Evidence from EEG of Abnormal Functional Connectivity and Microstates in GAD and PD

Evidence from EEG of Abnormal Functional Connectivity and Microstates in GAD and PD | 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 Evidence from EEG of Abnormal Functional Connectivity and Microstates in GAD and PD Danfeng Yuan, Xiangyun Yang, Pengchong Wang, Wenpeng Hou, Zhanjiang Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7035279/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Feb, 2026 Read the published version in Brain Topography → Version 1 posted 8 You are reading this latest preprint version Abstract Panic disorder (PD) and generalized anxiety disorder (GAD) are among the most prevalent anxiety disorders (ADs), yet their neural mechanisms remain unclear. This study aimed to characterize EEG microstate patterns and their functional connectivity (FC) in patients with GAD and PD, and to explore the neural mechanisms of anxiety symptoms through microstate analysis. Resting-state EEG was collected from 35 patients with PD, 31 patients with GAD, and 39 healthy controls (HCs). Microstate topologies (microstate-4) were selected to calculate the parameters, including the mean duration, time coverage, occurrence, mean global field power (GFP), and transitions. Furthermore, the FC patterns underlying each microstate class were analyzed. Correlation analyses were conducted between anxiety symptoms and microstate dynamics. Compared with HCs, ADs presented an increased duration of microstate D and a decreased time coverage of microstate A. The correlation analysis revealed that the microstate C features were positively associated with anxiety symptoms. In contrast, microstate A and B exhibited consistent negative correlations with anxiety symptoms. The PD and GAD groups exhibited distinct FC patterns in microstate A. These findings reveal distinct neural dynamics in ADs characterized by impaired sensory processing and executive functioning. The abnormalities were predominantly observed in patients with GAD. Anxiety symptoms may be associated with distinct microstate patterns: positively with microstate C (linked to self-referential processing) and negatively with microstates A and B (involved in sensory network functioning). FC differences in microstate A demonstrated discriminative value for distinguishing between GAD and PD. Electroencephalogram Microstate Functional connectivity Panic disorder Generalized anxiety disorder Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Anxiety disorders (ADs) constitute the most prevalent mental health condition globally (Penninx et al. 2021 ), with panic disorder (PD) and generalized anxiety disorder (GAD) representing the two most common anxiety disorders. GAD is characterized by persistent, uncontrolled worry, whereas PD presents as sudden episodes of extreme fear accompanied by physical symptoms and autonomic nervous system arousal (Locke et al. 2015 ). The 22.2% comorbidity rate between PD and GAD (Turki et al. 2017 ), despite their distinct symptoms, suggests a shared pathophysiology that complicates differential diagnosis. However, the pathophysiological mechanisms of GAD and PD remain incompletely understood. Research indicates that ADs are commonly associated with abnormalities in attentional control and heightened vigilance toward threatening stimuli (Price et al. 2013 ). Among them, individuals with GAD — characterized by worry as the core symptom — exhibit more pronounced impairments in attentional control. Poor attentional control may contribute to increased anxiety and inefficient attention shifting (Barthel et al. 2022 ). Magnetic resonance imaging (MRI)-based functional network studies further supported frontoparietal alternations in the attentional network among individuals with ADs (Lai 2020 ), with pronounced effects observed in individuals with GAD (Buff et al. 2016 ). Furthermore, anxiety appears to impair sensory processing through maladaptive amplification of ascending prediction error signals. This heightened signaling may compromise accurate threat detection by distorting stimulus processing, potentially underpinning some deleterious effects of anxiety on higher-order cognition (Cornwell et al. 2017 ). In particular, the advanced fear network model incorporating sensory regions of the temporal, occipital and parietal lobes was confirmed to be associated with PD pathophysiology (Lai 2019 ). Compared with MRI, electroencephalography (EEG) is a more cost-effective and noninvasive method that offers superior temporal resolution for directly tracking the dynamic patterns of brain electrical activity (Chizhikova 2024 ). Among EEG analysis methods, resting-state functional connectivity (rsFC) and EEG microstate are robust approaches that provide brain activity and connectivity information underlying neurological and psychiatric diseases. Microstate is a novel approach in resting-state EEG research that evaluates transient global brain activity states with stable topographies lasting approximately 60–120 ms (Michel and Koenig 2018 ). The spatial stability of microstates enables their use as indicators of whole-brain functional states in health and disease. Four predominant microstate classes (A-D) are consistently observed across populations (Britz et al. 2010 ). These microstates have been shown to correlate with specific neural networks through synchronized EEG-fMRI studies: auditory (microstate A), visual (microstate B), saliency (microstate C), and frontal-apical networks (microstate D) (Van de Ville et al. 2010 ). A growing number of studies have demonstrated abnormal EEG microstate characteristics in patients with neuropsychiatric disorders, including depression disorder (Cao et al. 2024 ), schizophrenia (Thirioux et al. 2024 ), and bipolar disorders (Wang et al. 2021 ). However, there are relatively few studies examining EEG microstate features in ADs and different types of ADs. To date, only two studies—with limited sample sizes and inconsistent findings have investigated microstate characteristics in PD patients and GAD patients separately (Kikuchi et al. 2011 ; Wang et al. 2023 ). The EEG microstate patterns in ADs—both generally and in PD/GAD specifically—remain understudied. In additional to microstates, EEG-based FC enables direct measurement of neural oscillations. This approach captures neural oscillations generated by synchronized rhythmic firing. The neural oscillations across different frequency bands reflect distinct neural activity within different brain structures and brain states. Evidence from EEG-based FC studies has revealed aberrant frequency-dependent FC in GAD patients, including reduced FC in the beta band in the left hemisphere (Mou et al. 2024 ) and decreased long-range interactions between the frontal cortex and other brain areas across all frequency bands (Shen et al. 2022 ). In contrast, EEG-based FC abnormalities in PD remain understudied. Combining microstate analysis with FC measures enables the computation of FC within specific microstate time windows, providing a more comprehensive understanding of the mechanisms underlying microstates (Michel and Koenig 2018 ). However, no studies have investigated the potential differences in microstate and microstate-specific FC between PD and GAD patients. Existing studies suggest that there may be a potential correlation between microstate dynamic features and the severity of clinical symptoms. For example, Xue et al. revealed that depression symtoms were correlated with microstate B, whereas, the anxiety symptoms were correlated with the microstate E (Xue et al. 2024 ). Another study in low social anxiety group revealed that the transition probability between microstates B and C was significantly negatively correlated with social anxiety symptoms (Zhang et al. 2025 ). The correlation analyses between anxiety symptoms and neurodynamic parameters may provide important clues to understanding the neural mechanisms of anxiety symptoms. However, current research findings remain inconsistent, and studies have primarily focused on subclinical samples, whereas research in anxiety disorder patients is notably lacking. In general, this study aims to characterize EEG microstate patterns in ADs generally as well as PD and GAD separately. In addition, the present study will further explore the association between anxiety symptom severity and microstate dynamic features in ADs overall, as well as in PD and GAD separately.The FC under microstate was also analysis to examine whether there were disorder-specific disruptions in attentional control and sensory networks among PD and GAD patients. Materials and methods Participants and procedure Thirty-five patients with PD, 31 with GAD, and 39 healthy controls (HCs) were recruited between May 2023 and October 2024 from the clinic at Beijing Anding Hospital. The inclusion criteria were as follows: (1) a primary diagnosis of GAD or PD according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5); (2) no changes in psychiatric medications in the past four weeks; (3) aged 18–60 years; and (4) right-handedness. We excluded individuals with (1) neurological disorders, including brain tumors, stroke, dementia, epilepsy, or seizure disorders; (2) head injury; (3) severe physical illness; (4) current alcohol/substance abuse or dependence; (5) a 17-item Hamilton Depression Rating Scale (HAMD-17) score ≥ 17; or (6) a diagnosis of schizoaffective disorder. Healthy controls were sex-, age-, and education-matched individuals without a history of psychiatric illness or first-degree relatives with diagnosed psychiatric disorders, reported no history of psychiatric medication use, and had no history of alcohol or substance abuse. All participants provided informed consent following a detailed description of the study. The study was approved by the Ethics Committee of Beijing Anding Hospital, affiliated with Capital Medical University (202395FS-2). Clinical assessments PD and GAD were diagnosed on the basis of the DSM-5 via the Mini-International Neuropsychiatric Interview (MINI 7.0.2) (Sheehan et al. 1998 ). Anxiety symptoms were assessed via the the State-Trait Anxiety Inventory (STAI). The STAI is a reliable self-reported anxiety scale comprising two 20-item state and trait anxiety subscales, with scores ≥ 40 indicating clinically significant anxiety (Julian 2011 ). The severity of depressive symptoms was evaluated via the 17-item Hamilton Rating Scale for Depression (HAMD-17) and categorized as no depression (0–7), mild depression (8–16), moderate depression (17–23), or severe depression (≥ 24) (Müller and Dragicevic 2003 ). The Penn State Worry Questionnaire (PSWQ) was used to assess uncontrollable pathological worry for GAD, with higher scores reflecting greater severity of worry (Behar et al. 2003 ). The Hamilton Anxiety Rating Scale (HAM-A) was used to assess the severity of GAD, utilizing a cutoff score of fifteen (Matza et al. 2010 ). The Panic Disorder Severity Scale (PDSS) was used to assess the severity of PD, utilizing a cutoff score of eight (Shear et al. 2001 ). EEG data preprocessing EEG data were collected with a 64-channel Ag-AgCl electrode (ActiCap64 system, Brain Products GmbH, Gilching, Germany) positioned according to the 10–20 international system. The reference electrode was placed at Fpz, with the ground at FCz. Five minutes of resting-state data with eyes closed were continuously recorded at a bandwidth of 1–40 Hz and sampled at 1000 Hz. EEG signals were downsampled to 256 Hz to optimize the processing time. Artifacts were removed through visual inspection, and independent component analysis (ICA) was used to eliminate eye blinks and electrocardiogram artifacts. The corrected channels were visually identified and spline interpolated. The EEG signals were rereferenced to the common average reference before microstate analysis. Microstate analysis The microstate analysis was performed by the “MICROSTATE” toolbox in EEGLAB (Nagabhushan Kalburgi et al. 2024 ). The EEG data were bandpass filtered (2–20 Hz) and processed to identify global field power (GFP) peaks. The GFP was defined as the spatial standard deviation of the average-referenced signal across all electrodes and was calculated as follows: $$\:\text{GFP}\left(t\right)=\sqrt{\frac{{\sum\:}_{i=1}^{n}{{u}_{i}}^{2}}{n}}$$ 1 where 𝑖 represents each electrode, 𝑛 represents the number of electrodes (𝑛 = 64 in this study), and 𝑢 represents the measured voltage of each channel. For individual GFP peaks, topographies were clustered by k-means algorithms for all topographies (Pascual-Marqui et al. 1995 ) to find 2–10 templates using the original maps. Individual templates were then aggregated and reclustered to create group-level templates, which were then aligned and labeled on the basis of standard microstate nomenclature. The common templates A-D were chosen for anxiety patients and healthy controls. The microstate parameters included the mean duration, coverage, occurrence, mean GFP, and transition probabilities (Michel and Koenig 2018 ). The mean duration represents the mean time (ms) that a specific microstate persisted continuously. The time coverage indicates the percentage of cumulative time occupied by a specific microstate. The occurrence reflects the number of appearances of a specific microstate per epoch. Mean GFP is the normalized average of GFP from the microstate time series that corresponds to the average strength of the electric field for a specific microstate. Transition probabilities refers to transition probabilities from one class to another. Microstate-wise functional connectivity analysis Time series data for each of the four microstate categories (A-D) were extracted for all participants. Then, we calculated the FC matrices between the 64 channels for each group within the four microstate classes. The Phase-Locking Value (PLV) was used to quantify FC between all channel pairs in each microstate, as it captures phase synchronization and has been shown to correlate with fMRI-based FC (Rizkallah et al. 2020 ). The PLV was calculated as follows: $$\:PL{V}_{i,j}^{m}=\left|\frac{1}{N}\sum\:_{n=1}^{N}{e}^{\text{i}({{\phi\:}}_{jn}-{{\phi\:}}_{in})}\right|$$ 2 where 𝑖 and j represent the electrode in the pair, n represents the index of the epochs in the experiment, and m represents the specific frequency band. The average PLV of each microstate was calculated by averaging the PLVs across time points labeled with the same microstate label. Statistical and visualization tools Statistical analyses were performed by the Statistical Package for the Social Sciences (SPSS Inc., Versob 26.0, USA). For the analysis of the participants’ demographic and clinical characteristics, continuous variables were analyzed by independent-samples t -test and one-way analysis of variance (ANOVA), whereas categorical variables were assessed by the chi-square test. Group differences in microstate parameters (duration, time coverage, occurrence, mean GFP) were assessed by repeated-measures ANOVA. microstate classes (A-D) were used as within-subject factors, and groups (ADs or HCs/PD, GAD, or HCs) were used as between-subject factors. The Greenhouse–Geisser correction was applied when the sphericity assumption was violated. For significant microstate × group interactions, post hoc comparison of three groups was conducted via the Bonferroni method. Independent samples t -tests and one-way ANOVA were employed to compare differences in transition probabilities between: (1) ADs and HC, and (2) among PD, GAD, and HC groups. For each group, False Discovery Rate (FDR) correction was performed for the p -values of multiple paired t -tests. Furthermore, Pearson correlation analysis was conducted in R (v.4.0.3) using the lm function to explore the relationship between microstate parameters and anxiety symptoms. The anxiety symptoms were assessed through multidimensional anxiety measures, including the STAI, PDSS, PSWQ, and HAMA. The analyses were conducted across the overall anxiety group as well as in specific diagnostic subgroups (GAD and PD). The level of significance was p < 0.05. Whole-brain matrices of the PLVs were examined to identify subnetworks or topological clusters showing significant differences between groups via network-based statistical analysis (NBS) in the GRETNA toolbox (Wang et al. 2015 ). A two-tailed p value < 0.05 was considered statistically significant. A Circos plot was used to present the connectivity relationships between electrodes, with the Circos plot being generated by the MNE-Connectivity package in Python (version 3.7.6) (Gramfort et al. 2013 ). Results Participant characteristics The demographic and clinical characteristics of the three groups are shown in Table 1 . There were no significant differences in age, sex, or education level between the ADs and HCs in addition to PD, GAD, HC groups. For the clinical characteristics, the mean HAMD scores for the PD and GAD groups were below the threshold for moderate depression, at 10.71 and 11.52, respectively. In terms of worry, the GAD group had a significantly higher mean score on the PSQW than the PD group (56.57 vs 49.09; t =-2.21, p = 0.032). In terms of panic symptom severity, the mean PDSS score for the PD group was 11.62 which falls within the range for moderate PD. The mean HAMA score for the GAD group was 17.17, which falls within the range for moderate anxiety (Matza et al. 2010 ) (Table 1 ). Table 1 Demographic and clinical characteristics of the participants Variables PD GAD ADs HC F or χ 2 ( p Value) t or χ 2 ( p Value) Number (N) 35 31 66 39 - - Age (years) (M ± SD) 31.2(9.6) 32.1(6.5) 31.7(5.3) 31.1(13.0) 0.84(0.437) 1.01(0.245) Gender (M/F) 12/23 9/22 21/45 11/28 0.37(0.835) 0.982(0.535) Education (years) (M ± SD) 12.9(2.0) 14.0(1.7) 13.8(1.5) 13.0(2.3) 2.36(0.102) 1.784(0.097) HAM-D (M ± SD) 10.71(4.07) 11.52(13.40) 11.05(3.48) 1.52(1.82) 117.23(0.000 a ) 17.62(0.000 a ) PSWQ (M ± SD) 49.09(11.84) 56.57(7.32) 52.48(6.73) 35.14(10.48) 41.61(0.000 a ) 7.42(0.000 a ) S-AI (M ± SD) 50.23(10.73) 50.77(9.83) 50.50(10.21) 31.66(9.45) 39.81(0.000 a ) 8.96(0.000 a ) T-AI (M ± SD) 49.41(8.68) 50.35(6.50) 50.05(9.45) 32.17(8.51) 56.14(0.000 a ) 10.64(0.000 a ) HAM-A (M ± SD) 17.16(2.90) - - - - - PDSS (M ± SD) 11.62(2.56) - - - - - Note: M ± SD, mean ± standard deviation, M: male, F: female, PSWQ: Penn State Worry Questionnaire; HAM-D: Hamilton Depression Scale, HAM-A: Hamilton Anxiety Scale, PDSS: Panic Disorder Severity Scale, S-AI: State Anxiety Inventory, T-AI: Trait Anxiety Inventory, GAD: generalized anxiety disorder, PD: panic disorder, ADs: anxiety disorders a p <0.05 Comparison of Microstate parameters between anxiety disorders and HCs The four typical EEG microstate topographies (A-D) were identified for the calculation of microstate parameters, following established protocols (Fig. 1 ). On average, the topographies of the four microstate classes explained 70.75%, and 68.2% of the total variance in the ADs and HC groups, respectively, all exceeding the 65% threshold (Michel and Koenig 2018 ). The four microstate parameters were compared between groups by repeated-measures ANOVA, with the EEG microstate (A-D) as a within-subject factor and the group (ADs or HCs) as a between-subject factor. The repeated-measures ANOVA result on mean duration revealed a significant interaction effect between group and microstate class ( F (3, 309) = 3.462, p = 0.017) (Fig. 2 a). Post hoc analyses indicated that the mean duration of microstate D was significantly increased in patients with ADs compared to HCs ( p = 0.025). The result of occurrence revealed no significant main effect of group ( F (1, 103) = 2.708, p = 0.103) and interaction effect between group and microstate class ( F (3, 309) = 1.941, p = 0.128) (Fig. 2 b). The result of time coverage revealed a significant main effect of group ( F (1, 103) = 17.630, p <0.001) and interaction effect between group and microstate class ( F (3, 309) = 4.134, p = 0.009). Simple effect analyses revealed that patients with ADs showed significantly decreased time coverage in microstates A compared to HCs ( p <0.001) (Fig. 2 c). The result of mean GFP revealed no significant main effect of group ( F (1, 103) = 2.416, p = 0.123), but a significant interaction effect between group and microstate class ( F (3, 309) = 3.165, p = 0.043). The simple effect analysis indicated no significant differences in mean GFP across microstates between anxiety group and HCs (Fig. 2 d) (Table 2 ). Table 2 Result of repeated-measures ANOVA for microstate parameter between anxiety disorder patients and healthy control groups Effect F p η 2 Post hoc Mean Duration Group 2.585 0.111 0.032 - Class*group 3.462 0.017 0.038 Microstate D: ADs >HC Occurrence Group 2.708 0.057 0.035 - Class*group 1.941 0.103 0.024 - Time coverage Group 17.630 0.000 0.150 - Class*group 4.134 0.009 0.040 Microstate A: ADs <HC Mean GFP Group 2.416 0.123 0.023 - Class*group 3.165 0.043 0.030 - ADs: anxiety disorders, HC:healthy controls; GFP: global field power; η 2 :eta squared Comparison of Microstate parameters between GAD patients, PD patients and HCs The four microstate classes explained 70.65%, 70.9%, and 68.2% of the total variance in the PD, GAD, and HC groups, respectively. The four microstate parameters were compared between groups by repeated-measures ANOVA, with the EEG microstate (A-D) as a within-subject factor and the group (PD, GAD, or HCs) as a between-subject factor. The analysis revealed a significant interaction effect between group and microstate class for mean duration ( F (6, 102) = 2.243, p = 0.039). Post hoc analyses with Bonferroni correction indicated that the mean duration of microstate D was significantly increased in patients with GAD compared to HCs ( p = 0.024), whereas no significant differences in mean duration were observed for microstates A, B, or C among the PD groups (Fig. 3 a). Repeated-measures ANOVA revealed no significant differences in occurrence between groups or across microstate classes (Fig. 3 b). Additionally, there were no significant main effects of group or interactions between groups and microstate classes on time coverage and mean GFP (Fig. 3 c, Fig. 3 d) (Table 3 ). Detailed microstate parameters are provided in Table S1 . Table 3 Results of repeated-measures ANOVA for microstate parameters in patients with PD, GAD, and HC. Effect F p η 2 Post hoc Mean Duration Group 2.217 0.114 0.042 - Class*group 2.243 0.039 0.042 Microstate D: GAD >HC Occurrence Group 2.176 0.119 0.041 - Class*group 1.647 0.134 0.031 - Time coverage Group 0.471 0.626 0.009 - Class*group 1.889 0.082 0.036 - Mean GFP Group 1.452 0.239 0.028 - Class*group 1.932 0.083 0.036 - GAD: Generalized anxiety disorder, PD: Panic disorder; GFP: global field power; η 2 :eta squared Comparison of Microstate transition between anxiety disorders and HCs The results showed that patients with ADs showed lower transition probabilities in A to B ( t =-2.507, p = 0.007) and B to A ( t =-2.223, p = 0.017) compared to the HCs group, while patients with ADs showed higher transition probabilities in C to D ( t = 2.105, p = 0.019) and D to C ( t = 1.993, p = 0.026) compared to the HCs group. However, applying FDR correction for multiple comparisons, none of the observed differences reached statistical significance (Table 4 ). Table 4 Comparison of microstate transition probabilities between ADs and HCs Variable AD HC t p FDR A to B 7.18(2.33) 8.70(2.98) -2.51 0.007 0.076 A to C 7.87(2.45) 8.26(2.36) -0.84 0.405 0.810 A to D 7.70(2.55) 7.61(2.45) 0.19 0.848 0.917 B to A 7.39(2.78) 8.73(2.87) -2.22 0.017 0.076 B to C 8.40(3.57) 8.56(2.24) -0.32 0.752 0.917 B to D 8.23(2.97) 7.97(2.45) 0.53 0.602 0.903 C to A 7.77(2.43) 8.36(2.34) -1.28 0.205 0.492 C to B 8.54(3.55) 8.60(2.29) -0.11 0.917 0.917 C to D 10.50(2.66) 8.83(3.31) 2.11 0.019 0.076 D to A 7.61(2.53) 7.48(2.50) 0.26 0.799 0.917 D to B 8.26(2.82) 7.97(2.55) 0.55 0.581 0.903 D to C 10.54(2.52) 8.92(3.49) 1.99 0.026 0.078 GAD: generalized anxiety disorder, PD: panic disorder, HCs: healthy controls; FDR:False Discovery Rate Comparison of Microstate transition between GAD, PD and HCs Analysis of transition probabilities revealed significant group differences from microstates A to B ( F (2,102) = 3.94, p = 0.022) and B to A ( F (2,102) = 3.18, p = 0.046). Analysis of transition probabilities revealed significant group differences from microstates C to D ( F (2,102) = 3.21, p = 0.045). However, the results did not reach statistical significance after FDR correction (Table 5 ). Table 5 Comparison of microstate transition probabilities among PD, GAD, and HC groups. Variable PD GAD HC F p -value *q*-value (FDR) A to B 7.36(2.73) 6.99(2.35) 8.70(2.98) 3.94 0.022 0.184 A to C 7.58(2.37) 8.20(2.27) 8.26(2.36) 0.95 0.390 0.585 A to D 7.86(2.65) 7.52(2.25) 7.61(2.45) 0.17 0.846 0.924 B to A 7.53(2.75) 7.23(2.28) 8.73(2.87) 3.18 0.046 0.184 B to C 8.29(2.71) 8.51(2.98) 8.56(2.24) 0.11 0.899 0.924 B to D 8.66(2.45) 7.75(2.54) 7.97(2.45) 1.23 0.296 0.578 C to A 7.57(2.21) 8.00(2.37) 8.36(2.34) 1.10 0.337 0.578 C to B 8.37(3.76) 8.73(3.22) 8.60(2.29) 0.14 0.867 0.924 C to D 10.15(3.29) 10.89(3.88) 8.83(3.31) 3.21 0.045 0.184 D to A 7.70(2.55) 7.51(2.33) 7.48(2.50) 0.08 0.924 0.924 D to B 8.72(2.70) 7.75(2.55) 7.97(2.55) 1.31 0.273 0.578 D to C 10.22(3.41) 10.91(3.72) 8.92(3.49) 2.86 0.062 0.186 GAD: generalized anxiety disorder; PD: panic disorder; HCs: healthy controls; FDR: False Discovery Rate Correlation between microstate parameters and transitions with anxiety symptom Correlation analysis in the anxiety group revealed that T-AI scores were positively associated with time coverage C ( R = 0.353, p = 0.0005, Fig. 5 a) and mean duration C ( R = 0.351, p = 0.005, Fig. 5 b), but negatively associated with occurrence A and B ( R = -0.272, p = 0.033, Fig. 5 c; R = -0.310, p = 0.014, Fig. 5 d ). In addition, the PSWQ scores were negatively associated with time coverage B ( R = -0.276, p = 0.030, Fig. 5 e). Similarly, in PD group, T-AI scores showed significant positive correlations with both time coverage C ( R 2 = 0.247, p = 0.005) and mean duration C( R 2 = 0.123, p = 0.0048). but negatively associated with occurrence A, occurrence B, and time coverage B ( R² = 0.074, p = 0.0049; R² = 0.271, p = 0.024; R² = 0.148, p = 0.033). In addition, the PSWQ scores were positively associated with mean duration C and time coverage C, but negatively associated with occurrence B ( R² = 0.127, p = 0.042; R² = 0.176, p = 0.015; R² = 0.248, p = 0.003). In the GAD group, a significant positive association was observed between HAM-A scores and time coverage C ( R 2 = 0.228, p = 0.016). Detailed correlation coefficients for overall, as well as for PD and GAD, are provided in Supplementary Tables S2, S3, S4. Between-group comparison of microstate-based FC Group differences in microstate-based FCs were analyzed via whole-brain NBS for each channel in each microstate. Compared with HCs, GAD patients presented an increasing trend in the PLV in microstate A, especially between the right parietal-occipital and right frontal-central areas (Fig. 5 a). In addition, microstate D showed stronger connections between the frontal and parietal‒occipital electrodes in the GAD group than in the HCs (Fig. 5 b). FC differences in microstates B and D were observed between PD patients and HCs, but these differences were not as pronounced as the differences between GAD patients and HCs. In microstate B, patients with PD presented stronger PLVs between frontal (FPz, AF4, F1, Fz) and left parietal‒occipital (PO3, P5) electrode sites (Fig. 5 c). In microstate D, increased PLV was observed between frontal (FP2, AF4, F1) and parietal-occipital (PO3, Oz, PO8) electrode sites (Fig. 5 d). Differences between PD patients and GAD patients were observed only in microstate A. Patients with PD showed stronger FC between electrode pairs C6-TP7 and Pz-TP7 but weaker FC between CP5-Pz and C5-Pz than patients with GAD (Fig. 5 e). Discussion This study provides the first comprehensive comparison of microstate parameters and microstate-based FC during resting-state EEG in patients with ADs, as well as PD and GAD specifically. Our study revealed common microstate characteristics in ADs, specifically an increase in microstate D and decreases in microstates A. Furthermore, microstate C parameters were positively associated with anxiety severity in the overall sample, PD, and GAD groups, whereas microstates A and B showed negative associations.In addition, Our findings revealed distinct patterns in GAD and PD patients in FC pattern. FC differences between GAD patients and PD patients were observed in microstate A, which involves sensory integration. Previous EEG/fMRI studies conducted in healthy individuals have shown that microstate D is associated with the ventral frontal cortex and temporoparietal regions, primarily in the dorsal attention network (DAN), which involves the allocation and maintenance of attentional resources. This study revealed an increased duration of microstate D in patients with ADs, specially in GAD, which might reflect greater activation of the frontal‒parietal attention network. This finding is in line with previous studies suggesting that patients with GAD are associated with cognitive control deficits and exhibit difficulty in shifting attention from potential threat stimuli (Bashford-Largo et al. 2021 ; Blair et al. 2012 ). A previous fMRI study reported that anxiety severity was associated with greater activation in the ventrolateral prefrontal cortex, left posterior temporal sulcus and temporoparietal junction, suggesting heightened stimulus-driven attention to distractors (Díaz et al. 2025 ). This study revealed increased microstate D in GAD patients but not in PD patients, suggesting that attention control—particularly deficits in attentional shifting—is more pronounced in individuals with GAD (Wei et al. 2021 ). The fMRI results also revealed that GAD patients displayed disorder-specific posterior activity, possibly reflecting exaggerated attention regulation and difficulty in shifting attention away from threats (Buff et al. 2016 ). Patients with ADs exhibited significantly decreased time coverage of microstate A. The microstate A is primarily associated with activiation in superior and middle temporal gyri, potentially corresponding to auditory network (Murphy et al. 2020 ). These results support the disturbance of the sensory network in ADs and aligns with prior research in which anxiety and sensory processing difficulties were found to be associated (McCombs et al. 2024 ; Storozheva et al. 2021 ). Contrary to our hypotheses, the microstate peremeters did not significantly differ between PD and HCs, nor between PD and GAD group, suggesting that microstate patterns may reflect common anxiety-related network dysfunction rather than disorder-specific signatures and may have limited discriminant validity for capturing distinct neural dynamics across anxiety disorder subtypes. The correlation analysis demonstrated a significant positive correlation between microstate C parameters and anxiety symptoms assessed by STAI and HAMA in patients with ADs. This finding aligns with previous research on social anxiety, which reported that high social characterized by heightened self-referential processing is associated with microstate C (Zhang et al. 2025 ). According to Britz et al., microstate C has been demonstrated to correlate with brain regions associated with self-referential processing and emotion regulation, including areas assigned to the the default mode network(DMN) (Britz et al. 2010 ). Additionally, previous studies have suggested that self-referential processes within the DMN are closely associated with anxiety symptoms (Burdwood et al. 2016 ). Our study provides further evidence that anxiety symptoms may be associated with self-referential processing. In contrast, microstate A and B parameters exhibited negative correlations with symptom severity. These findings potentially reflect anxiety-mediated impairments in sensory network. Moreover, the results of correlation analysis are consistent with our prior finding demonstrating reduced parameters in microstate A and B among anxiety disorders. We further compared the average microstate connectivity among GAD, PD, and HCs to explore the neural mechanisms underlying microstates. FC differences between GAD patients and healthy controls were primarily observed in microstates A and D. Compared with HCs, GAD patients presented increased connections in microstate D, suggesting abnormal activation of the attention network. Moreover, the activation in FC was primarily observed between frontal and parietal regions, reflecting abnormalities in top-down control in patients with GAD (Mochcovitch et al. 2014 ). Microstate A has been associated not only with auditory processing (Custo et al. 2017 ) but also with visual processing and brain arousal (Tarailis et al. 2024 ). Previous studies have reported associations between anxiety disorders and sensory processing difficulties (McCombs et al. 2024 ). This study revealed increased FC in microstate A in GAD patients compared with HCs, reflecting increased sensitivity to environmental sounds and sustained hypervigilance. Berggren et al. utilized a visual detection task and demonstrated that anxiety is associated with improved visual detection, suggesting that anxiety may modulate sensory processing (Berggren et al. 2015 ). The FC differences between PD patients and healthy controls were primarily observed in microstates B and D. Activated and deactivated connections in microstate B were identified between AF4/FP2 and Oz/POz/PO3/PO4, which can be mapped to the right frontal cortex (Brodmann areas: BA 9, BA 10) and occipital cortex (BA 18 L, BA 19) (Kaga and Minami 2017 ). These brain regions are involved primarily in higher-level cognitive functions and visual information processing. Abnormal FC in microstate B between the frontal and occipital cortex suggests aberrant visual processing in patients with PD. Additionally, the PD group exhibited increased activation of AF4/Fpz/F1/Fz and PO3/P5 in microstate D, corresponding to the frontal cortex (BA 9R, BA 10 L, BA 6 L, BA 8 L) and left parietal lobe (BA 19, BA 39), which are involved in anterior‒posterior cortical attention processes (Babiloni et al. 2011 ). The enhanced FC between these areas implies impaired visual processing in PD patients. An fMRI study also reported a potential correlation between sensory information processing and anxiety symptoms in PD patients (Pfleiderer et al. 2010 ). Furthermore, an event-related fMRI study revealed that brain responses to visual threats are directly related to anxiety symptoms in PD patients (Feldker et al. 2016 ). These findings support the notion that the severity of panic disorder is related to abnormalities in sensory information processing. The FC differences between the PD and GAD groups were observed in microstate A. Compared with the GAD group, PD patients presented deactivated connections in the right inferior parietal (BA 39) and superior parietal (BA 7) lobules, which are involved in language processing and sensory associations. In contrast, increased activation was observed in the left inferior parietal (BA 40) and lateral temporal (BA 21) lobes in the PD patients compared with the GAD patients. BA 40 and BA 21 are associated with mathematics performance and visual processing, respectively. These findings suggest differences in cognitive processing and sensory integration between GAD patients and PD patients. Gordeev et al. used neuropsychological and neurophysiological methods to demonstrate distinct cognitive impairments between GAD and PD patients (Gordeev et al. 2013 ). However, further research is needed to validate these results. Limitations This study has several noteworthy limitations. First, the absence of longitudinal data in this cross-sectional study limited our ability to track developmental trajectories of EEG microstate and FC pattern alterations. Additionally, this study employed EEG data exclusively, future studies could combine EEG with resting-state fMRI to provide complementary insights of temporal and spatial perspectives on brain network dynamics. Conclusions This study investigated the resting-state EEG microstate dynamics in individuals with patients with ADs overall and separately in patients with GAD and PD. Our study revealed an increased microstate D and reduced microstates A in the broad anxiety cohort. Prolonged microstate D duration was also observed in GAD patients. Furthermore, the exacerbation of anxiety symptoms may be associated with self-referential and sensory processing networks. PD and GAD patients presented distinct patterns of FC across microstate A, indicating disorder-specific impairments in sensory processing. These findings enhance our understanding of the neural mechanisms underlying ADs and suggest that microstate-specific FC patterns may serve as potential clinical biomarkers for PD and GAD. Abbreviations PD: Panic disorder GAD: Generalized anxiety disorder EEG: Electroencephalography DSM: Diagnostic and Statistical Manual of Mental Disorders PLV: Phase-Locking Value GFP: Global Field Power NBS: Network-based statistical analysis ML: Machine learning SVM: Support vector machine AUC: Area under the curve Declarations Funding: This work was supported by the Science and Technology Innovation 2030 -Major Project on Brain Science and Brain-like Research (grant number 2021ZD0202004) and Research on Optimization of Treatment and Brain Function of Acupuncture Therapy for Patients with Panic Disorders (grant number 2020-1-2121). Authors' contributions: Conceptualization, D.F.Y.; methodology, D.F.Y. and P.C.W.; resources, X.Y.Y. and Z.J.L.; data curation, D.F.Y; writing—original draft preparation, D.F.Y; writing—review and editing, X.Y.Y. and W.P.H.; visualization, D.F.Y.; supervision, X.Y.Y. and Z.J.L.; funding acquisition, Z.J.L. All authors have read and agreed to the published version of the manuscript. Acknowledgments: This manuscript has been approved by all the authors, who affirm the integrity of the work. All the authors made substantial contributions to the study design, data collection, analysis, and/or interpretation of the data, and most contributed to the writing and intellectual content of the article. Availability of data and materials: The data supporting the findings of this study are available from the corresponding author upon reasonable request. 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Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 28 Feb, 2026 Read the published version in Brain Topography → Version 1 posted Editorial decision: Revision requested 29 Oct, 2025 Reviews received at journal 28 Jul, 2025 Reviewers agreed at journal 19 Jul, 2025 Reviewers agreed at journal 14 Jul, 2025 Reviewers invited by journal 14 Jul, 2025 Editor assigned by journal 04 Jul, 2025 Submission checks completed at journal 03 Jul, 2025 First submitted to journal 03 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7035279","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485199462,"identity":"24a1530a-b109-43f0-8f47-26ae4e6db223","order_by":0,"name":"Danfeng Yuan","email":"","orcid":"","institution":"National Center for Mental Disorders, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Danfeng","middleName":"","lastName":"Yuan","suffix":""},{"id":485199463,"identity":"736e11fe-107d-4866-b3be-2915ad96afc6","order_by":1,"name":"Xiangyun Yang","email":"","orcid":"","institution":"National Center for Mental Disorders, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiangyun","middleName":"","lastName":"Yang","suffix":""},{"id":485199464,"identity":"403d97fc-c0df-4380-a496-9037eaa4eddc","order_by":2,"name":"Pengchong Wang","email":"","orcid":"","institution":"National Center for Mental Disorders, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Pengchong","middleName":"","lastName":"Wang","suffix":""},{"id":485199465,"identity":"80c30ddb-465e-4d64-89c9-0582d0c57fab","order_by":3,"name":"Wenpeng Hou","email":"","orcid":"","institution":"National Center for Mental Disorders, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenpeng","middleName":"","lastName":"Hou","suffix":""},{"id":485199466,"identity":"c2ac13c9-ac18-405f-b120-f0d72521a32d","order_by":4,"name":"Zhanjiang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYBACPmYGBoMPFVAeDzFa2IBaDGecIUkLEDPztpGkhZ39QeHMeXbyujMSGB+8bWOQNyfCYQkGH7clG267kcBsOLeNwXBnA2EtBwxnbjuQYHYjgU0a6MIEgwMEtTA2GPPOAWth/02kFmYGY94GiC3MRGphAwbyMaBfzjxslpxzTsJwAyEt/PzHnxl8qLGTNzuefPDDmzIbeYK2gCwygNCMDUBCgrB6IGB+QJSyUTAKRsEoGLkAAHe+ONUtEWAWAAAAAElFTkSuQmCC","orcid":"","institution":"National Center for Mental Disorders, Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zhanjiang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-07-03 07:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7035279/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7035279/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10548-026-01179-6","type":"published","date":"2026-02-28T15:58:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87037495,"identity":"2c9cb11b-1dd9-4cb3-b49c-b320704e4756","added_by":"auto","created_at":"2025-07-18 13:24:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":201708,"visible":true,"origin":"","legend":"\u003cp\u003eTopographic distributions of each microstate class in PD, GAD, and HC. Red and blue indicate positive and negative values, respectively. The polarity is ignored during microstate analysis.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7035279/v1/fdd8b793cd9bc0906e163563.png"},{"id":87037451,"identity":"d4a1c0a9-8206-4e0a-bf2e-0ac2847b575f","added_by":"auto","created_at":"2025-07-18 13:23:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":242323,"visible":true,"origin":"","legend":"\u003cp\u003eEEG microstate duration, occurrence, contribution, and mean GFP analysis results for ADs and HC groups. (a) Mean duration between ADs and HC groups. (b) Time coverage between ADs and HC groups. (c) Occurrence rate between ADs and HC groups. (d) Global field power between ADs and HC groups.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7035279/v1/72e57d69092592713c2ef5db.png"},{"id":87037499,"identity":"6a05dd54-36ae-4e5e-af64-2482047cc48d","added_by":"auto","created_at":"2025-07-18 13:24:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":247510,"visible":true,"origin":"","legend":"\u003cp\u003eEEG microstate duration, occurrence, contribution, and mean GFP analysis results for PD, GAD, and HC groups. (a) Mean duration between PD, GAD, and HC groups. (b) Time coverage between PD, GAD, and HC groups. (c) Occurrence rate between PD, GAD, and HC groups. (d) Global field power between PD, GAD, and HC groups.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7035279/v1/c82dbe83ae5e6fff0adaa4c3.png"},{"id":87037460,"identity":"eab1ce20-453d-4ee1-a1ce-fd4be27e5a54","added_by":"auto","created_at":"2025-07-18 13:23:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":213936,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between Microstate parameters and anxiety symptoms in overall anxiety. The solid lines indicate linear regression fits, with the shaded areas representing the 95% confidence intervals. (a) Association between time coverage of microstate A and T-AI scores. (b) Association between mean duration of microstate C and T-AI scores. (c) Association between occurrence of microstate A and T-AI scores. (d) Association between occurrence of microstate B and T-AI scores. (e) The relationship between time coverage B and PSWQ scores. T-AI: Trait Anxiety Inventory; PSWQ: Penn State Worry Questionnaire.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7035279/v1/8500cffc4641e4d7c62d524d.png"},{"id":87037463,"identity":"b6f84b56-fe5f-4fff-89d0-1e6ac581cbe4","added_by":"auto","created_at":"2025-07-18 13:23:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":312387,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant differences in functional connectivity (FC) of microstates for PD, GAD, and HC groups (P \u0026lt; 0.05, NBS correction) (a) Comparison of FC between GAD and HC in microstate A. (b) Comparison of FC between GAD and HC in microstate D. (c) Comparison of FC between PD and HC in microstate B. (d) Comparison of FC between PD and HC in microstate D. (e) Comparison of FC between PD and GAD in microstate A. Red color indicates greater connectivity, and blue color indicates weaker connectivity.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7035279/v1/cd08165d855b041a591665d5.png"},{"id":103765971,"identity":"22de6d75-9622-46ae-976b-5ee3ab8f826d","added_by":"auto","created_at":"2026-03-02 16:11:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2350152,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7035279/v1/ae409596-59cd-4336-afee-137c5a50770f.pdf"},{"id":87037455,"identity":"7eb1084b-7324-4b28-b0a7-ec3cc0c16c1b","added_by":"auto","created_at":"2025-07-18 13:23:56","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":32327,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7035279/v1/c78230b00352c1417d6d933d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evidence from EEG of Abnormal Functional Connectivity and Microstates in GAD and PD","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAnxiety disorders (ADs) constitute the most prevalent mental health condition globally (Penninx et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), with panic disorder (PD) and generalized anxiety disorder (GAD) representing the two most common anxiety disorders. GAD is characterized by persistent, uncontrolled worry, whereas PD presents as sudden episodes of extreme fear accompanied by physical symptoms and autonomic nervous system arousal (Locke et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The 22.2% comorbidity rate between PD and GAD (Turki et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), despite their distinct symptoms, suggests a shared pathophysiology that complicates differential diagnosis. However, the pathophysiological mechanisms of GAD and PD remain incompletely understood. Research indicates that ADs are commonly associated with abnormalities in attentional control and heightened vigilance toward threatening stimuli (Price et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Among them, individuals with GAD \u0026mdash; characterized by worry as the core symptom \u0026mdash; exhibit more pronounced impairments in attentional control. Poor attentional control may contribute to increased anxiety and inefficient attention shifting (Barthel et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Magnetic resonance imaging (MRI)-based functional network studies further supported frontoparietal alternations in the attentional network among individuals with ADs (Lai \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with pronounced effects observed in individuals with GAD (Buff et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Furthermore, anxiety appears to impair sensory processing through maladaptive amplification of ascending prediction error signals. This heightened signaling may compromise accurate threat detection by distorting stimulus processing, potentially underpinning some deleterious effects of anxiety on higher-order cognition (Cornwell et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In particular, the advanced fear network model incorporating sensory regions of the temporal, occipital and parietal lobes was confirmed to be associated with PD pathophysiology (Lai \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCompared with MRI, electroencephalography (EEG) is a more cost-effective and noninvasive method that offers superior temporal resolution for directly tracking the dynamic patterns of brain electrical activity (Chizhikova \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Among EEG analysis methods, resting-state functional connectivity (rsFC) and EEG microstate are robust approaches that provide brain activity and connectivity information underlying neurological and psychiatric diseases.\u003c/p\u003e\u003cp\u003eMicrostate is a novel approach in resting-state EEG research that evaluates transient global brain activity states with stable topographies lasting approximately 60\u0026ndash;120 ms (Michel and Koenig \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The spatial stability of microstates enables their use as indicators of whole-brain functional states in health and disease. Four predominant microstate classes (A-D) are consistently observed across populations (Britz et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). These microstates have been shown to correlate with specific neural networks through synchronized EEG-fMRI studies: auditory (microstate A), visual (microstate B), saliency (microstate C), and frontal-apical networks (microstate D) (Van de Ville et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). A growing number of studies have demonstrated abnormal EEG microstate characteristics in patients with neuropsychiatric disorders, including depression disorder (Cao et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), schizophrenia (Thirioux et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and bipolar disorders (Wang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, there are relatively few studies examining EEG microstate features in ADs and different types of ADs. To date, only two studies\u0026mdash;with limited sample sizes and inconsistent findings have investigated microstate characteristics in PD patients and GAD patients separately (Kikuchi et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The EEG microstate patterns in ADs\u0026mdash;both generally and in PD/GAD specifically\u0026mdash;remain understudied. In additional to microstates, EEG-based FC enables direct measurement of neural oscillations. This approach captures neural oscillations generated by synchronized rhythmic firing. The neural oscillations across different frequency bands reflect distinct neural activity within different brain structures and brain states. Evidence from EEG-based FC studies has revealed aberrant frequency-dependent FC in GAD patients, including reduced FC in the beta band in the left hemisphere (Mou et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and decreased long-range interactions between the frontal cortex and other brain areas across all frequency bands (Shen et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In contrast, EEG-based FC abnormalities in PD remain understudied. Combining microstate analysis with FC measures enables the computation of FC within specific microstate time windows, providing a more comprehensive understanding of the mechanisms underlying microstates (Michel and Koenig \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, no studies have investigated the potential differences in microstate and microstate-specific FC between PD and GAD patients.\u003c/p\u003e\u003cp\u003eExisting studies suggest that there may be a potential correlation between microstate dynamic features and the severity of clinical symptoms. For example, Xue et al. revealed that depression symtoms were correlated with microstate B, whereas, the anxiety symptoms were correlated with the microstate E (Xue et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Another study in low social anxiety group revealed that the transition probability between microstates B and C was significantly negatively correlated with social anxiety symptoms (Zhang et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The correlation analyses between anxiety symptoms and neurodynamic parameters may provide important clues to understanding the neural mechanisms of anxiety symptoms. However, current research findings remain inconsistent, and studies have primarily focused on subclinical samples, whereas research in anxiety disorder patients is notably lacking.\u003c/p\u003e\u003cp\u003eIn general, this study aims to characterize EEG microstate patterns in ADs generally as well as PD and GAD separately. In addition, the present study will further explore the association between anxiety symptom severity and microstate dynamic features in ADs overall, as well as in PD and GAD separately.The FC under microstate was also analysis to examine whether there were disorder-specific disruptions in attentional control and sensory networks among PD and GAD patients.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eParticipants and procedure\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThirty-five patients with PD, 31 with GAD, and 39 healthy controls (HCs) were recruited between May 2023 and October 2024 from the clinic at Beijing Anding Hospital. The inclusion criteria were as follows: (1) a primary diagnosis of GAD or PD according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5); (2) no changes in psychiatric medications in the past four weeks; (3) aged 18\u0026ndash;60 years; and (4) right-handedness. We excluded individuals with (1) neurological disorders, including brain tumors, stroke, dementia, epilepsy, or seizure disorders; (2) head injury; (3) severe physical illness; (4) current alcohol/substance abuse or dependence; (5) a 17-item Hamilton Depression Rating Scale (HAMD-17) score\u0026thinsp;\u0026ge;\u0026thinsp;17; or (6) a diagnosis of schizoaffective disorder. Healthy controls were sex-, age-, and education-matched individuals without a history of psychiatric illness or first-degree relatives with diagnosed psychiatric disorders, reported no history of psychiatric medication use, and had no history of alcohol or substance abuse. All participants provided informed consent following a detailed description of the study. The study was approved by the Ethics Committee of Beijing Anding Hospital, affiliated with Capital Medical University (202395FS-2).\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical assessments\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePD and GAD were diagnosed on the basis of the DSM-5 via the Mini-International Neuropsychiatric Interview (MINI 7.0.2) (Sheehan et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Anxiety symptoms were assessed via the the State-Trait Anxiety Inventory (STAI). The STAI is a reliable self-reported anxiety scale comprising two 20-item state and trait anxiety subscales, with scores\u0026thinsp;\u0026ge;\u0026thinsp;40 indicating clinically significant anxiety (Julian \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The severity of depressive symptoms was evaluated via the 17-item Hamilton Rating Scale for Depression (HAMD-17) and categorized as no depression (0\u0026ndash;7), mild depression (8\u0026ndash;16), moderate depression (17\u0026ndash;23), or severe depression (\u0026ge;\u0026thinsp;24) (M\u0026uuml;ller and Dragicevic \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The Penn State Worry Questionnaire (PSWQ) was used to assess uncontrollable pathological worry for GAD, with higher scores reflecting greater severity of worry (Behar et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The Hamilton Anxiety Rating Scale (HAM-A) was used to assess the severity of GAD, utilizing a cutoff score of fifteen (Matza et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The Panic Disorder Severity Scale (PDSS) was used to assess the severity of PD, utilizing a cutoff score of eight (Shear et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eEEG data preprocessing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEEG data were collected with a 64-channel Ag-AgCl electrode (ActiCap64 system, Brain Products GmbH, Gilching, Germany) positioned according to the 10\u0026ndash;20 international system. The reference electrode was placed at Fpz, with the ground at FCz. Five minutes of resting-state data with eyes closed were continuously recorded at a bandwidth of 1\u0026ndash;40 Hz and sampled at 1000 Hz. EEG signals were downsampled to 256 Hz to optimize the processing time. Artifacts were removed through visual inspection, and independent component analysis (ICA) was used to eliminate eye blinks and electrocardiogram artifacts. The corrected channels were visually identified and spline interpolated. The EEG signals were rereferenced to the common average reference before microstate analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMicrostate analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe microstate analysis was performed by the \u0026ldquo;MICROSTATE\u0026rdquo; toolbox in EEGLAB (Nagabhushan Kalburgi et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The EEG data were bandpass filtered (2\u0026ndash;20 Hz) and processed to identify global field power (GFP) peaks. The GFP was defined as the spatial standard deviation of the average-referenced signal across all electrodes and was calculated as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\text{GFP}\\left(t\\right)=\\sqrt{\\frac{{\\sum\\:}_{i=1}^{n}{{u}_{i}}^{2}}{n}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u0026#119894; represents each electrode, \u0026#119899; represents the number of electrodes (\u0026#119899; = 64 in this study), and \u0026#119906; represents the measured voltage of each channel. For individual GFP peaks, topographies were clustered by k-means algorithms for all topographies (Pascual-Marqui et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) to find 2\u0026ndash;10 templates using the original maps. Individual templates were then aggregated and reclustered to create group-level templates, which were then aligned and labeled on the basis of standard microstate nomenclature. The common templates A-D were chosen for anxiety patients and healthy controls. The microstate parameters included the mean duration, coverage, occurrence, mean GFP, and transition probabilities (Michel and Koenig \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The mean duration represents the mean time (ms) that a specific microstate persisted continuously. The time coverage indicates the percentage of cumulative time occupied by a specific microstate. The occurrence reflects the number of appearances of a specific microstate per epoch. Mean GFP is the normalized average of GFP from the microstate time series that corresponds to the average strength of the electric field for a specific microstate. Transition probabilities refers to transition probabilities from one class to another.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMicrostate-wise functional connectivity analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTime series data for each of the four microstate categories (A-D) were extracted for all participants. Then, we calculated the FC matrices between the 64 channels for each group within the four microstate classes. The Phase-Locking Value (PLV) was used to quantify FC between all channel pairs in each microstate, as it captures phase synchronization and has been shown to correlate with fMRI-based FC (Rizkallah et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The PLV was calculated as follows:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:PL{V}_{i,j}^{m}=\\left|\\frac{1}{N}\\sum\\:_{n=1}^{N}{e}^{\\text{i}({{\\phi\\:}}_{jn}-{{\\phi\\:}}_{in})}\\right|$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u0026#119894; and j represent the electrode in the pair, n represents the index of the epochs in the experiment, and m represents the specific frequency band. The average PLV of each microstate was calculated by averaging the PLVs across time points labeled with the same microstate label.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical and visualization tools\u003c/b\u003e\u003c/p\u003e\u003cp\u003eStatistical analyses were performed by the Statistical Package for the Social Sciences (SPSS Inc., Versob 26.0, USA). For the analysis of the participants\u0026rsquo; demographic and clinical characteristics, continuous variables were analyzed by independent-samples \u003cem\u003et\u003c/em\u003e-test and one-way analysis of variance (ANOVA), whereas categorical variables were assessed by the chi-square test. Group differences in microstate parameters (duration, time coverage, occurrence, mean GFP) were assessed by repeated-measures ANOVA. microstate classes (A-D) were used as within-subject factors, and groups (ADs or HCs/PD, GAD, or HCs) were used as between-subject factors. The Greenhouse\u0026ndash;Geisser correction was applied when the sphericity assumption was violated. For significant microstate \u0026times; group interactions, post hoc comparison of three groups was conducted via the Bonferroni method. Independent samples \u003cem\u003et\u003c/em\u003e-tests and one-way ANOVA were employed to compare differences in transition probabilities between: (1) ADs and HC, and (2) among PD, GAD, and HC groups. For each group, False Discovery Rate (FDR) correction was performed for the \u003cem\u003ep\u003c/em\u003e-values of multiple paired \u003cem\u003et\u003c/em\u003e-tests. Furthermore, Pearson correlation analysis was conducted in R (v.4.0.3) using the lm function to explore the relationship between microstate parameters and anxiety symptoms. The anxiety symptoms were assessed through multidimensional anxiety measures, including the STAI, PDSS, PSWQ, and HAMA. The analyses were conducted across the overall anxiety group as well as in specific diagnostic subgroups (GAD and PD). The level of significance was \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003eWhole-brain matrices of the PLVs were examined to identify subnetworks or topological clusters showing significant differences between groups via network-based statistical analysis (NBS) in the GRETNA toolbox (Wang et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). A two-tailed \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. A Circos plot was used to present the connectivity relationships between electrodes, with the Circos plot being generated by the MNE-Connectivity package in Python (version 3.7.6) (Gramfort et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eParticipant characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe demographic and clinical characteristics of the three groups are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There were no significant differences in age, sex, or education level between the ADs and HCs in addition to PD, GAD, HC groups. For the clinical characteristics, the mean HAMD scores for the PD and GAD groups were below the threshold for moderate depression, at 10.71 and 11.52, respectively. In terms of worry, the GAD group had a significantly higher mean score on the PSQW than the PD group (56.57 vs 49.09; \u003cem\u003et\u003c/em\u003e=-2.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032). In terms of panic symptom severity, the mean PDSS score for the PD group was 11.62 which falls within the range for moderate PD. The mean HAMA score for the GAD group was 17.17, which falls within the range for moderate anxiety (Matza et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eDemographic and clinical characteristics of the participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGAD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eADs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e or χ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(\u003cem\u003ep\u003c/em\u003e Value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e or χ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(\u003cem\u003ep\u003c/em\u003e Value)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber (N)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years) (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.2(9.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.1(6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.7(5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.1(13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.84(0.437)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.01(0.245)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (M/F)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12/23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9/22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21/45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11/28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.37(0.835)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.982(0.535)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation (years) (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.9(2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.0(1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.8(1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.0(2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.36(0.102)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.784(0.097)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHAM-D (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.71(4.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.52(13.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.05(3.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.52(1.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e117.23(0.000\u003csup\u003ea\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.62(0.000\u003csup\u003ea\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePSWQ (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49.09(11.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.57(7.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.48(6.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35.14(10.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e41.61(0.000\u003csup\u003ea\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.42(0.000\u003csup\u003ea\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS-AI (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50.23(10.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.77(9.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.50(10.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.66(9.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e39.81(0.000\u003csup\u003ea\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.96(0.000\u003csup\u003ea\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT-AI (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49.41(8.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.35(6.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.05(9.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32.17(8.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e56.14(0.000\u003csup\u003ea\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.64(0.000\u003csup\u003ea\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHAM-A (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.16(2.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePDSS (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.62(2.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, M: male, F: female, PSWQ: Penn State Worry Questionnaire; HAM-D: Hamilton Depression Scale, HAM-A: Hamilton Anxiety Scale, PDSS: Panic Disorder Severity Scale, S-AI: State Anxiety Inventory, T-AI: Trait Anxiety Inventory, GAD: generalized anxiety disorder, PD: panic disorder, ADs: anxiety disorders \u003csup\u003ea\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparison of Microstate parameters between anxiety disorders and HCs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe four typical EEG microstate topographies (A-D) were identified for the calculation of microstate parameters, following established protocols (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). On average, the topographies of the four microstate classes explained 70.75%, and 68.2% of the total variance in the ADs and HC groups, respectively, all exceeding the 65% threshold (Michel and Koenig \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The four microstate parameters were compared between groups by repeated-measures ANOVA, with the EEG microstate (A-D) as a within-subject factor and the group (ADs or HCs) as a between-subject factor.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe repeated-measures ANOVA result on mean duration revealed a significant interaction effect between group and microstate class (\u003cem\u003eF\u003c/em\u003e (3, 309)\u0026thinsp;=\u0026thinsp;3.462, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Post hoc analyses indicated that the mean duration of microstate D was significantly increased in patients with ADs compared to HCs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025). The result of occurrence revealed no significant main effect of group (\u003cem\u003eF\u003c/em\u003e (1, 103)\u0026thinsp;=\u0026thinsp;2.708, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.103) and interaction effect between group and microstate class (\u003cem\u003eF\u003c/em\u003e (3, 309)\u0026thinsp;=\u0026thinsp;1.941, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.128) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The result of time coverage revealed a significant main effect of group (\u003cem\u003eF\u003c/em\u003e (1, 103)\u0026thinsp;=\u0026thinsp;17.630, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) and interaction effect between group and microstate class (\u003cem\u003eF\u003c/em\u003e (3, 309)\u0026thinsp;=\u0026thinsp;4.134, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009). Simple effect analyses revealed that patients with ADs showed significantly decreased time coverage in microstates A compared to HCs (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The result of mean GFP revealed no significant main effect of group (\u003cem\u003eF\u003c/em\u003e (1, 103)\u0026thinsp;=\u0026thinsp;2.416, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.123), but a significant interaction effect between group and microstate class (\u003cem\u003eF\u003c/em\u003e (3, 309)\u0026thinsp;=\u0026thinsp;3.165, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043). The simple effect analysis indicated no significant differences in mean GFP across microstates between anxiety group and HCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ed) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\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\u003eResult of repeated-measures ANOVA for microstate parameter between anxiety disorder patients and healthy control groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEffect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eη\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePost hoc\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eMean Duration\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.585\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass*group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMicrostate D: ADs \u0026gt;HC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eOccurrence\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.708\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass*group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.941\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eTime coverage\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass*group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMicrostate A: ADs \u0026lt;HC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eMean GFP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass*group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eADs: anxiety disorders, HC:healthy controls; GFP: global field power; η\u003csup\u003e2\u003c/sup\u003e:eta squared\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparison of Microstate parameters between GAD patients, PD patients and HCs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe four microstate classes explained 70.65%, 70.9%, and 68.2% of the total variance in the PD, GAD, and HC groups, respectively. The four microstate parameters were compared between groups by repeated-measures ANOVA, with the EEG microstate (A-D) as a within-subject factor and the group (PD, GAD, or HCs) as a between-subject factor.\u003c/p\u003e\u003cp\u003eThe analysis revealed a significant interaction effect between group and microstate class for mean duration (\u003cem\u003eF\u003c/em\u003e (6, 102)\u0026thinsp;=\u0026thinsp;2.243, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039). Post hoc analyses with Bonferroni correction indicated that the mean duration of microstate D was significantly increased in patients with GAD compared to HCs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024), whereas no significant differences in mean duration were observed for microstates A, B, or C among the PD groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Repeated-measures ANOVA revealed no significant differences in occurrence between groups or across microstate classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Additionally, there were no significant main effects of group or interactions between groups and microstate classes on time coverage and mean GFP (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ed) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Detailed microstate parameters are provided in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of repeated-measures ANOVA for microstate parameters in patients with PD, GAD, and HC.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEffect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eη\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePost hoc\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eMean Duration\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass*group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMicrostate D: GAD \u0026gt;HC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eOccurrence\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass*group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.647\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eTime coverage\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.626\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass*group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eMean GFP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass*group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eGAD: Generalized anxiety disorder, PD: Panic disorder; GFP: global field power; η\u003csup\u003e2\u003c/sup\u003e:eta squared\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparison of Microstate transition between anxiety disorders and HCs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe results showed that patients with ADs showed lower transition probabilities in A to B (\u003cem\u003et\u003c/em\u003e=-2.507, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) and B to A (\u003cem\u003et\u003c/em\u003e=-2.223, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017) compared to the HCs group, while patients with ADs showed higher transition probabilities in C to D (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.105, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019) and D to C (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.993, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026) compared to the HCs group. However, applying FDR correction for multiple comparisons, none of the observed differences reached statistical significance (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of microstate transition probabilities between ADs and HCs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFDR\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=\".\" colname=\"c2\"\u003e\u003cp\u003e7.18(2.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.70(2.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.076\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=\".\" colname=\"c2\"\u003e\u003cp\u003e7.87(2.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.26(2.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.810\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=\".\" colname=\"c2\"\u003e\u003cp\u003e7.70(2.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.61(2.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.917\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=\".\" colname=\"c2\"\u003e\u003cp\u003e7.39(2.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.73(2.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.076\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=\".\" colname=\"c2\"\u003e\u003cp\u003e8.40(3.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.56(2.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.917\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=\".\" colname=\"c2\"\u003e\u003cp\u003e8.23(2.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.97(2.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.903\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=\".\" colname=\"c2\"\u003e\u003cp\u003e7.77(2.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.36(2.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.492\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=\".\" colname=\"c2\"\u003e\u003cp\u003e8.54(3.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.60(2.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.917\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC to D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.50(2.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.83(3.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.076\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=\".\" colname=\"c2\"\u003e\u003cp\u003e7.61(2.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.48(2.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.799\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.917\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=\".\" colname=\"c2\"\u003e\u003cp\u003e8.26(2.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.97(2.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.903\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=\".\" colname=\"c2\"\u003e\u003cp\u003e10.54(2.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.92(3.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eGAD: generalized anxiety disorder, PD: panic disorder, HCs: healthy controls; FDR:False Discovery Rate\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparison of Microstate transition between GAD, PD and HCs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAnalysis of transition probabilities revealed significant group differences from microstates A to B (\u003cem\u003eF\u003c/em\u003e (2,102)\u0026thinsp;=\u0026thinsp;3.94, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022) and B to A (\u003cem\u003eF\u003c/em\u003e (2,102)\u0026thinsp;=\u0026thinsp;3.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046). Analysis of transition probabilities revealed significant group differences from microstates C to D (\u003cem\u003eF\u003c/em\u003e (2,102)\u0026thinsp;=\u0026thinsp;3.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045). However, the results did not reach statistical significance after FDR correction (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of microstate transition probabilities among PD, GAD, and HC groups.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGAD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e*q*-value (FDR)\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=\".\" colname=\"c2\"\u003e\u003cp\u003e7.36(2.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.99(2.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.70(2.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.184\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=\".\" colname=\"c2\"\u003e\u003cp\u003e7.58(2.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.20(2.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.26(2.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.390\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.585\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=\".\" colname=\"c2\"\u003e\u003cp\u003e7.86(2.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.52(2.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.61(2.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.846\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.924\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=\".\" colname=\"c2\"\u003e\u003cp\u003e7.53(2.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.23(2.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.73(2.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.184\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=\".\" colname=\"c2\"\u003e\u003cp\u003e8.29(2.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.51(2.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.56(2.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.924\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=\".\" colname=\"c2\"\u003e\u003cp\u003e8.66(2.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.75(2.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.97(2.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.578\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=\".\" colname=\"c2\"\u003e\u003cp\u003e7.57(2.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.00(2.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.36(2.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.578\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=\".\" colname=\"c2\"\u003e\u003cp\u003e8.37(3.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.73(3.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.60(2.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.924\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC to D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.15(3.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.89(3.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.83(3.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.184\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=\".\" colname=\"c2\"\u003e\u003cp\u003e7.70(2.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.51(2.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.48(2.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.924\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=\".\" colname=\"c2\"\u003e\u003cp\u003e8.72(2.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.75(2.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.97(2.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.578\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=\".\" colname=\"c2\"\u003e\u003cp\u003e10.22(3.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.91(3.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.92(3.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.186\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eGAD: generalized anxiety disorder; PD: panic disorder; HCs: healthy controls; FDR: False Discovery Rate\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCorrelation between microstate parameters and transitions with anxiety symptom\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCorrelation analysis in the anxiety group revealed that T-AI scores were positively associated with time coverage C (\u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.353, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0005, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ea) and mean duration C (\u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.351, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), but negatively associated with occurrence A and B (\u003cem\u003eR\u003c/em\u003e= -0.272, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ec; \u003cem\u003eR\u003c/em\u003e = -0.310, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ed ). In addition, the PSWQ scores were negatively associated with time coverage B (\u003cem\u003eR\u003c/em\u003e= -0.276, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ee).\u003c/p\u003e\u003cp\u003eSimilarly, in PD group, T-AI scores showed significant positive correlations with both time coverage C (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.247, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) and mean duration C(\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.123, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0048). but negatively associated with occurrence A, occurrence B, and time coverage B (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.074, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0049; \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.271, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024; \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.148, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033). In addition, the PSWQ scores were positively associated with mean duration C and time coverage C, but negatively associated with occurrence B (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.127, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042; \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.176, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015; \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.248, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). In the GAD group, a significant positive association was observed between HAM-A scores and time coverage C (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.228, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016). Detailed correlation coefficients for overall, as well as for PD and GAD, are provided in Supplementary Tables S2, S3, S4.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eBetween-group comparison of microstate-based FC\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGroup differences in microstate-based FCs were analyzed via whole-brain NBS for each channel in each microstate. Compared with HCs, GAD patients presented an increasing trend in the PLV in microstate A, especially between the right parietal-occipital and right frontal-central areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). In addition, microstate D showed stronger connections between the frontal and parietal‒occipital electrodes in the GAD group than in the HCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). FC differences in microstates B and D were observed between PD patients and HCs, but these differences were not as pronounced as the differences between GAD patients and HCs. In microstate B, patients with PD presented stronger PLVs between frontal (FPz, AF4, F1, Fz) and left parietal‒occipital (PO3, P5) electrode sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). In microstate D, increased PLV was observed between frontal (FP2, AF4, F1) and parietal-occipital (PO3, Oz, PO8) electrode sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). Differences between PD patients and GAD patients were observed only in microstate A. Patients with PD showed stronger FC between electrode pairs C6-TP7 and Pz-TP7 but weaker FC between CP5-Pz and C5-Pz than patients with GAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ee).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides the first comprehensive comparison of microstate parameters and microstate-based FC during resting-state EEG in patients with ADs, as well as PD and GAD specifically. Our study revealed common microstate characteristics in ADs, specifically an increase in microstate D and decreases in microstates A. Furthermore, microstate C parameters were positively associated with anxiety severity in the overall sample, PD, and GAD groups, whereas microstates A and B showed negative associations.In addition, Our findings revealed distinct patterns in GAD and PD patients in FC pattern. FC differences between GAD patients and PD patients were observed in microstate A, which involves sensory integration.\u003c/p\u003e\u003cp\u003ePrevious EEG/fMRI studies conducted in healthy individuals have shown that microstate D is associated with the ventral frontal cortex and temporoparietal regions, primarily in the dorsal attention network (DAN), which involves the allocation and maintenance of attentional resources. This study revealed an increased duration of microstate D in patients with ADs, specially in GAD, which might reflect greater activation of the frontal‒parietal attention network. This finding is in line with previous studies suggesting that patients with GAD are associated with cognitive control deficits and exhibit difficulty in shifting attention from potential threat stimuli (Bashford-Largo et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Blair et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). A previous fMRI study reported that anxiety severity was associated with greater activation in the ventrolateral prefrontal cortex, left posterior temporal sulcus and temporoparietal junction, suggesting heightened stimulus-driven attention to distractors (D\u0026iacute;az et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This study revealed increased microstate D in GAD patients but not in PD patients, suggesting that attention control\u0026mdash;particularly deficits in attentional shifting\u0026mdash;is more pronounced in individuals with GAD (Wei et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The fMRI results also revealed that GAD patients displayed disorder-specific posterior activity, possibly reflecting exaggerated attention regulation and difficulty in shifting attention away from threats (Buff et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePatients with ADs exhibited significantly decreased time coverage of microstate A. The microstate A is primarily associated with activiation in superior and middle temporal gyri, potentially corresponding to auditory network (Murphy et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These results support the disturbance of the sensory network in ADs and aligns with prior research in which anxiety and sensory processing difficulties were found to be associated (McCombs et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Storozheva et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Contrary to our hypotheses, the microstate peremeters did not significantly differ between PD and HCs, nor between PD and GAD group, suggesting that microstate patterns may reflect common anxiety-related network dysfunction rather than disorder-specific signatures and may have limited discriminant validity for capturing distinct neural dynamics across anxiety disorder subtypes.\u003c/p\u003e\u003cp\u003eThe correlation analysis demonstrated a significant positive correlation between microstate C parameters and anxiety symptoms assessed by STAI and HAMA in patients with ADs. This finding aligns with previous research on social anxiety, which reported that high social characterized by heightened self-referential processing is associated with microstate C (Zhang et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). According to Britz et al., microstate C has been demonstrated to correlate with brain regions associated with self-referential processing and emotion regulation, including areas assigned to the the default mode network(DMN) (Britz et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Additionally, previous studies have suggested that self-referential processes within the DMN are closely associated with anxiety symptoms (Burdwood et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Our study provides further evidence that anxiety symptoms may be associated with self-referential processing. In contrast, microstate A and B parameters exhibited negative correlations with symptom severity. These findings potentially reflect anxiety-mediated impairments in sensory network. Moreover, the results of correlation analysis are consistent with our prior finding demonstrating reduced parameters in microstate A and B among anxiety disorders.\u003c/p\u003e\u003cp\u003eWe further compared the average microstate connectivity among GAD, PD, and HCs to explore the neural mechanisms underlying microstates. FC differences between GAD patients and healthy controls were primarily observed in microstates A and D. Compared with HCs, GAD patients presented increased connections in microstate D, suggesting abnormal activation of the attention network. Moreover, the activation in FC was primarily observed between frontal and parietal regions, reflecting abnormalities in top-down control in patients with GAD (Mochcovitch et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Microstate A has been associated not only with auditory processing (Custo et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) but also with visual processing and brain arousal (Tarailis et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Previous studies have reported associations between anxiety disorders and sensory processing difficulties (McCombs et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This study revealed increased FC in microstate A in GAD patients compared with HCs, reflecting increased sensitivity to environmental sounds and sustained hypervigilance. Berggren et al. utilized a visual detection task and demonstrated that anxiety is associated with improved visual detection, suggesting that anxiety may modulate sensory processing (Berggren et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe FC differences between PD patients and healthy controls were primarily observed in microstates B and D. Activated and deactivated connections in microstate B were identified between AF4/FP2 and Oz/POz/PO3/PO4, which can be mapped to the right frontal cortex (Brodmann areas: BA 9, BA 10) and occipital cortex (BA 18 L, BA 19) (Kaga and Minami \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These brain regions are involved primarily in higher-level cognitive functions and visual information processing. Abnormal FC in microstate B between the frontal and occipital cortex suggests aberrant visual processing in patients with PD. Additionally, the PD group exhibited increased activation of AF4/Fpz/F1/Fz and PO3/P5 in microstate D, corresponding to the frontal cortex (BA 9R, BA 10 L, BA 6 L, BA 8 L) and left parietal lobe (BA 19, BA 39), which are involved in anterior‒posterior cortical attention processes (Babiloni et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The enhanced FC between these areas implies impaired visual processing in PD patients. An fMRI study also reported a potential correlation between sensory information processing and anxiety symptoms in PD patients (Pfleiderer et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Furthermore, an event-related fMRI study revealed that brain responses to visual threats are directly related to anxiety symptoms in PD patients (Feldker et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These findings support the notion that the severity of panic disorder is related to abnormalities in sensory information processing.\u003c/p\u003e\u003cp\u003eThe FC differences between the PD and GAD groups were observed in microstate A. Compared with the GAD group, PD patients presented deactivated connections in the right inferior parietal (BA 39) and superior parietal (BA 7) lobules, which are involved in language processing and sensory associations. In contrast, increased activation was observed in the left inferior parietal (BA 40) and lateral temporal (BA 21) lobes in the PD patients compared with the GAD patients. BA 40 and BA 21 are associated with mathematics performance and visual processing, respectively. These findings suggest differences in cognitive processing and sensory integration between GAD patients and PD patients. Gordeev et al. used neuropsychological and neurophysiological methods to demonstrate distinct cognitive impairments between GAD and PD patients (Gordeev et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, further research is needed to validate these results.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study has several noteworthy limitations. First, the absence of longitudinal data in this cross-sectional study limited our ability to track developmental trajectories of EEG microstate and FC pattern alterations. Additionally, this study employed EEG data exclusively, future studies could combine EEG with resting-state fMRI to provide complementary insights of temporal and spatial perspectives on brain network dynamics.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study investigated the resting-state EEG microstate dynamics in individuals with patients with ADs overall and separately in patients with GAD and PD. Our study revealed an increased microstate D and reduced microstates A in the broad anxiety cohort. Prolonged microstate D duration was also observed in GAD patients. Furthermore, the exacerbation of anxiety symptoms may be associated with self-referential and sensory processing networks. PD and GAD patients presented distinct patterns of FC across microstate A, indicating disorder-specific impairments in sensory processing. These findings enhance our understanding of the neural mechanisms underlying ADs and suggest that microstate-specific FC patterns may serve as potential clinical biomarkers for PD and GAD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePD: Panic disorder\u003c/p\u003e\n\u003cp\u003eGAD: Generalized anxiety disorder\u003c/p\u003e\n\u003cp\u003eEEG: Electroencephalography\u003c/p\u003e\n\u003cp\u003eDSM:\u0026nbsp;Diagnostic and Statistical Manual of Mental Disorders\u003c/p\u003e\n\u003cp\u003ePLV:\u0026nbsp;Phase-Locking Value\u003c/p\u003e\n\u003cp\u003eGFP: Global Field\u0026nbsp;Power\u003c/p\u003e\n\u003cp\u003eNBS: Network-based statistical analysis\u003c/p\u003e\n\u003cp\u003eML: Machine learning\u003c/p\u003e\n\u003cp\u003eSVM:\u0026nbsp;Support vector machine\u003c/p\u003e\n\u003cp\u003eAUC: Area under the curve\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the Science and Technology Innovation 2030 -Major Project on Brain Science and Brain-like Research (grant number 2021ZD0202004) and Research on Optimization of Treatment and Brain Function of Acupuncture Therapy for Patients with Panic Disorders (grant number 2020-1-2121).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eConceptualization, D.F.Y.; methodology, D.F.Y. and P.C.W.; resources, X.Y.Y. and Z.J.L.; data curation, D.F.Y; writing\u0026mdash;original draft preparation, D.F.Y; writing\u0026mdash;review and editing, X.Y.Y. and W.P.H.; visualization, D.F.Y.; supervision, X.Y.Y. and Z.J.L.; funding acquisition, Z.J.L. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThis manuscript has been approved by all the authors, who affirm the integrity of the work. All the authors made substantial contributions to the study design, data collection, analysis, and/or interpretation of the data, and most contributed to the writing and intellectual content of the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe study was approved by the Ethics Committee of Beijing Anding Hospital, affiliated with Capital Medical University (202395FS-2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they do not have any competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBabiloni C, Del Percio C, Triggiani AI et al (2011) Attention cortical responses to enlarged faces are reduced in underweight subjects: An electroencephalographic study. 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Front Psychol\u003cem\u003e \u003c/em\u003e16: 1581517. https://doi.org/10.3389/fpsyg.2025.1581517\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"brain-topography","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"btop","sideBox":"Learn more about [Brain Topography](http://link.springer.com/journal/10548)","snPcode":"10548","submissionUrl":"https://submission.nature.com/new-submission/10548/3","title":"Brain Topography","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Electroencephalogram, Microstate, Functional connectivity, Panic disorder, Generalized anxiety disorder","lastPublishedDoi":"10.21203/rs.3.rs-7035279/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7035279/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePanic disorder (PD) and generalized anxiety disorder (GAD) are among the most prevalent anxiety disorders (ADs), yet their neural mechanisms remain unclear. This study aimed to characterize EEG microstate patterns and their functional connectivity (FC) in patients with GAD and PD, and to explore the neural mechanisms of anxiety symptoms through microstate analysis. Resting-state EEG was collected from 35 patients with PD, 31 patients with GAD, and 39 healthy controls (HCs). Microstate topologies (microstate-4) were selected to calculate the parameters, including the mean duration, time coverage, occurrence, mean global field power (GFP), and transitions. Furthermore, the FC patterns underlying each microstate class were analyzed. Correlation analyses were conducted between anxiety symptoms and microstate dynamics. Compared with HCs, ADs presented an increased duration of microstate D and a decreased time coverage of microstate A. The correlation analysis revealed that the microstate C features were positively associated with anxiety symptoms. In contrast, microstate A and B exhibited consistent negative correlations with anxiety symptoms. The PD and GAD groups exhibited distinct FC patterns in microstate A. These findings reveal distinct neural dynamics in ADs characterized by impaired sensory processing and executive functioning. The abnormalities were predominantly observed in patients with GAD. Anxiety symptoms may be associated with distinct microstate patterns: positively with microstate C (linked to self-referential processing) and negatively with microstates A and B (involved in sensory network functioning). FC differences in microstate A demonstrated discriminative value for distinguishing between GAD and PD.\u003c/p\u003e","manuscriptTitle":"Evidence from EEG of Abnormal Functional Connectivity and Microstates in GAD and PD","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 13:23:35","doi":"10.21203/rs.3.rs-7035279/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-29T12:27:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-28T10:34:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17229287211769380471140005392459689359","date":"2025-07-19T07:34:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65703262610559788345478615025123783130","date":"2025-07-14T13:38:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-14T12:01:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-04T08:54:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-03T07:43:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Brain Topography","date":"2025-07-03T07:14:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"brain-topography","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"btop","sideBox":"Learn more about [Brain Topography](http://link.springer.com/journal/10548)","snPcode":"10548","submissionUrl":"https://submission.nature.com/new-submission/10548/3","title":"Brain Topography","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6604111e-238b-487b-abc4-773022e1d1ce","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T16:08:27+00:00","versionOfRecord":{"articleIdentity":"rs-7035279","link":"https://doi.org/10.1007/s10548-026-01179-6","journal":{"identity":"brain-topography","isVorOnly":false,"title":"Brain Topography"},"publishedOn":"2026-02-28 15:58:39","publishedOnDateReadable":"February 28th, 2026"},"versionCreatedAt":"2025-07-18 13:23:35","video":"","vorDoi":"10.1007/s10548-026-01179-6","vorDoiUrl":"https://doi.org/10.1007/s10548-026-01179-6","workflowStages":[]},"version":"v1","identity":"rs-7035279","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7035279","identity":"rs-7035279","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}