Electroencephalography (EEG) spectral signatures of selective serotonin reuptake inhibitors (SSRIs), selective norepinephrine reuptake inhibitors (SNRIs) and vortioxetine in major depressive disorder: A systematic review
Bibliographic record
Abstract
BACKGROUND: Converging evidence suggests electroencephalography (EEG) methods may elucidate alterations in global structural and functional connectivity that underlie the pathophysiology of depressive disorders. Extant literature suggests SSRIs and SNRIs may broadly induce alterations to EEG-measured neural activity. Herein, this systematic review comprehensively evaluates changes to EEG spectral signatures associated with vortioxetine and each FDA-approved agent within the SSRI and SNRI class. METHODS: We conducted a systematic review of studies investigating changes to EEG spectral signatures associated with SSRI, SNRI, and/or vortioxetine treatment in persons with MDD. Database search occurred from database inception to May 3, 2024. RESULTS: Our search yielded 15 studies investigating overall spectral signature changes associated with SSRI- and/or SNRI-treatment. The existing literature presents with mixed findings. Notwithstanding, we did observe a pattern in which the SSRI and SNRI agents reproducibly affect EEG spectral signatures. We observed overlapping yet distinct spectral patterns for each agent within- and between-drug classes of SSRIs and SNRIs. Changes in resting/wake EEG were also observed. LIMITATIONS: The findings from our systematic review are mixed. Heterogeneity exists with sample size, composition, dosing of antidepressants, duration of antidepressant exposure, as well as the type of EEG devices used. DISCUSSIONS: Our findings provide support to the notion that although SSRIs, SNRIs and vortioxetine block reuptake of the serotonin transporter; they are different in their profile of pharmacology as evidenced by differential EEG signatures. EEG changes associated with SSRIs, SNRIs and vortioxetine are also highly replicated findings across mixed studies and populations.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".