MICROSTATELAB: The EEGLAB Toolbox for Resting-State Microstate Analysis

EEG microstates in adolescent depression: Effects of depression severity and overall symptoms.

Resting-state microstate dynamics abnormalities in children with ADHD and co-occurring sleep problems.

Background: Accurate assessment of prolonged disorders of consciousness (pDOC) is a critical clinical challenge. Misdiagnosis in pDOC can occur in up to 40% of cases, highlighting the need for more objective and reproducible biomarkers to support neurophysiological scales, thereby improving diagnosis and guiding therapeutic and prognostic decisions. Electroencephalography (EEG) microstate analysis is a promising, non-invasive method for tracking large-scale brain dynamics, but research in pDOC has predominantly relied on a canonical 4-class model. This methodological constraint may limit the ability to capture the full complexity of neural alterations present in these patients. Objective: This pilot study aimed to offer an objective method for assessing consciousness, complementing and enhancing the existing approaches established in the literature. The classical 4-class and an extended 7-class microstate model were compared to determine which more accurately characterizes the complexity of resting-state brain dynamics across different levels of consciousness in pDOC patients and healthy controls (HCs). Methods: Retrospective resting-state EEG (rsEEG) data from a cohort of pDOC patients and HC subjects were analyzed. Microstate analysis was performed using both 4-class and 7-class templates. The models were evaluated and compared based on three criteria: spatial correspondence with canonical maps (shared variance), the number of significant intra-group correlations between temporal features (Spearman test), and their ability to discriminate between the pDOC and HC groups (Wilcoxon test). Results: The 7-class microstate model provided a more accurate description of brain activity for most participants, with a greater number of microstate classes exceeding the 50% shared variance threshold compared to the 4-class model. In the pDOC group, both the 4-class and 7-class models showed a mean shared variance <50% in class D, which is associated with executive functioning across both templates. For the HC group, a prevalence of classes B and D emerged in both models, indicating higher engagement of executive functions. Furthermore, the 7-class model allowed for a group-specific analysis, which demonstrated that microstates A and F were consistently shared among 86% of pDOC patients. This suggests the potential preservation of specific intrinsic brain networks, particularly the sensory and default networks, even in the presence of severely impaired consciousness. Moreover, the 7-class model yielded a higher number of significant correlations within both groups and identified a broader set of temporal features that were significantly different between pDOC patients and HCs. These results highlight the enhanced sensitivity of the 7-class model in distinguishing subtle brain dynamics and improving the diagnostic capability for pDOC. Conclusions: The 7-class microstate model provides a more fine-grained and sensitive characterization of brain activity in both pDOC patients and healthy individuals. It demonstrated better performance in capturing individual brain dynamics, identifying shared network patterns, and discriminating between clinical populations. These findings suggest that the extended 7-class model holds greater potential for clinical utility and could lead to the development of more robust biomarkers for assessing consciousness.

Distinct patterns of cortical activity associated with cognitive and motor decline in community-dwelling older adults.

<h3>Objective:</h3> To develop and evaluate an EEG-based assay to predict cognitive functions in Parkinson’s disease (PD) using resting-state EEG data. <h3>Background:</h3> Cognitive dysfunction is a prominent feature of PD. PD related cognitive impairment is usually diagnosed by neuropsychological testing; however, these tests are complex, cannot capture cognitive fluctuations in PD patients, and are not readily compatible with repeated measurements, real-time feedback, or closed-loop neuromodulation therapies. <h3>Design/Methods:</h3> We recorded a few minutes of resting-state EEG data from a few electrodes. Our approach optimized a data-driven, machine-learning technique called ‘Linear predictive coding EEG Algorithms for Parkinson’s disease’ (LEAD-PD) to predict cognitive measures from 100 PD and 49 control participants. We compared our EEG-based cognitive index with the Montreal Cognitive Assessment (MOCA), as well as with different cognitive domains from NIH Toolbox, using multiple cross-validation schemes and randomization tests. We also investigated the relationship between them with multiple regression models, compared our approach with traditional EEG-based features and measured robustness with data truncation tests. <h3>Results:</h3> We found a strong correlation (rho = 0.69, <i>p</i> value &lt; 0.001) between our proposed LEAD-PD cognitive index and the MOCA score using data from only 8 EEG electrodes. LEAD-PD index also strongly predicted other cognitive domains from the NIH toolbox (Dimensional Change Card Sort, Flanker Attention and Inhibitory Control, Picture Vocabulary Task, Pattern Completion Speed Processing, and the Picture Sequence Memory tests). Our approach was consistently more predictive than traditional EEG spectral analysis, yielded 80.44% accuracy (0.90 AUC, 79.52 % sensitivity, and 81.14% specificity) in detecting cognitive impairment, and extended to both PD and control participants. The relationship between our approach and the MOCA was nonlinear with an R² of 0.46 for quadratic regression models. <h3>Conclusions:</h3> LEAD-PD approach using resting-state EEG is computationally-efficient and capable of providing continuous real-time control and feedback, facilitating the diagnosis and monitoring of cognitive impairment in PD. <b>Disclosure:</b> An immediate family member of Dr. Uc has received personal compensation in the range of $500-$4,999 for serving as an officer or member of the Board of Directors for American Board of Pediatrics. Dr. Uc has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Elsevier. The institution of Dr. Uc has received research support from Department of Veterans Affairs. The institution of Dr. Uc has received research support from Department of Defense. The institution of Dr. Uc has received research support from Michael J. Fox Foundation. The institution of an immediate family member of Dr. Uc has received research support from NIH. Dr. Anjum has nothing to disclose. Dr. Dasgupta has nothing to disclose. Dr. Narayanan has nothing to disclose.

Background: Electroencephalography (EEG) is a noninvasive technique for studying brain electrophysiology with high temporal resolution. Microstate analysis examines EEG recordings as a succession of quasi-stable microstates, allowing evaluation of extensive brain network activity linked to neuropsychiatric disorders like tinnitus. Objective: This study distinguishes tinnitus patients from healthy controls by using features acquired by microstate analysis. Methods: This study investigated EEG microstate differences between 16 healthy controls and 10 tinnitus patients. Four microstates were extracted and analyzed using Multivariate Analysis of Variance (MANOVA), revealing significant differences in duration, coverage, and occurrence between groups. Machine learning algorithms, including support vector machine (SVM) and K-Nearest Neighbors (KNN), and others were employed to classify tinnitus patients based on microstate features, achieving high accuracy, precision, specificity, recall, and F1-score. Results: MANOVA analysis revealed a significant difference in the duration of microstate A, which is associated with phonological processing and auditory perception, between the two groups. Additionally, significant differences in the coverage and occurrence of microstate B, related to visual networks, were observed. The SVM classifier achieved the highest accuracy of 96.44% in differentiating tinnitus patients from healthy controls, with impressive precision (97.64%), specificity (95.62%), and F1-score (97.24%). KNN also performed well, achieving a maximum recall of 97.24%. Conclusion: This study reveals the potential of EEG microstate analysis, incorporating time-related features, to improve tinnitus diagnosis and classification. Using SVM and KNN, we achieve high accuracy in identifying tinnitus-associated brain patterns, highlighting the clinical utility of EEG for neurological disease management.

Electroencephalography (EEG) microstate analysis is a neuroimaging analytical method that has received considerable attention in recent years and is widely used for analysing EEG signals. EEG is easily influenced by internal and external factors, which can affect the repeatability and stability of EEG microstate analysis. However, there have been few reports and publicly available datasets on the repeatability of EEG microstate analysis. In the current study, a 39-year-old healthy male underwent a total of 60 simultaneous electroencephalography and electrocardiogram measurements over a period of three months. After the EEG recording was completed, magnetic resonance imaging (MRI) was also conducted. To date, this EEG dataset has the highest number of repeated measurements for one individual. The dataset can be used to assess the stability and repeatability of EEG microstates and other analytical methods, to decode resting EEG states among subjects with open eyes, and to explore the stability and repeatability of cortical spatiotemporal dynamics through source analysis with individual MRI.

Early 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 who reported high (N = 38) versus low (N = 38) levels of depressive symptoms, while also examining the long-range dependencies 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, revealing significant differences in parameters 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 can predicted depressive symptom scores (R² = 0.145). 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.

Resting-state gamma oscillations, less studied compared to task-related gamma activity, have increasingly been considered to reflect baseline cognitive processes and readiness for future cognitive demands. Previous research has largely focused on clinical populations, identifying aberrant gamma activity as a marker of cognitive dysfunction in conditions such as schizophrenia, Alzheimer’s disease, and ADHD. This study investigates the relationship between resting-state EEG gamma power and neuropsychological performance in healthy adults. Specifically, it examines the associations between absolute and relative gamma power across different brain regions (frontal, temporal, parietal, and occipital) during resting-state and performance in cognitive domains such as working memory, verbal fluency, face recognition, short-term memory, and attention. A total of 45 healthy individuals who underwent routine neuropsychiatric evaluations at NP Istanbul Brain Hospital and exhibited no pathological findings were included in the study. Resting-state EEG data, collected retrospectively, were analyzed to extract gamma power values, which were subsequently correlated with neuropsychological test scores. The results revealed significant positive correlations between relative gamma power in the frontal and parietal regions and verbal fluency performance. This association suggests that specific brain regions contribute uniquely to cognitive functions during resting states, with gamma power providing insight into the neural substrates of language-related abilities. These findings highlight the relevance of resting-state gamma power in understanding individual differences in cognitive abilities. Future research should validate these findings with larger, more diverse samples and incorporate advanced techniques like MEG to better understand the neural mechanisms linking gamma oscillations to cognitive performance.

Slow wave activity in electroencephalography (EEG) is emerging as a promising early biomarker for neurodegenerative disorders, including Alzheimer's Disease (AD) and dementia. In this study, we applied non-parametric spectral analysis methods to automate the detection of slow wave activity and stratify dementia risk using resting-state EEG data from 79 middle-aged participants in the PEARL-Neuro Database. Power spectral density (PSD) was estimated using Welch's method, employing Hann-windowed 4 second epochs with 50 % overlap to ensure robust handling of the non-stationary and noisy nature of EEG signals. Relative power was calculated for canonical frequency bands: delta (0.5 – 4 Hz), theta (4 – 8 Hz), alpha (8 – 13 Hz), and beta (13 – 30 Hz). Additionally, delta/beta and theta/beta power ratios were derived as potential biomarkers of cortical dysregulation. To account for non-normality in the data, we used non-parametric statistical methods, including Spearman's rank correlation, to assess relationships between absolute and relative EEG power across all recording sites and clinical scores related to attention deficit and hyperactivity. Our results revealed that increased delta and theta power, along with elevated delta/beta and theta/beta ratios—particularly in frontal and parietal regions—were significantly associated with higher dementia risk profiles. These findings demonstrate that non-parametric spectral analysis of resting-state EEG provides a reliable and scalable approach for identifying early neurophysiological markers of dementia risk, supporting its potential utility in both clinical and research settings.

<h2>Abstract</h2><h3>Aim</h3> To investigate neural oscillatory networks in major depressive disorder (MDD), effects of home-based transcranial direct current stimulation (tDCS) treatment, and predictors of treatment remission. <h3>Methods</h3> In a randomized controlled trial, EEG data were acquired from 21 MDD participants (16 women, mean age 36.63 ± 9.71 years) with moderate to severe depressive episodes (mean HAMD score 18.42 ± 1.80). Participants were randomized to active (n = 11) or sham tDCS (n = 8). Home-based tDCS treatment was administered for 10 weeks, with 5 sessions per week for 3 weeks, then 3 sessions per week for 7 weeks. Active tDCS was 2 mA, and sham tDCS was 0 mA with brief ramp-up/down periods. Clinical remission was defined as HAMD score ≤ 7. Resting-state EEG data were collected at baseline and at the 10-week end of treatment using a portable 4-channel EEG device. EEG band power and functional connectivity (phase locking value, PLV) were analyzed. Deep learning identified predictors of treatment remission from baseline PLV features. <h3>Results</h3> The active tDCS group showed higher gamma PLV in frontal and temporal regions compared to the sham group. Positive correlations between changes in delta, theta, alpha, and beta PLV and depression improvement were observed in the active group. Combining PLV features from theta, alpha, and beta achieved the highest treatment remission prediction accuracy: 71.94 % (sensitivity 52.88 %, specificity 83.06 %). <h3>Conclusions</h3> Synchronized brain activity in gamma PLV may be a mechanism of active tDCS. Baseline resting-state EEG could predict treatment remission. Home-based EEG measures are feasible and useful predictors of clinical outcomes.

The human brain exhibits complex functions that emerge from interactions among spatially distributed neural regions. Electroencephalography (EEG) microstate analysis has been widely adopted to capture transient topographies reflecting large-scale network dynamics; moreover, it has been linked to cognitive functions, intrinsic brain networks, and neuropsychiatric disorders. Building on this framework, we recently proposed a novel approach based on instantaneous frequency (IF), defined as the temporal derivative of the instantaneous phase, which characterizes microstates in a dimension distinct from that of conventional amplitude-based microstates by explicitly capturing the phase leading and lagging. Although IF microstates have shown promise in characterizing the pathology and cognitive decline in Alzheimer's, their relevance to normal aging has not been investigated. This study aimed to identify age-group differences in large-scale EEG-dynamic properties using IF microstates. We recorded resting-state EEG with eyes closed from 29 younger and 18 middle-aged healthy adults. IF time series were extracted from sensor-level EEG signals in the theta and alpha bands. The IF microstates were identified using a hidden Markov model to ensure temporal continuity in state segmentation. Subsequently, we evaluated the sensor-level spatial distributions, mean dwell times, occupancy, and transition probabilities of the IF microstates and assessed age-group differences using appropriate statistical tests with false discovery rate correction. We identified several IF microstates characterized by frontal IF delay and occipital IF lead, as well as microstates deviating from these patterns. Group comparisons revealed age-group differences in dynamic properties; in the middle-aged group, mean dwell times increased in some states and decreased in others, while occupancy and transition probabilities also exhibited significant changes. IF microstate analysis provides a novel and informative perspective on age-group differences in spatiotemporal EEG dynamics. This approach, which is distinct from conventional amplitude-based microstates, may be useful for understanding healthy aging neural mechanisms.

Background and Objectives: Alzheimer’s disease (AD) and frontotemporal dementia (FTD) present overlapping clinical and neuroanatomical features, complicating early diagnosis. Therefore, this study evaluated whether EEG microstate analysis can provide reliable markers to distinguish patients with dementia from healthy controls. Materials and Methods: Resting-state EEG was recorded from 36 AD patients, 23 FTD patients, and 29 healthy controls. Preprocessing and microstate analysis were conducted using the MICROSTATELAB pipeline in EEGLAB. Clustering solutions ranging from four to seven classes were tested, with grand mean fitting and variance thresholds. Temporal parameters (duration, occurrence, and coverage) and their ratio-normalized forms were compared across groups using ANCOVA and nonparametric tests. Associations with Mini-Mental State Examination (MMSE) scores were assessed by regression analyses. Results: The four- and seven-class clustering solutions achieved high variance overlap with published microstate templates. In the four-class solution, temporal parameters of microstates B and D significantly differentiated controls from dementia groups, while in the seven-class solution, microstates C and G were the most informative. Ratio-normalized parameters improved group discrimination and were associated with MMSE scores. Conclusions: EEG microstates capture disease-related alterations in large-scale brain dynamics that differentiate patients with dementia from healthy individuals.

Spatiotemporal abnormalities in brain networks as a signature of neurological damage in Wilson's disease.

Background: Parkinson's disease (PD) is the second most common neurodegenerative disorder after Alzheimer's disease, and deep brain stimulation (DBS) can effectively alleviate PD symptoms. Although previous studies have detected network features of PD and DBS, few studies have considered their dynamic characteristics.Objective: We tested two hypotheses. (1) Reduced brain dynamics, as evidenced by slowed microstate dynamic change, is a characteristic of PD and is related to the movement disorders of patients with PD. (2) Therapeutic acute DBS can partially reverse slow brain dynamics in PD to healthy levels.Methods: We used electroencephalography (EEG) microstate analysis based on high density (256-channel) EEG to detect the effects of PD and DBS on brain dynamic changes on a sub-second timescale. We compared 21 healthy controls (HCs) with 20 patients with PD who were in either DBS-OFF or DBS-ON states. Assessment of movement disorder using the Unified Parkinson's Disease Rating Scale III was correlated with microstate parameters.Results: Compared with HCs, patients with PD displayed a longer mean microstate duration with reduced occurrence per second, which were significantly associated with movement disorders. In patients with PD, some parameters of microstate analysis were restored toward healthy levels after DBS.Conclusions: Resting-state EEG microstate analysis is an important tool for investigating brain dynamic changes in PD and DBS. PD can slow down brain dynamic change, and therapeutic acute DBS can partially reverse this change toward a healthy level.