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Event-related microstate dynamics represents working memory performance
Abstract
In recent years, EEG microstate analysis has attracted much attention as a tool for characterizing the spatial and temporal dynamics of large-scale electrophysiological activities in the human brain. Canonical 4 states (classes A, B, C, and D) have been widely reported, and they have been pointed out for their relationships with cognitive functions and several psychiatric disorders such as schizophrenia, in particular, through their static parameters such as average duration, occurrence, coverage, and transition probability. However, the relationships between event-related microstate changes and their related cognitive functions, as is often analyzed in event-related potentials under time-locked frameworks, is still not well understood. Furthermore, not enough attention has been paid to the relationship between microstate dynamics and static characteristics.
To clarify the relationships between the static microstate parameters and dynamic microstate changes, and between the dynamics and working memory (WM) function, we first examined the temporal profiles of the microstates during the N-back task. We found significant event-related microstate dynamics that differed predominantly with WM loads, which were not clearly observed in the static parameters. Furthermore, in the 2-back condition, patterns of state transitions from class A to C in the high- and low-performance groups showed prominent differences at 50–300 ms after stimulus onset. We also confirmed that the transition patterns of the specific time periods were able to predict the performance level (low or high) in the 2-back condition at a significant level, where a specific transition between microstates, namely from class A to C with specific polarity, contributed to the prediction robustly. Taken together, our findings indicate that event-related microstate dynamics at 50–300 ms after onset may be essential for WM function. This suggests that event-related microstate dynamics can reflect more highly-refined brain functions.
... The relationship between microstate categories and cognitive function remains a topic of debate (Jia et al. 2021). Additionally, few studies have explored the relationship between mental workload and microstates (Chen et al. 2024;Guan et al. 2022;Jia et al. 2021;Tamano et al. 2022). Three studies used n-back tasks as task models, but the results were inconsistent. ...
... Three studies used n-back tasks as task models, but the results were inconsistent. Guan et al. (2022) and Chen et al. (2024) found that a high mental workload could decrease microstate C, whereas Tamano et al. (2022) found that the parameters of the microstate remained unchanged at different mental workload levels. In a study on creative design tasks, Jia et al. (2021) found an increase in microstates C and D during the highest cognitive load stage. ...
... This indicates that higher order cognitive tasks require flexible coupling activities between the DMN and the DAN (Kim et al. 2021). Therefore, sufficient transition and balance between higher order networks are crucial for performing working memory tasks (Tamano et al. 2022) and are beneficial for regulating performance (Bagdasarov et al. 2024). ...
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.
... We calculated the absolute value of the resulting Pearson's r, and a sample was labeled with the most correlated microstate except in cases in which none of the resulting r values exceeded a threshold of 0.5 (Custo et al., 2017). We retained the original sign of the correlation to account for polarity in all subsequent analyses (Tamano, Ogawa, Katagiri, Cai, & Asai, 2022). We performed an additional round of thresholding based on the temporal extent of a given microstate (accounting for polarity). ...
... In line with this expectation, we find that microstates, which have previously been identified from spontaneous continuous EEG signals (e.g., Bréchet et al., 2019;Custo et al., 2017), are differentially engaged depending on the state of the memory system. Our current approach does leave open the question of whether there are task-specific microstates that dissociate memory states, although prior work did not find significant differences between resting-statederived microstates versus those derived during a working memory task (Tamano et al., 2022). Future work is necessary to disentangle task-specific from resting-state-derived patterns of network activity. ...
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.
... The temporal dynamics of these changes are particularly significant in pDoC (Panda et al., 2022;Panda et al., 2016). In recent years, numerous studies have reported that microstates may be associated with various psychological states (Baradits et al., 2020;Tamano et al., 2022;Bochet et al., 2021;Zanesco et al., 2020), providing insight into neural activity of the brain during resting-state. As a "quantitative indicator" of the distribution pattern of brain topographic maps, microstate analysis divides resting-state EEG signals into a limited number of distinct quasi-stable states (Khanna et al., 2015;Pascual-Marqui et al., 1995). ...
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.
... In this regard, Hazarika and Dasgupta [6] explore how action video game enhances brain network integration and visuospatial working memory. Tamano et al. [7] report that video game training enhances visual working memory (VWM) capacity. Toril et al. [8] show visuospatial and episodic memory improvements in older adults after video game training, indicating retained brain plasticity with age. ...
... Furthermore, the distribution of pinned nodes in microstate D had obvious left and right hemisphere trends, with the right-frontal being higher than the left-frontal in the SZ group, but the pinned nodes were distributed throughout the whole frontal lobe in the HC group. This suggests a reduced level of activation in the right brain in patients, which may lead to a significant decrease in attention and memory abilities, thereby affecting cognitive function [51]. ...
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.
... Recently, Guan et al. (2022) showed that the occurrence of all the microstates except microstate class E, the duration of microstate class A and C, as well as the coverage of microstate class A, C, and F, were significantly affected by MWL in four NBack tasks. In contrast, Tamano et al. (2022) reported that static microstate topographies and parameters were not affected by MWL in the NBack task, whereas only eventrelated microstate dynamics were affected. It is important to note that both studies focus on the same type of task: NBack. ...
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.
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.
EEG microstate sequences, representing whole-brain spatial potential distribution patterns of the EEG, offer valuable insights for capturing spatiotemporally continuous and fluctuating neural dynamics with high temporal resolution through appropriate discretization. Recent studies suggest that EEG microstate transitions are gradual and continuous phenomena, contrary to the classical view of binary transitions. This study aimed to update conventional microstate analysis to reflect continuous EEG dynamics and examine differences in age-related electrophysiological state transitions. We considered the relative positions of EEG microstates on the neural manifold and their topographical polarity. Transition probability results showed fewer transitions on the microstate D-C-E axis in older adults. In contrast, transitions among microstates A, D, and B increased in the older group and were mainly observed within polarity. Furthermore, the 100 microstate transitions, which are variations of the shortest transitions between 10 microstates, could be reduced to 8 principal components based on the co-occurrence of each transition, including hubs C and E, planar transitions through msA/B and D, and unidirectional transition components. Several transition components were potentially significant predictors of age group. These features were nearly replicated in independent data, indicating their robustness in characterizing age-related electrophysiological spatiotemporal dynamics.
+microstate is a MATLAB toolbox for brain functional microstate analysis. It builds upon previous EEG microstate literature and toolboxes by including algorithms for source-space microstate analysis. +microstate includes codes for performing individual- and group-level brain microstate analysis in resting-state and task-based data including event-related potentials/fields. Functions are included to visualise and perform statistical analysis of microstate sequences, including novel advanced statistical approaches such as statistical testing for associated functional connectivity patterns, cluster-permutation topographic ANOVAs, and χ2 analysis of microstate probabilities in response to stimuli. Additionally, codes for simulating microstate sequences and their associated M/EEG data are included in the toolbox, which can be used to generate artificial data with ground truth microstates and to validate the methodology. +microstate integrates with widely used toolboxes for M/EEG processing including Fieldtrip, SPM, LORETA/sLORETA, EEGLAB, and Brainstorm to aid with accessibility, and includes wrappers for pre-existing toolboxes for brain-state estimation such as Hidden Markov modelling (HMM-MAR) and independent component analysis (FastICA) to aid with direct comparison with these techniques. In this paper, we first introduce +microstate before subsequently performing example analyses using open access datasets to demonstrate and validate the methodology. MATLAB live scripts for each of these analyses are included in +microstate, to act as a tutorial and to aid with reproduction of the results presented in this manuscript.
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.
Background
Research on the electroencephalographic (EEG) signatures of attention-deficit hyperactivity disorder (ADHD) has historically concentrated on its frequency spectrum or event-related evoked potentials. In this work, we investigate EEG microstates, an alternative framework defined by the clustering of recurring topographical patterns, as a novel approach for examining large-scale cortical dynamics in ADHD.
Methods
Using kmeans clustering, we studied the spatio-temporal dynamics of ADHD during rest condition by comparing the microstate (MS) segmentations between adult ADHD patients and neurotypical controls, across 2 independent datasets: the first dataset consisted of 66 ADHD patients and 66 controls, while the second dataset comprised of 22 ADHD patients and 22 controls and was used for out-of-sample validation.
Results
Spatially, ADHD and control subjects displayed equivalent MS topographies (canonical maps), indicating preservation of prototypical EEG generators in ADHD. However, this concordance was accompanied by significant differences in temporal dynamics. At the group level, and across both datasets, ADHD diagnosis was associated with longer mean durations of a fronto-central topography (D), indicating its electrocortical generator(s) could be acting as pronounced “attractors” of global cortical dynamics. Lastly, in the first (larger) dataset, we also found evidence for decreased time coverage and mean duration of microstate A, which inversely correlated with ADHD scores, while microstate D metrics were correlated with sleep disturbance, the latter being known to have strong relation with ADHD.
Conclusions
Overall, our study underlines the value of EEG microstates as promising functional biomarkers for ADHD, offering an additional lens through which to examine its neurophysiological mechanisms.
Alterations 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.
Meditation practice is believed to foster states of mindful awareness and mental quiescence in everyday life. If so, then the cultivation of these qualities with training ought to leave its imprint on the activity of intrinsic functional brain networks. In an intensive longitudinal study, we investigated associations between meditation practitioners' experiences of felt mindful awareness and changes in the spontaneous electrophysiological dynamics of functional brain networks. Experienced meditators were randomly assigned to complete 3 months of full‐time training in focused‐attention meditation (during an initial intervention) or to serve as waiting‐list controls and receive training second (during a later intervention). We collected broadband electroencephalogram (EEG) during rest at the beginning, middle, and end of the two training periods. Using a data‐driven approach, we segmented the EEG into a time series of transient microstate intervals based on clustering of topographic voltage patterns. Participants also provided daily reports of felt mindful awareness and mental quiescence, and reported daily on four experiential qualities of their meditation practice during training. We found that meditation training led to increases in mindful qualities of awareness, which corroborate contemplative accounts of deepening mental calm and attentional focus. We also observed reductions in the strength and duration of EEG microstates across both interventions. Importantly, changes in the dynamic sequencing of microstates were associated with daily increases in felt attentiveness and serenity during training. Our results connect shifts in subjective qualities of meditative experience with the large‐scale dynamics of whole brain functional EEG networks at rest.
Electroencephalogram (EEG) microstate analysis is a promising and effective spatio-temporal method that can segment signals into several quasi-stable classes, providing a great opportunity to investigate short-range and long-range neural dynamics. However, there are still many controversies in terms of reproducibility and reliability when selecting different parameters or datatypes.
In this study, five electrode configurations (91, 64, 32, 19, and 8 channels) were used to measure the reliability of microstate analysis at different electrode densities during propofol-induced sedation.
First, the microstate topography and parameters at five different electrode densities were compared in the baseline (BS) condition and the moderate sedation (MD) condition, respectively. The intraclass correlation coefficient (ICC) and coefficient of variation (CV) were introduced to quantify the consistency of the microstate parameters. Second, statistical analysis and classification between BS and MD were performed to determine whether the microstate differences between different conditions remained stable at different electrode densities, and ICC was also calculated between the different conditions to measure the consistency of the results in a single condition.
The results showed that in both the BS or MD condition, respectively, there were few significant differences in the microstate parameters among the 91-, 64-, and 32-channel configurations, with most of the differences observed between the 19- or 8-channel configurations and the other configurations. The ICC and CV data also showed that the consistency among the 91-, 64-, and 32-channel configurations was better than that among all five electrode configurations after including the 19- and 8-channel configurations. Furthermore, the significant differences between the conditions in the 91-channel configuration remained stable at the 64- and 32-channel resolutions, but disappeared at the 19- and 8-channel resolutions. In addition, the classification and ICC results showed that the microstate analysis became unreliable with fewer than 20 electrodes.
The findings of this study support the hypothesis that microstate analysis of different brain states is more reliable with higher electrode densities; the use of a small number of channels is not recommended.
Sustained attention and working memory were improved in young adults after they engaged in a recently developed,
closed-loop, digital meditation practice. Whether this type of meditation also has a sustained effect on dominant
resting-state networks is currently unknown. In this study, we examined the resting brain states before and
after a period of breath-focused, digital meditation training versus placebo using an electroencephalography
(EEG) microstate approach. We found topographical changes in postmeditation rest, compared with baseline
rest, selectively for participants who were actively involved in the meditation training and not in participants
who engaged with an active, expectancy-match, placebo control paradigm. Our results suggest a reorganization
of brain network connectivity after 6 weeks of intensive meditation training in brain areas, mainly including the
right insula, the superior temporal gyrus, the superior parietal lobule, and the superior frontal gyrus bilaterally.
These findings provide an opening for the development of a novel noninvasive treatment of neuropathological states
by low-cost, breath-focused, digital meditation practice, which can be monitored by the EEG microstate approach.
In this study, we hypothesized that task performance could be evaluated applying EEG microstate to mental arithmetic task. This pilot study also aimed at evaluating the efficacy of microstates as novel features to discriminate task performance. Thirty-six subjects were divided into good and poor performers, depending on how well they performed the task. Microstate features were derived from EEG recordings during resting and task states. In the good performers, there was a decrease in type C and an increase in type D features during the task compared to the resting state. Mean duration and occurrence decreased and increased, respectively. In the poor performers, occurrence of type D feature, mean duration and occurrence showed greater changes. We investigated whether microstate features were suitable for task performance classification and eleven features including four archetypes were selected by recursive feature elimination (RFE). The model that implemented them showed the highest classification performance for differentiating between groups. Our pilot findings showed that the highest mean Area Under Curve (AUC) was 0.831. This study is the first to apply EEG microstate features to specific cognitive tasks in healthy subjects, suggesting that EEG microstate features can reflect task achievement.
Brain activity continuously and spontaneously fluctuates during tasks of sustained attention. This spontaneous activity reflects the intrinsic dynamics of neurocognitive networks, which have been suggested to differentiate moments of externally directed task focus from episodes of mind wandering. However, the contribution of specific electrophysiological brain states and their millisecond dynamics to the experience of mind wandering is still unclear. In this study, we investigated the association between electroencephalogram microstate temporal dynamics and self-reported mind wandering. Thirty-six participants completed a sustained attention to response task in which they were asked to respond to frequently occurring upright faces (nontargets) and withhold responses to rare inverted faces (targets). Intermittently, experience sampling probes assessed whether participants were focused on the task or whether they were mind wandering (i.e., off-task). Broadband electroencephalography was recorded and segmented into a time series of brain electric microstates based on data-driven clustering of topographic voltage patterns. The strength, prevalence, and rate of occurrence of specific microstates differentiated on- versus off-task moments in the prestimulus epochs of trials preceding probes. Similar associations were also evident between microstates and variability in response times. Together, these findings demonstrate that distinct microstates and their millisecond dynamics are sensitive to the experience of mind wandering.
Objective
Huntington’s disease (HD) is characterized by psychiatric, cognitive, and motor disturbances. The study aimed to determine electroencephalography (EEG) global state and microstate changes in HD and their relationship with cognitive and behavioral impairments.
Methods
EEGs from 20 unmedicated HD patients and 20 controls were compared using global state properties (connectivity and dimensionality) and microstate properties (EEG microstate analysis). For four microstate classes (A, B, C, D), three parameters were computed: duration, occurrence, coverage. Global- and microstate properties were compared between groups and correlated with cognitive test scores for patients.
Results
Global state analysis showed reduced connectivity in HD and an increasing dimensionality with increasing HD severity. Microstate analysis revealed parameter increases for classes A and B (coverage), decreases for C (occurrence) and D (coverage and occurrence). Disease severity and poorer test performances correlated with parameter increases for class A (coverage and occurrence), decreases for C (coverage and duration) and a dimensionality increase.
Conclusions
Global state changes may reflect higher functional dissociation between brain areas and the complex microstate changes possibly the widespread neuronal death and corresponding functional deficits in brain regions associated with HD symptomatology.
Significance
Combining global- and microstate analyses can be useful for a better understanding of progressive brain deterioration in HD.
Rumination is an important etiological factor of anxiety pathology, with its mechanism related to the deficit of working memory. The current study examined whether working memory training (WM-T) and emotional working memory training (EWM-T) could reduce rumination in anxious individuals. The participants with high trait anxiety underwent 21 days of mobile applications-based WM-T (n = 34), EWM-T (n = 36) or placebo control (n = 36), with questionnaires, cognitive tasks, and resting electroencephalogram (EEG) as the pre-post-test indicators. The results revealed that two training groups obtained comparable operation span increases (WM-T: d = 0.53; EWM-T: d = 0.65), updating improvement (WM-T: d = 0.43; EWM-T: d = 0.60) and shifting improvement (WM-T: d = 0.49; EWM-T: d = 0.72). Furthermore, compared to the control group, the EWM-T showed significant self-reported rumination reduction (d = 0.69), increased inhibition ability (d = 0.72), as well as modification of resting EEG microstate C parameters (Duration C: d = 0.42, Coverage C: d = 0.39), which were closely related to rumination level (r ~ 0.4). The WM-T group also showed the potential to reduced self-reported rumination (d = 0.45), but with the absence of the observable inhibition improvement and resting EEG changes. The correlation analysis suggested that the emotional benefits of WM-T depending more on improved updating and shifting, and that of EWM-T depending more on improved inhibition ability. The advantage to add emotional distractions into general working memory training for targeting rumination related anxiety has been discussed.
Brain functional connectivity measured by resting-state fMRI varies over multiple time scales, and recurrent dynamic functional connectivity (dFC) states have been identified. These have been found to be associated with different cognitive and pathological states, with potential as disease biomarkers, but their neuronal underpinnings remain a matter of debate. A number of recurrent microstates have also been identified in resting-state EEG studies, which are thought to represent the quasi-simultaneous activity of large-scale functional networks reflecting time-varying brain states. Here, we hypothesized that fMRI-derived dFC states may be associated with these EEG microstates. To test this hypothesis, we quantitatively assessed the ability of EEG microstates to predict concurrent fMRI dFC states in simultaneous EEG-fMRI data collected from healthy subjects at rest. By training a random forests classifier, we found that the four canonical EEG microstates predicted fMRI dFC states with an accuracy of 90%, clearly outperforming alternative EEG features such as spectral power. Our results indicate that EEG microstates analysis yields robust signatures of fMRI dFC states, providing evidence of the electrophysiological underpinnings of dFC while also further supporting that EEG microstates reflect the dynamics of large-scale brain networks.
The use of electroencephalography (EEG) to study overt speech production has increased substantially in the past 15 years and the alignment of evoked potential (ERPs) on the response onset has become an extremely useful method to target "lat-est" stages of speech production. Yet, response-locked ERPs raise a methodological issue: on which event should the point of alignment be placed? Response-locked ERPs are usually aligned to the vocal (acoustic) onset, although it is well known that articulatory movements may start up to a hundred milliseconds prior to the acoustic onset and that this "articulatory onset to acoustic onset interval" (AAI) depends on the phoneme properties. Given the previously reported difficulties to measure the AAI, the purpose of this study was to determine if the AAI could be reliably detected with EEG-microstates. High-density EEG was recorded during delayed speech production of monosyllabic pseudowords with four different onset consonants. Whereas the acoustic response onsets varied depending on the onset consonant, the response-locked spatiotem-poral EEG analysis revealed a clear asynchrony of the same sequence of microstates across onset consonants. A specific microstate, the latest observed in the ERPs locked to the vocal onset, presented longer duration for phonemes with longer acoustic response onsets. Converging evidences seemed to confirm that this microstate may be related to the articulatory onset of motor execution: its scalp topography corresponded to those previously associated with muscle activity and source localization highlighted the involvement of motor areas. Finally, the analyses on the duration of such microstate in single trials further fit with the AAI intervals for specific phonemes reported in previous studies. These results thus suggest that a particular ERP-microstate is a reliable index of articulation onset and of the AAI.
EEG microstate analysis offers a sparse characterisation of the spatio-temporal features of large-scale brain network activity. However, despite the concept of microstates is straight-forward and offers various quantifications of the EEG signal with a relatively clear neurophysiological interpretation, a few important aspects about the currently applied methods are not readily comprehensible. Here we aim to increase the transparency about the methods to facilitate widespread application and reproducibility of EEG microstate analysis by introducing a new EEGlab toolbox for Matlab. EEGlab and the Microstate toolbox are open source, allowing the user to keep track of all details in every analysis step. The toolbox is specifically designed to facilitate the development of new methods. While the toolbox can be controlled with a graphical user interface (GUI), making it easier for newcomers to take their first steps in exploring the possibilities of microstate analysis, the Matlab framework allows advanced users to create scripts to automatise analysis for multiple subjects to avoid tediously repeating steps for every subject. This manuscript provides an overview of the most commonly applied microstate methods as well as a tutorial consisting of a comprehensive walk-through of the analysis of a small, publicly available dataset.
The spontaneous activity of the brain is dynamic even at rest and the deviation from this normal pattern of dynamics can lead to different pathological states. EEG microstate analysis of resting-state neuronal activity in Parkinson’s disease (PD) could provide insight into altered brain dynamics of patients exhibiting dementia. Resting-state EEG microstate maps were derived from 128 channel EEG data in 20 PD without dementia (PDND), 18 PD with dementia (PDD) and 20 Healthy controls (CON) using Cartool and sLORETA softwares. Microstate map parameters including global explained variance, mean duration, frequency of occurrence (TF) and time coverage were compared statistically among the groups. Eight maps that explained 72% of the topographic variance were identified and only three maps differed significantly across the groups. TF of Map1 was lower in both PDND and PDD (p < 0.001) and that of Map3 (p = 0.02) in PDND compared to control. Cortical sources showed higher activation in precuneus, cuneus and superior parietal lobe (Threshold: Log-F = 1.74, p < 0.05) with maximum activity in the precuneus region (MNI co-ordinates: − 25, − 75, − 40; Log-F = 1.9) in PDND compared to control only for Map1. Lower TF of Map1 (prototypical microstate D) may potentially serve as a biomarker for PD with or without dementia whereas higher activation of precuneus, cuneus and superior parietal lobe at resting-state could favour signal processing, lack of which could be associated with dementia in Parkinson’s disorder.
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.
Evidence suggests that mindfulness meditation (MM) improves selective attention and reduces distractibility by enhancing top-down neural modulation. Altered P300 and alpha neural activity from MM have been identified and may reflect the neural changes that underpin these improvements. Given the proposed role of alpha activity in supressing processing of task-irrelevant information, it is theorised that altered alpha activity may underlie increased availability of neural resources in meditators. The present study investigated attentional function in meditators using a cross-modal study design, examining the P300 during working memory (WM) and alpha activity during concurrent distracting tactile stimuli. Thirty-three meditators and 27 healthy controls participated in the study. Meditators showed a more frontal distribution of P300 neural activity following WM stimuli (p = 0.005, η2 = 0.060) and more modulation of alpha activity at parietal-occipital regions between single (tactile stimulation only) and dual task demands (tactile stimulation plus WM task) (p < 0.001, η2 = 0.065). Additionally, meditators performed more accurately than controls (p = 0.038, η2 = 0.067). The altered distribution of neural activity concurrent with improved WM performance suggests greater attentional resources dedicated to task related functions, such as WM in meditators. Thus, meditation-related neural changes are likely multifaceted, involving both altered distribution and also amplitudes of brain activity, thereby enhancing attentional processes depending on task requirements.
The momentary global functional state of the brain is reflected in its electric field configuration and cluster analytical approaches have consistently shown four configurations, referred to as EEG microstate classes A to D. Changes in microstate parameters are associated with a number of neuropsychiatric disorders, task performance, and mental state establishing their relevance for cognition. However, the common practice to use eye-closed resting state data to assess the temporal dynamics of microstate parameters might induce systematic confounds related to vigilance levels. Here, we studied the dynamics of microstate parameters in two independent data sets and showed that the parameters of microstates are strongly associated with vigilance level assessed both by EEG power analysis and fMRI global signal. We found that the duration and contribution of microstate class C, as well as transition probabilities towards microstate class C were positively associated with vigilance, whereas the sign was reversed for microstate classes A and B. Furthermore, in looking for the origins of the correspondence between microstates and vigilance level, we found Granger-causal effects of vigilance levels on microstate sequence parameters. Collectively, our findings suggest that duration and occurrence of microstates have a different origin and possibly reflect different physiological processes. Finally, our findings indicate the need for taking vigilance levels into consideration in resting-sate EEG investigations.
Resting-state EEG microstates are brief (50–100 ms) periods, in which the spatial configuration of scalp global field power remains quasi-stable before rapidly shifting to another configuration. Changes in microstate parameters have been described in patients with psychotic disorders. These changes have also been observed in individuals with a clinical or genetic high risk, suggesting potential usefulness of EEG microstates as a biomarker for psychotic disorders. The present study aimed to investigate the potential of EEG microstates as biomarkers for psychotic disorders and future transition to psychosis in patients at ultra-high-risk (UHR). We used 19-channel clinical EEG recordings and orthogonal contrasts to compare temporal parameters of four normative microstate classes (A–D) between patients with first-episode psychosis (FEP; n = 29), UHR patients with (UHR-T; n = 20) and without (UHR-NT; n = 34) later transition to psychosis, and healthy controls (HC; n = 25). Microstate A was increased in patients (FEP & UHR-T & UHR-NT) compared to HC, suggesting an unspecific state biomarker of general psychopathology. Microstate B displayed a decrease in FEP compared to both UHR patient groups, and thus may represent a state biomarker specific to psychotic illness progression. Microstate D was significantly decreased in UHR-T compared to UHR-NT, suggesting its potential as a selective biomarker of future transition in UHR patients.
Electroencephalogram microstates are recurrent scalp potential configurations that remain stable for around 90 ms. The dynamics of two of the four canonical classes of microstates, commonly labeled as C and D, have been suggested as a potential endophenotype for schizophrenia. For endophenotypes, unaffected relatives of patients must show abnormalities compared to controls. Here, we examined microstate dynamics in resting-state recordings of unaffected siblings of patients with schizophrenia, patients with schizophrenia, healthy controls, and patients with first episodes of psychosis (FEP). Patients with schizophrenia and their siblings showed increased presence of microstate class C and decreased presence of microstate class D compared to controls. No difference was found between FEP and chronic patients. Our findings suggest that the dynamics of microstate classes C and D are a candidate endophenotype for schizophrenia. EEG microstate abnormalities have been reported in patients with schizophrenia. Here the authors demonstrate that patients and their siblings show similar microstate abnormalities compared to healthy controls.
In the last decades, several electrophysiological markers have been investigated to better understand how humans precede a signaled event. Among others, the pre-stimulus microstates’ topography, representing the whole brain activity, has been proposed as a promising index of the anticipatory period in several cognitive tasks. However, to date, a clear relationship between the metrics of the pre-stimulus microstates [i.e., the global explained variance (GEV) and the frequency of occurrence (FOO)] and well-known electroencephalography marker of the anticipation (i.e., the alpha power reduction) has not been investigated. Here, after extracting the microstates during the expectancy of the semantic memory task, we investigate the correlations between the microstate features and the anticipatory alpha (8–12 Hz) power reduction (i.e., the event-related de-synchronization of the alpha rhythms; ERD) that is widely interpreted as a functional correlate of brain activation. We report a positive correlation between the occurrence of the dominant, but not non-dominant, microstate and both the mean amplitude of high-alpha ERD and the magnitude of the alpha ERD peak so that the stronger the decrease (percentage) in the alpha power, the higher the FOO of the dominant microstate. Moreover, we find a positive correlation between the occurrence of the dominant microstate and the latency of the alpha ERD peak, suggesting that subjects with higher FOO present the stronger alpha ERD closely to the target. These correlations are not significant between the GEV and all anticipatory alpha ERD indices. Our results suggest that only the occurrence of the dominant, but not non-dominant, microstate should be considered as a useful electrophysiological correlate of the cortical activation.
Quasi-stable electrical fields in the EEG, called microstates carry information on the dynamics of large scale brain networks. Using machine learning techniques, we explored whether abnormalities in microstates can be used to classify patients with schizophrenia and healthy controls. We applied multivariate pattern analysis of microstate features to create a specified feature set to represent microstate characteristics. Machine learning approaches using these features for classification of patients with schizophrenia were compared with prior EEG based machine learning studies. Our microstate segmentation in both patients with schizophrenia and healthy controls yielded topographies that were similar to the normative database established earlier by Koenig et al. Our machine learning model was based on large sample size, low number of features and state-of-art K-fold cross-validation technique. The multivariate analysis revealed three patterns of correlated features, which yielded an AUC of 0.84 for the group separation (accuracy: 82.7%, sensitivity/specificity: 83.5%/85.3%). Microstate segmentation of resting state EEG results in informative features to discriminate patients with schizophrenia from healthy individuals. Moreover, alteration in microstate measures may represent disturbed activity of networks in patients with schizophrenia.
The clinical diagnosis of Parkinson's disease (PD) is very difficult, especially in the early stage of the disease, because there is no physiological indicator that can be referenced. Drug-free patients with early PD are characterized by clinical symptoms such as impaired motor function and cognitive decline, which was caused by the dysfunction of brain's dynamic activities. The indicators of brain dysfunction in patients with PD at an early unmedicated condition may provide a valuable basis for the diagnosis of early PD and later treatment. In order to find the spatiotemporal characteristic markers of brain dysfunction in PD, the resting-state EEG microstate analysis is used to explore the transient state of the whole brain of 23 drug-free patients with PD on the sub-second timescale compared to 23 healthy controls. EEG microstates reflect a transiently stable brain topological structure with spatiotemporal characteristics, and the spatial characteristic microstate classes and temporal parameters provide insight into the brain's functional activities in PD patients. The further exploration was to explore the relation between temporal microstate parameters and significant clinical symptoms to determine whether these parameters could be used as a basis for clinically assisted diagnosis. Therefore, we used a general linear model (GLM) to explore the relevance of microstate parameters to clinical scales and multiple patient attributes, and the Wilcoxon rank sum test was used to quantify the linear relation between influencing factors and microstate parameters. Results of microstate analysis revealed that there was an unique spatial microstate different from healthy controls in PD, and several other typical microstates had significant differences compared with the normal control group, and these differences were reflected in the microstate parameters, such as longer durations and more occurrences of one class of microstates in PD compared with healthy controls. Furthermore, correlation analysis showed that there was a significant correlation between multiple microstate classes' parameters and significant clinical symptoms, including impaired motor function and cognitive decline. These results indicate that we have found multiple quantifiable feature tags that reflect brain dysfunction in the early stage of PD. Importantly, such temporal dynamics in microstates are correlated with clinical scales which represent the motor function and recognize level. The obtained results may deepen our understanding of the brain dysfunction caused by PD, and obtain some quantifiable signatures to provide an auxiliary reference for the early diagnosis of PD.
In recent years, EEG microstate analysis has gained popularity as a tool to characterize spatio-temporal dynamics of large-scale electrophysiology data. It has been used in a wide range of EEG studies and the discovered microstates have been linked to cognitive function and brain diseases. EEG microstates are assumed to (1) be winner-take-all, meaning that the topography at any given time point is in one state; and (2) discretely transition from one state into another. In this study we investigated these assumptions by taking a geometric perspective of EEG data, treating microstate topographies as basis vectors for a subspace of the original channel space. We found that within- and across-microstate distance distributions were largely overlapping: for the low GFP (Global Field Power) range (lower 15%), individual time points labeled as one microstate are often equidistant to multiple microstate vectors, challenging the winner-take-all assumption. At high global field power, separability of microstates improved, but remained rather weak. Although many GFP peaks (which are the time points used for defining microstates) occur during high GFP ranges, low GFP ranges associated with poor separability also contain GFP peaks. Furthermore, the geometric analysis suggested that microstates and their transitions appear to be more continuous than discrete. The Analysis of rate of change of trajectory in sensor space suggests gradual microstate transitions as opposed to the classical binary view of EEG microstates. Taken together, our findings suggest that EEG microstates are better conceptualized as spatially and temporally continuous, rather than discrete activations or neural populations.
Background: Neuroimaging studies provided evidence for disrupted resting-state functional brain network activity in bipolar disorder (BD). Electroencephalographic (EEG) studies found altered temporal characteristics of functional EEG microstates during depressive episode within different affective disorders. Here we investigated whether euthymic patients with BD show deviant resting-state large-scale brain network dynamics as reflected by altered temporal characteristics of EEG microstates.
Methods: We used high-density EEG to explore between-group differences in duration, coverage, and occurrence of the resting-state functional EEG microstates in 17 euthymic adults with BD in on-medication state and 17 age- and gender-matched healthy controls. Two types of anxiety, state and trait, were assessed separately with scores ranging from 20 to 80.
Results: Microstate analysis revealed five microstates (A–E) in global clustering across all subjects. In patients compared to controls, we found increased occurrence and coverage of microstate A that did not significantly correlate with anxiety scores.
Conclusion: Our results provide neurophysiological evidence for altered large-scale brain network dynamics in BD patients and suggest the increased presence of A microstate to be an electrophysiological trait characteristic of BD.
Spontaneous mental activity is characterized by dynamic alterations of discrete and stabile brain states called functional microstates that are thought to represent distinct steps of human information processing. Electroencephalography (EEG) directly reflects functioning of brain synapses with a uniquely high temporal resolution, necessary for investigation of brain network dynamics. Since synaptic dysfunction is an early event and best correlate of cognitive status and decline in patients along Alzheimer's disease (AD) continuum, EEG microstates might serve as valuable early markers of AD. The present study investigated differences in EEG microstate topographies and parameters (duration, occurrence and contribution) between a large cohort of healthy elderly (n = 308) and memory clinic patients: subjective cognitive decline (SCD, n = 210); mild cognitive impairment (MCI, n = 230) and AD (n = 197) and how they correlate to conventional cerebrospinal fluid (CSF) markers of AD. Four most representative microstate maps assigned as classes A, B (asymmetrical), C and D (symmetrical) were computed from the resting state EEGs since it has been shown previously that this is sufficient to explain most of the resting state EEG data. Statistically different topography of microstate maps were found between the controls and the patient groups for microstate classes A, C and D. Changes in the topography of microstate class C were associated with the CSF Aβ42 levels, whereas changes in the topography of class B were linked with the CSF p-tau levels. Gradient-like increase in the contribution of asymmetrical (A and B) and gradient-like decrease in the contribution of symmetrical (C and D) maps were observed with the more severe stage of cognitive impairment. Our study demonstrated extensive relationship of resting state EEG microstates topographies and parameters with the stage of cognitive impairment and AD biomarkers. Resting state EEG microstates might therefore serve as functional markers of early disruption of neurocognitive networks in patients along AD continuum.
Electroencephalography (EEG) is a useful tool to inspect the brain activity in resting state and allows to characterize spontaneous brain activity that is not detected when a subject is cognitively engaged. Moreover, taking advantage of the high time resolution in EEG, it is possible to perform fast topographical reference-free analysis, since different scalp potential fields correspond to changes in the underlying sources within the brain. In this study, the spontaneous EEG resting state (eyes closed) was compared between 10 young adults ages 18–30 years with autism spectrum disorder (ASD) and 13 neurotypical controls. A microstate analysis was applied, focusing on four temporal parameters: mean duration, the frequency of occurrence, the ratio of time coverage, and the global explained variance (GEV). Using data that were acquired from a 65-channel EEG system, six resting-state topographies that best describe the dataset across all subjects were identified by running a two-step cluster analysis labeled as microstate classes A–F. The results indicated that microstates B and E displayed statistically significant differences between both groups among the temporal parameters evaluated. Classes B, D, E, and F were consistently more present in ASD, and class C in controls. The combination of these findings with the putative functional significance of the different classes suggests that during resting state, the ASD group was more focused on visual scene reconstruction, while the control group was more engaged with self-memory retrieval. Furthermore, from a connectivity perspective, the resting-state networks that have been previously associated with each microstate class overlap the brain regions implicated in impaired social communication and repetitive behaviors that characterize ASD.
Functional MRI neurofeedback (NF) allows humans to self-modulate neural patterns in specific brain areas. This technique is regarded as a promising tool to translate neuroscientific knowledge into brain-guided psychiatric interventions. However, its clinical implementation is restricted by unstandardized methodological practices, by clinical definitions that are poorly grounded in neurobiology, and by lack of a unifying framework that dictates experimental choices. Here we put forward a new framework, termed ‘process-based NF’, which endorses a process-oriented characterization of mental dysfunctions to form precise and effective psychiatric treatments. This framework relies on targeting specific dysfunctional mental processes by modifying their underlying neural mechanisms and on applying process-specific contextual feedback interfaces. Finally, process-based NF offers designs and a control condition that address the methodological shortcomings of current approaches, thus paving the way for a precise and personalized neuromodulation.
Lewy body dementia includes dementia with Lewy bodies and Parkinson’s disease dementia and is characterized by transient clinical symptoms such as fluctuating cognition, which might be driven by dysfunction of the intrinsic dynamic properties of the brain. In this context we investigated whole-brain dynamics on a subsecond timescale in 42 Lewy body dementia compared to 27 Alzheimer’s disease patients and 18 healthy controls using an EEG microstate analysis in a cross-sectional design. Microstates are transiently stable brain topographies whose temporal characteristics provide insight into the brain’s dynamic repertoire. Our additional aim was to explore what processes in the brain drive microstate dynamics. We therefore studied associations between microstate dynamics and temporal aspects of large-scale cortical-basal ganglia-thalamic interactions using dynamic functional MRI measures given the putative role of these subcortical areas in modulating widespread cortical function and their known vulnerability to Lewy body pathology. Microstate duration was increased in Lewy body dementia for all microstate classes compared to Alzheimer’s disease (P < 0.001) and healthy controls (P < 0.001), while microstate dynamics in Alzheimer’s disease were largely comparable to healthy control levels, albeit with altered microstate topographies. Correspondingly, the number of distinct microstates per second was reduced in Lewy body dementia compared to healthy controls (P < 0.001) and Alzheimer’s disease (P < 0.001). In the dementia with Lewy bodies group, mean microstate duration was related to the severity of cognitive fluctuations (ρ = 0.56, PFDR = 0.038). Additionally, mean microstate duration was negatively correlated with dynamic functional connectivity between the basal ganglia (r = − 0.53, P = 0.003) and thalamic networks (r = − 0.38, P = 0.04) and large-scale cortical networks such as visual and motor networks in Lewy body dementia. The results indicate a slowing of microstate dynamics and disturbances to the precise timing of microstate sequences in Lewy body dementia, which might lead to a breakdown of the intricate dynamic properties of the brain, thereby causing loss of flexibility and adaptability that is crucial for healthy brain functioning. When contrasted with the largely intact microstate dynamics in Alzheimer’s disease, the alterations in dynamic properties in Lewy body dementia indicate a brain state that is less responsive to environmental demands and might give rise to the apparent slowing in thinking and intermittent confusion which typify Lewy body dementia. By using Lewy body dementia as a probe pathology we demonstrate a potential link between dynamic functional MRI fluctuations and microstate dynamics, suggesting that dynamic interactions within the cortical-basal ganglia-thalamic loop might play a role in the modulation of EEG dynamics.
Autism spectrum disorder (ASD) involves aberrant organization and functioning of large-scale brain networks. The aim of this study was to examine 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 ASD. To achieve this goal, EEG microstate analysis was conducted on the resting-state EEG datasets of 15 patients with ASD and 18 healthy controls from the Healthy Brain Network. The parameters (i.e., duration, occurrence rate, time coverage and topographical configuration) of four classical microstate classes (i.e., class A, B, C and D) were statistically tested between two groups. The results showed that: (1) the occurrence rate and time coverage of microstate class B in ASD group were significantly larger than those in control group; (2) the duration of microstate class A, the duration and time coverage of microstate class C were significantly smaller than those in control group; (3) the map configuration and occurrence rate differed significantly between two groups for microstate class D. These results suggested that EEG microstate analysis could be used to detect the deviant functions of large-scale cortical activities in ASD, and may provide indices that could be used in clinical researches of ASD.
Objectives: Characterizing pharmacological response in Parkinson’s Disease (PD) patients may be a challenge in early stages but gives valuable clues for diagnosis. Neurotropic drugs may modulate Electroencephalography (EEG) microstates (MS). We investigated EEG-MS default-mode network changes in response to dopaminergic stimulation in PD.
Methods: Fourteen PD subjects in HY stage III or less were included, and twenty-one healthy controls. All patients were receiving dopaminergic stimulation with levodopa or dopaminergic agonists. Resting EEG activity was recorded before the first daily PD medication dose and 1 h after drug intake resting EEG activity was again recorded. Time and frequency variables for each MS were calculated.
Results: Parkinson’s disease subjects MS A duration decreases after levodopa intake, MS B appears more often than before levodopa intake. MS E was not present, but MS G was. There were no significant differences between control subjects and patients after medication intake.
Conclusion: Clinical response to dopaminergic drugs in PD is characterized by clear changes in MS profile.
Significance: This work demonstrates that there are clear EEG MS markers of PD dopaminergic stimulation state. The characterization of the disease and its response to dopaminergic medication may be of help for early therapeutic diagnosis.
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”.
Background
The “Avolition-apathy” domain of the negative symptoms was found to include different symptoms by factor analytic studies on ratings derived by different scales. In particular, the relationship of anhedonia with this domain is controversial. Recently introduced negative symptom rating scales provide a better assessment of anhedonia, allowing the distinction of anticipatory and consummatory aspects, which might be related to different psychopathological dimensions. The study of associations with external validators, such as electrophysiological, brain imaging or cognitive indices, might shed further light on the status of anhedonia within the Avolition-apathy domain.
Objectives
We used brain electrical microstates (MSs), which represent subsecond periods of quasi-stable scalp electrical field, associated with resting-state neural networks (and thus with global patterns of functional connectivity), to test whether the component symptoms of Avolition-apathy share the same correlates.
Method
We analyzed multichannel resting EEGs in 142 individuals with schizophrenia (SCZ) and in 64 healthy controls (HC), recruited within the add-on EEG study of the Italian Network for Research on Psychoses. Relative time contribution, duration and occurrence of four MS classes (MS-A/-B/-C/−D) were calculated. Group differences on MS parameters (contribution and duration) and their associations with negative symptom domains (assessed using the Brief Negative Symptoms Scale) were investigated.
Results
SCZ, in comparison to HC, showed increased contribution and duration of MS-C. The contribution of MS-A positively correlated with Avolition-apathy, but not with Expressive deficit. Within the Avolition-apathy domain, anticipatory anhedonia, avolition and asociality, but not consummatory anhedonia, showed the same correlations with MS-A contribution.
Conclusion
Our findings support the existence of distinct electrophysiological correlates of Avolition-apathy with respect to Expressive deficit, and lend support to the hypothesis that only the anticipatory component of anhedonia shares the same pathophysiological underpinnings of the Avolition-apathy domain.
While many insights on brain development and aging have been gained by studying resting-state networks with fMRI, relating these changes to cognitive functions is limited by the temporal resolution of fMRI. In order to better grasp short-lasting and dynamically changing mental activities, an increasing number of studies utilize EEG to define resting-state networks, thereby often using the concept of EEG microstates. These are brief (around 100 ms) periods of stable scalp potential fields that are influenced by cognitive states and are sensitive to neuropsychiatric diseases. Despite the rising popularity of the EEG microstate approach, information about age changes is sparse and nothing is known about sex differences. Here we investigated age and sex related changes of the temporal dynamics of EEG microstates in 179 healthy individuals (6-87 years old, 90 females, 204-channel EEG). We show strong sex-specific changes in microstate dynamics during adolescence as well as at older age. In addition, males and females differ in the duration and occurrence of specific microstates. These results are of relevance for the comparison of studies in populations of different age and sex and for the understanding of the changes in neuropsychiatric diseases.
The present review discusses a well-established method for characterizing resting-state activity of the human brain using multichannel electroencephalography (EEG). This method involves the examination of electrical microstates in the brain, which are defined as successive short time periods during which the configuration of the scalp potential field remains semi-stable, suggesting quasi-simultaneity of activity among the nodes of large-scale networks. A few prototypic microstates, which occur in a repetitive sequence across time, can be reliably identified across participants. Researchers have proposed that these microstates represent the basic building blocks of the chain of spontaneous conscious mental processes, and that their occurrence and temporal dynamics determine the quality of mentation. Several studies have further demonstrated that disturbances of mental processes associated with neurological and psychiatric conditions manifest as changes in the temporal dynamics of specific microstates. Combined EEG-fMRI studies and EEG source imaging studies have indicated that EEG microstates are closely associated with resting-state networks as identified using fMRI. The scale-free properties of the time series of EEG microstates explain why similar networks can be observed at such different time scales. The present review will provide an overview of these EEG microstates, available methods for analysis, the functional interpretations of findings regarding these microstates, and their behavioral and clinical correlates.
Using EEG to elucidate the spontaneous activation of brain resting state networks is non trivial as the signal of interest is of low amplitude and it is difficult to distinguish the underlying neural sources. Using the principles of electric field topographical analysis, it is possible to estimate the meta-stable states of the brain (i.e., the resting state topographies, so-called microstates). We estimated seven resting state topographies explaining the EEG dataset with k-means clustering (N=164, 256 electrodes). Using a method specifically designed to localize the sources of broadband EEG scalp topographies by matching sensor and source space temporal patterns, we demonstrated that we can estimate the EEG resting state networks reliably by measuring the reproducibility of our findings. After subtracting their mean from the seven EEG resting state networks, we identified seven state-specific networks. The mean map includes regions known to be densely anatomically and functionally connected (superior frontal, superior parietal, insula, and anterior cingulate cortices). While the mean map can be interpreted as a "router", cross-linking multiple functional networks, the seven state-specific resting state networks partly resemble and extend previous fMRI-based networks estimated as the hemodynamic correlates of four canonical EEG microstates.
Whereas some studies have demonstrated impaired working memory (WM) among patients with borderline personality disorder (BPD), these findings have not been consistent. Furthermore, there is a lack of neurophysiological evidence about WM function in patients with BPD. The goal of this study was to examine WM function in patients with BPD by using event-related potentials (ERPs). An additional goal was to explore whether characteristics of BPD (i.e., impulsiveness and emotional instability) are associated with WM impairment. A modified version of the N-back task (0- and 2-back) was used to measure WM. ERPs were recorded in 22 BPD patients and 21 age-, handedness-, and sex-matched healthy controls (HCs) while they performed the WM task. The results revealed that there were no significant group differences for behavioral variables (reaction time and accuracy rate) or for latencies and amplitudes of P1 and N1 (all p > 0.05). BPD patients had lower P3 amplitudes and longer N2 latencies than HC, independent of WM load (low load: 0-back; high load: 2-back). Impulsiveness was not correlated with N2 latency or P3 amplitude, and no correlations were found between N2 latency or P3 amplitude and affect intensity scores in any WM load (all p > 0.05). In conclusion, the lower P3 amplitudes and longer N2 latencies in BPD patients suggested that they might have some dysfunction of neural activities in sub-processing in WM, while impulsiveness and negative affect might not have a close relationship with these deficits.
EEG studies of wakeful rest have shown that there are brief periods in which global electrical brain activity on the scalp remains semi-stable (so-called microstates). Topographical analyses of this activity have revealed that much of the variance is explained by four distinct microstates that occur in a repetitive sequence. A recent fMRI study showed that these four microstates correlated with four known functional systems, each of which is activated by specific cognitive functions and sensory inputs. The present study used high density EEG to examine the degree to which spatial and temporal properties of microstates may be altered by manipulating cognitive task (a serial subtraction task vs. wakeful rest) and the availability of visual information (eyes open vs. eyes closed conditions). The hypothesis was that parameters of microstate D would be altered during the serial subtraction task because it is correlated with regions that are part of the dorsal attention functional system. It was also expected that the sequence of microstates would preferentially transition from all other microstates to microstate D during the task as compared to rest. Finally, it was hypothesized that the eyes open condition would significantly increase one or more microstate parameters associated with microstate B, which is associated with the visual system. Topographical analyses indicated that the duration, coverage, and occurrence of microstate D were significantly higher during the cognitive task compared to wakeful rest; in addition, microstate C, which is associated with regions that are part of the default mode and cognitive control systems, was very sensitive to the task manipulation, showing significantly decreased duration, coverage, and occurrence during the task condition compared to rest. Moreover, microstate B was altered by manipulations of visual input, with increased occurrence and coverage in the eyes open condition. In addition, during the eyes open condition microstates A and D had significantly shorter durations, while C had increased occurrence. Microstate D had decreased coverage in the eyes open condition. Finally, at least 15 microstates (identified via k-means clustering) were required to explain a similar amount of variance of EEG activity as previously published values. These results support important aspects of our hypotheses and demonstrate that cognitive manipulation of microstates is possible, but the relationships between microstates and their corresponding functional systems are complex. Moreover, there may be more than four primary microstates.
Spontaneous fluctuations of neuronal activity in large-scale distributed networks are a hallmark of the resting brain. In relapsing-remitting multiple sclerosis (RRMS) several fMRI studies have suggested altered resting-state connectivity patterns. Topographical EEG analysis reveals much faster temporal fluctuations in the tens of milliseconds time range (termed “microstates”), which showed altered properties in a number of neuropsychiatric conditions.
We investigated whether these microstates were altered in patients with RRMS, and if the microstates' temporal properties reflected a link to the patients' clinical features.
We acquired 256-channel EEG in 53 patients (mean age 37.6 years, 45 females, mean disease duration 9.99 years, Expanded Disability Status Scale ≤ 4, mean 2.2) and 49 healthy controls (mean age 36.4 years, 33 females). We analyzed segments of a total of 5 min of EEG during resting wakefulness and determined for both groups the four predominant microstates using established clustering methods.
We found significant differences in the temporal dynamics of two of the four microstates between healthy controls and patients with RRMS in terms of increased appearance and prolonged duration. Using stepwise multiple linear regression models with 8-fold cross-validation, we found evidence that these electrophysiological measures predicted a patient's total disease duration, annual relapse rate, disability score, as well as depression score, and cognitive fatigue measure.
In RRMS patients, microstate analysis captured altered fluctuations of EEG topographies in the sub-second range. This measure of high temporal resolution provided potentially powerful markers of disease activity and neuropsychiatric co-morbidities in RRMS.
Objective: Diagnosis of semantic dementia relies on cost-intensive MRI or PET, although resting EEG markers of other dementias have been reported. Yet the view still holds that resting EEG in patients with semantic dementia is normal. However, studies using increasingly sophisticated EEG analysis methods have demonstrated that slightest alterations of functional brain states can be detected. Methods: We analyzed the common four resting EEG microstates (A, B, C, and D) of 8 patients with semantic dementia in comparison with 8 healthy controls and 8 patients with Alzheimer's disease. Results: Topographical differences between the groups were found in microstate classes B and C, while microstate classes A and D were comparable. The data showed that the semantic dementia group had a peculiar microstate E, but the commonly found microstate C was lacking. Furthermore, the presence of microstate E was significantly correlated with lower MMSE and language scores. Conclusion: Alterations in resting EEG can be found in semantic dementia. Topographical shifts in microstate C might be related to semantic memory deficits. Significance: This is the first study that discovered resting state EEG abnormality in semantic dementia. The notion that resting EEG in this dementia subtype is normal has to be revised.
Spontaneous EEG signal can be parsed into sub-second periods of stable functional states (microstates) that assumingly correspond to brief large scale synchronization events. In schizophrenia, a specific class of microstate (class "D") has been found to be shorter than in healthy controls and to be correlated with positive symptoms. To explore potential new treatment options in schizophrenia, we tested in healthy controls if neurofeedback training to self-regulate microstate D presence is feasible and what learning patterns are observed. Twenty subjects underwent EEG-neurofeedback training to up-regulate microstate D presence. The protocol included 20 training sessions, consisting of baseline trials (resting state), regulation trials with auditory feedback contingent on microstate D presence, and a transfer trial. Response to neurofeedback was assessed with mixed effects modelling. All participants increased the percentage of time spent producing microstate D in at least one of the three conditions (p < 0.05). Significant between-subjects across-sessions results showed an increase of 0.42 % of time spent producing microstate D in baseline (reflecting a sustained change in the resting state), 1.93 % of increase during regulation and 1.83 % during transfer. Within-session analysis (performed in baseline and regulation trials only) showed a significant 1.65 % increase in baseline and 0.53 % increase in regulation. These values are in a range that is expected to have an impact upon psychotic experiences. Additionally, we found a negative correlation between alpha power and microstate D contribution during neurofeedback training. Given that microstate D has been related to attentional processes, this result provides further evidence that the training was to some degree specific for the attentional network. We conclude that microstate-neurofeedback training proved feasible in healthy subjects. The implementation of the same protocol in schizophrenia patients may promote skills useful to reduce positive symptoms by means of EEG-neurofeedback.
Sustained attention and working memory were improved in young adults after they engaged in a recently developed, closed-loop digital meditation practice. Whether this type of meditation also has a sustained effect on dominant resting state networks is currently unknown. Here, we examined the resting brain states before and after a period of breath-focused digital meditation training vs. placebo using an electroencephalography (EEG) microstates approach. We found topographical changes in post-meditation rest compared to baseline rest, selectively for participants who were actively involved in the meditation training and not in participants who engaged with an active, expectancy-match placebo control paradigm. Our results suggest a re-organization of brain network connectivity after 6 weeks of intensive meditation training in brain areas mainly including the right insula, the superior temporal gyrus, the superior parietal lobule and the superior frontal gyrus bilaterally. These findings provide an opening for the development of novel, non-invasive treatment of neuropathological states by low-cost breath-focused digital meditation practice, which can be monitored by the EEG microstate approach.
Neuroimaging studies have shown that major depressive disorder (MDD) is characterized by abnormal neural activity and connectivity. However, hemodynamic imaging techniques lack the temporal resolution needed to resolve the dynamics of brain mechanisms underlying MDD. Moreover, it is unclear whether putative abnormalities persist after remission. To address these gaps, we used microstate analysis to study resting-state brain activity in major depressive disorder (MDD). Electroencephalographic (EEG) “microstates” are canonical voltage topographies that reflect brief activations of components of resting-state brain networks. We used polarity-insensitive k-means clustering to segment resting-state high-density (128-channel) EEG data into microstates. Data from 79 healthy controls (HC), 63 individuals with MDD, and 30 individuals with remitted MDD (rMDD) were included. The groups produced similar sets of five microstates, including four widely-reported canonical microstates (A-D). The proportion of microstate D was decreased in MDD and rMDD compared to the HC group (Cohen’s d = 0.63 and 0.72, respectively) and the duration and occurrence of microstate D was reduced in the MDD group compared to the HC group (Cohen’s d = 0.43 and 0.58, respectively). Among the MDD group, proportion and duration of microstate D were negatively correlated with symptom severity (Spearman’s rho = −0.34 and −0.46, respectively). Finally, microstate transition probabilities were nonrandom and the MDD group, relative to the HC and the rMDD groups, exhibited multiple distinct transition probabilities, primarily involving microstates A and C. Our findings highlight both state and trait abnormalities in resting-state brain activity in MDD.
The temporal structure of self-generated cognition is a key attribute to the formation of a meaningful stream of consciousness. When at rest, our mind wanders from thought to thought in distinct mental states. Despite the marked importance of ongoing mental processes, it is challenging to capture and relate these states to specific cognitive contents. In this work, we employed ultra-high field functional magnetic resonance imaging (fMRI) and high-density electroencephalography (EEG) to study the ongoing thoughts of participants instructed to retrieve self-relevant past episodes for periods of 22sec. These task-initiated, participant-driven activity patterns were compared to a distinct condition where participants performed serial mental arithmetic operations, thereby shifting from self-related to self-unrelated thoughts. BOLD activity mapping revealed selective enhanced activity in temporal, parietal and occipital areas during the memory compared to the mental arithmetic condition, evincing their role in integrating the re-experienced past events into conscious representations during memory retrieval. Functional connectivity analysis showed that these regions were organized in two major subparts, previously associated to “scene-reconstruction” and “self-experience” subsystems. EEG microstate analysis allowed studying these participant-driven thoughts in the millisecond range by determining the temporal dynamics of brief periods of stable scalp potential fields. This analysis revealed selective modulation of occurrence and duration of specific microstates in the memory and in the mental arithmetic condition, respectively. EEG source analysis revealed similar spatial distributions of the sources of these microstates and the regions identified with fMRI. These findings imply a functional link between BOLD activity changes in regions related to a certain mental activity and the temporal dynamics of mentation, and support growing evidence that specific fMRI networks can be captured with EEG as repeatedly occurring brief periods of integrated coherent neuronal activity, lasting only fractions of seconds.
Fluid reasoning is considered central to general intelligence. How its psychometric structure relates to brain function remains poorly understood. For instance, what is the dynamic composition of ability-specific processes underlying fluid reasoning? We investigated whether distinct fluid reasoning abilities could be differentiated by electroencephalography (EEG) microstate profiles. EEG microstates specifically capture rapidly altering activity of distributed cortical networks with a high temporal resolution as scalp potential topographies that dynamically vary over time in an organized manner. EEG was recorded simultaneously with functional magnetic resonance imaging (fMRI) in twenty healthy adult participants during cognitively distinct fluid reasoning tasks: induction, spatial relationships and visualization. Microstate parameters successfully discriminated between fluid reasoning and visuomotor control tasks as well as between the fluid reasoning tasks. Mainly, microstate B coverage was significantly higher during spatial relationships and visualization, compared to induction, while microstate C coverage was significantly decreased during spatial relationships and visualization, compared to induction. Additionally, microstate D coverage was highest during spatial relationships and microstate A coverage was most strongly reduced during the same condition. Consistently, multivariate analysis with a leave-one-out cross-validation procedure accurately classified the fluid reasoning tasks based on the coverage parameter. These EEG data and their correlation with fMRI data suggest that especially the tasks most strongly relying on visuospatial processing modulated visual and default mode network activity. We propose that EEG microstates can provide valuable information about neural activity patterns with a dynamic and complex temporal structure during fluid reasoning, suggesting cognitive ability-specific interplays between multiple brain networks.
OBJECTIVE: The momentary spatial configuration of the brain electric field at the scalp reflects quasi-stable "functional microstates" caused by activity of different intracranial generators. There is paucity in literature on the intracranial generators of resting state EEG microstate alterations in stable patients with schizophrenia. The present study aimed to investigate resting state microstate alterations and their neural generators in patients with schizophrenia and their first-degree relatives as compared to healthy controls in an attempt to establish state and trait marker.
METHOD: Thirty-four patients with schizophrenia (DSM-5 criteria), 29 first-degree relatives and 25 matched healthy controls participated in the study. Brain activity during eyes closed condition was recorded using 128 channel electroencephalography. Microstates were clustered into 5 maps across groups according to their topography. Microstate map parameters and their cortical sources were compared among groups.
RESULTS: Map 5 mean duration (χ2(2) = 7.617, p = 0.022) was significantly lower in patients compared to controls (U = 256, p = 0.010). Maximum activation was seen in left inferior parietal lobule (MNI coordinates: -65, -35, 25, Log-Fmax = 0.748). Suprathreshold cortical voxels with increased activations were found localized at left temporal gyri.
CONCLUSION: Hyperactivation in left inferior parietal lobule and temporal gyri might have shortened Map 5 duration at rest in patients with schizophrenia. This could imply microstate alterations as the potential state marker of schizophrenia.
Copyright © 2018 Elsevier B.V. All rights reserved.
KEYWORDS: EEG microstates; First-degree relatives; Second generation antipsychotics; Sensory integration; State marker; sLORETA
PMID: 29925477
DOI: 10.1016/j.schres.2018.06.020
The neurobiological correlates of human fluid intelligence (Gf) remain elusive. Here, we demonstrate that spatiotemporal dynamics of EEG activity correlate with baseline measures of Gf and with its modulation by cognitive training. EEG dynamics were assessed in 74 healthy participants by examination of fast-changing, recurring, topographically-defined electric patterns termed "microstates", which characterize the electrophysiological activity of distributed cortical networks. We find that the frequency of appearance of specific brain topographies, spatially associated with visual (microstate B) and executive control (microstate C) networks, respectively, is inversely related to Gf scores. Moreover, changes in Gf scores with cognitive training are inversely correlated with changes in microstate properties, indicating that the changes in brain network dynamics are behaviorally relevant. Finally, we find that cognitive training that increases Gf scores results in a posterior shift in the topography of microstate C. These results highlight the role of fast-changing brain electrical states in individual variability in Gf and in the response to cognitive training.
A healthy adult human is able to report conscious perceptions and thoughts; they are experienced as an uninterrupted, continuous “stream of consciousness” during wakefulness. Consciousness cannot be thought of as consisting of identifiable subportions that still possess the properties of consciousness, even though it is clear that consciousness occurs only if large numbers of the brain’s neural subsystems operate and cooperate adequately.
The momentary, global functional state of the brain is reflected by its electric field configuration. Cluster analytical approaches consistently extracted four head-surface brain electric field configurations that optimally explain the variance of their changes across time in spontaneous EEG recordings. These four configurations are referred to as EEG microstate classes A, B, C, and D and have been associated with verbal/phonological, visual, attention reorientation, and subjective interoceptive-autonomic processing, respectively. The present study tested these associations via an intra-individual and inter-individual analysis approach. The intra-individual approach tested the effect of task-induced increased modality-specific processing on EEG microstate parameters. The inter-individual approach tested the effect of personal modality-specific parameters on EEG microstate parameters.
Background:
Impaired P300 (P3) generation is one of the most robust indices of brain dysfunction in schizophrenia. This study investigates the integrity of cognitive eventrelated potentials that precede P3 in an "oddball" paradigm to determine the earliest stages at which auditory information processing is impaired in schizophrenia.
Methods:
Cognitive event-related potential components including mismatch negativity (MMN), N2, and P3 were recorded from subjects with chronic schizophrenia who were receiving medication (n=20), from those who were withdrawn from drug treatment (n=11), and from healthy volunteers (n=11) during an auditory oddball paradigm. Recordings were made in both passive and active response conditions. The MMN, N2, and P3 amplitudes were compared across groups and the degree of MMN deficit was correlated with the degree of P3 reduction as a function of diagnostic group.
Results:
Schizophrenic subjects showed severe impairments in the generation of MMN and N2 as well as P3. Across groups, the decrement in MMN amplitude correlated significantly with the decrement in P3 amplitude. There were no significant between-group differences in MMN topography.
Conclusions:
The present study demonstrates that the neurophysiological deficits associated with schizophrenia, as reflected in cognitive event-related potential generation, are pervasive, extending even to the level of the sensory cortex. Mismatch negativity indexes the functioning of an automatic alerting mechanism designed to stimulate individuals to explore unexpected environmental events. Dysfunction of this mechanism may contribute to the deficit state associated with schizophrenia.