Enhancement of synchronizability in chronic migraine patients related to the increase of connectome interhemispheric connectivity

Enhancement of synchronizability in chronic migraine patients related to the increase of connectome interhemispheric connectivity | 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 Enhancement of synchronizability in chronic migraine patients related to the increase of connectome interhemispheric connectivity Tomás Hüttebräucker, Álvaro Planchuelo-Gómez, David García-Azorín, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3154214/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 The structure of the brain can be characterized as a set of interconnected regions, using the concept of connectome . It has been shown that the connectome is altered in patients with migraine, presenting a series of structural connections differences with respect to healthy subjects. Relating these structural alterations to the specific characteristics of migraine is a difficult task. One approach is to study the effects of these alterations on the dynamic processes that can take place in the connectome. An ubiquitous process in brain networks is the synchronization of neuronal populations. It depends not only on the network but also on the properties of the units being synchronized. Fortunately, the contribution of the network structure can be calculated independently, thus defining the network synchronizability . Connectome synchronizability and structural connectivity were assessed using diffusion-weighted magnetic resonance imaging data from 56 female patients with episodic migraine, 60 female patients with chronic migraine (CM) and 39 female healthy controls (HC). We found that whole-brain synchronizability was significantly larger in CM than in HC. The analysis of the synchronizability of subnetworks showed that differences between CM and HC were larger for subnetworks involving regions from different hemispheres. Moreover, the number of interhemispheric streamlines was significantly larger in CM than in HC, whereas no such difference appeared between intrahemispheric streamlines. The largest contributions to these differences come from the interhemispheric connections from three regions: left superior frontal gyrus, right precentral cortex, and right caudate. Migraine Connectome Synchronization Interhemispheric connectivity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Migraine is a primary headache disorder characterized by recurrent attacks with unilateral location, pulsatile character and moderate to severe intensity. It is a highly prevalent condition (14%, Stovner et al., 2022), two to three times more prevalent in women than in men (Vetvik and MacGregor, 2017). Migraine remains the second most frequent cause of disability worldwide, and the first among young women (Steiner et al., 2020). According to the International Classification of Headache Disorders (ICHD-3, 2018), migraine can be classified as chronic (CM, headache occurring on 15 or more days/month with at least 8 days per month of migraineous pain, for more than 3 months) or episodic (EM, headache occurring on less than 15 days/month). Many theories about migraine pathophysiology have been proposed, but there is a growing consensus that it can be considered as a brain disorder (Charles, 2013; Goadsby et al., 2017; Szabo et al., 2022). Thus, it might be related to structural and functional alterations of the brain. The brains of migraine patients and healthy controls have been reported to be different in several ways (Borsook et al., 2014), in particular in their morphometrical characteristics. A meta-analysis reported gray matter volume increases and decreases in migraine patients in multiple brain regions associated with sensation, affection, cognition, and descending modulation aspects of pain (Zhang et al., 2023). Regarding the structural integrity of white matter, some have reported no differences between healthy controls (HC) and CM patients (Neeb et al., 2015; Planchuelo-Gómez et al., 2020a), but it has been argued that this could be due to symptomatic treatment overuse, since such differences appear when this feature is controlled for (Coppola et al., 2020). Differences between the brains of patients with CM and EM have also been found, both in gray and white matter (Schwedt et al., 2016; Filippi and Messina, 2021; Planchuelo-Gómez et al., 2020a, Planchuelo-Gómez et al., 2020b). One aspect that has received less attention in migraine studies is the connectivity between brain areas, quantified using the concept of connectome (Sporns, 2010). This is a representation of the brain as a network, whose vertices, representing brain areas, are connected by edges, representing nerve fibers or some proxy for them (structural connectome), or temporal correlations (functional connectome). This has allowed researchers to apply concepts and measures from Graph Theory to study the brain (Rubinov and Sporns, 2010). Connectomes are built using non-invasive imaging approaches, such as diffusion-weighted magnetic resonance imaging (dMRI) for structural connectomes or functional (fMRI) for functional connectomes. Images obtained using dMRI provide information about the diffusion properties of water inside axonal bundles (Mori, 2007). Tractography algorithms (Zhang et al., 2022) are used to reconstruct the paths of fiber bundles between brain regions, sometimes called streamlines . Connectomes are best suited to understand brain processes that involve several brain areas. Connectome alterations have been associated with diverse brain disorders (Fornito et al., 2015). Even though migraine has been much less studied in this context, structural connectome alterations have also been found in migraine patients (Li et al., 2017; Planchuelo-Gómez et al., 2020; Silvestro et al., 2021). In general, the relationship between these alterations and the pathophysiology of a disease is inferred either from general considerations or from modeling exercises (Alstott et al., 2009; Taylor et al., 2018). But the specific contribution of the connectome is difficult to disentangle from the contribution of the specific dynamical model used for each node. An important exception is the phenomenon of synchronization (Arenas et al., 2008). Many collective phenomena happening in the brain, from rhythms to seizures, are based in synchronization of neural populations. But synchronization is not only associated with processes in the healthy brain, but also with some brain disorders, such as schizophrenia, epilepsy, autism, Alzheimer’s and Parkinson’s diseases (Uhlhaass and Singer, 2006). In the mathematical analysis of networks of oscillating systems there is a formalism (Master Stability Function, MSF, Pecora and Carroll, 1998) that provides a condition for synchronization that is independent of the oscillators, leading to the definition of the synchronizability of a network. In biological terms, the synchronizability coefficient measures the capability of a brain to sustain processes involving the synchronization of several regions. In other words, in less synchronizable brains synchronization processes would die out faster. This measure is largely independent of the details of the processes involved, since the MSF formalism applies to a large class of synchronizing systems (Arenas et al., 2015). To study the relationship between migraine and synchronizability, the structural connectomes of patients with EM and CM, and HC were analyzed. We hypothesized that there may be differences between the synchronizabilities of HC and patients with migraine, particularly CM. Furthermore, we aimed at finding the most important features of the connectomes responsible for these differences. Methods Study design We conducted an analytical observational study with a case-control design. Most participants were a subset of the sample included in Planchuelo-Gómez et al. (2020a), and all were recruited in the same environment. Briefly, the clinical inclusion criteria for patients included diagnosis of migraine according to the ICHD-3 and a clinical stable situation. Controls were recruited based on the absence of prior history of migraine. Patients with high-frequency EM were excluded, and both cases and controls were excluded if they had other painful and neurological disorders different to migraine, major psychiatric disease, drug or substance abuse or pregnancy. Only female participants were included in the final sample. Full details are available in the Supplementary Material. MRI acquisition The full MRI acquisition protocol, described in Planchuelo-Gómez et al. (2020a), is available in the Supplementary Material. Image processing T1- and diffusion-weighted images were processed to obtain structural connectivity matrices, following a pipeline described in detail in Planchuelo-Gómez et al.(2020a). Briefly, the preprocessing for the T1-weighted data included non-brain tissue removal with BET (Jenkinson et al., 2012; Smith, 2002) and gray matter parcellation. For the DWI volumes the steps were Marchenko-Pastur Principal Component Analysis denoising (Tournier et al., 2019; Veraart et al., 2016), and correction for eddy currents, motion and B1 field inhomogeneity (Andersson et al., 2015). Probabilistic anatomically-constrained tractography (ACT) was performed from five-tissue-type (5TT) segmented images and the estimated fiber orientation distributions (FOD) (Dhollander et al., 2016). Structural connectivity matrices were computed from tractography output and the registered gray matter parcellation maps. 84x84 connectivity matrices, corresponding to the 84 cortical and subcortical regions from the Desikan-Killiany atlas (Desikan et al., 2006), were obtained using the number of streamlines in each connection as connectome metrics. Due to the employed tractography method, the streamlines obtained were non-directional. Therefore, the matrices obtained were symmetric. Furthermore, streamlines starting and ending in different points of the same gray matter region (“self-connections”) were removed. Thus, the diagonal elements of the connectome were 0. To avoid false positives, removal of weak connections ( pruning ) was performed until only 20% of the matrix elements presented non-zero values. Because the cerebellum is weakly connected to other regions, the two regions (left and right) were removed from the connectome. Measures of synchronizability Following the MSF formalism, we consider a network whose nodes are identical oscillating systems (each one encompassing many neurons), interacting through the connections specified by a connectome. The strength of the coupling between regions is the product of the corresponding elements in the connectome and a coupling constant s that depends on the oscillator type. The method is independent of the exact nature of the oscillators, since the results apply to two large classes of dynamical systems (type I and type II, Boccaletti et al., 2006). For type II oscillators there exists a threshold value s min such that for s>s min synchronization is always robust. For type I oscillators the synchronized state is robust only in the range s min <ss max or s<s min the oscillators cannot synchronize (Pecora and Carroll, 1998, Arenas et al., 2008). For type II oscillators, the synchronization threshold s min is inversely proportional to l 2 , the second smallest eigenvalue of the Laplacian matrix L. In networks with smaller l 2 , oscillators will be easier to synchronize. Thus, l 2 provides a measure of type II synchronizability of the network. For type I oscillators, s min has the same properties as for type II, but s max is inversely proportional to l n , the largest eigenvalue of L. Thus, the larger the distance between l 2 and l n , the easier it is to synchronize a system of oscillators. Therefore R=l n /l 2 provides a measure of type I synchronizability . Synchronizability in the whole brain and in subnetworks The MSF formalism was applied to the whole-brain connectivity matrix, and separately to regions from each hemisphere. We afterwards generated 300 subnetworks of 30 regions each. To avoid any systematic bias, the 30 regions of each subnetwork were randomly chosen from the regions in a single hemisphere, or from both hemispheres without restriction. For each subnetwork the connections between the 30 regions were the corresponding direct connections in the connectome. All other regions were removed, giving a 30x30 connectivity matrix. Intra- and inter-hemispheric connectivity The inter-hemispheric connectivity of each connectome was characterized as the number of streamlines having their extremes in each hemisphere. The remaining streamlines were characterized as intra-hemispheric . The relative improvements (or increases) of synchronizabilities in CM when compared with HC are defined as 100*(l 2 CM -l 2 HC )/l 2 HC (Type II), and 100*(R CM -R HC )/R HC ) (Type I). Statistical analysis Statistical analyses were performed using R version 4.2.2 (R Core Team, 2022). Shapiro-Wilk and Levene’s tests were used to assess normality and homogeneity of variance, respectively. To test for significant differences in the age of HC, EM and CM patients, a Kruskal-Wallis test was used, since the assumption of normality was not met. To test for significant differences in duration of migraine history, headache frequency, and migraine frequency Mann-Whitney-Wilcoxon tests were used, since normality was not met. To test for significant differences in synchronizability measures and in numbers of streamlines between the groups a one-way ANOVA was used if the assumptions of normality and homogeneity of variance were met. If normality was met but there was no homogeneity, a Welch’s ANOVA test was used. If the normality was not met, a Kruskal-Wallis test was used. When significant differences were found, a post-hoc analysis was performed. Tukey’s test was used for ANOVA, Games-Howell test for Welch’s ANOVA, and Conover-Iman test for Kruskal-Wallis. To correct for multiple comparisons, the Benjamini-Hochberg (Benjamini and Hochberg, 1995) method was used. Cohen’s d was used to describe the effect size of the comparisons. Results Demographic and clinical characteristics A total of 39 HC, 57 EM and 61 CM patients were enrolled. Two patients (one CM and one EM) were excluded because of inconsistencies in the obtained connectome. Demographic and clinical data for the three groups are summarized in Table 1. Statistically significant differences were found in duration of migraine history, and headache and migraine frequency between the two migraine groups. Table 1 Participants characteristics HC (n=39) EM (n=56) CM (n=60) Statistical test Age (years) 36.4 (14.7) 37.4 (11.4) 39.8 (9.9) c²(2) = 3.93, p= 0.14 † Duration of migraine history (years) 13.1 (10.5) 20 (11) W = 1189, p= 0.005 ‡ Time from onset of chronic migraine (months) 26.8 (35.2) Headache frequency (days/month) 3.61 (1.93) 23.7 (6.5) W = 1711, p<0.001 ‡ Migraine frequency (days/month) 3.61 (1.93) 13.2 (6.3) W = 1685, p<0.001 ‡ Values are means (SD). † Kruskal-Wallis, ‡: Mann-Whitney-Wilcoxon. Synchronizability of migraine connectomes Both synchronizability measures (l 2 and R) were significantly larger for CM patients than for HC (Fig. 1) and showed medium effect size (d > 0.5, d=0.53 for l 2 and d=0.56 for R). In all the assessments of this study the values for EM patients were intermediate between those of HC and CM, but the differences were not statistically significant. No significant difference between patients with migraine and HC in the synchronizability of each single hemisphere was found (Fig. 1). Synchronizability of smaller brain circuits Given the results of the previous section, we focused here in the comparison between CM and HC for the subnetworks assessment. Fig. 2 shows, for 300 random brain circuits, the relative improvement of synchronizabilities in CM (defined in Methods). These quantities were much larger for subnetworks composed from regions from different hemispheres, than for circuits composed by regions from the same hemisphere (Fig 2). Inter- vs intra-hemispheric connectivity in migraineurs and controls Fig. 3 shows that there was a significant difference in inter-hemispheric streamlines between CM and both HC and EM (medium effect sizes d=0.52 and 0.5, respectively), whereas no such difference was found for intra hemispheric streamlines. Figure 4 shows the regions that contributed most to these differences within each hemisphere. Approximately 75% of the total interhemispheric connections involved the same five regions. We analyzed the number of interhemispheric streamlines involving these five bilateral regions and found statistically significant differences in the interhemispheric connectivities of three regions: left superior frontal gyrus, right precentral cortex, and right caudate. In all these cases, CM patients showed increased number of streamlines compared to HC (Fig. 5). Discussion In the present study we analyzed the structural connectomes, computed from dMRI data, of female migraine patients and HC using synchronizability measures derived from the MSF formalism. These measures are used to assess the ability of a network of brain regions to sustain a synchronization process. Our two main findings were that, when compared with HC, the connectomes of CM patients presented higher synchronizability and more inter-hemispheric streamlines. Neuronal synchronization (Varela et al., 2001; Guevara Erra et al., 2017), underlies many brain processes involving the propagation of neuronal activity. There is a growing consensus that migraine should be considered as a neuronal disorder (Charles 2013; Goadsby et al., 2017; Szabo et al., 2022) and, since migraineous pain affects large areas of the head and causes multiple symptoms, it seems reasonable to expect that some form of collective neuronal activity could occur during migraine attacks. It has been proposed that a wave of hyperpolarizability followed by depression of neuronal activity, the cortical spreading depolarization (CSD), activates the trigeminal system to cause a headache (Dalkara et al., 2006; Harriott et al., 2021). However, there are probably other mechanisms that remain largely unknown. Here, instead of studying a specific synchronization process, a formalism was applied that allows us to isolate the contribution of the network for a large class of synchronization processes. The rigidity of some assumptions has led some to question the utility of the concept of synchronizability for brain networks (Papo and Buldú 2019), but they can be considerably relaxed. For example, it is assumed that all oscillators are identical, but recently it was shown (Sun et al., 2009, Pereira et al., 2013) that when oscillators are different they may deviate from the synchronized state, but the fluctuations are inversely proportional to l 2 . Here we assessed whole-brain synchronizability and, to represent realistic situations, subsets of the whole connectome were also assessed. We found that, for brain circuits including areas from both hemispheres, synchronizability was higher for CM than for HC, whereas no difference appeared for intrahemispheric circuits. Consistently, synchronizability of the whole connectome was also larger in CM. An increased functional synchronizability has also been found in schizophrenia (Zhu et al., 2020), Alzheimer’s disease (de Haan et al., 2012) and epilepsy (Schindler et al., 2008). Note that larger synchronizability does not imply the synchronization of any particular process. It just implies that almost any process involving synchronization would be facilitated. Interestingly, in migraine patients under periodic visual stimulation EEG recordings in the alpha band were more synchronized across many sensors in the cortex than in HC undergoing the same stimulation (Angellini et al., 2009; De Tomasso et al., 2013). The increase in synchronizability for CM was associated with a significant increase in interhemispheric connectivity. The main differences were in the streamlines that originate in (or end in) the left superior frontal and right precentral areas, and in the right caudate. Superior frontal cortex has been associated with pain regulation (Zhang et al., 2012). When compared with HC, an increased functional connectivity with the left superior frontal cortex has been observed in migraine patients (Cao et al., 2022). Precentral cortex is involved in pain modulation and it was proposed as one of the possible targets for transcranial magnetic stimulation or deep brain stimulation in neuropathic pain patients (Lefaucheur et al., 2001; Mertens et al., 1999; Ayache 2016). Anatomically, the caudate nucleus is connected with the amygdala and the insula, relevant regions in migraine pathophysiology, associated with cognitive and emotional aspects of migraine attacks (Çırak et al., 2020). In addition, the firing pattern of caudate nucleus neurons seems to change after cortical CSD (Seghatoleslam et al., 2020). There are clinical limitations worth describing. As most CM patients overused medication, we were unable to separately assess medication effects. In the sample of the study, only women were included, and no patients suffered from anxiety or depression. Thus, the effects of sex or frequent psychiatric comorbidities could not be assessed. In the case of sex, considering that CM is more prevalent in women compared to EM (Scher et al., 2019), and that interhemispheric connectivity is larger in women (Ingalhalikar et al., 2014, Hänggi et al., 2014), the analysis of men and women could help to understand whether interhemispheric connectivity is a causative factor for migraine. It is important to mention that, even though it has been argued that differences in interhemispheric connectivity only reflect differences in brain size or intracranial volume (Hänggi et al. 2014), we have found no correlation between brain size and migraine (see Supplementary Material). Regarding technical limitations, connections with few streamlines were eliminated from the connectome because they are likely to be spurious. To show that our results are not an artifact of pruning, the analysis was repeated without removing any connections from the connectome, and qualitatively the same results were found (see Supplementary Material). Conclusions The structural connectome of CM female patients is more synchronizable that the one of HC, also when considering smaller subnetworks involving regions from different hemispheres. This was associated with a significant increase in structural interhemispheric connectivity in CM patients. The largest contributions to these differences come from the interhemispheric connections from left superior frontal gyrus, right precentral cortex, and right caudate. Declarations Acknowledgments This work was supported by research grant TED2021-130758B-I00, funded by MCIN/AEI/10.13039/501100011033 and the European Union “NextGenerationEU/PRTR” and PRE2019-089176 funded by MCIN/AEI/10.13039/501100011033 and ESF ”ESF invests in your future”. Author contributions Author contributions included conception and study design (SRG), data collection or acquisition (DG-A, ÁLG and RdL-G), data analysis (TH, AP-G, SRG), interpretation of results (TH, AP-G. SRG, RdL-G), drafting and revising the manuscript critically, and approval of final version to be published and agreement to be accountable on all aspects of the work (All authors). Funding Sources This work was supported by research grant TED2021-130758B-I00, funded by MCIN/AEI/10.13039/501100011033 and the European Union “NextGenerationEU/PRTR” and PRE2019-089176 funded by MCIN/AEI/10.13039/501100011033 and ESF ”ESF invests in your future”. Compliance with ethical standards The local Ethics Committee of Hospital Clínico Universitario de Valladolid approved the study (PI: 14–197). All participants read and signed a written consent form before their participation. Availability of data and material Anonymized data that support the findings of this study are available from the authors upon request. Funding This work was supported by research grant TED2021-130758B-I00, funded by MCIN/AEI/10.13039/501100011033 and the European Union “NextGenerationEU/PRTR” and PRE2019-089176 funded by MCIN/AEI/10.13039/501100011033 and ESF ”ESF invests in your future”. Competing interests All authors declare no conflicts of interest relevant to the content of this article. Ethical approval The local Ethics Committee of Hospital Clínico Universitario de Valladolid approved the study (PI: 14–197). All participants read and signed a written consent form before their participation. Consent to participate All patients who participated in the study signed a written informed consent. Consent for publication Not applicable. Availability of data and material Anonymized data that support the findings of this study are available from the authors upon request. Code availability Not applicable. References Alstott, J., Breakspear, M., Hagmann, P., Cammoun, L., & Sporns, O. (2009). Modeling the impact of lesions in the human brain. PLoS computational biology, 5(6) , e1000408. https://doi.org/10.1371/journal.pcbi.1000408 Andersson, J. L. R., & Sotiropoulos, S. N. (2016). An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage, 125, 1063–1078. https://doi.org/10.1016/j.neuroimage.2015.10.019 Angelini, L., de Tommaso, M., Guido, M., Hu, K., Ivanov, Pch., Marinazzo, D., Nardulli, G., Nitti, L., Pellicoro, M., Pierro, C., & Stramaglia, S. (2004). Steady-state visual evoked potentials and phase synchronization in migraine patients. Physical Review Letters, 93 , 038103. https://doi.org/10.1103/PhysRevLett.93.038103. Arenas, A., Díaz-Guilera, A., Kurths, J., Moreno, Y., & Zhou, C. (2008). Synchronization in complex networks, Physics Reports, 469 , 93-153. https://doi.org/10.1016/j.physrep.2008.09.002. Ayache, S.S., Ahdab, R., Chalah, M.A., Farhat, W.H., Mylius, V., Goujon. C., Sorel, M., & Lefaucheur, J.-P. (2016). Analgesic effects of navigated motor cortex rTMS in patients with chronic neuropathic pain. European Journal of Pain, 20 , 1413-1422. https://doi.org/10.1002/ejp.864. Benjamini, Y. & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 57(1):289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x. Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., & Hwang, D.-U. (2006). Complex Networks: Structure and Dynamics. Physics Reports, 424 , 175-308. https://doi.org/10.1016/j.physrep.2005.10.009. Borsook, D., Erpelding, N., Lebel, A., Linnman, C., Veggeberg, R., Grant, P.E., Buettner, C., Becerra, L., & Burstein, R. (2014). Sex and the migraine brain. Neurobiology of Disease, 68 , 200-214. https://doi.org/10.1016/j.nbd.2014.03.008. Cao, Z., Yu, W., Zhang, Z., Xu, M., Lin, J., Zhang, L., & Song, W. (2022). Decreased Gray Matter Volume in the Frontal Cortex of Migraine Patients with Associated Functional Connectivity Alterations: A VBM and rs-FC Study. Pain Research & Management, 25 , 2115956. https://doi.org/10.1155/2022/2115956. Charles A. (2013). Migraine: a brain state. Current opinion in neurology, 26(3), 235–239. https://doi.org/10.1097/WCO.0b013e32836085f4 Çırak, M., Yağmurlu, K., Kearns, K.N., Ribas, E.C., Urgun, K., Shaffrey, M.E., & Kalani, M.Y.S. (2020). The Caudate Nucleus: Its Connections, Surgical Implications, and Related Complications. World Neurosurgery, 139, e428-e438. https://doi.org/10.1016/j.wneu.2020.04.027. Coppola, G., Di Renzo, A., Tinelli, E., Petolicchio, B., Di Lorenzo, C., Parisi, V., Serrao, M., Calistri, V., Tardioli, S., Cartocci, G. et al., (2020). Patients with chronic migraine without history of medication overuse are characterized by a peculiar white matter fiber bundle profile. The Journal of Headache and Pain , 21, 92. https://doi.org/10.1186/s10194-020-01159-6 Dalkara, T., Zervas, N.T., & Moskowitz, M.A. (2006). From spreading depression to the trigeminovascular system. Neurological Sciences, 27 Supp2, S86-90. https://doi.org/10.1007/s10072-006-0577-z. de Tommaso, M., Stramaglia, S., Marinazzo, D., Trotta, G., & Pellicoro, M. (2013). Functional and effective connectivity in EEG alpha and beta bands during intermittent flash stimulation in migraine with and without aura. Cephalalgia , 33 , 938-947. https://doi.org/10.1177/0333102413477741. Desikan, R.S., Ségonne, F., Fischl, B., Quinn,B.T., Dickerson,B.C., Blacker, D., Buckner, R.L., Dale, A.M., Maguire, R.P., Hyman, B.T., et al., (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31(3), 968–980. https://doi.org/10.1016/j.neuroimage.2006.01.021 Dhollander, T., Raffelt, D. & Connelly, A. (2016). Unsupervised 3-tissue response function estimation from single-shell or multi-shell diffusion MR data without a co-registered T1 image. ISMRM Workshop on Breaking the Barriers of Diffusion MRI, 5 . https://mrtrix.readthedocs.io/en/latest/ reference/commands/dwi2mask.html . Filippi, M., & Messina, R. (2020). The Chronic Migraine Brain: What Have We Learned From Neuroimaging? Frontiers in Neurology, 10, 1356. https://doi.org/10.3389/fneur.2019.01356. Fornito, A., Zalesky, A., & Breakspear, M. (2015).The connectomics of brain disorders. Nature Reviews Neuroscience, 16, 159-72. https://doi.org/10.1038/nrn3901. Goadsby, P.J., Holland, P.R., Martins-Oliveira, M., Hoffmann, J., Schankin, C., & Akerman, S. (2017). Pathophysiology of Migraine: A Disorder of Sensory Processing. Physiological Reviews, 97, 553-622. https://doi.org/10.1152/physrev.00034.2015. Guevara Erra, R., Perez Velazquez, J.L., & Rosenblum, M. (2017). Neural Synchronization from the Perspective of Non-linear Dynamics. Frontiers in Computational Neuroscience, 11 , 98. https://doi.org/10.3389/fncom.2017.00098 de Haan, W., van der Flier, W. M., Wang, H., Van Mieghem, P. F., Scheltens, P., & Stam, C. J. (2012). Disruption of functional brain networks in Alzheimer's disease: what can we learn from graph spectral analysis of resting-state magnetoencephalography?. Brain connectivity, 2(2), 45–55. https://doi.org/10.1089/brain.2011.0043 Hänggi, J., Fövenyi, L., Liem, F., Meyer, M., & Jäncke, L. (2014). The hypothesis of neuronal interconnectivity as a function of brain size-a general organization principle of the human connectome. Frontiers in Human Neuroscience, 8 , 915. https://doi.org/10.3389/fnhum.2014.00915. Harriott, A.M., Chung, D.Y., Uner, A., Bozdayi, R.O., Morais, A., Takizawa, T., Qin, T., & Ayata, C. (2021). Optogenetic Spreading Depression Elicits Trigeminal Pain and Anxiety Behavior. Annals of Neurology, 89 , 99-110. https://doi.org/10.1002/ana.25926. Headache Classification Committee of the International Headache Society. (2018). The International Classification of Headache Disorders, 3rd edition. Cephalalgia , 38, 1-211. Ingalhalikar, M., Smith, A., Parker, D., Satterthwaite, T. D., Elliott, M. A., Ruparel, K., Hakonarson, H., Gur, R. E., Gur, R. C., & Verma, R. (2014). Sex differences in the structural connectome of the human brain. Proceedings of the National Academy of Sciences of the United States of America, 111(2), 823–828. https://doi.org/10.1073/pnas.1316909110 Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., & Smith, S. M. (2012). FSL. NeuroImage, 62(2), 782–790. https://doi.org/10.1016/j.neuroimage.2011.09.015 Lefaucheur, J.P., Drouot, X., Keravel, Y., & Nguyen, J.P. (2001). Pain relief induced by repetitive transcranial magnetic stimulation of precentral cortex. Neuroreport. 12 , 2963-29655. https://doi.org/10.1097/00001756-200109170-00041. Li, K., Liu, L., Yin, Q., Dun, W., Xu, X., Liu, J., & Zhang, M. (2017). Abnormal rich club organization and impaired correlation between structural and functional connectivity in migraine sufferers. Brain imaging and behavior, 11(2), 526–540. https://doi.org/10.1007/s11682-016-9533-6 Mertens, P., Nuti, C., Sindou, M,, Guenot, M., Peyron, R., Garcia-Larrea, L., & Laurent, B. (1999). Precentral cortex stimulation for the treatment of central neuropathic pain: results of a prospective study in a 20-patient series. Stereotactic and Functional Neurosurgery, 73 , 122-125. https://doi.org/10.1159/000029769. Mori, S. (2007). Introduction to Diffusion Tensor Imaging . Elsevier. ISBN 0444528288, 9780444528285 Neeb, L., Bastian, K., Villringer, K., Gits, H.C., Israel,H., Reuter,U., & Fiebach, J.B. (2015). No microstructural white matter alterations in chronic and episodic Migraineurs: a case-control diffusion tensor magnetic resonance imaging study. Headache, 55, 241–251. https://doi.org/10.1111/head.12496 Papo, D., & Buldú, J.M. (2019). Brain synchronizability, a false friend. NeuroImage, 196, 195-199. https://doi.org/10.1016/j.neuroimage.2019.04.029. Pecora, L. M., & Carroll, T. L. (1998). Master stability functions for synchronized coupled systems. Physical Review Letters, 80, 2109-2112.https://doi.org/10.1103/PhysRevLett.80.2109. Pereira, T., Eroglu, D., Bagci, G. B., Tirnakli, U., & Jensen, H. J. (2013). Connectivity-driven coherence in complex networks. Physical review letters, 110(23), 234103. https://doi.org/10.1103/PhysRevLett.110.234103 Planchuelo-Gómez, Á., García-Azorín, D., Guerrero, Á. L., Aja-Fernández, S., Rodríguez, M., & de Luis-García, R. (2020a). Structural connectivity alterations in chronic and episodic migraine: A diffusion magnetic resonance imaging connectomics study. Cephalalgia : an international journal of headache, 40(4), 367–383. https://doi.org/10.1177/0333102419885392 Planchuelo-Gómez, A., Garcia-Azorin, D., Guerrero, A. L., Aja-Fernandez, S., Rodriguez, M., & de Luis-Garcia, R. (2020b) White matter changes in chronic and episodic migraine: a diffusion tensor imaging study. The Journal of Headache and Pain, 21, 1. https://doi.org/10.1186/s10194-019-1071-3 R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.https://www.R-project.org/. Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. NeuroImage, 52(3), 1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003 Scher, A. I., Wang, S. J., Katsarava, Z., Buse, D. C., Fanning, K. M., Adams, A. M., & Lipton, R. B. (2019). Epidemiology of migraine in men: Results from the Chronic Migraine Epidemiology and Outcomes (CaMEO) Study. Cephalalgia : an international journal of headache, 39(2), 296–305. https://doi.org/10.1177/0333102418786266 Schindler, K. A., Bialonski, S., Horstmann, M. T., Elger, C. E., & Lehnertz, K. (2008). Evolving functional network properties and synchronizability during human epileptic seizures. Chaos (Woodbury, N.Y.), 18(3), 033119. https://doi.org/10.1063/1.2966112. Schwedt, T.J., Chong, C.D., Wu, T., Gaw, N., Fu, Y., & Li, J. (2015). Accurate classification of chronic migraine via brain magnetic resonance imaging. Headache, 55, 762–77. https://doi.org/10.1111/head.12584 Seghatoleslam, M., Ghadiri, M.K., Ghaffarian, N., Speckmann, E.J., & Gorji, A. (2014). Cortical spreading depression modulates the caudate nucleus activity. Neuroscience, 267 , 83-90. https://doi.org/10.1016/j.neuroscience.2014.02.029. Silvestro, M., Tessitore, A., Caiazzo, G. , Scotto di Clemente, F., Trojsi,F., Cirillo,M., Esposito, F., Tedeschi, G., & Russo, A. (2021). Disconnectome of the migraine brain: a “connectopathy” model. The Journal of Headache and Pain, 22, 102. https://doi.org/10.1186/s10194-021-01315-6 Smith S. M. (2002). Fast robust automated brain extraction. Human brain mapping, 17(3), 143–155. https://doi.org/10.1002/hbm.10062 Sporns, O. (2010) Networks of the Brain. MIT Press. Steiner, T.J., Stovner, L.J., Jensen, R., Uluduz, D., & Katsarava, Z. (2020). Lifting The Burden: the Global Campaign against Headache. Migraine remains second among the world's causes of disability, and first among young women: findings from GBD2019. The Journal of Headache and Pain, 21 , 137. https://doi.org/10.1186/s10194-020-01208-0. Stovner, L.J., Hagen, K., Linde. M., & Steiner, T.J. (2022). The global prevalence of headache: an update, with analysis of the influences of methodological factors on prevalence estimates. The Journal of Headache and Pain, 23, 34. https://doi.org/10.1186/s10194-022-01402-2. Sun, J., Bollt, E. M., & Nishikawa, T. (2009). Master stability functions for coupled nearly identical dynamical systems. Europhysics Letters, 85(6) , 60011. https://doi.org/10.1209/0295-5075/85/60011 Szabo, E., Green, S., Karunakaran, K.D., Sieberg, C.B., Elman, I., Burstein, R., & Borsook, D. (2022). Migraine: interactions between brain's trait and state. CNS Spectrums, 27 , 561-569. https://doi.org/10.1017/S109285292100064X. Taylor, P.N., Sinha, N., Wang, Y., Vos, S.B., de Tisi, J., Miserocchi, A., McEvoy, A.W., Winston, G.P., & Duncan, J.S. (2018). The impact of epilepsy surgery on the structural connectome and its relation to outcome, NeuroImage: Clinical, 18 , 202-214. https://doi.org/10.1016/j.nicl.2018.01.028. Tournier, J-D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., Jeurissen, B., Yeh, C.-H., & Connelly, A. (2019). MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage, 202, 116137. https://doi.org/10.1016/j.neuroimage.2019.116137 Uhlhaas, P.J., & Singer, W. (2006). Neural synchrony in brain disorders: relevance for cognitive dysfunctions and pathophysiology. Neuron, 52 , 155-68. https://doi.org/10.1016/j.neuron.2006.09.020. Varela, F., Lachaux, JP., Rodriguez, E. & Martinerie, J. (2001). The brainweb: Phase synchronization and large-scale integration. Nature Reviews Neuroscience, 2 , 229–239. https://doi.org/10.1038/35067550 Veraart, J., Novikov, D. S., Christiaens, D., Ades-Aron, B., Sijbers, J., & Fieremans, E. (2016). Denoising of diffusion MRI using random matrix theory. NeuroImage, 142 , 394–406. https://doi.org/10.1016/j.neuroimage.2016.08.016 Vetvik, K.G., & MacGregor, E.A. (2017). Sex differences in the epidemiology, clinical features, and pathophysiology of migraine. Lancet Neurology , 16, 76-87. https://doi.org/10.1016/S1474-4422(16)30293-9. Zhang, F., Daducci, A., He, Y., Schiavi, S., Seguin, C., Smith, R.E., Yeh, C.H., Zhao, T., & O'Donnell, L.J. (2022). Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review. NeuroImage. 249 , 118870. https://doi.org/10.1016/j.neuroimage.2021.118870. Zhang, S., Ide, J.S., & Li, C.S. (2012). Resting-state functional connectivity of the medial superior frontal cortex. Cerebral Cortex, 22, 99-111. https://doi.org/10.1093/cercor/bhr088. Zhang, X., Zhou, J., Guo, M., Cheng, S., Chen, Y., Jiang, N., Li, X., Hu, S., Tian, Z., Li, Z., et al., (2023). A systematic review and meta-analysis of voxel-based morphometric studies of migraine. Journal of Neurology, 270 , 152-170. https://doi.org/10.1007/s00415-022-11363-w. Zhu, J., Qian, Y., Zhang, B., Li, X., Bai, Y., Li, X., & Yu, Y. (2020). Abnormal synchronization of functional and structural networks in schizophrenia. Brain Imaging and Behavior, 14 , 2232-2241. https://doi.org/10.1007/s11682-019-00175-8. Additional Declarations No competing interests reported. Supplementary Files HuttebrauckerBIABESM.docx 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3154214","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":217073840,"identity":"8e7103fc-d235-4407-8384-b7ec94ed2119","order_by":0,"name":"Tomás Hüttebräucker","email":"","orcid":"","institution":"Balseiro Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tomás","middleName":"","lastName":"Hüttebräucker","suffix":""},{"id":217073841,"identity":"2f78a31c-b45b-4be4-baf9-32d70c2cc5e1","order_by":1,"name":"Álvaro Planchuelo-Gómez","email":"","orcid":"","institution":"University of Valladolid","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Álvaro","middleName":"","lastName":"Planchuelo-Gómez","suffix":""},{"id":217073842,"identity":"b0ca4e2c-adef-4457-9946-a27ab00a6aa5","order_by":2,"name":"David García-Azorín","email":"","orcid":"","institution":"Hospital Clínico Universitario de Valladolid","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"García-Azorín","suffix":""},{"id":217073843,"identity":"6a2f5b2f-9339-49e6-ac5e-7f121f5d6f4c","order_by":3,"name":"Ángel Guerrero","email":"","orcid":"","institution":"Hospital Clínico Universitario de Valladolid","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ángel","middleName":"","lastName":"Guerrero","suffix":""},{"id":217073844,"identity":"5b4fd4f9-df6b-4c17-b8a8-e4b986840635","order_by":4,"name":"Rodrigo de Luis-García","email":"","orcid":"","institution":"University of Valladolid","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rodrigo","middleName":"","lastName":"de Luis-García","suffix":""},{"id":217073845,"identity":"9ad5e778-7d49-4ede-b5a4-76eec67ddbd7","order_by":5,"name":"Sebastián Risau-Gusman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYFACxgYgYcPAIEGiljSStIDBYRK0yM8+3PzhZ9t5ef7ZDYyfCyrsGOTdewwYfu7BrcXgXGKbZG/bbcMZdw4wS884k8xgeOaMAWPPMzxaeBjbGHjbbicw3EhgY+ZtY2YwnJGWwMBzAI/DehibP/5tO5cgD9byrx6shfEPHi0MZxgbpHnbDiQYgLU0HGaQl0g+wIzPFoMzjG3SMueSDTfeOdgszXPsOI8Bz+EDh2XwOoz98cc3ZXbycrebD37mqamWk29vbHz4Bp/DEAAcpww8BkDVxGlA2NtAmvpRMApGwSgY/gAAxzROS7yX2zcAAAAASUVORK5CYII=","orcid":"","institution":"National Scientific and Technical Research Council","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Sebastián","middleName":"","lastName":"Risau-Gusman","suffix":""}],"badges":[],"createdAt":"2023-07-09 16:14:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3154214/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3154214/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":39980669,"identity":"21d3f650-f723-49ff-8b73-f50612237c60","added_by":"auto","created_at":"2023-07-13 14:12:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":159451,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eComparison between synchronizabilities of connectomes of healthy controls (HC), chronic migraine (CM), and episodic migraine (EM) patients.\u003c/em\u003e A: Type II whole brain synchronizability. B: Type I whole brain synchronizability. C: Type II left and right hemisphere synchronizability. D: Type I left and right hemisphere synchronizability. Each point represents the value for a given patient. Boxplots show median, second and third quartile. *: p\u0026lt;0.05, **: p\u0026lt;0.01, ns: non-significant (A: Welch’s ANOVA, B: ANOVA, C and D: Kruskal-Wallis.)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3154214/v1/6ba119cb710f2083c6b040d5.png"},{"id":39982409,"identity":"75892143-26fa-47f8-b3d6-01dbb4cee22c","added_by":"auto","created_at":"2023-07-13 14:20:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":156671,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eEnhancement in the synchronizability of brain subnetworks with 30 regions in chronic migraine patients. \u003c/em\u003eEach point represents the mean synchronizability increase for a given subnetwork as a percentage of the mean synchronizability of the same subnetwork for healthy controls. IntraL and IntraR designate subnetworks composed by regions from the left or right hemispheres, respectively. Inter designates subnetworks composed by regions from both hemispheres. A: Type II synchronizability. B: Type I synchronizability.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3154214/v1/6f0b0900e588c723b08df585.png"},{"id":39980665,"identity":"087e1520-9bd5-46ff-86fa-de4eb1ae57c6","added_by":"auto","created_at":"2023-07-13 14:12:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":103532,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eComparison of intra- and inter-hemispheric connectivities of\u003c/em\u003e \u003cem\u003echronic migraine (CM) patients, episodic migraine (EM) patients and healthy controls (HC).\u003c/em\u003e Connectivity is quantified by the number of streamlines joining regions inside the left hemisphere (Panel A), inside the right hemisphere (Panel B) or from both hemispheres (Panel C). Each point represents the value for a given patient. Boxplots show median, second and third quartile.\u003c/p\u003e\n\u003cp\u003e*: p\u0026lt;0.05, ns: non- significant (A and B: ANOVA. C: Welch’s ANOVA).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3154214/v1/e1018966357f80f8dc5144d3.png"},{"id":39982410,"identity":"5f0be986-e2bd-46cd-974c-ccbac7637a73","added_by":"auto","created_at":"2023-07-13 14:20:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":178601,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eContribution of brain regions to interhemispheric connectivity. \u003c/em\u003eEach point represents the mean number of streamlines joining a region with the regions from the opposite hemisphere. Red and blue points correspond to regions in the right or left hemispheres, respectively. Means were performed over the set of healthy controls (left panel), episodic migraine patients (center panel), and chronic migraine patients (right panel).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3154214/v1/b9e2e2cbff7fc0a686059e6f.png"},{"id":39980666,"identity":"7667e2df-0c8d-4cbf-9ba2-2922c4eb5eed","added_by":"auto","created_at":"2023-07-13 14:12:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":199076,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eComparison of inter-hemispheric connectivity of regions in the Left or Right hemispheres.\u003c/em\u003e Connectivity is quantified by the number of streamlines joining regions from the left or right hemispheres with all the regions in the opposite hemisphere. Each point represents the value for a given patient. Boxplots show median, second and third quartile. ns: non-significant, *: p\u0026lt;0.05, **: p\u0026lt;0.01, ***: p\u0026lt;0.001 (Kruskal-Wallis for all comparisons except A (Right), C (Right), E (Left), where ANOVA was used).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3154214/v1/e3234a8ab32c01bda2de3c6c.png"},{"id":41198378,"identity":"e9539fac-0861-478b-8aff-45ae21a01413","added_by":"auto","created_at":"2023-08-07 16:37:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1190761,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3154214/v1/5bff04de-4267-4acb-b677-9c96d400dbb7.pdf"},{"id":39980664,"identity":"6bddfcc3-4eb4-49f4-af39-1069360c1569","added_by":"auto","created_at":"2023-07-13 14:12:00","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10256,"visible":true,"origin":"","legend":"","description":"","filename":"HuttebrauckerBIABESM.docx","url":"https://assets-eu.researchsquare.com/files/rs-3154214/v1/646ecef677b74e37cbdef8c3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancement of synchronizability in chronic migraine patients related to the increase of connectome interhemispheric connectivity ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMigraine is a primary headache disorder characterized by recurrent attacks with unilateral location, pulsatile character and moderate to severe intensity. It is a highly prevalent condition (14%, Stovner et al., 2022), two to three times more prevalent in women than in men (Vetvik and MacGregor, 2017). Migraine remains the second most frequent cause of disability worldwide, and the first among young women (Steiner et al., 2020). According to the International Classification of Headache Disorders (ICHD-3, 2018), migraine can be classified as \u003cem\u003echronic\u003c/em\u003e (CM, headache occurring on 15 or more days/month with at least 8 days per month of migraineous pain, for more than 3 months) or \u003cem\u003eepisodic\u003c/em\u003e (EM, headache occurring on less than 15 days/month).\u003c/p\u003e\n\u003cp\u003eMany theories about migraine pathophysiology have been proposed, but there is a growing consensus that it can be considered as a brain disorder (Charles, 2013; Goadsby et al., 2017; Szabo et al., 2022). Thus, it might be related to structural and functional alterations of the brain. The brains of migraine patients \u0026nbsp;and healthy controls have been reported to be different in several ways (Borsook et al., 2014), in particular in their morphometrical characteristics. A\u0026nbsp;meta-analysis reported gray matter volume increases and decreases in migraine patients in multiple brain regions associated with sensation, affection, cognition, and descending modulation aspects of pain (Zhang et al., 2023).\u003c/p\u003e\n\u003cp\u003eRegarding the structural integrity of white matter, some have reported no differences between healthy controls (HC) and CM patients (Neeb et al., 2015; Planchuelo-Gómez et al., 2020a), but it has been argued that this could be due to symptomatic treatment overuse, since such differences appear when this feature is controlled for (Coppola et al., 2020). Differences between the brains of patients with CM and EM have also been found, both in gray and white matter (Schwedt et al., 2016; Filippi and Messina, 2021; Planchuelo-Gómez et al., 2020a, Planchuelo-Gómez et al., 2020b).\u003c/p\u003e\n\u003cp\u003eOne aspect that has received less attention in migraine studies is the connectivity between brain areas, quantified using the concept of \u003cem\u003econnectome\u003c/em\u003e (Sporns, 2010). This is a representation of the brain as a network, whose vertices, representing brain areas, are connected by edges, representing nerve fibers or some proxy for them (structural connectome), or temporal correlations (functional connectome). This has allowed researchers to apply concepts and measures from Graph Theory to study the brain (Rubinov and Sporns, 2010). Connectomes are built using non-invasive imaging approaches, such as diffusion-weighted magnetic resonance imaging (dMRI) for structural connectomes or functional (fMRI) for functional connectomes. Images obtained using dMRI provide information about the diffusion properties of water inside axonal bundles (Mori, 2007). \u003cem\u003eTractography\u003c/em\u003e algorithms (Zhang et al., 2022) are used to reconstruct the paths of fiber bundles between brain regions, sometimes called \u003cem\u003estreamlines\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eConnectomes are best suited to understand brain processes that involve several brain areas. Connectome alterations have been associated with diverse brain disorders (Fornito et al., 2015). Even though migraine has been much less studied in this context, structural connectome alterations have also been found in migraine patients (Li et al., 2017; Planchuelo-Gómez et al., 2020; Silvestro et al., 2021). In general, the relationship between these alterations and the pathophysiology of a disease is inferred either from general considerations or from modeling exercises (Alstott et al., 2009; Taylor et al., 2018). But the specific contribution of the connectome is difficult to disentangle from the contribution of the specific dynamical model used for each node.\u003c/p\u003e\n\u003cp\u003eAn important exception is the phenomenon of \u003cem\u003esynchronization\u0026nbsp;\u003c/em\u003e(Arenas et al., 2008). Many collective phenomena happening in the brain, from rhythms to seizures, are based in synchronization of neural populations. But synchronization is not only associated with processes in the healthy brain, but also with some brain disorders, such as schizophrenia, epilepsy, autism, Alzheimer’s and Parkinson’s diseases (Uhlhaass and Singer, 2006). In the mathematical analysis of networks of oscillating systems there is a formalism (Master Stability Function, MSF, Pecora and Carroll, 1998) that provides a condition for synchronization that is independent of the oscillators, leading to the definition of the \u003cem\u003esynchronizability\u003c/em\u003e of a network. In biological terms, the synchronizability coefficient measures the capability of a brain to \u003cem\u003esustain\u003c/em\u003e processes involving the synchronization of several regions. In other words, in less synchronizable brains synchronization processes would die out faster. This measure is largely independent of the details of the processes involved, since the MSF formalism applies to a large class of synchronizing systems (Arenas et al., 2015).\u003c/p\u003e\n\u003cp\u003eTo study the relationship between migraine and synchronizability, the structural connectomes of patients with EM and CM, and HC were analyzed. We hypothesized that there may be differences between the synchronizabilities of HC and patients with migraine, particularly CM. Furthermore, we aimed at finding the most important features of the connectomes responsible for these differences.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy design\u003c/h2\u003e\n\u003cp\u003eWe conducted an analytical observational study with a case-control design. Most participants were a subset of the sample included in Planchuelo-Gómez et al. (2020a), and all were recruited in the same environment. Briefly, the clinical inclusion criteria for patients included diagnosis of migraine according to the ICHD-3 and a clinical stable situation. Controls were recruited based on the absence of prior history of migraine. Patients with high-frequency EM were excluded, and both cases and controls were excluded if they had other painful and neurological disorders different to migraine, major psychiatric disease, drug or substance abuse or pregnancy. Only female participants were included in the final sample. Full details are available in the Supplementary Material.\u003c/p\u003e\n\u003ch2\u003eMRI acquisition\u003c/h2\u003e\n\u003cp\u003eThe full MRI acquisition protocol, described in Planchuelo-Gómez et al. (2020a), is available in the Supplementary Material.\u003c/p\u003e\n\u003ch2\u003eImage processing\u003c/h2\u003e\n\u003cp\u003eT1- and diffusion-weighted images were processed to obtain structural connectivity matrices, following a pipeline described in detail in Planchuelo-Gómez et al.(2020a).\u003c/p\u003e\n\u003cp\u003eBriefly, the preprocessing for the T1-weighted data included non-brain tissue removal with BET (Jenkinson et al., 2012; Smith, 2002) and gray matter parcellation. For the DWI volumes the steps were Marchenko-Pastur Principal Component Analysis denoising (Tournier et al., 2019; Veraart et al., 2016), and correction for eddy currents, motion and B1 field inhomogeneity (Andersson et al., 2015). Probabilistic anatomically-constrained tractography (ACT) was performed from five-tissue-type (5TT) segmented images and the estimated fiber orientation distributions (FOD) (Dhollander et al., 2016).\u003c/p\u003e\n\u003cp\u003eStructural connectivity matrices were computed from tractography output and the registered gray matter parcellation maps. 84x84 connectivity matrices, corresponding to the 84 cortical and subcortical regions from the Desikan-Killiany atlas (Desikan et al., 2006), were obtained using the number of streamlines in each connection as connectome metrics. Due to the employed tractography method, the streamlines obtained were non-directional. Therefore, the matrices obtained were symmetric. Furthermore, streamlines starting and ending in different points of the same gray matter region (“self-connections”) were removed. Thus, the diagonal elements of the connectome were 0. To avoid false positives, removal of weak connections (\u003cem\u003epruning\u003c/em\u003e) was performed until only 20% of the matrix elements presented non-zero values. Because the cerebellum is weakly connected to other regions, the two regions (left and right) were removed from the connectome.\u003c/p\u003e\n\u003ch2\u003eMeasures of synchronizability\u003c/h2\u003e\n\u003cp\u003eFollowing the MSF formalism, we consider a network whose nodes are identical oscillating systems (each one encompassing many neurons), interacting through the connections specified by a connectome. The strength of the coupling between regions is the product of the corresponding elements in the connectome and a \u003cem\u003ecoupling constant\u003c/em\u003e s\u0026nbsp;that depends on the oscillator type. The method is independent of the exact nature of the oscillators, since the results apply to two large classes of dynamical systems (type I and type II, Boccaletti et al., 2006). For type II oscillators there exists a threshold value s\u003csub\u003emin\u003c/sub\u003e\u0026nbsp; such that for s\u0026gt;s\u003csub\u003emin\u003c/sub\u003e synchronization is always robust. For type I oscillators the synchronized state is robust only in the range s\u003csub\u003emin\u003c/sub\u003e\u0026lt;s\u0026lt;s\u003csub\u003emax\u003c/sub\u003e. Thus, if s\u0026gt;s\u003csub\u003emax\u003c/sub\u003e or s\u0026lt;s\u003csub\u003emin\u003c/sub\u003e the oscillators cannot synchronize (Pecora and Carroll, 1998, Arenas et al., 2008).\u003c/p\u003e\n\u003cp\u003eFor type II oscillators, the synchronization threshold s\u003csub\u003emin\u003c/sub\u003e is inversely proportional to l\u003csub\u003e2\u003c/sub\u003e, the second smallest eigenvalue of the Laplacian matrix L. In networks with smaller l\u003csub\u003e2\u003c/sub\u003e, oscillators will be easier to synchronize. Thus, l\u003csub\u003e2\u003c/sub\u003e provides a measure of \u003cem\u003etype II synchronizability\u003c/em\u003e of the network.\u003c/p\u003e\n\u003cp\u003eFor type I oscillators, s\u003csub\u003emin\u003c/sub\u003e has the same properties as for type II, but s\u003csub\u003emax\u0026nbsp;\u003c/sub\u003eis inversely proportional to l\u003csub\u003en\u003c/sub\u003e, the largest eigenvalue of L. Thus, the larger the distance between l\u003csub\u003e2\u003c/sub\u003e and l\u003csub\u003en\u003c/sub\u003e, the easier it is to synchronize a system of oscillators. Therefore R=l\u003csub\u003en\u003c/sub\u003e/l\u003csub\u003e2\u003c/sub\u003e provides a measure of \u003cem\u003etype I synchronizability\u003c/em\u003e.\u003c/p\u003e\n\u003ch2\u003eSynchronizability in the whole brain and in subnetworks\u003c/h2\u003e\n\u003cp\u003eThe MSF formalism was applied to the whole-brain connectivity matrix, and separately to regions from each hemisphere. We afterwards generated 300 subnetworks of 30 regions each. To avoid any systematic bias, the 30 regions of each subnetwork were randomly chosen from the regions in a single hemisphere, or from both hemispheres without restriction. For each subnetwork the connections between the 30 regions were the corresponding direct connections in the connectome. All other regions were removed, giving a 30x30 connectivity matrix.\u003c/p\u003e\n\u003ch2\u003eIntra- and inter-hemispheric connectivity\u003c/h2\u003e\n\u003cp\u003eThe \u003cem\u003einter-hemispheric connectivity\u003c/em\u003e of each connectome was characterized as the number of streamlines having their extremes in each hemisphere. The remaining streamlines were characterized as \u003cem\u003eintra-hemispheric\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThe relative improvements (or increases) of synchronizabilities in CM when compared with HC are defined as 100*(l\u003csub\u003e2\u003c/sub\u003e\u003csup\u003eCM\u003c/sup\u003e-l\u003csub\u003e2\u003c/sub\u003e\u003csup\u003eHC\u003c/sup\u003e)/l\u003csub\u003e2\u003c/sub\u003e\u003csup\u003eHC\u003c/sup\u003e(Type II), and 100*(R\u003csup\u003eCM\u003c/sup\u003e-R\u003csup\u003eHC\u003c/sup\u003e)/R\u003csup\u003eHC\u003c/sup\u003e) (Type I).\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eStatistical analyses were performed using R version 4.2.2 (R Core Team, 2022). Shapiro-Wilk and Levene’s tests were used to assess normality and homogeneity of variance, respectively. To test for significant differences in the age of HC, EM and CM patients, a Kruskal-Wallis test was used, since the assumption of normality was not met. To test for significant differences in duration of migraine history, headache frequency, and migraine frequency Mann-Whitney-Wilcoxon tests were used, since normality was not met.\u003c/p\u003e\n\u003cp\u003eTo test for significant differences in synchronizability measures and in numbers of streamlines between the groups a one-way ANOVA was used if the assumptions of normality and homogeneity of variance were met. If normality was met but there was no homogeneity, a Welch’s ANOVA test was used. If the normality was not met, a Kruskal-Wallis test was used.\u003c/p\u003e\n\u003cp\u003eWhen significant differences were found, a post-hoc analysis was performed. Tukey’s test was used for ANOVA, Games-Howell test for Welch’s ANOVA, and Conover-Iman test for Kruskal-Wallis. To correct for multiple comparisons, the Benjamini-Hochberg (Benjamini and Hochberg, 1995) method was used.\u003c/p\u003e\n\u003cp\u003eCohen’s d was used to describe the effect size of the comparisons.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eDemographic and clinical characteristics\u003c/h2\u003e\n\u003cp\u003eA total of 39 HC, 57 EM and 61 CM patients were enrolled. Two patients (one CM and one EM) were excluded because of inconsistencies in the obtained connectome. Demographic and clinical data for the three groups are summarized in Table 1. Statistically significant differences were found in duration of migraine history, and headache and migraine frequency between the two migraine groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Participants characteristics\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"660\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003eHC (n=39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003eEM (n=56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003eCM (n=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"top\"\u003e\n \u003cp\u003eStatistical test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e36.4 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e37.4 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e39.8 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"top\"\u003e\n \u003cp\u003ec\u0026sup2;(2) = 3.93, p= 0.14 \u0026nbsp;\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003eDuration of migraine history (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e13.1 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e20 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"top\"\u003e\n \u003cp\u003eW = 1189, p= 0.005 \u0026nbsp;\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003eTime from onset of chronic migraine (months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e26.8 (35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003eHeadache frequency (days/month)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e3.61 (1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e23.7 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"top\"\u003e\n \u003cp\u003eW = 1711, p\u0026lt;0.001\u0026nbsp;\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003eMigraine frequency (days/month)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e3.61 (1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.181818181818183%\" valign=\"top\"\u003e\n \u003cp\u003e13.2 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.545454545454547%\" valign=\"top\"\u003e\n \u003cp\u003eW = 1685, p\u0026lt;0.001\u0026nbsp;\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are means (SD). \u0026dagger; Kruskal-Wallis, \u0026Dagger;: Mann-Whitney-Wilcoxon.\u003c/p\u003e\n\u003ch2\u003eSynchronizability of migraine connectomes\u003c/h2\u003e\n\u003cp\u003eBoth synchronizability measures (l\u003csub\u003e2\u003c/sub\u003e and R) were significantly larger for CM patients than for HC (Fig. 1) and showed medium effect size (d \u0026gt; 0.5, d=0.53 for l\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eand d=0.56 for R). In all the assessments of this study the values for EM patients were intermediate between those of HC and CM, but the differences were not statistically significant. No significant difference between patients with migraine and HC in the synchronizability of each single hemisphere was found (Fig. 1).\u003c/p\u003e\n\u003ch2\u003eSynchronizability of smaller brain circuits\u003c/h2\u003e\n\u003cp\u003eGiven the results of the previous section, we focused here in the comparison between CM and HC for the subnetworks assessment. Fig. 2 shows, for 300 random brain circuits, the relative improvement of synchronizabilities in CM (defined in Methods). These quantities were much larger for subnetworks composed from regions from different hemispheres, than for circuits composed by regions from the same hemisphere (Fig 2).\u003c/p\u003e\n\u003ch2\u003eInter- vs intra-hemispheric connectivity in migraineurs and controls\u003c/h2\u003e\n\u003cp\u003eFig. 3 shows that there was a significant difference in inter-hemispheric streamlines between CM and both HC and EM (medium effect sizes d=0.52 and 0.5, respectively), whereas no such difference was found for intra hemispheric streamlines.\u003c/p\u003e\n\u003cp\u003eFigure 4 shows the regions that contributed most to these differences within each hemisphere. Approximately 75% of the total interhemispheric connections involved the same five regions. We analyzed the number of interhemispheric streamlines involving these five bilateral regions and found statistically significant differences in the interhemispheric connectivities of three regions: left superior frontal gyrus, right precentral cortex, and right caudate. In all these cases, CM patients showed increased number of streamlines compared to HC (Fig. 5).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study we analyzed the structural connectomes, computed from dMRI data, of female migraine patients and HC using synchronizability measures derived from the MSF formalism. These measures are used to assess the ability of a network of brain regions to sustain a synchronization process. Our two main findings were that, when compared with HC, the connectomes of CM patients presented higher synchronizability and more inter-hemispheric streamlines.\u003c/p\u003e\n\u003cp\u003eNeuronal synchronization (Varela et al., 2001; Guevara Erra et al., 2017), underlies many brain processes involving the propagation of neuronal activity. There is a growing consensus that migraine should be considered as a neuronal disorder (Charles 2013; Goadsby et al., 2017; Szabo et al., 2022) and, since migraineous pain affects large areas of the head and causes multiple symptoms, it seems reasonable to expect that some form of collective neuronal activity could occur during migraine attacks. It has been proposed that a wave of hyperpolarizability followed by depression of neuronal activity, the cortical spreading depolarization (CSD), activates the trigeminal system to cause a headache (Dalkara et al., 2006; Harriott et al., 2021).\u003c/p\u003e\n\u003cp\u003eHowever, there are probably other mechanisms that remain largely unknown. Here, instead of studying a specific synchronization process, a formalism was applied that allows us to isolate the contribution of the network for a large class of synchronization processes. The rigidity of some assumptions has led some to question the utility of the concept of synchronizability for brain networks (Papo and Buldú 2019), but they can be considerably relaxed. For example, it is assumed that all oscillators are identical, but recently it was shown (Sun et al., 2009, Pereira et al., 2013) that when oscillators are different they may deviate from the synchronized state, but the fluctuations are inversely proportional to l\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003eHere we assessed whole-brain synchronizability and, to represent realistic situations, subsets of the whole connectome were also assessed. We found that, for brain circuits including areas from both hemispheres, synchronizability was higher for CM than for HC, whereas no difference appeared for intrahemispheric circuits. Consistently, synchronizability of the whole connectome was also larger in CM. An increased functional synchronizability has also been found in schizophrenia (Zhu et al., 2020), Alzheimer’s disease (de Haan et al., 2012) and epilepsy (Schindler et al., 2008). Note that larger synchronizability does not imply the synchronization of any particular process. It just implies that almost any process involving synchronization would be facilitated. Interestingly, in migraine patients under periodic visual stimulation EEG recordings in the alpha band were more synchronized across many sensors in the cortex than in HC undergoing the same stimulation (Angellini et al., 2009; De Tomasso et al., 2013).\u003c/p\u003e\n\u003cp\u003eThe increase in synchronizability for CM was associated with a significant increase in interhemispheric connectivity. The main differences were in the streamlines that originate in (or end in) the left superior frontal and right precentral areas, and in the right caudate.\u003c/p\u003e\n\u003cp\u003eSuperior frontal cortex has been associated with pain regulation (Zhang et al., 2012). When compared with HC, an increased functional connectivity with the left superior frontal cortex has been observed in migraine patients (Cao et al., 2022).\u003c/p\u003e\n\u003cp\u003ePrecentral cortex is involved in pain modulation and it was proposed as one of the possible targets for transcranial magnetic stimulation or deep brain stimulation in neuropathic pain patients (Lefaucheur et al., 2001; Mertens et al., 1999; Ayache 2016). Anatomically, the caudate nucleus is connected with the amygdala and the insula, relevant regions in migraine pathophysiology, associated with cognitive and emotional aspects of migraine attacks (Çırak et al., 2020). In addition, the firing pattern of caudate nucleus neurons seems to change after cortical CSD (Seghatoleslam et al., 2020).\u003c/p\u003e\n\u003cp\u003eThere are clinical limitations worth describing. As most CM patients overused medication, we were unable to separately assess medication effects. In the sample of the study, only women were included, and no patients suffered from anxiety or depression. Thus, the effects of sex or frequent psychiatric comorbidities could not be assessed. In the case of sex, considering that CM is more prevalent in women compared to EM (Scher et al., 2019), and that interhemispheric connectivity is larger in women (Ingalhalikar et al., 2014, Hänggi et al., 2014), the analysis of men and women could help to understand whether interhemispheric connectivity is a causative factor for migraine. It is important to mention that, even though it has been argued that differences in interhemispheric connectivity only reflect differences in brain size or intracranial volume (Hänggi et al. 2014), we have found no correlation between brain size and migraine (see Supplementary Material).\u003c/p\u003e\n\u003cp\u003eRegarding technical limitations, connections with few streamlines were eliminated from the connectome because they are likely to be spurious. To show that our results are not an artifact of pruning, the analysis was repeated without removing any connections from the connectome, and qualitatively the same results were found (see Supplementary Material).\u003c/p\u003e\n"},{"header":"Conclusions","content":"\u003cp\u003eThe structural connectome of CM female patients is more synchronizable that the one of HC, also when considering smaller subnetworks involving regions from different hemispheres. This was associated with a significant increase in structural interhemispheric connectivity in CM patients. The largest contributions to these differences come from the interhemispheric connections from left superior frontal gyrus, right precentral cortex, and right caudate.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThis work was supported by research grant TED2021-130758B-I00, funded by MCIN/AEI/10.13039/501100011033 and the European Union \u0026ldquo;NextGenerationEU/PRTR\u0026rdquo; and PRE2019-089176 funded by MCIN/AEI/10.13039/501100011033 and ESF \u0026rdquo;ESF invests in your future\u0026rdquo;.\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eAuthor contributions included conception and study design (SRG), data collection or acquisition (DG-A, \u0026Aacute;LG and RdL-G), data analysis (TH, AP-G, SRG), interpretation of results (TH, AP-G. SRG, RdL-G), drafting and revising the manuscript critically, and approval of final version to be published and agreement to be accountable on all aspects of the work (All authors).\u003c/p\u003e\n\u003ch2\u003eFunding Sources\u003c/h2\u003e\n\u003cp\u003eThis work was supported by research grant TED2021-130758B-I00, funded by MCIN/AEI/10.13039/501100011033 and the European Union \u0026ldquo;NextGenerationEU/PRTR\u0026rdquo; and PRE2019-089176 funded by MCIN/AEI/10.13039/501100011033 and ESF \u0026rdquo;ESF invests in your future\u0026rdquo;.\u003c/p\u003e\n\u003ch2\u003eCompliance with ethical standards\u003c/h2\u003e\n\u003cp\u003eThe local Ethics Committee of Hospital Cl\u0026iacute;nico Universitario de Valladolid approved the study (PI: 14\u0026ndash;197). All participants read and signed a written consent form before their participation.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and material\u003c/h2\u003e\n\u003cp\u003eAnonymized data that support the findings of this study are available from the authors upon request.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by research grant TED2021-130758B-I00, funded by MCIN/AEI/10.13039/501100011033 and the European Union \u0026ldquo;NextGenerationEU/PRTR\u0026rdquo; and PRE2019-089176 funded by MCIN/AEI/10.13039/501100011033 and ESF \u0026rdquo;ESF invests in your future\u0026rdquo;.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eAll authors declare no conflicts of interest relevant to the content of this article.\u003c/p\u003e\n\u003ch2\u003eEthical approval\u003c/h2\u003e\n\u003cp\u003eThe local Ethics Committee of Hospital Cl\u0026iacute;nico Universitario de Valladolid approved the study (PI: 14\u0026ndash;197). All participants read and signed a written consent form before their participation.\u003c/p\u003e\n\u003ch2\u003eConsent to participate\u003c/h2\u003e\n\u003cp\u003eAll patients who participated in the study signed a written informed consent.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and material\u003c/h2\u003e\n\u003cp\u003eAnonymized data that support the findings of this study are available from the authors upon request.\u003c/p\u003e\n\u003ch2\u003eCode availability\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlstott, J., Breakspear, M., Hagmann, P., Cammoun, L., \u0026amp; Sporns, O. (2009). Modeling the impact of lesions in the human brain. \u003cem\u003ePLoS computational biology, 5(6)\u003c/em\u003e, e1000408. https://doi.org/10.1371/journal.pcbi.1000408\u003c/li\u003e\n\u003cli\u003eAndersson, J. L. R., \u0026amp; Sotiropoulos, S. N. (2016). An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. \u003cem\u003eNeuroImage, 125,\u003c/em\u003e 1063\u0026ndash;1078. https://doi.org/10.1016/j.neuroimage.2015.10.019\u003c/li\u003e\n\u003cli\u003eAngelini, L., de Tommaso, M., Guido, M., Hu, K., Ivanov, Pch., Marinazzo, D., Nardulli, G., Nitti, L., Pellicoro, M., Pierro, C., \u0026amp; Stramaglia, S. (2004). Steady-state visual evoked potentials and phase synchronization in migraine patients. \u003cem\u003ePhysical Review Letters,\u003c/em\u003e \u003cem\u003e93\u003c/em\u003e, 038103. https://doi.org/10.1103/PhysRevLett.93.038103.\u003c/li\u003e\n\u003cli\u003eArenas, A., D\u0026iacute;az-Guilera, A., Kurths, J., Moreno, Y., \u0026amp; Zhou, C. (2008). Synchronization in complex networks, \u003cem\u003ePhysics Reports,\u003c/em\u003e \u003cem\u003e469\u003c/em\u003e, 93-153. https://doi.org/10.1016/j.physrep.2008.09.002.\u003c/li\u003e\n\u003cli\u003eAyache, S.S., Ahdab, R., Chalah, M.A., Farhat, W.H., Mylius, V., Goujon. C., Sorel, M., \u0026amp; Lefaucheur, J.-P. (2016). Analgesic effects of navigated motor cortex rTMS in patients with chronic neuropathic pain. \u003cem\u003eEuropean Journal of Pain, 20\u003c/em\u003e, 1413-1422. https://doi.org/10.1002/ejp.864.\u003c/li\u003e\n\u003cli\u003eBenjamini, Y. \u0026amp; Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 57(1):289\u0026ndash;300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x.\u003c/li\u003e\n\u003cli\u003eBoccaletti, S., Latora, V., Moreno, Y., Chavez, M., \u0026amp; Hwang, D.-U. (2006). Complex Networks: Structure and Dynamics. \u003cem\u003ePhysics Reports,\u003c/em\u003e \u003cem\u003e424\u003c/em\u003e, 175-308. https://doi.org/10.1016/j.physrep.2005.10.009.\u003c/li\u003e\n\u003cli\u003eBorsook, D., Erpelding, N., Lebel, A., Linnman, C., Veggeberg, R., Grant, P.E., Buettner, C., Becerra, L., \u0026amp; Burstein, R. (2014). Sex and the migraine brain. \u003cem\u003eNeurobiology of Disease, 68\u003c/em\u003e, 200-214. https://doi.org/10.1016/j.nbd.2014.03.008.\u003c/li\u003e\n\u003cli\u003eCao, Z., Yu, W., Zhang, Z., Xu, M., Lin, J., Zhang, L., \u0026amp; Song, W. (2022). Decreased Gray Matter Volume in the Frontal Cortex of Migraine Patients with Associated Functional Connectivity Alterations: A VBM and rs-FC Study. \u003cem\u003ePain Research \u0026amp; Management, 25\u003c/em\u003e, 2115956. https://doi.org/10.1155/2022/2115956.\u003c/li\u003e\n\u003cli\u003eCharles A. (2013). Migraine: a brain state. \u003cem\u003eCurrent opinion in neurology,\u003c/em\u003e 26(3), 235\u0026ndash;239. https://doi.org/10.1097/WCO.0b013e32836085f4\u003c/li\u003e\n\u003cli\u003e\u0026Ccedil;ırak, M., Yağmurlu, K., Kearns, K.N., Ribas, E.C., Urgun, K., Shaffrey, M.E., \u0026amp; Kalani, M.Y.S. (2020). The Caudate Nucleus: Its Connections, Surgical Implications, and Related Complications. \u003cem\u003eWorld Neurosurgery, 139,\u003c/em\u003e e428-e438. https://doi.org/10.1016/j.wneu.2020.04.027.\u003c/li\u003e\n\u003cli\u003eCoppola, G., Di Renzo, A., Tinelli, E., Petolicchio, B., Di Lorenzo, C., Parisi, V., Serrao, M., Calistri, V., Tardioli, S., Cartocci, G. et al., (2020). Patients with chronic migraine without history of medication overuse are characterized by a peculiar white matter fiber bundle profile. \u003cem\u003eThe Journal of Headache and Pain\u003c/em\u003e, 21, 92. https://doi.org/10.1186/s10194-020-01159-6\u003c/li\u003e\n\u003cli\u003eDalkara, T., Zervas, N.T., \u0026amp; Moskowitz, M.A. (2006). From spreading depression to the trigeminovascular system. \u003cem\u003eNeurological Sciences, 27\u003c/em\u003e Supp2, S86-90. https://doi.org/10.1007/s10072-006-0577-z.\u003c/li\u003e\n\u003cli\u003ede Tommaso, M., Stramaglia, S., Marinazzo, D., Trotta, G., \u0026amp; Pellicoro, M. (2013). Functional and effective connectivity in EEG alpha and beta bands during intermittent flash stimulation in migraine with and without aura. \u003cem\u003eCephalalgia\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e, 938-947. https://doi.org/10.1177/0333102413477741.\u003c/li\u003e\n\u003cli\u003eDesikan, R.S., S\u0026eacute;gonne, F., Fischl, B., Quinn,B.T., Dickerson,B.C., Blacker, D., Buckner, R.L., Dale, A.M., Maguire, R.P., Hyman, B.T., et al., (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. \u003cem\u003eNeuroImage, 31(3),\u003c/em\u003e 968\u0026ndash;980. https://doi.org/10.1016/j.neuroimage.2006.01.021\u003c/li\u003e\n\u003cli\u003eDhollander, T., Raffelt, D. \u0026amp; Connelly, A. (2016). Unsupervised 3-tissue response function estimation from single-shell or multi-shell diffusion MR data without a co-registered T1 image. \u003cem\u003eISMRM Workshop on Breaking the Barriers of Diffusion MRI, 5\u003c/em\u003e. https://mrtrix.readthedocs.io/en/latest/ reference/commands/dwi2mask.html .\u003c/li\u003e\n\u003cli\u003eFilippi, M., \u0026amp; Messina, R. (2020). The Chronic Migraine Brain: What Have We Learned From Neuroimaging? \u003cem\u003eFrontiers in Neurology, 10,\u003c/em\u003e 1356. https://doi.org/10.3389/fneur.2019.01356.\u003c/li\u003e\n\u003cli\u003eFornito, A., Zalesky, A., \u0026amp; Breakspear, M. (2015).The connectomics of brain disorders. \u003cem\u003eNature Reviews Neuroscience, 16,\u003c/em\u003e159-72. https://doi.org/10.1038/nrn3901.\u003c/li\u003e\n\u003cli\u003eGoadsby, P.J., Holland, P.R., Martins-Oliveira, M., Hoffmann, J., Schankin, C., \u0026amp; Akerman, S. (2017). Pathophysiology of Migraine: A Disorder of Sensory Processing. \u003cem\u003ePhysiological Reviews, 97,\u003c/em\u003e 553-622. https://doi.org/10.1152/physrev.00034.2015.\u003c/li\u003e\n\u003cli\u003eGuevara Erra, R., Perez Velazquez, J.L., \u0026amp; Rosenblum, M. (2017). Neural Synchronization from the Perspective of Non-linear Dynamics. \u003cem\u003eFrontiers in Computational Neuroscience, 11\u003c/em\u003e, 98. https://doi.org/10.3389/fncom.2017.00098\u003c/li\u003e\n\u003cli\u003ede Haan, W., van der Flier, W. M., Wang, H., Van Mieghem, P. F., Scheltens, P., \u0026amp; Stam, C. J. (2012). Disruption of functional brain networks in Alzheimer\u0026apos;s disease: what can we learn from graph spectral analysis of resting-state magnetoencephalography?. Brain connectivity, 2(2), 45\u0026ndash;55. https://doi.org/10.1089/brain.2011.0043\u003c/li\u003e\n\u003cli\u003eH\u0026auml;nggi, J., F\u0026ouml;venyi, L., Liem, F., Meyer, M., \u0026amp; J\u0026auml;ncke, L. (2014). The hypothesis of neuronal interconnectivity as a function of brain size-a general organization principle of the human connectome. \u003cem\u003eFrontiers in Human Neuroscience, 8\u003c/em\u003e, 915. https://doi.org/10.3389/fnhum.2014.00915.\u003c/li\u003e\n\u003cli\u003eHarriott, A.M., Chung, D.Y., Uner, A., Bozdayi, R.O., Morais, A., Takizawa, T., Qin, T., \u0026amp; Ayata, C. (2021). Optogenetic Spreading Depression Elicits Trigeminal Pain and Anxiety Behavior. \u003cem\u003eAnnals of Neurology, 89\u003c/em\u003e, 99-110. https://doi.org/10.1002/ana.25926.\u003c/li\u003e\n\u003cli\u003eHeadache Classification Committee of the International Headache Society. (2018). The International Classification of Headache Disorders, 3rd edition. \u003cem\u003eCephalalgia\u003c/em\u003e,\u003cem\u003e 38,\u003c/em\u003e 1-211.\u003c/li\u003e\n\u003cli\u003eIngalhalikar, M., Smith, A., Parker, D., Satterthwaite, T. D., Elliott, M. A., Ruparel, K., Hakonarson, H., Gur, R. E., Gur, R. C., \u0026amp; Verma, R. (2014). Sex differences in the structural connectome of the human brain. Proceedings of the National Academy of Sciences of the United States of America, 111(2), 823\u0026ndash;828. https://doi.org/10.1073/pnas.1316909110\u003c/li\u003e\n\u003cli\u003eJenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., \u0026amp; Smith, S. M. (2012). FSL. NeuroImage, 62(2), 782\u0026ndash;790. https://doi.org/10.1016/j.neuroimage.2011.09.015\u003c/li\u003e\n\u003cli\u003eLefaucheur, J.P., Drouot, X., Keravel, Y., \u0026amp; Nguyen, J.P. (2001). Pain relief induced by repetitive transcranial magnetic stimulation of precentral cortex. \u003cem\u003eNeuroreport. 12\u003c/em\u003e, 2963-29655. https://doi.org/10.1097/00001756-200109170-00041.\u003c/li\u003e\n\u003cli\u003eLi, K., Liu, L., Yin, Q., Dun, W., Xu, X., Liu, J., \u0026amp; Zhang, M. (2017). Abnormal rich club organization and impaired correlation between structural and functional connectivity in migraine sufferers. \u003cem\u003eBrain imaging and behavior, 11(2),\u003c/em\u003e 526\u0026ndash;540. https://doi.org/10.1007/s11682-016-9533-6\u003c/li\u003e\n\u003cli\u003eMertens, P., Nuti, C., Sindou, M,, Guenot, M., Peyron, R., Garcia-Larrea, L., \u0026amp; Laurent, B. (1999). Precentral cortex stimulation for the treatment of central neuropathic pain: results of a prospective study in a 20-patient series. \u003cem\u003eStereotactic and Functional Neurosurgery, 73\u003c/em\u003e, 122-125. https://doi.org/10.1159/000029769.\u003c/li\u003e\n\u003cli\u003eMori, S. (2007). \u003cem\u003eIntroduction to Diffusion Tensor Imaging\u003c/em\u003e. Elsevier. ISBN 0444528288, 9780444528285\u003c/li\u003e\n\u003cli\u003eNeeb, L., Bastian, K., Villringer, K., Gits, H.C., Israel,H., Reuter,U., \u0026amp; Fiebach, J.B. (2015). No microstructural white matter alterations in chronic and episodic Migraineurs: a case-control diffusion tensor magnetic resonance imaging study. \u003cem\u003eHeadache, 55,\u003c/em\u003e 241\u0026ndash;251. https://doi.org/10.1111/head.12496\u003c/li\u003e\n\u003cli\u003ePapo, D., \u0026amp; Buld\u0026uacute;, J.M. (2019). Brain synchronizability, a false friend. \u003cem\u003eNeuroImage, 196,\u003c/em\u003e 195-199. https://doi.org/10.1016/j.neuroimage.2019.04.029.\u003c/li\u003e\n\u003cli\u003ePecora, L. M., \u0026amp; Carroll, T. L. (1998). Master stability functions for synchronized coupled systems. Physical Review Letters, 80, 2109-2112.https://doi.org/10.1103/PhysRevLett.80.2109.\u003c/li\u003e\n\u003cli\u003ePereira, T., Eroglu, D., Bagci, G. B., Tirnakli, U., \u0026amp; Jensen, H. J. (2013). Connectivity-driven coherence in complex networks. Physical review letters, 110(23), 234103. https://doi.org/10.1103/PhysRevLett.110.234103\u003c/li\u003e\n\u003cli\u003ePlanchuelo-G\u0026oacute;mez, \u0026Aacute;., Garc\u0026iacute;a-Azor\u0026iacute;n, D., Guerrero, \u0026Aacute;. L., Aja-Fern\u0026aacute;ndez, S., Rodr\u0026iacute;guez, M., \u0026amp; de Luis-Garc\u0026iacute;a, R. (2020a). Structural connectivity alterations in chronic and episodic migraine: A diffusion magnetic resonance imaging connectomics study. Cephalalgia : an international journal of headache, 40(4), 367\u0026ndash;383. https://doi.org/10.1177/0333102419885392\u003c/li\u003e\n\u003cli\u003ePlanchuelo-G\u0026oacute;mez, A., Garcia-Azorin, D., Guerrero, A. L., Aja-Fernandez, S., Rodriguez, M., \u0026amp; de Luis-Garcia, R. (2020b) White matter changes in chronic and episodic migraine: a diffusion tensor imaging study. \u003cem\u003eThe Journal of Headache and Pain, 21,\u003c/em\u003e 1. https://doi.org/10.1186/s10194-019-1071-3\u003c/li\u003e\n\u003cli\u003eR Core Team (2022). \u003cem\u003eR: A language and environment for statistical \u003c/em\u003e\u003cem\u003ecomputing.\u003c/em\u003e R Foundation for Statistical Computing, Vienna, Austria.https://www.R-project.org/.\u003c/li\u003e\n\u003cli\u003eRubinov, M., \u0026amp; Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. NeuroImage, 52(3), 1059\u0026ndash;1069. https://doi.org/10.1016/j.neuroimage.2009.10.003\u003c/li\u003e\n\u003cli\u003eScher, A. I., Wang, S. J., Katsarava, Z., Buse, D. C., Fanning, K. M., Adams, A. M., \u0026amp; Lipton, R. B. (2019). Epidemiology of migraine in men: Results from the Chronic Migraine Epidemiology and Outcomes (CaMEO) Study. Cephalalgia : an international journal of headache, 39(2), 296\u0026ndash;305. https://doi.org/10.1177/0333102418786266\u003c/li\u003e\n\u003cli\u003eSchindler, K. A., Bialonski, S., Horstmann, M. T., Elger, C. E., \u0026amp; Lehnertz, K. (2008). Evolving functional network properties and synchronizability during human epileptic seizures. Chaos (Woodbury, N.Y.), 18(3), 033119. https://doi.org/10.1063/1.2966112.\u003c/li\u003e\n\u003cli\u003eSchwedt, T.J., Chong, C.D., Wu, T., Gaw, N., Fu, Y., \u0026amp; Li, J. (2015). Accurate classification of chronic migraine via brain magnetic resonance imaging. \u003cem\u003eHeadache, 55,\u003c/em\u003e 762\u0026ndash;77. https://doi.org/10.1111/head.12584\u003c/li\u003e\n\u003cli\u003eSeghatoleslam, M., Ghadiri, M.K., Ghaffarian, N., Speckmann, E.J., \u0026amp; Gorji, A. (2014). Cortical spreading depression modulates the caudate nucleus activity. \u003cem\u003eNeuroscience, 267\u003c/em\u003e, 83-90. https://doi.org/10.1016/j.neuroscience.2014.02.029.\u003c/li\u003e\n\u003cli\u003eSilvestro, M., Tessitore, A., Caiazzo, G.\u003cem\u003e, \u003c/em\u003eScotto di Clemente, F., Trojsi,F., Cirillo,M., Esposito, F., Tedeschi, G., \u0026amp; Russo, A. (2021). Disconnectome of the migraine brain: a \u0026ldquo;connectopathy\u0026rdquo; model. The \u003cem\u003eJournal of Headache and Pain, 22,\u003c/em\u003e 102. https://doi.org/10.1186/s10194-021-01315-6\u003c/li\u003e\n\u003cli\u003eSmith S. M. (2002). Fast robust automated brain extraction. Human brain mapping, 17(3), 143\u0026ndash;155. https://doi.org/10.1002/hbm.10062\u003c/li\u003e\n\u003cli\u003eSporns, O. (2010) \u003cem\u003eNetworks of the Brain.\u003c/em\u003e MIT Press.\u003c/li\u003e\n\u003cli\u003eSteiner, T.J., Stovner, L.J., Jensen, R., Uluduz, D., \u0026amp; Katsarava, Z. (2020). Lifting The Burden: the Global Campaign against Headache. Migraine remains second among the world\u0026apos;s causes of disability, and first among young women: findings from GBD2019. \u003cem\u003eThe Journal of Headache and Pain, 21\u003c/em\u003e, 137. https://doi.org/10.1186/s10194-020-01208-0.\u003c/li\u003e\n\u003cli\u003eStovner, L.J., Hagen, K., Linde. M., \u0026amp; Steiner, T.J. (2022). The global prevalence of headache: an update, with analysis of the influences of methodological factors on prevalence estimates. \u003cem\u003eThe Journal of Headache and Pain,\u003c/em\u003e 23, 34. https://doi.org/10.1186/s10194-022-01402-2.\u003c/li\u003e\n\u003cli\u003eSun, J., Bollt, E. M., \u0026amp; Nishikawa, T. (2009). Master stability functions for coupled nearly identical dynamical systems. \u003cem\u003eEurophysics Letters, 85(6)\u003c/em\u003e, 60011. https://doi.org/10.1209/0295-5075/85/60011\u003c/li\u003e\n\u003cli\u003eSzabo, E., Green, S., Karunakaran, K.D., Sieberg, C.B., Elman, I., Burstein, R., \u0026amp; Borsook, D. (2022). Migraine: interactions between brain\u0026apos;s trait and state. \u003cem\u003eCNS Spectrums, 27\u003c/em\u003e, 561-569. https://doi.org/10.1017/S109285292100064X.\u003c/li\u003e\n\u003cli\u003eTaylor, P.N., Sinha, N., Wang, Y., Vos, S.B., de Tisi, J., Miserocchi, A., McEvoy, A.W., Winston, G.P., \u0026amp; Duncan, J.S. (2018). The impact of epilepsy surgery on the structural connectome and its relation to outcome, \u003cem\u003eNeuroImage: Clinical, 18\u003c/em\u003e, 202-214. https://doi.org/10.1016/j.nicl.2018.01.028.\u003c/li\u003e\n\u003cli\u003eTournier, J-D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., Jeurissen, B., Yeh, C.-H., \u0026amp; Connelly, A. (2019). MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. \u003cem\u003eNeuroImage, 202,\u003c/em\u003e 116137. https://doi.org/10.1016/j.neuroimage.2019.116137\u003c/li\u003e\n\u003cli\u003eUhlhaas, P.J., \u0026amp; Singer, W. (2006). Neural synchrony in brain disorders: relevance for cognitive dysfunctions and pathophysiology. \u003cem\u003eNeuron, 52\u003c/em\u003e, 155-68. https://doi.org/10.1016/j.neuron.2006.09.020.\u003c/li\u003e\n\u003cli\u003eVarela, F., Lachaux, JP., Rodriguez, E. \u0026amp; Martinerie, J. (2001). The brainweb: Phase synchronization and large-scale integration. \u003cem\u003eNature Reviews Neuroscience, 2\u003cu\u003e,\u003c/u\u003e\u003c/em\u003e 229\u0026ndash;239. https://doi.org/10.1038/35067550\u003c/li\u003e\n\u003cli\u003eVeraart, J., Novikov, D. S., Christiaens, D., Ades-Aron, B., Sijbers, J., \u0026amp; Fieremans, E. (2016). Denoising of diffusion MRI using random matrix theory. \u003cem\u003eNeuroImage, 142\u003c/em\u003e, 394\u0026ndash;406. https://doi.org/10.1016/j.neuroimage.2016.08.016\u003c/li\u003e\n\u003cli\u003eVetvik, K.G., \u0026amp; MacGregor, E.A. (2017). Sex differences in the epidemiology, clinical features, and pathophysiology of migraine. \u003cem\u003eLancet Neurology\u003c/em\u003e, 16, 76-87. https://doi.org/10.1016/S1474-4422(16)30293-9.\u003c/li\u003e\n\u003cli\u003eZhang, F., Daducci, A., He, Y., Schiavi, S., Seguin, C., Smith, R.E., Yeh, C.H., Zhao, T., \u0026amp; O\u0026apos;Donnell, L.J. (2022). Quantitative mapping of the brain\u0026apos;s structural connectivity using diffusion MRI tractography: A review. \u003cem\u003eNeuroImage. 249\u003c/em\u003e, 118870. https://doi.org/10.1016/j.neuroimage.2021.118870.\u003c/li\u003e\n\u003cli\u003eZhang, S., Ide, J.S., \u0026amp; Li, C.S. (2012). Resting-state functional connectivity of the medial superior frontal cortex. \u003cem\u003eCerebral Cortex, 22,\u003c/em\u003e 99-111. https://doi.org/10.1093/cercor/bhr088.\u003c/li\u003e\n\u003cli\u003eZhang, X., Zhou, J., Guo, M., Cheng, S., Chen, Y., Jiang, N., Li, X., Hu, S., Tian, Z., Li, Z., \u003cem\u003eet al.,\u003c/em\u003e (2023). A systematic review and meta-analysis of voxel-based morphometric studies of migraine. \u003cem\u003eJournal of Neurology, 270\u003c/em\u003e, 152-170. https://doi.org/10.1007/s00415-022-11363-w.\u003c/li\u003e\n\u003cli\u003eZhu, J., Qian, Y., Zhang, B., Li, X., Bai, Y., Li, X., \u0026amp; Yu, Y. (2020). Abnormal synchronization of functional and structural networks in schizophrenia. \u003cem\u003eBrain Imaging and Behavior, 14\u003c/em\u003e, 2232-2241. https://doi.org/10.1007/s11682-019-00175-8.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[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, Connectome, Synchronization, Interhemispheric connectivity","lastPublishedDoi":"10.21203/rs.3.rs-3154214/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3154214/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe structure of the brain can be characterized as a set of interconnected regions, using the concept of \u003cem\u003econnectome\u003c/em\u003e. It has been shown that the connectome is altered in patients with migraine, presenting a series of structural connections differences with respect to healthy subjects. Relating these structural alterations to the specific characteristics of migraine is a difficult task. One approach is to study the effects of these alterations on the dynamic processes that can take place in the connectome. An ubiquitous process in brain networks is the synchronization of neuronal populations. It depends not only on the network but also on the properties of the units being synchronized. Fortunately, the contribution of the network structure can be calculated independently, thus defining the network \u003cem\u003esynchronizability\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eConnectome synchronizability and structural connectivity were assessed using diffusion-weighted magnetic resonance imaging data from 56 female patients with episodic migraine, 60 female patients with chronic migraine (CM) and 39 female healthy controls (HC). We found that whole-brain synchronizability was significantly larger in CM than in HC. The analysis of the synchronizability of subnetworks showed that differences between CM and HC were larger for subnetworks involving regions from different hemispheres. Moreover, the number of interhemispheric streamlines was significantly larger in CM than in HC, whereas no such difference appeared between intrahemispheric streamlines. The largest contributions to these differences come from the interhemispheric connections from three regions: left superior frontal gyrus, right precentral cortex, and right caudate.\u003c/p\u003e","manuscriptTitle":"Enhancement of synchronizability in chronic migraine patients related to the increase of connectome interhemispheric connectivity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-13 14:11:56","doi":"10.21203/rs.3.rs-3154214/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":"093d4669-2e22-4033-b8ce-34e53b1fe5b6","owner":[],"postedDate":"July 13th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-08-07T16:29:31+00:00","versionOfRecord":[],"versionCreatedAt":"2023-07-13 14:11:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3154214","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3154214","identity":"rs-3154214","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}