
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39256409
71000
10.1038/s41598-024-71000-6
Article
Structural brain characteristics of epilepsy patients with comorbid migraine without aura
Zhang Shujiang 1
Liu Wenyu 2
Li Jinmei lijinmei@wchscu.cn

2
Zhou Dong zhoudong66@yahoo.de

2
1 https://ror.org/0014a0n68 grid.488387.8 Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
2 grid.13291.38 0000 0001 0807 1581 Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
10 9 2024
10 9 2024
2024
14 211676 2 2024
23 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Migraine is a common bi-directional comorbidity of epilepsy, indicating potential complex interactions between the two conditions. However, no previous studies have used brain morphology analysis to assess possible interactions between epilepsy and migraine. Voxel-based morphometry (VBM), surface-based morphometry (SBM), and structural covariance networks (SCNs) can be used to detect morphological changes with high accuracy. We recruited 30 individuals with epilepsy and comorbid migraine without aura (EM), along with 20 healthy controls (HC) and 30 epilepsy controls (EC) without migraine. We used VBM, SBM, and SCN analysis to compare differences in gray matter volume, cortical thickness, and global level and local level graph theory indexes between the EM, EC, and HC groups to investigate structural brain changes in the EM patients. VBM analysis showed that the EM group had gray matter atrophy in the right temporal pole compared with the HC group (p < 0.001, false discovery rate correction [FDR]). Furthermore, the headache duration in the EM group was negatively correlated with the gray matter volume of the right temporal pole (p < 0.05). SBM analysis showed cortical atrophy in the left insula, left posterior cingulate gyrus, left postcentral gyrus, left middle temporal gyrus, and left fusiform gyrus in the EM compared with the HC group (p < 0.001, family wise error correction). We found a positive correlation between headache frequency and the cortical thickness of the left middle temporal gyrus (p < 0.05). SCN analysis revealed no differences in global parameters between the three groups. The area under the curve (AUC) of the nodal betweenness centrality in the right postcentral gyrus was lower in the EM group compared with the HC group (p < 0.001, FDR correction), and the AUC of the nodal degree in the right fusiform gyrus was lower in the EM group compared with the EC group (p < 0.001, FDR correction). We found clear differences in brain structure in the EM patients compared with the HC group. Accordingly, migraine episodes may influence brain structure in epilepsy patients. Conversely, abnormal brain structure may be an important factor in the development of epilepsy with comorbid migraine without aura. Further studies are needed to investigate the role of brain structure in individuals with epilepsy and comorbid migraine without aura.

Keywords

Epilepsy
Comorbid migraine without aura
Brain structure
Gray matter volume
Cortical thickness
Structural covariance network
Subject terms

Neuroscience
Structural biology
issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Migraine is a common comorbidity of epilepsy. The reported prevalence of comorbid migraine in epilepsy patients ranges from 12.4 to 26.3%1–3. In general, the prevalence of migraine is higher in individuals with epilepsy than in the general population4,5, suggesting a close association between epilepsy and migraine. The prevalence of migraine varies among epilepsy patients according to epilepsy origin and age. For instance, Begasse et al.6 reported that the prevalence of comorbid migraine in patients with epilepsy of focal origin was 21.2%, while Kelley et al.7 reported this prevalence to be 25% in pediatric patients with epilepsy.

The early treatment response to anti-seizure medicine (ASM) in patients with comorbid migraine is worse than that in patients without migraine8. Indeed, comorbid migraine appears to have a negative impact on epilepsy prognosis9,10, and is known to affect the quality of life while increasing the mental and economic burden of patients with epilepsy11,12. Comorbid migraine has a bi-directional effect on epilepsy: migraine and epilepsy share environmental and genetic risk factors13,14, and also have common treatment drugs15,16. At present, the specific pathogenesis of epilepsy with comorbid migraine is unclear. Accordingly, the use of imaging technologies to explore the basis of migraine comorbidity could increase our understanding of the pathogenesis of epilepsy with comorbid migraine. Epilepsy and migraine are both paroxysmal disorders of the nervous system. Epilepsy patients with comorbid migraine most often have migraines without an aura17. Patients with epilepsy with comorbid migraine without an aura (EM) may have specific differences in brain structure that could be revealed via imaging. However, few imaging studies have examined individuals with epilepsy with comorbid migraine. Wang et al.18 reported decreased functional connectivity between the periaqueductal gray (PAG) and pontine nucleus in temporal lobe epilepsy patients with comorbid migraine. This decrease in functional connectivity may be a feature of temporal lobe epilepsy with comorbid migraine. In a study on morphological changes in white matter in epilepsy patients with comorbid migraine, patients with epilepsy with comorbid migraine had characteristic white matter structural changes compared with epilepsy patients without migraine19. In patients with epilepsy, comorbid migraine might be caused by abnormal white matter in the brainstem, fornix, or right uncinate tract19. Comorbid epilepsy and migraine might also involve changes in the hippocampus, insula, cingulate, and thalamus, which are involved in epileptic drug resistance as well as pain regulation. Voxel based morphometry (VBM), surface-based morphometry (SBM), and structural covariant network (SCN) analysis are commonly used for cortical morphological analysis20–22. The aim of the present study was to explore morphological changes and the pathogenesis of migraine without aura in patients with epilepsy.

Methods

Design and study duration

In this cross-sectional study, we enrolled both in-patients and out-patients at the Epilepsy Center of the West China Hospital of Sichuan University from June 2021 to June 2022. The study was approved by the institutional review board of the West China Hospital of Sichuan University. All participants received detailed written information regarding the study objectives, and written informed consent was obtained in accordance with the Declaration of Helsinki.

Participants

We recruited 20 right-handed healthy controls (HC), 30 right-handed EM patients, and 30 epilepsy controls (EC) without migraine. All subjects underwent 3D-T1 scans, during which they were asked not to fall asleep and to not think of anything in particular. The detailed demographic data and epilepsy related details are given in Table 1. Patients in the EM group were required to meet the following criteria: (1) epilepsy according to the diagnostic criteria23 and course of epilepsy > 6 months; (2) headache characteristics meeting the diagnostic criteria of migraine without aura according to the International Classification of Headache Disorders, 3rd edition3; (3) interictal migraine episodes that occurred after the onset of epileptic symptoms; (4) age 18–75 years; (5) no structural abnormalities according to conventional brain magnetic resonance imaging (MRI). During the enrollment process, we excluded all patients with a history of acute migraine episodes and those who had used analgesic medication within the 3 days prior to the scan. Patients in the EC group were required to be free of any type of headache during the course of epilepsy. The EC group members were matched with the EM patients regarding the epilepsy origin. For all participants, the exclusion criteria were organic brain disease, other mental disease, intellectual disability, a family history of dementia, a history of alcoholism, MRI contraindications, an inability to cooperate with the examination, and a previous history of craniocerebral surgery.Table 1 Demographic details of the study participants.

	EM	EC	HC	p value	
N	30	30	20	–	
Age (years, mean ± SD)	30.87 ± 10.33	28.10 ± 7.33	28.85 ± 8.53	0.466	
Gender, female, n (%)	25 (83.33%)	25 (83.33%)	12 (60.00%)	0.115	
Low education, high school and below, n (%)	17 (56.67%)	21 (70.00%)	11 (55.00%)	0.457	
Epilepsy duration (median, years)	9	8		0.546	
Seizure type				0.838	
 Partial seizures	2 (6.67%)	1 (3.33%)	–		
 Generalized seizures	10 (33.33%)	12 (40.00%)	–		
 Multiple seizure types	18 (60.00%)	17 (56.67%)			
Etiology				0.508	
 Infection-related	2 (6.67%)	6 (20.00%)	–		
 Gene-related	2 (6.67%)	1 (3.33%)	–		
 Immune-related	4 (13.33%)	3 (10.00%)	–		
 Unknown	22 (73.33%)	20 (66.67%)	–		
Type of epilepsy				0.493	
 Focal origin	15 (50.00%)	11 (36.67%)	–		
 Frontal lobe epilepsy	3 (10.00%)	2 (6.67%)	–		
 Temporal lobe epilepsy	8 (26.67%)	7 (23.33%)	–		
 Occipital lobe epilepsy	2 (6.67%)	1 (3.33%)	–		
 Multifocal origin	2 (6.67%)	1 (3.33%)	–		
 Generalized origin	4 (13.33%)	7 (23.33%)	–		
 Unknown origin	11 (36.67%)	12 (40.00%)	–		
ASM use				0.115	
 Monotherapy	11 (36.67%)	7 (23.33%)	–		
 Polytherapy	15 (50.00%)	12 (40.00%)	–		
 No therapy	4 (13.33%)	11 (36.67%)	–		
 Frequent seizures, n (%)	9 (30.00%)	17 (56.67%)	–	0.067	
EM epilepsy with comorbid migraine without aura, EC epilepsy controls, SD standard deviation, ASM antiseizure medications.

Data acquisition

Whole-brain MRI was performed using a 3 T scanner (Siemens Germany) with an 8-channel receive-only head coil. Scanning was conducted at the neuroimaging center at the West China Hospital of Sichuan University. A sagittal T1-weighted sequence was used for 3D scanning, and the scanning parameters were as follows: pulse repetition time = 2250 ms, echo time = 2.6 ms, flip angle = 90°, layer thickness = 1 mm, matrix = 256 × 256. The field of view was 256 × 256 mm2, and the voxel size was 1 × 1 × 1 mm3. Each acquisition contained 192 structural images. An experienced neuroradiologist examined the scans for any major anatomical abnormalities.

Data analysis

VBM

For all subjects, we used SPM 12 (https://www.fil.ion.ucl.ac.uk/spm/) for raw data format conversion. We used CAT12 (http://www.neuro.uni-jena.de/cat/) for data segmentation, reconstruction, correction, and registration. We used diffeomorphic anatomical registration through exponentiated lie algebra (DARTEL) to register the structural images of the subjects, and the gray and white matter volumes were calculated after tissue segmentation. The total volume of the gray matter, white matter, and cerebral spinal fluid (CSF) were included as covariates in the statistical analysis. Finally, the gray matter volume was spatially smoothed, and the full width half maximum (FWHM) was set to 8 mm. The region of interest (ROI) parameters were set as follows: voxel p < 0.001 and cluster < 0.05, with false discovery rate (FDR) correction.

SBM

The data preprocessing steps, software, and ROI parameters were the same as in the VBM analysis. The cortical thickness was obtained after segmentation reconstruction correction and registration. Finally, the cortical thickness was spatially smoothed, the FWHM parameter was set to 15 mm, and family wise error rate (FWE) correction was performed.

SCN

For the three subject groups, the whole brain was divided into 90 ROIs using the automatic anatomical marker template map24. Then, the gray matter volume values were extracted. Linear regression analysis was used to eliminate the influence of gender, age, and years of education, and the 90 brain regions were defined as network nodes. After calculating the Pearson correlation coefficient of the gray matter volume between the nodes, a correlation matrix of 90 × 90 was obtained. The SCN was based on the group level, and we determined the sparsity of the same structure network based on the cluster level for analysis. The sparsity range was 6% ≤ S ≤ 25%, and the step size was 0.01. The global and local levels of the graph were analyzed using MATLAB 2013b (https://ww2.mathworks.cn/products/matlab.html) and the BCT toolkit (https://www.nitrc.org/projects/bct). The specific parameters was listed as follows:

The global parameters were as follows: the clustering coefficient (Cp) measured the degree of network collectivization, and was an important parameter for measuring the network because it reflected the interconnection between a node and its neighbors; the standardized Cp (Gamma) reflected the ratio of the Cp of the actual network to the Cp of the random network; the characteristic path length (Lp) was the path with the least number of edges connecting two nodes; the average shortest characteristic path was the average distance among all of the point pairs in the network; the standardized Lp (Lambda) was the ratio of the Cp of the actual network to the Cp of the random network; the small-world attribute (Sigma) was the ratio of Gamma to Lambda; the global efficiency (Eglob) was a measure of information transmission capacity between nodes; the local efficiency (Eloc) was the global efficiency of a subgraph composed of its nearest neighbors, and measured the local information transmission capacity of the network.

The local parameters included the following: the nodal degree was used to calculate the number of directly connected nodes or the number of connected edges of the selected node; the nodal betweenness centrality calculated the contribution value of the shortest path of the selected node; the nodal efficiency was used to examine the information transmission capability of each node in the network. After the above processing, the area under the curve (AUC) of the graph theory index was calculated, and 1000 permutation tests were used for pairwise comparisons between the groups. All of the local parameters were corrected using the FDR for multiple comparisons, where p < 0.05 was considered statistically significant.

Statistical analysis

In the VBM and SBM analyses, age, gender, and level of education were used as covariates in a one-way analysis of variance. The VBM and SBM analyses were based on the differences in brain regions at the cluster level, and we conducted post-hoc analyses for pairwise comparisons between the groups. We used the Bonferroni method to conduct multiple comparisons of the results, and the corrected significance level was p < 0.05. In the SCN analysis, pairwise comparisons were performed based on the group level. We performed a correlation analysis to determine how gray matter volume and cortical thickness were related to headache course, headache duration, headache frequency, and headache degree (according to a visual analog scale) in the EM group, and headache course was calculated from the first day of migraine symptoms after the diagnosis of epilepsy and was measured in years. We calculated Spearman correlation coefficients (r values), and the significance was set at p < 0.05.

Ethics approval and consent to participate

The study was approved by the institutional review board of the West China Hospital of Sichuan University (2022301), China. All subjects provided written informed consent prior to study enrollment.

Results

Gray matter volume

We found a difference in gray matter volume among the three groups in the right temporal pole (voxel p < 0.001, cluster p < 0.05, FDR corrected). The specific peak information is given in Table 2. The location of the right temporal pole is shown in Fig. 1.Table 2 Brain regions with differences in gray matter volume among the three groups.

Peak point brain region	MNI peak coordinates	F value	Cluster Size	
X	Y	Z	
Right temporal pole	42	12	− 42	15.5069	693	
(voxel p < 0.001, cluster p < 0.05, FDR corrected).

Fig. 1 Location of discrepant regions in VBM analysis. The yellow area represents the right temporal pole.

The results of the group comparison suggested that compared with the HC group, gray matter atrophy of the right temporal pole was present in the EM group (p < 0.001, FDR corrected). However, there was no difference in gray matter volume between the EM group and EC group, as shown in Fig. 2.Fig. 2 Comparison of gray matter volume of the right temporal pole in three groups. Gray matter atrophy of the right temporal pole was present in the EM group compared with the HC group, and there was no difference in gray matter volume compared with the EC group. ***p < 0.001; EM: epilepsy with comorbid migraine without aura; EC: epilepsy controls; GMV: gray matter volume.

The course of headache, frequency of headache, and degree of headache were not correlated with the gray matter volume of the above-mentioned discrepant regions in the EM group, and the duration of headache was negatively correlated with the gray matter volume of the right temporal pole (p < 0.05), as shown in Fig. 3.Fig. 3 Correlation analysis between headache duration and gray matter volume in the right temporal pole. The duration of headache was negatively correlated with the gray matter volume of the right temporal pole. GMV: gray matter volume.

Cortical thickness

We identified five brain regions with differences in cortical thickness among the three groups. The maximum overlap rates all corresponded with regions in the left hemisphere, and included the left insula, left posterior cingulate, left postcentral gyrus, left middle temporal gyrus, and left fusiform gyrus (voxel p < 0.001, cluster p < 0.05, FWE corrected), as shown in Table 3. Cortical indicators of brain regions were identified according to the Desikan-Killiany map. The locations of the different brain regions are shown in Fig. 4.Table 3 Brain regions with differences in cortical thickness among the three groups.

Cluster	Overlapping ratio (%)	Brain regions	number of vertices	p value	
Cluster 1	68	Insula	3113	< 0.001	
22	Pars opercularis			
6	Postcentral			
4	Precentral			
Cluster 2	36	Posterior cingulate	1387	< 0.001	
33	Precuneus			
30	Paracentral			
Cluster 3	86	Postcentral	1088	< 0.001	
14	Supramarginal			
Cluster 4	96	Middle temporal	819	< 0.001	
4	Inferior temporal			
Cluster 5	66	Fusiform	796	< 0.001	
29	Lateral occipital			
5	Lingual			
(voxel p < 0.001, cluster p < 0.05, FWE corrected).

Fig. 4 Location of discrepant regions in SBM analysis. The blue area represents the discrepant regions with the maximum overlap rate in the three groups, which are located in the left insula, left posterior cingulate, left postcentral gyrus, left middle temporal gyrus, and left fusiform gyrus.

Compared with the HC group, the EM group had cortical atrophy in the left insula, left posterior cingulate, left postcentral gyrus, left middle temporal gyrus, left fusiform gyrus, and other brain regions (p < 0.001, FWE corrected). However, there was no difference in cortical thickness between the EM and EC groups, as shown in Fig. 5.Fig. 5 Comparison of the cortical thickness of the discrepant regions in the three groups. A-E represent the left insula, left posterior cingulate, left postcentral gyrus, left middle temporal gyrus, and left fusiform gyrus respectively. The results showed a difference in cortical thickness between the EM and HC groups, but no difference between the EM and EC groups. ***p < 0.001; **p < 0.01. EM: epilepsy with comorbid migraine without aura; EC: epilepsy controls.

The course of headache, duration of headache, and degree of headache were not correlated with the gray matter volume of the above-mentioned discrepant regions in the EM group. Headache frequency was positively correlated with the thickness of the left middle temporal cortex (p < 0.05), as shown in Fig. 6.Fig. 6 Correlation analysis between headache frequency and cortical thickness in the left middle temporal gyrus. The headache frequency was positively correlated with the thickness of the left middle temporal cortex.

SCN

SCN analysis showed that there was no change in global parameters in the EM group compared with the HC group (Table 4), although the area under the nodal medium curve of the right postcentral gyrus was decreased (p < 0.001, FDR corrected) (Table 5). The location of the right posterior central gyrus is shown in Fig. 7. There were no differences in global parameters or local parameters between the EC and HC groups (Table 6). There were no differences in global parameters between the EM and EC groups (Table 7), although the area under the nodal curvature of the right fusiform gyrus was reduced (p < 0.001, FDR corrected) (Table 8). The location of the right fusiform gyrus is shown in Fig. 8.Table 4 Comparison of global parameters between the EM group and HC group.

	EM	HC	p value	
n	30	20	
aCp	0.1022	0.1027	0.9430	
aLp	0.4384	0.4520	0.6510	
aGamma	0.3994	0.3889	0.8940	
aLambda	0.2245	0.2197	0.2197	
aSigma	0.3336	0.3311	0.9630	
aEglob	0.0861	0.0838	0.7060	
aEloc	0.1332	0.1328	0.9480	
EM epilepsy with comorbid migraine without aura, HC healthy controls.

Table 5 Regions with discrepant levels of nodal interleukin between the EM group and HC group.

Discrepant regions	EM	HC	p value	
Postcentral_R	3.5009	95.9092	< 0.001	
EM epilepsy with comorbid migraine without aura, HC healthy controls.

Fig. 7 Location of the right posterior central gyrus.

Table 6 Comparison of global parameters between the EC group and HC group.

	EC	HC	p value	
n	30	20	
aCp	0.0954	0.0993	0.6750	
aLp	0.4614	0.4199	0.3310	
aGamma	0.3254	0.3953	0.1820	
aLambda	0.2181	0.2131	0.7380	
aSigma	0.2800	0.3475	0.1830	
aEglob	0.0820	0.0899	0.2670	
aEloc	0.1211	0.1315	0.3170	
EC epilepsy controls, HC healthy controls.

Table 7 Comparison of global parameters between the EM group and EC group.

	EM	EC	p value	
n	30	20	
aCp	0.0993	0.0955	0.0955	
aLp	0.4291	0.4739	0.2740	
aGamma	0.3864	0.3162	0.3370	
aLambda	0.2184	0.2190	0.9520	
aSigma	0.3319	0.2715	0.3560	
aEglob	0.0881	0.0798	0.2210	
aEloc	0.1299	0.1210	0.3620	
EM epilepsy with comorbid migraine without aura, EC epilepsy controls.

Table 8 Node degree of discrepant regions in the EM group and EC group.

Discrepant regions	EM	EC	p value	
Fusiform_R	2.1150	7.7150	< 0.001	
EM epilepsy with comorbid migraine without aura, EC epilepsy controls.

Fig. 8 Location of the right fusiform gyrus.

Discussion

Migraine is one of the most common comorbidities of epilepsy, and it has a similar pathology. At present, the pathogenesis of epilepsy with comorbid migraine is not fully understood. Migraine is often neglected in epilepsy patients, and treatment is not standardized. Migraine without aura is one of the most common types of migraine in individuals with epilepsy. EM patients may have specific changes in brain structure, and so a greater understanding of the pathogenesis of epilepsy with comorbid migraine may lead to standardized clinical treatments in the future.

In this study, we found that both the EM and EC groups had differences in structural morphology compared with the HCs. In the EM group, the duration of headache was negatively correlated with the gray matter volume of the right temporal pole, while headache frequency was positively correlated with the thickness of the left middle temporal cortex. Compared with the EC group, the area under the nodal curvature of the right fusiform gyrus was reduced in the EM group. Based on these results, we conclude that the differences in brain morphology between the EM and EC groups were limited.

SBM is extremely useful for estimating the indices of cortical morphology, such as volume, thickness, area, and gyrification. In contrast, VBM is a typical form of gray matter volumetry that includes cortical measurement. Previous literature has shown that VBM and SBM have different advantages for assessing different diseases, and that they can provide different types of information. Goto et al.20 summarized the advantages of VBM and SBM morphometry for cortical morphology analysis, and concluded that VBM and SBM should be conducted concurrently. SCN refers to the correlative patterns of diverse brain morphological features among differential brain regions comprising the brain, as calculated per participant or across the participants25. Researchers could use SCN to estimate the dynamics of brain morphological changes among several brain regions from cross-sectional data, and might be able to find potential target(s) of therapeutic intervention among hub regions26. Using VBM in conjunction with SBM and SCN appears to be effective for morphological analysis.

Gray matter changes in EM patients

Braga et al.27 used VBM analysis to study changes in gray matter volume in the cingulate gyrus of 32 patients with idiopathic generalized epilepsy, and found gray matter atrophy in the cingulate gyrus, which was concentrated in the anterior cingulate area and isthmus. Lu et al.28 used VBM to analyze brain morphological changes in 120 patients with temporal lobe epilepsy, and found that patients with left temporal lobe epilepsy had gray matter atrophy in the left lower half of the hippocampus, bilateral medial thalamus, hypothalamus, and other brain regions, while patients with right temporal lobe epilepsy had gray matter atrophy in the right lateral part of the hippocampus. Taken together, these data indicate that gray matter atrophy occurs in both focal and generalized epilepsy.

Similarly, gray matter atrophy has been reported in migraine patients. Li et al.29 analyzed changes in gray matter volume in 72 patients with migraine without aura, and reported a decrease in gray matter volume in the bilateral superior and inferior colliculus, periaqueductal gray, locus ceruleus, raphe nucleus, and the dorsal junction of the pons and medulla. A systematic review of VBM analyses involving 1616 migraine patients by Zhang et al.30 showed that gray matter atrophy in multiple brain regions was related to changes in sensation, emotion, and cognition, along with pain reduction. Correlation analyses showed that changes in these brain regions were related to the frequency and course of migraine.

Overall, it appears that both epilepsy and migraine are associated with atrophy in gray matter, which indicates that the occurrence of migraine in patients with epilepsy might be related to changes in gray matter. At present, the mechanism that could link epilepsy to comorbid migraine is unknown, and it is not clear whether brain morphological changes are involved in the development of migraine in patients with epilepsy. In our study, the onset of migraine symptoms occurred after the diagnosis of epilepsy, so our comparison of differences in gray matter volume between patients with and without comorbid migraine served to explore whether the occurrence of migraine with comorbid epilepsy is related to gray matter changes. No previous studies have examined gray matter changes in epilepsy patients with comorbid migraine. However, several brain morphological changes have been identified in epilepsy patients with comorbid migraine, and there is evidence to suggest that white matter changes might be involved in the pathogenesis of migraine with epilepsy. Huang et al.19 compared white matter differences in 13 individuals with EM and 12 with EC. They found increased fractional anisotropy (FA) of the fornix, increased mean diffusivity (MD) of the middle cerebellar peduncle, left superior cerebellar peduncle, and right uncinate fasciculus, and increased axial diffusivity (AD) of the middle cerebellar peduncle and the right medial lemulus in EM compared with EC patients. The changes in the above indicators were related to the type of migraine and the duration of headache. Changes in the composition of brainstem white matter may be involved in the development of migraine in patients with epilepsy, while the composition of the fornix and right uncinate white matter tracts could mediate the co-occurrence of migraine and epilepsy19.

Our results showed that the gray matter volume of the right temporal pole was decreased in the EM group compared with the HC group, but we found no significant difference in gray matter volume in the EM group compared with EC patients. Gray matter volume was not related to the course of headache, frequency of headache, or degree of headache, and the headache duration was negatively correlated with the gray matter volume of the right temporal pole in EM patients such that longer headaches were associated with more significant gray matter atrophy. The temporal pole is involved in cognitive processes including visual processing of complex objects, facial recognition, autobiographical memory, naming, word-object labeling, semantic processing, and social-emotional processing31. Recent studies have shown that the temporal pole might also play an important role in chronic pain regulation. Lin et al.32 studied glucose metabolism in patients with head and neck squamous cell carcinoma and esophageal cancer, and found that uncontrollable cancer pain was associated with enhanced activity in multiple brain regions, including the temporal pole. Furthermore, Moulton et al.33 used thermal imaging to identify physiological changes in the cortical areas of 11 patients with non-ictal migraine. Rs-fMRI analysis showed that the temporal pole had increased functional connectivity with respect to other brain regions, suggesting that anterior temporal pole hyperexcitation may lead to functional abnormalities in migraine patients. They also found that Rs-fMRI activation of the anterior temporal pole during pain heat sensitivity was exacerbated during migraine, suggesting that recurrent migraine may enhance the sensitivity of the anterior temporal pole. The observed temporal lobe abnormalities may lead to many of the perceptual changes documented in migraine patients. Indeed, Cortese et al.34 used brain imaging to demonstrate altered temporal pole structure in migraine patients, and reported that the density and functional activity of the temporal pole fluctuated with changes in the migraine cycle. Our findings suggest that the right temporal pole might be involved in the occurrence of epilepsy with comorbid migraine without aura, and that migraine episodes might impact the gray matter volume of the right temporal pole.

Cortical thickness changes in EM patients

Previous studies have reported that cortical atrophy tends to occur in both epilepsy and migraine patients, and that cortical atrophy is associated with the course of epilepsy. For instance, Zhang et al.35 found that the cortical thickness of the right inferior temporal gyrus, right insular gyrus, and right cingulate gyrus was significantly negatively correlated with the course of epilepsy in adolescent myoclonic epilepsy patients. Furthermore, a meta-analysis showed that atrophy of the medial dorsal thalamic nucleus occurred in patients with mesial temporal lobe epilepsy36. Furthermore, Chan et al.37 confirmed the presence of thalamus and inferior corpus callosum atrophy in pediatric epilepsy patients with absence seizures. Cortical atrophy has also been reported in migraine patients. Specifically, patients with or without aura had cortical atrophy in the superior middle frontal gyrus, anteroposterior central cortex, and occipital lobe, or in the parietal lobe and insula, respectively, and the duration of headache was linked with cortical thickness38,39.

In the present study, we found that EM patients had cortical atrophy in the left insula, left posterior cingulate, left postcentral gyrus, left middle temporal gyrus, and left fusiform gyrus compared with the HCs. However, there was no difference in the cortical thickness of the above-mentioned regions when compared with the EC group. We also found a positive correlation between the frequency of headache and the thickness of the left middle temporal cortex. The insula participates in human sensory perception, emotional processing, advanced cognition, and other important physiological processes40, and plays an important role in pain processing. Liu et al.41 used an electrode to stimulate the anterior insula in epilepsy patients, and found that the stimulation increased their pain threshold. The posterior cingulate cortex is an important part of the emotional circuit, as it is involved in emotional processing and self-evaluation, and is closely related to depression. However, recent research has suggested that the posterior cingulate cortex might also be involved in pain processing. For instance, posterior cingulate cortex atrophy was found in human immunodeficiency virus (HIV)-positive patients with distal neuralgia42. The postcentral gyrus is a cortical center that processes both deep and superficial sensations, and is involved in pain regulation and other processes43. The mesial temporal lobe is mainly involved in language and auditory processing, although it might also be involved in pain regulation pathways. The fusiform gyrus is primarily involved in the processing of visual information, but it also plays an important role in pain processing. Strong pain-induced activation of the fusiform gyrus, hypothalamic nucleus, hippocampus, middle cingulate cortex, and other brain regions was found in migraine patients, while the activation intensity of the left fusiform gyrus was significantly correlated with headache frequency and migraine duration43.

SCN changes in EM patients

We found no differences in the global parameters between the three groups. The area under the nodal degree curve of the right fusiform gyrus in the EM group was smaller than that in the EC group. This is consistent with previous literature reporting that the fusiform gyrus was involved in the regulation of migraine, and that pain-induced activation of the fusiform gyrus was increased in migraine patients44.

Combined with the results of brain structural imaging studies, our data indicate that migraine without aura has a potential impact on morphological features such as gray matter, cortical thickness, and the size of the right fusiform gyrus in epilepsy patients, although we found no difference in gray matter or cortical thickness between the EM and EC groups. Thus, studies with larger sample sizes are needed to further explore the influencing factors of brain morphological changes in EM patients.

Limitations

The main limitations of this study were its cross-sectional design and limited sample size. Furthermore, we excluded epilepsy patients with comorbid migraine with aura because of their low prevalence, which would make it difficult to explore brain structure differences in these patients. A multi-center, longitudinal study with a larger sample size is needed in the future. Nonetheless, our results provide new information about brain morphology and assessment approaches for epilepsy patients with comorbid migraine without aura.

Conclusions

We found clear structural brain changes in EM patients compared with HCs. Migraine episodes may have potential effects on brain structure in epilepsy patients. Meanwhile, brain structural changes may be an important factor for the development of epilepsy with comorbid migraine without aura. Further studies are needed to investigate the structural changes that occur in epilepsy patients with comorbid migraine without aura.

Abbreviations

EM Epilepsy with comorbid migraine without aura

EC Epilepsy controls

HC Healthy controls

FDR False discovery rate

FWE Family wise error

ASM Anti-seizure medicine

VBM Voxel based morphometry

SBM Surface-based morphometry

SCN Structural covariant networks

MRI Magnetic resonance imaging

VAS Visual analog scale

ICHD-III International classification headache disorders-3rd edition

FWHM Full width half maximum

Acknowledgements

We thank all subjects for their participation in this study.

Author contributions

Conceived and designed the experiments: S.Z. and J.L.; analyzed the data: S.Z. and W.L.; wrote the paper: S.Z.; revised the manuscript: J.L. and D.Z. The author(s) read and approved the final manuscript.

Funding

We greatly appreciate the financial support of the National Natural Science Foundation of China (81871017).

Data availability

All data generated or analyzed in this study are included in this article.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Shujiang Zhang and Wenyu Liu.
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