraine headaches. This argument is partly supported by aberrant syntax patterns in the MwoA patient group compared to the HC group observed in the present study. A reduction in transitions from microstate class C to microstate class B and A seems to implicate decreased connectivity from the SN to the primary sensory networks. Such a pattern may then lead to an increase in cortical excitability and sensory gain, as implied by our observation of increased microstate class B in association with more engagement of the VN in MwoA patients. This finding is in agreement with previous work showing decreased SN and CEN connectivity ( 57 ) and decreased SN and VN connectivity in migraineurs ( 63 – 65 ). More importantly, these findings further indicate that the SN may stand at a ‘crossroads’ in the network architecture of the migraine brain and consequently may represent a potential target for improving the adverse impact of headache on daily functions in sufferers ( 63 ). Our observation of negative associations in the MwoA patient group between the mean duration of microstate class C and Hit-6 scores, which measure the adverse impact of headache on social functioning, role functioning, vitality, cognitive functioning and psychological distress, appears to support this argument. Microstate class D and the dorsal attention network (DAN) Finally, we observed a significant increase in time coverage and occurrence of the microstate class D in the MwoA patient group compared to the HC group. In accordance with previous work ( 46 ), this microstate class is related to activities in the DAN including the dorsal areas of the frontal and parietal cortex. Our finding thus implies a functional impairment in this network among MwoA patients. Increases in these two parameters for microstate class D at baseline seems to suggest a potential hyperexcitability of the DAN to incoming sensory stimuli. This is consistent with recent brain imaging studies showing an increase in neural responses to both attended and unattended stimuli in the key regions of the DAN ( 66 , 67 ). Furthermore, the functional significance of the DAN has been argued to reflect reflexive aspects of attention, such as switching and reorientation of attention to relevant information ( 68 ). From this, we can speculate that migraineurs may exhibit an exaggerated pattern of reflexive orienting responses to incoming sensory stimuli. This argument is indeed supported by previous work showing heightened reflexive visual-spatial orienting to attended and nonattended events ( 69 – 71 ). In this sense, the change in microstate class D at baseline that we observed in MwoA patients would thus be associated with alterations in top-down and/or bottom-up attention during task performance. Such an interpretation receives support from our observation of aberrant syntax patterns in the MwoA patient group compared to the HC group. Here, we observed increased transitions from microstate class D to microstate class B, a finding that is also consistent with previous resting-state studies showing an increased functional connectivity between the DAN and VN ( 64 , 72 ). Thus, it is possible that such atypical syntax patterns in the MwoA patient group at baseline may provide a potential neurobiological explanation for the enhanced attentional focus toward visual events described above. Potential limitations Despite the relatively large sample size used in the present study, we should take several potential limitations into account. First, only MwoA patients were involved in the present study. Thus, it remains unclear as to whether these findings can be generalized to other types of migraine groups, such as patients suffering from migraine with aura (MA) and chronic migraine patients. It would be important to address this issue in future studies mainly because different pathophysiological mechanisms have been found to play a role in these different types of headache syndromes ( 73 , 74 ). Second, the present study is not capable of allowing us to identify whether deviant temporal microstate profiles found in MwoA patients represent the trait or state nature of microstate abnormalities. Taking this into account in future studies would further help to shed additional light on migraine pathophysiology. Conclusion In sum, in this study we showed divergent temporal microstate profiles and aberrant microstate syntax patterns in the MwoA group compared to the HC group. The divergent temporal microstate profiles in the MwoA patient group reflect an increase in baseline brain activities in the VN and the DAN, alongside a reduction in neural activities in the SN at baseline. Moreover, we were also able to observe a decrease in transitions from the SN to the VN as well as an increase in transitions from the DAN and the VN in MwoA patients. This is consistent with previous research demonstrating aberrant functional connectivity among diversely distributed resting-state brain networks in migraineurs. Taken together, these findings may provide a neurophysiological mechanism to explain the altered cortical excitability, enhanced attentional focus toward sensory events and enhanced sensory gain that is present in migraineurs. Abbreviations MwoA: migraine without aura; HC: healthy controls; MA: migraine with aura; EEG: Electroencephalogram; VN: visual network; SN: salience network; DAN: dorsal attention network; HIT: Headache Impact Test; CEN: central-executive network; IFC: inferior frontal cortex; dACC: dorsal anterior cingulate cortex; BMI: body mass index; ANOVA: analyses of variance; GFP: Global Field Power; VEOG: vertical electrooculogram; HEOG: horizontal electrooculogram; HAMA: Hamilton Anxiety Scale; HAMD:Hamilton Depression Scale; ICHD: International Classification of Headache Disorders; Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of the Chinese PLA General Hospital and informed written consent was obtained for all participants. Consent for publication Written informed consent for publication was obtained. Availability of data and materials The datasets used and analyzed during the present study are available from the corresponding authors on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the National Natural Science Foundation of China (Grant No. 82071226, 81671077 and 81771200 to S.Y.), Beijing Natural Science Foundation Essential Research Project (Grant No. Z170002 to S.Y.). Author contributions G.C.,Y.L. and S.Y. conceived and designed the study; G.C., M.S., J.L, L.H., Z.D.,R.W. and S.Y. collected the data. G.C. analyzed the data under the supervision of Y.L. G.C. and Y.L. wrote the draft; Y.L. provided the final corrections on the draft. All authors approved the final manuscript. Acknowledgements The authors wish to thank the volunteers involved in this study. 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Translational Psychiatry 10:1–9 Nishida K, Morishima Y, Yoshimura M, Isotani T, Irisawa S, Jann K et al (2013) EEG microstates associated with salience and frontoparietal networks in frontotemporal dementia, schizophrenia and Alzheimer’s disease. Clin Neurophysiol 124:1106–1114 Koenig T, Prichep L, Lehmann D, Sosa PV, Braeker E, Kleinlogel H et al (2002) Millisecond by millisecond, year by year: normative EEG microstates and developmental stages. Neuroimage 16:41–48 Wackermann J, Lehmann D, Michel C, Strik W (1993) Adaptive segmentation of spontaneous EEG map series into spatially defined microstates. Int J Psychophysiol 14:269–283 Pascual-Marqui RD, Michel CM, Lehmann D (1995) Segmentation of brain electrical activity into microstates: model estimation and validation. IEEE Trans Biomed Eng 42:658–665 Koenig T, Lehmann D, Merlo MC, Kochi K, Hell D, Koukkou M (1999) A deviant EEG brain microstate in acute, neuroleptic-naive schizophrenics at rest. Eur Arch Psychiatry Clin NeuroSci 249:205–211 Lehmann D, Faber PL, Galderisi S, Herrmann WM, Kinoshita T, Koukkou M et al (2005) EEG microstate duration and syntax in acute, medication-naive, first-episode schizophrenia: a multi-center study. Psychiatry Research: Neuroimaging 138:141–156 Tomescu MI, Rihs TA, Becker R, Britz J, Custo A, Grouiller F et al (2014) Deviant dynamics of EEG resting state pattern in 22q11. 2 deletion syndrome adolescents: a vulnerability marker of schizophrenia? Schizophr Res 157:175–181 Britz J, Van De Ville D, Michel CM (2010) BOLD correlates of EEG topography reveal rapid resting-state network dynamics. NeuroImage 52:1162–1170 Granziera C, DaSilva AFM, Snyder J, Tuch DS, Hadjikhani N (2006) Anatomical alterations of the visual motion processing network in migraine with and without aura. PLoS Med 3:e402 Chronicle E, Mulleners W (1996) Visual system dysfunction in migraine: a review of clinical and psychophysical findings. Cephalalgia 16:525–535 De Tommaso M, Trotta G, Vecchio E, Ricci K, Siugzdaite R, Stramaglia S (2017) Brain networking analysis in migraine with and without aura. J Headache Pain 18:1–12 Brighina F, Bolognini N, Cosentino G, Maccora S, Paladino P, Baschi R et al (2015) Visual cortex hyperexcitability in migraine in response to sound-induced flash illusions. Neurology 84:2057–2061 Burke MJ, Joutsa J, Cohen AL, Soussand L, Cooke D, Burstein R et al (2020) Mapping migraine to a common brain network. Brain 143:541–553 Wei H-L, Zhou X, Chen Y-C, Yu Y-S, Guo X, Zhou G-P et al (2019) Impaired intrinsic functional connectivity between the thalamus and visual cortex in migraine without aura. J Headache Pain 20:1–9 Mulleners WM, Chronicle EP, Palmer JE, Koehler PJ, Vredeveld JW (2001) Visual cortex excitability in migraine with and without aura. Headache: The Journal of Head and Face Pain 41:565–572 Brigo F, Storti M, Tezzon F, Manganotti P, Nardone R (2013) Primary visual cortex excitability in migraine: a systematic review with meta-analysis. Neurol Sci 34:819–830 Purdy RA (2008) Migraine with and without aura share the same pathogenic mechanisms. Neurol Sci 29:44–46 Dai Z, Zhong J, Xiao P, Zhu Y, Chen F, Pan P et al (2015) Gray matter correlates of migraine and gender effect: A meta-analysis of voxel-based morphometry studies. Neuroscience 299:88–96 Xue T, Yuan K, Zhao L, Yu D, Zhao L, Dong T et al (2012) Intrinsic brain network abnormalities in migraines without aura revealed in resting-state fMRI. PLoS ONE 7:e52927 Uddin LQ (2015) Salience processing and insular cortical function and dysfunction. Nat Rev Neurosci 16:55–61 Menon V, Uddin LQ (2010) Saliency, switching, attention and control: a network model of insula function. Brain structure and function 214:655–667 Ghahremani A, Rastogi A, Lam S (2015) The role of right anterior insula and salience processing in inhibitory control. J Neurosci 35:3291–3292 Cai W, Ryali S, Chen T, Li C-SR, Menon V (2014) Dissociable roles of right inferior frontal cortex and anterior insula in inhibitory control: evidence from intrinsic and task-related functional parcellation, connectivity, and response profile analyses across multiple datasets. J Neurosci 34:14652–14667 Putcha D, Ross RS, Cronin-Golomb A, Janes AC, Stern CE (2016) Salience and default mode network coupling predicts cognition in aging and Parkinson’s disease. J Int Neuropsychol Soc 22:205–215 Yu D, Yuan K, Luo L, Zhai J, Bi Y, Xue T et al (2017) Abnormal functional integration across core brain networks in migraine without aura. Mol Pain 13:1744806917737461 Niddam DM, Lai K-L, Fuh J-L, Chuang C-YN, Chen W-T, Wang S-J (2016) Reduced functional connectivity between salience and visual networks in migraine with aura. Cephalalgia 36:53–66 Hodkinson DJ, Veggeberg R, Kucyi A, van Dijk KR, Wilcox SL, Scrivani SJ et al (2016) : Cortico–cortical connections of primary sensory areas and associated symptoms in migraine. Eneuro . 3 Masson R, Lévêque Y, Demarquay G, ElShafei H, Fornoni L, Lecaignard F et al (2020) Auditory attention alterations in migraine: a behavioral and MEG/EEG study. Clin Neurophysiol 131:1933–1946 Mickleborough MJ, Ekstrand C, Gould L, Lorentz EJ, Ellchuk T, Babyn P et al (2016) Attentional network differences between migraineurs and non-migraine controls: fMRI evidence. Brain Topogr 29:419–428 Corbetta M, Shulman GL (2002) Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci 3:201–215 Mickleborough MJ, Hayward J, Chapman C, Chung J, Handy TC (2011) Reflexive attentional orienting in migraineurs: The behavioral implications of hyperexcitable visual cortex. Cephalalgia 31:1642–1651 Mickleborough MJ, Truong G, Handy TC (2011) Top–down control of visual cortex in migraine populations. Neuropsychologia 49:1006–1015 Han M, Hou X, Xu S, Hong Y, Chen J, Ma Y et al (2019) Selective attention network impairment during the interictal period of migraine without aura. Journal of Clinical Neuroscience 60:73–78 Skorobogatykh K, Van Hoogstraten WS, Degan D, Prischepa A, Savitskaya A, Ileen BM et al (2019) Functional connectivity studies in migraine: what have we learned? J Headache Pain 20:1–10 Ozturk V, Cakmur R, Donmez B, Yener GG, Kursad F, Idiman F (2002) Comparison of cortical excitability in chronic migraine (transformed migraine) and migraine without aura. J Neurol 249:1268–1271 Blau J (1995) Migraine with aura and migraine without aura are not different entities. Cephalalgia 15:186–190 Cite Share Download PDF Status: Published Journal Publication published 05 Apr, 2022 Read the published version in The Journal of Headache and Pain → Version 1 posted Reviews received at journal 27 Nov, 2021 Reviewers invited by journal 31 Oct, 2021 Editor assigned by journal 31 Oct, 2021 Submission checks completed at journal 30 Oct, 2021 Editor invited by journal 30 Oct, 2021 First submitted to journal 27 Oct, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-1023439","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":60370063,"identity":"ee4a1923-1a32-4a0a-ab8a-afcd01ab762e","order_by":0,"name":"Yansong Li","email":"","orcid":"","institution":"Nanjing University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yansong","middleName":"","lastName":"Li","suffix":""},{"id":60370064,"identity":"8a3ee485-3e93-4464-a6c1-e19f84385198","order_by":1,"name":"Guoliang Chen","email":"","orcid":"","institution":"Medical School of Chinese PLA","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guoliang","middleName":"","lastName":"Chen","suffix":""},{"id":60370065,"identity":"3260ca86-2ffa-4b2d-a387-510e39899ee3","order_by":2,"name":"Jing Lv","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Lv","suffix":""},{"id":60370066,"identity":"eb80017b-5611-4feb-a1be-7ccf735a6f77","order_by":3,"name":"Zhao Dong","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhao","middleName":"","lastName":"Dong","suffix":""},{"id":60370067,"identity":"fa0c5301-f64a-4d99-b147-d497e327aa48","order_by":4,"name":"Rongfei Wang","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rongfei","middleName":"","lastName":"Wang","suffix":""},{"id":60370068,"identity":"cab938f3-874c-40aa-93e5-9dcce45aca6a","order_by":5,"name":"Min Su","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Su","suffix":""},{"id":60370069,"identity":"31eb318a-c58b-49e1-9baf-ff40c372c7fb","order_by":6,"name":"Shengyuan Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYLCChAobO35m5oMPiNfy4ExasmQ7W7IB0ToYH7YdYtxwnsdMgCjlBjfSn0kkth1gNj7MYMbAUGMTTYSWHDOJhHN3+MwOM6Q9YDiWlttAhBY2iYSyZ8xALccNGBsOE6MF6LAEtsOMm5sZ2ySI1JIAdFjbYcYNzMxsxGmRPPPG2CIBGMgSh9mYDRKI8Qvf8fSHN3+AorL//McHH2psCGtROMDAIgHnJRBSDgLyDQzMH4hROApGwSgYBSMYAAA15kQOKE1GVwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-3961-2464","institution":"Chinese PLA general hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Shengyuan","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2021-10-27 09:58:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1023439/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1023439/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s10194-022-01414-y","type":"published","date":"2022-04-05T11:55:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":15187934,"identity":"dd538538-8684-40cd-b727-b4f21d13c86a","added_by":"auto","created_at":"2021-11-03 14:44:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":354357,"visible":true,"origin":"","legend":"Spatial configuration of the four microstate classes, separately for MwoA patients and healthy controls. Each row shows the four topographic configurations (A-D) for each group. MwoA, migraine without aura. ","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1023439/v1/acfed39eae073302d7ff84d8.png"},{"id":15188329,"identity":"4c480caf-1e29-4c09-be91-75ac28d6fb50","added_by":"auto","created_at":"2021-11-03 14:47:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":152065,"visible":true,"origin":"","legend":"Microstate analysis of temporal parameter results. (A) Violin plots showing the mean duration of each microstate class in each of the two groups. The decreased duration of microstate class C was found in the MwoA patient group compared to the HC group; (B) Violin plots showing the occurence of each microstate class in each of the two groups. The increased occurence of microstate classes B and D, but decreased occurence of microstate class C, were found in the MwoA patient group compared to the HC group; (C) Violin plots showing the time coverage of each microstate class in each of the two groups. The increased time coverage of microstate classes B and D, but decreased time coverage of microstate class C, were found in the MwoA patient group compared to the HC group. MwoA, migraine without aura; HC, healthy controls; *p \u003c .05, **p \u003c .01, ***p \u003c .001.","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1023439/v1/f4f8971a712377c4f3def6e7.png"},{"id":15187932,"identity":"91eedddd-b5b1-471a-a45b-e98ad3f0435e","added_by":"auto","created_at":"2021-11-03 14:44:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":293086,"visible":true,"origin":"","legend":"Schematic view of microstate syntax analysis results. (A) Significant differences in transition probabilities for each pair of microstate class between MwoA patients and healthy controls were found. The MwoA patient group had a bias toward fewer transitions from A to C, B to C, C to A and C to B than the HC group. In contrast, the MwoA patient group had a bias toward more transitions from B to D and D to B than the HC group. MwoA, migraine without aura; HC, healthy controls.","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1023439/v1/6dede7fb3fa61e78d78faac0.png"},{"id":15188330,"identity":"b7ad4ac0-4736-4400-8159-4039d575b89e","added_by":"auto","created_at":"2021-11-03 14:47:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":34693,"visible":true,"origin":"","legend":"Microstate class C association with clinical measures. Scatterplot of mean duration of microstate class C and the six-item Headache Impact Test (HIT-6) scores in MwoA patients. MwoA, migraine without aura.","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-1023439/v1/bf44addb939ccff2a833da13.png"},{"id":19969021,"identity":"fa171ebd-76ba-4ba4-97d5-cbb6ae787243","added_by":"auto","created_at":"2022-04-05 11:55:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1386402,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1023439/v1/7695fd33-3feb-4014-842a-882a4a1cf245.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eAbnormalities in Resting-State EEG Microstates are a Vulnerability Marker of Migraine\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMigraine is characterized by recurrent headache attacks which exert a significant impact on the daily lives of sufferers (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Although our understanding of the exact pathogenetic mechanisms behind migraine remain incomplete, it is now widely accepted that migraine, at its core, represents a complex brain network disorder (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Over the last decade, we have witnessed remarkable progress in understanding the causes of migraine. Such progress can, to a large degree, be ascribed to an increased effort into examining the neural function of the migraine brain. Among others, measuring neural function with electrophysiological methods (i.e., Electroencephalogram, EEG) has been demonstrated to be an effective approach in describing migraine pathophysiology (\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin this domain, findings regarding migraine pathophysiology were primarily derived from task-oriented EEG studies that sought to determine electrophysiological activity associated with the performance of an explicit task (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Such studies have provided compelling evidence showing that migraine is associated with a state of functional cortical disexcitability, usually reflected by abnormal cortical evoked responses, across sensory, affective and cognitive processes (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Despite these encouraging results, this type of task-oriented EEG is unable to fully capture all aspects of migraine pathophysiology. Migraine-related neural abnormalities at rest represent a clear example of the limitations of such a method (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). For this reason, over the past decade we have witnessed a surge in interest in the use of resting-state EEG, in which migraineurs refrain explicit activity and usually keep their eyes closed, to evaluate neural abnormalities that cannot be identified using task-oriented EEG (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The majority of previous resting-state EEG studies have focused on dynamic changes in spectral patterns and functional connectivity networks in migraineurs (\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18 CR19\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). This conventional resting-state EEG analysis relied primarily on the examination of power- or oscillation-related variation in different frequency bands that integrate brain activity over seconds. Although these studies have revealed some new insights into migraine-related neural abnormalities, such a traditional analysis is not capable of detecting spatial and temporal properties of resting-state brain networks that occur on shorter time scales (e.g., within fractions of seconds).\u003c/p\u003e \u003cp\u003eCompared to the traditional resting-state EEG analysis, by using multichannel EEG on a sub-second time scale, EEG microstate analysis offers the promise of capturing the spatiotemporal dynamics of several components of resting-state brain networks at the whole-brain level (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). EEG microstates are often referred to as global patterns of spatial configurations of electric potentials that dynamically evolve over time in an organized manner (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Two key properties of EEG microstates have consistently been identified across studies (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). First, although there are a large number of possible spatial configurations, they can typically be classified into four canonical classes, labeled A, B, C and D. These four classes typically explain 65\u0026ndash;84% of total topographic variance (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Second, a single configuration is a brief period (about 60\u0026ndash;120 ms) in which its spatial configuration remains dominant and quasi-stable before rapidly transitioning to another configuration. These periods of quasi-stability of a single configuration are thus called \u0026ldquo;microstates\u0026rdquo;. These key properties may serve EEG microstates well in the detection of alterations in rapid, dynamic activity in large-scale resting state brain networks in neuropsychiatric disorders. Over the past decade, an increasing number of resting-state EEG studies have demonstrated the potential utility of a set of EEG microstate parameters (duration, occurrence, time coverage and syntax) in detecting neurophysiological changes underlying certain neuropsychiatric disorders (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). To date, previous research has identified alterations in certain microstate parameters in neuropsychiatric conditions such as schizophrenia (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), major depressive disorder (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), panic disorder (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), autism spectrum disorder (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), Alzheimer's disease (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) and Parkinson\u0026rsquo;s disease (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Overall, previous work clearly suggests an intriguing relationship between features of certain EEG microstates and the neurophysiological basis of these neuropsychiatric disorders. These findings can in turn provide us with novel insights into the pathophysiological mechanisms underlying such disorders.\u003c/p\u003e \u003cp\u003eSurprisingly, our current understanding of the characteristics of resting-state EEG microstates in migraineurs is still lacking. To this end, we evaluated EEG microstates in MwoA patients during the interictal period, the period between seizures, in comparison with a group of HC participants. Based on previous brain imaging studies reporting aberrant resting-state brain networks associated with migraine (\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), we expected to observe changes in certain microstate parameters in the MwoA patient group compared to the HC group.\u003c/p\u003e"},{"header":"Methods And Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eWe based the participant recruitment procedure on our recent study (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The patient group comprised of 61 MwoA patients (age =32.79 \u0026plusmn; 0.88, 50 females), all of whom had received diagnoses by trained neurologists (Z.D. and S.Y.) as well as neuropsychologists (G.C. and J.L.). Each patient kept a headache diary and completed structured questionnaires on demographics, headache profile, medical history, and medication use. The headache profile examined migraine history (years), the frequency (amount per month) and duration (days per month) of headaches, the severity of migraines, and included the six-item Headache Impact Test (HIT)-6, Hamilton Anxiety Scale (HAMA) and Hamilton Depression Scale (HAMD). The inclusion criteria for MwoA patients were: 1) fulfilling the diagnosed criteria for migraine according to the International Classification of Headache Disorders, 3rd edition (ICHD-3); 2) a history of at least two years of migraine and at least one migraine episode per month and 3) the period, during the resting state EEG recording, between the last migraine or headache and the next one (the interictal period). Moreover, individuals were to be excluded according to the following criteria: 1) neurological diseases (i.e., epilepsy, cerebral infarction, encephalitis, neuromuscular disorders); 2) mental retardation; 3) a current or past history of substance dependence; 4) receiving prophylactic anti-migraine therapy; 5) depressive and anxiety disorders (scores more than 7 points in HAMA and HAMD). We also employed an age- and sex-matched healthy control group consisting of 66 healthy volunteers (age = 31.44 \u0026plusmn; 0.57, 50 females), none of whom reported any personal or family history of psychiatric or neurological disorders. This was confirmed by both a self-reported past history and a psychiatric examination of present mental state using the DSM-IV criteria of axis I. None of the female participants from either group took any oral contraceptives for at least 1 week prior to involvement in this study. All participants in the two groups were right-handed and signed informed consent forms. The study protocol was approved by the Ethics Committee of the Chinese PLA General Hospital. Demographic and clinical characteristics are described in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of the study sample.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMwoA patients\u003c/p\u003e \u003cp\u003e(n = 61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003cp\u003e(n = 66)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroup comparison\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e(M \u0026plusmn; SD)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e(M \u0026plusmn; SD)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.79 \u0026plusmn; 6.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.44 \u0026plusmn; 4.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et(125) = -1.31, p = 0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (F/M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(50/11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(50/16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.41, p = 0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.76 \u0026plusmn; 3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.87 \u0026plusmn; 3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et(125) = -0.20, p = 0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI [kg/m\u003csup\u003e2\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.56 \u0026plusmn; 3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.81 \u0026plusmn; 2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et(125) = 0.50, p = 0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.18 \u0026plusmn; 1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.50 \u0026plusmn; 0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et(125) = 1.78, p = 0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of migraine, days per month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.38 \u0026plusmn; 3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of migraine, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.38 \u0026plusmn; 6.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMigraine frequency, times per month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.15 \u0026plusmn; 1.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeverity of headache\u003c/p\u003e \u003cp\u003e(VAS scale)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.72 \u0026plusmn; 1.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIT-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.85 \u0026plusmn; 6.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eVAS, visual analog scale, with 0 indicating no pain and 10 worst possible pain; BMI, body mass index;MoCA, the Montreal Cognitive Assessment; HIT-6, the six-item Headache Impact Test; M, mean; SD, standard deviation; HC, healthy controls; MwoA, migraine without aura.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEEG data acquisition and preprocessing\u003c/h2\u003e \u003cp\u003eWe employed an EEG data recording procedure similar to that described in our previous studies (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Resting-state EEG data were recorded (SynAmps amplifier, NeuroScan) with a quick cap carrying 64 Ag/AgCl electrodes placed at standard locations covering the whole scalp (the extended international 10\u0026ndash;20 system). The reference electrode was attached to the right mastoid (M2), and the ground electrode was placed on the forehead. The vertical electrooculogram (VEOG) was recorded with electrodes placed above and below the left eye. The horizontal electrooculogram (HEOG) was recorded using electrodes placed beside the two eyes. Impedance was kept below 5 kΩ. Electrophysiological data were continuously recorded with a bandwidth 0.05\u0026ndash;100 Hz and sampled at a rate of 1000 Hz. All participants were asked to keep their eyes closed and to relax throughout the recording period (4 min).\u003c/p\u003e \u003cp\u003eOffline EEG data were down-sampled to 500 Hz and preprocessed using EEGLAB 2021.0 (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Preprocessing analysis was consistent with the procedure reported in previous work (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Specifically, the raw EEG data were filtered with a bandpass of 0.5 \u0026ndash; 70 Hz and a notch (50 Hz) filter. Upon visual inspection, epochs with artefacts caused by movement or poor signal were detected and removed manually. Independent component analysis (ICA) was then used to remove eye movements-, muscular- and bad channel-related artifacts. Subsequently, data were divided into 2s duration segments. Finally, data were re-referenced to common average reference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eEEG microstate analysis\u003c/h2\u003e \u003cp\u003eConsistent with recent work (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), we performed microstate analysis using the Microstate Analysis plug-in (Version 1.1) for EEGLAB (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). This adhered to well-established standard procedures reported in previous studies (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Briefly, preprocessed EEG data were digitally filtered with a band pass of 2\u0026ndash;20 Hz. Then, we computed the Global Field Power (GFP) for each participant, which represents the overall potential variance across all electrodes at each sample in time. Given that EEG scalp topographies around the peaks of the GFP remain stable, we only extracted and submitted topographies at the momentary peaks of GFP to subsequent analysis. Four classes of microstate topography have previously been found to optimally account for EEG data variance and have frequently been adopted in the existing research on neuropsychiatric disorders (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). We thus computed four microstate class topographies in the present study using a modified version of the K-mean clustering algorithm (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Microstate class topographies for each group were calculated separately using a permutation algorithm that minimized common variance across participants (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). We labeled the four microstate classes as A, B, C or D according to their similarities to the microstate class topographies reported in previous work (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). For each participant, four microstate parameters were computed for each class: mean duration (ms) (the average time that a given microstate was continuously present), occurrence (the mean number of a given microstate per second), time coverage (%) (the percentage of total analysis time spent in a given microstate) and syntax (the transition from each of the four microstate classes to any other microstate classes).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe used a non-parametric chi-square test to assess group differences in gender ratio. Independent sample t-tests were employed to examine between-group differences in age, years of education and body mass index (BMI). To assess between-group differences in microstate parameters (mean duration, occurrence and time coverage), we ran three separate mixed analyses of variance (ANOVA), with group as a between-participants factor (MwoA patients versus HC) and microstate class (A versus B versus C versus D) as a within-participants factor. Regarding microstate syntax analysis, we followed a procedure described in previous work (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), in which we computed the percentage of transitions from one microstate class to another. We achieved this by calculating the relative observed occurrence of transitions from one microstate class to all other classes. After normalization, we obtained the percentage for each possible transition for every participant in this study. Subsequently, while Bonferroni correcting for multiple comparisons, we performed a two-sample t-test on each pair of microstate class transitions to examine whether there were significant between-group differences in the transition probabilities. Finally, for microstates showing significant between-group differences, we performed Pearson\u0026rsquo;s correlations to assess the relationships between microstate parameters and clinical measures.\u003c/p\u003e \u003cp\u003eAll data were analyzed using R (version 4.1.0). Statistical comparisons were made at p-values of p \u0026lt; .05, with the Greenhouse\u0026ndash;Geisser correction when violations of sphericity occurred.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMicrostate maps\u003c/h2\u003e \u003cp\u003eThe topographies of 4 dominant microstate classes strongly resembled those reported in previous work (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). These four microstate classes explained more than 77% of the global variance in each group (79.58% in the MwoA patient group and 77.45% in the HC group) (Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Therefore, we were able to categorize them as microstate classes A, B, C, and D.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMean duration (ms)\u003c/h2\u003e \u003cp\u003eA mixed ANOVA did not reveal a significant main effect of either group (F(1, 125) = 1.72, p = 0.19) or microstate class (F(3, 375) = 1.33, p = 0.26). However, we found a significant group \u0026times; microstate class interaction (F(3, 375) = 15.49, p \u0026lt; .001). An analysis of simple effects revealed that the mean duration of microstate class C was shorter in the MwoA patient group than in the HC group (p \u0026lt; .001) (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMwoA patients vs. healthy controls for all microstate parameters and for each microstate class.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicrostate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMwoA patients\u003c/p\u003e \u003cp\u003e(M \u0026plusmn; SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003cp\u003e(M \u0026plusmn; SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean duration (ms)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.13 \u0026plusmn; 10.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.06 \u0026plusmn; 8.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.99 \u0026plusmn; 8.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.74 \u0026plusmn; 9.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.44 \u0026plusmn; 8.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.45 \u0026plusmn; 11.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.61 \u0026plusmn; 10.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.25 \u0026plusmn; 9.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccurrence (/s)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.86 \u0026plusmn; 0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.83 \u0026plusmn; 0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.99 \u0026plusmn; 0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.72 \u0026plusmn; 0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.70 \u0026plusmn; 0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.04 \u0026plusmn; 0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.18 \u0026plusmn; 0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.76 \u0026plusmn; 0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTime coverage (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.55 \u0026plusmn; 5.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.51 \u0026plusmn; 4.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.41 \u0026plusmn; 4.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.64 \u0026plusmn; 4.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.41 \u0026plusmn; 4.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.30 \u0026plusmn; 4.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.62 \u0026plusmn; 5.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.56 \u0026plusmn; 4.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eM, mean; SD, standard deviation;HC, healthy controls; MwoA, migraine without aura\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eOccurrence (Times/S)\u003c/h2\u003e \u003cp\u003eUsing a mixed ANOVA, we found no significant main effects of either group (F(1, 125) = 1.65, p = 0.20) or microstate class (F(3, 375) = 1.64, p = 0.18). However, there was a significant interaction between group and microstate class (F(3, 375) = 12.58, p \u0026lt; .001). An analysis of simple effects revealed that microstate classes B (p \u0026lt; .05) and D (p \u0026lt; .001) were significantly more frequent in the MwoA patient group than in the HC group, while the microstate class C was significantly less frequent in the MwoA patient group than in the HC group (p \u0026lt; .01) (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTime Coverage (%)\u003c/h2\u003e \u003cp\u003eA mixed ANOVA failed to reveal significant main effects for either group (F(1, 125) = 1.02, p = 0.32) or microstate class (F(3, 375) = 0.85, p = 0.46). However, we did find a significant interaction between group and microstate class (F(3, 375) = 15.84, p \u0026lt; .001). An analysis of simple effects revealed that microstate classes B (p \u0026lt; .05) and D (p \u0026lt; .001) covered significantly more time in the MwoA patient group than in the HC group, while microstate class C covered significantly less time in the MwoA patient group than in the HC group (p \u0026lt; .001) (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMicrostate syntax\u003c/h2\u003e \u003cp\u003eThe percentage of observed transitions from one microstate class to all other classes in the MwoA patient and HC groups is shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. We performed t tests to examine observed transition probabilities between groups and found that, compared to the HC group, the MwoA patient group showed a bias toward making fewer transition from one microstate to another. Specifically, we observed this bias between the following microstates: A to C (t(125) = 4.81, p \u0026lt; .001); from B to C (t(125) = 3.39, p \u0026lt; .05); from C to A (t(125) = 5.23, p \u0026lt; .001) and from C to B (t(125) = 3.54, p \u0026lt; .01) (Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast, we found that the MwoA patient group showed a bias toward making more transitions from B to D (t(125) = -4.69, p \u0026lt; .001) and from D to B (t(125) = -5.64, p \u0026lt; .001) than the HC group (Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePercentage of transitions from one microstate class to all other classes in MwoA patients and healthy controls.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMwoA patients\u003c/p\u003e \u003cp\u003e(M \u0026plusmn; SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003cp\u003e(M \u0026plusmn; SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA to B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.92 \u0026plusmn; 1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.42 \u0026plusmn; 1.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA to C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.00 \u0026plusmn; 1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8.58 \u0026plusmn; 1.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA to D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.67 \u0026plusmn; 1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.92 \u0026plusmn; 2.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB to A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.06 \u0026plusmn; 1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.33 \u0026plusmn; 1.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB to C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.38 \u0026plusmn; 1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8.54 \u0026plusmn; 1.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB to D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.88 \u0026plusmn; 2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.29 \u0026plusmn; 1.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC to A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.99 \u0026plusmn; 1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8.71 \u0026plusmn; 1.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC to B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.30 \u0026plusmn; 1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8.51 \u0026plusmn; 1.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC to D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.24 \u0026plusmn; 1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8.29 \u0026plusmn; 1.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD to A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.53 \u0026plusmn; 1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.88 \u0026plusmn; 2.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD to B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e9.14 \u0026plusmn; 2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.34 \u0026plusmn; 1.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD to C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.15 \u0026plusmn; 1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8.36 \u0026plusmn; 1.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eM, mean; SD, standard deviation; HC, healthy controls; MwoA, migraine without aura\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between microstate parameters and clinical measures\u003c/h2\u003e \u003cp\u003eOur correlation analysis only revealed a significantly negative correlation between the mean duration of microstate class C and HIT-6 scores in the MwoA patient group (r = -0.27, p \u0026lt; .05) (Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study demonstrate the presence of abnormalities in resting-state EEG microstates among MwoA patients. Overall, MwoA patients exhibited divergent temporal microstate profiles compared to those in the HC group. Moreover, the MwoA patient group, relative to the HC group, displayed multiple distinct microstate transition probabilities, which primarily involved microstate classes B, C and D. These findings Here, we will discuss how these findings may help to shed light on migraine pathophysiology.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMicrostate class B and the visual network (VN)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe found a significant change in this microstate class B (time coverage and occurrence) in the MwoA patient group compared to the HC group. Microstate class B has been shown to be linked to activities in the VN encompassing the bilateral lateral extrastriate visual areas (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). A clear change in this microstate class may thus represent underlying structural abnormalities in this area in MwoA patients. This argument is supported by previous anatomical imaging studies reporting anatomical alterations in the VN (e.g., increased cortical thickness) in MwoA patients (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Consistent with the notion that brain areas showing structural abnormalities in migraineurs also show functional alterations, structural alterations in the VN may provide an explanation for the functional changes that we observe in microstate class B.\u003c/p\u003e \u003cp\u003eMeanwhile, such a change in microstate class B may also indicate functional alterations of the VN in MwoA patients at baseline. That is, these results reflect activity occurring default functional states that are independent of any task performance. Occurrence and time coverage of a particular microstate class have usually been interpreted to reflect the tendency of its underlying cortical and subcortical sources to be activated as well as the corresponding relative time coverage of such underlying neural activities (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). It is thus reasonable to speculate that an increase in these parameters of microstate class B at rest in MwoA patients may indicate an enhanced likelihood of neural activation of the VN in response to visual events. This speculation is indeed supported by previous functional studies in migraineurs which have converged to reveal a functional impairment in the form of visual cortex hyperexcitability in migraineurs (\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). In this sense, our observation of increased microstate class B activity at rest may provide a potential neurobiological mechanism to explain the visual disturbances and visual hyperactivity in the visual system of MwoA patients.\u003c/p\u003e \u003cp\u003eIt should be noted that patients in the present study all suffered from migraines without aura. The effect observed in these patients adds to a growing literature showing that visual hyperactivity in the visual system can also be found in MwoA patients (\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). In spite of some ongoing controversy (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e) and their differing clinical symptoms (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e), our finding provides adds support to the viewpoint that similar pathogenic mechanisms may be shared among all migraine patients, both with visual aura (usually with coexisting visual disturbances) and without aura.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMicrostate class C and the salience network (SN)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn contrast to microstate class B, we found a significant decrease in microstate class (mean duration, occurrence and time coverage) in the MwoA patient group compared to the HC group. This microstate class has been related to the salience network (SN) focusing mainly on the dorsal anterior cingulate cortex (dACC) and anterior insula (AI) (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Decreased microstate class C may be associated with the structural abnormalities in the SN that have been reported by previous anatomical imaging studies (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). These three microstate parameters have been interpreted to reflect three complents: the average length of time a given microstate class remains stable; the tendency of its underlying cortical and subcortical sources to be activated; and the corresponding relative time coverage of such underlying neural activities (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Thus, a decrease in these three parameters in microstate class C may suggest a functional impairment of the SN in MwoA patients at baseline. This would be consistent with previous resting-state fMRI studies showing reduced intrinsic connectivity within the SN in these patients (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding the functional significance of the SN, several views have emerged to provide a possible explanation of its activity during resting state. A prevailing view points to the well-established role of the SN in interceptive awareness and sensory processing of salient events (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). This view emphasizes its role in detecting and filtering salient stimuli and in coordinating other brain networks (e.g., central-executive network (CEN)) to guide behavior. In addition to this view, recent studies have begun to identify a specific role of this network in inhibitory control (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). Despite some controversy, one line of evidence supports the notion that the SN integrates salient information that is subsequently used by the CEN, including the inferior frontal cortex (IFC), for recruiting inhibition (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). This implies an indirect involvement of salience processing in inhibitory control. Therefore, we can speculate that dysfunctional SN may lead to an aberrant assignment of salience to sensory stimuli. This is then improperly, or incompletely, processed by the CEN and ultimately causes reduced involvement of the CEN in recruiting inhibition in MwoA patients. From this perspective, it is possible that the aberrant role of salience processing in inhibitory control in MwoA patients is related to a hypervigilance to salient events (e.g., ongoing pain and sensory stimuli), which are common triggers for migraine headaches. This argument is partly supported by aberrant syntax patterns in the MwoA patient group compared to the HC group observed in the present study. A reduction in transitions from microstate class C to microstate class B and A seems to implicate decreased connectivity from the SN to the primary sensory networks. Such a pattern may then lead to an increase in cortical excitability and sensory gain, as implied by our observation of increased microstate class B in association with more engagement of the VN in MwoA patients. This finding is in agreement with previous work showing decreased SN and CEN connectivity (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e) and decreased SN and VN connectivity in migraineurs (\u003cspan additionalcitationids=\"CR64\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). More importantly, these findings further indicate that the SN may stand at a \u0026lsquo;crossroads\u0026rsquo; in the network architecture of the migraine brain and consequently may represent a potential target for improving the adverse impact of headache on daily functions in sufferers (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). Our observation of negative associations in the MwoA patient group between the mean duration of microstate class C and Hit-6 scores, which measure the adverse impact of headache on social functioning, role functioning, vitality, cognitive functioning and psychological distress, appears to support this argument.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMicrostate class D and the dorsal attention network (DAN)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFinally, we observed a significant increase in time coverage and occurrence of the microstate class D in the MwoA patient group compared to the HC group. In accordance with previous work (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), this microstate class is related to activities in the DAN including the dorsal areas of the frontal and parietal cortex. Our finding thus implies a functional impairment in this network among MwoA patients. Increases in these two parameters for microstate class D at baseline seems to suggest a potential hyperexcitability of the DAN to incoming sensory stimuli. This is consistent with recent brain imaging studies showing an increase in neural responses to both attended and unattended stimuli in the key regions of the DAN (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). Furthermore, the functional significance of the DAN has been argued to reflect reflexive aspects of attention, such as switching and reorientation of attention to relevant information (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e). From this, we can speculate that migraineurs may exhibit an exaggerated pattern of reflexive orienting responses to incoming sensory stimuli. This argument is indeed supported by previous work showing heightened reflexive visual-spatial orienting to attended and nonattended events (\u003cspan additionalcitationids=\"CR70\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). In this sense, the change in microstate class D at baseline that we observed in MwoA patients would thus be associated with alterations in top-down and/or bottom-up attention during task performance. Such an interpretation receives support from our observation of aberrant syntax patterns in the MwoA patient group compared to the HC group. Here, we observed increased transitions from microstate class D to microstate class B, a finding that is also consistent with previous resting-state studies showing an increased functional connectivity between the DAN and VN (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e). Thus, it is possible that such atypical syntax patterns in the MwoA patient group at baseline may provide a potential neurobiological explanation for the enhanced attentional focus toward visual events described above.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePotential limitations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDespite the relatively large sample size used in the present study, we should take several potential limitations into account. First, only MwoA patients were involved in the present study. Thus, it remains unclear as to whether these findings can be generalized to other types of migraine groups, such as patients suffering from migraine with aura (MA) and chronic migraine patients. It would be important to address this issue in future studies mainly because different pathophysiological mechanisms have been found to play a role in these different types of headache syndromes (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e). Second, the present study is not capable of allowing us to identify whether deviant temporal microstate profiles found in MwoA patients represent the trait or state nature of microstate abnormalities. Taking this into account in future studies would further help to shed additional light on migraine pathophysiology.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn sum, in this study we showed divergent temporal microstate profiles and aberrant microstate syntax patterns in the MwoA group compared to the HC group. The divergent temporal microstate profiles in the MwoA patient group reflect an increase in baseline brain activities in the VN and the DAN, alongside a reduction in neural activities in the SN at baseline. Moreover, we were also able to observe a decrease in transitions from the SN to the VN as well as an increase in transitions from the DAN and the VN in MwoA patients. This is consistent with previous research demonstrating aberrant functional connectivity among diversely distributed resting-state brain networks in migraineurs. Taken together, these findings may provide a neurophysiological mechanism to explain the altered cortical excitability, enhanced attentional focus toward sensory events and enhanced sensory gain that is present in migraineurs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMwoA: migraine without aura; HC: healthy controls; MA: migraine with aura; EEG: Electroencephalogram; VN: visual network; SN: salience network; DAN: dorsal attention network; HIT: Headache Impact Test; CEN: central-executive network; IFC: inferior frontal cortex; dACC: dorsal anterior cingulate cortex; BMI: body mass index; ANOVA: analyses of variance; GFP: Global Field Power; VEOG: vertical electrooculogram; HEOG: horizontal electrooculogram; HAMA: Hamilton Anxiety Scale; HAMD:Hamilton Depression Scale; ICHD: International Classification of Headache Disorders; \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the Chinese PLA General Hospital and informed written consent was obtained for all participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent for publication was obtained.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the present study are available from the corresponding authors on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Grant No. 82071226, 81671077 and 81771200 to S.Y.), Beijing Natural Science Foundation Essential Research Project (Grant No. Z170002 to S.Y.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eG.C.,Y.L. and S.Y. conceived and designed the study; G.C., M.S., J.L, L.H., Z.D.,R.W. and S.Y. collected the data. G.C. analyzed the data under the supervision of Y.L. G.C. and Y.L. wrote the draft; Y.L. provided the final corrections on the draft. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank the volunteers involved in this study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArnold M (2018) Headache classification committee of the international headache society (IHS) the international classification of headache disorders. Cephalalgia 38:1\u0026ndash;211\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePietrobon D, Moskowitz MA (2013) Pathophysiology of migraine. Annu Rev Physiol 75:365\u0026ndash;391\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVecchia D, Pietrobon D (2012) Migraine: a disorder of brain excitatory\u0026ndash;inhibitory balance? 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Cephalalgia 15:186\u0026ndash;190\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-journal-of-headache-and-pain","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tjhp","sideBox":"Learn more about [The Journal of Headache and Pain](https://thejournalofheadacheandpain.biomedcentral.com/)","snPcode":"10194","submissionUrl":"https://submission.nature.com/new-submission/10194/3","title":"The Journal of Headache and Pain","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Microstate, Resting-state, Migraine, MwoA, EEG","lastPublishedDoi":"10.21203/rs.3.rs-1023439/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1023439/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eResting-state EEG microstates are thought to reflect brief activations of several interacting components of resting-state brain networks. Surprisingly, we still know little about the role of these microstates in migraine. In the present study, we attempted to address this issue by examining EEG microstates in patients with migraine without aura (MwoA) during the interictal period and comparing them with those of a group of healthy controls (HC). \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eResting-state EEG was recorded in 61 MwoA patients (50 females) and 66 HC (50 females). Microstate parameters were compared between the two groups. We computed four widely identified canonical microstate classes A-D.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eMicrostate classes B and D displayed higher time coverage and occurrence in the MwoA patient group than in the HC group, while microstate class C exhibited significantly lower time coverage and occurence in the MwoA patient group. Meanwhile, the mean duration of microstate class C was significantly shorter in the MwoA patient group than in the HC group. Moreover, among the MwoA patient group, the duration of microstate class C correlated negatively with clinical measures of headache‐related disability as assessed by the six-item Headache Impact Test (HIT-6). Finally, microstate syntax analysis showed significant differences in transition probabilities between the two groups, primarily involving microstate classes B, C and D. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e By exploring EEG microstate characteristics at baseline we were able to explore the neurobiological mechanisms underlying altered cortical excitability and aberrant sensory, affective and cognitive processing, thus deepening our understanding of migraine pathophysiology.\u003c/p\u003e","manuscriptTitle":"Abnormalities in Resting-State EEG Microstates are a Vulnerability Marker of Migraine","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-11-03 14:44:56","doi":"10.21203/rs.3.rs-1023439/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2021-11-27T13:57:59+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-11-01T02:20:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-10-31T20:55:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-10-30T23:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-10-30T23:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Journal of Headache and Pain","date":"2021-10-27T05:57:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-journal-of-headache-and-pain","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tjhp","sideBox":"Learn more about [The Journal of Headache and Pain](https://thejournalofheadacheandpain.biomedcentral.com/)","snPcode":"10194","submissionUrl":"https://submission.nature.com/new-submission/10194/3","title":"The Journal of Headache and Pain","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cd6cca22-5f38-4aa2-bf16-d9a1e9b6aa67","owner":[],"postedDate":"November 3rd, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":8293731,"name":"Anesthesiology \u0026 Pain Medicine"},{"id":8293732,"name":"Neurology"}],"tags":[],"updatedAt":"2022-04-05T11:55:10+00:00","versionOfRecord":{"articleIdentity":"rs-1023439","link":"https://doi.org/10.1186/s10194-022-01414-y","journal":{"identity":"the-journal-of-headache-and-pain","isVorOnly":false,"title":"The Journal of Headache and Pain"},"publishedOn":"2022-04-05 11:55:10","publishedOnDateReadable":"April 5th, 2022"},"versionCreatedAt":"2021-11-03 14:44:56","video":"","vorDoi":"10.1186/s10194-022-01414-y","vorDoiUrl":"https://doi.org/10.1186/s10194-022-01414-y","workflowStages":[]},"version":"v1","identity":"rs-1023439","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1023439","identity":"rs-1023439","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}