Unveiling Hidden Executive Dysfunction by EEG Microstates and Functional Connectivity in Newly Diagnosed, Drug-Naïve Temporal Lobe Epilepsy

Unveiling Hidden Executive Dysfunction by EEG Microstates and Functional Connectivity in Newly Diagnosed, Drug-Naïve Temporal Lobe Epilepsy | 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 Unveiling Hidden Executive Dysfunction by EEG Microstates and Functional Connectivity in Newly Diagnosed, Drug-Naïve Temporal Lobe Epilepsy Zihan Hu, Zhihai Shao, Rong Tang, Weifeng Peng, Xin Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9000959/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 Objectives This study aimed to investigate the resting-state EEG microstate dynamics and functional connectivity in newly diagnosed temporal lobe epilepsy (TLE) patients, exploring whether these patients exhibit brain network impairment. Methods This is a retrospective case-control study. Seventeen newly diagnosed TLE patients and 16 age- and gender-matched non-epileptic controls were selected from 514 inpatients for long-term EEG during the period of 2020.1-2023.2 in Zhongshan Hospital Xiamen Branch, Xiamen, China. Resting-state EEG data epochs extracted from long-term EEG monitoring were preprocessed and analyzed for microstates using the MICROSTATELAB toolbox in EEGLAB. Functional connectivity was measured and presented by Coherence (COH), Phase-Locking value (PLV), and Phase Lag Index/weighted Phase Lag Index (PLI/wPLI). Results For microstate analysis, microstate D representing executive Control networks in TLE patients had a significantly longer mean duration, higher time coverage, and different GEV values compared to non-epileptic controls. The transition probability from microstate C representing self-referential processing to D was higher in TLE patients. In terms of functional connectivity, TLE patients showed lower PLI and wPLI in the gamma band representing cognitive function and higher values in the theta band representing epileptic network connectivity compared to controls. Conclusion Our findings demonstrate that executive dysfunction emerges at the earliest stage of temporal lobe epilepsy, prior to medication, as detected by EEG microstate and functional connectivity analyses. These results suggest novel electrophysiological markers for subclinical cognitive impairment and underscore the need for early monitoring and tailored interventions. Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Epilepsy is a prevalent and severe neurological disorder affecting approximately 70 million people worldwide, manifesting as recurrent seizures often accompanied by comorbidities[ 1 ]. Temporal lobe epilepsy (TLE), the most common form of adult focal epilepsy, has traditionally been viewed as a condition originating in the temporal lobe. However, recent studies have highlighted the role of large-scale brain network dysfunctions that persist during inter-ictal periods, revealing that TLE involves disrupted neural dynamics beyond focal region[ 2 ], which may be responsible for the comorbidities such as cognitive impairments. Studies using advanced neuroimaging techniques have consistently demonstrated altered connectivity within these networks, suggesting a network-level pathology in TLE[ 3 , 4 ], usually involving temporal and frontal lobes which are crucial for cognitive processes such as memory, attention, and executive control. In newly diagnosed TLE patients, evidence of brain network impairment is emerging, indicating that network dysfunction could be a primary feature of the disease rather than a secondary consequence of long-term seizure activity[ 5 , 6 ]. Resting-state Electroencephalogram (EEG) is a valuable tool for examining the spontaneous neural activity of the brain and offers unique insights into the temporal dynamics of brain networks[ 7 ]. EEG microstates are brief periods of quasi-stable topographical patterns in the EEG signal, reflecting the coordinated activity of large-scale neural networks. These microstates are considered the building blocks of brain function and are linked to distinct cognitive and perceptual processes[ 8 ]. Changes in EEG microstate dynamics, such as alterations in duration, frequency, and transition rates between states, have been associated with various neurological and psychiatric conditions, indicating their potential role as biomarkers of brain network dysfunction[ 9 ]. In epilepsy, disrupted microstate dynamics have been linked to abnormalities in brain connectivity and information processing, which might serve as a distinctive biomarker for predicting the severity of epilepsy and distinguishing between unilateral and bilateral TLE [ 10 , 11 ]. Furthermore, functional connectivity analysis based on EEG data reflected functional changes between different brain areas, which may supply additional information about brain connectivity and epileptogenic focus localization[ 12 ]. As there is still limited research on brain network in newly diagnosed drug-naive TLE patients, this study aims to combine the dynamics of resting-state EEG microstates and function connectivity analysis to investigate their associations with cognitive function. Our study would provide new neurobiological evidence linking brain network dysfunction with cognitive deficits in newly diagnosed TLE, contributing to a deeper understanding of the pathophysiological mechanisms for comorbidities of epilepsy. 2. Methods This is a retrospective case-control study. The dataset includes data from hospitalized patients who underwent long-term EEG examination during the period of 2020.1-2023.2 in Zhongshan Hospital Fudan University Xiamen Branch, Xiamen, China. Patients who have been newly diagnosed with temporal lobe epilepsy (TLE) prior to anti-seizure medications (ASMs) and non-epileptic patients of corresponding age and gender were selected. This study was approved by the Ethics Committee of Zhongshan Hospital Fudan University, Xiamen, China (the ethical number is B2022-023). 2.1. Participants and groups In the dataset, TLE Patients included in this study met the inclusion criteria as follows: 1) aged between 18 and 60 years with no gender limitation; 2) met the diagnostic criteria of for TLE as defined by the International League Against Epilepsy (ILAE) in 2014 and 2017[ 13 , 14 ]; 3) were newly diagnosed with TLE patients, confirmed by clinical features, electroencephalogram (EEG), and cerebral magnetic resonance imaging (MRI). Patients with acute cerebrovascular disease, central nervous system infections, severe psychiatric disorders, or those who had taken sedatives, hypnotics, antidepressants, antipsychotics, or anti-seizure medications (ASMs) within two weeks prior to the EEG recording were excluded. Participants in the Control group were selected from those who had experienced transient dizziness and headache and subsequently underwent long-term EEG monitoring to rule out epilepsy. They were matched by age and sex with the TLE group and assigned to the non-epileptic Control group. 2.2. EEG recording and post-analysis The EEG data were recorded using identical instruments in the same EEG laboratory, under the supervision of a single technician. A cap with 25-channel EEG electrodes was put on the head of each participant following the revised international 10–20 system[ 15 ]. Twenty-five Ag/AgCl scalp electrodes (Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T7, T8, P7, P8, F9, T9, P9, F10, T10, P10, Fz, Cz, Pz) were linked to 25 data channels. The resting-state EEG signals were recorded continuously using Nihon Kohden digital amplifiers system linked to these data channels to amplify and digitize the EEG signals, with a sample rate of 1000Hz. A ten-minute recording of resting-state EEG in wakefulness was obtained for each participant, showing no detectable epileptiform discharges or other pathological waveforms. The reference channel was set to average. The raw EEG data were band pass filtered from 1 to 70 Hz and segmented into non-overlapping 2 s epochs. An additional 48 ~ 52 Hz notch filtering was applied. Main artifacts (the muscle artifacts, the ocular artifacts, the ECG artifacts and the channel artifacts) on the raw EEG data were identified by independent component analysis (maximum iteration = 2000) using the EEGLAB toolbox[ 16 ]. After performing the decomposition on each EEG recording, the independent components related to eye, heart, and muscle movements and channel artifacts were removed. Then the artifact corrected EEG data were filtered between 1 and 30Hz, based on previous studies. 2.3. The process of microstate analysis The microstate was calculated by using the MICROSTATELAB[ 17 ] toolbox in the EEGLAB. In the first step of microstate analysis, individual microstate maps were identified by using modified k-means algorithm cluster analysis[ 18 ] which based in the topographic similarity on the time series of the electric potential field map topographies. Then the individual microstate maps were reordered and labeled refer to the published templates[ 19 ] in the way that maximizes their shared variance across subjects. After data quality check and outlier detection, the temporal dynamics of individual recordings were quantified to be used for further statistical analyses, which included global explained variance (GEV), global field power (GFP), mean duration, occurrence, and contribution of microstate. The GEV, is expressed as a ratio that quantifies the degree to which each template represents the assigned time points across the entire dataset. The GFP is a measure that quantifies the strength of the electric field generated by the brain at a particular instant. The mean duration of microstate is defined as the average time span during which a microstate persists continuously. The frequency of occurrence is defined as the number of times a microstate recurs per second. The contribution and time coverage of microstate is defined as the cumulative amount of time, aggregated across the entire dataset, during which a particular microstate is present. 2.4. The process of functional connectivity analysis The Coherence (COH), Phase-Locking value (PLV) and Phase Lag Index/ weighted Phase Lag Index (PLI/wPLI) were calculated in this study to measure the functional connectivity. The COH is defined as \({COH}_{xy}\left(f\right)=\frac{{\left|{S}_{xy}\left(f\right)\right|}^{2}}{{S}_{xx}\left(f\right){S}_{yy}\left(f\right)}\) .[ 20 ] ( \({S}_{xx}\left(f\right)\) and \({S}_{yy}\left(f\right)\) are the respective self-spectrum of the signals x(t) and y(t), and \({S}_{xy}\left(f\right)\) is the cross-spectrum of them.) The PLV is defined as \(PLV=\frac{1}{K}\left|{\sum}_{K=1}^{K}exp\left(j\theta\left({t}_{k}\right)\right)\right|\) .[ 21 ] ( \(\theta\left({t}_{k}\right)={\varnothing}_{1}\left({t}_{k}\right)-{\varnothing}_{2}\left({t}_{k}\right)\) , which means the phase difference between different signals at t .) The PLV range is [0,1]. When the phase difference was constant throughout the whole time series, the PLV was equal to 1. When the phase difference was distributed evenly within the range of (0,2π), the PLV was equal to 0. The PLI is defined as \(\text{P}\text{L}\text{I}=\left|⟨sign\left[\varDelta\varnothing\left({t}_{k}\right)\right]⟩\right|\) .[ 22 ] The wPLI is further calculated to improve the discontinuity of PLI by weighing each phase differences according to the magnitude of the lag.[ 23 ] A larger PLI/wPLI showed a stronger phase locking. 2.5. Statistical analysis Statistical analysis was performed using SPSS 25.0. All the results were checked for normality. There were no missing data and no data interpolation in this study. Data conforming to the normal distribution were expressed as mean ± SD, while the parameters not conforming to the normal distribution were expressed as quartiles. The differences in the GEV, duration, occurrence and contribution of microstate were analyzed using independent samples t-test. The transition probabilities from each microstate to another were also calculated by using the Wilcoxon rank sum test. The differences in the functional connectivity including mean GFP, COH, PLV and PLI/wPLI were assessed using the t-test. Post hoc analyses were corrected using Bonferroni correction. Statistical significance was set at P < 0.05. 3. Results 3.1. Clinical features of included participants As is shown in the workflow of Fig-1 , a total of 514 hospitalized patients who underwent long-term EEG examination from 2020.1 to 2023.2 in Fudan University Zhongshan Hospital Xiamen Branch were reviewed. After screening, 132 patients with TLE, aged 18–60 years, who met the diagnostic criteria based on clinical symptoms, imaging findings, and EEG characteristics, were included for further review. Among them, 17 were newly diagnosed TLE patients who had not received any ASMs prior to EEG monitoring. In addition, 16 age-, sex-, and education-matched non-epileptic participants were selected from the dataset as the control group. The demographic and clinical characteristics of all the included patients with TLE were listed in Table 1 , including age, gender, the onset age of TLE, disease duration, seizure frequency, and the lateralization of IEDs. The average ages of the matched control group were 37.00 ± 12.89 years, with the range of 21 to 60, and the ratio of male to female was 11:5. They all had normal long-term EEG monitoring and structural brain MRI, and no complaints of cognition decline in their case histories. Table 1 The clinical features of the included patients in the TLE group TLE patients' ID age gender onset age of TLE disease duration (year) seizure frequency lateralization of IEDs 1 27 female 26 1 ≤ 1/y bilateral 2 43 male 42 2 ≤ 1/y bilateral 3 51 female 51 0.5 ≤ 1/y right 4 36 female 36 0.01 ≤ 1/y left 5 46 male 46 0.17 ≤ 1/y bilateral 6 37 male 37 0.05 ≤ 1/y left 7 36 male 36 0.12 1/m ~ 1/w left 8 34 male 34 0.12 ≤ 1/y right 9 35 male 34 0.67 ≤ 1/y left 10 58 female 57 0.5 ≤ 1/y bilateral 11 52 female 52 0.5 ≤ 1/y left 12 46 female 45 1 ≥ 1/w bilateral 13 19 female 18 1 1/y ~ 1/m bilateral 14 54 female 54 0.08 ≤ 1/y bilateral 15 58 male 58 0.02 ≤ 1/y bilateral 16 29 male 27 1.5 1/y ~ 1/m bilateral 17 57 male 50 7 1/m ~ 1/w bilateral 3.2. Significantly different microstate C and D coverage and transition between the TLE and Control groups There were 7 microstates calculated in the study, named from A to G. The Microstate topographies were analyzed separately in the TLE and Control groups. As shown in Fig. 2 , no significant difference was found between TLE group and Control group in all the grand mean microstate topographies. All the 7 classifications (A-G) of microstate topographies showed a consistency with the previously published templates[ 19 ]. The results in Fig. 2 showed that there was no significant difference in the GFP and occurrence of each microstate between the TLE group and the Control group. The microstate C GEV in the TLE group was significantly lower than the Control group (P = 0.045, t=-2.087, t-test). Meanwhile, the microstate D GEV in the TLE group was significantly higher than the Control group (P = 0.038, t = 2.166, t-test). Moreover, the mean microstate D duration in the TLE group was significantly longer than the Control group (P = 0.016, t = 2.556, t-test), and the microstate D time coverage in the TLE group was higher than the Control group (P = 0.016, t = 2.545, t-test). The transition probability between different microstates which indicates the relative probability of transformation from current microstate to another microstate was calculated. As shown in Table 2 , the TLE group had higher transition probability from microstate C to microstate D than in the Control group (P = 0.044, t = 2.105, t-test). Table 2 The transition probability of microstates compared between the TLE and Control groups Relative Probability TLE Control P A to B 2.30 ± 0.21 2.52 ± 0.21 0.455 A to C 2.32 ± 0.37 3.08 ± 0.34 0.148 A to D 3.15 ± 0.46 2.27 ± 0.23 0.103 B to A 2.24 ± 0.20 2.51 ± 0.21 0.352 B to C 2.24 ± 0.19 2.31 ± 0.24 0.811 B to D 3.36 ± 0.28 2.58 ± 0.33 0.078 C to A 2.35 ± 0.38 3.05 ± 0.31 0.166 C to B 2.15 ± 0.22 2.20 ± 0.19 0.853 C to D 2.88 ± 0.31 2.07 ± 0.23 0.044* D to A 3.19 ± 0.47 2.40 ± 0.25 0.153 D to B 3.24 ± 0.29 2.69 ± 0.37 0.251 D to C 2.97 ± 0.32 2.22 ± 0.27 0.082 3.3. TLE patients showed lower PLI and wPLI in the gamma band and higher values in the theta band compared to controls In this study, we quantified EEG functional connectivity across frequency bands and calculated connectivity indices including coherence (COH), phase-locking value (PLV), phase lag index (PLI), and weighted phase lag index (wPLI). Figure 3 shows the intergroup comparisons of PLI of in the gamma and theta frequency bands in the TLE group and the Control group via EEG functional connectivity analysis. In the gamma band, both the PLI and wPLI across all leads were significantly reduced in the TLE group compared with the Control group (P < 0.05). In contrast, in the theta band, PLI and wPLI were significantly elevated in the TLE group (P < 0.05), with the most pronounced PLI increase observed between the Fz-T5 leads. In the alpha band, a downward tendency in PLI and wPLI was observed in the TLE group, although this did not reach statistical significance compared with controls. Across the theta, alpha, and gamma bands, no statistically significant differences were found in the PLV and COH between the TLE group and the Control group. Similarly, in the beta and delta bands, no significant intergroup differences in EEG functional connectivity indices were detected. 4. Discussion Our investigation of untreated, newly diagnosed TLE patients revealed distinct alterations in EEG microstate dynamics and functional connectivity compared to non-epileptic controls. Key findings include reduced global explained variance (GEV) for microstate C, increased GEV and prolonged duration/time coverage of microstate D, heightened transition probability from microstate C to D, and divergent theta/gamma-band connectivity patterns. These results demonstrate disrupted network dynamics in TLE, particularly involving self-referential processing (microstate C) and executive Control networks (microstate D). Critically, our exclusion of ASMs effects strengthens the validity of these findings as intrinsic neurophysiological features of early-stage TLE. In this study, we employed a novel microstate classification framework (A-G) to enhance cross-study comparability[ 19 ]. The spatiotemporal-functional profiles revealed that: Microstate A (right fronto-left posterior configuration) was associated with auditory/visual processing and arousal modulation, localized to the left middle/superior temporal gyri and insular cortex (Brodmann areas, BA41/22) [ 24 , 25 ]. Microstate B (left fronto-right posterior configuration) participated in self-visualization and autobiographical memory, anchored in bilateral occipital cortices (BA17/18) [ 26 , 27 ]. Microstate C (anteroposterior axial configuration) mediated self-referential processing, involving the precuneus and posterior cingulate cortex (PCC) [ 26 , 28 ]. Microstate D (frontocentral dominance) governed executive functions (working memory/attentional control), mapped to the right inferior parietal lobule (BA40) [ 29 , 30 ]. Microstate E (left-hemispheric predominance) encoded interoceptive-emotional signals, engaging the left middle frontal gyrus (BA8) and dorsal anterior cingulate cortex[ 31 , 32 ]. Microstate F (centroparietal activation) operated as a default mode network component supporting mental simulation[ 19 , 26 ]. Microstate G (right-hemispheric predominance) potentially interfaced with somatosensory networks, spanning the right superior temporal gyrus and cerebellum[ 19 ]. This taxonomy systematically delineates the correspondence between microstate topographies and cognitive-affective neural circuits. Although no intergroup differences emerged in occurrence rates for microstates, the triad of increased GEV, prolonged duration, and expanded time coverage of microstate D that anchored in frontoparietal regions align with functional MRI (fMRI) evidence of frontoparietal network dysfunction in TLE [ 33 ], suggesting the impairment of brain network connectivity in the right inferior parietal lobe and the right middle and superior frontal gyri. This "sticky" microstate pattern, coupled with heightened C→D transitions, suggests the following mechanisms: 1) Prolonged microstate D engagement may counterbalance degraded executive Control, which reflects compensatory mechanisms for impaired cognition[ 29 , 30 ]; 2) Frequent C→D shifts imply fragility in self-referential processing (microstate C)[ 28 ], potentially triggering maladaptive recruitment of attentional networks, which may induce instability cascade. Notably, the alterations of microstates C and D remained independent of IED laterality, contrasting with Wei et al.'s global microstate changes that higher mean duration and lower occurrence of all microstates from A to G were found in TLE patients[ 34 ]. This discrepancy may reflect our cohort's unique profile (58.8% bilateral IEDs), underscoring the need to disentangle epileptogenic vs. compensatory microstate dynamics in future lateralization studies. Ricci et al [ 35 ] focused on the effects of Levetiracetam (LEV) therapy in TLE patients and found that LEV treatment modulates resting-state EEG microstate dynamics, specifically enhancing transition probabilities from microstate A to C and microstate B to D, which underscore the necessity to Control for ASMs effects in microstate analyses. To mitigate such pharmacological confounders, we exclusively acquired pretreatment EEG data from TLE patients prior to ASM initiation. In this study, the TLE group demonstrated significantly increased PLI/wPLI in the left temporal and frontal regions within the theta frequency band compared to the non-epileptic controls. This elevated frontotemporal theta hyperconnectivity (particularly Fz-T5) aligns with ictal-interictal continuum models, where theta synchronization facilitates epileptic network recruitment and positively correlated with seizure severity [ 36 ]. These findings may represent a "double-edged sword" that promoting pathological synchronization while attempting functional compensation through long-range integration. In addition, TLE patients demonstrated significantly reduced PLI/wPLI across nearly all channels within the gamma frequency band compared to non-epileptic Controls in this study. The gamma hypoconnectivity have a consistency with a fMRI findings of default mode network disintegration by González et al.[ 37 ], which reported that TLE patients exhibited significantly lower undirected functional connectivity in the default mode network compared to healthy controls. These connectivity abnormalities negatively correlated with processing speed index, language comprehension ability, and cognitive function, suggesting that altered functional connectivity may represent one of the neurophysiological bases for cognitive deficits in TLE patients. These findings of abnormal reductions in global functional connectivity potentially explain cognitive comorbidities of TLE through impaired cross-frequency coupling. Moreover, this inverse theta-gamma relationship suggests a spectral trade-off mechanism that pathological theta dominance disrupts gamma-mediated cognitive integration, facilitating the comorbidity of epileptogenesis and cognitive decline. Limitation While excluding ASMs confounders strengthens causality attribution, the retrospective design and modest sample size may limit generalizability of our results. Future studies should integrate longitudinal EEG monitoring with detailed neuropsychological assessments to clarify temporal relationships between microstate alterations, connectivity changes, and clinical progression. The predominance of bilateral IEDs in our cohort, though not statistically confounding, suggests cautious interpretation regarding lateralization effects. Declarations Funding No funding was received for conducting this study. Competing interests The authors have no competing interests to declare that are relevant to the content of this article. Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Zhongshan Hospital Fudan University, Xiamen, China (Ethics approval number: B2022-023). Author Contribution Zihan Hu: Conceptualization, Methodology, Writing – original draft.Zhihai Shao: Data curation, Formal analysis, Visualization.Rong Tang: Data curation, Formal analysis, Visualization.Weifeng Peng: Resources, Supervision, Writing – review & editing.Xin Wang: Project administration.All authors have read and agreed to the published version of the manuscript. Data Availability The original raw research data generated and analyzed in this study include resting-state EEG recordings, de-identified neuropsychological assessment data, and derived EEG microstate and functional connectivity datasets. Due to the nature of this clinical research involving human participants, the full raw EEG data contain potentially identifiable sensitive information, and are not publicly available in compliance with the study’s ethical approval, participant informed consent terms, and institutional data privacy policies.The de-identified analytical dataset supporting the core findings of this study is available from the corresponding author [Dr. Weifeng Peng, [email protected] ] upon reasonable, non-commercial academic request. Access to the data will be subject to institutional review and a formal data use agreement to ensure the protection of participant privacy and compliance with all relevant ethical regulations. 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Int J Psychophysiol 169:63–70. 10.1016/j.ijpsycho.2021.09.001 Englot DJ et al (2018) Relating structural and functional brainstem connectivity to disease measures in epilepsy, (in eng), Neurology , vol. 91, no. 1, pp. e67-e77, Jul 3 10.1212/wnl.0000000000005733 Wei Z et al (2024) Microstate-based brain network dynamics distinguishing temporal lobe epilepsy patients: A machine learning approach, Neuroimage , vol. 296, p. 120683, Aug 1 10.1016/j.neuroimage.2024.120683 Ricci L et al (2022) Levetiracetam Modulates EEG Microstates in Temporal Lobe Epilepsy, (in eng), Brain Topogr , vol. 35, no. 5–6, pp. 680–691, Nov 10.1007/s10548-022-00911-2 Mao L et al (2022) Frontotemporal phase lag index correlates with seizure severity in patients with temporal lobe epilepsy. Front Neurol 13. 10.3389/fneur.2022.855842 González HFJ et al (2023) Arousal and salience network connectivity alterations in surgical temporal lobe epilepsy. J Neurosurg 138(3):810–820. 10.3171/2022.5.Jns22837 Additional Declarations No competing interests reported. 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-9000959","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612053453,"identity":"94de0938-fbd1-4f4c-987a-bbab2667b041","order_by":0,"name":"Zihan Hu","email":"","orcid":"","institution":"Shanghai Geriatric Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Zihan","middleName":"","lastName":"Hu","suffix":""},{"id":612053454,"identity":"2715f7d6-60e5-4890-ba8d-e18f4de7ac0e","order_by":1,"name":"Zhihai Shao","email":"","orcid":"","institution":"Zhongshan Hospital Xiamen Branch","correspondingAuthor":false,"prefix":"","firstName":"Zhihai","middleName":"","lastName":"Shao","suffix":""},{"id":612053455,"identity":"8916bf89-88e5-46e2-aed9-93adfc0dc155","order_by":2,"name":"Rong Tang","email":"","orcid":"","institution":"Zhongshan Hospital Xiamen Branch","correspondingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Tang","suffix":""},{"id":612053457,"identity":"d9c9b2f1-542f-4ef4-91ec-e6a69ff0e851","order_by":3,"name":"Weifeng Peng","email":"data:image/png;base64,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","orcid":"","institution":"Shanghai Geriatric Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Weifeng","middleName":"","lastName":"Peng","suffix":""},{"id":612053459,"identity":"5e63033c-e694-49c8-a99e-729d14bfa954","order_by":4,"name":"Xin Wang","email":"","orcid":"","institution":"Zhongshan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-03-01 11:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9000959/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9000959/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105640942,"identity":"23f4d6c8-1afa-4a7c-bf2a-c4b072299786","added_by":"auto","created_at":"2026-03-28 16:25:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56534,"visible":true,"origin":"","legend":"\u003cp\u003eThe filtration of the participants. a total of 514 hospitalized patients who underwent long-term EEG examination from 2020.1 to 2023.2 in Fudan University Zhongshan Hospital Xiamen Branch were reviewed. After screening, a total of 132 patients with TLE whose clinical symptoms, imaging findings, and EEG manifestations met the diagnostic criteria and were aged between 18 and 60 years old were included. Among them, there were 17 newly diagnosed TLE patients who had not taken any ASMs prior to the time of EEG monitoring and 16 non-epileptic participants in the Control group included in the study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9000959/v1/feecf9f8b71f0fdfbd2a6a7d.png"},{"id":105640917,"identity":"85b266c6-b343-4196-be6d-88b7ab486af5","added_by":"auto","created_at":"2026-03-28 16:25:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":381537,"visible":true,"origin":"","legend":"\u003cp\u003eThe topography of each microstate (A to G). The first line showed the microstate topographies of TLE group. The second line showed the microstate topographies of the Control group. The last line were the grand mean microstate topographies of all the participants.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9000959/v1/92e319c9b8bf17782344999c.png"},{"id":105640916,"identity":"60d46ea2-2748-4f09-bb33-56ccb5a0f8d7","added_by":"auto","created_at":"2026-03-28 16:25:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":112704,"visible":true,"origin":"","legend":"\u003cp\u003eThe temporal parameters of each microstate (A to G). Group comparison of mean duration， time coverage, occurrence, GFP and GEV were shown above. Mean duration means the average duration of each microstate in the EEG data. Time coverage means the summarized duration of each microstate as a percentage of the total EEG recording time. Occurrence means the frequency of the occurrence of each microstate per second. GFP (Global Field Power) means the standard deviation of voltage values of all electrodes in each microstate topography. GEV (Global Explained Variance) means the sum of the weighted variances of GFP for all the time points at which the clustering label is assigned.\u003c/p\u003e\n\u003cp\u003e*P\u0026lt;0.05\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9000959/v1/570a3ebf0f92a16c60ec68c6.png"},{"id":105640915,"identity":"22f0b182-b1aa-4419-bc1e-e0f00ecbadb6","added_by":"auto","created_at":"2026-03-28 16:25:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":196137,"visible":true,"origin":"","legend":"\u003cp\u003eThe functional connectivity indexes of each band. Group comparison of the PLI and wPLI between TLE and Control was shown above. A. In the theta band, the PLI and wPLI of the TLE group were higher than those of the Control group, with the most significant PLI increasing between the Fz-T5 leads. B. In the gamma band, the PLI and wPLI between all leads in the TLE group were significantly lower than those in the Control group. (P\u0026lt;0.05, t-test)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9000959/v1/d6b68d9cd6bb615f20d09a81.png"},{"id":106401624,"identity":"eab3e357-635c-4769-8436-d763c5254aba","added_by":"auto","created_at":"2026-04-08 09:08:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1461971,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9000959/v1/3e16e101-ba13-4b6a-bf72-3e96c48b4e02.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unveiling Hidden Executive Dysfunction by EEG Microstates and Functional Connectivity in Newly Diagnosed, Drug-Naïve Temporal Lobe Epilepsy","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEpilepsy is a prevalent and severe neurological disorder affecting approximately 70\u0026nbsp;million people worldwide, manifesting as recurrent seizures often accompanied by comorbidities[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Temporal lobe epilepsy (TLE), the most common form of adult focal epilepsy, has traditionally been viewed as a condition originating in the temporal lobe. However, recent studies have highlighted the role of large-scale brain network dysfunctions that persist during inter-ictal periods, revealing that TLE involves disrupted neural dynamics beyond focal region[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], which may be responsible for the comorbidities such as cognitive impairments.\u003c/p\u003e \u003cp\u003eStudies using advanced neuroimaging techniques have consistently demonstrated altered connectivity within these networks, suggesting a network-level pathology in TLE[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], usually involving temporal and frontal lobes which are crucial for cognitive processes such as memory, attention, and executive control. In newly diagnosed TLE patients, evidence of brain network impairment is emerging, indicating that network dysfunction could be a primary feature of the disease rather than a secondary consequence of long-term seizure activity[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResting-state Electroencephalogram (EEG) is a valuable tool for examining the spontaneous neural activity of the brain and offers unique insights into the temporal dynamics of brain networks[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. EEG microstates are brief periods of quasi-stable topographical patterns in the EEG signal, reflecting the coordinated activity of large-scale neural networks. These microstates are considered the building blocks of brain function and are linked to distinct cognitive and perceptual processes[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChanges in EEG microstate dynamics, such as alterations in duration, frequency, and transition rates between states, have been associated with various neurological and psychiatric conditions, indicating their potential role as biomarkers of brain network dysfunction[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In epilepsy, disrupted microstate dynamics have been linked to abnormalities in brain connectivity and information processing, which might serve as a distinctive biomarker for predicting the severity of epilepsy and distinguishing between unilateral and bilateral TLE [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Furthermore, functional connectivity analysis based on EEG data reflected functional changes between different brain areas, which may supply additional information about brain connectivity and epileptogenic focus localization[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs there is still limited research on brain network in newly diagnosed drug-naive TLE patients, this study aims to combine the dynamics of resting-state EEG microstates and function connectivity analysis to investigate their associations with cognitive function. Our study would provide new neurobiological evidence linking brain network dysfunction with cognitive deficits in newly diagnosed TLE, contributing to a deeper understanding of the pathophysiological mechanisms for comorbidities of epilepsy.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThis is a retrospective case-control study. The dataset includes data from hospitalized patients who underwent long-term EEG examination during the period of 2020.1-2023.2 in Zhongshan Hospital Fudan University Xiamen Branch, Xiamen, China. Patients who have been newly diagnosed with temporal lobe epilepsy (TLE) prior to anti-seizure medications (ASMs) and non-epileptic patients of corresponding age and gender were selected. This study was approved by the Ethics Committee of Zhongshan Hospital Fudan University, Xiamen, China (the ethical number is B2022-023).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Participants and groups\u003c/h2\u003e \u003cp\u003eIn the dataset, TLE Patients included in this study met the inclusion criteria as follows: 1) aged between 18 and 60 years with no gender limitation; 2) met the diagnostic criteria of for TLE as defined by the International League Against Epilepsy (ILAE) in 2014 and 2017[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]; 3) were newly diagnosed with TLE patients, confirmed by clinical features, electroencephalogram (EEG), and cerebral magnetic resonance imaging (MRI). Patients with acute cerebrovascular disease, central nervous system infections, severe psychiatric disorders, or those who had taken sedatives, hypnotics, antidepressants, antipsychotics, or anti-seizure medications (ASMs) within two weeks prior to the EEG recording were excluded.\u003c/p\u003e \u003cp\u003eParticipants in the Control group were selected from those who had experienced transient dizziness and headache and subsequently underwent long-term EEG monitoring to rule out epilepsy. They were matched by age and sex with the TLE group and assigned to the non-epileptic Control group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. EEG recording and post-analysis\u003c/h2\u003e \u003cp\u003eThe EEG data were recorded using identical instruments in the same EEG laboratory, under the supervision of a single technician. A cap with 25-channel EEG electrodes was put on the head of each participant following the revised international 10\u0026ndash;20 system[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Twenty-five Ag/AgCl scalp electrodes (Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T7, T8, P7, P8, F9, T9, P9, F10, T10, P10, Fz, Cz, Pz) were linked to 25 data channels. The resting-state EEG signals were recorded continuously using Nihon Kohden digital amplifiers system linked to these data channels to amplify and digitize the EEG signals, with a sample rate of 1000Hz. A ten-minute recording of resting-state EEG in wakefulness was obtained for each participant, showing no detectable epileptiform discharges or other pathological waveforms.\u003c/p\u003e \u003cp\u003eThe reference channel was set to average. The raw EEG data were band pass filtered from 1 to 70 Hz and segmented into non-overlapping 2 s epochs. An additional 48\u0026thinsp;~\u0026thinsp;52 Hz notch filtering was applied. Main artifacts (the muscle artifacts, the ocular artifacts, the ECG artifacts and the channel artifacts) on the raw EEG data were identified by independent component analysis (maximum iteration\u0026thinsp;=\u0026thinsp;2000) using the EEGLAB toolbox[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. After performing the decomposition on each EEG recording, the independent components related to eye, heart, and muscle movements and channel artifacts were removed. Then the artifact corrected EEG data were filtered between 1 and 30Hz, based on previous studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. The process of microstate analysis\u003c/h2\u003e \u003cp\u003eThe microstate was calculated by using the MICROSTATELAB[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] toolbox in the EEGLAB. In the first step of microstate analysis, individual microstate maps were identified by using modified k-means algorithm cluster analysis[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] which based in the topographic similarity on the time series of the electric potential field map topographies. Then the individual microstate maps were reordered and labeled refer to the published templates[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] in the way that maximizes their shared variance across subjects. After data quality check and outlier detection, the temporal dynamics of individual recordings were quantified to be used for further statistical analyses, which included global explained variance (GEV), global field power (GFP), mean duration, occurrence, and contribution of microstate. The GEV, is expressed as a ratio that quantifies the degree to which each template represents the assigned time points across the entire dataset. The GFP is a measure that quantifies the strength of the electric field generated by the brain at a particular instant. The mean duration of microstate is defined as the average time span during which a microstate persists continuously. The frequency of occurrence is defined as the number of times a microstate recurs per second. The contribution and time coverage of microstate is defined as the cumulative amount of time, aggregated across the entire dataset, during which a particular microstate is present.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. The process of functional connectivity analysis\u003c/h2\u003e \u003cp\u003eThe Coherence (COH), Phase-Locking value (PLV) and Phase Lag Index/ weighted Phase Lag Index (PLI/wPLI) were calculated in this study to measure the functional connectivity.\u003c/p\u003e \u003cp\u003eThe COH is defined as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({COH}_{xy}\\left(f\\right)=\\frac{{\\left|{S}_{xy}\\left(f\\right)\\right|}^{2}}{{S}_{xx}\\left(f\\right){S}_{yy}\\left(f\\right)}\\)\u003c/span\u003e\u003c/span\u003e.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({S}_{xx}\\left(f\\right)\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({S}_{yy}\\left(f\\right)\\)\u003c/span\u003e\u003c/span\u003e are the respective self-spectrum of the signals x(t) and y(t), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({S}_{xy}\\left(f\\right)\\)\u003c/span\u003e\u003c/span\u003e is the cross-spectrum of them.)\u003c/p\u003e \u003cp\u003eThe PLV is defined as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(PLV=\\frac{1}{K}\\left|{\\sum}_{K=1}^{K}exp\\left(j\\theta\\left({t}_{k}\\right)\\right)\\right|\\)\u003c/span\u003e\u003c/span\u003e.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\theta\\left({t}_{k}\\right)={\\varnothing}_{1}\\left({t}_{k}\\right)-{\\varnothing}_{2}\\left({t}_{k}\\right)\\)\u003c/span\u003e\u003c/span\u003e, which means the phase difference between different signals at \u003cem\u003et\u003c/em\u003e.) The PLV range is [0,1]. When the phase difference was constant throughout the whole time series, the PLV was equal to 1. When the phase difference was distributed evenly within the range of (0,2π), the PLV was equal to 0.\u003c/p\u003e \u003cp\u003eThe PLI is defined as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{P}\\text{L}\\text{I}=\\left|\u0026lang;sign\\left[\\varDelta\\varnothing\\left({t}_{k}\\right)\\right]\u0026rang;\\right|\\)\u003c/span\u003e\u003c/span\u003e.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] The wPLI is further calculated to improve the discontinuity of PLI by weighing each phase differences according to the magnitude of the lag.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] A larger PLI/wPLI showed a stronger phase locking.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using SPSS 25.0. All the results were checked for normality. There were no missing data and no data interpolation in this study. Data conforming to the normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, while the parameters not conforming to the normal distribution were expressed as quartiles.\u003c/p\u003e \u003cp\u003eThe differences in the GEV, duration, occurrence and contribution of microstate were analyzed using independent samples t-test. The transition probabilities from each microstate to another were also calculated by using the Wilcoxon rank sum test. The differences in the functional connectivity including mean GFP, COH, PLV and PLI/wPLI were assessed using the t-test. Post hoc analyses were corrected using Bonferroni correction. Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Clinical features of included participants\u003c/h2\u003e \u003cp\u003eAs is shown in the workflow of \u003cb\u003eFig-1\u003c/b\u003e, a total of 514 hospitalized patients who underwent long-term EEG examination from 2020.1 to 2023.2 in Fudan University Zhongshan Hospital Xiamen Branch were reviewed. After screening, 132 patients with TLE, aged 18\u0026ndash;60 years, who met the diagnostic criteria based on clinical symptoms, imaging findings, and EEG characteristics, were included for further review. Among them, 17 were newly diagnosed TLE patients who had not received any ASMs prior to EEG monitoring. In addition, 16 age-, sex-, and education-matched non-epileptic participants were selected from the dataset as the control group.\u003c/p\u003e \u003cp\u003eThe demographic and clinical characteristics of all the included patients with TLE were listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, including age, gender, the onset age of TLE, disease duration, seizure frequency, and the lateralization of IEDs. The average ages of the matched control group were 37.00\u0026thinsp;\u0026plusmn;\u0026thinsp;12.89 years, with the range of 21 to 60, and the ratio of male to female was 11:5. They all had normal long-term EEG monitoring and structural brain MRI, and no complaints of cognition decline in their case histories.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe clinical features of the included patients in the TLE group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLE patients' ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003egender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eonset age of TLE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003edisease duration (year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eseizure frequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003elateralization of IEDs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1/y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ebilateral\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1/y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ebilateral\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1/y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eright\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1/y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eleft\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1/y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ebilateral\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1/y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eleft\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1/m\u0026thinsp;~\u0026thinsp;1/w\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eleft\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1/y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eright\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1/y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eleft\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1/y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ebilateral\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1/y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eleft\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1/w\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ebilateral\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1/y\u0026thinsp;~\u0026thinsp;1/m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ebilateral\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1/y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ebilateral\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1/y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ebilateral\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1/y\u0026thinsp;~\u0026thinsp;1/m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ebilateral\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1/m\u0026thinsp;~\u0026thinsp;1/w\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ebilateral\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Significantly different microstate C and D coverage and transition between the TLE and Control groups\u003c/h2\u003e \u003cp\u003eThere were 7 microstates calculated in the study, named from A to G. The Microstate topographies were analyzed separately in the TLE and Control groups. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, no significant difference was found between TLE group and Control group in all the grand mean microstate topographies. All the 7 classifications (A-G) of microstate topographies showed a consistency with the previously published templates[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed that there was no significant difference in the GFP and occurrence of each microstate between the TLE group and the Control group. The microstate C GEV in the TLE group was significantly lower than the Control group (P\u0026thinsp;=\u0026thinsp;0.045, t=-2.087, t-test). Meanwhile, the microstate D GEV in the TLE group was significantly higher than the Control group (P\u0026thinsp;=\u0026thinsp;0.038, t\u0026thinsp;=\u0026thinsp;2.166, t-test). Moreover, the mean microstate D duration in the TLE group was significantly longer than the Control group (P\u0026thinsp;=\u0026thinsp;0.016, t\u0026thinsp;=\u0026thinsp;2.556, t-test), and the microstate D time coverage in the TLE group was higher than the Control group (P\u0026thinsp;=\u0026thinsp;0.016, t\u0026thinsp;=\u0026thinsp;2.545, t-test).\u003c/p\u003e \u003cp\u003eThe transition probability between different microstates which indicates the relative probability of transformation from current microstate to another microstate was calculated. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the TLE group had higher transition probability from microstate C to microstate D than in the Control group (P\u0026thinsp;=\u0026thinsp;0.044, t\u0026thinsp;=\u0026thinsp;2.105, t-test).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe transition probability of microstates compared between the TLE and Control groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eRelative Probability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTLE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA to B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA to C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA to D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB to A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB to C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB to D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC to A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC to B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC to D\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.044*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD to A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD to B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD to C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e3.3. \u003cb\u003eTLE patients showed lower PLI and wPLI in the gamma band and higher values in the theta band compared to controls\u003c/b\u003e \u003c/p\u003e\u003cp\u003eIn this study, we quantified EEG functional connectivity across frequency bands and calculated connectivity indices including coherence (COH), phase-locking value (PLV), phase lag index (PLI), and weighted phase lag index (wPLI). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the intergroup comparisons of PLI of in the gamma and theta frequency bands in the TLE group and the Control group via EEG functional connectivity analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the gamma band, both the PLI and wPLI across all leads were significantly reduced in the TLE group compared with the Control group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, in the theta band, PLI and wPLI were significantly elevated in the TLE group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with the most pronounced PLI increase observed between the Fz-T5 leads. In the alpha band, a downward tendency in PLI and wPLI was observed in the TLE group, although this did not reach statistical significance compared with controls. Across the theta, alpha, and gamma bands, no statistically significant differences were found in the PLV and COH between the TLE group and the Control group. Similarly, in the beta and delta bands, no significant intergroup differences in EEG functional connectivity indices were detected.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur investigation of untreated, newly diagnosed TLE patients revealed distinct alterations in EEG microstate dynamics and functional connectivity compared to non-epileptic controls. Key findings include reduced global explained variance (GEV) for microstate C, increased GEV and prolonged duration/time coverage of microstate D, heightened transition probability from microstate C to D, and divergent theta/gamma-band connectivity patterns. These results demonstrate disrupted network dynamics in TLE, particularly involving self-referential processing (microstate C) and executive Control networks (microstate D). Critically, our exclusion of ASMs effects strengthens the validity of these findings as intrinsic neurophysiological features of early-stage TLE.\u003c/p\u003e \u003cp\u003eIn this study, we employed a novel microstate classification framework (A-G) to enhance cross-study comparability[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The spatiotemporal-functional profiles revealed that: Microstate A (right fronto-left posterior configuration) was associated with auditory/visual processing and arousal modulation, localized to the left middle/superior temporal gyri and insular cortex (Brodmann areas, BA41/22) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Microstate B (left fronto-right posterior configuration) participated in self-visualization and autobiographical memory, anchored in bilateral occipital cortices (BA17/18) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Microstate C (anteroposterior axial configuration) mediated self-referential processing, involving the precuneus and posterior cingulate cortex (PCC) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Microstate D (frontocentral dominance) governed executive functions (working memory/attentional control), mapped to the right inferior parietal lobule (BA40) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Microstate E (left-hemispheric predominance) encoded interoceptive-emotional signals, engaging the left middle frontal gyrus (BA8) and dorsal anterior cingulate cortex[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Microstate F (centroparietal activation) operated as a default mode network component supporting mental simulation[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Microstate G (right-hemispheric predominance) potentially interfaced with somatosensory networks, spanning the right superior temporal gyrus and cerebellum[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This taxonomy systematically delineates the correspondence between microstate topographies and cognitive-affective neural circuits.\u003c/p\u003e \u003cp\u003eAlthough no intergroup differences emerged in occurrence rates for microstates, the triad of increased GEV, prolonged duration, and expanded time coverage of microstate D that anchored in frontoparietal regions align with functional MRI (fMRI) evidence of frontoparietal network dysfunction in TLE [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], suggesting the impairment of brain network connectivity in the right inferior parietal lobe and the right middle and superior frontal gyri. This \"sticky\" microstate pattern, coupled with heightened C\u0026rarr;D transitions, suggests the following mechanisms: 1) Prolonged microstate D engagement may counterbalance degraded executive Control, which reflects compensatory mechanisms for impaired cognition[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]; 2) Frequent C\u0026rarr;D shifts imply fragility in self-referential processing (microstate C)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], potentially triggering maladaptive recruitment of attentional networks, which may induce instability cascade.\u003c/p\u003e \u003cp\u003eNotably, the alterations of microstates C and D remained independent of IED laterality, contrasting with Wei et al.'s global microstate changes that higher mean duration and lower occurrence of all microstates from A to G were found in TLE patients[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This discrepancy may reflect our cohort's unique profile (58.8% bilateral IEDs), underscoring the need to disentangle epileptogenic vs. compensatory microstate dynamics in future lateralization studies.\u003c/p\u003e \u003cp\u003eRicci et al [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] focused on the effects of Levetiracetam (LEV) therapy in TLE patients and found that LEV treatment modulates resting-state EEG microstate dynamics, specifically enhancing transition probabilities from microstate A to C and microstate B to D, which underscore the necessity to Control for ASMs effects in microstate analyses. To mitigate such pharmacological confounders, we exclusively acquired pretreatment EEG data from TLE patients prior to ASM initiation.\u003c/p\u003e \u003cp\u003eIn this study, the TLE group demonstrated significantly increased PLI/wPLI in the left temporal and frontal regions within the theta frequency band compared to the non-epileptic controls. This elevated frontotemporal theta hyperconnectivity (particularly Fz-T5) aligns with ictal-interictal continuum models, where theta synchronization facilitates epileptic network recruitment and positively correlated with seizure severity [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These findings may represent a \"double-edged sword\" that promoting pathological synchronization while attempting functional compensation through long-range integration.\u003c/p\u003e \u003cp\u003eIn addition, TLE patients demonstrated significantly reduced PLI/wPLI across nearly all channels within the gamma frequency band compared to non-epileptic Controls in this study. The gamma hypoconnectivity have a consistency with a fMRI findings of default mode network disintegration by Gonz\u0026aacute;lez et al.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], which reported that TLE patients exhibited significantly lower undirected functional connectivity in the default mode network compared to healthy controls. These connectivity abnormalities negatively correlated with processing speed index, language comprehension ability, and cognitive function, suggesting that altered functional connectivity may represent one of the neurophysiological bases for cognitive deficits in TLE patients. These findings of abnormal reductions in global functional connectivity potentially explain cognitive comorbidities of TLE through impaired cross-frequency coupling. Moreover, this inverse theta-gamma relationship suggests a spectral trade-off mechanism that pathological theta dominance disrupts gamma-mediated cognitive integration, facilitating the comorbidity of epileptogenesis and cognitive decline.\u003c/p\u003e \u003cp\u003eLimitation\u003c/p\u003e \u003cp\u003eWhile excluding ASMs confounders strengthens causality attribution, the retrospective design and modest sample size may limit generalizability of our results. Future studies should integrate longitudinal EEG monitoring with detailed neuropsychological assessments to clarify temporal relationships between microstate alterations, connectivity changes, and clinical progression. The predominance of bilateral IEDs in our cohort, though not statistically confounding, suggests cautious interpretation regarding lateralization effects.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e \u003cp\u003eCompeting interests\u003c/p\u003e \u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e \u003cp\u003eEthics approval\u003c/p\u003e \u003cp\u003e This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Zhongshan Hospital Fudan University, Xiamen, China (Ethics approval number: B2022-023).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZihan Hu: Conceptualization, Methodology, Writing \u0026ndash; original draft.Zhihai Shao: Data curation, Formal analysis, Visualization.Rong Tang: Data curation, Formal analysis, Visualization.Weifeng Peng: Resources, Supervision, Writing \u0026ndash; review \u0026amp; editing.Xin Wang: Project administration.All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe original raw research data generated and analyzed in this study include resting-state EEG recordings, de-identified neuropsychological assessment data, and derived EEG microstate and functional connectivity datasets. Due to the nature of this clinical research involving human participants, the full raw EEG data contain potentially identifiable sensitive information, and are not publicly available in compliance with the study\u0026rsquo;s ethical approval, participant informed consent terms, and institutional data privacy policies.The de-identified analytical dataset supporting the core findings of this study is available from the corresponding author [Dr. Weifeng Peng, [email protected]] upon reasonable, non-commercial academic request. Access to the data will be subject to institutional review and a formal data use agreement to ensure the protection of participant privacy and compliance with all relevant ethical regulations. No third-party datasets were used or analyzed in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eThijs RD, Surges R, O'Brien TJ, Sander JW Epilepsy in adults, (in eng), \u003cem\u003eLancet\u003c/em\u003e, vol. 393, no. 10172, pp. 689\u0026ndash;701, Feb 16 2019. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0140-6736(18)32596-0\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(18)32596-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCourtiol J, Guye M, Bartolomei F, Petkoski S, Jirsa VK Dynamical Mechanisms of Interictal Resting-State Functional Connectivity in Epilepsy, (in eng), \u003cem\u003eJ Neurosci\u003c/em\u003e, vol. 40, no. 29, pp. 5572\u0026ndash;5588, Jul 15 2020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1523/jneurosci.0905-19.2020\u003c/span\u003e\u003cspan address=\"10.1523/jneurosci.0905-19.2020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoyer J et al (2023) Cortical microstructural gradients capture memory network reorganization in temporal lobe epilepsy, \u003cem\u003eBrain\u003c/em\u003e, vol. 146, no. 9, pp. 3923\u0026ndash;3937, Sep 1 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/brain/awad125\u003c/span\u003e\u003cspan address=\"10.1093/brain/awad125\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoito A et al Dynamic directed interictal connectivity in left and right temporal lobe epilepsy, (in eng), \u003cem\u003eEpilepsia\u003c/em\u003e, vol. 56, no. 2, pp. 207\u0026thinsp;\u0026ndash;\u0026thinsp;17, Feb 2015. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/epi.12904\u003c/span\u003e\u003cspan address=\"10.1111/epi.12904\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichardson MP (Dec 2012) Large scale brain models of epilepsy: dynamics meets connectomics. 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J Neurosurg 138(3):810\u0026ndash;820. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3171/2022.5.Jns22837\u003c/span\u003e\u003cspan address=\"10.3171/2022.5.Jns22837\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":"","lastPublishedDoi":"10.21203/rs.3.rs-9000959/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9000959/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThis study aimed to investigate the resting-state EEG microstate dynamics and functional connectivity in newly diagnosed temporal lobe epilepsy (TLE) patients, exploring whether these patients exhibit brain network impairment.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis is a retrospective case-control study. Seventeen newly diagnosed TLE patients and 16 age- and gender-matched non-epileptic controls were selected from 514 inpatients for long-term EEG during the period of 2020.1-2023.2 in Zhongshan Hospital Xiamen Branch, Xiamen, China. Resting-state EEG data epochs extracted from long-term EEG monitoring were preprocessed and analyzed for microstates using the MICROSTATELAB toolbox in EEGLAB. Functional connectivity was measured and presented by Coherence (COH), Phase-Locking value (PLV), and Phase Lag Index/weighted Phase Lag Index (PLI/wPLI).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFor microstate analysis, microstate D representing executive Control networks in TLE patients had a significantly longer mean duration, higher time coverage, and different GEV values compared to non-epileptic controls. The transition probability from microstate C representing self-referential processing to D was higher in TLE patients. In terms of functional connectivity, TLE patients showed lower PLI and wPLI in the gamma band representing cognitive function and higher values in the theta band representing epileptic network connectivity compared to controls.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur findings demonstrate that executive dysfunction emerges at the earliest stage of temporal lobe epilepsy, prior to medication, as detected by EEG microstate and functional connectivity analyses. These results suggest novel electrophysiological markers for subclinical cognitive impairment and underscore the need for early monitoring and tailored interventions.\u003c/p\u003e","manuscriptTitle":"Unveiling Hidden Executive Dysfunction by EEG Microstates and Functional Connectivity in Newly Diagnosed, Drug-Naïve Temporal Lobe Epilepsy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-28 16:24:59","doi":"10.21203/rs.3.rs-9000959/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":"f8076dbd-9df9-47ac-8898-e1df967a7fb8","owner":[],"postedDate":"March 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T09:29:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-28 16:24:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9000959","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9000959","identity":"rs-9000959","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}