EEG Microstates in Major Depressive Disorder: Evidence Against Trait Markers of Illness
Bibliographic record
Abstract
Abstract Dysconnectivity between cortical networks is a core feature of major depressive disorder (MDD). Electroencephalography (EEG) derived microstates are a cost effective and time sensitive method of examining the dynamics of large-scale brain networks. Previous studies examining the four traditional microstate classes of A, B, C, and D in MDD have used mixed diagnostic samples and have displayed varied results. More recently, there have been reports of altered microstate parameters in studies where five microstate classes best fit the data. To continue the examination of this phenomenon, the current study examined five microstate classes in individuals with MDD compared to healthy controls. Although a five-microstate model best fit our data, we failed to replicate any alterations in the duration, occurrence, contribution, or transitional probabilities of any microstate class in MDD. However, the number of observed transitions varied significantly from what would be expected between microstates A, B, and E in the MDD group, suggesting abnormal connectivity between regions responsible for generating these states. There was also a significant relationship found between anxiety symptoms and the occurrence of microstate class E in our MDD sample. Potential explanations for our findings, such as illness severity and the presence of comorbid anxiety symptoms are discussed. The results provide skepticism for the utilization of microstate alterations as an independent biomarker for MDD. Future research is needed to clarify the observed relationship between anxiety symptoms and microstate class E.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".