EEG Microstate Dynamics Consistently Predict Depressive Symptoms in Healthy Young Adults

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Previous research has identified altered temporal and transition patterns of EEG microstates in clinical populations diagnosed with major depressive disorder (MDD) as well as in healthy individuals exhibiting elevated depressive symptoms. In this study, we aimed to replicate recent EEG microstate findings in young, generally healthy adults reporting high (N = 38) versus low (N = 38) levels of depressive symptoms, while also examining the long-term temporal memory of microstate sequences. Microstate analysis was performed on 5-minute resting-state EEG recordings obtained with eyes closed. EEG data were categorized into five microstate classes, and significant parameter differences were observed between groups. Participants with high depressive symptoms exhibited decreased occurrence of microstate A, reduced coverage of microstates A and D, and diminished bidirectional transition probabilities between microstates A and D. Conversely, increased values were found for the Hurst exponent and bidirectional transition probabilities between microstates B and C, between microstates C and E, and from microstate B to E. Linear regression analysis demonstrated that these microstate parameters significantly predicted depressive symptom scores (R² = 0.389). Our results underscore the potential of resting-state EEG microstate temporal and sequence parameters as biomarkers for the early identification of depressive symptoms in generally healthy young adults. EEG microstates depressive symptoms linear regression Hurst sequence analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Depression is a highly prevalent and heterogeneous disorder characterized by significant cognitive, emotional, and physiological impairments (Marx et al., 2023 ). Delayed diagnosis frequently leads to chronic symptoms, treatment resistance, and increased risk for comorbid conditions such as anxiety, cardiovascular disease, and neurodegeneration. Consequently, early detection of depression is essential for enhancing clinical outcomes, reducing disease burden, and improving treatment efficacy. Notably, depressive symptoms are highly prevalent in the general population (Goodwin et al., 2022 ; Maurer et al., 2018 ), and their incidence continues to rise (Hao et al., 2023 ). Subthreshold depressive states are considered to exist on a continuum with major depression, differing from clinical depression primarily in severity rather than in fundamental characteristics (Rodríguez et al., 2012 ; Solomon et al., 2001 ). Electrical brain activity (electroencephalogram, EEG) has been used to study and evaluate the state of the subject in various types of neuropsychiatric and neurodegenerative disorders (Alexander et al., 2008 ; Koenig et al., 2012 ; Nicholson et al., 2023 ; Simpraga et al., 2017 ; Smailovic et al., 2022 ). It provides a non-invasive and cost-effective approach to assessing brain function, making it particularly valuable for identifying neural abnormalities linked to neuropsychiatric conditions. This capability facilitates early diagnosis, effective treatment monitoring, and personalized interventions. One of the methods allowing assessment of EEG signal produced by the large-scale brain networks is an EEG microstates approach (MS) that provides a unique evaluation of global brain electrical activity (Koenig et al., 2023 ; Michel & Koenig, 2018 ; Tarailis et al., 2023 ; Zanesco, 2023 ). In microstate analysis, each time point is defined as a non-overlapping voltage map, which is generated by approximately simultaneously active large-scale functional brain networks (Britz et al., 2010 ; Custo et al., 2017 ; Seeber & Michel, 2021 ; Tarailis, Lory, et al., 2025 ). In this context, EEG microstates enable the quantification of both spatial aspects (microstate classes or topographies) and temporal characteristics, including duration ("how long"), occurrence ("how often"), and coverage ("what proportion of time each microstate occupies"). The most prevalent microstate topographies are consistently replicated across various studies and have been associated with specific functional and physiological processes. This consistency facilitates relatively standardized evaluations of brain functioning, making EEG microstate analysis particularly suitable for assessing both normal and pathological states (for review (Tarailis et al., 2023 )). MDD has previously been examined using EEG microstate analysis in both clinical (Atluri et al., 2018 ; Cao et al., 2023 ; Che et al., 2025 ; He et al., 2021 ; Lei et al., 2022 ; Li et al., 2023 ; Murphy et al., 2020 ; Nishida et al., 2025 ; Sun et al., 2022 ) and preclinical populations (Liang et al., 2021 ; Qin et al., 2022 ; Xue et al., 2024 ; S. Zhao et al., 2022 ). Microstate parameters have shown correlations with well-established depressive symptom questionnaires such as the Beck Depression Inventory (BDI-II), Montgomery–Åsberg Depression Rating Scale, Hamilton Depression Scale, and the Self-Rating Depression Scale (Cao et al., 2023 ; Che et al., 2025 ; Damborská et al., 2019 ; Liang et al., 2021 ; Xue et al., 2024 ). A recent meta-analysis by (Chivu et al., 2023 ), summarized findings regarding the mean duration and occurrence rate of microstate classes, consistently revealing increased activity of MS B and decreased activity of MS D in populations with MDD. Similar alterations in temporal microstate parameters were also reported in a recent study conducted among generally healthy college students (N = 68), in which a subgroup (N = 34) presenting high depressive symptoms (BDI-II ≥ 14) exhibited increased duration, occurrence, and coverage of MS B, alongside decreased occurrence and coverage of MS D and MS E, as well as changes in transition probabilities(Qin et al., 2022 ).Taken together, these findings highlight the potential predictive value of microstate parameters for depressive states and emphasize the necessity for further research in this area. Such research is particularly important given the steadily rising prevalence of depressive conditions, which have lifelong impacts and social and clinical consequences comparable in severity to those associated with MDD (Cox et al., 2001 ; Flett et al., 1997 ; Goodwin et al., 2022 ; Maurer et al., 2018 ). To further validate the use of EEG microstates as potential biomarkers for depressive states, the present study aimed to replicate the findings of (Qin et al., 2022 ) in a sample of generally healthy young adults (19–35 years old). Based on previous results (Qin et al., 2022 ) and a recent meta-analysis (Chivu et al., 2023 ), we expected to observe differences in the temporal characteristics of MS B, MS C, MS D, and MS E, which would correlate with self-reported depressive symptoms. Additionally, we assessed MS transition dynamics (using transition probabilities, (Haydock et al., 2025 ; Lehmann et al., 2005 )) and long-range temporal correlations (using Hurst exponent (Van De Ville et al., 2010 ; von Wegner et al., 2018 )) of microstate sequences to further investigate the relationship between sequence parameters and self-reported levels of depression. Materials and methods Participants We re-used the data from a larger dataset (Tarailis et al., 2021 ). Following group selection approach by (Qin et al., 2022 ), participants who scored 14 or more on the Beck Depression Inventory-II (BDI-II) questionnaire (further referred to as experimental group, N = 38), and those matching in age and sex with a total BDI-II score of 13 or less (further referred to as control group, N = 38) were included in the current analysis. All subjects gave their written informed consent to participate, and the study was approved by the Vilnius Regional Biomedical Research Ethics Committee (Nr.2019/10-1159-649, approval date: 8 October 2019). Participants with any reported neurological or psychiatric disorders, any kind of addiction, or the use of psychotropic substances were excluded. Participants were asked not to use nicotine and caffeine 2 h prior to the study. Depression symptoms assessment Before the EEG recording session, all participants filled in the BDI-II (Beck et al., 1996). BDI-II is a self-rated questionnaire to evaluate the severity of depressive symptoms over the period of last 2 weeks. It contains 21 statements, which participants have to rate on a Likert-type scale ranging from 0 (completely disagree) to 3 (completely agree). The scores for BDI-II range from 0 to 63 where scores 0–7 indicate minimal, 8–15 indicate mild, 16–25 indicate moderate, and 26–63 indicate severe depression levels. Participants with a total BDI-II score of 14 or more were included in the experimental group. The comparison group was comprised of age- and sex-matched participants from the remaining dataset with a total BDI-II score of 13 or less. EEG recordings and preprocessing The details on EEG collection and preprocessing can be found in our original publication (Tarailis et al., 2021 ). Briefly, the recordings took place in the dim-lighted, sound-attenuated, and electrically shielded laboratory while participants were comfortably seated in the upright position. Five minutes of eyes closed resting state EEG was collected using 64 channels mounted on an elastic WaveGuard EEG cap and EEG equipment (ANT Neuro, The Netherlands). All electrodes were referenced against mastoids (M1 and M2) and a ground electrode was attached close to Fz. The impedance of the electrodes was kept below 20 kΩ. Two additional pairs of electrodes were used to track eye movements and blinks. Data was sampled at 2048 Hz. 50 Hz power line noise was removed using the Thomas F-statistics implemented in the CleanLine plugin for EEGLAB (Mullen, 2012 ). The artefacts caused by eye blinks and horizontal eye movements and cardiac pulses were corrected using an ICA approach. Channels with excessive artefacts were manually rejected and reconstructed using spherical spline method (Perrin et al., 1989 ). Data were downsampled to 512 Hz, recomputed to average reference and filtered between 1 and 40 Hz using Butterworth filter of 2nd order. Microstate segmentation Detailed information about EEG microstates analysis is reported in our previous work (Tarailis et al., 2021 ). Briefly, topographic clustering of voltage maps at GFP peaks was conducted individually for each subject’s EEG using k-means algorithm for cluster solutions between 2 and 10. In the second step, most dominant individual topographies for every number of clusters were averaged using permutation algorithm (Koenig et al., 1999 ). The optimal number of clusters was determined by maximum value of Silhouette method as it is described in our previous study (Tarailis et al., 2021 ). Silhouette evaluates how similar each data point (individual topography) is to other data points in its own cluster compared to the data points in other clusters (Rousseeuw, 1987 ). To minimize a potential bias in group level analysis (Murphy et al., 2023 ), group level microstates were determined from all 76 subjects. Backfitting was done on the GFP peaks, while all the time points in between peaks were split in half and labeled as the topography at the nearest GFP peak. After backfitting, we extracted the average duration, occurrence rate and time coverage for each microstate. Microstate sequence analysis Microstate syntax analysis of the sequence was based on the steps described in detail in (Lehmann et al., 2005 ; Wackermann et al., 1993 ). First, the transition matrix was extracted by counting the number of transitions from microstate i to microstate j . The transitions were computed from segments where all consecutive time points of identical label stand for the same state, meaning that self-transitions were not allowed. Counts between microstates were normalized by the occurrence rate of each state (Bréchet et al., 2019 ; Tomescu et al., 2018 ). Finally, the entire transition matrix was normalized by the total number of transition matrix counts, so that the sum of the whole transition matrix is equal to 1 (Lehmann et al., 2005 ). To assess if there are any differences between groups in a long-range memory of the sequence, we utilized Hurst exponent, following analysis steps described in (Tarailis et al., 2025 ). Briefly, individual symbolic microstates sequences were embedded into random walk by parting microstates into two classes and associating each class with a negative and a positive step respectively and generating a cumulative sum (Van De Ville et al., 2010 ). Fifty logarithmically spaced time scales (S) between 0.2 and 30 seconds duration were used to segment the sequence into a certain number of time windows (von Wegner et al., 2023 ). Each epoch was detrended (F) by subtracting the local trend using least-squares fit (Ros et al., 2017 ). The Hurst parameter of the random walk corresponds to the slope between F and S on the log-log coordinates plane. Statistical analysis Two-tailed independent samples permutation test for equality of means was used to test if there are any differences between groups for scores on BDI-II, and for temporal metrics and transition parameters of microstates. P values for each comparison were estimated by 5000 random permutations and further were corrected for multiple comparisons using False Discovery Rate (FDR) (Benjamini & Hochberg, 1995 ) separately for temporal parameters and transition metrics. The significance threshold for all comparisons was set to α = 0.05. Standardized mean difference (Cohen’s d) was calculated to report the effect size in case of significant differences between the groups. To evaluate the associations between BDI-II scores of participants and microstate parameters that were significantly different between the groups, Pearson’s correlation was calculated. Again, all p values were corrected using FDR. Linear regression models (LRM) were employed to predict the severity of depression symptoms based on microstate parameters that statistically differed between the groups. Five times repeated 5-fold cross validation tests were used, meaning that the dataset was split into two parts: a training set (80%) used to fit LRM and testing set (20%), used only to evaluate models’ performance. Since features have different scales, to improve models’ performance before the analysis, all parameters were z scored, so that each feature would have mean of 0 and standard deviation of 1. To quantify LRM performance, for each iteration, R 2 and Root Mean Square Error (RMSE) were computed and averaged across all five iterations (Hakim et al., 2021 ). R 2 is a statistical measure that indicates how well the data fits a regression model, with higher values indicating a better fit of the model. RMSE represents the difference between predicted and actual values, with lower values suggesting that model's predictions are very close to the actual values. Lastly, following the previous study (Xue et al., 2024 ) we computed Pearson’s correlation coefficient between predicted and observed values across all five iterations as a measure to evaluate how well models predict scores for depression symptoms. Results Psychometric characteristics of the groups The demographic characteristics of the groups are summarized in Table 1 . Experimental and control groups did not differ in age (p = 0.675). Statistical analysis confirmed that total BDI-II scores were higher in the experimental group (p < 0.001, d = 2.693). Table 1 The demographic characteristics of the study groups. Control Experimental N 38 38 Males 14 14 Females 24 24 Mean age and ± SD 23.32 ± 4.4 23.74 ± 4.3 BDI-II mean and ± SD 6.24 ± 3.96 22.66 ± 7.66 Topographic features of EEG microstates and group differences in temporal parameters Silhouette coefficient (mean value of silhouettes for each number of clusters) yielded a maximum value at k = 5, suggesting that 5 microstate classes allow for the best description of the data sample. Microstate topography with right frontal to left posterior, topography with left frontal to right posterior, topography with frontal to occipital, topography with fronto-central and topography with posterior maximum configurations matched the most frequently reported microstate classes in the literature (Koenig et al., 2023 ; Tarailis et al., 2023 ) and were labeled accordingly as microstates A, B, C, D and E (Fig. 1 A). All extracted temporal parameters for each microstate class fell in line with 95% of predictive intervals as estimated from 93 studies (Zanesco, 2023 ). The descriptive statistics of means and standard deviations of the temporal characteristics of each of the five microstates are presented in Table 2 . Table 2 Temporal parameters of extracted microstates. Duration (ms) Occurrence rate/s Coverage (%) Experimental Control Experimental Control Experimental Control MS A 41 ± 8 43 ± 7 3.73 ± 1.20 4.56 ± 1.22 14.9 ± 4.4 18.9 ± 4 MS B 47 ± 10 43 ± 9 4.59 ± 1.26 4.36 ± 1.12 20.7 ± 5.5 18 ± 4.3 MS C 63 ± 20 59 ± 25 6.02 ± 1.05 5.56 ± 0.97 34.7 ± 7.2 30.6 ± 9.4 MS D 39 ± 7 41 ± 8 3.50 ± 1.09 4.19 ± 1.32 13.4 ± 4.1 16.6 ± 4.5 MS E 40 ± 7 40 ± 7 4.13 ± 1.36 4.15 ± 1.47 15.8 ± 4.8 16.3 ± 4.9 After correcting for multiple testing, no differences in microstate durations between the groups were observed. MS A occurred less frequently in the experimental group (p = 0.022, d =-0.681), while there was a trend for increased occurrence of MS C (p = 0.061, d = 0.449) and decreased occurrence rate of MS D (p = 0.095, d=-0.722). Decreased coverage of MS A (p = 0.003, d=-0.956) and MS D (p = 0.015, d=-0.722) were observed in the experimental group, while trends for increased coverages of MS B (d = 0.541) and MS C (d = 0.487) were approaching significance level (p = 0.065 and p = 0.096 respectively). Individual values for parameters of each microstate are plotted in Fig. 1 (B). Figure 2 A shows the statistically significant differences in transition probabilities between the experimental and control groups. Out of 20 possible transitions between microstate classes, statistical analysis revealed significant differences in 8 possible transitions. In the experimental group bidirectional transition probabilities between MS A and MS D, and from MS A to MS E were decreased. Bidirectional transition probabilities between MS B and MS C, and between MS C and MS E and transition probability from MS B to MS E were significantly higher in the experimental group compared to the control group. We also observed an increased long-range memory of microstate sequence as estimated with Hurst exponent in the experimental group (p = 0.037, d = 0.488) (Fig. 2 B). Prediction of BDI-II scores using microstate parameters We used linear regression model to predict BDI-II scores based on the microstate parameters that were statistically different between two groups (occurrence and coverage of MS A, coverage of MS D, transition probabilities of MS A → MS D, MS A → MS E, MS B → MS C, MS B → MS E, MS C → MS B, MS C → MS E, MS D → MS A, AND E → MS C and Hurst exponent). 5-fold cross validated model for BDI-II resulted in R 2 = 0.389 and RMSE = 9.076. To determine how well predictive scores align with the actual scores of linear regression model, Pearson’s r was calculated and revealed a moderate positive association (r = 0.510) (Fig. 4 ). In this study we aimed to evaluate the potential of temporal and sequence properties of EEG microstates to distinguish between young generally healthy participants experiencing high and low intensity of depressive symptoms and to compare results to those recently reported by Qin et al. ( 2022 ) in order to further establish the potential utility of EEG microstates as biomarkers sensitive to mental conditions, and depressive states in particular. Using a data-driven approach, we estimated the optimal number of microstate classes that best represented our data and identified five microstates matching the topographies that are most frequently reported in existing literature (Tarailis et al., 2023 ). In addition to standard metrics used to describe microstates (duration, occurrence rate, and coverage), we investigated microstate sequence characteristics measured by transition probabilities Hurst exponent. Lastly, microstate metrics demonstrating significant differences between the subgroups were used in linear regression models to predict depressive symptom scores. In our sample of overall healthy young participants, we showed that the coverage and occurrence rate of MS A, and the coverage of MS D were decreased in the experimental group (Fig. 1 A), i.e. in subjects with higher levels of self-reported depression symptoms. These findings align closely with those reported by Qin et al. and are consistent with EEG microstate results from clinical samples of patients diagnosed with MDD (Lei et al., 2022 ; Murphy et al., 2020 ; Nishida et al., 2025 ; Sun et al., 2022 ). A recent meta-analysis suggested that overall decreased presence of MS D in MDD might be a vulnerability trait marker and could even be considered as a transdiagnostic marker (Chivu et al., 2023 ). Our findings of reduced MS D parameters in participants with elevated depressive scores further support this hypothesis. Additionally, the significantly lower occurrence rate and coverage of MS A observed in the high-depression group might reflect neural changes associated with anxiety symptoms rather than depression alone, as previously proposed by (Yan et al., 2021 ). Our recent systematic review(Tarailis et al., 2023 ) indicated that MS A is associated with auditory and visual processing as well as arousal and arousability, whereas MS D relates primarily to executive functioning. The decreased parameters observed for these microstates, along with reduced bidirectional transition probabilities between them in the experimental group, could reflect diminished arousability in individuals with more pronounced depressive symptoms(Surova et al., 2021 ). Additionally, these findings may highlight impairments in cognitive processing and valence-congruent emotional functioning(Dhami et al., 2022 ; Goldstein-Piekarski et al., 2021 ; LeDuke et al., 2023 ; Miljevic et al., 2023 ) aligning with commonly reported characteristics of MDD(Rude & McCarthy, 2003 ; Schmidt et al., 2017 ; Ulke et al., 2024 ) and further supporting the conceptualization of depressive states along a continuum. Although several studies have reported limited short-term and long-term reliability of EEG syntax parameters (Antonova et al., 2022 ; Kleinert et al., 2023 ; Tarailis, Artoni, et al., 2025 ), our findings partially replicated previously observed alterations in microstate transition patterns within the experimental group. Importantly, consistent with Qin et al. ( 2022 ) we observed decreased bidirectional transition probabilities between MS A and MS D and increased bidirectional transitions between MS B and MS C (Fig. 2 A). Additionally, transition probabilities showed similar correlation patterns with BDI-II scores across both studies. Specifically, a positive correlation between BDI-II scores and transitions from MS B to MS C, and vice versa, was evident in both our sample and Qin et al.'s study. Qin et al. also reported a significant negative correlation between BDI-II scores and transitions from MS A to MS E; in our study, a similar negative correlation was observed but did not reach statistical significance (p = 0.139). Overall, the altered microstate transition patterns observed in current study align with previous findings reported in several other works investigating MDD subjects and healthy controls (Al Zoubi et al., 2019 ; He et al., 2021 ; Liang et al., 2021 ; Z. Zhao et al., 2024 ). The similarity between results obtained from generally healthy samples experiencing depressive symptoms and those reported in clinical populations is notable, further supporting the continuum model of depressive states(Flett et al., 1997 ). These observations hold significant translational value, emphasizing the potential of EEG microstate transitions as biomarkers for depressive states and clinical manifestations of MDD. To the best of our knowledge, our study is the first to apply the Hurst exponent for analyzing microstate sequences in a preclinical population, specifically to determine whether long-range temporal memory of EEG microstate sequences differs between individuals with high versus low levels of depressive symptoms. Functionally, the Hurst exponent relates to the dynamic structural memory, i.e. how patterns of brain activity retain information about prior states, underlying neural network activation, which shapes perception and behavior(Linkenkaer-Hansen et al., 2001 , 2004 ). Furthermore, it indicates the degree of neural adaptation to upcoming stimuli, which is essential for maximizing information transfer and storage within neural networks (Alamian et al., 2022 ; Fosque et al., 2022 ; Haldeman & Beggs, 2005 ; Socolar & Kauffman, 2003 ). We observed an increased Hurst exponent in the experimental group (Fig. 2 B). Higher values of Hurst exponent indicate that sequence is more predictable and repetitive and has very similar structure to itself at all time scales (Díaz & Córdova, 2021 ). In our study, the presence of more repetitive microstate patterns may be driven by the observed decrease in transitions between MS A and MS D, along with increased transitions between MS B and MS C, as revealed by our EEG syntax assessment. The increased long-range memory (as indicated by the Hurst exponent) observed in the experimental group aligns with findings from previous EEG studies using different metrics to examine EEG alterations in depression. For instance, Lee et al.(Lee et al., 2007 ) reported increased long-range dependency in brain activity among patients with depression. Similarly, Gärtner et al. (Gärtner et al., 2017 ), demonstrated an increased Hurst exponent in the theta frequency range, which decreased following mindfulness and stress-reduction training, with the magnitude of reduction correlating positively with decreases in BDI scores. As the final step, we employed linear regression models with five repetitions of 5-fold cross-validation to predict BDI-II scores based on microstate parameters. To prevent model performance degradation due to excessive features, we included only parameters that significantly differed between the two groups. The resulting model demonstrated an R² of 0.389, an RMSE of 9.076, and a strong correlation between observed and predicted depressive symptom scores (r = 0.510). Although the results of the linear regression seem promising, the small sample size might have resulted in the inflated R 2 values (Flint et al., 2021 ; Varoquaux, 2018 ). Nevertheless, findings from the present study, together with previous work involving preclinical populations (Liang et al., 2021 ; Qin et al., 2022 ; Xue et al., 2024 ; S. Zhao et al., 2022 ), suggest that microstate dynamics may have predictive value for depressive states, highlighting the need for further investigation. Conclusions In this study we showed that young participants with more severe depressive symptoms demonstrate changed EEG microstates temporal parameters and transition patterns compared to the age- and sex-matched subjects with lower scores on depressive ratings. We partly replicated the results reported in a previous study with healthy young college students. Moreover, our results are in line with those reported in the clinical populations. This indicates that the dynamics of EEG microstates consistently predict depressive symptoms, thus microstates can further be utilized as biomarkers in the assessment of depression levels. Declarations Conflicts of Interest The authors declare no conflict of interest. Funding This research was funded by the Research Council of Lithuania (LMTLT agreement no. S-LJB-20-1). Author Contribution PT: data collection, EEG data analysis, visualization, writing original manuscript, reviewing and editing. IG-B: conceptualization, funding acquisition, supervision, reviewing and editing. Acknowledgement We would like to thank Dovilė Šimkutė for her help with data collection. Data Availability The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. References Al Zoubi O, Mayeli A, Tsuchiyagaito A, Misaki M, Zotev V, Refai H, Paulus M, Bodurka J, Aupperle RL, Khalsa SS, Feinstein JS, Savitz J, Cha YH, Kuplicki R, Victor TA (2019) EEG Microstates Temporal Dynamics Differentiate Individuals with Mood and Anxiety Disorders From Healthy Subjects. 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Cite Share Download PDF Status: Published Journal Publication published 03 Feb, 2026 Read the published version in Cognitive Neurodynamics → Version 1 posted Editorial decision: Revision requested 28 Oct, 2025 Reviews received at journal 22 Oct, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviews received at journal 09 Sep, 2025 Reviews received at journal 08 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers agreed at journal 01 Sep, 2025 Reviewers invited by journal 29 Aug, 2025 Editor assigned by journal 24 Aug, 2025 Submission checks completed at journal 24 Aug, 2025 First submitted to journal 21 Aug, 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. 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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-7430033","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":509547375,"identity":"79b8b704-84f3-42d8-8fe5-f1ee97d06c20","order_by":0,"name":"Povilas Tarailis","email":"","orcid":"","institution":"Simon Fraser University","correspondingAuthor":false,"prefix":"","firstName":"Povilas","middleName":"","lastName":"Tarailis","suffix":""},{"id":509547376,"identity":"ac55dc97-ae14-4614-aa30-1eaecd534966","order_by":1,"name":"Inga Griškova-Bulanova","email":"data:image/png;base64,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","orcid":"","institution":"Vilnius University","correspondingAuthor":true,"prefix":"","firstName":"Inga","middleName":"","lastName":"Griškova-Bulanova","suffix":""}],"badges":[],"createdAt":"2025-08-22 02:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7430033/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7430033/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11571-026-10409-3","type":"published","date":"2026-02-03T15:57:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90804787,"identity":"5f9fa9fe-75d9-4a25-8785-6b6c801fc215","added_by":"auto","created_at":"2025-09-08 10:40:32","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":77428,"visible":true,"origin":"","legend":"\u003cp\u003eA) Five data-driven microstates obtained from all subjects (N=76). (B) Left - individual values of temporal parameters. White dots indicate mean values and error bars indicate standard deviations. Right - mean value differences (Experimental – Control) between groups. Error bars indicate 95% confidence intervals. Asterisks indicate significant FDR corrected differences between groups (* p\u0026lt;0.05, ** p\u0026lt;0.01).\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7430033/v1/7ff269f39d1c2d0a69c690a3.jpg"},{"id":90804788,"identity":"03ff239a-40c0-4c55-8af2-016460320a27","added_by":"auto","created_at":"2025-09-08 10:40:32","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":22087,"visible":true,"origin":"","legend":"\u003cp\u003eSequence analysis results. (A) The statistically significant differences in transition probabilities between two groups. The red arrows indicate increased transitions in the experimental group, while the blue arrows indicate decreased transition probabilities in the experimental group compared to the control group. P values are FDR corrected. (B) Box plot for Hust exponent values.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7430033/v1/9033f76a408acdabafc4586b.jpg"},{"id":90804793,"identity":"60a4d4da-287d-44c4-8423-6a33b86d022a","added_by":"auto","created_at":"2025-09-08 10:40:32","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":219632,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant correlations between BDI-II scores and EEG microstate parameters that were statistically different between two groups. P values are FDR corrected.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7430033/v1/f1bd18d8a8db6cb841adc468.jpg"},{"id":90804790,"identity":"a24480f3-2f9c-46ea-ac94-99bc8efb8f06","added_by":"auto","created_at":"2025-09-08 10:40:32","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":56785,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot between actual scores versus linear regression model predicted scores for BDI-II.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7430033/v1/57637f2d3c8180ff7235e0bc.jpg"},{"id":102233984,"identity":"0252e074-5aa6-4080-ad2e-6b197c81bc51","added_by":"auto","created_at":"2026-02-09 16:02:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1120426,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7430033/v1/0baf32ed-4e2d-4bc2-aec9-ce11907ec524.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"EEG Microstate Dynamics Consistently Predict Depressive Symptoms in Healthy Young Adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDepression is a highly prevalent and heterogeneous disorder characterized by significant cognitive, emotional, and physiological impairments (Marx et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Delayed diagnosis frequently leads to chronic symptoms, treatment resistance, and increased risk for comorbid conditions such as anxiety, cardiovascular disease, and neurodegeneration. Consequently, early detection of depression is essential for enhancing clinical outcomes, reducing disease burden, and improving treatment efficacy. Notably, depressive symptoms are highly prevalent in the general population (Goodwin et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Maurer et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and their incidence continues to rise (Hao et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Subthreshold depressive states are considered to exist on a continuum with major depression, differing from clinical depression primarily in severity rather than in fundamental characteristics (Rodr\u0026iacute;guez et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Solomon et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eElectrical brain activity (electroencephalogram, EEG) has been used to study and evaluate the state of the subject in various types of neuropsychiatric and neurodegenerative disorders (Alexander et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Koenig et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Nicholson et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Simpraga et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Smailovic et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It provides a non-invasive and cost-effective approach to assessing brain function, making it particularly valuable for identifying neural abnormalities linked to neuropsychiatric conditions. This capability facilitates early diagnosis, effective treatment monitoring, and personalized interventions. One of the methods allowing assessment of EEG signal produced by the large-scale brain networks is an EEG microstates approach (MS) that provides a unique evaluation of global brain electrical activity (Koenig et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Michel \u0026amp; Koenig, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tarailis et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zanesco, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In microstate analysis, each time point is defined as a non-overlapping voltage map, which is generated by approximately simultaneously active large-scale functional brain networks (Britz et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Custo et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Seeber \u0026amp; Michel, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tarailis, Lory, et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In this context, EEG microstates enable the quantification of both spatial aspects (microstate classes or topographies) and temporal characteristics, including duration (\"how long\"), occurrence (\"how often\"), and coverage (\"what proportion of time each microstate occupies\"). The most prevalent microstate topographies are consistently replicated across various studies and have been associated with specific functional and physiological processes. This consistency facilitates relatively standardized evaluations of brain functioning, making EEG microstate analysis particularly suitable for assessing both normal and pathological states (for review (Tarailis et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)).\u003c/p\u003e\u003cp\u003eMDD has previously been examined using EEG microstate analysis in both clinical (Atluri et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cao et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Che et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; He et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lei et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Murphy et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nishida et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and preclinical populations (Liang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Qin et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Xue et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; S. Zhao et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Microstate parameters have shown correlations with well-established depressive symptom questionnaires such as the Beck Depression Inventory (BDI-II), Montgomery\u0026ndash;\u0026Aring;sberg Depression Rating Scale, Hamilton Depression Scale, and the Self-Rating Depression Scale (Cao et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Che et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Damborsk\u0026aacute; et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Liang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xue et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A recent meta-analysis by (Chivu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), summarized findings regarding the mean duration and occurrence rate of microstate classes, consistently revealing increased activity of MS B and decreased activity of MS D in populations with MDD. Similar alterations in temporal microstate parameters were also reported in a recent study conducted among generally healthy college students (N\u0026thinsp;=\u0026thinsp;68), in which a subgroup (N\u0026thinsp;=\u0026thinsp;34) presenting high depressive symptoms (BDI-II\u0026thinsp;\u0026ge;\u0026thinsp;14) exhibited increased duration, occurrence, and coverage of MS B, alongside decreased occurrence and coverage of MS D and MS E, as well as changes in transition probabilities(Qin et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).Taken together, these findings highlight the potential predictive value of microstate parameters for depressive states and emphasize the necessity for further research in this area. Such research is particularly important given the steadily rising prevalence of depressive conditions, which have lifelong impacts and social and clinical consequences comparable in severity to those associated with MDD (Cox et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Flett et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Goodwin et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Maurer et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo further validate the use of EEG microstates as potential biomarkers for depressive states, the present study aimed to replicate the findings of (Qin et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in a sample of generally healthy young adults (19\u0026ndash;35 years old). Based on previous results (Qin et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and a recent meta-analysis (Chivu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), we expected to observe differences in the temporal characteristics of MS B, MS C, MS D, and MS E, which would correlate with self-reported depressive symptoms. Additionally, we assessed MS transition dynamics (using transition probabilities, (Haydock et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lehmann et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2005\u003c/span\u003e)) and long-range temporal correlations (using Hurst exponent (Van De Ville et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; von Wegner et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)) of microstate sequences to further investigate the relationship between sequence parameters and self-reported levels of depression.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eWe re-used the data from a larger dataset (Tarailis et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Following group selection approach by (Qin et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), participants who scored 14 or more on the Beck Depression Inventory-II (BDI-II) questionnaire (further referred to as experimental group, N\u0026thinsp;=\u0026thinsp;38), and those matching in age and sex with a total BDI-II score of 13 or less (further referred to as control group, N\u0026thinsp;=\u0026thinsp;38) were included in the current analysis. All subjects gave their written informed consent to participate, and the study was approved by the Vilnius Regional Biomedical Research Ethics Committee (Nr.2019/10-1159-649, approval date: 8 October 2019). Participants with any reported neurological or psychiatric disorders, any kind of addiction, or the use of psychotropic substances were excluded. Participants were asked not to use nicotine and caffeine 2 h prior to the study.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDepression symptoms assessment\u003c/h3\u003e\n\u003cp\u003eBefore the EEG recording session, all participants filled in the BDI-II (Beck et al., 1996). BDI-II is a self-rated questionnaire to evaluate the severity of depressive symptoms over the period of last 2 weeks. It contains 21 statements, which participants have to rate on a Likert-type scale ranging from 0 (completely disagree) to 3 (completely agree). The scores for BDI-II range from 0 to 63 where scores 0\u0026ndash;7 indicate minimal, 8\u0026ndash;15 indicate mild, 16\u0026ndash;25 indicate moderate, and 26\u0026ndash;63 indicate severe depression levels. Participants with a total BDI-II score of 14 or more were included in the experimental group. The comparison group was comprised of age- and sex-matched participants from the remaining dataset with a total BDI-II score of 13 or less.\u003c/p\u003e\n\u003ch3\u003eEEG recordings and preprocessing\u003c/h3\u003e\n\u003cp\u003eThe details on EEG collection and preprocessing can be found in our original publication (Tarailis et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Briefly, the recordings took place in the dim-lighted, sound-attenuated, and electrically shielded laboratory while participants were comfortably seated in the upright position. Five minutes of eyes closed resting state EEG was collected using 64 channels mounted on an elastic WaveGuard EEG cap and EEG equipment (ANT Neuro, The Netherlands). All electrodes were referenced against mastoids (M1 and M2) and a ground electrode was attached close to Fz. The impedance of the electrodes was kept below 20 kΩ. Two additional pairs of electrodes were used to track eye movements and blinks. Data was sampled at 2048 Hz.\u003c/p\u003e\u003cp\u003e50 Hz power line noise was removed using the Thomas F-statistics implemented in the CleanLine plugin for EEGLAB (Mullen, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The artefacts caused by eye blinks and horizontal eye movements and cardiac pulses were corrected using an ICA approach. Channels with excessive artefacts were manually rejected and reconstructed using spherical spline method (Perrin et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). Data were downsampled to 512 Hz, recomputed to average reference and filtered between 1 and 40 Hz using Butterworth filter of 2nd order.\u003c/p\u003e\n\u003ch3\u003eMicrostate segmentation\u003c/h3\u003e\n\u003cp\u003eDetailed information about EEG microstates analysis is reported in our previous work (Tarailis et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Briefly, topographic clustering of voltage maps at GFP peaks was conducted individually for each subject\u0026rsquo;s EEG using k-means algorithm for cluster solutions between 2 and 10. In the second step, most dominant individual topographies for every number of clusters were averaged using permutation algorithm (Koenig et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The optimal number of clusters was determined by maximum value of Silhouette method as it is described in our previous study (Tarailis et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Silhouette evaluates how similar each data point (individual topography) is to other data points in its own cluster compared to the data points in other clusters (Rousseeuw, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). To minimize a potential bias in group level analysis (Murphy et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), group level microstates were determined from all 76 subjects. Backfitting was done on the GFP peaks, while all the time points in between peaks were split in half and labeled as the topography at the nearest GFP peak. After backfitting, we extracted the average duration, occurrence rate and time coverage for each microstate.\u003c/p\u003e\n\u003ch3\u003eMicrostate sequence analysis\u003c/h3\u003e\n\u003cp\u003eMicrostate syntax analysis of the sequence was based on the steps described in detail in (Lehmann et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Wackermann et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). First, the transition matrix was extracted by counting the number of transitions from microstate \u003cem\u003ei\u003c/em\u003e to microstate \u003cem\u003ej\u003c/em\u003e. The transitions were computed from segments where all consecutive time points of identical label stand for the same state, meaning that self-transitions were not allowed. Counts between microstates were normalized by the occurrence rate of each state (Br\u0026eacute;chet et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tomescu et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Finally, the entire transition matrix was normalized by the total number of transition matrix counts, so that the sum of the whole transition matrix is equal to 1 (Lehmann et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo assess if there are any differences between groups in a long-range memory of the sequence, we utilized Hurst exponent, following analysis steps described in (Tarailis et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Briefly, individual symbolic microstates sequences were embedded into random walk by parting microstates into two classes and associating each class with a negative and a positive step respectively and generating a cumulative sum (Van De Ville et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Fifty logarithmically spaced time scales (S) between 0.2 and 30 seconds duration were used to segment the sequence into a certain number of time windows (von Wegner et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Each epoch was detrended (F) by subtracting the local trend using least-squares fit (Ros et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The Hurst parameter of the random walk corresponds to the slope between F and S on the log-log coordinates plane.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eTwo-tailed independent samples permutation test for equality of means was used to test if there are any differences between groups for scores on BDI-II, and for temporal metrics and transition parameters of microstates. P values for each comparison were estimated by 5000 random permutations and further were corrected for multiple comparisons using False Discovery Rate (FDR) (Benjamini \u0026amp; Hochberg, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) separately for temporal parameters and transition metrics. The significance threshold for all comparisons was set to α\u0026thinsp;=\u0026thinsp;0.05. Standardized mean difference (Cohen\u0026rsquo;s d) was calculated to report the effect size in case of significant differences between the groups.\u003c/p\u003e\u003cp\u003eTo evaluate the associations between BDI-II scores of participants and microstate parameters that were significantly different between the groups, Pearson\u0026rsquo;s correlation was calculated. Again, all p values were corrected using FDR.\u003c/p\u003e\u003cp\u003eLinear regression models (LRM) were employed to predict the severity of depression symptoms based on microstate parameters that statistically differed between the groups. Five times repeated 5-fold cross validation tests were used, meaning that the dataset was split into two parts: a training set (80%) used to fit LRM and testing set (20%), used only to evaluate models\u0026rsquo; performance. Since features have different scales, to improve models\u0026rsquo; performance before the analysis, all parameters were z scored, so that each feature would have mean of 0 and standard deviation of 1. To quantify LRM performance, for each iteration, R\u003csup\u003e2\u003c/sup\u003e and Root Mean Square Error (RMSE) were computed and averaged across all five iterations (Hakim et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). R\u003csup\u003e2\u003c/sup\u003e is a statistical measure that indicates how well the data fits a regression model, with higher values indicating a better fit of the model. RMSE represents the difference between predicted and actual values, with lower values suggesting that model's predictions are very close to the actual values. Lastly, following the previous study (Xue et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) we computed Pearson\u0026rsquo;s correlation coefficient between predicted and observed values across all five iterations as a measure to evaluate how well models predict scores for depression symptoms.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003ePsychometric characteristics of the groups\u003c/h2\u003e\u003cp\u003eThe demographic characteristics of the groups are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Experimental and control groups did not differ in age (p\u0026thinsp;=\u0026thinsp;0.675). Statistical analysis confirmed that total BDI-II scores were higher in the experimental group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d\u0026thinsp;=\u0026thinsp;2.693).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe demographic characteristics of the study groups.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExperimental\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean age and \u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.32\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.74\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBDI-II mean and \u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.24\u0026thinsp;\u0026plusmn;\u0026thinsp;3.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.66\u0026thinsp;\u0026plusmn;\u0026thinsp;7.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eTopographic features of EEG microstates and group differences in temporal parameters\u003c/h2\u003e\u003cp\u003eSilhouette coefficient (mean value of silhouettes for each number of clusters) yielded a maximum value at k\u0026thinsp;=\u0026thinsp;5, suggesting that 5 microstate classes allow for the best description of the data sample. Microstate topography with right frontal to left posterior, topography with left frontal to right posterior, topography with frontal to occipital, topography with fronto-central and topography with posterior maximum configurations matched the most frequently reported microstate classes in the literature (Koenig et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tarailis et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and were labeled accordingly as microstates A, B, C, D and E (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). All extracted temporal parameters for each microstate class fell in line with 95% of predictive intervals as estimated from 93 studies (Zanesco, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The descriptive statistics of means and standard deviations of the temporal characteristics of each of the five microstates are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eTemporal parameters of extracted microstates.\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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDuration (ms)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eOccurrence rate/s\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eCoverage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExperimental\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExperimental\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eExperimental\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMS A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMS B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMS C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59\u0026thinsp;\u0026plusmn;\u0026thinsp;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e34.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e30.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMS D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMS E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAfter correcting for multiple testing, no differences in microstate durations between the groups were observed. MS A occurred less frequently in the experimental group (p\u0026thinsp;=\u0026thinsp;0.022, d =-0.681), while there was a trend for increased occurrence of MS C (p\u0026thinsp;=\u0026thinsp;0.061, d\u0026thinsp;=\u0026thinsp;0.449) and decreased occurrence rate of MS D (p\u0026thinsp;=\u0026thinsp;0.095, d=-0.722). Decreased coverage of MS A (p\u0026thinsp;=\u0026thinsp;0.003, d=-0.956) and MS D (p\u0026thinsp;=\u0026thinsp;0.015, d=-0.722) were observed in the experimental group, while trends for increased coverages of MS B (d\u0026thinsp;=\u0026thinsp;0.541) and MS C (d\u0026thinsp;=\u0026thinsp;0.487) were approaching significance level (p\u0026thinsp;=\u0026thinsp;0.065 and p\u0026thinsp;=\u0026thinsp;0.096 respectively). Individual values for parameters of each microstate are plotted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (B).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA shows the statistically significant differences in transition probabilities between the experimental and control groups. Out of 20 possible transitions between microstate classes, statistical analysis revealed significant differences in 8 possible transitions. In the experimental group bidirectional transition probabilities between MS A and MS D, and from MS A to MS E were decreased. Bidirectional transition probabilities between MS B and MS C, and between MS C and MS E and transition probability from MS B to MS E were significantly higher in the experimental group compared to the control group. We also observed an increased long-range memory of microstate sequence as estimated with Hurst exponent in the experimental group (p\u0026thinsp;=\u0026thinsp;0.037, d\u0026thinsp;=\u0026thinsp;0.488) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePrediction of BDI-II scores using microstate parameters\u003c/h2\u003e\u003cp\u003eWe used linear regression model to predict BDI-II scores based on the microstate parameters that were statistically different between two groups (occurrence and coverage of MS A, coverage of MS D, transition probabilities of MS A \u0026rarr; MS D, MS A \u0026rarr; MS E, MS B \u0026rarr; MS C, MS B \u0026rarr; MS E, MS C \u0026rarr; MS B, MS C \u0026rarr; MS E, MS D \u0026rarr; MS A, AND E \u0026rarr; MS C and Hurst exponent). 5-fold cross validated model for BDI-II resulted in R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.389 and RMSE\u0026thinsp;=\u0026thinsp;9.076. To determine how well predictive scores align with the actual scores of linear regression model, Pearson\u0026rsquo;s r was calculated and revealed a moderate positive association (r\u0026thinsp;=\u0026thinsp;0.510) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn this study we aimed to evaluate the potential of temporal and sequence properties of EEG microstates to distinguish between young generally healthy participants experiencing high and low intensity of depressive symptoms and to compare results to those recently reported by Qin et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in order to further establish the potential utility of EEG microstates as biomarkers sensitive to mental conditions, and depressive states in particular. Using a data-driven approach, we estimated the optimal number of microstate classes that best represented our data and identified five microstates matching the topographies that are most frequently reported in existing literature (Tarailis et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition to standard metrics used to describe microstates (duration, occurrence rate, and coverage), we investigated microstate sequence characteristics measured by transition probabilities Hurst exponent. Lastly, microstate metrics demonstrating significant differences between the subgroups were used in linear regression models to predict depressive symptom scores.\u003c/p\u003e\u003cp\u003eIn our sample of overall healthy young participants, we showed that the coverage and occurrence rate of MS A, and the coverage of MS D were decreased in the experimental group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), i.e. in subjects with higher levels of self-reported depression symptoms. These findings align closely with those reported by Qin et al. and are consistent with EEG microstate results from clinical samples of patients diagnosed with MDD (Lei et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Murphy et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nishida et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A recent meta-analysis suggested that overall decreased presence of MS D in MDD might be a vulnerability trait marker and could even be considered as a transdiagnostic marker (Chivu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our findings of reduced MS D parameters in participants with elevated depressive scores further support this hypothesis. Additionally, the significantly lower occurrence rate and coverage of MS A observed in the high-depression group might reflect neural changes associated with anxiety symptoms rather than depression alone, as previously proposed by (Yan et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur recent systematic review(Tarailis et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) indicated that MS A is associated with auditory and visual processing as well as arousal and arousability, whereas MS D relates primarily to executive functioning. The decreased parameters observed for these microstates, along with reduced bidirectional transition probabilities between them in the experimental group, could reflect diminished arousability in individuals with more pronounced depressive symptoms(Surova et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, these findings may highlight impairments in cognitive processing and valence-congruent emotional functioning(Dhami et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Goldstein-Piekarski et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; LeDuke et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Miljevic et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) aligning with commonly reported characteristics of MDD(Rude \u0026amp; McCarthy, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Schmidt et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ulke et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and further supporting the conceptualization of depressive states along a continuum.\u003c/p\u003e\u003cp\u003eAlthough several studies have reported limited short-term and long-term reliability of EEG syntax parameters (Antonova et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kleinert et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tarailis, Artoni, et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), our findings partially replicated previously observed alterations in microstate transition patterns within the experimental group. Importantly, consistent with Qin et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) we observed decreased bidirectional transition probabilities between MS A and MS D and increased bidirectional transitions between MS B and MS C (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Additionally, transition probabilities showed similar correlation patterns with BDI-II scores across both studies. Specifically, a positive correlation between BDI-II scores and transitions from MS B to MS C, and vice versa, was evident in both our sample and Qin et al.'s study. Qin et al. also reported a significant negative correlation between BDI-II scores and transitions from MS A to MS E; in our study, a similar negative correlation was observed but did not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.139). Overall, the altered microstate transition patterns observed in current study align with previous findings reported in several other works investigating MDD subjects and healthy controls (Al Zoubi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; He et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Z. Zhao et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The similarity between results obtained from generally healthy samples experiencing depressive symptoms and those reported in clinical populations is notable, further supporting the continuum model of depressive states(Flett et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). These observations hold significant translational value, emphasizing the potential of EEG microstate transitions as biomarkers for depressive states and clinical manifestations of MDD.\u003c/p\u003e\u003cp\u003eTo the best of our knowledge, our study is the first to apply the Hurst exponent for analyzing microstate sequences in a preclinical population, specifically to determine whether long-range temporal memory of EEG microstate sequences differs between individuals with high versus low levels of depressive symptoms. Functionally, the Hurst exponent relates to the dynamic structural memory, i.e. how patterns of brain activity retain information about prior states, underlying neural network activation, which shapes perception and behavior(Linkenkaer-Hansen et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Furthermore, it indicates the degree of neural adaptation to upcoming stimuli, which is essential for maximizing information transfer and storage within neural networks (Alamian et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fosque et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Haldeman \u0026amp; Beggs, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Socolar \u0026amp; Kauffman, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). We observed an increased Hurst exponent in the experimental group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Higher values of Hurst exponent indicate that sequence is more predictable and repetitive and has very similar structure to itself at all time scales (D\u0026iacute;az \u0026amp; C\u0026oacute;rdova, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In our study, the presence of more repetitive microstate patterns may be driven by the observed decrease in transitions between MS A and MS D, along with increased transitions between MS B and MS C, as revealed by our EEG syntax assessment. The increased long-range memory (as indicated by the Hurst exponent) observed in the experimental group aligns with findings from previous EEG studies using different metrics to examine EEG alterations in depression. For instance, Lee et al.(Lee et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) reported increased long-range dependency in brain activity among patients with depression. Similarly, G\u0026auml;rtner et al. (G\u0026auml;rtner et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), demonstrated an increased Hurst exponent in the theta frequency range, which decreased following mindfulness and stress-reduction training, with the magnitude of reduction correlating positively with decreases in BDI scores.\u003c/p\u003e\u003cp\u003eAs the final step, we employed linear regression models with five repetitions of 5-fold cross-validation to predict BDI-II scores based on microstate parameters. To prevent model performance degradation due to excessive features, we included only parameters that significantly differed between the two groups. The resulting model demonstrated an R\u0026sup2; of 0.389, an RMSE of 9.076, and a strong correlation between observed and predicted depressive symptom scores (r\u0026thinsp;=\u0026thinsp;0.510). Although the results of the linear regression seem promising, the small sample size might have resulted in the inflated R\u003csup\u003e2\u003c/sup\u003e values (Flint et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Varoquaux, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Nevertheless, findings from the present study, together with previous work involving preclinical populations (Liang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Qin et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Xue et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; S. Zhao et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), suggest that microstate dynamics may have predictive value for depressive states, highlighting the need for further investigation.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study we showed that young participants with more severe depressive symptoms demonstrate changed EEG microstates temporal parameters and transition patterns compared to the age- and sex-matched subjects with lower scores on depressive ratings. We partly replicated the results reported in a previous study with healthy young college students. Moreover, our results are in line with those reported in the clinical populations. This indicates that the dynamics of EEG microstates consistently predict depressive symptoms, thus microstates can further be utilized as biomarkers in the assessment of depression levels.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflicts of Interest\u003c/h2\u003e\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was funded by the Research Council of Lithuania (LMTLT agreement no. S-LJB-20-1).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePT: data collection, EEG data analysis, visualization, writing original manuscript, reviewing and editing. IG-B: conceptualization, funding acquisition, supervision, reviewing and editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank Dovilė Šimkutė for her help with data collection.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAl Zoubi O, Mayeli A, Tsuchiyagaito A, Misaki M, Zotev V, Refai H, Paulus M, Bodurka J, Aupperle RL, Khalsa SS, Feinstein JS, Savitz J, Cha YH, Kuplicki R, Victor TA (2019) EEG Microstates Temporal Dynamics Differentiate Individuals with Mood and Anxiety Disorders From Healthy Subjects. \u003cem\u003eFrontiers in Human Neuroscience\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/FNHUM.2019.00056\u003c/span\u003e\u003cspan address=\"10.3389/FNHUM.2019.00056\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlamian G, Lajnef T, Pascarella A, Lina JM, Knight L, Walters J, Singh KD, Jerbi K (2022) Altered Brain Criticality in Schizophrenia: New Insights From Magnetoencephalography. \u003cem\u003eFrontiers in Neural Circuits\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fncir.2022.630621\u003c/span\u003e\u003cspan address=\"10.3389/fncir.2022.630621\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlexander DM, Hermens DF, Keage HAD, Clark CR, Williams LM, Kohn MR, Clarke SD, Lamb C, Gordon E (2008) Event-related wave activity in the EEG provides new marker of ADHD. 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J Neural Eng 19(5). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1741-2552/ac88f6\u003c/span\u003e\u003cspan address=\"10.1088/1741-2552/ac88f6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao Z, Ran X, Wang J, Lv S, Qiu M, Niu Y, Wang C, Xu Y, Gao Z, Ren W, Zhou X, Fan X, Song J, Yu Y (2024) Common and differential EEG microstate of major depressive disorder patients with and without response to rTMS treatment. J Affect Disord 367:777\u0026ndash;787. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/J.JAD.2024.09.040\u003c/span\u003e\u003cspan address=\"10.1016/J.JAD.2024.09.040\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":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":"cognitive-neurodynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cody","sideBox":"Learn more about [Cognitive Neurodynamics](http://link.springer.com/journal/11571)","snPcode":"11571","submissionUrl":"https://submission.nature.com/new-submission/11571/3","title":"Cognitive Neurodynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"EEG microstates, depressive symptoms, linear regression, Hurst, sequence analysis","lastPublishedDoi":"10.21203/rs.3.rs-7430033/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7430033/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEarly detection of depressive symptoms is crucial for reducing their impact on social and cognitive functioning and can be effectively supported by non-invasive, cost-effective biomarkers derived from brain electrical activity. Previous research has identified altered temporal and transition patterns of EEG microstates in clinical populations diagnosed with major depressive disorder (MDD) as well as in healthy individuals exhibiting elevated depressive symptoms. In this study, we aimed to replicate recent EEG microstate findings in young, generally healthy adults reporting high (N\u0026thinsp;=\u0026thinsp;38) versus low (N\u0026thinsp;=\u0026thinsp;38) levels of depressive symptoms, while also examining the long-term temporal memory of microstate sequences.\u003c/p\u003e\u003cp\u003eMicrostate analysis was performed on 5-minute resting-state EEG recordings obtained with eyes closed. EEG data were categorized into five microstate classes, and significant parameter differences were observed between groups. Participants with high depressive symptoms exhibited decreased occurrence of microstate A, reduced coverage of microstates A and D, and diminished bidirectional transition probabilities between microstates A and D. Conversely, increased values were found for the Hurst exponent and bidirectional transition probabilities between microstates B and C, between microstates C and E, and from microstate B to E. Linear regression analysis demonstrated that these microstate parameters significantly predicted depressive symptom scores (R\u0026sup2; = 0.389).\u003c/p\u003e\u003cp\u003eOur results underscore the potential of resting-state EEG microstate temporal and sequence parameters as biomarkers for the early identification of depressive symptoms in generally healthy young adults.\u003c/p\u003e","manuscriptTitle":"EEG Microstate Dynamics Consistently Predict Depressive Symptoms in Healthy Young Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-08 10:40:27","doi":"10.21203/rs.3.rs-7430033/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-28T05:10:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-23T03:02:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281896353308606567949409956135049559925","date":"2025-09-16T02:27:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-09T07:04:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-08T19:26:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233953754702582784129183040257694687879","date":"2025-09-08T09:43:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210565483726360980818861369795568539530","date":"2025-09-03T08:27:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14959104551895799606420642798955204955","date":"2025-09-02T03:25:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-29T04:32:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-25T01:24:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-25T01:23:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cognitive Neurodynamics","date":"2025-08-22T02:26:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cognitive-neurodynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cody","sideBox":"Learn more about [Cognitive Neurodynamics](http://link.springer.com/journal/11571)","snPcode":"11571","submissionUrl":"https://submission.nature.com/new-submission/11571/3","title":"Cognitive Neurodynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0ae29984-1ba4-4237-900f-0dcffbc2683c","owner":[],"postedDate":"September 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:00:49+00:00","versionOfRecord":{"articleIdentity":"rs-7430033","link":"https://doi.org/10.1007/s11571-026-10409-3","journal":{"identity":"cognitive-neurodynamics","isVorOnly":false,"title":"Cognitive Neurodynamics"},"publishedOn":"2026-02-03 15:57:20","publishedOnDateReadable":"February 3rd, 2026"},"versionCreatedAt":"2025-09-08 10:40:27","video":"","vorDoi":"10.1007/s11571-026-10409-3","vorDoiUrl":"https://doi.org/10.1007/s11571-026-10409-3","workflowStages":[]},"version":"v1","identity":"rs-7430033","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7430033","identity":"rs-7430033","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}