Related Experiment Video
Updated: Mar 28, 2026
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Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
Published on: June 15, 2018
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EEG Unpredictability in the Resting State of Major Depressive Disorder: A Multidomain EEG Analysis
Kassra Ghassemkhani1, Blake T Dotta2,3
1Behavioural Neuroscience & Biology Programs, Schools of Natural Science, Laurentian University, Sudbury, ON, P3E2C6, Canada.
Brain Topography
|March 27, 2026
Summary
Scalp electroencephalography (EEG) reveals distinct brain activity patterns in Major Depressive Disorder (MDD). Increased beta power and altered network complexity are key EEG markers differentiating MDD from controls.
Area of Science:
- Neuroscience
- Computational Psychiatry
- Biomedical Engineering
Background:
- Major Depressive Disorder (MDD) diagnosis relies on clinical symptoms, lacking objective biomarkers.
- Scalp electroencephalography (EEG) offers a non-invasive, cost-effective method for neurophysiological assessment.
- Understanding EEG-based biomarkers can improve MDD identification and treatment monitoring.
Purpose of the Study:
- To investigate spectral, complexity, and network dynamics in the resting-state EEG of individuals with MDD compared to healthy controls.
- To identify reliable EEG features that can serve as objective biomarkers for MDD.
- To explore alterations in brain microstates and network topology associated with MDD.
Main Methods:
- Analysis of a publicly available eyes-closed resting-state EEG dataset.
- Calculation of relative band power, aperiodic exponent, multiscale entropy, and Higuchi fractal dimension.
- Assessment of brain microstate temporal stability and network topology using phase locking value (PLV).
- Feature ranking using the area under the receiver operating characteristic curve (ROC).
Main Results:
- MDD group exhibited significantly higher relative beta power (13-30 Hz) and lower aperiodic exponent compared to controls.
- Increased multiscale entropy and Higuchi fractal dimension were observed in MDD.
- Reduced temporal stability of brain microstates and lower small-worldness index (indicating more random network topology) were found in MDD.
- Relative beta power, Higuchi fractal dimension, aperiodic exponent, and short-scale entropy were identified as top predictors for MDD.
Conclusions:
- EEG spectral and complexity features, alongside microstate and network dynamics, reveal significant differences between MDD and controls.
- Objective EEG biomarkers, particularly relative beta power and complexity metrics, show promise for MDD diagnosis.
- EEG analysis highlights widespread alterations in brain activity and connectivity in MDD, suggesting unpredictable neural dynamics.
