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Kong, Wanzeng, Luyun Wang, Jianhai Zhang, Qibin Zhao, and Junfeng Sun. "The Dynamic EEG Microstates in Mental Rotation." Sensors 18, no. 9 (September 3, 2018): 2920. http://dx.doi.org/10.3390/s18092920.
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Mental rotation is generally analyzed based on event-related potential (ERP) in a time domain with several characteristic electrodes, but neglects the whole spatial-temporal brain pattern in the cognitive process which may reflect the underlying cognitive mechanism. In this paper, we mainly proposed an approach based on microstates to examine the encoding of mental rotation from the spatial-temporal changes of EEG signals. In particular, we collected EEG data from 11 healthy subjects in a mental rotation cognitive task using 12 different stimulus pictures representing left and right hands at various rotational angles. We applied the microstate method to investigate the microstates conveyed by the event-related potential extracted from EEG data during mental rotation, and obtained four microstate modes (referred to as modes A, B, C, D, respectively). Subsequently, we defined several measures, including microstate sequences, topographical map, hemispheric lateralization, and duration of microstate, to characterize the dynamics of microstates during mental rotation. We observed that (1) the microstates sequence had a specified progressing mode, i.e., A → B → A ; (2) the activation of the right parietal occipital region was stronger than that of the left parietal occipital region according to the hemispheric lateralization of the microstates mode A; and (3) the duration of the second microstates mode A showed the shorter duration in the vertical stimuli, named “angle effect”.
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Shaw, Saurabh Bhaskar, Kiret Dhindsa, James P. Reilly, and Suzanna Becker. "Capturing the Forest but Missing the Trees: Microstates Inadequate for Characterizing Shorter-Scale EEG Dynamics." Neural Computation 31, no. 11 (November 2019): 2177–211. http://dx.doi.org/10.1162/neco_a_01229.
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The brain is known to be active even when not performing any overt cognitive tasks, and often it engages in involuntary mind wandering. This resting state has been extensively characterized in terms of fMRI-derived brain networks. However, an alternate method has recently gained popularity: EEG microstate analysis. Proponents of microstates postulate that the brain discontinuously switches between four quasi-stable states defined by specific EEG scalp topologies at peaks in the global field potential (GFP). These microstates are thought to be “atoms of thought,” involved with visual, auditory, salience, and attention processing. However, this method makes some major assumptions by excluding EEG data outside the GFP peaks and then clustering the EEG scalp topologies at the GFP peaks, assuming that only one microstate is active at any given time. This study explores the evidence surrounding these assumptions by studying the temporal dynamics of microstates and its clustering space using tools from dynamical systems analysis, fractal, and chaos theory to highlight the shortcomings in microstate analysis. The results show evidence of complex and chaotic EEG dynamics outside the GFP peaks, which is being missed by microstate analysis. Furthermore, the winner-takes-all approach of only one microstate being active at a time is found to be inadequate since the dynamic EEG scalp topology does not always resemble that of the assigned microstate, and there is competition among the different microstate classes. Finally, clustering space analysis shows that the four microstates do not cluster into four distinct and separable clusters. Taken collectively, these results show that the discontinuous description of EEG microstates is inadequate when looking at nonstationary short-scale EEG dynamics.
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Fu, Yunfa, Jian Chen, and Xin Xiong. "Calculation and Analysis of Microstate Related to Variation in Executed and Imagined Movement of Force of Hand Clenching." Computational Intelligence and Neuroscience 2018 (August 27, 2018): 1–15. http://dx.doi.org/10.1155/2018/9270685.
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Objective. In order to investigate electroencephalogram (EEG) instantaneous activity states related to executed and imagined movement of force of hand clenching (grip force: 4 kg, 10 kg, and 16 kg), we utilized a microstate analysis in which the spatial topographic map of EEG behaves in a certain number of discrete and stable global brain states. Approach. Twenty subjects participated in EEG collection; the global field power of EEG and its local maximum were calculated and then clustered using cross validation and statistics; the 4 parameters of each microstate (duration, occurrence, time coverage, and amplitude) were calculated from the clustering results and statistically analyzed by analysis of variance (ANOVA); finally, the relationship between the microstate and frequency band was analyzed. Main Results. The experimental results showed that all microstates related to executed and imagined grip force tasks were clustered into 3 microstate classes (A, B, and C); these microstates generally transitioned from A to B and then from B to C. With the increase of the target value of executed and imagined grip force, the duration and time coverage of microstate B gradually decreased, while these parameters of microstate C gradually increased. The occurrence times of microstate B and C related to executed grip force were significantly more than those related to imagined grip force; furthermore, the amplitudes of these 3 microstates related to executed grip force were significantly greater than those related to imagined grip force. The correlation coefficients between the microstates and the frequency bands indicated that the microstates were correlated to mu rhythm and beta frequency bands, which are consistent with event-related desynchronization/synchronization (ERD/ERS) phenomena of sensorimotor rhythm. Significance. It is expected that this microstate analysis may be used as a new method for observing EEG instantaneous activity patterns related to variation in executed and imagined grip force and also for extracting EEG features related to these tasks. This study may lay a foundation for the application of executed and imagined grip force training for rehabilitation of hand movement disorders in patients with stroke in the future.
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Shi, Wen, Yamin Li, Zhian Liu, Jing Li, Qiang Wang, Xiangguo Yan, and Gang Wang. "Non-Canonical Microstate Becomes Salient in High Density EEG During Propofol-Induced Altered States of Consciousness." International Journal of Neural Systems 30, no. 02 (January 23, 2020): 2050005. http://dx.doi.org/10.1142/s0129065720500057.
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Dynamically assessing the level of consciousness is still challenging during anesthesia. With the help of Electroencephalography (EEG), the human brain electric activity can be noninvasively measured at high temporal resolution. Several typical quasi-stable states are introduced to represent the oscillation of the global scalp electric field. These so-called microstates reflect spatiotemporal dynamics of coherent neural activities and capture the switch of brain states within the millisecond range. In this study, the microstates of high-density EEG were extracted and investigated during propofol-induced transition of consciousness. To analyze microstates on the frequency domain, a novel microstate-wise spectral analysis was proposed by the means of multivariate empirical mode decomposition and Hilbert–Huang transform. During the transition of consciousness, a map with a posterior central maximum denoted as microstate F appeared and became salient. The current results indicated that the coverage, occurrence, and power of microstate F significantly increased in moderate sedation. The results also demonstrated that the transition of brain state from rest to sedation was accompanied by significant increase in mean energy of all frequency bands in microstate F. Combined with studies on the possible cortical sources of microstates, the findings reveal that non-canonical microstate F is highly associated with propofol-induced altered states of consciousness. The results may also support the inference that this distinct topography can be derived from canonical microstate C (anterior-posterior orientation). Finally, this study further develops pertinent methodology and extends possible applications of the EEG microstate during propofol-induced anesthesia.
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Lehmann, Dietrich, Roberto Pascual-Marqui, and Christoph Michel. "EEG microstates." Scholarpedia 4, no. 3 (2009): 7632. http://dx.doi.org/10.4249/scholarpedia.7632.
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Khazaei, Mohammad, Khadijeh Raeisi, Pierpaolo Croce, Gabriella Tamburro, Anton Tokariev, Sampsa Vanhatalo, Filippo Zappasodi, and Silvia Comani. "Characterization of the Functional Dynamics in the Neonatal Brain during REM and NREM Sleep States by means of Microstate Analysis." Brain Topography 34, no. 5 (July 13, 2021): 555–67. http://dx.doi.org/10.1007/s10548-021-00861-1.
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AbstractNeonates spend most of their life sleeping. During sleep, their brain experiences fast changes in its functional organization. Microstate analysis permits to capture the rapid dynamical changes occurring in the functional organization of the brain by representing the changing spatio-temporal features of the electroencephalogram (EEG) as a sequence of short-lasting scalp topographies—the microstates. In this study, we modeled the ongoing neonatal EEG into sequences of a limited number of microstates and investigated whether the extracted microstate features are altered in REM and NREM sleep (usually known as active and quiet sleep states—AS and QS—in the newborn) and depend on the EEG frequency band. 19-channel EEG recordings from 60 full-term healthy infants were analyzed using a modified version of the k-means clustering algorithm. The results show that ~ 70% of the variance in the datasets can be described using 7 dominant microstate templates. The mean duration and mean occurrence of the dominant microstates were significantly different in the two sleep states. Microstate syntax analysis demonstrated that the microstate sequences characterizing AS and QS had specific non-casual structures that differed in the two sleep states. Microstate analysis of the neonatal EEG in specific frequency bands showed a clear dependence of the explained variance on frequency. Overall, our findings demonstrate that (1) the spatio-temporal dynamics of the neonatal EEG can be described by non-casual sequences of a limited number of microstate templates; (2) the brain dynamics described by these microstate templates depends on frequency; (3) the features of the microstate sequences can well differentiate the physiological conditions characterizing AS and QS.
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Wang, Tianjun, Yun-Hsuan Chen, and Mohamad Sawan. "Exploring the Role of Visual Guidance in Motor Imagery-Based Brain-Computer Interface: An EEG Microstate-Specific Functional Connectivity Study." Bioengineering 10, no. 3 (February 21, 2023): 281. http://dx.doi.org/10.3390/bioengineering10030281.
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Motor imagery-based brain–computer interfaces (BCI) have been widely recognized as beneficial tools for rehabilitation applications. Moreover, visually guided motor imagery was introduced to improve the rehabilitation impact. However, the reported results to support these techniques remain unsatisfactory. Electroencephalography (EEG) signals can be represented by a sequence of a limited number of topographies (microstates). To explore the dynamic brain activation patterns, we conducted EEG microstate and microstate-specific functional connectivity analyses on EEG data under motor imagery (MI), motor execution (ME), and guided MI (GMI) conditions. By comparing sixteen microstate parameters, the brain activation patterns induced by GMI show more similarities to ME than MI from a microstate perspective. The mean duration and duration of microstate four are proposed as biomarkers to evaluate motor condition. A support vector machine (SVM) classifier trained with microstate parameters achieved average accuracies of 80.27% and 66.30% for ME versus MI and GMI classification, respectively. Further, functional connectivity patterns showed a strong relationship with microstates. Key node analysis shows clear switching of key node distribution between brain areas among different microstates. The neural mechanism of the switching pattern is discussed. While microstate analysis indicates similar brain dynamics between GMI and ME, graph theory-based microstate-specific functional connectivity analysis implies that visual guidance may reduce the functional integration of the brain network during MI. Thus, we proposed that combined MI and GMI for BCI can improve neurorehabilitation effects. The present findings provide insights for understanding the neural mechanism of microstates, the role of visual guidance in MI tasks, and the experimental basis for developing new BCI-aided rehabilitation systems.
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Tayade, Prashant, Simran Kaur, Suriya Prakash Muthukrishnan, Ratna Sharma, and Gaurav Saini. "EEG microstates in resting condition in young indians." Indian Journal of Physiology and Pharmacology 66 (October 10, 2022): 175–80. http://dx.doi.org/10.25259/ijpp_44_2022.
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Objectives: The map topography analysis gives an idea of temporal dynamics of electric fields, which is reference independent, making the results unambiguous. These topographic maps remain stable for 80 to 100 milliseconds, abruptly shifting to a new topographic map configuration and remains stable in that state are called the ‘functional microstates’ as described by Lehmann et al (1987). There has been no study done in the resting state eye closed and eye open conditions showing the microstate maps in healthy Indian subjects in resting eyes open and resting eyes closed condition using 128 channel EEG. So our study aim was to assess the microstates in resting eyes closed and eyes open condition. And to compare the microstate parameters such as mean duration, global explained variance (GEV) and time coverage between eyes closed and eyes open condition. Materials and Methods: A cross-sectional and observational study on 20 indian subjects (Mean age- 26.65 and (SD) - 2.78 years) was done on resting eyes closed and eyes open conditions. After EEG acquisition using 128 channel EEG machine, EEG was preprocessed and microstate analysis using CARTOOL software was performed on the EEG data. Results: After microstate analysis four maps topography were obtained. There was statistically non-significant difference observed in microstate maps between resting eyes closed and resting eyes open condition for the all parameters i.e. mean duration, time coverage and GEV. Conclusion: Our findings suggests that, both eyes closed and eyes open conditions were similar to each other in terms of stability and predominance of Maps.
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Jabès, Adeline, Giuliana Klencklen, Paolo Ruggeri, Christoph M. Michel, Pamela Banta Lavenex, and Pierre Lavenex. "Resting‐State EEG Microstates Parallel Age‐Related Differences in Allocentric Spatial Working Memory Performance." Brain Topography 34, no. 4 (April 19, 2021): 442–60. http://dx.doi.org/10.1007/s10548-021-00835-3.
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AbstractAlterations of resting-state EEG microstates have been associated with various neurological disorders and behavioral states. Interestingly, age-related differences in EEG microstate organization have also been reported, and it has been suggested that resting-state EEG activity may predict cognitive capacities in healthy individuals across the lifespan. In this exploratory study, we performed a microstate analysis of resting-state brain activity and tested allocentric spatial working memory performance in healthy adult individuals: twenty 25–30-year-olds and twenty-five 64–75-year-olds. We found a lower spatial working memory performance in older adults, as well as age-related differences in the five EEG microstate maps A, B, C, C′ and D, but especially in microstate maps C and C′. These two maps have been linked to neuronal activity in the frontal and parietal brain regions which are associated with working memory and attention, cognitive functions that have been shown to be sensitive to aging. Older adults exhibited lower global explained variance and occurrence of maps C and C′. Moreover, although there was a higher probability to transition from any map towards maps C, C′ and D in young and older adults, this probability was lower in older adults. Finally, although age-related differences in resting-state EEG microstates paralleled differences in allocentric spatial working memory performance, we found no evidence that any individual or combination of resting-state EEG microstate parameter(s) could reliably predict individual spatial working memory performance. Whether the temporal dynamics of EEG microstates may be used to assess healthy cognitive aging from resting-state brain activity requires further investigation.
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Tarailis, Povilas, Dovilė Šimkutė, Thomas Koenig, and Inga Griškova-Bulanova. "Relationship between Spatiotemporal Dynamics of the Brain at Rest and Self-Reported Spontaneous Thoughts: An EEG Microstate Approach." Journal of Personalized Medicine 11, no. 11 (November 17, 2021): 1216. http://dx.doi.org/10.3390/jpm11111216.
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Rationale: The resting-state paradigm is frequently applied in electroencephalography (EEG) research; however, it is associated with the inability to control participants’ thoughts. To quantify subjects’ subjective experiences at rest, the Amsterdam Resting-State Questionnaire (ARSQ) was introduced covering ten dimensions of mind wandering. We aimed to estimate associations between subjective experiences and resting-state microstates of EEG. Methods: 5 min resting-state EEG data of 197 subjects was used to evaluate temporal properties of seven microstate classes. Bayesian correlation approach was implemented to assess associations between ARSQ domains assessed after resting and parameters of microstates. Results: Several associations between Comfort, Self and Somatic Awareness domains and temporal properties of neuroelectric microstates were revealed. The positive correlation between Comfort and duration of microstates E showed the strongest evidence (BF10 > 10); remaining correlations showed substantial evidence (10 > BF10 > 3). Conclusion: Our study indicates the relevance of assessments of spontaneous thought occurring during the resting-state for the understanding of the intrinsic brain activity reflected in microstates.
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Gu, Feng, Anmin Gong, Yi Qu, Hui Xiao, Jin Wu, Wenya Nan, Changhao Jiang, and Yunfa Fu. "Research on Top Archer’s EEG Microstates and Source Analysis in Different States." Brain Sciences 12, no. 8 (July 31, 2022): 1017. http://dx.doi.org/10.3390/brainsci12081017.
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The electroencephalograph (EEG) microstate is a method used to describe the characteristics of the EEG signal through the brain scalp electrode potential’s spatial distribution; as such, it reflects the changes in the brain’s functional state. The EEGs of 13 elite archers from China’s national archery team and 13 expert archers from China’s provincial archery team were recorded under the alpha rhythm during the resting state (with closed eyes) and during archery aiming. By analyzing the differences between the EEG microstate parameters and the correlation between these parameters with archery performance, as well as by combining our findings through standardized low-resolution brain electromagnetic tomography source analysis (sLORETA), we explored the changes in the neural activity of professional archers of different levels, under different states. The results of the resting state study demonstrated that the duration, occurrence, and coverage in microstate D of elite archers were significantly higher than those of expert archers and that their other microstates had the greatest probability of transferring to microstate D. During the archery aiming state, the average transition probability of the other microstates transferring to microstate in the left temporal region was the highest observed in the two groups of archers. Moreover, there was a significant negative correlation between the duration and coverage of microstates in the frontal region of elite archers and their archery performance. Our findings indicate that elite archers are more active in the dorsal attention system and demonstrate a higher neural efficiency during the resting state. When aiming, professional archers experience an activation of brain regions associated with archery by suppressing brain regions unrelated to archery tasks. These findings provide a novel theoretical basis for the study of EEG microstate dynamics in archery and related cognitive motor tasks, particularly from the perspective of the subject’s mental state.
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Nguyen, Tien Dat, Van Toi Vo, and Thi Thanh Huong Ha. "Bipolar disorder traits: An electroencephalogram systematic review." Ministry of Science and Technology, Vietnam 64, no. 4 (December 15, 2022): 84–90. http://dx.doi.org/10.31276/vjste.64(4).84-90.
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Bipolar disorder (BD) is a serious mental disorder that globally affected 40 million people in 2019. According to the National Alliance on Mental Illness (NAMI), the present state of scientific knowledge only permits psychiatrists to diagnose BD using subjective and imprecise questionnaires. Therefore, developing a diagnostic tool with objective and precise biomarkers should be a major focus of research in this field. Among the potential biomarkers for BD, electroencephalogram (EEG)-based signatures of BD are considered to be the most optimal marker due to their strong links with behavioural symptoms and also their non-invasiveness. The goal of this review is to give a detailed summary of current techniques for investigating the traces of BD through EEG abnormalities. In this review, 13 studies from databases such as ScienceDirect and PubMed seeking to utilize EEG characteristics to diagnose BD were selected. The search keywords were “EEG in BD diagnosis”, “EEG microstates in BD”, and “EEG features for BD patients”. The publication date was set from 2007 to 2021. From these studies, we synthesize the effects of BD on each EEG feature, as well as detail the pros and cons when using each feature as a biomarker for BD. Results showed that EEG microstates demonstrate their potential among the seven EEG properties discussed in this article, as shown by several studies. By definition, EEG microstates are a dynamic representation of the spatial distribution of the scalp's electric potential as it varies over time. Specifically, four microstate classes recorded in different brain regions are classified into A (right-frontal left-posterior), B (left-frontal right-posterior), C (midline frontal-occipital), and D (midline frontal topographies). Greater presence of microstate class B in BD patients during task-free resting states are a distinctive characteristic of BD patients from which BD can be differentiated from other psychiatric illnesses. Besides microstates, EEG resting states are also considered to have a bright future in BD diagnosis. Specifically, by investigating brain frequency bands, researchers have discovered that BD patients exhibit abnormal delta and alpha signals as compared to healthy controls (HCs). The abnormalities of microstate B in EEG microstate characteristics would be the most promising biomarker for detecting BD. In addition, anomalies in delta and alpha signals during resting EEG states are possible BD diagnostic indicators.
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Cui, Yujie, Songyun Xie, Yingxin Fu, and Xinzhou Xie. "Predicting Motor Imagery BCI Performance Based on EEG Microstate Analysis." Brain Sciences 13, no. 9 (September 6, 2023): 1288. http://dx.doi.org/10.3390/brainsci13091288.
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Motor imagery (MI) electroencephalography (EEG) is natural and comfortable for controllers, and has become a research hotspot in the field of the brain–computer interface (BCI). Exploring the inter-subject MI-BCI performance variation is one of the fundamental problems in MI-BCI application. EEG microstates with high spatiotemporal resolution and multichannel information can represent brain cognitive function. In this paper, four EEG microstates (MS1, MS2, MS3, MS4) were used in the analysis of the differences in the subjects’ MI-BCI performance, and the four microstate feature parameters (the mean duration, the occurrences per second, the time coverage ratio, and the transition probability) were calculated. The correlation between the resting-state EEG microstate feature parameters and the subjects’ MI-BCI performance was measured. Based on the negative correlation of the occurrence of MS1 and the positive correlation of the mean duration of MS3, a resting-state microstate predictor was proposed. Twenty-eight subjects were recruited to participate in our MI experiments to assess the performance of our resting-state microstate predictor. The experimental results show that the average area under curve (AUC) value of our resting-state microstate predictor was 0.83, and increased by 17.9% compared with the spectral entropy predictor, representing that the microstate feature parameters can better fit the subjects’ MI-BCI performance than spectral entropy predictor. Moreover, the AUC of microstate predictor is higher than that of spectral entropy predictor at both the single-session level and average level. Overall, our resting-state microstate predictor can help MI-BCI researchers better select subjects, save time, and promote MI-BCI development.
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Chen, Chen, Jinying Han, Shuang Zheng, Xintong Zhang, Haibo Sun, Ting Zhou, Shunyin Hu, et al. "Dynamic Changes of Brain Activity in Different Responsive Groups of Patients with Prolonged Disorders of Consciousness." Brain Sciences 13, no. 1 (December 20, 2022): 5. http://dx.doi.org/10.3390/brainsci13010005.
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As medical technology continues to improve, many patients diagnosed with brain injury survive after treatments but are still in a coma. Further, multiple clinical studies have demonstrated recovery of consciousness after transcranial direct current stimulation. To identify possible neurophysiological mechanisms underlying disorders of consciousness (DOCs) improvement, we examined the changes in multiple resting-state EEG microstate parameters after high-definition transcranial direct current stimulation (HD-tDCS). Because the left dorsolateral prefrontal cortex is closely related to consciousness, it is often chosen as a stimulation target for tDCS treatment of DOCs. A total of 21 patients diagnosed with prolonged DOCs were included in this study, and EEG microstate analysis of resting state EEG datasets was performed on all patients before and after interventions. Each of them underwent 10 anodal tDCS sessions of the left dorsolateral prefrontal cortex over 5 consecutive working days. According to whether the clinical manifestations improved, DOCs patients were divided into the responsive (RE) group and the non-responsive (N-RE) group. The dynamic changes of resting state EEG microstate parameters were also analyzed. After multiple HD-tDCS interventions, the duration and coverage of class C microstates in the RE group were significantly increased. This study also found that the transition between microstates A and C increased, while the transition between microstates B and D decreased in the responsive group. However, these changes in EEG microstate parameters in the N-RE group have not been reported. Our findings suggest that EEG neural signatures have the potential to assess consciousness states and that improvement in the dynamics of brain activity was associated with the recovery of DOCs. This study extends our understanding of the neural mechanism of DOCs patients in consciousness recovery.
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Kim, Kyungwon, Nguyen Thanh Duc, Min Choi, and Boreom Lee. "EEG microstate features for schizophrenia classification." PLOS ONE 16, no. 5 (May 14, 2021): e0251842. http://dx.doi.org/10.1371/journal.pone.0251842.
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Electroencephalography (EEG) microstate analysis is a method wherein spontaneous EEG activity is segmented at sub-second levels to analyze quasi-stable states. In particular, four archetype microstates and their features are known to reflect changes in brain state in neuropsychiatric diseases. However, previous studies have only reported differences in each microstate feature and have not determined whether microstate features are suitable for schizophrenia classification. Therefore, it is necessary to validate microstate features for schizophrenia classification. Nineteen microstate features, including duration, occurrence, and coverage as well as thirty-one conventional EEG features, including statistical, frequency, and temporal characteristics were obtained from resting-state EEG recordings of 14 patients diagnosed with schizophrenia and from 14 healthy (control) subjects. Machine-learning based multivariate analysis was used to evaluate classification performance. EEG recordings of patients and controls showed different microstate features. More importantly, when differentiating among patients and controls, EEG microstate features outperformed conventional EEG ones. The performance of the microstate features exceeded that of conventional EEG, even after optimization using recursive feature elimination. EEG microstate features applied with conventional EEG features also showed better classification performance than conventional EEG features alone. The current study is the first to validate the use of microstate features to discriminate schizophrenia, suggesting that EEG microstate features are useful for schizophrenia classification.
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Keihani, Ahmadreza, Seyed Saman Sajadi, Mahsa Hasani, and Fabio Ferrarelli. "Bayesian Optimization of Machine Learning Classification of Resting-State EEG Microstates in Schizophrenia: A Proof-of-Concept Preliminary Study Based on Secondary Analysis." Brain Sciences 12, no. 11 (November 4, 2022): 1497. http://dx.doi.org/10.3390/brainsci12111497.
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Resting-state electroencephalography (EEG) microstates reflect sub-second, quasi-stable states of brain activity. Several studies have reported alterations of microstate features in patients with schizophrenia (SZ). Based on these findings, it has been suggested that microstates may represent neurophysiological biomarkers for the classification of SZ. To explore this possibility, machine learning approaches can be employed. Bayesian optimization is a machine learning approach that selects the best-fitted machine learning model with tuned hyperparameters from existing models to improve the classification. In this proof-of-concept preliminary study based on secondary analysis, 20 microstate features were extracted from 14 SZ patients and 14 healthy controls’ EEG signals. These parameters were then ranked as predictors based on their importance, and an optimized machine learning approach was applied to evaluate the performance of the classification. SZ patients had altered microstate features compared to healthy controls. Furthermore, Bayesian optimization outperformed conventional multivariate analyses and showed the highest accuracy (90.93%), AUC (0.90), sensitivity (91.37%), and specificity (90.48%), with reliable results using just six microstate predictors. Altogether, in this proof-of-concept study, we showed that machine learning with Bayesian optimization can be utilized to characterize EEG microstate alterations and contribute to the classification of SZ patients.
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Chang, Qi, Cancheng Li, Jicong Zhang, and Chuanyue Wang. "Dynamic brain functional network based on EEG microstate during sensory gating in schizophrenia." Journal of Neural Engineering 19, no. 2 (March 11, 2022): 026007. http://dx.doi.org/10.1088/1741-2552/ac5266.
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Abstract Objective. Cognitive impairment is one of the core symptoms of schizophrenia, with an emphasis on dysfunctional information processing. Sensory gating deficits have consistently been reported in schizophrenia, but the underlying physiological mechanism is not well-understood. We report the discovery and characterization of P50 dynamic brain connections based on microstate analysis. Approach. We identify five main microstates associated with the P50 response and the difference between the first and second click presentation (S1-S2-P50) in first-episode schizophrenia (FESZ) patients, ultra-high-risk individuals (UHR) and healthy controls (HCs). We used the signal segments composed of consecutive time points with the same microstate label to construct brain functional networks. Main results. The microstate with a prefrontal extreme location during the response to the S1 of P50 are statistically different in duration, occurrence and coverage among the FESZ, UHR and HC groups. In addition, a microstate with anterior–posterior orientation was found to be associated with S1-S2-P50 and its coverage was found to differ among the FESZ, UHR and HC groups. Source location of microstates showed that activated brain regions were mainly concentrated in the right temporal lobe. Furthermore, the connectivities between brain regions involved in P50 processing of HC were widely different from those of FESZ and UHR. Significance. Our results indicate that P50 suppression deficits in schizophrenia may be due to both aberrant baseline sensory perception and adaptation to repeated stimulus. Our findings provide new insight into the mechanisms of P50 suppression in the early stage of schizophrenia.
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de Grosbois, J., F. Colino, O. Krigolson, M. Heath, and G. Binsted. "EEG microstates during visually guided reaching." Journal of Vision 10, no. 7 (August 13, 2010): 1068. http://dx.doi.org/10.1167/10.7.1068.
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Mishra, Ashutosh, Bernhard Englitz, and Michael X. Cohen. "EEG microstates as a continuous phenomenon." NeuroImage 208 (March 2020): 116454. http://dx.doi.org/10.1016/j.neuroimage.2019.116454.
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Kang, Jiannan, Xiwang Fan, Yiwen Zhong, Manuel F. Casanova, Estate M. Sokhadze, Xiaoli Li, Zikang Niu, and Xinling Geng. "Transcranial Direct Current Stimulation Modulates EEG Microstates in Low-Functioning Autism: A Pilot Study." Bioengineering 10, no. 1 (January 11, 2023): 98. http://dx.doi.org/10.3390/bioengineering10010098.
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Autism spectrum disorder (ASD) is a heterogeneous disorder that affects several behavioral domains of neurodevelopment. Transcranial direct current stimulation (tDCS) is a new method that modulates motor and cognitive function and may have potential applications in ASD treatment. To identify its potential effects on ASD, differences in electroencephalogram (EEG) microstates were compared between children with typical development (n = 26) and those with ASD (n = 26). Furthermore, children with ASD were divided into a tDCS (experimental) and sham stimulation (control) group, and EEG microstates and Autism Behavior Checklist (ABC) scores before and after tDCS were compared. Microstates A, B, and D differed significantly between children with TD and those with ASD. In the experimental group, the scores of microstates A and C and ABC before tDCS differed from those after tDCS. Conversely, in the control group, neither the EEG microstates nor the ABC scores before the treatment period (sham stimulation) differed from those after the treatment period. This study indicates that tDCS may become a viable treatment for ASD.
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Ruiz, Pablo, Raquel Tinoco-Egas, and Carlos Cevallos. "Neural States in Tourism Travel Videos." Proceedings 71, no. 1 (November 18, 2020): 6. http://dx.doi.org/10.3390/iecbs-08465.
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In marketing, there are many methods to relate reactions to products to customer preference. Current electroencephalography (EEG) signal analysis in the neuromarketing field focuses mainly on correlations between selected electrodes and hemisphere-based analysis on single scalp measures. The present study shows microstate analysis of brain EEG signals in goal-oriented videos. We measured a 16 channel EEG with an Emotiv EPOC+ device. We used two oriented videos from the Ecuadorian Government to publicize Ecuador as a tourist destination. We used a Topographic Atomize and Agglomerate Hierarchical Clustering (TAAHC) microstate analysis for the duration of the EEG as the participants watched each video. We picked the four predominant, in total time and repeatability, microstate maps that represent more than 50% of the entire recording time. We also show, in time, how topographies are represented along the video, which in a later step could be correlated with the images observed in the videos. We show the existing relations between the existing microstates. A microstate analysis of brain signal behavior across time might be a valid methodology and useful tool to analyze videos with marketing purposes.
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de Bock, Renate. "Resting-State EEG Microstates in Psychotic Disorders." International Journal of Psychophysiology 168 (October 2021): S22—S23. http://dx.doi.org/10.1016/j.ijpsycho.2021.07.067.
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Brodbeck, Verena, Alena Kuhn, Frederic von Wegner, Astrid Morzelewski, Enzo Tagliazucchi, Sergey Borisov, Christoph M. Michel, and Helmut Laufs. "EEG microstates of wakefulness and NREM sleep." NeuroImage 62, no. 3 (September 2012): 2129–39. http://dx.doi.org/10.1016/j.neuroimage.2012.05.060.
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Schlegel, Felix, Dietrich Lehmann, Pascal L. Faber, Patricia Milz, and Lorena R. R. Gianotti. "EEG Microstates During Resting Represent Personality Differences." Brain Topography 25, no. 1 (June 5, 2011): 20–26. http://dx.doi.org/10.1007/s10548-011-0189-7.
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Férat, Victor, Mathieu Scheltienne, Denis Brunet, Tomas Ros, and Christoph Michel. "Pycrostates: a Python library to study EEG microstates." Journal of Open Source Software 7, no. 78 (October 13, 2022): 4564. http://dx.doi.org/10.21105/joss.04564.
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Croce, Pierpaolo, Sara Spadone, Filippo Zappasodi, Antonello Baldassarre, and Paolo Capotosto. "rTMS affects EEG microstates dynamic during evoked activity." Cortex 138 (May 2021): 302–10. http://dx.doi.org/10.1016/j.cortex.2021.02.014.
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Croce, Pierpaolo, Filippo Zappasodi, Sara Spadone, and Paolo Capotosto. "Magnetic stimulation selectively affects pre-stimulus EEG microstates." NeuroImage 176 (August 2018): 239–45. http://dx.doi.org/10.1016/j.neuroimage.2018.04.061.
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Zappasodi, Filippo, Pierpaolo Croce, Alessandro Giordani, Giovanni Assenza, Nadia M. Giannantoni, Paolo Profice, Giuseppe Granata, Paolo M. Rossini, and Franca Tecchio. "Prognostic Value of EEG Microstates in Acute Stroke." Brain Topography 30, no. 5 (May 25, 2017): 698–710. http://dx.doi.org/10.1007/s10548-017-0572-0.
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Ricci, Lorenzo, Pierpaolo Croce, Jacopo Lanzone, Marilisa Boscarino, Filippo Zappasodi, Mario Tombini, Vincenzo Di Lazzaro, and Giovanni Assenza. "Transcutaneous Vagus Nerve Stimulation Modulates EEG Microstates and Delta Activity in Healthy Subjects." Brain Sciences 10, no. 10 (September 25, 2020): 668. http://dx.doi.org/10.3390/brainsci10100668.
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Transcutaneous vagus nerve stimulation (tVNS) is an alternative non-invasive method for the electrical stimulation of the vagus nerve with the goal of treating several neuropsychiatric disorders. The objective of this study is to assess the effects of tVNS on cerebral cortex activity in healthy volunteers using resting-state microstates and power spectrum electroencephalography (EEG) analysis. Eight male subjects aged 25–45 years were recruited in this randomized sham-controlled double-blind study with cross-over design. Real tVNS was administered at the left external acoustic meatus, while sham stimulation was performed at the left ear lobe, both of them for 60 min. The EEG recording lasted 5 min and was performed before and 60 min following the tVNS experimental session. We observed that real tVNS induced an increase in the metrics of microstate A mean duration (p = 0.039) and an increase in EEG power spectrum activity in the delta frequency band (p < 0.01). This study confirms that tVNS is an effective way to stimulate the vagus nerve, and the mechanisms of action of this activation can be successfully studied using scalp EEG quantitative metrics. Future studies are warranted to explore the clinical implications of these findings and to focus the research of the prognostic biomarkers of tVNS therapy for neuropsychiatric diseases.
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Kondakor, I., D. Lehmann, C. M. Michel, D. Brandeis, K. Kochi, and T. Koenig. "Prestimulus EEG microstates influence visual event-related potential microstates in field maps with 47 channels." Journal of Neural Transmission 104, no. 2-3 (February 1997): 161–73. http://dx.doi.org/10.1007/bf01273178.
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Michalopoulos, Kostas, Michalis Zervakis, Marie-Pierre Deiber, and Nikolaos Bourbakis. "Classification of EEG Single Trial Microstates Using Local Global Graphs and Discrete Hidden Markov Models." International Journal of Neural Systems 26, no. 06 (July 19, 2016): 1650036. http://dx.doi.org/10.1142/s0129065716500362.
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We present a novel synergistic methodology for the spatio-temporal analysis of single Electroencephalogram (EEG) trials. This new methodology is based on the novel synergy of Local Global Graph (LG graph) to characterize define the structural features of the EEG topography as a global descriptor for robust comparison of dominant topographies (microstates) and Hidden Markov Models (HMM) to model the topographic sequence in a unique way. In particular, the LG graph descriptor defines similarity and distance measures that can be successfully used for the difficult comparison of the extracted LG graphs in the presence of noise. In addition, hidden states represent periods of stationary distribution of topographies that constitute the equivalent of the microstates in the model. The transitions between the different microstates and the formed syntactic patterns can reveal differences in the processing of the input stimulus between different pathologies. We train the HMM model to learn the transitions between the different microstates and express the syntactic patterns that appear in the single trials in a compact and efficient way. We applied this methodology in single trials consisting of normal subjects and patients with Progressive Mild Cognitive Impairment (PMCI) to discriminate these two groups. The classification results show that this approach is capable to efficiently discriminate between control and Progressive MCI single trials. Results indicate that HMMs provide physiologically meaningful results that can be used in the syntactic analysis of Event Related Potentials.
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Das, Sushmit, Reza Zomorrodi, Peter Enticott, Melissa Kirkovski, Daniel M. Blumberger, Tarek K. Rajji, and Pushpal Desarkar. "Atypical Resting State EEG Microstates in Autism: Preliminary Results." Biological Psychiatry 89, no. 9 (May 2021): S347. http://dx.doi.org/10.1016/j.biopsych.2021.02.865.
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Zappasodi, Filippo, Mauro Gianni Perrucci, Aristide Saggino, Pierpaolo Croce, Pasqua Mercuri, Roberta Romanelli, Roberto Colom, and Sjoerd J. H. Ebisch. "EEG microstates distinguish between cognitive components of fluid reasoning." NeuroImage 189 (April 2019): 560–73. http://dx.doi.org/10.1016/j.neuroimage.2019.01.067.
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Ricci, Lorenzo, Pierpaolo Croce, Patrizia Pulitano, Marilisa Boscarino, Filippo Zappasodi, Flavia Narducci, Jacopo Lanzone, et al. "Levetiracetam Modulates EEG Microstates in Temporal Lobe Epilepsy." Brain Topography, September 13, 2022. http://dx.doi.org/10.1007/s10548-022-00911-2.
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AbstractTo determine the effects of Levetiracetam (LEV) therapy using EEG microstates analysis in a population of newly diagnosed Temporal Lobe Epilepsy (TLE) patients. We hypothesized that the impact of LEV therapy on the electrical activity of the brain can be globally explored using EEG microstates. Twenty-seven patients with TLE were examined. We performed resting-state microstate EEG analysis and compared microstate metrics between the EEG performed at baseline (EEGpre) and after 3 months of LEV therapy (EEGpost). The microstates A, B, C and D emerged as the most stable. LEV induced a reduction of microstate B and D mean duration and occurrence per second (p < 0.01). Additionally, LEV treatment increased the directional predominance of microstate A to C and microstate B to D (p = 0.01). LEV treatment induces a modulation of resting-state EEG microstates in newly diagnosed TLE patients. Microstates analysis has the potential to identify a neurophysiological indicator of LEV therapeutic activity. This study of EEG microstates in people with epilepsy opens an interesting path to identify potential LEV activity biomarkers that may involve increased neuronal inhibition of the epileptic network.
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Xiong, Xin, Yuyan Ren, Shenghan Gao, Jianhua Luo, Jiangli Liao, Chunwu Wang, Sanli Yi, Ruixiang Liu, Yan Xiang, and Jianfeng He. "EEG microstate in obstructive sleep apnea patients." Scientific Reports 11, no. 1 (August 25, 2021). http://dx.doi.org/10.1038/s41598-021-95749-2.
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AbstractObstructive sleep apnea (OSA) is a common sleep respiratory disease. Previous studies have found that the wakefulness electroencephalogram (EEG) of OSA patients has changed, such as increased EEG power. However, whether the microstates reflecting the transient state of the brain is abnormal is unclear during obstructive hypopnea (OH). We investigated the microstates of sleep EEG in 100 OSA patients. Then correlation analysis was carried out between microstate parameters and EEG markers of sleep disturbance, such as power spectrum, sample entropy and detrended fluctuation analysis (DFA). OSA_OH patients showed that the microstate C increased presence and the microstate D decreased presence compared to OSA_withoutOH patients and controls. The fifth microstate E appeared during N1-OH, but the probability of other microstates transferring to microstate E was small. According to the correlation analysis, OSA_OH patients in N1-OH showed that the microstate D was positively correlated with delta power, and negatively correlated with beta and alpha power; the transition probability of the microstate B → C and E → C was positively correlated with alpha power. In other sleep stages, the microstate parameters were not correlated with power, sample entropy and FDA. We might interpret that the abnormal transition of brain active areas of OSA patients in N1-OH stage leads to abnormal microstates, which might be related to the change of alpha activity in the cortex.
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Sun, Qiaoling, Linlin Zhao, and Liwen Tan. "Abnormalities of Electroencephalography Microstates in Drug-Naïve, First-Episode Schizophrenia." Frontiers in Psychiatry 13 (March 14, 2022). http://dx.doi.org/10.3389/fpsyt.2022.853602.
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ObjectiveMicrostate analysis is a powerful tool to probe the brain functions, and changes in microstates under electroencephalography (EEG) have been repeatedly reported in patients with schizophrenia. This study aimed to investigate the dynamics of EEG microstates in drug-naïve, first-episode schizophrenia (FE-SCH) and to test the relationship between EEG microstates and clinical symptoms.MethodsResting-state EEG were recorded for 23 patients with FE-SCH and 23 healthy controls using a 64-channel cap. Three parameters, i.e., contribution, duration, and occurrence, of the four microstate classes were calculated. Group differences in EEG microstates and their clinical symptoms [assessed using the Positive and Negative Syndrome Scale (PANSS)] were analyzed.ResultsCompared with healthy controls, patients with FE-SCH showed increased duration, occurrence and contribution of microstate class C and decreased contribution and occurrence of microstate class D. In addition, the score of positive symptoms in PANSS was negatively correlated with the occurrence of microstate D.ConclusionOur findings showed abnormal patterns of EEG microstates in drug-naïve, first-episode schizophrenia, which might help distinguish individuals with schizophrenia in the early stage and develop early intervention strategies.
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Sun, Qiaoling, Jiansong Zhou, Huijuan Guo, Ningzhi Gou, Ruoheng Lin, Ying Huang, Weilong Guo, and Xiaoping Wang. "EEG Microstates and Its Relationship With Clinical Symptoms in Patients With Schizophrenia." Frontiers in Psychiatry 12 (October 28, 2021). http://dx.doi.org/10.3389/fpsyt.2021.761203.
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Schizophrenia is a complex and devastating disorder with unclear pathogenesis. Electroencephalogram (EEG) microstates have been suggested as a potential endophenotype for this disorder. However, no clear dynamic pattern of microstates has been found. This study aims to identify the dynamics of EEG microstates in schizophrenia and to test whether schizophrenia patients with altered clinical symptoms severity showed different microstates abnormalities compared with healthy controls. Resting-state EEG data in 46 individuals who met the ICD-10 diagnostic criteria for schizophrenia and 39 healthy controls was recorded. The patients with schizophrenia were divided into subgroups based on the level of their negative or positive symptoms assessed using the Positive and Negative Syndrome Scale. Microstate parameters (contribution, occurrence, and duration) of four prototypical microstate classes (A–D) were investigated. Compared with healthy controls, individuals with schizophrenia showed increased duration and contribution of microstate class C, decreased contribution and occurrence of microstate class B. Different microstate patterns were found between subgroups and healthy controls. Results in this study support the consistent observation of abnormal EEG microstates patterns in patients with schizophrenia and highlight the necessity to divide subjects into subgroups according to their clinical symptoms.
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Qiu, Shuang, Xiaohan Lyu, Qianqian Zheng, Huiguang He, Richu Jin, and Weiwei Peng. "Temporal dynamics of electroencephalographic microstates during sustained pain." Cerebral Cortex, April 27, 2023. http://dx.doi.org/10.1093/cercor/bhad143.
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Abstract Brain dynamics can be modeled by a sequence of transient, nonoverlapping patterns of quasi-stable electrical potentials named “microstates.” While electroencephalographic (EEG) microstates among patients with chronic pain remained inconsistent in the literature, this study characterizes the temporal dynamics of EEG microstates among healthy individuals during experimental sustained pain. We applied capsaicin (pain condition) or control (no-pain condition) cream to 58 healthy participants in different sessions and recorded resting-state EEG 15 min after application. We identified 4 canonical microstates (A–D) that are related to auditory, visual, salience, and attentional networks. Microstate C had less occurrence, as were bidirectional transitions between microstate C and microstates A and B during sustained pain. In contrast, sustained pain was associated with more frequent and longer duration of microsite D, as well as more bidirectional transitions between microstate D and microstates A and B. Microstate D duration positively correlated with intensity of ongoing pain. Sustained pain improved global integration within microstate C functional network, but weakened global integration and efficiency within microstate D functional network. These results suggest that sustained pain leads to an imbalance between processes that load on saliency (microstate C) and processes related to switching and reorientation of attention (microstate D).
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Das, Sushmit, Reza Zomorrodi, Peter G. Enticott, Melissa Kirkovski, Daniel M. Blumberger, Tarek K. Rajji, and Pushpal Desarkar. "Resting state electroencephalography microstates in autism spectrum disorder: A mini-review." Frontiers in Psychiatry 13 (December 1, 2022). http://dx.doi.org/10.3389/fpsyt.2022.988939.
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Atypical spatial organization and temporal characteristics, found via resting state electroencephalography (EEG) microstate analysis, have been associated with psychiatric disorders but these temporal and spatial parameters are less known in autism spectrum disorder (ASD). EEG microstates reflect a short time period of stable scalp potential topography. These canonical microstates (i.e., A, B, C, and D) and more are identified by their unique topographic map, mean duration, fraction of time covered, frequency of occurrence and global explained variance percentage; a measure of how well topographical maps represent EEG data. We reviewed the current literature for resting state microstate analysis in ASD and identified eight publications. This current review indicates there is significant alterations in microstate parameters in ASD populations as compared to typically developing (TD) populations. Microstate parameters were also found to change in relation to specific cognitive processes. However, as microstate parameters are found to be changed by cognitive states, the differently acquired data (e.g., eyes closed or open) resting state EEG are likely to produce disparate results. We also review the current understanding of EEG sources of microstates and the underlying brain networks.
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Hu, Wanrou, Zhiguo Zhang, Huilin Zhao, Li Zhang, Linling Li, Gan Huang, and Zhen Liang. "EEG microstate correlates of emotion dynamics and stimulation content during video watching." Cerebral Cortex, March 9, 2022. http://dx.doi.org/10.1093/cercor/bhac082.
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Abstract Introduction EEG microstates have been widely adopted to understand the complex and dynamic-changing process in dynamic brain systems, but how microstates are temporally modulated by emotion dynamics is still unclear. An investigation of EEG microstates under video-evoking emotion dynamics modulation would provide a novel insight into the understanding of temporal dynamics of functional brain networks. Methods In the present study, we postulate that emotional states dynamically modulate the microstate patterns, and perform an in-depth investigation between EEG microstates and emotion dynamics under a video-watching task. By mapping from subjective-experienced emotion states and objective-presented stimulation content to EEG microstates, we gauge the comprehensive associations among microstates, emotions, and multimedia stimulation. Results The results show that emotion dynamics could be well revealed by four EEG microstates (MS1, MS2, MS3, and MS4), where MS3 and MS4 are found to be highly correlated to different emotion states (emotion task effect and level effect) and the affective information involved in the multimedia content (visual and audio). Conclusion In this work, we reveal the microstate patterns related to emotion dynamics from sensory and stimulation dimensions, which deepens the understanding of the neural representation under emotion dynamics modulation and will be beneficial for the future study of brain dynamic systems.
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Tait, Luke, Francesco Tamagnini, George Stothart, Edoardo Barvas, Chiara Monaldini, Roberto Frusciante, Mirco Volpini, et al. "EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease." Scientific Reports 10, no. 1 (October 19, 2020). http://dx.doi.org/10.1038/s41598-020-74790-7.
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Abstract The dynamics of the resting brain exhibit transitions between a small number of discrete networks, each remaining stable for tens to hundreds of milliseconds. These functional microstates are thought to be the building blocks of spontaneous consciousness. The electroencephalogram (EEG) is a useful tool for imaging microstates, and EEG microstate analysis can potentially give insight into altered brain dynamics underpinning cognitive impairment in disorders such as Alzheimer’s disease (AD). Since EEG is non-invasive and relatively inexpensive, EEG microstates have the potential to be useful clinical tools for aiding early diagnosis of AD. In this study, EEG was collected from two independent cohorts of probable AD and cognitively healthy control participants, and a cohort of mild cognitive impairment (MCI) patients with four-year clinical follow-up. The microstate associated with the frontoparietal working-memory/attention network was altered in AD due to parietal inactivation. Using a novel measure of complexity, we found microstate transitioning was slower and less complex in AD. When combined with a spectral EEG measure, microstate complexity could classify AD with sensitivity and specificity > 80%, which was tested on an independent cohort, and could predict progression from MCI to AD in a small preliminary test cohort of 11 participants. EEG microstates therefore have potential to be a non-invasive functional biomarker of AD.
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Mikutta, Christian A., Robert T. Knight, Daniela Sammler, Thomas J. Müller, and Thomas Koenig. "Electrocorticographic Activation Patterns of Electroencephalographic Microstates." Brain Topography, March 20, 2023. http://dx.doi.org/10.1007/s10548-023-00952-1.
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AbstractElectroencephalography (EEG) microstates are short successive periods of stable scalp field potentials representing spontaneous activation of brain resting-state networks. EEG microstates are assumed to mediate local activity patterns. To test this hypothesis, we correlated momentary global EEG microstate dynamics with the local temporo-spectral evolution of electrocorticography (ECoG) and stereotactic EEG (SEEG) depth electrode recordings. We hypothesized that these correlations involve the gamma band. We also hypothesized that the anatomical locations of these correlations would converge with those of previous studies using either combined functional magnetic resonance imaging (fMRI)-EEG or EEG source localization. We analyzed resting-state data (5 min) of simultaneous noninvasive scalp EEG and invasive ECoG and SEEG recordings of two participants. Data were recorded during the presurgical evaluation of pharmacoresistant epilepsy using subdural and intracranial electrodes. After standard preprocessing, we fitted a set of normative microstate template maps to the scalp EEG data. Using covariance mapping with EEG microstate timelines and ECoG/SEEG temporo-spectral evolutions as inputs, we identified systematic changes in the activation of ECoG/SEEG local field potentials in different frequency bands (theta, alpha, beta, and high-gamma) based on the presence of particular microstate classes. We found significant covariation of ECoG/SEEG spectral amplitudes with microstate timelines in all four frequency bands (p = 0.001, permutation test). The covariance patterns of the ECoG/SEEG electrodes during the different microstates of both participants were similar. To our knowledge, this is the first study to demonstrate distinct activation/deactivation patterns of frequency-domain ECoG local field potentials associated with simultaneous EEG microstates.
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Wang, Fanglan, Khamlesh Hujjaree, and Xiaoping Wang. "Electroencephalographic Microstates in Schizophrenia and Bipolar Disorder." Frontiers in Psychiatry 12 (February 26, 2021). http://dx.doi.org/10.3389/fpsyt.2021.638722.
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Schizophrenia (SCH) and bipolar disorder (BD) are characterized by many types of symptoms, damaged cognitive function, and abnormal brain connections. The microstates are considered to be the cornerstones of the mental states shown in EEG data. In our study, we investigated the use of microstates as biomarkers to distinguish patients with bipolar disorder from those with schizophrenia by analyzing EEG data measured in an eyes-closed resting state. The purpose of this article is to provide an electron directional physiological explanation for the observed brain dysfunction of schizophrenia and bipolar disorder patients.Methods: We used microstate resting EEG data to explore group differences in the duration, coverage, occurrence, and transition probability of 4 microstate maps among 20 SCH patients, 26 BD patients, and 35 healthy controls (HCs).Results: Microstate analysis revealed 4 microstates (A–D) in global clustering across SCH patients, BD patients, and HCs. The samples were chosen to be matched. We found the greater presence of microstate B in BD patients, and the less presence of microstate class A and B, the greater presence of microstate class C, and less presence of D in SCH patients. Besides, a greater frequent switching between microstates A and B and between microstates B and A in BD patients than in SCH patients and HCs and less frequent switching between microstates C and D and between microstates D and C in BD patients compared with SCH patients.Conclusion: We found abnormal features of microstate A, B in BD patients and abnormal features of microstate A, B, C, and D in SCH patients. These features may indicate the potential abnormalities of SCH patients and BD patients in distributing neural resources and influencing opportune transitions between different states of activity.
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Teipel, Stefan J., Katharina Brüggen, Anna Gesine Marie Temp, Kristina Jakobi, Marc-André Weber, and Christoph Berger. "Simultaneous Assessment of Electroencephalography Microstates and Resting State Intrinsic Networks in Alzheimer's Disease and Healthy Aging." Frontiers in Neurology 12 (June 17, 2021). http://dx.doi.org/10.3389/fneur.2021.637542.
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Electroencephalography (EEG) microstate topologies may serve as building blocks of functional brain activity in humans. Here, we studied the spatial and temporal correspondences between simultaneously acquired EEG microstate topologies and resting state functional MRI (rs-fMRI) intrinsic networks in 14 patients with Alzheimer's disease (AD) and 14 healthy age and sex matched controls. We found an anteriorisation of EEG microstates' topologies in AD patients compared with controls; this corresponded with reduced spatial expression of default mode and increased expression of frontal lobe networks in rs-fMRI. In a hierarchical cluster analysis the time courses of the EEG microstates were associated with the time courses of spatially corresponding rs-fMRI networks. We found prevalent negative correlations of time courses between anterior microstate topologies and posterior rs-fMRI components as well as between posterior microstate topology and anterior rs-fMRI components. These negative correlations were significantly more expressed in controls than in AD patients. In conclusion, our data support the notion that the time courses of EEG microstates underlie the temporal expression of rs-fMRI networks. Furthermore, our findings indicate that the anterior-to-posterior connectivity of microstates and rs-fMRI components may be reduced in AD, indicative of a break-down of long-reaching intrahemispheric connections.
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Bréchet, Lucie, Denis Brunet, Lampros Perogamvros, Giulio Tononi, and Christoph M. Michel. "EEG microstates of dreams." Scientific Reports 10, no. 1 (October 13, 2020). http://dx.doi.org/10.1038/s41598-020-74075-z.
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Abstract Why do people sometimes report that they remember dreams, while at other times they recall no experience? Despite the interest in dreams that may happen during the night, it has remained unclear which brain states determine whether these conscious experiences will occur and what prevents us from waking up during these episodes. Here we address this issue by comparing the EEG activity preceding awakenings with recalled vs. no recall of dreams using the EEG microstate approach. This approach characterizes transiently stable brain states of sub-second duration that involve neural networks with nearly synchronous dynamics. We found that two microstates (3 and 4) dominated during NREM sleep compared to resting wake. Further, within NREM sleep, microstate 3 was more expressed during periods followed by dream recall, whereas microstate 4 was less expressed. Source localization showed that microstate 3 encompassed the medial frontal lobe, whereas microstate 4 involved the occipital cortex, as well as thalamic and brainstem structures. Since NREM sleep is characterized by low-frequency synchronization, indicative of neuronal bistability, we interpret the increased presence of the “frontal” microstate 3 as a sign of deeper local deactivation, and the reduced presence of the “occipital” microstate 4 as a sign of local activation. The latter may account for the occurrence of dreaming with rich perceptual content, while the former may account for why the dreaming brain may undergo executive disconnection and remain asleep. This study demonstrates that NREM sleep consists of alternating brain states whose temporal dynamics determine whether conscious experience arises.
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Chen, Jing, Haifeng Li, Lin Ma, Hongjian Bo, Frank Soong, and Yaohui Shi. "Dual-Threshold-Based Microstate Analysis on Characterizing Temporal Dynamics of Affective Process and Emotion Recognition From EEG Signals." Frontiers in Neuroscience 15 (July 14, 2021). http://dx.doi.org/10.3389/fnins.2021.689791.
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Recently, emotion classification from electroencephalogram (EEG) data has attracted much attention. As EEG is an unsteady and rapidly changing voltage signal, the features extracted from EEG usually change dramatically, whereas emotion states change gradually. Most existing feature extraction approaches do not consider these differences between EEG and emotion. Microstate analysis could capture important spatio-temporal properties of EEG signals. At the same time, it could reduce the fast-changing EEG signals to a sequence of prototypical topographical maps. While microstate analysis has been widely used to study brain function, few studies have used this method to analyze how brain responds to emotional auditory stimuli. In this study, the authors proposed a novel feature extraction method based on EEG microstates for emotion recognition. Determining the optimal number of microstates automatically is a challenge for applying microstate analysis to emotion. This research proposed dual-threshold-based atomize and agglomerate hierarchical clustering (DTAAHC) to determine the optimal number of microstate classes automatically. By using the proposed method to model the temporal dynamics of auditory emotion process, we extracted microstate characteristics as novel temporospatial features to improve the performance of emotion recognition from EEG signals. We evaluated the proposed method on two datasets. For public music-evoked EEG Dataset for Emotion Analysis using Physiological signals, the microstate analysis identified 10 microstates which together explained around 86% of the data in global field power peaks. The accuracy of emotion recognition achieved 75.8% in valence and 77.1% in arousal using microstate sequence characteristics as features. Compared to previous studies, the proposed method outperformed the current feature sets. For the speech-evoked EEG dataset, the microstate analysis identified nine microstates which together explained around 85% of the data. The accuracy of emotion recognition achieved 74.2% in valence and 72.3% in arousal using microstate sequence characteristics as features. The experimental results indicated that microstate characteristics can effectively improve the performance of emotion recognition from EEG signals.
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Zhang, Chi, Xiaoguang Wang, Zhiwei Ding, Hanwen Zhou, Peng Liu, Xinmiao Xue, Li Wang, et al. "Study on tinnitus-related electroencephalogram microstates in patients with vestibular schwannomas." Frontiers in Neuroscience 17 (April 6, 2023). http://dx.doi.org/10.3389/fnins.2023.1159019.
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Tinnitus is closely associated with cognition functioning. In order to clarify the central reorganization of tinnitus in patients with vestibular schwannoma (VS), this study explored the aberrant dynamics of electroencephalogram (EEG) microstates and their correlations with tinnitus features in VS patients. Clinical and EEG data were collected from 98 VS patients, including 76 with tinnitus and 22 without tinnitus. Microstates were clustered into four categories. Our EEG microstate analysis revealed that VS patients with tinnitus exhibited an increased frequency of microstate C compared to those without tinnitus. Furthermore, correlation analysis demonstrated that the Tinnitus Handicap Inventory (THI) score was negatively associated with the duration of microstate A and positively associated with the frequency of microstate C. These findings suggest that the time series and syntax characteristics of EEG microstates differ significantly between VS patients with and without tinnitus, potentially reflecting abnormal allocation of neural resources and transition of functional brain activity. Our results provide a foundation for developing diverse treatments for tinnitus in VS patients.
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Rubega, Maria, Massimiliano Facca, Vittorio Curci, Giovanni Sparacino, Franco Molteni, Eleonora Guanziroli, Stefano Masiero, Emanuela Formaggio, and Alessandra Del Felice. "EEG Microstates as a Signature of Hemispheric Lateralization in Stroke." Brain Topography, May 17, 2023. http://dx.doi.org/10.1007/s10548-023-00967-8.
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AbstractStroke recovery trajectories vary substantially. The need for tracking and prognostic biomarkers in stroke is utmost for prognostic and rehabilitative goals: electroencephalography (EEG) advanced signal analysis may provide useful tools toward this aim. EEG microstates quantify changes in configuration of neuronal generators of short-lasting periods of coordinated synchronized communication within large-scale brain networks: this feature is expected to be impaired in stroke. To characterize the spatio-temporal signatures of EEG microstates in stroke survivors in the acute/subacute phase, EEG microstate analysis was performed in 51 first-ever ischemic stroke survivors [(28–82) years, 24 with right hemisphere (RH) lesion] who underwent a resting-state EEG recording in the acute and subacute phase (from 48 h up to 42 days after the event). Microstates were characterized based on 4 parameters: global explained variance (GEV), mean duration, occurrences per second, and percentage of coverage. Wilcoxon Rank Sum tests were performed to compare features of each microstate across the two groups [i.e., left hemisphere (LH) and right hemisphere (RH) stroke survivors]. The canonical microstate map D, characterized by a mostly frontal topography, displayed greater GEV, occurrence per second, and percentage of coverage in LH than in RH stroke survivors (p < 0.05). The EEG microstate map B, with a left-frontal to right-posterior topography, and F, with an occipital-to-frontal topography, exhibited a greater GEV in RH than in LH stroke survivors (p = 0.015). EEG microstates identified specific topographic maps which characterize stroke survivors’ lesioned hemisphere in the acute and early subacute phase. Microstate features offer an additional tool to identify different neural reorganization.
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Kleinert, Tobias, Kyle Nash, Thomas Koenig, and Edmund Wascher. "Normative Intercorrelations Between EEG Microstate Characteristics." Brain Topography, July 14, 2023. http://dx.doi.org/10.1007/s10548-023-00988-3.
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AbstractEEG microstates are brief, recurring periods of stable brain activity that reflect the activation of large-scale neural networks. The temporal characteristics of these microstates, including their average duration, number of occurrences, and percentage contribution have been shown to serve as biomarkers of mental and neurological disorders. However, little is known about how microstate characteristics of prototypical network types relate to each other. Normative intercorrelations among these parameters are necessary to help researchers better understand the functions and interactions of underlying networks, interpret and relate results, and generate new hypotheses. Here, we present a systematic analysis of intercorrelations between EEG microstate characteristics in a large sample representative of western working populations (n = 583). Notably, we find that microstate duration is a general characteristic that varies across microstate types. Further, microstate A and B show mutual reinforcement, indicating a relationship between auditory and visual sensory processing at rest. Microstate C appears to play a special role, as it is associated with longer durations of all other microstate types and increased global field power, suggesting a relationship of these parameters with the anterior default mode network. All findings could be confirmed using independent EEG recordings from a retest-session (n = 542).
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Lamoš, Martin, Martina Bočková, Sabina Goldemundová, Marek Baláž, Jan Chrastina, and Ivan Rektor. "The effect of deep brain stimulation in Parkinson’s disease reflected in EEG microstates." npj Parkinson's Disease 9, no. 1 (April 17, 2023). http://dx.doi.org/10.1038/s41531-023-00508-x.
Full textAbstract:
AbstractMechanisms of deep brain stimulation (DBS) on cortical networks were explored mainly by fMRI. Advanced analysis of high-density EEG is a source of additional information and may provide clinically useful biomarkers. The presented study evaluates EEG microstates in Parkinson’s disease and the effect of DBS of the subthalamic nucleus (STN). The association between revealed spatiotemporal dynamics of brain networks and changes in oscillatory activity and clinical examination were assessed. Thirty-seven patients with Parkinson’s disease treated by STN-DBS underwent two sessions (OFF and ON stimulation conditions) of resting-state EEG. EEG microstates were analyzed in patient recordings and in a matched healthy control dataset. Microstate parameters were then compared across groups and were correlated with clinical and neuropsychological scores. Of the five revealed microstates, two differed between Parkinson’s disease patients and healthy controls. Another microstate differed between ON and OFF stimulation conditions in the patient group and restored parameters in the ON stimulation state toward to healthy values. The mean beta power of that microstate was the highest in patients during the OFF stimulation condition and the lowest in healthy controls; sources were localized mainly in the supplementary motor area. Changes in microstate parameters correlated with UPDRS and neuropsychological scores. Disease specific alterations in the spatiotemporal dynamics of large-scale brain networks can be described by EEG microstates. The approach can reveal changes reflecting the effect of DBS on PD motor symptoms as well as changes probably related to non-motor symptoms not influenced by DBS.