Impaired brain functional hubs and effective connectivity of striatum-cortical network in migraine without aura: a resting-state fMRI study

Impaired brain functional hubs and effective connectivity of striatum-cortical network in migraine without aura: a resting-state fMRI study | 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 Impaired brain functional hubs and effective connectivity of striatum-cortical network in migraine without aura: a resting-state fMRI study Zhiyang Zhang, Chaorong Xie, Linglin Dong, Yangxu Ou, Xixiu Ni, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4594035/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Migraine without aura (MWoA) is a brain network disorder involving abnormal activity in subcortical and cortical brain regions. However, the functional alteration of key nodes and the flow of information within and between brain network in MWoA remain unclear. Thus, we aim to explore functional and effective connectivity (EC) to investigate relationship between impaired brain connectivity and migraine onsets. Methods: Fifty-three MWoA patients and 51 age- and sex-matched healthy controls (HCs) were enrolled in this study. Degree centrality (DC) analysis was used to measure the whole brain functional connectivity, and the abnormal brain regions found by DC were regarded as seeds to perform Granger causality analysis (GCA) to explore EC. Furthermore, a correlation analysis was conducted to determine the relationship between brain abnormalities and clinical symptoms in MWoA. Results: MWoA patients exhibited decreased DC value in left putamen (PUT.L) and increased DC value in left angular gyrus (ANG.L) in whole brain functional integration compared with HCs. In EC, from subcortex to cortex, we found altered EC values from PUT.L to right superior frontal gyrus, medial, right supramarginal gyrus, right superior frontal gyrus, dorsolateral (SFGdor.R) and postcentral gyrus (PoCG.R), and altered EC from bilateral caudate (CAU) to ANG.L. From cortex to subcortex, we observed altered EC value from SFGdor.R to PUT.L, and from ANG.L to left caudate. Furthermore, we found that the EC value from PUT.L to PoCG.R was inversely correlated with the frequency of headache attack and the EC value from CAU.R to ANG.L was positively correlated with duration of illness in MWoA. Conclusion: Our study validated the hypothesis that the functional and effective connectivity between subcortex and cortex were abnormal in MWoA patients compared with HCs, manifesting as alteration in striatum-cortex network, and the inflow and outflow information in striatum-cortex network were correlated with the frequency of headache attack and duration of illness, which may contribute to clarify neuroimaging mechanism of pain sensory during migraine onset, and the abnormality may be an adjunctive biomarker in evaluating severity of migraine and the efficacy of therapeutic intervention. migraine without aura resting-state fMRI degree centrality effective connectivity granger causality analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Migraine is one of the most frequent neurological disorders and is considered to be the second most disabling condition in terms of disability-adjusted life-years[ 1 ]. According to the International Headache Society[ 2 ], more than 70% patients suffer from migraine without aura (MWoA). Migraine is considered to be a common brain disorder[ 3 ], and a study regarded migraine attack as a brain condition with potential alteration in the activities of several brain network[ 4 ]. Another study supported that migraine may cause progressive brain damage with an increase in the frequency of attacks and the duration of disease[ 5 ]. The previous studies indicated that the pathology of migraine is closely related to brain dysfunction. Therefore, it is necessary to understand the neuroimaging mechanism of migraine in order to clarify the pain sensory and modulation during occurrence and progression of migraine. Resting-state functional magnetic resonance imaging (rs-fMRI) is a widely used tool for investigating brain function. Several studies[ 6 – 8 ] supported that migraine is a brain network disorder involving abnormal activity in subcortical and cortical brain regions such as default mode network (DMN), sensorimotor network (SMN), visual cortex, thalamo-cortical, amygdala and striatum[ 9 – 13 ], among which, the striatum containing putamen, caudate and pallidum is a key region modulates information between cortex and subcortical involving pain signal[ 14 , 15 ], and nucleus accumbens (subregions of striatum) and the medial prefrontal cortex were shown to directly correlated with stimulus-pain[ 16 ]. Previous study reported the interaction between putamen and sensorimotor regions may be the basis for clinical pain in complex regional pain syndrome[ 17 ]. Several studies reported abnormal FC within striatum and cortex regions, such as primary motor cortex[ 18 ], insula[ 19 ] and SMN[ 20 ] in migraine patients, and the disrupted FC in striatum associated with headache attack frequency[ 19 ], disease duration[ 21 ]. It seems that there is interaction between subcortical and cortical brain network in MWoA, but the key nodes of MWoA in subcortical and cortical and their corresponding directional connections remains unclear. Degree centrality (DC) is a voxel-wise data-driven method that can reflect the importance of a certain brain node in the whole brain as well as its functional connectivity within the whole-brain network[ 22 , 23 ], and may avoid the bias caused by selecting brain regions according to a priori assumption[ 24 ]. Granger causality analysis (GCA) is widely employed to depict the flow of information across brain network and explore the directional or effective connections between brain regions[ 25 ]. Thus, combining the two methods may identify key nodes of the whole brain and further explore directional inflow of information within and between the pivotal areas, which may contribute to decode disease-related neuroimaging mechanisms. Due to the remarkable capability in quantifying the significance and directional connectivity within the brain network, a study found stronger DC connection in the pain matrix in chronic migraine[ 26 ], and several studies reported impaired effective connectivity in SMN[ 20 ] and amygdala[ 12 ] to explore abnormal multisensory integration and pain modulation in migraine patients. Therefore, we attempt to identify pivotal brain regions in MWoA which establish altered functional connectivity within the whole-brain regions relying on DC analysis, and then use GCA to further explore altered effective functional connections based on dysfunctional brain regions determined by DC method. We hypothesized that the functional and effective connections between subcortical and cortical brain regions were abnormal in MWoA, and the abnormality was associated with the pain sensory during occurrence and progression of migraine. 2. Materials and methods 2.1 Study participants Fifty-three eligible MWoA patients were recruited from the neurology departments of the Hospital of Chengdu University of Traditional Chinese Medicine and the Third Affiliated Hospital of Chengdu University of Chinese Medicine. This trial conformed to the principles of the Declaration of Helsinki, was approved by the Ethics Committee of the Hospital of Chengdu University of TCM (Ethics Approval No. 2020KL-003), and was registered with the China Clinical Trial Registry (Registration No. ChiCTR2000032308). All of the MWoA patients and healthy controls (HCs) provided written consents. The diagnosis of MWoA was established according to the international Classification of Headache Disorders, 3rd Editon (ICHD-3). The inclusion criteria were as follows: (1) patients aged between 18 and 55, with an age of first onset less than or equal to 50; (2) patients matched with the diagnosis of ICHD-3; (3) experienced at least two but no more than fifteen headache attacks every four weeks during the last three months; (4) the intensity of headache pain was moderate during the baseline period, with visual analogue scale (VAS) scores ranging from 3 to 7; (5) the disease of duration more than 1 year; (6) patients were required to complete a baseline headache diary; (7) 51 age- and sex-matched HCs with no history of migraine or any other headache were enrolled. The exclusion criteria of MWoA participants and HCs included: (1) individuals suffering from other types of primary headache or undiagnosed headache; (2) individuals with severe primary diseases such as cardiovascular, cerebrovascular, liver, renal, and hematopoietic systems, as well as other organic lesions; (3) individuals with a history of head injury, mental and intellectual disabilities who are unable to cooperate with the questionnaire; (4) individuals with bleeding tendencies, allergies, and skin diseases; (5) pregnant women, lactating women and individuals who have fertility requirements in the past 6 months; (6) alcohol or drug abusers; (7) participants who have received acupuncture or other preventive treatment within the past 4 weeks; (8) individuals with metal implants in their bodies or suffer from claustrophobia and other contraindications for MRI examination; (9) severe head anatomical asymmetry or definite lesions were found in MRI scan; (10) inability to understand or record a headache diary; (11) participated in similar research within 3 months. 2.2 Clinical Symptom Evaluation For each participant, we recorded demographic information including age and gender. We also evaluated the headache intensity and frequency based on the headache diaries kept during the observation period, the details are as follows: (1) medical history, (2) diseases duration (duration of each attack), (3) attack frequency (at least 48 hours between attacks), (4) VAS scores (“0” indicates no pain and “10” indicates the worst pain imaginable), (5) headache impact test (HIT-6) scores and (6) migraine specific quality of life questionnaire (MSQ) were used to evaluate the emotional status of MwoA participants. The details are provided in Table 1 . 2.3 MRI acquisition MRI scan was performed after collecting demography and clinical symptom data. All MwoA patients were pain-free for at least 72 h before the MRI scans. The imaging data were obtained using a 3 Tesla Siemens MRI system (Allegra, Siemens Medical System, Erlangen, Germany) at the Hospital of Chengdu University of Traditional Chinese Medicine. Ear plugs and sponge pads were supplied to decline the scan noise and head movements and participants were instructed to avoid falling asleep and engaging in thought. The T1 images were acquired using a fast spoiled gradient recalled sequence (Parameters: TR = 2530 ms, TE = 3.4 ms, flip angle = 12°, FOV = 64 × 64, slice thickness = 1 mm). The resting-state functional images were obtained with echo-planar imaging (EPI) (30 continuous slices with a slice thickness = 5 mm, TR = 2000 ms, TE = 30 ms, flip angle = 90°, FOV = 240 mm × 240 mm, matrix = 64 × 64, voxel size = 3.75mm × 3.75mm × 5mm). 2.4 Data preprocessing Resting-state fMRI data preprocessing was conducted with statistical parametric mapping software (SPM 8; https://www.fil.ion.ucl.ac.uk/spm/software/spm8/ ) and Data Processing & Analysis for Brain Imaging (DPABI v4.3; http://rfmri.org/dpabi ). The following steps were taken: (1) the first 5 time points were removed avoid scanner instability and to allow the participant adapt to the scanning circumstances; (2) slice-timing was applied to correct the differences in image acquisition time between slices; (3) head motion correction was performed; (4) spatial normalization to the Montreal Neurological Institute (MNI) space via the deformation fields derived from tissue segmentation of structural images (resampling voxel size = 3 mm × 3 mm × 3 mm); (5) the functional images were smoothed using a 6-mm full-width at half maximum Gaussian kerne; (6) the time course of linear trend was removed; (7) covariates including Friston-24 parameters were regressed out [ 27 ]; (8) a band-pass filtering (0.01–0.08 Hz) was applied. 2.5 Whole-Brain functional integration— DC analysis DC was used to observe the functional connectivity at the whole-brain scale. The time series of each voxel from the processed fMRI data of the volunteer were correlated with the time series of every other voxel using Pearson's correlation and a matrix of Pearson's correlation coefficients were obtained. We eliminated low temporal correlations caused by signal noise by thresholding the correlation coefficients at r > 0.25. To calculate degree centrality, we counted the sum of the weights of the positive connections of each voxel. To allow comparison of maps across subjects, the DC value of each voxel was then converted into a standardized z-score by subtracting the mean DC value and dividing the standard deviation of the whole-brain DC map. Finally, we spatially smoothed the standardized DC maps with a 6 mm FWHM Gaussian kernel. 2.6 Voxel-wise effective connectivity— GCA Based on the results of the DC analysis, we determined seed region that showed significant difference between MWoA and HCs. The causal connectivity was analyzed by using REST-GCA in the REST toolbox[ 28 ]. The time series of seed region was designated as the seed time series x, while the time series of whole-brain regions were defined as y. The signed path coefficients were subsequently calculated as GCA values, including the linear direct effect of x to y (x→y) and y to x (y→x). Therefore, two Granger causality maps were constructed based on each subject's influence measures. Finally, the GCA maps were converted to z-values maps using Fisher’s r-to-z transformation to improve normality. 2.7 Correlation analysis A Sparman correlation analysis was used to assess the association between each measure of DC and GCA with the clinical data, including disease duration, attack frequency, and the VAS, HIT-6, and MSQ scores. Only correlations with P values less than 0.05 were considered significant. 2.8 Statistical analysis Demographic and clinical data were analyzed using SPSS statistical analysis software (SPSS 25.0). Continuous variables with normal distribution were described as mean ± standard deviation (SD), while others were described as median (first quartile, third quartile). The difference between MWoA patients and HCs in age was tested with nonparametric Mann-Whitney U test and gender difference was tested with chi-square test. To analyze brain intrinsic activity, we used DPABI v4.3 for statistical analyses[ 29 ]. A two-sample t-test was conducted to compare the DC and GCA values (x to y and y to x respectively) between MWoA patients and HCs. Individual age and gender were treated as covariates during the group comparison to minimize their potential effects on the results. The results that remained after False Discovery Rate (FDR) correlation with voxel P < 0.05 were consider to be significant. 3. Results 3.1 Participants characteristics The demographic characteristics and clinical assessment of all patients were summarized in Table 1 . There was no significant difference in age ( P = 0.423) and gender ( P = 0.705) between MWoA patients and HCs. Table 1 Demography and clinical scores of the MWoA patients and healthy control Items MWoA patients (N = 53) Healthy controls (N = 51) P value Age (year) 33.00 (25.50, 42.50) 26.00 (25.00, 45.00) 0.423 Gender (male/female) 12/41 10/41 0.705 Duration of illness (months) 84.00 (42.00, 121.00) - - Attack frequency per month 4 (3, 5) - - Average duration of each attack (hours) 7.80 (4.10, 15.25) - - Unable to study and work during hours 4.00 (0.25, 13.75) - - VAS scores (cm) 5.50 (4.28, 6.50) - - HIT-6 64.00 (58.00, 67.00) - - MSQ score, Restrictive Subscale 65.00 (54.29, 80.00) - - MSQ score, Preventive Subscale 80.00 (65.00, 90.00) - - MSQ score, Emotional Function Subscale 80.00 (66.67, 93.33) - - VAS: visual analogue scale; HIT-6: headache impact test; MSQ: migraine-specific quality of life questionnaire; “-”: no data. a Values are represented as the mean ± standard deviation; b Values are represented as the median (P 25 , P 75 ). P values for age were obtained using the nonparametric Mann-Whitney U test, and the P value for sex was obtained using the chi squared test. 3.2 Degree centrality analysis DC depicts the functional connectivity of whole brain. Compared with HCs, we found significantly increased DC value in left angular grey and decreased DC value in left putamen nucleus in MWoA patients (Table 2 and Fig. 1 ). Specifically, the brain regions we found locate in DMN and striatum respectively. Table 2 Difference in DC values between MWoA patients and HCs Brain regions Peak MNI coordinates Voxel size DC T value x y z PUT.L -21 21 6 213 ↓ -6.61 ANG.L -42 -63 36 392 ↑ 5.67 Two-sample t-test (FDR corrected, P < 0.05). 3.3 Effective connectivity from and to the PUT.L. Compared with HCs, patients with MWoA exhibited increased EC values from PUT.L to SFGmed.R, and decreased EC values from PUT.L to SMG.R, SFGdor.R, and PoCG.R (Table 3 and Fig. 2 A). In contrast, MWoA patients showed increased EC value to PUT.L from SFGdor.R (Table 3 and Fig. 2 B, FDR corrected, P < 0.05). Table 3 Difference in EC in WMoA patients compared to HCs from and to the PUT.L Brain region Peak MNI coordinates Voxel size T value X Y Z Causal outflow from PUT.L to the rest of brain (x to y) SFGmed.R 6 60 33 282 4.61 SMG.R 66 -21 18 70 -3.61 SFGdor.R 27 12 57 92 -4.18 PoCG.R 48 -33 60 67 -4.52 Causal inflow to PUT.L from the rest of brain (y to x) SFGdor.R 24 12 57 63 3.82 Two-sample t-test (FDR corrected, P < 0.05). SFGmed.R: right cerebrum of superior frontal gyrus, media; SMG: supramarginal gyrus; PoCG.R: right cerebrum of postcentral gyrus. SFGdor.R: right cerebrum of superior frontal gyrus, dorsolateral. 3.4 Effective connectivity from and to the ANG.L. Compared with HCs, patients with MWoA exhibited decreased EC values from ANG.L to CAU.L, ORBmid.L and ORBsupmed.R (Table 4 and Fig. 3 A, FDR corrected, P < 0.05). In contrast, MWoA patients showed increased EC value to ANG.L from bilateral caudate and CUN.R (Table 4 and Fig. 3 B, FDR corrected, P < 0.05). Table 4 Difference in EC in WMoA patients compared to HCs from and to the ANG.L Brain region Peak MNI coordinates Voxel size T value X Y Z Causal outflow from ANG.L to the rest of brain (x to y) CAU.L -3 18 -3 267 -4.49 ORBmid.L -6 51 -6 49 -4.17 ORBsupmed.R 9 69 0 55 -3.65 Causal inflow to ANG.L from the rest of brain (y to x) CAU.R 14 18 -3 93 5.10 CAU.L -12 15 -12 98 4.28 CUN.R 12 -78 36 58 3.63 Two-sample t-test (FDR corrected, P < 0.05). CAU: caudate nucleus; ORBmid.L: left cerebrum of middle frontal gyrus, orbital part. ORBsupmed.R: right cerebrum of superior frontal gyrus, medial orbital. CUN.R: right cerebrum of cuneus. 3.5 Correlation with clinical scores We found that the signed-path coefficients of PUT.L to PoCG.R was inversely correlated with the headache attack frequency per month ( r = -0.275, P = 0.046, Fig. 4 A); and the signed-coefficients of CAU.R to ANG.L was positively correlated with the medical history ( r = 0.306, P = 0.026, Fig. 4 B). No significant correlation was found in DC analysis between clinical scores, and EC analysis between PUT.L or ANG.L and average duration of each attack, VAS scores, HIT-6 scores and MSQ scores. 4. Discussion To the best of our knowledge, this study is the first to combine whole brain functional connectivity and EC to characterize abnormal connectivity in MWoA patients compared to HCs. We utilized DC analysis to investigate impaired functional hubs in MWoA patients we found decreased DC value in PUT.L and increased DC value in ANG.L, which respectively belongs to subcortical region (striatum) and cortical area (DMN). To further investigate the directional influence, we employed PUT.L and ANG.L as seeds to evaluate their EC with whole brain applying GCA and the altered EC were mainly in striatum-cortical network. In addition, the EC abnormalities from PUT.L to PoCG.R and CAU.R to ANG.L were significantly correlated with headache attack frequency and duration of illness. Conclusively, these finding confirmed the hypothesis that MWoA exhibited abnormal functional and effective connectivity in subcortical brain regions (striatum) and cortical network including DMN, SMN and attention network, and the dysfunction were related to pain sensory during the occurrence and progression of migraine. Specifically, on the one hand, the pain sensory of migraine is amplified by the weakened modulation of pain signals from the subcortex (striatum) to the cortex network, and on the other hand, the cortical network (attention network) pays too much attention to pain signals from the subcortex (striatum), resulting in excessive convergence of pain signals. 4.1 Impaired whole-brain functional hubs in MWoA Migraine involves extensive functional abnormalities in the cortex and subcortical brain regions[ 30 ]. Our study found that the dysfunctional brain regions belong to cortical (DMN) and subcortical areas (striatum). Degree centrality represents the status and role of voxels in the whole-brain network[ 24 ]. We discovered the DC value of left putamen significantly decreased in MWoA patients compared with HCs. The putamen is part of striatum, being activated frequently during pain attacking[ 31 ]. Previous study reported that putamen may play a significant role in the mechanisms that convert nociceptive information into pain sensory[ 32 ], and a diffusion tensor imaging (DTI) study which could quantify the chance from one brain region to other areas showed that the putamen connected with several regions pertained to the procession of pain sensory such as DMN (middle frontal gyrus), insula and hippocampus[ 33 ]. Thus, the decreased DC value indicated weaker functional connectivity with other regions, which may lead to dysregulation of pain sensation. We also observed increased DC value in left angular in MWoA patients compared with HCs. The angular locates at posterior part of the inferior parietal, a study using diffusion tensor imaging and tractography techniques explored rich structural link between angular with other areas including precuneus, caudate, frontal and temporal gyrus[ 34 ], which provided structural support that angular participates in consisting of DMN and its relationship with striatum. In addition, the angular converges multisensory information involving pain[ 35 ], increased DC value may result in excessive convergence of pain signal, then amplified the pain sensory. 4.2 Effective connectivity from striatum to cortical network To further investigate the direction of functional connectivity, we selected PUT.L and ANG.L as seeds to perform the GCA. Our study indicated significantly increased EC from PUT.L to SFGmed.R, and decreased EC from PUT.L to SMG.R, SFGdor.R and PoCG.R. The SFGmed.R and SMG.R were part of DMN, the medial frontal cortex was correlated with cognitive control, pain and emotion especially negative emotion[ 36 , 37 ], increased EC value may indicate the pathway from pain sensory to pain emotion was overactivated, which may lead to emotion disorder such as depression[ 38 , 39 ], and anxiety[ 40 , 41 ]; the supramarginal gyrus is part of somatosensory association cortex, participating in somatosensory integration and interpretation, and right supramarginal gyrus is further related to attention reorientation and distribution[ 42 – 44 ], several studies reported that distracting attention can relief pain[ 45 , 46 ], decreased EC value from PUT.L to SMG.R may imply that the integration of pain signals input from subcortical areas as well as the ability to distract attention were inhibited in MWoA patients, contributing to intolerable pain sensory. In addition, we explored decreased EC value from PUT.L to PoCG.R correlating with headache attack frequency, postcentral belongs to SMN, previous study focusing on alteration in sensorimotor network effective connectivity promoted that the SMN may be affected by abnormal inflow or outflow information from putamen[ 20 ], and another study inferred the frequency of pain correlated with SMN and dorsal striatum[ 47 ]. Therefore, our study may further confirm the abnormal directional connection between the striatum and SMN, and the alteration is related to the frequency of headache attack. Moreover, we observed markedly increased effective connectivity from bilateral caudate to ANG.L as well as decreased effective connectivity from ANG.L to CAU.L. As the key part of basal ganglia, the caudate participates in cognitive, sensory and pain modulation[ 48 ], our study found interaction between the angular and the caudate, manifesting as the effective connectivity from angular to caudate decreased while that from caudate to angular increased in MWoA patients, which may imply that the processing of pain information in the striatum affects the perception of pain signal in the cortical network. Previous study reported the abnormal functional connectivity of right caudate in chronic migraine[ 21 ], and we also investigated altered effective connectivity from right caudate to angular, which was positively correlated with duration of illness. It further suggested that dysregulation of caudate may be an important neuroimaging marker in migraine progression. 4.3 Effective connectivity from cortical network to striatum We observed increased EC value to from SFGdor.R to PUT.L. The dorsolateral prefrontal cortex (DLPFC) is a pivotal structure in dorsal attention network (DAN) involving in top-down attention orientation, together with attention reorientation system of right-lateralized ventral attention network (VAN), consist of two attention systems in human brain[ 49 ], our study observed increased EC from right DLPFC to left putamen, and the EC was inhibited from left putamen to right DLPFC, implying that the excessive attention of DAN on regulation pain information of the striatum may trigger pain, we also found abnormal EC between right-lateralized ventral attention network and striatum (decreased EC value from PUT.L to SMG.R), it can be speculated that the abnormal EC between the two attention networks may be the neuroimaging mechanism of the relationship between pain sensation and attention. In conclusion, the striatum-DMN may play a role in modulating the transition from pain sensory to pain emotion, while the striatum-SMN may be associated with the frequency of pain sensory experiences. Additionally, the striatum-attention network may be involved in processing both pain sensory information and attentional aspects in patients with MWoA. 5. Limitations There are several limitations to our study that need further study in the future. Firstly, we currently focus on abnormal EC between the default network and the striatum, but it seems that there are also abnormal EC among the attention network and the sensory motor network, further research is needed to investigate the altered connectivity across networks. Secondly, we did not perform any cerebral structural alteration based on the abnormal brain regions, future studies are warranted to assess the structural brain changes using VBM or DTI to further clarify the neuroimaging mechanism in MWoA. 6. Conclusion Our study validated the hypothesis that the functional and effective connectivity between subcortex and cortex were abnormal in MWoA patients compared with HCs, manifesting as alteration in striatum-cortex network, and the inflow and outflow information in striatum-cortex network were correlated with the frequency of headache attack and duration of illness, which may contribute to clarify neuroimaging mechanism of pain sensory during migraine onset, and the functional abnormality may be an adjunctive biomarker in evaluating severity of migraine and the efficacy of therapeutic intervention. Abbreviations MWoA Migraine without aura EC Effective connectivity HCs Healthy controls DC Degree centrality GCA Granger causality analysis PUT.L Left putamen ANG.L Left angular gyrus SFGmed.R Right superior frontal gyrus, medial SMG.R Right supramarginal gyrus SFGdor.R Right superior frontal gyrus, dorsolateral PoCG.R Right postcentral gyrus DMN Default mode network SMN Sensorimotor network VAS Visual analogue scale HIT-6 Headache impact test MSQ Migraine-specific quality of life questionnaire FDR False Discovery Rate DAN Dorsal attention network VAN Ventral attention network Declarations Ethics approval and consent to participate: all participants were informed in detail about the study and volunteered to sign an informed consent form. Our study was approved by the Ethics Committee of the Hospital of Chengdu University of TCM (Ethics Approval No. 2020KL-003), and was registered with the China Clinical Trial Registry (Registration No. ChiCTR2000032308). Consent for publication: all authors consent for the publication. Availability of data and materials: data can be made available upon request. Conflict of interest statement: the authors declare no conflicts of interest. Funding This work was supported by the National Natural Science Foundation of China (Grant Number: 81973962 and 82204919), the Innovation Team and Talents Cultivation Program of the National Administration of Traditional Chinese Medicine (Grant Number: ZYYCXTD-D-202003), and China Postdoctoral Science Foundation (Grant Number: 2022MD713681). Acknowledgements We would like to thank Figdraw ( www.figdraw.com ) for visual abstract editing. Author contribution statement Study protocol and design: LZ, MS and XW; acquisition of data: CX, XN, XG, LD; analysis and interpretation of data: ZZ, XW, YO; and drafting of the manuscript: ZZ, XW, LZ, QY, QF. All author(s) read and approved the final manuscript. 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Magn Reson Med 35(3):346–355 Zang ZX et al (2012) Granger causality analysis implementation on MATLAB: a graphic user interface toolkit for fMRI data processing. J Neurosci Methods 203(2):418–426 Yan CG et al (2016) DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. Neuroinformatics 14(3):339–351 Messina R, Filippi M, Goadsby PJ (2018) Recent advances in headache neuroimaging. Curr Opin Neurol 31(4):379–385 Bingel U et al (2004) Somatotopic representation of nociceptive information in the putamen: an event-related fMRI study. Cereb Cortex 14(12):1340–1345 Starr CJ et al (2011) The contribution of the putamen to sensory aspects of pain: insights from structural connectivity and brain lesions. Brain 134(Pt 7):1987–2004 Tomycz ND, Friedlander RM (2011) The experience of pain and the putamen: a new link found with functional MRI and diffusion tensor imaging. Neurosurgery, 69(4): p. N12-3. Seghier ML (2013) The angular gyrus: multiple functions and multiple subdivisions. Neuroscientist 19(1):43–61 Ramanan S, Piguet O, Irish M (2018) Rethinking the Role of the Angular Gyrus in Remembering the Past and Imagining the Future: The Contextual Integration Model. Neuroscientist 24(4):342–352 Kragel PA et al (2018) Generalizable representations of pain, cognitive control, and negative emotion in medial frontal cortex. Nat Neurosci 21(2):283–289 Cao J et al (2022) Inhibition of glutamatergic neurons in layer II/III of the medial prefrontal cortex alleviates paclitaxel-induced neuropathic pain and anxiety. Eur J Pharmacol 936:175351 Ma M et al (2018) Exploration of intrinsic brain activity in migraine with and without comorbid depression. J Headache Pain 19(1):48 Liu J et al (2017) Brain structural properties predict psychologically mediated hypoalgesia in an 8-week sham acupuncture treatment for migraine. Hum Brain Mapp 38(9):4386–4397 Yavuz BG et al (2013) Association between somatic amplification, anxiety, depression, stress and migraine. J Headache Pain 14(1):53 Krimmel SR et al (2022) Three Dimensions of Association Link Migraine Symptoms and Functional Connectivity. J Neurosci 42(31):6156–6166 Timmers I et al (2022) Amygdala functional connectivity mediates the association between catastrophizing and threat-safety learning in youth with chronic pain. Pain 163(4):719–728 Wilterson AI et al (2021) Attention, awareness, and the right temporoparietal junction. Proc Natl Acad Sci U S A, 118(25) Behrmann M, Geng JJ, Shomstein S (2004) Parietal cortex and attention. Curr Opin Neurobiol 14(2):212–217 Wauters A et al (2021) The Moderating Role of Attention Control in the Relationship Between Pain Catastrophizing and Negatively-Biased Pain Memories in Youth With Chronic Pain. J Pain 22(10):1303–1314 Janssen SA, Arntz A (1996) Anxiety and pain: attentional and endorphinergic influences. Pain 66(2–3):145–150 Mancini F, Zhang S, Seymour B (2022) Computational and neural mechanisms of statistical pain learning. Nat Commun 13(1):6613 Borsook D et al (2010) A key role of the basal ganglia in pain and analgesia–insights gained through human functional imaging. Mol Pain 6:27 Fox MD et al (2006) Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc Natl Acad Sci U S A 103(26):10046–10051 Additional Declarations No competing interests reported. Supplementary Files VisualAbstract.png Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4594035","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":321780313,"identity":"7908ea71-ddb3-4f1a-8456-1e6e9191af23","order_by":0,"name":"Zhiyang Zhang","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhiyang","middleName":"","lastName":"Zhang","suffix":""},{"id":321780316,"identity":"d93ecec2-25e0-411e-af71-bd8a1c70fbdd","order_by":1,"name":"Chaorong Xie","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chaorong","middleName":"","lastName":"Xie","suffix":""},{"id":321780319,"identity":"b90a68fc-4b0f-4703-9dbe-69873e9009c0","order_by":2,"name":"Linglin Dong","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Linglin","middleName":"","lastName":"Dong","suffix":""},{"id":321780320,"identity":"c0be6753-21a1-4d2f-bff7-f2e1252bedea","order_by":3,"name":"Yangxu Ou","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yangxu","middleName":"","lastName":"Ou","suffix":""},{"id":321780322,"identity":"ecb59f6e-8a11-4a20-b5be-cbe1b1c80391","order_by":4,"name":"Xixiu Ni","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xixiu","middleName":"","lastName":"Ni","suffix":""},{"id":321780324,"identity":"5a48358d-339f-46be-ac60-305e247dce4e","order_by":5,"name":"Mingsheng Sun","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mingsheng","middleName":"","lastName":"Sun","suffix":""},{"id":321780325,"identity":"8bc5fe90-c89c-4396-951a-a12cb7e24955","order_by":6,"name":"Xiaoyu Gao","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Gao","suffix":""},{"id":321780326,"identity":"522feabb-9a38-4e59-ac02-09e3f34bb1f6","order_by":7,"name":"Qixuan Fu","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qixuan","middleName":"","lastName":"Fu","suffix":""},{"id":321780327,"identity":"2ee72575-9cd6-48ca-bbd4-f843cb0481d3","order_by":8,"name":"Qinyi Yan","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qinyi","middleName":"","lastName":"Yan","suffix":""},{"id":321780328,"identity":"096b936d-6730-4811-b93e-b0bf44a561a9","order_by":9,"name":"Xiao Wang","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Wang","suffix":""},{"id":321780329,"identity":"e029bc24-a216-46cf-8776-f06b7e3411b4","order_by":10,"name":"Ling Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYDACCSB+YMDAwM/AYAAVSiBCSwJQsWQDUMsB4rUAscEBYrXIz25++CCh4I7d5uOHt0l/qNnGwM+eY8DwcwduLYxzjhkbJBg8S952Jq1M4sCx2wySPW8MGHvP4NbCLJFgJpFgcDjZ7AaPmcTBhtsMBjdyDJgZ23BrYZNI/wbWYjwDqsWekBYeiRywLXYGEjBbJAhokZDIKQb65XCCxJm0Yoszx27zSJx5VnCwF48W+RnpGx98+HPYnr/98MYbFTW35fjbkzc++IlHCwwkNsBcCiIOENbAwGBPjKJRMApGwSgYoQAAeg9Sf8Jg3qsAAAAASUVORK5CYII=","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Ling","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-06-17 12:05:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4594035/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4594035/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60093062,"identity":"33de6c8b-8a58-45a3-99f4-6720c6a1a640","added_by":"auto","created_at":"2024-07-11 16:50:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":596457,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant difference in DC values between MWoA patients and HCs. The warm color indicates increased DC value while cool color indicates decreased DC value compared with HCs in MWoA.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4594035/v1/38badb8f2b68bde86167853c.png"},{"id":60093057,"identity":"a8545c07-2951-4b4b-9d2c-e8649fc17746","added_by":"auto","created_at":"2024-07-11 16:50:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":989246,"visible":true,"origin":"","legend":"\u003cp\u003eEC from and to PUT.L. A: Causal outflow from PUT.L to the whole brain (x to y). B: Causal inflow to PUT.L from the whole brain (y to x). The red arrows indicate increased EC from the PUT.L to the whole brain; the blue arrows indicate decreased EC from the PUT.L to the whole brain in MWoA compared to HCs.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4594035/v1/aec18e5d9b755c983afecb04.png"},{"id":60093059,"identity":"ba7f9413-5033-4b0e-bd12-495ba619ce75","added_by":"auto","created_at":"2024-07-11 16:50:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1100309,"visible":true,"origin":"","legend":"\u003cp\u003eEC from and to ANG.L. A: Causal inflow from ANG.L to the whole brain (x to y). B: Causal inflow to ANG.L from the whole brain (y to x). The red arrows indicate increased EC from ANG.L to the whole brain; the blue arrows indicate decreased EC from ANG.L to the whole brain in MWoA compared to HCs.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4594035/v1/968ce88d84fa55f735f48355.png"},{"id":60093838,"identity":"4f6f1a9c-bcd3-4f94-916d-f28b8b24fe14","added_by":"auto","created_at":"2024-07-11 16:58:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":168220,"visible":true,"origin":"","legend":"\u003cp\u003eA: signed-path coefficients of PUT.L to PoCG.R was inversely correlated with the headache attack frequency per month. B: signed-coefficients of CAU.R to ANG.L was positively correlated with the medical history.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4594035/v1/5a52e79fffdfce6d1320c026.png"},{"id":60093060,"identity":"5ef7c68f-9975-480e-bd43-0478f312cdab","added_by":"auto","created_at":"2024-07-11 16:50:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":524752,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpaired striatum-cortical network related to clinical symptoms in migraine.\u003c/strong\u003e The increased EC from striatum to SMN was inversely correlated with headache attack frequency, and the decreased EC from striatum to DMN was positively correlated with duration of illness. The solid red lines represent increased EC and the solid blue lines represent decreased EC values. The transparent red lines represent increased EC and the transparent blue lines represent decreased EC values while unrelated to clinical symptoms. The visual abstract was edited by Figdraw.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4594035/v1/da15798c5b32a03224542ae9.png"},{"id":60094889,"identity":"d36c0005-78c8-47dd-998c-0f22e86c26d5","added_by":"auto","created_at":"2024-07-11 17:14:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9881036,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4594035/v1/0cd1f1ad-86fe-4705-b510-c2c881f9d11b.pdf"},{"id":60093061,"identity":"fd84cbb2-8e9b-4216-95dc-49cfd65a404f","added_by":"auto","created_at":"2024-07-11 16:50:25","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2368737,"visible":true,"origin":"","legend":"","description":"","filename":"VisualAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-4594035/v1/1dac22c3aacd430d97a8cfe2.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impaired brain functional hubs and effective connectivity of striatum-cortical network in migraine without aura: a resting-state fMRI study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMigraine is one of the most frequent neurological disorders and is considered to be the second most disabling condition in terms of disability-adjusted life-years[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to the International Headache Society[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], more than 70% patients suffer from migraine without aura (MWoA). Migraine is considered to be a common brain disorder[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and a study regarded migraine attack as a brain condition with potential alteration in the activities of several brain network[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Another study supported that migraine may cause progressive brain damage with an increase in the frequency of attacks and the duration of disease[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The previous studies indicated that the pathology of migraine is closely related to brain dysfunction. Therefore, it is necessary to understand the neuroimaging mechanism of migraine in order to clarify the pain sensory and modulation during occurrence and progression of migraine.\u003c/p\u003e \u003cp\u003eResting-state functional magnetic resonance imaging (rs-fMRI) is a widely used tool for investigating brain function. Several studies[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] supported that migraine is a brain network disorder involving abnormal activity in subcortical and cortical brain regions such as default mode network (DMN), sensorimotor network (SMN), visual cortex, thalamo-cortical, amygdala and striatum[\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], among which, the striatum containing putamen, caudate and pallidum is a key region modulates information between cortex and subcortical involving pain signal[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and nucleus accumbens (subregions of striatum) and the medial prefrontal cortex were shown to directly correlated with stimulus-pain[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Previous study reported the interaction between putamen and sensorimotor regions may be the basis for clinical pain in complex regional pain syndrome[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Several studies reported abnormal FC within striatum and cortex regions, such as primary motor cortex[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], insula[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and SMN[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] in migraine patients, and the disrupted FC in striatum associated with headache attack frequency[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], disease duration[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. It seems that there is interaction between subcortical and cortical brain network in MWoA, but the key nodes of MWoA in subcortical and cortical and their corresponding directional connections remains unclear.\u003c/p\u003e \u003cp\u003eDegree centrality (DC) is a voxel-wise data-driven method that can reflect the importance of a certain brain node in the whole brain as well as its functional connectivity within the whole-brain network[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and may avoid the bias caused by selecting brain regions according to a priori assumption[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Granger causality analysis (GCA) is widely employed to depict the flow of information across brain network and explore the directional or effective connections between brain regions[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Thus, combining the two methods may identify key nodes of the whole brain and further explore directional inflow of information within and between the pivotal areas, which may contribute to decode disease-related neuroimaging mechanisms. Due to the remarkable capability in quantifying the significance and directional connectivity within the brain network, a study found stronger DC connection in the pain matrix in chronic migraine[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and several studies reported impaired effective connectivity in SMN[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and amygdala[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] to explore abnormal multisensory integration and pain modulation in migraine patients.\u003c/p\u003e \u003cp\u003eTherefore, we attempt to identify pivotal brain regions in MWoA which establish altered functional connectivity within the whole-brain regions relying on DC analysis, and then use GCA to further explore altered effective functional connections based on dysfunctional brain regions determined by DC method. We hypothesized that the functional and effective connections between subcortical and cortical brain regions were abnormal in MWoA, and the abnormality was associated with the pain sensory during occurrence and progression of migraine.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study participants\u003c/h2\u003e \u003cp\u003eFifty-three eligible MWoA patients were recruited from the neurology departments of the Hospital of Chengdu University of Traditional Chinese Medicine and the Third Affiliated Hospital of Chengdu University of Chinese Medicine. This trial conformed to the principles of the Declaration of Helsinki, was approved by the Ethics Committee of the Hospital of Chengdu University of TCM (Ethics Approval No. 2020KL-003), and was registered with the China Clinical Trial Registry (Registration No. ChiCTR2000032308). All of the MWoA patients and healthy controls (HCs) provided written consents.\u003c/p\u003e \u003cp\u003eThe diagnosis of MWoA was established according to the international Classification of Headache Disorders, 3rd Editon (ICHD-3). The inclusion criteria were as follows: (1) patients aged between 18 and 55, with an age of first onset less than or equal to 50; (2) patients matched with the diagnosis of ICHD-3; (3) experienced at least two but no more than fifteen headache attacks every four weeks during the last three months; (4) the intensity of headache pain was moderate during the baseline period, with visual analogue scale (VAS) scores ranging from 3 to 7; (5) the disease of duration more than 1 year; (6) patients were required to complete a baseline headache diary; (7) 51 age- and sex-matched HCs with no history of migraine or any other headache were enrolled.\u003c/p\u003e \u003cp\u003eThe exclusion criteria of MWoA participants and HCs included: (1) individuals suffering from other types of primary headache or undiagnosed headache; (2) individuals with severe primary diseases such as cardiovascular, cerebrovascular, liver, renal, and hematopoietic systems, as well as other organic lesions; (3) individuals with a history of head injury, mental and intellectual disabilities who are unable to cooperate with the questionnaire; (4) individuals with bleeding tendencies, allergies, and skin diseases; (5) pregnant women, lactating women and individuals who have fertility requirements in the past 6 months; (6) alcohol or drug abusers; (7) participants who have received acupuncture or other preventive treatment within the past 4 weeks; (8) individuals with metal implants in their bodies or suffer from claustrophobia and other contraindications for MRI examination; (9) severe head anatomical asymmetry or definite lesions were found in MRI scan; (10) inability to understand or record a headache diary; (11) participated in similar research within 3 months.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Clinical Symptom Evaluation\u003c/h2\u003e \u003cp\u003eFor each participant, we recorded demographic information including age and gender. We also evaluated the headache intensity and frequency based on the headache diaries kept during the observation period, the details are as follows: (1) medical history, (2) diseases duration (duration of each attack), (3) attack frequency (at least 48 hours between attacks), (4) VAS scores (\u0026ldquo;0\u0026rdquo; indicates no pain and \u0026ldquo;10\u0026rdquo; indicates the worst pain imaginable), (5) headache impact test (HIT-6) scores and (6) migraine specific quality of life questionnaire (MSQ) were used to evaluate the emotional status of MwoA participants. The details are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 MRI acquisition\u003c/h2\u003e \u003cp\u003eMRI scan was performed after collecting demography and clinical symptom data. All MwoA patients were pain-free for at least 72 h before the MRI scans. The imaging data were obtained using a 3 Tesla Siemens MRI system (Allegra, Siemens Medical System, Erlangen, Germany) at the Hospital of Chengdu University of Traditional Chinese Medicine. Ear plugs and sponge pads were supplied to decline the scan noise and head movements and participants were instructed to avoid falling asleep and engaging in thought. The T1 images were acquired using a fast spoiled gradient recalled sequence (Parameters: TR\u0026thinsp;=\u0026thinsp;2530 ms, TE\u0026thinsp;=\u0026thinsp;3.4 ms, flip angle\u0026thinsp;=\u0026thinsp;12\u0026deg;, FOV\u0026thinsp;=\u0026thinsp;64 \u0026times; 64, slice thickness\u0026thinsp;=\u0026thinsp;1 mm). The resting-state functional images were obtained with echo-planar imaging (EPI) (30 continuous slices with a slice thickness\u0026thinsp;=\u0026thinsp;5 mm, TR\u0026thinsp;=\u0026thinsp;2000 ms, TE\u0026thinsp;=\u0026thinsp;30 ms, flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg;, FOV\u0026thinsp;=\u0026thinsp;240 mm \u0026times; 240 mm, matrix\u0026thinsp;=\u0026thinsp;64 \u0026times; 64, voxel size\u0026thinsp;=\u0026thinsp;3.75mm \u0026times; 3.75mm \u0026times; 5mm).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data preprocessing\u003c/h2\u003e \u003cp\u003eResting-state fMRI data preprocessing was conducted with statistical parametric mapping software (SPM 8; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fil.ion.ucl.ac.uk/spm/software/spm8/\u003c/span\u003e\u003cspan address=\"https://www.fil.ion.ucl.ac.uk/spm/software/spm8/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Data Processing \u0026amp; Analysis for Brain Imaging (DPABI v4.3; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rfmri.org/dpabi\u003c/span\u003e\u003cspan address=\"http://rfmri.org/dpabi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The following steps were taken: (1) the first 5 time points were removed avoid scanner instability and to allow the participant adapt to the scanning circumstances; (2) slice-timing was applied to correct the differences in image acquisition time between slices; (3) head motion correction was performed; (4) spatial normalization to the Montreal Neurological Institute (MNI) space via the deformation fields derived from tissue segmentation of structural images (resampling voxel size\u0026thinsp;=\u0026thinsp;3 mm \u0026times; 3 mm \u0026times; 3 mm); (5) the functional images were smoothed using a 6-mm full-width at half maximum Gaussian kerne; (6) the time course of linear trend was removed; (7) covariates including Friston-24 parameters were regressed out [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]; (8) a band-pass filtering (0.01\u0026ndash;0.08 Hz) was applied.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Whole-Brain functional integration\u0026mdash; DC analysis\u003c/h2\u003e \u003cp\u003eDC was used to observe the functional connectivity at the whole-brain scale. The time series of each voxel from the processed fMRI data of the volunteer were correlated with the time series of every other voxel using Pearson's correlation and a matrix of Pearson's correlation coefficients were obtained. We eliminated low temporal correlations caused by signal noise by thresholding the correlation coefficients at \u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.25. To calculate degree centrality, we counted the sum of the weights of the positive connections of each voxel. To allow comparison of maps across subjects, the DC value of each voxel was then converted into a standardized z-score by subtracting the mean DC value and dividing the standard deviation of the whole-brain DC map. Finally, we spatially smoothed the standardized DC maps with a 6 mm FWHM Gaussian kernel.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Voxel-wise effective connectivity\u0026mdash; GCA\u003c/h2\u003e \u003cp\u003eBased on the results of the DC analysis, we determined seed region that showed significant difference between MWoA and HCs. The causal connectivity was analyzed by using REST-GCA in the REST toolbox[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The time series of seed region was designated as the seed time series x, while the time series of whole-brain regions were defined as y. The signed path coefficients were subsequently calculated as GCA values, including the linear direct effect of x to y (x\u0026rarr;y) and y to x (y\u0026rarr;x). Therefore, two Granger causality maps were constructed based on each subject's influence measures. Finally, the GCA maps were converted to z-values maps using Fisher\u0026rsquo;s r-to-z transformation to improve normality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Correlation analysis\u003c/h2\u003e \u003cp\u003eA Sparman correlation analysis was used to assess the association between each measure of DC and GCA with the clinical data, including disease duration, attack frequency, and the VAS, HIT-6, and MSQ scores. Only correlations with \u003cem\u003eP\u003c/em\u003e values less than 0.05 were considered significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Statistical analysis\u003c/h2\u003e \u003cp\u003eDemographic and clinical data were analyzed using SPSS statistical analysis software (SPSS 25.0). Continuous variables with normal distribution were described as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), while others were described as median (first quartile, third quartile). The difference between MWoA patients and HCs in age was tested with nonparametric Mann-Whitney U test and gender difference was tested with chi-square test.\u003c/p\u003e \u003cp\u003eTo analyze brain intrinsic activity, we used DPABI v4.3 for statistical analyses[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A two-sample t-test was conducted to compare the DC and GCA values (x to y and y to x respectively) between MWoA patients and HCs. Individual age and gender were treated as covariates during the group comparison to minimize their potential effects on the results. The results that remained after False Discovery Rate (FDR) correlation with voxel \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were consider to be significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Participants characteristics\u003c/h2\u003e \u003cp\u003eThe demographic characteristics and clinical assessment of all patients were summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There was no significant difference in age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.423) and gender (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.705) between MWoA patients and HCs.\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\u003eDemography and clinical scores of the MWoA patients and healthy control\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMWoA patients\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealthy controls\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.00 (25.50, 42.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.00 (25.00, 45.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (male/female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12/41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10/41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of illness (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.00 (42.00, 121.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttack frequency per month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (3, 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage duration of each attack (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.80 (4.10, 15.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnable to study and work during hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.00 (0.25, 13.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAS scores (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.50 (4.28, 6.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIT-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.00 (58.00, 67.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSQ score, Restrictive Subscale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.00 (54.29, 80.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSQ score, Preventive Subscale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.00 (65.00, 90.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSQ score, Emotional Function Subscale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.00 (66.67, 93.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eVAS: visual analogue scale; HIT-6: headache impact test; MSQ: migraine-specific quality of life questionnaire; \u0026ldquo;-\u0026rdquo;: no data. \u003csup\u003ea\u003c/sup\u003e Values are represented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation; \u003csup\u003eb\u003c/sup\u003e Values are represented as the median (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e). \u003cem\u003eP\u003c/em\u003e values for age were obtained using the nonparametric Mann-Whitney U test, and the \u003cem\u003eP\u003c/em\u003e value for sex was obtained using the chi squared test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Degree centrality analysis\u003c/h2\u003e \u003cp\u003eDC depicts the functional connectivity of whole brain. Compared with HCs, we found significantly increased DC value in left angular grey and decreased DC value in left putamen nucleus in MWoA patients (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Specifically, the brain regions we found locate in DMN and striatum respectively.\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\u003eDifference in DC values between MWoA patients and HCs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBrain regions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePeak MNI coordinates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVoxel size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eT value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ey\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePUT.L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-6.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANG.L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTwo-sample t-test (FDR corrected, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Effective connectivity from and to the PUT.L.\u003c/h2\u003e \u003cp\u003eCompared with HCs, patients with MWoA exhibited increased EC values from PUT.L to SFGmed.R, and decreased EC values from PUT.L to SMG.R, SFGdor.R, and PoCG.R (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In contrast, MWoA patients showed increased EC value to PUT.L from SFGdor.R (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, FDR corrected, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifference in EC in WMoA patients compared to HCs from and to the PUT.L\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBrain region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003ePeak MNI coordinates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVoxel size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eT value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCausal outflow from PUT.L to the rest of brain (x to y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSFGmed.R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSMG.R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSFGdor.R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoCG.R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCausal inflow to PUT.L from the rest of brain (y to x)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSFGdor.R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTwo-sample t-test (FDR corrected, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). SFGmed.R: right cerebrum of superior frontal gyrus, media; SMG: supramarginal gyrus; PoCG.R: right cerebrum of postcentral gyrus. SFGdor.R: right cerebrum of superior frontal gyrus, dorsolateral.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Effective connectivity from and to the ANG.L.\u003c/h2\u003e \u003cp\u003eCompared with HCs, patients with MWoA exhibited decreased EC values from ANG.L to CAU.L, ORBmid.L and ORBsupmed.R (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, FDR corrected, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). In contrast, MWoA patients showed increased EC value to ANG.L from bilateral caudate and CUN.R (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, FDR corrected, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifference in EC in WMoA patients compared to HCs from and to the ANG.L\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBrain region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003ePeak MNI coordinates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVoxel size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eT value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCausal outflow from ANG.L to the rest of brain (x to y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAU.L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-4.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eORBmid.L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-4.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eORBsupmed.R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCausal inflow to ANG.L from the rest of brain (y to x)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAU.R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAU.L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCUN.R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTwo-sample t-test (FDR corrected, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). CAU: caudate nucleus; ORBmid.L: left cerebrum of middle frontal gyrus, orbital part. ORBsupmed.R: right cerebrum of superior frontal gyrus, medial orbital. CUN.R: right cerebrum of cuneus.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Correlation with clinical scores\u003c/h2\u003e \u003cp\u003eWe found that the signed-path coefficients of PUT.L to PoCG.R was inversely correlated with the headache attack frequency per month (\u003cem\u003er\u003c/em\u003e = -0.275, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA); and the signed-coefficients of CAU.R to ANG.L was positively correlated with the medical history (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.306, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). No significant correlation was found in DC analysis between clinical scores, and EC analysis between PUT.L or ANG.L and average duration of each attack, VAS scores, HIT-6 scores and MSQ scores.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e \u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this study is the first to combine whole brain functional connectivity and EC to characterize abnormal connectivity in MWoA patients compared to HCs. We utilized DC analysis to investigate impaired functional hubs in MWoA patients we found decreased DC value in PUT.L and increased DC value in ANG.L, which respectively belongs to subcortical region (striatum) and cortical area (DMN). To further investigate the directional influence, we employed PUT.L and ANG.L as seeds to evaluate their EC with whole brain applying GCA and the altered EC were mainly in striatum-cortical network. In addition, the EC abnormalities from PUT.L to PoCG.R and CAU.R to ANG.L were significantly correlated with headache attack frequency and duration of illness. Conclusively, these finding confirmed the hypothesis that MWoA exhibited abnormal functional and effective connectivity in subcortical brain regions (striatum) and cortical network including DMN, SMN and attention network, and the dysfunction were related to pain sensory during the occurrence and progression of migraine. Specifically, on the one hand, the pain sensory of migraine is amplified by the weakened modulation of pain signals from the subcortex (striatum) to the cortex network, and on the other hand, the cortical network (attention network) pays too much attention to pain signals from the subcortex (striatum), resulting in excessive convergence of pain signals.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Impaired whole-brain functional hubs in MWoA\u003c/h2\u003e \u003cp\u003eMigraine involves extensive functional abnormalities in the cortex and subcortical brain regions[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Our study found that the dysfunctional brain regions belong to cortical (DMN) and subcortical areas (striatum). Degree centrality represents the status and role of voxels in the whole-brain network[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. We discovered the DC value of left putamen significantly decreased in MWoA patients compared with HCs. The putamen is part of striatum, being activated frequently during pain attacking[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Previous study reported that putamen may play a significant role in the mechanisms that convert nociceptive information into pain sensory[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and a diffusion tensor imaging (DTI) study which could quantify the chance from one brain region to other areas showed that the putamen connected with several regions pertained to the procession of pain sensory such as DMN (middle frontal gyrus), insula and hippocampus[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Thus, the decreased DC value indicated weaker functional connectivity with other regions, which may lead to dysregulation of pain sensation.\u003c/p\u003e \u003cp\u003eWe also observed increased DC value in left angular in MWoA patients compared with HCs. The angular locates at posterior part of the inferior parietal, a study using diffusion tensor imaging and tractography techniques explored rich structural link between angular with other areas including precuneus, caudate, frontal and temporal gyrus[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], which provided structural support that angular participates in consisting of DMN and its relationship with striatum. In addition, the angular converges multisensory information involving pain[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], increased DC value may result in excessive convergence of pain signal, then amplified the pain sensory.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Effective connectivity from striatum to cortical network\u003c/h2\u003e \u003cp\u003eTo further investigate the direction of functional connectivity, we selected PUT.L and ANG.L as seeds to perform the GCA. Our study indicated significantly increased EC from PUT.L to SFGmed.R, and decreased EC from PUT.L to SMG.R, SFGdor.R and PoCG.R.\u003c/p\u003e \u003cp\u003eThe SFGmed.R and SMG.R were part of DMN, the medial frontal cortex was correlated with cognitive control, pain and emotion especially negative emotion[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], increased EC value may indicate the pathway from pain sensory to pain emotion was overactivated, which may lead to emotion disorder such as depression[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and anxiety[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]; the supramarginal gyrus is part of somatosensory association cortex, participating in somatosensory integration and interpretation, and right supramarginal gyrus is further related to attention reorientation and distribution[\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], several studies reported that distracting attention can relief pain[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], decreased EC value from PUT.L to SMG.R may imply that the integration of pain signals input from subcortical areas as well as the ability to distract attention were inhibited in MWoA patients, contributing to intolerable pain sensory.\u003c/p\u003e \u003cp\u003eIn addition, we explored decreased EC value from PUT.L to PoCG.R correlating with headache attack frequency, postcentral belongs to SMN, previous study focusing on alteration in sensorimotor network effective connectivity promoted that the SMN may be affected by abnormal inflow or outflow information from putamen[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and another study inferred the frequency of pain correlated with SMN and dorsal striatum[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Therefore, our study may further confirm the abnormal directional connection between the striatum and SMN, and the alteration is related to the frequency of headache attack.\u003c/p\u003e \u003cp\u003eMoreover, we observed markedly increased effective connectivity from bilateral caudate to ANG.L as well as decreased effective connectivity from ANG.L to CAU.L. As the key part of basal ganglia, the caudate participates in cognitive, sensory and pain modulation[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], our study found interaction between the angular and the caudate, manifesting as the effective connectivity from angular to caudate decreased while that from caudate to angular increased in MWoA patients, which may imply that the processing of pain information in the striatum affects the perception of pain signal in the cortical network. Previous study reported the abnormal functional connectivity of right caudate in chronic migraine[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and we also investigated altered effective connectivity from right caudate to angular, which was positively correlated with duration of illness. It further suggested that dysregulation of caudate may be an important neuroimaging marker in migraine progression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Effective connectivity from cortical network to striatum\u003c/h2\u003e \u003cp\u003eWe observed increased EC value to from SFGdor.R to PUT.L. The dorsolateral prefrontal cortex (DLPFC) is a pivotal structure in dorsal attention network (DAN) involving in top-down attention orientation, together with attention reorientation system of right-lateralized ventral attention network (VAN), consist of two attention systems in human brain[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], our study observed increased EC from right DLPFC to left putamen, and the EC was inhibited from left putamen to right DLPFC, implying that the excessive attention of DAN on regulation pain information of the striatum may trigger pain, we also found abnormal EC between right-lateralized ventral attention network and striatum (decreased EC value from PUT.L to SMG.R), it can be speculated that the abnormal EC between the two attention networks may be the neuroimaging mechanism of the relationship between pain sensation and attention.\u003c/p\u003e \u003cp\u003eIn conclusion, the striatum-DMN may play a role in modulating the transition from pain sensory to pain emotion, while the striatum-SMN may be associated with the frequency of pain sensory experiences. Additionally, the striatum-attention network may be involved in processing both pain sensory information and attentional aspects in patients with MWoA.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Limitations","content":"\u003cp\u003eThere are several limitations to our study that need further study in the future. Firstly, we currently focus on abnormal EC between the default network and the striatum, but it seems that there are also abnormal EC among the attention network and the sensory motor network, further research is needed to investigate the altered connectivity across networks. Secondly, we did not perform any cerebral structural alteration based on the abnormal brain regions, future studies are warranted to assess the structural brain changes using VBM or DTI to further clarify the neuroimaging mechanism in MWoA.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eOur study validated the hypothesis that the functional and effective connectivity between subcortex and cortex were abnormal in MWoA patients compared with HCs, manifesting as alteration in striatum-cortex network, and the inflow and outflow information in striatum-cortex network were correlated with the frequency of headache attack and duration of illness, which may contribute to clarify neuroimaging mechanism of pain sensory during migraine onset, and the functional abnormality may be an adjunctive biomarker in evaluating severity of migraine and the efficacy of therapeutic intervention.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMWoA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMigraine without aura\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eEC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEffective connectivity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHCs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealthy controls\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDegree centrality\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGCA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGranger causality analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePUT.L\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeft putamen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eANG.L\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeft angular gyrus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSFGmed.R\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRight superior frontal gyrus, medial\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSMG.R\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRight supramarginal gyrus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSFGdor.R\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRight superior frontal gyrus, dorsolateral\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePoCG.R\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRight postcentral gyrus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDMN\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDefault mode network\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSMN\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSensorimotor network\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eVAS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVisual analogue scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHIT-6\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHeadache impact test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMSQ\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMigraine-specific quality of life questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFDR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFalse Discovery Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDAN\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDorsal attention network\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eVAN\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVentral attention network\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eall participants were informed in detail about the study and volunteered to sign an informed consent form. Our study was approved by the Ethics Committee of the Hospital of Chengdu University of TCM (Ethics Approval No. 2020KL-003), and was registered with the China Clinical Trial Registry (Registration No. ChiCTR2000032308).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e all authors consent for the publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003edata can be made available upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement:\u003c/strong\u003e the authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Grant Number: 81973962 and 82204919), the Innovation Team and Talents Cultivation Program of the National Administration of Traditional Chinese Medicine (Grant Number: ZYYCXTD-D-202003), and China Postdoctoral Science Foundation (Grant Number: 2022MD713681).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Figdraw (\u003ca href=\"http://www.figdraw.com\"\u003ewww.figdraw.com\u003c/a\u003e) for visual abstract editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy protocol and design: LZ, MS and XW; acquisition of data: CX, XN, XG, LD; analysis and interpretation of data: ZZ, XW, YO; and drafting of the manuscript: ZZ, XW, LZ, QY, QF. All author(s) read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlobal, regional, and national burden of neurological disorders, 1990\u0026ndash;2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol, (2019) 18(5): p. 459\u0026ndash;480\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eHeadache Classification Committee of the International Headache Society (IHS) The International Classification of Headache Disorders, 3rd edition.\u003c/em\u003e Cephalalgia, (2018) 38(1): pp. 1-211\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkerman S, Romero-Reyes M, Holland PR (2017) Current and novel insights into the neurophysiology of migraine and its implications for therapeutics. Pharmacol Ther 172:151\u0026ndash;170\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCharles A (2013) Migraine: a brain state. 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Pain 66(2\u0026ndash;3):145\u0026ndash;150\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMancini F, Zhang S, Seymour B (2022) Computational and neural mechanisms of statistical pain learning. Nat Commun 13(1):6613\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorsook D et al (2010) A key role of the basal ganglia in pain and analgesia\u0026ndash;insights gained through human functional imaging. Mol Pain 6:27\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFox MD et al (2006) Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc Natl Acad Sci U S A 103(26):10046\u0026ndash;10051\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"migraine without aura, resting-state fMRI, degree centrality, effective connectivity, granger causality analysis","lastPublishedDoi":"10.21203/rs.3.rs-4594035/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4594035/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction: \u003c/strong\u003eMigraine without aura (MWoA) is a brain network disorder involving abnormal activity in subcortical and cortical brain regions. However, the functional alteration of key nodes and the flow of information within and between brain network in MWoA remain unclear. Thus, we aim to explore functional and effective connectivity (EC) to investigate relationship between impaired brain connectivity and migraine onsets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eFifty-three MWoA patients and 51 age- and sex-matched healthy controls (HCs) were enrolled in this study. Degree centrality (DC) analysis was used to measure the whole brain functional connectivity, and the abnormal brain regions found by DC were regarded as seeds to perform Granger causality analysis (GCA) to explore EC. Furthermore, a correlation analysis was conducted to determine the relationship between brain abnormalities and clinical symptoms in MWoA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eMWoA patients exhibited decreased DC value in left putamen (PUT.L) and increased DC value in left angular gyrus (ANG.L) in whole brain functional integration compared with HCs. In EC, from subcortex to cortex, we found altered EC values from PUT.L to right superior frontal gyrus, medial, right supramarginal gyrus, right superior frontal gyrus, dorsolateral (SFGdor.R) and postcentral gyrus (PoCG.R), and altered EC from bilateral caudate (CAU) to ANG.L. From cortex to subcortex, we observed altered EC value from SFGdor.R to PUT.L, and from ANG.L to left caudate. Furthermore, we found that the EC value from PUT.L to PoCG.R was inversely correlated with the frequency of headache attack and the EC value from CAU.R to ANG.L was positively correlated with duration of illness in MWoA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eOur study validated the hypothesis that the functional and effective connectivity between subcortex and cortex were abnormal in MWoA patients compared with HCs, manifesting as alteration in striatum-cortex network, and the inflow and outflow information in striatum-cortex network were correlated with the frequency of headache attack and duration of illness, which may contribute to clarify neuroimaging mechanism of pain sensory during migraine onset, and the abnormality may be an adjunctive biomarker in evaluating severity of migraine and the efficacy of therapeutic intervention.\u003c/p\u003e","manuscriptTitle":"Impaired brain functional hubs and effective connectivity of striatum-cortical network in migraine without aura: a resting-state fMRI study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-11 16:50:18","doi":"10.21203/rs.3.rs-4594035/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2605247f-a352-47a5-8cb2-51bf6775a0ee","owner":[],"postedDate":"July 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-11T16:50:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-11 16:50:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4594035","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4594035","identity":"rs-4594035","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}