EEG Microstate Dynamics in Cognitive Neuroscience

Electroencephalographic microstates are brief intervals during which the scalp potential field remains quasi-stable, reflecting the coordinated activity of large-scale neural networks on a subsecond timescale. Typically classified into a few prototypical topographies, these microstates are thought to represent fundamental building blocks of spontaneous cognitive processes. Measures such as duration, occurrence, coverage and transition probabilities offer quantitative insight into how the brain switches between discrete functional states. Combined with source imaging and concurrent functional MRI, microstate analysis has revealed close associations between canonical microstates and resting-state networks, such as frontoparietal attention and default mode systems. Variations in temporal dynamics have been linked to individual traits and cognitive states, while alterations in microstate parameters are increasingly recognised as potential biomarkers in neurological and psychiatric conditions. The non-invasive and cost-effective nature of EEG microstate analysis lends itself to clinical and developmental applications, providing a window into the dynamic organisation of the healthy and disordered brain.

Research from Nature Portfolio

Recent studies have highlighted the potential of microstate metrics as trait and state markers. One investigation into schizophrenia and unaffected siblings found that class C microstates were enhanced and class D reduced in both patients and relatives, pointing to these dynamics as candidate endophenotypes. Another line of work has focused on early Alzheimer’s disease, where an index of microstate complexity revealed slower, less varied transitions, particularly affecting the frontoparietal working-memory network state. When combined with spectral EEG measures, microstate complexity achieved classification accuracy exceeding 80% for distinguishing Alzheimer’s patients from controls and showed promise in predicting progression from mild cognitive impairment. Together, these findings underscore the clinical utility of microstate dynamics in capturing trait vulnerability and early pathological changes.

EEG Microstate Dynamics in Cognitive Neuroscience publication trend

The graph below shows the total number of articles in eeg microstate dynamics in cognitive neuroscience across all publications each year (not limited to Nature Index journals).

Technical terms

Microstate: A fleeting epoch (tens of milliseconds) during which the scalp potential field remains topographically stable, reflecting a coherent network state.

Microstate class: One of a small set of recurring scalp potential configurations, often labelled A, B, C and D, each associated with specific functional networks.

Duration: The average length of time a microstate persists before transitioning to another state.

Coverage: The proportion of total recording time occupied by a given microstate class.

Chaos game representation: A fractal-based method for mapping symbolic sequences—such as microstate labels—into geometric patterns to quantify oscillatory and sequential properties.

Complexity: A measure of the variability and unpredictability in the sequence of microstate transitions, reflecting the richness of brain dynamics.

References

1. Temporal and spatial variability of dynamic microstate brain network in early Parkinson’s disease. npj Parkinson's Disease (2023).
2. Changes in oscillatory patterns of microstate sequence in patients with first-episode psychosis. Scientific Data (2024).
3. EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review. NeuroImage (2017).
4. EEG microstates are a candidate endophenotype for schizophrenia. Nature Communications (2020).
5. EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease. Scientific Reports (2020).
6. Within and between-person correlates of the temporal dynamics of resting EEG microstates. NeuroImage (2020).