Event-related microstate dynamics represents working memory performance

Divergent spatiotemporal signatures characterize impaired facial emotional recognition in major depressive disorder: An event-related microstate study

BACKGROUND

Major depressive disorder (MDD) is a chronic affective mental disorder with intricate neuropathological characteristics. Microstate analysis has proved its ability to reveal the relatively stable features in a specific brain process. However, the relationship between event-related microstate networks and affective dysfunctions in patients with MDD is not well known.

METHODS

The 128-channel electroencephalogram (EEG) data from 24 MDD patients and 29 healthy controls (HCs) with facial emotion recognition (FER) tasks were used in this study. The analysis encompassed both event-related microstate parameters and specific microstate network metrics. The microstate parameters included Mean Global Field Power (mGFP), Mean Duration (mDur), Time Coverage (TC), and Segment Count Density (SegD). The network metrics evaluated were the clustering coefficient (CC), path length (Lp), global efficiency (Eg), and local efficiency (Eloc).

RESULTS

Three event-related microstates (MS-P1, MS-N170, and MS-P2) were estimated. Compared with HCs, the MDD patients showed significantly increased mGFP in MS-P1 with the sad emotion and decreased microstate parameters in MS-P2 with happy (mDur and TC) and sad (SegD and TC) emotions. Correlation results showed that MS-P1 with the sad emotion was positively related to clinical outcomes. MS-P2 with happy and sad emotions negatively correlated with clinical scores. Additionally, the microstate networks confirmed that MDD patients had decreased network efficiency of the happy emotion in MS-P1 while increased efficiency in dealing with the negative emotion in MS-P2.

CONCLUSIONS

By analyzing event-related microstates and brain networks, we provided a novel approach to demonstrate the divergent patterns for FER processing and the atypical dynamic coordination and integration of affective mechanisms underlying emotional deficits in MDD.

Gamma Transcranial Alternating Current Stimulation Enhances Working Memory Ability in Healthy People: An EEG Microstate Study

BACKGROUND

Working memory (WM) is a core cognitive function closely linked to various cognitive processes including language, decision making, and reasoning. Transcranial alternating current stimulation (tACS), a non-invasive brain stimulation technique, has been shown to modulate cognitive abilities and treat psychiatric disorders. Although gamma tACS (γ-tACS) has demonstrated positive effects on WM, its underlying neural mechanisms remain unclear.

METHODS

In this study, we employed electroencephalogram (EEG) microstate analysis to investigate the spatiotemporal dynamics of γ-tACS effects on WM performance. Healthy participants (N = 104) participated in two-back and three-back WM tasks before and after two types (sine and triangular) of γ-tACS, with sham stimulation as a control.

RESULTS

Our results revealed that γ-tACS improved performance in both the two-back and three-back tasks, with triangular γ-tACS showing greater accuracy improvement in the three-back task than the sham group. Furthermore, γ-tACS significantly modulated EEG microstate dynamics, specifically downregulating microstate Class C and upregulating microstate Classes D and B. These changes were positively correlated with reduced reaction times in the three-back task.

CONCLUSIONS

Our findings establish microstate analysis as an effective approach for evaluating γ-tACS-induced changes in global brain activity and advance the understanding of how γ-tACS influences WM.

Neural impact of anti-G suits on pilots: Analyzing microstates and functional connectivity

Overload represents a significant challenge for pilots in flight, with a substantial impact on flight safety. Currently, the primary method of protection is the utilization of inflatable anti-G suit to address instances where blood is concentrated in the lower extremities. The inflatable air pressure of the anti-G suit varies in response to different overload conditions, which in turn affects the pilot's sensory and brain loads. However, this change has not yet been fully explored. To investigate the neural effects of pressure from the anti-G suit under different degrees of overload, this paper employs a pressurized simulation methodology. The subjects' brain state changes during the simulation are measured through electroencephalogram (EEG), and comparative calculations are performed using microstate and functional connectivity. The final results demonstrate that varying inflation levels of the bladder anti-G suit can influence the microstate and functional connectivity. The Duration, Coverage, Occurrence, and transition probability (TP) characteristics of microstate C demonstrated significant variance across three distinct levels of overload. The mean increase in Phase Locking Value (PLV) for overload 3 relative to the absence of overload was 13.8%, and the number of channel synchronizations underwent a transition from 7 to 62.

Multiple patterns of EEG parameters and their role in the prediction of patients with prolonged disorders of consciousness

Introduction

Prognostication in patients with prolonged disorders of consciousness (pDoC) remains a challenging task. Electroencephalography (EEG) is a neurophysiological method that provides objective information for evaluating overall brain function. In this study, we aim to investigate the multiple features of pDoC using EEG and evaluate the prognostic values of these indicators.

Methods

We analyzed the EEG features: (i) spectral power; (ii) microstates; and (iii) mismatch negativity (MMN) and P3a of healthy controls, patients in minimally conscious state (MCS), and unresponsive wakefulness syndrome (UWS). Patients were followed up for 6 months. A combination of machine learning and SHapley Additive exPlanations (SHAP) were used to develop predictive model and interpret the results.

Results

The results indicated significant abnormalities in low-frequency spectral power, microstate parameters, and amplitudes of MMN and P3a in MCS and UWS. A predictive model constructed using support vector machine achieved an area under the curve (AUC) of 0.95, with the top 10 SHAP values being associated with transition probability (TP) from state C to F, time coverage of state E, TP from state D to F and D to F, mean duration of state A, TP from state F to C, amplitude of MMN, time coverage of state F, TP from state C to D, and mean duration of state E. Predictive models constructed for each component using support vector machine revealed that microstates had the highest AUC (0.95), followed by MMN and P3a (0.65), and finally spectral power (0.05).

Discussion

This study provides preliminary evidence for the application of microstate-based multiple EEG features for prognosis prediction in pDoC.

Clinical trial registration

chictr.org.cn, identifier ChiCTR2200064099.

Neuroimaging features for cognitive fatigue and its recovery with VR intervention: An EEG microstates analysis

INTRODUCTION

Cognitive fatigue is mainly caused by enduring mental stress or monotonous work, impairing cognitive and physical performance. Natural scene exposure is a promising intervention for relieving cognitive fatigue, but the efficacy of virtual reality (VR) simulated natural scene exposure is unclear. We aimed to investigate the effect of VR natural scene on cognitive fatigue and further explored its underlying neurophysiological alterations with electroencephalogram (EEG) microstates analysis.

METHODS

Ten participants performed a 20-minute 1-back task before and after VR intervention while EEG was recorded (pre-task, post-task). Performance was measured with mean accuracy rate (MAR) and mean reaction time (MRT) of the continuous 1-back task. VR simulation of the Canal Town scene was utilized to alleviate cognitive fatigue caused by 1-back tasks. Four resting-state phases were identified: beginning, pre, post, and end phases.

RESULTS

Post-task had a higher MAR and a lower MRT than pre-task. For pre-task, MAR was negatively correlated with trials, while MRT was positively correlated with trials. Four EEG microstates classes (A-D) were identified, and their temporal parameters (mean duration, time coverage and occurrence) and transition probabilities were calculated. After intervention, mean duration and time coverage of class B decreased, all parameters of class C increased, while all parameters of class D decreased. Transition probabilities between classes B and D decreased but increased between classes A and C.

CONCLUSION

VR simulation of Canal Town scene is a potentially effective method to alleviate cognitive fatigue. Microstate is an electrophysiological trait characteristic of cognitive fatigue and might be used to indicate the effect of VR intervention.

Effects of Mental Workload Manipulation on Electroencephalography Spectrum Oscillation and Microstates in Multitasking Environments

INTRODUCTION

Multitasking during flights leads to a high mental workload, which is detrimental for maintaining task performance. Electroencephalography (EEG) power spectral analysis based on frequency-band oscillations and microstate analysis based on global brain network activation can be used to evaluate mental workload. This study explored the effects of a high mental workload during simulated flight multitasking on EEG frequency-band power and microstate parameters.

METHODS

Thirty-six participants performed multitasking with low and high mental workloads after 4 consecutive days of training. Two levels of mental workload were set by varying the number of subtasks. EEG signals were acquired during the task. Power spectral and microstate analyses were performed on the EEG. The indices of four frequency bands (delta, theta, alpha, and beta) and four microstate classes (A-D) were calculated, changes in the frequency-band power and microstate parameters under different mental workloads were compared, and the relationships between the two types of EEG indices were analyzed.

RESULTS

The theta-, alpha-, and beta-band powers were higher under the high than under the low mental workload condition. Compared with the low mental workload condition, the high mental workload condition had a lower global explained variance and time parameters of microstate B but higher time parameters of microstate D. Less frequent transitions between microstates A and B and more frequent transitions between microstates C and D were observed during high mental workload conditions. The time parameters of microstate B were positively correlated with the delta-, theta-, and beta-band powers, whereas the duration of microstate C was negatively correlated with the beta-band power.

CONCLUSION

EEG frequency-band power and microstate parameters can be used to detect a high mental workload. Power spectral analyses based on frequency-band oscillations and microstate analyses based on global brain network activation were not completely isolated during multitasking.

Microstate D as a Biomarker in Schizophrenia: Insights from Brain State Transitions

Objectives. There is a significant correlation between EEG microstate and the neurophysiological basis of mental illness, brain state, and cognitive function. Given that the unclear relationship between network dynamics and different microstates, this paper utilized microstate, brain network, and control theories to understand the microstate characteristics of short-term memory task, aiming to mechanistically explain the most influential microstates and brain regions driving the abnormal changes in brain state transitions in patients with schizophrenia. Methods. We identified each microstate and analyzed the microstate abnormalities in schizophrenia patients during short-term memory tasks. Subsequently, the network dynamics underlying the primary microstates were studied to reveal the relationships between network dynamics and microstates. Finally, using control theory, we confirmed that the abnormal changes in brain state transitions in schizophrenia patients are driven by specific microstates and brain regions. Results. The frontal-occipital lobes activity of microstate D decreased significantly, but the left frontal lobe of microstate B increased significantly in schizophrenia, when the brain was moving toward the easy-to-reach states. However, the frontal-occipital lobes activity of microstate D decreased significantly in schizophrenia, when the brain was moving toward the hard-to-reach states. Microstate D showed that the right-frontal activity had a higher priority than the left-frontal, but microstate B showed that the left-frontal priority decreased significantly in schizophrenia, when changes occur in the synchronization state of the brain. Conclusions. In conclusion, microstate D may be a biomarker candidate of brain abnormal activity during the states transitions in schizophrenia, and microstate B may represent a compensatory mechanism that maintains brain function and exchanges information with other brain regions. Microstate and brain network provide complementary perspectives on the neurodynamics, offering potential insights into brain function in health and disease.

Revealing brain's cognitive process deeply: a study of the consistent EEG patterns of audio-visual perceptual holistic

Introduction

To investigate the brain's cognitive process and perceptual holistic, we have developed a novel method that focuses on the informational attributes of stimuli.

Methods

We recorded EEG signals during visual and auditory perceptual cognition experiments and conducted ERP analyses to observe specific positive and negative components occurring after 400ms during both visual and auditory perceptual processes. These ERP components represent the brain's perceptual holistic processing activities, which we have named Information-Related Potentials (IRPs). We combined IRPs with machine learning methods to decode cognitive processes in the brain.

Results

Our experimental results indicate that IRPs can better characterize information processing, particularly perceptual holism. Additionally, we conducted a brain network analysis and found that visual and auditory perceptual holistic processing share consistent neural pathways.

Discussion

Our efforts not only demonstrate the specificity, significance, and reliability of IRPs but also reveal their great potential for future brain mechanism research and BCI applications.

Evidence for modulation of EEG microstates by mental workload levels and task types

Electroencephalography (EEG) microstate analysis has become a popular tool for studying the spatial and temporal dynamics of large-scale electrophysiological activities in the brain in recent years. Four canonical topographies of the electric field (classes A, B, C, and D) have been widely identified, and changes in microstate parameters are associated with several psychiatric disorders and cognitive functions. Recent studies have reported the modulation of EEG microstate by mental workload (MWL). However, the common practice of evaluating MWL is in a specific task. Whether the modulation of microstate by MWL is consistent across different types of tasks is still not clear. Here, we studied the topographies and dynamics of microstate in two independent MWL tasks: NBack and the multi-attribute task battery (MATB) and showed that the modulation of MWL on microstate topographies and parameters depended on tasks. We found that the parameters of microstates A and C, and the topographies of microstates A, B, and D were significantly different between the two tasks. Meanwhile, all four microstate topographies and parameters of microstates A and C were different during the NBack task, but no significant difference was found during the MATB task. Furthermore, we employed a support vector machine recursive feature elimination procedure to investigate whether microstate parameters were suitable for MWL classification. An averaged classification accuracy of 87% for within-task and 78% for cross-task MWL discrimination was achieved with at least 10 features. Collectively, our findings suggest that topographies and parameters of microstates can provide valuable information about neural activity patterns with a dynamic temporal structure at different levels of MWL, but the modulation of MWL depends on tasks and their corresponding functional systems. Moreover, as a potential indicator, microstate parameters could be used to distinguish MWL.

Spatiotemporal Dynamics of Memory Encoding and Memory Retrieval States

Memory encoding and memory retrieval are neurally distinct brain states that can be differentiated on the basis of cortical network activity. However, it is unclear whether sustained engagement of one network or fluctuations between multiple networks give rise to these memory states. The spatiotemporal dynamics of memory states may have important implications for memory behavior and cognition; however, measuring temporally resolved signals of cortical networks poses a challenge. Here, we recorded scalp electroencephalography from participants performing a mnemonic state task in which they were biased toward memory encoding or retrieval. We performed a microstate analysis to measure the temporal dynamics of cortical networks throughout this mnemonic state task. We find that Microstate E, a putative analog of the default mode network, shows temporally sustained dissociations between memory encoding and retrieval, with greater engagement during retrieve compared with encode trials. We further show that decreased engagement of Microstate E is a general property of encoding, rather than a reflection of retrieval suppression. Thus, memory success, as well as cognition more broadly, may be influenced by the ability to engage or disengage Microstate E in a goal-dependent manner.