Microstate analysis of resting-state electroencephalography in patients with epilepsy with comorbid anxiety and depression

Microstate analysis of resting-state electroencephalography in patients with epilepsy with comorbid anxiety and depression | 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 Microstate analysis of resting-state electroencephalography in patients with epilepsy with comorbid anxiety and depression Rong Yan, Lijia Zhang, Fangjing Li, Wanyu Liu, Zhenzhen Tai, Juan Yang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3777110/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 Purpose To explore the characteristics of microstates in patients with epilepsy with comorbid anxiety and depression based on resting-state electroencephalography (EEG). Methods We recruited patients with epilepsy who were monitored using video EEG between November 2021 and December 2022 at the affiliated hospital of Zunyi Medical University. Thirty patients with epilepsy with comorbid anxiety and depression (PAD) and 32 patients with epilepsy without anxiety and depression (nPAD) were recruited for this study. Resting-state EEG was conducted for 5 min (in eyes-closed, relaxed, and awake states). EEGLAB and MATLAB were used to process EEG data. Four typical microstate types were observed, including A (auditory), B (visual), C (insular-cingulate), and D (attention). The duration, occurrence, coverage, and transition probabilities of microstates A, B, C, and D of the patients in the two groups were compared, and their correlations with anxiety and depression were analyzed. Results Compared to the nPAD group, patients in the PAD group had a shorter disease course and a higher frequency of seizures. Second, the occurrence of microstate C was decreased in patients in the PAD group. Third, the level of anxiety in patients with epilepsy was negatively correlated with the occurrence of microstate C and the transition probabilities from C to A and C to B. However, it was positively correlated with the transition probability from microstate D to A. The level of depression was negatively correlated with the occurrence of microstate C and the transition probabilities from C to A and C to B. Conclusion The more frequently patients had seizures (> 2 times per year), the more likely they were to have comorbid anxiety and depression. Moreover, the network connections between the insula and cingulate regions were weakened in patients with epilepsy with comorbid anxiety and depression. Epilepsy Anxiety Depression Resting-state EEG Microstate analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Epilepsy, one of the most prevalent diseases of the nervous system, is characterized by recurrent aberrant discharges of brain neurons [ 1 ]. There are over 70 million patients with epilepsy globally [ 2 ]. The everyday activities and quality of life of patients are usually affected by recurrent seizures [ 3 ]. Additionally, individuals with epilepsy maybe experience comorbidities such as depression, anxiety, dementia, migraines, and heart disease [ 4 ]. Among epilepsy patients, the prevalence of comorbid depression is about 22.9%, and the prevalence of comorbid anxiety is about 20.2% [ 5 ]. Anxiety and depression, the most prevalent psychiatric comorbidities in epilepsy, significantly affect patients’ well-being [ 5 ]. Research has indicated that individuals with mixed depression and anxiety disorders tend to experience more pronounced negative effects on their quality of life [ 6 ]. Electroencephalography (EEG) is a crucial adjunct tool for the diagnosis and evaluation of epilepsy. With its high temporal resolution, EEG enables the assessment of network activity across the entire cerebral cortex in both healthy individuals and those with neurological conditions [ 7 ]. Microstate analysis based on resting-state EEG is an emerging field, which is a method of segmenting spontaneous EEG activity at the sub-second level to analyze steady states. This reflects the simultaneous activation of several cortical regions that are dominant in different functions and are stable between 60 and 120 ms. It can be utilized to assess the general level of brain functionality [ 8 , 9 ]. EEG microstates can be categorized into four distinct classes: A, B, C, and D. Each class represents a different brain network region and its respective function. Microstate A primarily involves the upper and middle temporal lobes and reflects the auditory resting-state network. Microstate B corresponds to the visual resting-state network and exhibits significant correlations with blood oxygen level-dependent changes in the striatum, extrastriatal cortex, and bilateral occipital cortices. Microstate C is associated with the cognitive control network and linked to the activity of the insula and cingulate cortex. Microstate D is an attention network primarily associated with the activity of the right frontoparietal cortex. By analyzing these different microstates, researchers can gain insights into the functional organization and connectivity patterns of the brain in various states and cognitive processes [ 10 ]. Transitions between the four microstate types reflect coordinated interactions among different brain networks. These transitions become more activated when the brain is exposed to external stimuli or environmental changes [ 9 ]. Microstate analysis based on resting-state EEG is increasingly used to define brain networks in various neuropsychiatric diseases. According to one study that used resting-state EEG, the duration and occurrence of microstate D were lower in patients with simple depressive disorder than in healthy individuals [ 11 ]. However, the occurrence of microstate A was higher, and the duration of microstate D was negatively correlated with the degree of depression in the patients. Microstate B may be associated with episodic autobiographical memory impairment and increased self-focusing in patients with bipolar disorder [ 12 ]. Moreover, EEG microstate analysis can be used to predict clinical illness responses and assess the clinical prognosis [ 13 , 14 ]. Wang et al. found that patients with temporal lobe epilepsy and depression exhibited more pronounced dynamic changes in brain networks than patients without depressive symptoms [ 15 ]. Specifically, they observed that the duration of microstates B, C, and D decreased, and the occurrence of microstates A and B increased. This implies that the occurrence of recurrent seizures and the presence of comorbidities in patients are correlated with brain network abnormalities. Anxiety and depressive disorders impose a significant burden on both families and society. Previous studies on mood disorder comorbidities in patients with epilepsy have primarily focused on depression. However, recent studies have suggested that the detrimental effects of combined anxiety and depression surpass those of anxiety and depression alone [ 6 ]. Therefore, this study aimed to investigate changes in microstate characteristics among patients with epilepsy with comorbid anxiety and depression regarding the pathogenesis, clinical diagnosis, and treatment efficacy evaluation based on resting-state EEG. 2 Material and methods 2.1 Participants We enrolled patients with epilepsy who underwent video EEG monitoring at the Affiliated Hospital of Zunyi Medical University between November 2021 and December 2022. The inclusion criteria for this study were as follows: (1) age between 14 and 60 years; (2) epilepsy diagnosis in accordance with the 2017 ILAE Guidelines; (3) Neurological Disorders Depression Inventory for Epilepsy (NDDI-E) score > 12 [ 16 ] and Generalized Anxiety Disorder-7 (GAD-7) score > 6 [ 17 ]; and (4) ability to participate in scale assessments and EEG recordings. The exclusion criteria were as follows: (1) cerebral infarction, intracranial space-occupying lesions, traumatic brain injury, encephalitis, or other nervous system disorders; (2) cognitive impairment (assessed using the Mini-Mental State Examination); (3) patients who received anxiolytic and antidepressant treatment prior to this study. Thirty patients with epilepsy with comorbid anxiety and depression (PAD) and 32 patients with epilepsy without anxiety and depression (nPAD) were included in this study. This study was approved by the Ethics Committee of the Affiliated Hospital of Zunyi Medical University, and all participating patients provided signed informed consent. 2.2 Resting-state EEG recording and preprocessing Before the EEG measurements, the patients were instructed to rest comfortably in the bed and relax their facial muscles. During the recording, the environment was kept quiet, and the patients' eyes were closed for 5 min to minimize artifacts caused by eye movements. The recordings were conducted using a NICOLETONE long-term video EEG monitoring system. Following the international 10–20 system, 16 electrodes (FP1, FP2, F3, F4, F7, F8, T3, T4, T5, T6, C3, C4, P3, P4, O1, and O2) and reference electrodes A1 and A2 were positioned on the scalp. The EEG results were evaluated by the same physician following the acquisition. We used EEGLAB, a MATLAB software toolkit, to preprocess the EEG data. The following steps were performed: First, the raw EEG data were imported into EEGLAB for lead localization. Subsequently, a bandpass filter ranging from 2 to 20 Hz was applied to the EEG data. The data were further segmented into 2-second epochs. Independent component analysis was then used to remove interference signals, such as those from electromyography and electrooculograms. Artifacts were detected and excluded from the data. Finally, assuming that the average potential of the entire brain was 0 volts, the average value of all electrode potentials post-acquisition was used as the reference signal. The preprocessed EEG data segments were used for the microstate analysis. 2.3 Microstate analysis Analyses were performed using the microstate analysis tool, which is a plug-in for EEGLAB. First, the instantaneous Global Field Power (GFP) was computed for each participant, and the topography at the peak of the GFP was extracted. The extracted topographies were then combined with similar ones using a k-means clustering algorithm. The optimal number of microstates was determined to be four, using cross-validation principles. Subsequently, the duration, occurrence, coverage, and transition probabilities of the four types of microstates were analyzed. 2.4 Statistical analysis The data were analyzed using SPSS version 20. Categorical variables were examined using the chi-square test and are shown as counts (n) and percentages (%). For continuous variables, the mean (M) and standard deviation (SD) are presented. For comparisons between the two groups, an independent samples t-test was utilized. If the data weren't distributed normally, they were described using medians and quartiles (P25, P75), and the Mann-Whitney U test was used for between-group comparisons. Spearman’s correlation analysis was conducted to examine the relationship between anxiety and depression scores and EEG microstate metrics. The level of statistical significance was set at p < 0.05. 3 Results 3.1 Demographics and clinical variables During the study period, a total of 188 patients with epilepsy were initially recruited. However, after applying the inclusion and exclusion criteria, only 62 patients with epilepsy were included. Among them, 30 patients had anxiety and depression, while 32 patients did not have anxiety and depression (Fig. 1). The demographic and relevant clinical data of patients in both groups are presented in Table 1. No discernible variations were found in sex, age, age at first onset, years of education, types of antiseizure medications (ASMs), or medication compliance (all p > 0.05). However, significant differences were found in the disease duration and seizure frequency between the two groups (all p < 0.05). Furthermore, the GAD-7 scores were significantly higher in the PAD group than in the nPAD group [11.0 (9.0, 14.0) vs. 2.0 (1.0, 5.0), p < 0.01]. Similarly, the NDDI-E scores were significantly higher in the PAD group than in the nPAD group [17.0 (14.0, 19.0) vs. 7.0 (6.0, 10.0), p < 0.01]. Table 1. Demographic and clinical characteristics of the participants Variable PAD (n=30) nPAD (n=32) P-value Sex 0.611 Male 14 (46.7%) 17 (53.1%) Female 16 (53.3%) 15 (46.9%) Age (year) 26 (16, 46) 25 (17, 34) 0.682 Age at first onset (year) 19 (15, 36) 17 (14, 26) 0.180 Disease duration (months) 30 (6, 72) 72 (27, 100) 0.043 * Education years 0.280 Primary school 6 (20.0%) 2 (6.2%) Junior high school 16 (53.3%) 17 (53.1%) High school 6 (20.0%) 7 (21.9%) University and above 2 (6.7%) 6 (18.8%) Seizure frequency 0.000 * ≤2 times/year 5 (16.7%) 21 (65.6%) >2 times/year 25 (83.3%) 11 (34.4%) Number of ASMs 0.534 0 13 (43.3%) 10 (31.2%) 1 11 (36.7%) 16 (50.0%) ≥2 6 (20.0%) 6 (18.8%) Take drugs regularly Yes 11 (36.7%) 18 (56.2%) 0.122 No 19 (63.3%) 14 (43.8%) GAD-7 score 11 (9,14) 2 (1,5) 0.000* NDDI-E score 17 (14,19) 7 (6,10) 0.000* PAD, patients with epilepsy with comorbid anxiety and depression; nPAD, patients with epilepsy without comorbid anxiety and depression; ASMs, antiseizure medications; GAD-7, Generalized Anxiety Disorder-7; NDDI-E, Neurological Disorders Depression Inventory for Epilepsy. 3.2 Risk factors for epilepsy with comorbid anxiety and depression Logistic regression analysis was performed to further explore the risk factors for epilepsy with comorbid anxiety and depression. The results identified seizure frequency (> 2 times/year) as an independent risk factor for epilepsy with comorbid anxiety and depression (odds ratio = 0.047, 95% confidence interval: 0.008–0.271, p = 0.001). 3.3 EEG microstate analysis Following analysis of the EEG data from both groups, four different EEG microstate topographies were identified, as shown in Fig.2 microstate A was predominantly observed in the right frontal-left posterior region, microstate B in the left frontal-right posterior region, microstate C in the frontal-posterior occipital region, and microstate D in the middle frontal region. The global explained variance was 60.08% (SD: 12.13%) in the PAD group and 57.09% (SD: 10.60%) in the nPAD group, without a significant difference (p > 0.05) between the two groups. 3.4 Microstate metrics Fig. 3 illustrates the duration, coverage, and occurrence of the four microstates. The findings indicated that there were no significant differences in the duration and coverage of the four microstates between the two groups (p > 0.05). Similarly, there were no significant differences in the occurrence of microstates A, B, and D between the two groups (p > 0.05). However, the occurrence of microstate C in the PAD group was significantly lower than that in the nPAD group (p < 0.05). Regarding the transition probabilities, no significant difference was observed between the two groups (Supplementary Table 1). 3.5 Relationship between microstate metrics and anxiety and depression scores Lastly, we examined the relationship between the severity of anxiety and depression (as indicated by the GAD-7 and NDDI-E scores, respectively) and the microstate metrics. Our findings revealed negative correlations between GAD-7 scores, the occurrence of microstate C, and the transition probabilities of C to A and C to B. Additionally, a positive correlation was observed between GAD-7 scores and the transition probabilities of D to A. Similarly, the occurrence of microstate C and the transition probabilities of C to A and C to B were negatively correlated with the NDDI-E scores (Fig.4) 4 Discussion This study explored the clinical features and EEG microstate characteristics of patients with epilepsy with comorbid anxiety and depression. Our findings can be summarized as follows: First, compared to the nPAD group, the PAD group exhibited a shorter disease duration and a higher frequency of seizures. Second, there was a decrease in the occurrence of microstate C in the PAD group. Finally, we observed significant correlations between microstate metrics and the severity of anxiety and depressive symptoms. Specifically, the occurrence of microstate C and the transition probabilities from C to A and C to B were negatively correlated with the GAD-7 scores. However, the transition probability from microstate D to A was positively correlated with the GAD-7 scores. Additionally, the occurrence of microstate C and the transition probabilities from C to A and C to B were negatively correlated with the NDDI-E scores. Epilepsy is a chronic neurological disorder that places patients at a heightened risk of psychiatric comorbidities, affecting approximately one-third of individuals [ 18 ]. Comorbid depression and anxiety are reported to affect patients with epilepsy at rates of 22,9% and 20,2%, respectively [ 5 ]. A survey on psychiatric comorbidity in epilepsy conducted in rural areas of western China revealed that 52.6% of patients with epilepsy had comorbid depression, 33.4% had comorbid anxiety, and 27.9% had comorbid anxiety and depression. Additionally, a significant proportion of patients did not receive proper diagnosis and treatment for their conditions [ 19 ]. Multiple factors contribute to the development of comorbid mood disorders in patients with epilepsy. In our study, we observed a significant difference in seizure frequency between the PAD and nPAD groups. Seizure frequency was identified as an independent risk factor for comorbid anxiety and depression in patients with epilepsy, consistent with previous research findings [ 19 , 20 ]. Essentially, individuals with a higher frequency of seizures are more likely to experience anxiety and depression [ 21 , 22 ]. In addition, this study found that there were differences in the course of disease between the two groups, mainly manifested as a shorter course of disease and a later age at first onset in the PAD group. The impact of age at first onset on the co-occurrence of psychiatric disorders in patients with epilepsy remains debatable. Sabbagh et al. suggested that a later age of onset of epilepsy is associated with greater levels of anxiety and depression [ 23 ]. However, one study found that an earlier age of onset was independently associated with the level of anxiety disorders [ 24 ]. Another study found that age at first onset had no effect on whether patients had comorbid depression [ 19 ]. We believe that the effects of age at first onset and the course of disease on patients may need to be analyzed in conjunction with other factors such as seizure control, response to medication, and quality of life. However, this study did not establish a correlation between sex, literacy, medication adherence, combination therapy, and the risk of comorbid mood disorders in patients. Nevertheless, a study has proposed that women with epilepsy are more susceptible to mood disorders [ 25 ]. In contrast, our study observed comorbid anxiety and depression in both men and women, which could be attributed to the specific population characteristics as the patients were predominantly from local areas with relatively limited economic development. Low income and low level of education will impact the mood of epilepsy patients. Just as prior research has suggested that lower educational levels are associated with a higher likelihood of experiencing anxiety and depression among individuals with epilepsy [ 26 ]. Additionally, two investigations have demonstrated that the use of multiple ASMs is connected to a higher risk of depression and anxiety among individuals with epilepsy [ 27 , 28 ]. However, it is worth noting that a study has reported conflicting findings regarding this perspective [ 19 ]. In contrast, our study found no significant effect of combination treatment on the mood of patients. Some ASMs play a role in mood regulation. Experts have recommended that patients with epilepsy with comorbid depression should use lamotrigine, oxcarbazepine, and valproic acid, but levetiracetam, topiramate, and zonisamide should be used with caution [ 29 ]. However, owing to the small sample size, this study could not specifically analyze the differences between the two groups of patients taking ASMs or explore whether different ASMs have a regulatory effect on mood. Epilepsy is also characterized by abnormal connectivity within the brain, leading to seizures and disruption of neuronal networks [ 30 ]. During seizures, there is an increase in neural activity, cerebral blood flow, and oxygen consumption in the epileptogenic zone [ 31 ]. Furthermore, repeated seizures can result in reduced connectivity between the subcortical and cortical structures, potentially leading to cognitive decline and neuropsychological sequelae [ 32 ]. Burianová et al. investigated the characteristics of brain network connectivity in patients with epilepsy. Their findings revealed reduced connectivity in the insula and dorsal anterior cingulate cortex, suggesting the presence of abnormal network connectivity in individuals with epilepsy [ 33 ]. The default mode network (DMN) is a spontaneous mode of brain activity that occurs when the brain is at rest and not engaged in specific tasks. It involves the participation of several brain regions, including the precuneus/posterior cingulate cortex, medial prefrontal cortex, and medial, lateral, and inferior parietal cortices, which collectively contribute to its modulation [ 34 ]. Functional magnetic resonance imaging studies have shown that the DMN is involved in various neuropsychiatric disorders [ 35 ] and plays a role in human emotion regulation [ 36 ]. Notably, microstate C has been found to represent a component of the DMN [ 9 ]. Few studies have investigated EEG microstate characteristics in patients with epilepsy with comorbid mood disorders. Only one study has examined EEG microstate characteristics in patients with depression in temporal lobe epilepsy. The study reported a shorter duration of microstate C in patients with depression in temporal lobe epilepsy; But there was little difference in the occurrence of microstate C between patients with epilepsy with and without depression [ 15 ]. Our research focused on patients with epilepsy with comorbid anxiety and depression. The findings revealed a significant difference in the occurrence of microstate C between the two groups, with a lower occurrence observed in the PAD group. Furthermore, a negative correlation was observed between the occurrence of microstate C and the severity of anxiety and depression. Microstate C is closely linked to brain regions responsible for cognitive processes, and its decreased occurrence and duration are significant factors that contribute to cognitive decline in patients. Additionally, its association with psychiatric symptoms further underscores its relevance in understanding the mental well-being of individuals [ 14 , 37 ]. These findings provide evidence for the presence of abnormal cognitive evaluations of one's surroundings and self-reflection in patients with epilepsy with comorbid anxiety and depression. Furthermore, the activation of the microstate C network is associated with the activity of the insula and cingulate cortex, indicating that in patients with epilepsy with comorbid anxiety and depression, abnormal activation and reduced network connectivity in these regions are present. Previous studies have suggested a correlation between the increased occurrence of microstate A and the severity of depressive symptoms in patients with depression [ 38 ]. In contrast, another study found that in patients with epilepsy with comorbid depression, the occurrence of microstate A increased but did not show a correlation with the severity of depression [ 15 ]. Different views exist regarding the characteristics of the changes in microstate A. For instance, according to Zhao et al., patients with depression exhibit a decrease in the occurrence and coverage of microstate A in their EEG characteristics [ 39 ]. In the current research, there was a negative correlation found between microstate C-to-A transition and the anxiety and depression levels in patients. Microstate A is primarily associated with auditory stimulation and temporal lobe activity. However, it has been suggested that additional brain regions, such as the insula, prefrontal cortex, occipital gyrus, and left lingual gyrus, are also involved in the activation of microstate A [ 40 , 41 ]. Furthermore, patients with epilepsy and patients with depression have been found to exhibit abnormal connectivity in these regional networks [ 42 , 43 ]. These disruptions in connectivity can impact the transition from the DMN to the auditory network, potentially affecting patients' self-emotional regulation. Microstate B is a network that reflects alterations in resting-state visual [ 44 ]. Studies investigating visual network alterations in patients with epilepsy have found no evidence of visual network damage in these individuals [ 45 ]. However, in a study on epilepsy associated with left temporal lobe glioma, the patients exhibited concurrent damage to the visual network, which was likely attributed to the proximity of the glioma to the visual network [ 46 ]. Abnormalities in visual networks are regarded as a fundamental characteristic of depression [ 47 ]. In patients with pure depression, there is an elevation in the occurrence and coverage of microstate B compared to those without depression [ 48 ]. Furthermore, a shorter duration of microstate B is associated with milder depressive symptoms [ 14 ]. In the current study, we observed no significant differences in the duration, coverage, and occurrence of microstate B between the PAD and nPAD groups. Moreover, the transition between microstates B and C exhibited a negative association with the severity of anxiety and depression. However, it is essential to note that this dynamic change indirectly reflects the brain's ability to rapidly reorganize its networks and adapt to the environment, thereby playing a role in mood regulation [ 14 ]. Previous studies have indicated that microstate D is an attentional network primarily associated with activity in the right frontoparietal cortex and mainly influences attentional flexibility [ 44 , 49 ]. The frontoparietal network, which supports executive functions, plays a crucial role in the higher cognitive regulation of negative emotions [ 50 ]. The reduced duration and occurrence of microstate D in patients with depression and its negative correlation with symptom severity are believed to be associated with abnormal brain network connectivity [ 11 ]. This study found a positive correlation between the transition probability from microstate D to A and the level of anxiety, suggesting a potential association with patients shifting their attention to themselves. In this study, we investigated the functional brain characteristics of patients with epilepsy with comorbid anxiety and depression at the brain network level. Our findings suggest abnormal connectivity in the insular and cingulate cortex networks in patients with epilepsy with comorbid anxiety and depression. However, it is vital to acknowledge the limitations of this study. First, the sample size was small, which may limit the generalizability of the results. Second, there was no specific analysis of the effects of ASMs on the brain network connectivity in the patients. Third, we did not perform a comparative analysis involving healthy people, patients with epilepsy with comorbid anxiety alone, and patients with epilepsy with comorbid depression alone. Additionally, we did not compare the differences in brain networks between the patients with epilepsy at their initial presentation and follow-ups. To validate the findings of this study further, future research should involve multicenter studies with larger sample sizes. This would enable a more robust examination of the results and facilitate the application of resting-state EEG microstate analysis in the field of epilepsy with comorbid anxiety and depression. 5 Conclusions In this study, we found that the more frequently patients experienced seizures (> two times per year), the more likely they were to have comorbid anxiety and depression. The microstate analysis of the resting-state EEG revealed that the network connections of the insula and cingulate regions were weakened in patients with epilepsy with comorbid anxiety and depression. Declarations Ethics approval and consent to participate This research was approved by the Ethics Committee of the Affiliated Hospital of Zunyi Medical University (the Ethical Approval Number: KLLY-2021-085）. This experiment was performed in accordance with declaration of Helsinki and principles of Good Clinical Practice. And all participating patients or their guardian(s) agree this research and provided signed informed consent. Consent for publication Not Applicable. Availability of data and materials All data during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare no competing interests Funding This work was supported by grants from the Guizhou epilepsy basic and clinical research scientific and technological innovation talent team project (No: CXTD[2022]013), the Collaborative Innovation Center of Chinese Ministry of Education (No: 2020-39), the Guizhou provincial "hundred" level innovative talents funds (No: GCC-2022-038-1), the Guizhou Provincial Science and Technology Foundation (No: ZK2022-656), and the Zunyi City Science and Technology Foundation (No: 2019-71 and 2021-30). Authors' contributions Conceptualization and supervision, Zucai Xu, Changyin Yu and Haiqing Zhang; writing—original draft, preparation, Rong Yan; methodology and data curation, Rong Yan, Lijia Zhang, Fangjing Li, Wanyu Liu, Zhenzhen Tai, Juan Yang, Jinmei Tuo. All authors have read and agreed to the published version of the manuscript. References Falco-Walter J, Epilepsy-Definition. 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Altered functional connectivity in mesial temporal lobe epilepsy. Epilepsy Res. 2017;137:45–52. Mohan A, Roberto AJ, Mohan A, Lorenzo A, Jones K, Carney MJ, Liogier-Weyback L, Hwang S, Lapidus KA. The Significance of the Default Mode Network (DMN) in Neurological and Neuropsychiatric Disorders: A Review. Yale J Biol Med. 2016;89(1):49–57. Anticevic A, Cole MW, Murray JD, Corlett PR, Wang XJ, Krystal JH. The role of default network deactivation in cognition and disease. Trends Cogn Sci. 2012;16(12):584–92. Sheline YI, Barch DM, Price JL, Rundle MM, Vaishnavi SN, Snyder AZ, Mintun MA, Wang S, Coalson RS, Raichle ME. The default mode network and self-referential processes in depression. Proc Natl Acad Sci U S A. 2009;106(6):1942–7. Tomescu MI, Rihs TA, Rochas V, Hardmeier M, Britz J, Allali G, Fuhr P, Eliez S, Michel CM. From swing to cane: Sex differences of EEG resting-state temporal patterns during maturation and aging. Dev Cogn Neurosci. 2018;31:58–66. Damborská A, Tomescu MI, Honzírková E, Barteček R, Hořínková J, Fedorová S, Ondruš Š, Michel CM. EEG Resting-State Large-Scale Brain Network Dynamics Are Related to Depressive Symptoms. Front Psychiatry. 2019;10:548. Zhao Z, Niu Y, Zhao X, Zhu Y, Shao Z, Wu X, Wang C, Gao X, Wang C, Xu Y et al. EEG microstate in first-episode drug-naive adolescents with depression. J Neural Eng. 2022;19(5). Bréchet L, Brunet D, Birot G, Gruetter R, Michel CM, Jorge J. Capturing the spatiotemporal dynamics of self-generated, task-initiated thoughts with EEG and fMRI. NeuroImage. 2019;194:82–92. Custo A, Van De Ville D, Wells WM, Tomescu MI, Brunet D, Michel CM. Electroencephalographic Resting-State Networks: Source Localization of Microstates. Brain Connect. 2017;7(10):671–82. Xiao F, Lei D, An D, Li L, Chen S, Chen F, Yang T, Ren J, Huang X, Gong Q, et al. Functional brain connectome and sensorimotor networks in rolandic epilepsy. Epilepsy Res. 2015;113:113–25. Zou K, Gao Q, Long Z, Xu F, Sun X, Chen H, Sun X. Abnormal functional connectivity density in first-episode, drug-naive adult patients with major depressive disorder. J Affect Disord. 2016;194:153–8. Britz J, Van De Ville D, Michel CM. BOLD correlates of EEG topography reveal rapid resting-state network dynamics. NeuroImage. 2010;52(4):1162–70. Jiang Y, Zhu M, Hu Y, Wang K. Altered Resting-State Electroencephalography Microstates in Idiopathic Generalized Epilepsy: A Prospective Case-Control Study. Front Neurol. 2021;12:710952. Fang S, Zhou C, Fan X, Jiang T, Wang Y. Epilepsy-Related Brain Network Alterations in Patients With Temporal Lobe Glioma in the Left Hemisphere. Front Neurol. 2020;11:684. Lu F, Cui Q, Huang X, Li L, Duan X, Chen H, Pang Y, He Z, Sheng W, Han S, et al. Anomalous intrinsic connectivity within and between visual and auditory networks in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry. 2020;100:109889. He Y, Yu Q, Yang T, Zhang Y, Zhang K, Jin X, Wu S, Gao X, Huang C, Cui X, et al. Abnormalities in Electroencephalographic Microstates Among Adolescents With First Episode Major Depressive Disorder. Front Psychiatry. 2021;12:775156. Liang A, Zhao S, Song J, Zhang Y, Zhang Y, Niu X, Xiao T, Chi A. Treatment Effect of Exercise Intervention for Female College Students with Depression: Analysis of Electroencephalogram Microstates and Power Spectrum. Sustainability. 2021;13. Niendam TA, Laird AR, Ray KL, Dean YM, Glahn DC, Carter CS. Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions. Cogn Affect Behav Neurosci. 2012;12(2):241–68. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.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-3777110","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":263824072,"identity":"ce9cdbdf-c324-418e-b36d-d6c2c2649f1c","order_by":0,"name":"Rong Yan","email":"","orcid":"","institution":"Affiliated Hospital of Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Yan","suffix":""},{"id":263824073,"identity":"6b02ebba-0789-43c7-8860-be0492582901","order_by":1,"name":"Lijia Zhang","email":"","orcid":"","institution":"Affiliated Hospital of Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lijia","middleName":"","lastName":"Zhang","suffix":""},{"id":263824075,"identity":"39ca3111-0bad-4808-a46e-d461c4dd17f0","order_by":2,"name":"Fangjing Li","email":"","orcid":"","institution":"Affiliated Hospital of Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fangjing","middleName":"","lastName":"Li","suffix":""},{"id":263824076,"identity":"8184f4ad-6eb3-4282-b0e3-4a0eb1786f1b","order_by":3,"name":"Wanyu Liu","email":"","orcid":"","institution":"Affiliated Hospital of Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wanyu","middleName":"","lastName":"Liu","suffix":""},{"id":263824077,"identity":"27e77fc1-5151-4c1f-ad03-aaa944de837f","order_by":4,"name":"Zhenzhen Tai","email":"","orcid":"","institution":"Affiliated Hospital of Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhenzhen","middleName":"","lastName":"Tai","suffix":""},{"id":263824078,"identity":"1d54bf78-268f-469b-a9f8-f63cde2a41a9","order_by":5,"name":"Juan Yang","email":"","orcid":"","institution":"Affiliated Hospital of Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Yang","suffix":""},{"id":263824079,"identity":"8b20e290-3b20-4302-ab12-a3cfbf5ccbce","order_by":6,"name":"Jinmei Tuo","email":"","orcid":"","institution":"Affiliated Hospital of Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jinmei","middleName":"","lastName":"Tuo","suffix":""},{"id":263824080,"identity":"5b93123c-5ea6-4453-be5d-0b42d6c0c400","order_by":7,"name":"Changyin Yu","email":"","orcid":"","institution":"Affiliated Hospital of Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Changyin","middleName":"","lastName":"Yu","suffix":""},{"id":263824081,"identity":"77beae3f-2916-46c2-91ad-61490f009894","order_by":8,"name":"Haiqing Zhang","email":"","orcid":"","institution":"Affiliated Hospital of Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Haiqing","middleName":"","lastName":"Zhang","suffix":""},{"id":263824082,"identity":"2e388d3e-bc16-4d3d-929c-ee43057d30b0","order_by":9,"name":"zucai Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAoUlEQVRIiWNgGAWjYDCCA2DShoefvYE0LWkykj0HSNNy2MbghgOROviONz+T/FFznofhBgPjh485RGiRPHPM2EDi2G0extkNzJIztxGhxeBGDuMDw4bbPMwyB9iYeYnUwnAgseEcD5tEAvFaGB8cbDjAw0O0FpBfDBuOJfNI8BxsJs4v0BCzs7c/3nzww0ditCABxgbS1I+CUTAKRsEowA0AiyQ2NAmi0nIAAAAASUVORK5CYII=","orcid":"","institution":"Affiliated Hospital of Zunyi Medical University","correspondingAuthor":true,"prefix":"","firstName":"zucai","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2023-12-19 13:29:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3777110/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3777110/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49082084,"identity":"ce1683c8-9c6a-4a31-9854-adc999f69301","added_by":"auto","created_at":"2024-01-02 20:10:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":153979,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe inclusion of the participants\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3777110/v1/1e94e50d0a789a4c7c84d7ca.png"},{"id":49083147,"identity":"7963e8b7-434b-4166-8af0-e0e625185173","added_by":"auto","created_at":"2024-01-02 20:18:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":281971,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrostate topographic maps (A–D). \u003c/strong\u003ePAD, patients with epilepsy with comorbid anxiety and depression; nPAD, patients with epilepsy without comorbid anxiety and depression.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3777110/v1/8b9e7253473906544739cfd3.png"},{"id":49082086,"identity":"3021b4d2-8da3-4bbc-b461-c9f50281a4ed","added_by":"auto","created_at":"2024-01-02 20:10:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":383630,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparisons between the two groups based on the duration, coverage, and occurrence of each microstate. \u003c/strong\u003ePAD, patients with epilepsy with comorbid anxiety and depression; nPAD, patients with epilepsy without comorbid anxiety and depression. ns, not significant. *, p\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3777110/v1/8b67d5fade04e73cc3c652ee.png"},{"id":49082087,"identity":"9126dfbf-2aea-4094-9e92-9e0df89d5217","added_by":"auto","created_at":"2024-01-02 20:10:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":643510,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between the microstate metrics and the GAD-7 and NDDI-E scores. \u003c/strong\u003eThe data in the figure are presented as correlation coefficients. In the correlation analysis between the microstate metrics and GAD-7 scores, we found that the occurrence of microstate C and the conversion probabilities from C to A, C to B, and D to A exhibited significant correlations with the GAD-7 scores (p \u0026lt; 0.05). In the correlation analysis between the microstate metrics and the NDDI-E scores, we found that the occurrence of microstate C and the conversion probabilities from C to A and C to B exhibited significant correlations with the NDDI-E scores (p \u0026lt; 0.05). GAD-7, Generalized Anxiety Disorder-7; NDDI-E, Neurological Disorders Depression Inventory for Epilepsy.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3777110/v1/6fdc9421bb1ae8a0edccae27.png"},{"id":54103788,"identity":"618fa137-bf13-49bc-b6aa-c63109395fae","added_by":"auto","created_at":"2024-04-04 16:25:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1754301,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3777110/v1/a273ea10-5cf7-4b97-bd5d-e7139e560286.pdf"},{"id":49082088,"identity":"712b947f-167c-4796-87ba-8ac9d30230d8","added_by":"auto","created_at":"2024-01-02 20:10:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17506,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3777110/v1/935f5122f76b0753f4a6bae6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Microstate analysis of resting-state electroencephalography in patients with epilepsy with comorbid anxiety and depression","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eEpilepsy, one of the most prevalent diseases of the nervous system, is characterized by recurrent aberrant discharges of brain neurons [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. There are over 70\u0026nbsp;million patients with epilepsy globally [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The everyday activities and quality of life of patients are usually affected by recurrent seizures [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Additionally, individuals with epilepsy maybe experience comorbidities such as depression, anxiety, dementia, migraines, and heart disease [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Among epilepsy patients, the prevalence of comorbid depression is about 22.9%, and the prevalence of comorbid anxiety is about 20.2% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Anxiety and depression, the most prevalent psychiatric comorbidities in epilepsy, significantly affect patients\u0026rsquo; well-being [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Research has indicated that individuals with mixed depression and anxiety disorders tend to experience more pronounced negative effects on their quality of life [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eElectroencephalography (EEG) is a crucial adjunct tool for the diagnosis and evaluation of epilepsy. With its high temporal resolution, EEG enables the assessment of network activity across the entire cerebral cortex in both healthy individuals and those with neurological conditions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Microstate analysis based on resting-state EEG is an emerging field, which is a method of segmenting spontaneous EEG activity at the sub-second level to analyze steady states. This reflects the simultaneous activation of several cortical regions that are dominant in different functions and are stable between 60 and 120 ms. It can be utilized to assess the general level of brain functionality [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. EEG microstates can be categorized into four distinct classes: A, B, C, and D. Each class represents a different brain network region and its respective function. Microstate A primarily involves the upper and middle temporal lobes and reflects the auditory resting-state network. Microstate B corresponds to the visual resting-state network and exhibits significant correlations with blood oxygen level-dependent changes in the striatum, extrastriatal cortex, and bilateral occipital cortices. Microstate C is associated with the cognitive control network and linked to the activity of the insula and cingulate cortex. Microstate D is an attention network primarily associated with the activity of the right frontoparietal cortex. By analyzing these different microstates, researchers can gain insights into the functional organization and connectivity patterns of the brain in various states and cognitive processes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Transitions between the four microstate types reflect coordinated interactions among different brain networks. These transitions become more activated when the brain is exposed to external stimuli or environmental changes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Microstate analysis based on resting-state EEG is increasingly used to define brain networks in various neuropsychiatric diseases. According to one study that used resting-state EEG, the duration and occurrence of microstate D were lower in patients with simple depressive disorder than in healthy individuals [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, the occurrence of microstate A was higher, and the duration of microstate D was negatively correlated with the degree of depression in the patients. Microstate B may be associated with episodic autobiographical memory impairment and increased self-focusing in patients with bipolar disorder [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Moreover, EEG microstate analysis can be used to predict clinical illness responses and assess the clinical prognosis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Wang et al. found that patients with temporal lobe epilepsy and depression exhibited more pronounced dynamic changes in brain networks than patients without depressive symptoms [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Specifically, they observed that the duration of microstates B, C, and D decreased, and the occurrence of microstates A and B increased. This implies that the occurrence of recurrent seizures and the presence of comorbidities in patients are correlated with brain network abnormalities.\u003c/p\u003e \u003cp\u003eAnxiety and depressive disorders impose a significant burden on both families and society. Previous studies on mood disorder comorbidities in patients with epilepsy have primarily focused on depression. However, recent studies have suggested that the detrimental effects of combined anxiety and depression surpass those of anxiety and depression alone [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, this study aimed to investigate changes in microstate characteristics among patients with epilepsy with comorbid anxiety and depression regarding the pathogenesis, clinical diagnosis, and treatment efficacy evaluation based on resting-state EEG.\u003c/p\u003e"},{"header":"2 Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eWe enrolled patients with epilepsy who underwent video EEG monitoring at the Affiliated Hospital of Zunyi Medical University between November 2021 and December 2022. The inclusion criteria for this study were as follows: (1) age between 14 and 60 years; (2) epilepsy diagnosis in accordance with the 2017 ILAE Guidelines; (3) Neurological Disorders Depression Inventory for Epilepsy (NDDI-E) score\u0026thinsp;\u0026gt;\u0026thinsp;12 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and Generalized Anxiety Disorder-7 (GAD-7) score\u0026thinsp;\u0026gt;\u0026thinsp;6 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]; and (4) ability to participate in scale assessments and EEG recordings. The exclusion criteria were as follows: (1) cerebral infarction, intracranial space-occupying lesions, traumatic brain injury, encephalitis, or other nervous system disorders; (2) cognitive impairment (assessed using the Mini-Mental State Examination); (3) patients who received anxiolytic and antidepressant treatment prior to this study. Thirty patients with epilepsy with comorbid anxiety and depression (PAD) and 32 patients with epilepsy without anxiety and depression (nPAD) were included in this study.\u003c/p\u003e \u003cp\u003e This study was approved by the Ethics Committee of the Affiliated Hospital of Zunyi Medical University, and all participating patients provided signed informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Resting-state EEG recording and preprocessing\u003c/h2\u003e \u003cp\u003eBefore the EEG measurements, the patients were instructed to rest comfortably in the bed and relax their facial muscles. During the recording, the environment was kept quiet, and the patients' eyes were closed for 5 min to minimize artifacts caused by eye movements. The recordings were conducted using a NICOLETONE long-term video EEG monitoring system. Following the international 10\u0026ndash;20 system, 16 electrodes (FP1, FP2, F3, F4, F7, F8, T3, T4, T5, T6, C3, C4, P3, P4, O1, and O2) and reference electrodes A1 and A2 were positioned on the scalp. The EEG results were evaluated by the same physician following the acquisition.\u003c/p\u003e \u003cp\u003eWe used EEGLAB, a MATLAB software toolkit, to preprocess the EEG data. The following steps were performed: First, the raw EEG data were imported into EEGLAB for lead localization. Subsequently, a bandpass filter ranging from 2 to 20 Hz was applied to the EEG data. The data were further segmented into 2-second epochs. Independent component analysis was then used to remove interference signals, such as those from electromyography and electrooculograms. Artifacts were detected and excluded from the data. Finally, assuming that the average potential of the entire brain was 0 volts, the average value of all electrode potentials post-acquisition was used as the reference signal. The preprocessed EEG data segments were used for the microstate analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Microstate analysis\u003c/h2\u003e \u003cp\u003eAnalyses were performed using the microstate analysis tool, which is a plug-in for EEGLAB. First, the instantaneous Global Field Power (GFP) was computed for each participant, and the topography at the peak of the GFP was extracted. The extracted topographies were then combined with similar ones using a k-means clustering algorithm. The optimal number of microstates was determined to be four, using cross-validation principles. Subsequently, the duration, occurrence, coverage, and transition probabilities of the four types of microstates were analyzed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe data were analyzed using SPSS version 20. Categorical variables were examined using the chi-square test and are shown as counts (n) and percentages (%). For continuous variables, the mean (M) and standard deviation (SD) are presented. For comparisons between the two groups, an independent samples t-test was utilized. If the data weren't distributed normally, they were described using medians and quartiles (P25, P75), and the Mann-Whitney U test was used for between-group comparisons. Spearman\u0026rsquo;s correlation analysis was conducted to examine the relationship between anxiety and depression scores and EEG microstate metrics. The level of statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Demographics and clinical variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the study period, a total of 188 patients with epilepsy were initially recruited. However, after applying the inclusion and exclusion criteria, only 62 patients with epilepsy were included. Among them, 30 patients had anxiety and depression, while 32 patients did not have anxiety and depression (Fig. 1).\u003c/p\u003e\n\u003cp\u003eThe demographic and relevant clinical data of patients in both groups are presented in Table 1. No discernible variations were found in sex, age, age at first onset, years of education, types of antiseizure medications (ASMs), or medication compliance (all p \u0026gt; 0.05). However, significant differences were found in the disease duration and seizure frequency between the two groups (all p \u0026lt; 0.05). Furthermore, the GAD-7 scores were significantly higher in the PAD group than in the nPAD group [11.0 (9.0, 14.0) vs. 2.0 (1.0, 5.0), p \u0026lt; 0.01]. Similarly, the NDDI-E scores were significantly higher in the PAD group than in the nPAD group [17.0 (14.0, 19.0) vs. 7.0 (6.0, 10.0), p \u0026lt; 0.01].\u003c/p\u003e\n\u003cp\u003eTable 1. Demographic and clinical characteristics of the participants\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"532\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.015065913371%\" valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.436911487758945%\" valign=\"top\"\u003e\n \u003cp\u003ePAD\u003c/p\u003e\n \u003cp\u003e(n=30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.800376647834273%\" valign=\"top\"\u003e\n \u003cp\u003enPAD\u003c/p\u003e\n \u003cp\u003e(n=32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.747645951035782%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.015065913371%\" valign=\"top\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.436911487758945%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.800376647834273%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.747645951035782%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.96943231441048%\" valign=\"top\"\u003e\n \u003cp\u003e14 (46.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e17 (53.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.96943231441048%\" valign=\"top\"\u003e\n \u003cp\u003e16 (53.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e15 (46.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.015065913371%\" valign=\"top\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.436911487758945%\" valign=\"top\"\u003e\n \u003cp\u003e26 (16, 46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.800376647834273%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;25 (17, 34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.747645951035782%\" valign=\"top\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.015065913371%\" valign=\"top\"\u003e\n \u003cp\u003eAge at first onset (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.436911487758945%\" valign=\"top\"\u003e\n \u003cp\u003e19 (15, 36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.800376647834273%\" valign=\"top\"\u003e\n \u003cp\u003e17 (14, 26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.747645951035782%\" valign=\"top\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.015065913371%\" valign=\"top\"\u003e\n \u003cp\u003eDisease duration\u0026nbsp;(months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.436911487758945%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;30 (6, 72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.800376647834273%\" valign=\"top\"\u003e\n \u003cp\u003e72 (27, 100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.747645951035782%\" valign=\"top\"\u003e\n \u003cp\u003e0.043\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.015065913371%\" valign=\"top\"\u003e\n \u003cp\u003eEducation years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.436911487758945%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.800376647834273%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.747645951035782%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003ePrimary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.96943231441048%\" valign=\"top\"\u003e\n \u003cp\u003e6 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e2 (6.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003eJunior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.96943231441048%\" valign=\"top\"\u003e\n \u003cp\u003e16 (53.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e17 (53.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.96943231441048%\" valign=\"top\"\u003e\n \u003cp\u003e6 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e7 (21.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003eUniversity and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.96943231441048%\" valign=\"top\"\u003e\n \u003cp\u003e2 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e6 (18.8%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.015065913371%\" valign=\"top\"\u003e\n \u003cp\u003eSeizure frequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.436911487758945%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.800376647834273%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.747645951035782%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;0.000\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026le;2 times/year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.96943231441048%\" valign=\"top\"\u003e\n \u003cp\u003e5 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e21 (65.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;2 times/year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.96943231441048%\" valign=\"top\"\u003e\n \u003cp\u003e25 (83.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e11 (34.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.015065913371%\" valign=\"top\"\u003e\n \u003cp\u003eNumber of ASMs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.436911487758945%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.800376647834273%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.747645951035782%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.96943231441048%\" valign=\"top\"\u003e\n \u003cp\u003e13 (43.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e10 (31.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.96943231441048%\" valign=\"top\"\u003e\n \u003cp\u003e11 (36.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e16 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.96943231441048%\" valign=\"top\"\u003e\n \u003cp\u003e6 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e6 (18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.015065913371%\" valign=\"top\"\u003e\n \u003cp\u003eTake drugs regularly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.436911487758945%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.800376647834273%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.747645951035782%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.015065913371%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.436911487758945%\" valign=\"top\"\u003e\n \u003cp\u003e11 (36.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.800376647834273%\" valign=\"top\"\u003e\n \u003cp\u003e18 (56.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.747645951035782%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.96943231441048%\" valign=\"top\"\u003e\n \u003cp\u003e19 (63.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e14 (43.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.015065913371%\" valign=\"top\"\u003e\n \u003cp\u003eGAD-7 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.436911487758945%\" valign=\"top\"\u003e\n \u003cp\u003e11 (9,14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.800376647834273%\" valign=\"top\"\u003e\n \u003cp\u003e2 (1,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.747645951035782%\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.015065913371%\" valign=\"top\"\u003e\n \u003cp\u003eNDDI-E score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.436911487758945%\" valign=\"top\"\u003e\n \u003cp\u003e17 (14,19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.800376647834273%\" valign=\"top\"\u003e\n \u003cp\u003e7 (6,10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.747645951035782%\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePAD, patients with epilepsy with comorbid anxiety and depression; nPAD, patients with epilepsy without comorbid anxiety and depression; ASMs, antiseizure medications; GAD-7, Generalized Anxiety Disorder-7; NDDI-E, Neurological Disorders Depression Inventory for Epilepsy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Risk factors for epilepsy with comorbid anxiety and depression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLogistic regression analysis was performed to further explore the risk factors for epilepsy with comorbid anxiety and depression. The results identified seizure frequency (\u0026gt; 2 times/year) as an independent risk factor for epilepsy with comorbid anxiety and depression (odds ratio = 0.047, 95% confidence interval: 0.008\u0026ndash;0.271, p = 0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 EEG microstate analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing analysis of the EEG data from both groups, four different EEG microstate topographies were identified, as shown in Fig.2 microstate A was predominantly observed in the right frontal-left posterior region, microstate B in the left frontal-right posterior region, microstate C in the frontal-posterior occipital region, and microstate D in the middle frontal region. The global explained variance was 60.08% (SD: 12.13%) in the PAD group and 57.09% (SD: 10.60%) in the nPAD group, without a significant difference (p \u0026gt; 0.05) between the two groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Microstate metrics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFig. 3 illustrates the duration, coverage,\u0026nbsp;and occurrence of the four microstates. The findings indicated that there were no significant differences in the duration and coverage of the four microstates between the two groups (p \u0026gt; 0.05). Similarly, there were no significant differences in the occurrence of microstates A, B, and D between the two groups (p \u0026gt; 0.05). However, the occurrence of microstate C in the PAD group was significantly lower\u0026nbsp;than\u0026nbsp;that in the nPAD group (p \u0026lt; 0.05).\u003c/p\u003e\n\u003cdiv align=\"center\" style=\"text-align: left;\"\u003eRegarding the transition probabilities, no significant difference was observed between the two groups (Supplementary Table 1).\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Relationship between microstate metrics and anxiety and depression scores\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLastly, we examined the relationship between the severity of anxiety and depression (as indicated by the GAD-7 and NDDI-E scores, respectively) and the microstate metrics. Our findings revealed negative correlations between GAD-7 scores, the occurrence of microstate C, and the transition probabilities of C to A and C to B. Additionally, a positive correlation was observed between GAD-7 scores and the transition probabilities of D to A. Similarly, the occurrence of microstate C and the transition probabilities of C to A and C to B were negatively correlated with the NDDI-E scores (Fig.4)\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study explored the clinical features and EEG microstate characteristics of patients with epilepsy with comorbid anxiety and depression. Our findings can be summarized as follows: First, compared to the nPAD group, the PAD group exhibited a shorter disease duration and a higher frequency of seizures. Second, there was a decrease in the occurrence of microstate C in the PAD group. Finally, we observed significant correlations between microstate metrics and the severity of anxiety and depressive symptoms. Specifically, the occurrence of microstate C and the transition probabilities from C to A and C to B were negatively correlated with the GAD-7 scores. However, the transition probability from microstate D to A was positively correlated with the GAD-7 scores. Additionally, the occurrence of microstate C and the transition probabilities from C to A and C to B were negatively correlated with the NDDI-E scores.\u003c/p\u003e \u003cp\u003eEpilepsy is a chronic neurological disorder that places patients at a heightened risk of psychiatric comorbidities, affecting approximately one-third of individuals [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Comorbid depression and anxiety are reported to affect patients with epilepsy at rates of 22,9% and 20,2%, respectively [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A survey on psychiatric comorbidity in epilepsy conducted in rural areas of western China revealed that 52.6% of patients with epilepsy had comorbid depression, 33.4% had comorbid anxiety, and 27.9% had comorbid anxiety and depression. Additionally, a significant proportion of patients did not receive proper diagnosis and treatment for their conditions [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMultiple factors contribute to the development of comorbid mood disorders in patients with epilepsy. In our study, we observed a significant difference in seizure frequency between the PAD and nPAD groups. Seizure frequency was identified as an independent risk factor for comorbid anxiety and depression in patients with epilepsy, consistent with previous research findings [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Essentially, individuals with a higher frequency of seizures are more likely to experience anxiety and depression [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In addition, this study found that there were differences in the course of disease between the two groups, mainly manifested as a shorter course of disease and a later age at first onset in the PAD group.\u003c/p\u003e \u003cp\u003eThe impact of age at first onset on the co-occurrence of psychiatric disorders in patients with epilepsy remains debatable. Sabbagh et al. suggested that a later age of onset of epilepsy is associated with greater levels of anxiety and depression [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, one study found that an earlier age of onset was independently associated with the level of anxiety disorders [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Another study found that age at first onset had no effect on whether patients had comorbid depression [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. We believe that the effects of age at first onset and the course of disease on patients may need to be analyzed in conjunction with other factors such as seizure control, response to medication, and quality of life. However, this study did not establish a correlation between sex, literacy, medication adherence, combination therapy, and the risk of comorbid mood disorders in patients.\u003c/p\u003e \u003cp\u003eNevertheless, a study has proposed that women with epilepsy are more susceptible to mood disorders [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In contrast, our study observed comorbid anxiety and depression in both men and women, which could be attributed to the specific population characteristics as the patients were predominantly from local areas with relatively limited economic development. Low income and low level of education will impact the mood of epilepsy patients. Just as prior research has suggested that lower educational levels are associated with a higher likelihood of experiencing anxiety and depression among individuals with epilepsy [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, two investigations have demonstrated that the use of multiple ASMs is connected to a higher risk of depression and anxiety among individuals with epilepsy [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, it is worth noting that a study has reported conflicting findings regarding this perspective [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In contrast, our study found no significant effect of combination treatment on the mood of patients. Some ASMs play a role in mood regulation. Experts have recommended that patients with epilepsy with comorbid depression should use lamotrigine, oxcarbazepine, and valproic acid, but levetiracetam, topiramate, and zonisamide should be used with caution [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, owing to the small sample size, this study could not specifically analyze the differences between the two groups of patients taking ASMs or explore whether different ASMs have a regulatory effect on mood.\u003c/p\u003e \u003cp\u003eEpilepsy is also characterized by abnormal connectivity within the brain, leading to seizures and disruption of neuronal networks [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. During seizures, there is an increase in neural activity, cerebral blood flow, and oxygen consumption in the epileptogenic zone [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Furthermore, repeated seizures can result in reduced connectivity between the subcortical and cortical structures, potentially leading to cognitive decline and neuropsychological sequelae [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Burianov\u0026aacute; et al. investigated the characteristics of brain network connectivity in patients with epilepsy. Their findings revealed reduced connectivity in the insula and dorsal anterior cingulate cortex, suggesting the presence of abnormal network connectivity in individuals with epilepsy [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe default mode network (DMN) is a spontaneous mode of brain activity that occurs when the brain is at rest and not engaged in specific tasks. It involves the participation of several brain regions, including the precuneus/posterior cingulate cortex, medial prefrontal cortex, and medial, lateral, and inferior parietal cortices, which collectively contribute to its modulation [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Functional magnetic resonance imaging studies have shown that the DMN is involved in various neuropsychiatric disorders [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and plays a role in human emotion regulation [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Notably, microstate C has been found to represent a component of the DMN [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Few studies have investigated EEG microstate characteristics in patients with epilepsy with comorbid mood disorders. Only one study has examined EEG microstate characteristics in patients with depression in temporal lobe epilepsy. The study reported a shorter duration of microstate C in patients with depression in temporal lobe epilepsy; But there was little difference in the occurrence of microstate C between patients with epilepsy with and without depression [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Our research focused on patients with epilepsy with comorbid anxiety and depression. The findings revealed a significant difference in the occurrence of microstate C between the two groups, with a lower occurrence observed in the PAD group. Furthermore, a negative correlation was observed between the occurrence of microstate C and the severity of anxiety and depression. Microstate C is closely linked to brain regions responsible for cognitive processes, and its decreased occurrence and duration are significant factors that contribute to cognitive decline in patients. Additionally, its association with psychiatric symptoms further underscores its relevance in understanding the mental well-being of individuals [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. These findings provide evidence for the presence of abnormal cognitive evaluations of one's surroundings and self-reflection in patients with epilepsy with comorbid anxiety and depression. Furthermore, the activation of the microstate C network is associated with the activity of the insula and cingulate cortex, indicating that in patients with epilepsy with comorbid anxiety and depression, abnormal activation and reduced network connectivity in these regions are present.\u003c/p\u003e \u003cp\u003ePrevious studies have suggested a correlation between the increased occurrence of microstate A and the severity of depressive symptoms in patients with depression [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In contrast, another study found that in patients with epilepsy with comorbid depression, the occurrence of microstate A increased but did not show a correlation with the severity of depression [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Different views exist regarding the characteristics of the changes in microstate A. For instance, according to Zhao et al., patients with depression exhibit a decrease in the occurrence and coverage of microstate A in their EEG characteristics [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In the current research, there was a negative correlation found between microstate C-to-A transition and the anxiety and depression levels in patients. Microstate A is primarily associated with auditory stimulation and temporal lobe activity. However, it has been suggested that additional brain regions, such as the insula, prefrontal cortex, occipital gyrus, and left lingual gyrus, are also involved in the activation of microstate A [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Furthermore, patients with epilepsy and patients with depression have been found to exhibit abnormal connectivity in these regional networks [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. These disruptions in connectivity can impact the transition from the DMN to the auditory network, potentially affecting patients' self-emotional regulation.\u003c/p\u003e \u003cp\u003eMicrostate B is a network that reflects alterations in resting-state visual [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Studies investigating visual network alterations in patients with epilepsy have found no evidence of visual network damage in these individuals [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. However, in a study on epilepsy associated with left temporal lobe glioma, the patients exhibited concurrent damage to the visual network, which was likely attributed to the proximity of the glioma to the visual network [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Abnormalities in visual networks are regarded as a fundamental characteristic of depression [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In patients with pure depression, there is an elevation in the occurrence and coverage of microstate B compared to those without depression [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Furthermore, a shorter duration of microstate B is associated with milder depressive symptoms [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In the current study, we observed no significant differences in the duration, coverage, and occurrence of microstate B between the PAD and nPAD groups. Moreover, the transition between microstates B and C exhibited a negative association with the severity of anxiety and depression. However, it is essential to note that this dynamic change indirectly reflects the brain's ability to rapidly reorganize its networks and adapt to the environment, thereby playing a role in mood regulation [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have indicated that microstate D is an attentional network primarily associated with activity in the right frontoparietal cortex and mainly influences attentional flexibility [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The frontoparietal network, which supports executive functions, plays a crucial role in the higher cognitive regulation of negative emotions [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The reduced duration and occurrence of microstate D in patients with depression and its negative correlation with symptom severity are believed to be associated with abnormal brain network connectivity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This study found a positive correlation between the transition probability from microstate D to A and the level of anxiety, suggesting a potential association with patients shifting their attention to themselves.\u003c/p\u003e \u003cp\u003eIn this study, we investigated the functional brain characteristics of patients with epilepsy with comorbid anxiety and depression at the brain network level. Our findings suggest abnormal connectivity in the insular and cingulate cortex networks in patients with epilepsy with comorbid anxiety and depression. However, it is vital to acknowledge the limitations of this study. First, the sample size was small, which may limit the generalizability of the results. Second, there was no specific analysis of the effects of ASMs on the brain network connectivity in the patients. Third, we did not perform a comparative analysis involving healthy people, patients with epilepsy with comorbid anxiety alone, and patients with epilepsy with comorbid depression alone. Additionally, we did not compare the differences in brain networks between the patients with epilepsy at their initial presentation and follow-ups. To validate the findings of this study further, future research should involve multicenter studies with larger sample sizes. This would enable a more robust examination of the results and facilitate the application of resting-state EEG microstate analysis in the field of epilepsy with comorbid anxiety and depression.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eIn this study, we found that the more frequently patients experienced seizures (\u0026gt;\u0026thinsp;two times per year), the more likely they were to have comorbid anxiety and depression. The microstate analysis of the resting-state EEG revealed that the network connections of the insula and cingulate regions were weakened in patients with epilepsy with comorbid anxiety and depression.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was approved by the Ethics Committee of the Affiliated Hospital of Zunyi Medical University (the Ethical Approval Number: KLLY-2021-085）. This experiment was performed in accordance with declaration of Helsinki and principles of Good Clinical Practice. And all participating patients or their guardian(s) agree this research and provided signed informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the Guizhou epilepsy basic and clinical research scientific and technological innovation talent team project (No: CXTD[2022]013), the Collaborative Innovation Center of Chinese Ministry of Education (No: 2020-39), the Guizhou provincial \u0026quot;hundred\u0026quot; level innovative talents funds (No: GCC-2022-038-1), the Guizhou Provincial Science and Technology Foundation (No: ZK2022-656), and the Zunyi City Science and Technology Foundation (No: 2019-71 and 2021-30).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization and supervision, Zucai Xu, Changyin Yu and Haiqing Zhang; writing\u0026mdash;original draft, preparation, Rong Yan; methodology and data curation, Rong Yan, Lijia Zhang, Fangjing Li, Wanyu Liu, Zhenzhen Tai, Juan Yang, Jinmei Tuo. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFalco-Walter J, Epilepsy-Definition. Classification, Pathophysiology, and Epidemiology. Semin Neurol. 2020;40(6):617\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh A, Trevick S. The Epidemiology of Global Epilepsy. Neurol Clin. 2016;34(4):837\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalpekar JA, Mula M. Common psychiatric comorbidities in epilepsy: How big of a problem is it. Epilepsy Behav. 2019;98(Pt B):293\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeezer MR, Sisodiya SM, Sander JW. Comorbidities of epilepsy: current concepts and future perspectives. Lancet Neurol. 2016;15(1):106\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScott AJ, Sharpe L, Hunt C, Gandy M. 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Cogn Affect Behav Neurosci. 2012;12(2):241\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Epilepsy, Anxiety, Depression, Resting-state EEG, Microstate analysis","lastPublishedDoi":"10.21203/rs.3.rs-3777110/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3777110/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo explore the characteristics of microstates in patients with epilepsy with comorbid anxiety and depression based on resting-state electroencephalography (EEG).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe recruited patients with epilepsy who were monitored using video EEG between November 2021 and December 2022 at the affiliated hospital of Zunyi Medical University. Thirty patients with epilepsy with comorbid anxiety and depression (PAD) and 32 patients with epilepsy without anxiety and depression (nPAD) were recruited for this study. Resting-state EEG was conducted for 5 min (in eyes-closed, relaxed, and awake states). EEGLAB and MATLAB were used to process EEG data. Four typical microstate types were observed, including A (auditory), B (visual), C (insular-cingulate), and D (attention). The duration, occurrence, coverage, and transition probabilities of microstates A, B, C, and D of the patients in the two groups were compared, and their correlations with anxiety and depression were analyzed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCompared to the nPAD group, patients in the PAD group had a shorter disease course and a higher frequency of seizures. Second, the occurrence of microstate C was decreased in patients in the PAD group. Third, the level of anxiety in patients with epilepsy was negatively correlated with the occurrence of microstate C and the transition probabilities from C to A and C to B. However, it was positively correlated with the transition probability from microstate D to A. The level of depression was negatively correlated with the occurrence of microstate C and the transition probabilities from C to A and C to B.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe more frequently patients had seizures (\u0026gt;\u0026thinsp;2 times per year), the more likely they were to have comorbid anxiety and depression. Moreover, the network connections between the insula and cingulate regions were weakened in patients with epilepsy with comorbid anxiety and depression.\u003c/p\u003e","manuscriptTitle":"Microstate analysis of resting-state electroencephalography in patients with epilepsy with comorbid anxiety and depression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-02 20:10:47","doi":"10.21203/rs.3.rs-3777110/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":"0012ecfc-14d3-4dab-a175-96b56d86af36","owner":[],"postedDate":"January 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-04T16:17:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-02 20:10:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3777110","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3777110","identity":"rs-3777110","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}