microstate analysis
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2022 ◽  
Vol 15 ◽  
Author(s):  
Tirapoot Jatupornpoonsub ◽  
Paramat Thimachai ◽  
Ouppatham Supasyndh ◽  
Yodchanan Wongsawat

The Malnutrition-Inflammation Score (MIS) was initially proposed to evaluate malnutrition-inflammation complex syndrome (MICS) in end-stage renal disease (ESRD) patients. Although MICS should be routinely evaluated to reduce the hospitalization and mortality rate of ESRD patients, the inconvenience of the MIS might limit its use. Cerebral complications in ESRD, possibly induced by MICS, were previously assessed by using spectral electroencephalography (EEG) via the delta/theta ratio and microstate analysis. Correspondingly, EEG could be used to directly assess MICS in ESRD patients, but the relationships among MICS and these EEG features remain inconclusive. Thus, we aimed to investigate the delta/theta ratio and microstates in ESRD patients with high and low risks of MICS. We also attempted to identify the correlation among the MIS, delta/theta ratio, and microstate parameters, which might clarify their relationships. To achieve these objectives, a total of forty-six ESRD subjects were willingly recruited. We collected their blood samples, MIS, and EEGs after receiving written informed consent. Sixteen women and seven men were allocated to low risk group (MIS ≤ 5, age 57.57 ± 14.88 years). Additionally, high risk group contains 15 women and 8 men (MIS > 5, age 59.13 ± 11.77 years). Here, we discovered that delta/theta ratio (p < 0.041) and most microstate parameters (p < 0.001) were significantly different between subject groups. We also found that the delta/theta ratio was not correlated with MIS but was strongly with the average microstate duration (ρ = 0.708, p < 0.001); hence, we suggested that the average microstate duration might serve as an alternative encephalopathy biomarker. Coincidentally, we noticed positive correlations for most parameters of microstates A and B (0.54 ≤ ρ ≤ 0.68, p < 0.001) and stronger negative correlations for all microstate C parameters (−0.75 ≤ ρ ≤ −0.61, p < 0.001). These findings unveiled a novel EEG biomarker, the MIC index, that could efficiently distinguish ESRD patients at high and low risk of MICS when utilized as a feature in a binary logistic regression model (accuracy of train-test split validation = 1.00). We expected that the average microstate duration and MIC index might potentially contribute to monitor ESRD patients in the future.


2022 ◽  
Vol 12 ◽  
Author(s):  
Yueqian Sun ◽  
Guoping Ren ◽  
Jiechuan Ren ◽  
Qun Wang

Background: Depression is the most common psychiatric comorbidity of temporal lobe epilepsy (TLE). In the recent years, studies have focused on the common pathogenesis of TLE and depression. However, few of the studies focused on the dynamic characteristics of TLE with depression. We tested the hypotheses that there exist abnormalities in microstates in patients with TLE with depression.Methods: Participants were classified into patients with TLE with depression (PDS) (n = 19) and patients with TLE without depression (nPDS) (n = 19) based upon the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V). Microstate analysis was applied based on 256-channel electroencephalography (EEG) to detect the dynamic changes in whole brain. The coverage (proportion of time spent in each state), frequency of occurrence, and duration (average time of each state) were calculated.Results: Patients with PDS showed a shorter mean microstate duration with higher mean occurrence per second compared to patients with nPDS. There was no difference between the two groups in the coverage of microstate A–D.Conclusion: This is the first study to present the temporal fluctuations of EEG topography in comorbid depression in TLE using EEG microstate analysis. The temporal characteristics of the four canonical EEG microstates were significantly altered in patients with TLE suffer from comorbid depression.


2021 ◽  
Vol 12 ◽  
Author(s):  
Yuqiong He ◽  
Qianting Yu ◽  
Tingyu Yang ◽  
Yaru Zhang ◽  
Kun Zhang ◽  
...  

Background: Recent studies have reported changes in the electroencephalograms (EEG) of patients with major depressive disorder (MDD). However, little research has explored EEG differences between adolescents with MDD and healthy controls, particularly EEG microstates differences. The aim of the current study was to characterize EEG microstate activity in adolescents with MDD and healthy controls (HCs).Methods: A total of 35 adolescents with MDD and 35 HCs were recruited in this study. The depressive symptoms were assessed by Hamilton Depression Scale (HAMD) and Children's Depression Inventory (CDI), and the anxiety symptoms were assessed by Chinese version of DSM-5 Level 2-Anxiety-Child scale. A 64-channel EEG was recorded for 5 min (eye closed, resting-state) and analyzed using microstate analysis. Microstate properties were compared between groups and correlated with patients' depression scores.Results: We found increased occurrence and contribution of microstate B in MDD patients compared to HCs, and decreased occurrence and contribution of microstate D in MDD patients compared to HCs. While no significant correlation between depression severity (HAMD score) and the microstate metrics (occurrence and contribution of microstate B and D) differing between MDD adolescents and HCs was found.Conclusions: Adolescents with MDD showed microstate B and microstate D changes. The obtained results may deepen our understanding of dynamic EEG changes among adolescents with MDD and provide some evidence of changes in brain development in adolescents with MDD.


2021 ◽  
Author(s):  
Qiaoling Sun ◽  
Linlin Zhao ◽  
Liwen Tan

Abstract Objective: Microstate 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.Methods: Resting-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) were analyzed.Results: Compared 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.Conclusions: Our 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.


2021 ◽  
Vol 12 ◽  
Author(s):  
YuBao Jiang ◽  
MingYu Zhu ◽  
Ying Hu ◽  
Kai Wang

Objective: Idiopathic generalized epilepsy (IGE) involves aberrant organization and functioning of large-scale brain networks. This study aims to investigate whether the resting-state EEG microstate analysis could provide novel insights into the abnormal temporal and spatial properties of intrinsic brain activities in patients with IGE.Methods: Three groups of participants were chosen for this study (namely IGE-Seizure, IGE-Seizure Free, and Healthy Controls). EEG microstate analysis on the resting-state EEG datasets was conducted for all participants. The average duration (“Duration”), the average number of microstates per second (“Frequency”), as well as the percentage of total analysis time occupied in that state (“Coverage”) of the EEG microstate were compared among the three groups.Results: For microstate classes B and D, the differences in Duration, Frequency, and Coverage among the three groups were not statistically significant. Both Frequency and Coverage of microstate class A were statistically significantly larger in the IGE-Seizure group than in the other two groups. The Duration and Coverage of microstate class C were statistically significantly smaller in the IGE-Seizure group than those in the other two groups.Conclusions: The Microstate class A was regarded as a sensorimotor network and Microstate class C was mainly related to the salience network, this study indicated an altered sensorimotor and salience network in patients with IGE, especially in those who had experienced seizures in the past 2 years, while the visual and attention networks seemed to be intact.Significance: The temporal dynamics of resting-state networks were studied through EEG microstate analysis in patients with IGE, which is expected to generate indices that could be utilized in clinical researches of epilepsy.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Josephine Zerna ◽  
Alexander Strobel ◽  
Christoph Scheffel

AbstractIn electroencephalography (EEG), microstates are distributions of activity across the scalp that persist for several tens of milliseconds before changing into a different pattern. Microstate analysis is a way of utilizing EEG as both temporal and spatial imaging tool, but has rarely been applied to task-based data. This study aimed to conceptually replicate microstate findings of valence and emotional arousal processing and investigate the effects of emotion regulation on microstates, using data of an EEG paradigm with 107 healthy adults who actively viewed emotional pictures, cognitively detached from them, or suppressed facial reactions. Within the first 600 ms after stimulus onset only the comparison of viewing positive and negative pictures yielded significant results, caused by different electrodes depending on the microstate. Since the microstates associated with more and less emotionally arousing pictures did not differ, sequential processing could not be replicated. When extending the analysis to 2000 ms after stimulus onset, differences were exclusive to the comparison of viewing and detaching from negative pictures. Intriguingly, we observed the novel phenomenon of a microstate difference that could not be attributed to single electrodes. This suggests that microstate analysis can detect differences beyond those detected by event-related potential analysis.


2021 ◽  
Author(s):  
Wanrou Hu ◽  
Zhiguo Zhang ◽  
Huilin Zhao ◽  
Li Zhang ◽  
Linling Li ◽  
...  

Emotions dynamically change in response to ever-changing environments. It is of great importance, both clinically and scientifically, to investigate the neural representation and evoking mechanism of emotion dynamics. But, there are many unknown places in this stream of research, such as consistent and conclusive findings are still lacking. In this work, we perform an in-depth investigation of emotion dynamics under a video-watching task by gauging the dynamic associations among evoked emotions, electroencephalography (EEG) responses, and multimedia stimulation. Here, we introduce EEG microstate analysis to study emotional EEG signals, which provides a spatial-temporal neural representation of emotion dynamics. To investigate the temporal characteristics of evoking emotions during video watching with its neural mechanism, we conduct two studies from the perspective of EEG microstates. In Study 1, the dynamic microstate activities under different emotion states and emotion levels are explored to identify EEG spatial-temporal correlates of emotion dynamics. In Study 2, the stimulation effects of multimedia content (visual and audio) on EEG microstate activities are examined to learn about the involved affective information and investigate the emotion-evoking mechanism. The results show that emotion dynamics could be well reflected by four EEG microstates (MS1, MS2, MS3, and MS4). Specifically, emotion tasks lead to an increase in MS2 and MS4 coverage but a decrease in MS3 coverage, duration, and occurrence. Meanwhile, there exists a negative association between valence and MS4 occurrence as well as a positive association between arousal and MS3 coverage and occurrence. Further, we find that MS4 and MS3 activities are significantly affected by visual and audio content, respectively. In this work, we verify the possibility to reveal emotion dynamics through EEG microstate analysis from sensory and stimulation dimensions, where EEG microstate features are found to be highly correlated to different emotion states (emotion task effect and level effect) and different affective information involved in the multimedia content (visual and audio). Our work deepens the understanding of the neural representation and evoking mechanism of emotion dynamics, which can be beneficial for future development in the applications of emotion decoding and regulation.


2021 ◽  
Vol 168 ◽  
pp. S108
Author(s):  
Shuaiyang Li ◽  
Yuxia Hu ◽  
Hongcan Zhu ◽  
Rui Zhang ◽  
Mingming Chen ◽  
...  

2021 ◽  
Author(s):  
Luke Tait ◽  
Jiaxiang Zhang

Abstract EEG microstate analysis is an approach to study brain states and their fast transitions in healthy cognition and disease. A key limitation of conventional microstate analysis is that it must be performed at the sensor level, and therefore gives limited anatomical insight. Here, we generalise the microstate methodology to be applicable to source-reconstructed electrophysiological data. Using simulations of a neural-mass network model, we first established the validity and robustness of the proposed method. Using MEG resting-state data, we uncovered ten microstates with distinct spatial distributions of cortical activation. Multivariate pattern analysis demonstrated that source-level microstates were associated with distinct functional connectivity patterns. We further demonstrated that the occurrence probability of MEG microstates were altered by auditory stimuli, exhibiting a hyperactivity of the microstate including the auditory cortex. Our results support the use of source-level microstates as a method for investigating brain dynamic activity and connectivity at the millisecond scale.


2021 ◽  
Vol 14 (5) ◽  
pp. 1412
Author(s):  
Michael C. Gold ◽  
Shiwen Yuan ◽  
Eric Tirrell ◽  
Eugenia F. Kronenberg ◽  
JeeWon Kang ◽  
...  
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