EEG Microstate-Specific Functional Connectivity and Stroke-Related Alterations in Brain Dynamics

Analysis of microstate features for Parkinson's disease based on reliability validation

BACKGROUND

Parkinson's disease (PD) is a disorder with abnormal changes in brain activity. The lack of objective indicators makes the assessment of PD progression difficult. Assessment of brain activity changes in PD may offer a potential solution.

NEW METHOD

Electroencephalogram (EEG) microstates reflect global dynamic changes in the brain. Therefore, we utilized microstates to assess changes in PD brain activity. However, the effect of epoch duration on the reliability of microstate analyses in PD is unclear. Thus, we first assessed the effect of data duration on the reliability of microstate topography and temporal features in PD and older healthy individuals. According to the reliability assessment, EEG epochs with high reliability were selected for microstate analysis in PD. Finally, we investigated the correlation between microstate features and clinical scales to determine whether these features could serve as objective indicators to evaluate PD progression.

RESULTS

Microstate analysis features that show high reliability for 3 min and above epoch durations. The topology of microstate D was significantly changed in PD compared to healthy controls, as well as the temporal features of microstates C and D. Additionally, the occurrence of C was negatively correlated with MoCA, and the duration of D was positively correlated with UPDRS.

COMPARISON WITH EXISTING METHOD(S)

High reliability of PD microstate features obtained by our approach.

CONCLUSION

EEG for PD microstate analysis should be at least 3 min. Microstate analysis is expected to provide new ideas and objective indicators for assessing Parkinson's disease progression in the clinical setting.

EEG Microstates as a Signature of Hemispheric Lateralization in Stroke

Stroke 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.

Propofol Reversibly Attenuates Short-Range Microstate Ordering and 20 Hz Microstate Oscillations

Microstate sequences summarize the changing voltage patterns measured by electroencephalography, using a clustering approach to reduce the high dimensionality of the underlying data. A common approach is to restrict the pattern matching step to local maxima of the global field power (GFP) and to interpolate the microstate fit in between. In this study, we investigate how the anesthetic propofol affects microstate sequence periodicity and predictability, and how these metrics are changed by interpolation. We performed two frequency analyses on microstate sequences, one based on time-lagged mutual information, the other based on Fourier transform methodology, and quantified the effects of interpolation. Resting-state microstate sequences had a 20 Hz frequency peak related to dominant 10 Hz (alpha) rhythms, and the Fourier approach demonstrated that all five microstate classes followed this frequency. The 20 Hz periodicity was reversibly attenuated under moderate propofol sedation, as shown by mutual information and Fourier analysis. Characteristic microstate frequencies could only be observed in non-interpolated microstate sequences and were masked by smoothing effects of interpolation. Information-theoretic analysis revealed faster microstate dynamics and larger entropy rates under propofol, whereas Shannon entropy did not change significantly. In moderate sedation, active information storage decreased for non-interpolated sequences. Signatures of non-equilibrium dynamics were observed in non-interpolated sequences, but no changes were observed between sedation levels. All changes occurred while subjects were able to perform an auditory perception task. In summary, we show that low dose propofol reversibly increases the randomness of microstate sequences and attenuates microstate oscillations without correlation to cognitive task performance. Microstate dynamics between GFP peaks reflect physiological processes that are not accessible in interpolated sequences.

Normative Temporal Dynamics of Resting EEG Microstates

The large-scale electrophysiological events known as electroencephalographic microstates provide an important window into the intrinsic activity of whole-brain neuronal networks. The spontaneous activity of coordinated brain networks, including the ongoing temporal dynamics expressed by microstates, are thought to reflect individuals' neurocognitive functioning, and predict development, disease progression, and psychological differences among varied populations. A comprehensive understanding of human brain function therefore requires characterizing typical and atypical patterns in the temporal dynamics of microstates. But population-level estimates of normative microstate temporal dynamics are still unknown. To address this gap, I conducted a systematic search of the literature and accompanying meta-analysis of the average dynamics of microstates obtained from studies investigating spontaneous brain activity in individuals during periods of eyes-closed and eyes-open rest. Meta-analyses provided estimates of the average temporal dynamics of microstates across 93 studies totaling 6583 unique individual participants drawn from diverse populations. Results quantified the expected range of plausible estimates of average microstate dynamics across study samples, as well as characterized heterogeneity resulting from sampling variability and systematic differences in development, clinical diagnoses, or other study methodological factors. Specifically, microstate dynamics significantly differed for samples with specific developmental differences or clinical diagnoses, relative to healthy, typically developing samples. This research supports the notion that microstates and their dynamics reflect functionally relevant properties of large-scale brain networks, encoding typical and atypical neurocognitive functioning.

Temporal and spatial variability of dynamic microstate brain network in disorders of consciousness

BACKGROUND

Accurately diagnosing patients with the vegetative state (VS) and the minimally conscious state (MCS) reached a misdiagnosis of approximately 40%.

METHODS

A method combined microstate and dynamic functional connectivity (dFC) to study the spatiotemporal variability of the brain in disorders of consciousness (DOC) patients was proposed. Resting-state EEG data were obtained from 16 patients with MCS and 16 patients with VS. Mutual information (MI) was used to assess the EEG connectivity in each microstate. MI-based features with statistical differences were selected as the total feature subset (TFS), then the TFS was utilized to feature selection and fed into the classifier, obtaining the optimal feature subsets (OFS) in each microstate. Subsequently, an OFS-based MI functional connectivity network (MIFCN) was constructed in the cortex.

RESULTS

The group-average MI connectivity matrix focused on all channels revealed that all five microstates exhibited stronger information interaction in the MCS when comparing with the VS. While OFS-based MIFCN, which only focused on a few channels, revealed greater MI flow in VS patients than in MCS patients under microstates A, B, C, and E, except for microstate D. Additionally, the average classification accuracy of OFS in the five microstates was 96.2%.

CONCLUSION

Constructing features based on microstates to distinguish between two categories of DOC patients had effectiveness.

EEG microstates analysis after TMS in patients with subacute stroke during the resting state

To investigate whether intermittent theta burst stimulation over the cerebellum induces changes in resting-state electroencephalography microstates in patients with subacute stroke and its correlation with cognitive and emotional function. Twenty-four stroke patients and 17 healthy controls were included in this study. Patients and healthy controls were assessed at baseline, including resting-state electroencephalography and neuropsychological scales. Fifteen patients received lateral cerebellar intermittent theta burst stimulation as well as routine rehabilitation training (intermittent theta burst stimulation-RRT group), whereas 9 patients received only conventional rehabilitation training (routine rehabilitation training group). After 2 wk, baseline data were recorded again in both groups. Stroke patients exhibited reduced parameters in microstate D and increased parameters in microstate C compared with healthy controls. However, after the administration of intermittent theta burst stimulation over the lateral cerebellum, significant alterations were observed in the majority of metrics for both microstates D and C. Lateral cerebellar intermittent theta burst stimulation combined with conventional rehabilitation has a stronger tendency to improve emotional and cognitive function in patients with subacute stroke than conventional rehabilitation. The improvement of mood and cognitive function was significantly associated with microstates C and D. We identified electroencephalography microstate spatiotemporal dynamics associated with clinical improvement following a course of intermittent theta burst stimulation therapy.

Functional network dynamics revealed by EEG microstates reflect cognitive decline in amyotrophic lateral sclerosis

Recent electroencephalography (EEG) studies have shown that patterns of brain activity can be used to differentiate amyotrophic lateral sclerosis (ALS) and control groups. These differences can be interrogated by examining EEG microstates, which are distinct, reoccurring topographies of the scalp's electrical potentials. Quantifying the temporal properties of the four canonical microstates can elucidate how the dynamics of functional brain networks are altered in neurological conditions. Here we have analysed the properties of microstates to detect and quantify signal-based abnormality in ALS. High-density resting-state EEG data from 129 people with ALS and 78 HC were recorded longitudinally over a 24-month period. EEG topographies were extracted at instances of peak global field power to identify four microstate classes (labelled A-D) using K-means clustering. Each EEG topography was retrospectively associated with a microstate class based on global map dissimilarity. Changes in microstate properties over the course of the disease were assessed in people with ALS and compared with changes in clinical scores. The topographies of microstate classes remained consistent across participants and conditions. Differences were observed in coverage, occurrence, duration, and transition probabilities between ALS and control groups. The duration of microstate class B and coverage of microstate class C correlated with lower limb functional decline. The transition probabilities A to D, C to B and C to B also correlated with cognitive decline (total ECAS) in those with cognitive and behavioural impairments. Microstate characteristics also significantly changed over the course of the disease. Examining the temporal dependencies in the sequences of microstates revealed that the symmetry and stationarity of transition matrices were increased in people with late-stage ALS. These alterations in the properties of EEG microstates in ALS may reflect abnormalities within the sensory network and higher-order networks. Microstate properties could also prospectively predict symptom progression in those with cognitive impairments.

The accuracy of different mismatch negativity amplitude representations in predicting the levels of consciousness in patients with disorders of consciousness

Introduction

The mismatch negativity (MMN) index has been used to evaluate consciousness levels in patients with disorders of consciousness (DoC). Indeed, MMN has been validated for the diagnosis of vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state (MCS). In this study, we evaluated the accuracy of different MMN amplitude representations in predicting levels of consciousness.

Methods

Task-state electroencephalography (EEG) data were obtained from 67 patients with DoC (35 VS and 32 MCS). We performed a microstate analysis of the task-state EEG and used four different representations (the peak amplitude of MMN at electrode Fz (Peak), the average amplitude within a time window -25- 25 ms entered on the latency of peak MMN component (Avg for peak ± 25 ms), the average amplitude of averaged difference wave for 100-250 ms (Avg for 100-250 ms), and the average amplitude difference between the standard stimulus ("S") and the deviant stimulus ("D") at the time corresponding to Microstate 1 (MS1) (Avg for MS1) of the MMN amplitude to predict the levels of consciousness.

Results

The results showed that among the four microstates clustered, MS1 showed statistical significance in terms of time proportion during the 100-250 ms period. Our results confirmed the activation patterns of MMN through functional connectivity analysis. Among the four MMN amplitude representations, the microstate-based representation showed the highest accuracy in distinguishing different levels of consciousness in patients with DoC (AUC = 0.89).

Conclusion

We discovered a prediction model based on microstate calculation of MMN amplitude can accurately distinguish between MCS and VS states. And the functional connection of the MS1 is consistent with the activation mode of MMN.

CAMBA framework: Unveiling the brain asymmetry alterations and longitudinal changes after stroke using resting-state EEG

Hemispheric asymmetry or lateralization is a fundamental principle of brain organization. However, it is poorly understood to what extent the brain asymmetries across different levels of functional organizations are evident in health or altered in brain diseases. Here, we propose a framework that integrates three degrees of brain interactions (isolated nodes, node-node, and edge-edge) into a unified analysis pipeline to capture the sliding window-based asymmetry dynamics at both the node and hemisphere levels. We apply this framework to resting-state EEG in healthy and stroke populations and investigate the stroke-induced abnormal alterations in brain asymmetries and longitudinal asymmetry changes during poststroke rehabilitation. We observe that the mean asymmetry in patients was abnormally enhanced across different frequency bands and levels of brain interactions, with these abnormal patterns strongly associated with the side of the stroke lesion. Compared to healthy controls, patients displayed significant alterations in asymmetry fluctuations, disrupting and reconfiguring the balance of inter-hemispheric integration and segregation. Additionally, analyses reveal that specific abnormal asymmetry metrics in patients tend to move towards those observed in healthy controls after short-term brain-computer interface rehabilitation. Furthermore, preliminary evidence suggests that baseline clinical and asymmetry features can predict poststroke improvements in the Fugl-Meyer assessment of the lower extremity (mean absolute error of about 2). Overall, these findings advance our understanding of hemispheric asymmetry. Our framework offers new insights into the mechanisms underlying brain alterations and recovery after a brain lesion, may help identify prognostic biomarkers, and can be easily extended to different functional modalities.

Neural oscillations after acute large artery atherosclerotic cerebral infarction during resting state and sleep spindles

Electroencephalogram-microstate analysis was conducted using low-resolution electromagnetic tomography (LORETA)-KEY to evaluate dynamic brain network changes in patients with acute large artery atherosclerotic cerebral infarction (LAACI) during the rest and sleep stages. This study included 35 age- and sex-matched healthy controls and 34 patients with acute LAACI. Each participant performed a 3-h, 19-channel video electroencephalogram test. Subsequently, 20 epochs of 2-s sleep spindles during stage N2 sleep and five epochs of 10-s electroencephalogram data in the resting state for each participant were obtained. In both the resting state and sleep spindles, patients with LAACI displayed altered neural oscillations. The parameters of microstate A (coverage, occurrence, and duration) increased during the resting state in the patients with LAACI compared with healthy controls. The coverage and occurrence of microstate B and D were reduced in the LAACI group compared with the healthy controls (p < 0.05). Moreover, during sleep spindles, the duration of microstate A and the transition probability from microstate A and B to C decreased, but the coverage of microstate B and the transition rate from microstate B to D increased (p < 0.05) in the LAACI group compared with the healthy controls. These results enable better understanding of how neural oscillations are modified in patients with LAACI during the resting state and sleep spindles. Following LAACI, the dynamic brain network undergoes changes during sleep spindles and the resting state. Continued long-term investigations are required to determine how well these changes in brain dynamics reflect the clinical characteristics of patients with LAACI.

Resting-state electroencephalography microstate dynamics altered in patients with migraine with and without aura-A pilot study

OBJECTIVE

To evaluate electroencephalography (EEG) microstate differences between patients with migraine with aura (MWA), patients with migraine without aura (MWoA), and healthy controls (HC).

BACKGROUND

Previous research employing microstate analysis found unique microstate alterations in patients with MWoA; however, it is uncertain how microstates appear in patients with MWA.

METHODS

This study was conducted at the Headache Clinic of the First Affiliated Hospital of Xi'an Jiaotong University. In total, 30 patients with MWA, 30 with MWoA, and 30 HC were enrolled in this cross-sectional study. An EEG was recorded for all participants under resting state. The microstate parameters of four widely recognized microstate classes A-D were calculated and compared across the three groups.

RESULTS

The occurrence of microstate B (MsB) in the MWoA group was significantly higher than in the HC (p = 0.006, Cohen's d = 0.72) and MWA (p = 0.016, Cohen's d = 0.57) groups, while the contribution of MsB was significantly increased in the MWoA group compared to the HC group (p = 0.016, Cohen's d = 0.64). Microstate A (MsA) displayed a longer duration in the MWA group compared to the MWoA group (p = 0.007, Cohen's d = 0.69). Furthermore, the transition probability between MsB and microstate D was significantly increased in the MWoA group compared to the HC group (p = 0.009, Cohen's d = 0.68 for B to D; p = 0.007, Cohen's d = 0.71 for D to B). Finally, the occurrence and contribution of MsB were positively related to headache characteristics in the MWoA group but negatively in the MWA group, whereas the duration of MsA was positively related to the visual analog scale in the MWA group (all p < 0.05).

CONCLUSIONS

Patients with MWA and MWoA have altered microstate dynamics, indicating that resting-state brain network disorders may play a role in migraine pathogenesis. Microstate parameters may have the potential to aid clinical management, which needs to be investigated further.

Functional brain connectivity indexes derived from low-density EEG of pre-implanted patients as VNS outcome predictors

Objective. In 1/3 of patients, anti-seizure medications may be insufficient, and resective surgery may be offered whenever the seizure onset is localized and situated in a non-eloquent brain region. When surgery is not feasible or fails, vagus nerve stimulation (VNS) therapy can be used as an add-on treatment to reduce seizure frequency and/or severity. However, screening tools or methods for predicting patient response to VNS and avoiding unnecessary implantation are unavailable, and confident biomarkers of clinical efficacy are unclear.Approach. To predict the response of patients to VNS, functional brain connectivity measures in combination with graph measures have been primarily used with respect to imaging techniques such as functional magnetic resonance imaging, but connectivity graph-based analysis based on electrophysiological signals such as electroencephalogram, have been barely explored. Although the study of the influence of VNS on functional connectivity is not new, this work is distinguished by using preimplantation low-density EEG data to analyze discriminative measures between responders and non-responder patients using functional connectivity and graph theory metrics.Main results. By calculating five functional brain connectivity indexes per frequency band upon partial directed coherence and direct transform function connectivity matrices in a population of 37 refractory epilepsy patients, we found significant differences (p< 0.05) between the global efficiency, average clustering coefficient, and modularity of responders and non-responders using the Mann-Whitney U test with Benjamini-Hochberg correction procedure and use of a false discovery rate of 5%.Significance. Our results indicate that these measures may potentially be used as biomarkers to predict responsiveness to VNS therapy.