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Updated: Jan 19, 2026
Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
Published on: June 15, 2018
Electroencephalogram Microstate Abnormalities in Early-Course Psychosis
Michael Murphy1, Robert Stickgold2, Dost Öngür1
1Department of Psychiatry, Harvard Medical School, Boston, Massachusetts; Schizophrenia and Bipolar Disorder Research Program, McLean Hospital, Belmont, Massachusetts.
Background:
Microstates are periods of characteristic electroencephalographic signal topography that are related to activity in brain networks. Previous work has identified abnormal microstate parameters in individuals with psychotic disorders. We combined microstate analysis with sample entropy analysis to study the dynamics of resting-state networks in patients with early-course psychosis.
Methods:
We used microstate analysis to transform resting-state high-density electroencephalography data from 22 patients with early-course psychosis and 22 healthy control subjects into sequences of characteristic scalp topographies. Sample entropy was used to calculate the complexity of microstate sequences across a range of template lengths.
Results:
Patients and control subjects produced similar sets of 4 microstates that agree with a widely reported canonical set (A, B, C, and D). Relative to control subjects, patients had decreased frequency of microstate A. In control subjects, sample entropy decreased as template length increased, suggesting that sequence of microstate transitions is self-similar across multiple transitions. In patients, sample entropy did not decrease, suggesting a lack of self-similarity in transition sequences. This finding was unrelated to data length or microstate topography. Entropy was elevated in unmedicated patients, and it decreased in patients who were administered medication. We identified patterns of transitions between microstates that were overrepresented in control data compared with representation in patient data.
Conclusions:
Our findings suggest that patients with early-course psychosis have abnormally chaotic transitions between brain networks. This chaos may reflect an underlying abnormality in allocating neural resources and effecting appropriate transitions between distinct activity states in psychosis.
Insights
Patients with early-course psychosis exhibit chaotic brain network transitions, indicated by abnormal microstate sequences. This suggests altered neural resource allocation and state transitions in psychosis.
Area of Science:
- Neuroscience
- Psychiatry
- Computational Neuroscience
Background:
- Electroencephalography (EEG) microstates reflect brain network activity.
- Abnormal microstate parameters are observed in psychotic disorders.
- Early-course psychosis presents unique challenges for understanding brain dynamics.
Purpose of the Study:
- To investigate resting-state network dynamics in early-course psychosis using microstate and sample entropy analysis.
- To identify alterations in brain network transitions associated with psychosis.
Main Methods:
- High-density EEG data from 22 early-course psychosis patients and 22 controls were analyzed.
- Microstate analysis identified characteristic scalp topographies.
- Sample entropy quantified the complexity of microstate transition sequences.
Main Results:
- Patients and controls showed similar microstate sets (A, B, C, D).
- Patients had decreased frequency of microstate A and lacked self-similarity in microstate transitions.
- Elevated entropy in unmedicated patients decreased with medication; specific transition patterns differed between groups.
Conclusions:
- Early-course psychosis is associated with abnormally chaotic brain network transitions.
- This chaos may stem from impaired neural resource allocation and state transitions.
- Microstate and entropy analysis offer insights into psychosis pathophysiology.
