Pre-treatment EEG signal variability is associated with treatment success in depression
Bibliographic record
Abstract
BACKGROUND: Previous work suggests that major depressive disorder (MDD) is associated with disturbances in global connectivity among brain regions, as well as local connectivity within regions. However, the relative importance of these global versus local changes for successful antidepressant treatment is unknown. We used multiscale entropy (MSE), a measure of brain signal variability, to examine how the propensity for local (fine scale MSE) versus global (coarse scale MSE) neural processing measured prior to antidepressant treatment is related to subsequent treatment response. METHODS: We collected resting-state EEG activity during eyes-open and closed conditions from unmedicated individuals with MDD prior to antidepressant pharmacotherapy (N = 36) as well as from non-depressed controls (N = 36). Treatment response was assessed after 12 weeks of treatment using the Montgomery-Åsberg Depression Rating Scale (MADRS), at which time participants with MDD were characterized as either responders (≥ 50% MADRS decrease) or non-responders. MSE was calculated from baseline EEG, and compared between controls, future treatment responders and non-responders. Putative interactions with the well-documented age effect on signal variability (increased reliance on local neural communication with increasing age, indexed by greater finer-scale variability) were assessed. RESULTS: Only in responders, we found that reduced MSE at fine temporal scales (especially fronto-centrally) and increased MSE diffusely at coarser temporal scales was related to the magnitude of the antidepressant response. In controls and MDD non-responders, but not MDD responders, there was an increase in MSE with age at fine temporal scales and a decrease in MSE with age at coarse temporal scales. CONCLUSION: Our results suggest that an increased propensity toward global processing, indexed by greater MSE at coarser timescales, at baseline appears to facilitate eventual antidepressant treatment response.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".