Effect of short-term exposure to Raag Bilawal of North Indian classical music on young Indian adults: a high-density electroencephalogram microstate study.

Introduction: the objective of the study was to find out the microstate map topographies and their parameters generated during the resting state and during listening to North Indian classical Music Raag 'the Raag Bilawal'. It was hypothesized that in the resting state and during listening to music conditions, there would be a difference in microstate parameters i.e. mean duration, global explained variance (GEV), and time coverage. Methods: a 128-channel electroencephalogram (EEG) was recorded for 12 Indian subjects (average age 26.1+1.4 years) while resting and listening to music using the EEG microstate investigation. Investigation and comparison of the microstate parameters were the mean duration, global explained variance (GEV), and time coverage between both conditions were performed. Results: seven microstate maps were found to represent the resting state and listening to music condition, four canonical and three novel maps. No statistically significant difference was found between the two conditions for time coverage and mean duration. The statistical significance levels of the map-1, map-2, map-3, map-4, map-5, map-6, and map-7 for the mean duration were 0.4, 0.6, 0.97, 0.34, 0.32, 0.69, and 0.29 respectively; and for time coverage were 0.92, 0.92, 0.96, 0.64, 0.78, 0.38, and 0.76 respectively. Map-1, map-4, and map-7 were the three novel maps we found in our study. Conclusion: similarities regarding stability and predominance of maps with small vulnerability exist in both conditions indicating that phonological, visual, and dorsal attention networks may be activated in both resting state and listening to music condition.

Brain Evoked Response Qualification Using Multi-Set Consensus Clustering: Toward Single-Trial EEG Analysis.

In event-related potential (ERP) analysis, it is commonly assumed that individual trials from a subject share similar properties and originate from comparable neural sources, allowing reliable interpretation of group-averages. Nevertheless, traditional group-level ERP analysis methods, including cluster analysis, often overlook critical information about individual subjects' neural processes due to using fixed measurement intervals derived from averaging. We developed a multi-set consensus clustering pipeline to examine cognitive processes at the individual subject level. Initially, consensus clustering from diverse methods was applied to single-trial EEG epochs of individual subjects. Subsequently, a second level of consensus clustering was performed across the trials of each subject. A newly modified time window determination method was then employed to identify individual subjects' ERP(s) of interest. We validated our method with simulated data for ERP components N2 and P3, and real data from a visual oddball task to confirm the P3 component. Our findings revealed that estimated time windows for individual subjects provide precise ERP identification compared to fixed time windows across all subjects. Additionally, Monte Carlo simulations with synthetic single-trial data demonstrated stable scores for the N2 and P3 components, confirming the reliability of our method. The proposed method enhances the examination of brain-evoked responses at the individual subject level by considering single-trial EEG data, thereby extracting mutual information relevant to the neural process. This approach offers a significant improvement over conventional ERP analysis, which relies on the averaging mechanism and fixed measurement interval.

Application of EEG in the Diagnosis and Classification of Migraine: A Scoping Review.

Migraine is a chronic debilitating disease affecting a significant number of people, more often women than men. The gold standard for diagnosis is the International Classification of Headache Disorders-3 (ICHD-3). Authors have identified multiple tight spots in the present method of diagnosis. An alternative method of diagnosis has always been coveted. Electroencephalogram (EEG) is one of the most researched of such alternatives. The visually evoked potential is the most studied; auditory evoked potentials and transcranial direct current stimulation are also being studied. Cortical hyperexcitability and habituation deficit to sensory stimuli are some of the consistent findings. Alpha oscillations are among the most frequently studied bands; spectral analysis of EEG waves has often shown more reliable and consistent results than features read off the EEG directly. EEG microstate is a novel and promising method showing characteristic identifiable features that may help diagnose Migraine patients. An alternative to the ICHD-3 criterion for diagnosing Migraines would be instrumental in promptly diagnosing the disease. EEG is one of the most explored alternatives within which enumerable features can be used to identify Migraines, of which the most promising are EEG microstates.

Default mode network aberrance in subjects of alcohol and opioid use disorders during working memory task: An exploratory EEG microstates study.

Background: Aberrance in switching from default mode network (DMN) to fronto-parietal network (FPN) is proposed to underlie working memory deficits in subjects with substance use disorders, which can be studied using neuro-imaging techniques during cognitive tasks. The current study used EEG to investigate pre-stimulus microstates during the performance of Sternberg's working memory task in subjects with substance use disorders. Methods: 128-channel EEG was acquired and processed in ten age and gender-matched subjects, each with alcohol use disorder, opioid use disorder, and controls while they performed Sternberg's task. Behavioral parameters, pre-stimulus EEG microstate, and underlying sources were analyzed and compared between subjects with substance use disorders and controls. Results: Both alcohol and opioid use disorder subjects had significantly lower accuracy (P < 0.01), while reaction times were significantly higher only in subjects of alcohol use disorder compared to controls (P < 0.01) and opioid use disorder (P < 0.01), reflecting working memory deficits of varying degrees in subjects with substance use disorders. Pre-stimulus EEG microstate revealed four topographic Maps 1-4: subjects of alcohol and opioid use disorder showing significantly lower mean duration of Map 3 (visual processing) and Map 2 (saliency and DMN switching), respectively, compared to controls (P < 0.05). Conclusion: Reduced mean durations in Map 3 and 2 in subjects of alcohol and opioid use disorder can underlie their poorer performance in Sternberg's task. Furthermore, cortical sources revealed higher activity in both groups of substance use disorders in the parahippocampal gyrus- a hub of DMN; superior and middle temporal gyri associated with impulsivity; and insula that maintains balance between executive reflective system and impulsive system. EEG microstates can be used to envisage neural underpinnings implicated for working memory deficits in subjects of alcohol and opioid use disorders, reflected by aberrant switching between neural networks and information processing mechanisms.

MEG Microstates: An Investigation of Underlying Brain Sources and Potential Neurophysiological Processes.

Microstates are transient scalp configurations of brain activity measured by electroencephalography (EEG). The application of microstate analysis in magnetoencephalography (MEG) data remains challenging. In one MEG dataset (N = 113), we aimed to identify MEG microstates at rest, explore their brain sources, and relate them to changes in brain activity during open-eyes (ROE) or closed-eyes resting state (RCE) and an auditory Mismatch Negativity (MMN) task. In another dataset of simultaneously recorded EEG-MEG data (N = 21), we investigated the association between MEG and EEG microstates. Six MEG microstates (mMS) provided the best clustering of resting-state activity, each linked to different brain sources: mMS 1-2: left/right occipito-parietal; mMS 3: fronto-temporal; mMS 4: centro-medial; mMS 5-6: left/right fronto-parietal. Increases in occipital alpha power in RCE relative to ROE correlated with greater mMS 1-2 time coverage (τbs < 0.20, ps > .002), while the lateralization of deviance detection in MMN was associated with mMS 5-6 time coverage (τbs < 0.16, ps > .012). No temporal correlation was found between EEG and MEG microstates (ps > .05), despite some overlap in brain sources and global explained variance between mMS 2-3 and EEG microstates B-C (rs > 0.60, ps < .002). Hence, the MEG signal can be decomposed into microstates, but mMS brain activity clustering captures phenomena different from EEG microstates. Source reconstruction and task-related modulations link mMS to large-scale networks and localized activities. Thus, mMSs offer insights into brain dynamics and task-specific processes, complementing EEG microstates in studying physiological and dysfunctional brain activity.

EEG microstates are associated with music training experience.

Background: Music training facilitates the development of individual cognitive functions and influences brain plasticity. A comprehensive understanding of the pathways and processes through which music affects the human brain, as well as the neurobiological mechanisms underlying human brain perception of music, is necessary to fully harness the plasticity that music offers for brain development. Aims: To investigate the resting-state electroencephalogram (EEG) activity of individuals with and without music training experience, and explore the microstate patterns of EEG signals. Method: In this study, an analysis of electroencephalogram (EEG) microstates from 57 participants yielded temporal parameters(mean duration, time coverage, occurrence, and transition probability)of four classic microstate categories (Categories A, B, C, and D) for two groups: those with music training experience and those without. Statistical analysis was conducted on these parameters between groups. Results: The results indicate that compared to individuals without music training experience, participants with music training experience exhibit significantly longer mean durations of microstate A, which is associated with speech processing. Additionally, they show a greater time coverage of microstate B, which is associated with visual processing. Transition probabilities from microstate A to microstate B were greater in participants with music training experience compared to those without. Conversely, transition probabilities from microstate A to microstate C and from microstate C to microstate D were greater in participants without music training experience. Conclusion: Our study found differences in characteristic parameters of certain microstates between individuals with and without music training experience. This suggests distinct brain activity patterns during tasks related to speech, vision, and attention regulation among individuals with varying levels of music training experience. These findings support an association between music training experience and specific neural activities. Furthermore, they endorse the hypothesis of music training experience influencing brain activity during resting states. Additionally, they imply a facilitative role of music training in tasks related to speech, vision, and attention regulation, providing initial evidence for further empirical investigation into the cognitive processes influenced by music training.

Investigating the relationship between resting-state EEG microstates and executive functions: A null finding.

Recent advances in cognitive neurosciences suggest that intrinsic brain networks dynamics are associated with cognitive functioning. Despite this emerging perspective, limited research exists to validate this hypothesis. This Registered Report aimed to specifically test the relationship between intrinsic brain spatio-temporal dynamics and executive functions. Resting-state EEG microstates were used to assess brain spatio-temporal dynamics, while a comprehensive battery of nine cognitive function tasks was employed to evaluate executive functions in 140 participants. We hypothesized that microstates (class C and D) metrics would correlate with an executive functions composite score. Contrary to expectations, our hypotheses were not supported by the data. We however observed a small, non-significant trend with a negative correlation between microstate D occurrences and executive functions scores (r = -.18, 95% CI [-.33, -.01]) which however did not meet the adjusted threshold for significance. In light of the inconclusive or minor effect sizes observed, the assertion that intrinsic brain networks dynamics - as measured by resting-state EEG microstate metrics - are a reliable signature of executive functioning remains unsupported.

Are brain activity changes underlying rare word production after learning specific or do they extend to semantically related rare words?

Learning words in the mother tongue is a fundamental lifelong skill that involves complex cognitive and neural changes. In adults, newly learned words affect the organization of the lexical-semantic network and, compared to words that have been in the lexicon for longer, they activate the same cortical areas, but more extensively and/or intensively. It is however still unclear (1) which brain and cognitive processes underlying word production change when infrequent/unknown words are compared before and after learning and (2) whether integrating newly learned words impacts word specific processes or has a broader impact on unlearned words. The present study aims to investigate the electrophysiological changes underlying the production of rare words induced by learning and the effect of learning on an unlearned list of rare words belonging to the same semantic categories. To this end, 24 neurotypical adults learned one of two matched lists of 40 concrete rare words from 4 semantic categories. EEG (electroencephalographic) recordings were acquired during a referential word production task (picture naming) of the learned and unlearned words before and after the learning phase. The results show that the production of rare word is associated with event-related (ERP) differences between before and after learning in the period from 300 to 800 msec following the presentation of the imaged concept (picture). These differences consisted in a larger involvement of left temporal and parietal regions after learning between 300 and 400 msec i.e., the time window likely corresponding to lexical and phonological encoding processes. Crucially, the ERP changes are not restricted to the production of the learned rare words, but are also observed when participants try to retrieve words of a list of semantically and lexically matched rare words that they have not learned. The ERP changes on unlearned rare words are weaker and suggest that learning new words induces boarder effects also on unlearned words.

Valence-specific EEG microstate modulations during self-generated affective states.

Introduction: This study aims to explore the temporal dynamics of brain networks involved in self-generated affective states, specifically focusing on modulating these states in both positive and negative valences. The overarching goal is to contribute to a deeper understanding of the neurodynamic patterns associated with affective regulation, potentially informing the development of biomarkers for therapeutic interventions in mood and anxiety disorders. Methods: Utilizing EEG microstate analysis during self-generated affective states, we investigated the temporal dynamics of five distinct microstates across different conditions, including baseline resting state and self-generated states of positive valence (e.g., awe, contentment) and negative valence (e.g., anger, fear). Results: The study revealed noteworthy modulations in microstate dynamics during affective states. Additionally, valence-specific mechanisms of spontaneous affective regulation were identified. Negative valence affective states were characterized by the heightened presence of attention-associated microstates and reduced occurrence of salience-related microstates during negative valence states. In contrast, positive valence affective states manifested a prevalence of microstates related to visual/autobiographical memory and a reduced presence of auditory/language-associated microstates compared to both baseline and negative valence states. Discussion: This study contributes to the field by employing EEG microstate analysis to discern the temporal dynamics of brain networks involved in self-generated affective states. Insights from this research carry significant implications for understanding neurodynamic patterns in affective regulation. The identification of valence-specific modulations and mechanisms has potential applications in developing biomarkers for mood and anxiety disorders, offering novel avenues for therapeutic interventions.

EEG Microstate as a Marker of Adolescent Idiopathic Scoliosis.

The pathophysiology of Adolescent Idiopathic Scoliosis (AIS) is not yet fully understood, but multifactorial hypotheses have been proposed that include defective central nervous system (CNS) control of posture, biomechanics, and body schema alterations. To deepen CNS control of posture in AIS, electroencephalographic (EEG) activity during a simple balance task in adolescents with and without AIS was parsed into EEG microstates. Microstates are quasi-stable spatial distributions of the electric potential of the brain that last tens of milliseconds. The spatial distribution of the EEG characterised by the orientation from left-frontal to right-posterior remains stable for a greater amount of time in AIS compared to controls. This spatial distribution of EEG, commonly named in the literature as class B, has been found to be correlated with the visual resting state network. Both vision and proprioception networks provide critical information in mapping the extrapersonal environment. This neurophysiological marker probably unveils an alteration in the postural control mechanism in AIS, suggesting a higher information processing load due to the increased postural demands caused by scoliosis.

Fatigue in Multiple Sclerosis: A Resting-State EEG Microstate Study.

Fatigue affects approximately 80% of people with Multiple Sclerosis (PwMS) and can impact several domains of daily life. However, the neural underpinnings of fatigue in MS are still not completely clear. The aim of our study was to investigate the spontaneous large-scale networks functioning associated with fatigue in PwMS using the EEG microstate approach with a spectral decomposition. Forty-three relapsing-remitting MS patients and twenty-four healthy controls (HCs) were recruited. All participants underwent an administration of Modified Fatigue Impact scale (MFIS) and a 15-min resting-state high-density EEG recording. We compared the microstates of healthy subjects, fatigued (F-MS) and non-fatigued (nF-MS) patients with MS; correlations with clinical and behavioral fatigue scores were also analyzed. Microstates analysis showed six templates across groups and frequencies. We found that in the F-MS emerged a significant decrease of microstate F, associated to the salience network, in the broadband and in the beta band. Moreover, the microstate B, associated to the visual network, showed a significant increase in fatigued patients than healthy subjects in broadband and beta bands. The multiple linear regression showed that the high cognitive fatigue was predicted by both an increase and decrease, respectively, in delta band microstate B and beta band microstate F. On the other hand, higher physical fatigue was predicted with lower occurrence microstate F in beta band. The current findings suggest that in MS the higher level of fatigue might be related to a maladaptive functioning of the salience and visual network.

Abnormal EEG microstates in Alzheimer's disease: predictors of ß-amyloid deposition degree and disease classification.

Electroencephalography (EEG) microstates are used to study cognitive processes and brain disease-related changes. However, dysfunctional patterns of microstate dynamics in Alzheimer's disease (AD) remain uncertain. To investigate microstate changes in AD using EEG and assess their association with cognitive function and pathological changes in cerebrospinal fluid (CSF). We enrolled 56 patients with AD and 38 age- and sex-matched healthy controls (HC). All participants underwent various neuropsychological assessments and resting-state EEG recordings. Patients with AD also underwent CSF examinations to assess biomarkers related to the disease. Stepwise regression was used to analyze the relationship between changes in microstate patterns and CSF biomarkers. Receiver operating characteristics analysis was used to assess the potential of these microstate patterns as diagnostic predictors for AD. Compared with HC, patients with AD exhibited longer durations of microstates C and D, along with a decreased occurrence of microstate B. These microstate pattern changes were associated with Stroop Color Word Test and Activities of Daily Living scale scores (all P < 0.05). Mean duration, occurrences of microstate B, and mean occurrence were correlated with CSF Aß 1-42 levels, while duration of microstate C was correlated with CSF Aß 1-40 levels in AD (all P < 0.05). EEG microstates are used to predict AD classification with moderate accuracy. Changes in EEG microstate patterns in patients with AD correlate with cognition and disease severity, relate to Aß deposition, and may be useful predictors for disease classification.

Unraveling the complexity of rapid eye movement microstates: insights from nonlinear EEG analysis.

Although rapid eye movement (REM) sleep is conventionally treated as a unified state, it comprises two distinct microstates: phasic and tonic REM. Recent research emphasizes the importance of understanding the interplay between these microstates, hypothesizing their role in transient shifts between sensory detachment and external awareness. Previous studies primarily employed linear metrics to probe cognitive states, such as oscillatory power, while in this study, we adopt Lempel-Ziv Complexity (LZC), to examine the nonlinear features of electroencephalographic (EEG) data from the REM microstates and to gain complementary insights into neural dynamics during REM sleep. Our findings demonstrate a noteworthy reduction in LZC during phasic REM compared to tonic REM states, signifying diminished EEG complexity in the former. Additionally, we noted a negative correlation between decreased LZC and delta band power, along with a positive correlation with alpha band power. This study highlights the potential of nonlinear EEG metrics, particularly LZC, in elucidating the distinct features of REM microstates. Overall, this research contributes to advancing our understanding of the complex dynamics within REM sleep and opens new avenues for exploring its implications in both clinical and nonclinical contexts.

Magnetoencephalography-derived oscillatory microstate patterns across lifespan: the Cambridge centre for ageing and neuroscience cohort.

The aging brain represents the primary risk factor for many neurodegenerative disorders. Whole-brain oscillations may contribute novel early biomarkers of aging. Here, we investigated the dynamic oscillatory neural activities across lifespan (from 18 to 88 years) using resting Magnetoencephalography (MEG) in a large cohort of 624 individuals. Our aim was to examine the patterns of oscillation microstates during the aging process. By using a machine-learning algorithm, we identify four typical clusters of microstate patterns across different age groups and different frequency bands: left-to-right topographic MS1, right-to-left topographic MS2, anterior-posterior MS3 and fronto-central MS4. We observed a decreased alpha duration and an increased alpha occurrence for sensory-related microstate patterns (MS1 & MS2). Accordingly, theta and beta changes from MS1 & MS2 may be related to motor decline that increased with age. Furthermore, voluntary 'top-down' saliency/attention networks may be reflected by the increased MS3 & MS4 alpha occurrence and complementary beta activities. The findings of this study advance our knowledge of how the aging brain shows dysfunctions in neural state transitions. By leveraging the identified microstate patterns, this study provides new insights into predicting healthy aging and the potential neuropsychiatric cognitive decline.

Shared and distinct electroencephalogram microstate abnormalities across schizophrenia, bipolar disorder, and depression.

BACKGROUND: Microstates of an electroencephalogram (EEG) are canonical voltage topographies that remain quasi-stable for 90 ms, serving as the foundational elements of brain dynamics. Different changes in EEG microstates can be observed in psychiatric disorders like schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD). However, the similarities and disparatenesses in whole-brain dynamics on a subsecond timescale among individuals diagnosed with SCZ, BD, and MDD are unclear. METHODS: This study included 1112 participants (380 individuals diagnosed with SCZ, 330 with BD, 212 with MDD, and 190 demographically matched healthy controls [HCs]). We assembled resting-state EEG data and completed a microstate analysis of all participants using a cross-sectional design. RESULTS: Our research indicates that SCZ, BD, and MDD exhibit distinct patterns of transition among the four EEG microstate states (A, B, C, and D). The analysis of transition probabilities showed a higher frequency of switching from microstates A to B and from B to A in each patient group compared to the HC group, and less frequent transitions from microstates A to C and from C to A in the SCZ and MDD groups compared to the HC group. And the probability of the microstate switching from C to D and D to C in the SCZ group significantly increased compared to those in the patient and HC groups. CONCLUSIONS: Our findings provide crucial insights into the abnormalities involved in distributing neural assets and enabling proper transitions between different microstates in patients with major psychiatric disorders.

The Role of EEG microstates in predicting oxcarbazepine treatment outcomes in patients with newly-diagnosed focal epilepsy.

PURPOSE: Microstates represent the global and topographical distribution of electrical brain activity from scalp-recorded EEG. This study aims to explore EEG microstates of patients with focal epilepsy prior to medication, and employ extracted microstate metrics for predicting treatment outcomes with Oxcarbazepine monotherapy. METHODS: This study involved 25 newly-diagnosed focal epilepsy patients (13 females), aged 12 to 68, with various etiologies. Patients were categorized into Non-Seizure-Free (NSF) and Seizure-Free (SF) groups according to their first follow-up outcomes. From pre-medication EEGs, four representative microstates were identified by using clustering. The temporal parameters and transition probabilities of microstates were extracted and analyzed to discern group differences. With generating sample method, Support Vector Machine (SVM), Logistic Regression (LR), and Naïve Bayes (NB) classifiers were employed for predicting treatment outcomes. RESULTS: In the NSF group, Microstate 1 (MS1) exhibited a significantly higher duration (mean±std. = 0.092±0.008 vs. 0.085±0.008, p = 0.047), occurrence (mean±std. = 2.587±0.334 vs. 2.260±0.278, p = 0.014), and coverage (mean±std. = 0.240±0.046 vs. 0.194±0.040, p = 0.014) compared to the SF group. Additionally, the transition probabilities from Microstate 2 (MS2) and Microstate 3 (MS3) to MS1 were increased. In MS2, the NSF group displayed a stronger correlation (mean±std. = 0.618±0.025 vs. 0.571±0.034, p < 0.001) and a higher global explained variance (mean±std. = 0.083±0.035 vs. 0.055±0.023, p = 0.027) than the SF group. Conversely, Microstate 4 (MS4) in the SF group demonstrated significantly greater coverage (mean±std. = 0.388±0.074 vs. 0.334±0.052, p = 0.046) and more frequent transitions from MS2 to MS4, indicating a distinct pattern. Temporal parameters contribute major predictive role in predicting treatment outcomes of Oxcarbazepine, with area under curves (AUCs) of 0.95, 0.70, and 0.86, achieved by LR, NB and SVM, respectively. CONCLUSION: This study underscores the potential of EEG microstates as predictive biomarkers for Oxcarbazepine treatment responses in newly-diagnosed focal epilepsy patients.

Social functioning predicts individual changes in EEG microstates following intranasal oxytocin administration: A double-blind, cross-over randomized clinical trial.

Oxytocin (OXT) modulates social behaviors. However, the administration of exogenous OXT in humans produces inconsistent behavioral changes, affecting future consideration of OXT as a treatment for autism and other disorders with social symptoms. Inter-individual variability in social functioning traits might play a key role in how OXT changes brain activity and, therefore, behavior. Here, we investigated if inter-individual variability might dictate how single-dose intranasal OXT administration (IN-OXT) changes spontaneous neural activity during the eyes-open resting state. We used a double-blinded, randomized, placebo-controlled, cross-over design on 30 typically developing young adult men to investigate the dynamics of EEG microstates corresponding to activity in defined neural networks. We confirmed previous reports that, at the group level, IN-OXT increases the representation of the attention and salience microstates. Furthermore, we identified a decreased representation of microstates associated with the default mode network. Using multivariate partial least square statistical analysis, we found that social functioning traits associated with IN-OXT-induced changes in microstate dynamics in specific spectral bands. Correlation analysis further revealed that the higher the social functioning, the more IN-OXT increased the appearance of the visual network-associated microstate, and suppressed the appearance of a default mode network-related microstate. The lower the social functioning, the more IN-OXT increases the appearance of the salience microstate. The effects we report on the salience microstate support the hypothesis that OXT regulates behavior by enhancing social salience. Moreover, our findings indicate that social functioning traits modulate responses to IN-OXT and could partially explain the inconsistent reports on IN-OXT effects.

EEG Microstate Associated with Trait Nostalgia.

Nostalgia, a self-related emotion characterized by its bittersweet yet predominantly positive nature, plays a vital role in shaping individual psychology and behavior. This includes impacts on mental and physical health, behavioral patterns, and cognitive functions. However, higher levels of trait nostalgia may be linked to potential adverse outcomes, such as increased loneliness, heightened neuroticism, and more intense experiences of grief. The specific electroencephalography (EEG) feature associated with individuals exhibiting trait nostalgia, and how it differs from others, remains an area of uncertainty. To address this, our study employs microstate analysis to investigate the differences in resting-state EEG between individuals with varying levels of trait nostalgia. We assessed trait nostalgia in 63 participants using the Personal Inventory of Nostalgia and collected their resting-state EEG signals with eyes closed. The results of the regression analysis indicate a significant correlation between trait nostalgia and the temporal characteristics of microstates A, B, and C. Further, the occurrence of microstate B was significantly more frequent in the high trait nostalgia group than in the low trait nostalgia group. Independent samples t-test results showed that the transition probability between microstates A and B was significantly higher in the high trait nostalgia group. These results support the hypothesis that trait nostalgia is reflected in the resting state brain activity. Furthermore, they reveal a deeper sensory immersion in nostalgia experiences among individuals with high levels of trait nostalgia, and highlight the critical role of self-referential and autobiographical memory processes in nostalgia.

Corrigendum: The effect of acupuncture at the Taiyang acupoint on visual function and EEG microstates in myopia.

[This corrects the article DOI: 10.3389/fnint.2023.1234471.].

Microstate Analysis of Continuous Infant EEG: Tutorial and Reliability.

Microstate analysis of resting-state EEG is a unique data-driven method for identifying patterns of scalp potential topographies, or microstates, that reflect stable but transient periods of synchronized neural activity evolving dynamically over time. During infancy - a critical period of rapid brain development and plasticity - microstate analysis offers a unique opportunity for characterizing the spatial and temporal dynamics of brain activity. However, whether measurements derived from this approach (e.g., temporal properties, transition probabilities, neural sources) show strong psychometric properties (i.e., reliability) during infancy is unknown and key information for advancing our understanding of how microstates are shaped by early life experiences and whether they relate to individual differences in infant abilities. A lack of methodological resources for performing microstate analysis of infant EEG has further hindered adoption of this cutting-edge approach by infant researchers. As a result, in the current study, we systematically addressed these knowledge gaps and report that most microstate-based measurements of brain organization and functioning except for transition probabilities were stable with four minutes of video-watching resting-state data and highly internally consistent with just one minute. In addition to these results, we provide a step-by-step tutorial, accompanying website, and open-access data for performing microstate analysis using a free, user-friendly software called Cartool. Taken together, the current study supports the reliability and feasibility of using EEG microstate analysis to study infant brain development and increases the accessibility of this approach for the field of developmental neuroscience.