Impact of acute administration of escitalopram on the processing of emotional and neutral images: a randomized crossover fMRI study of healthy women
Bibliographic record
Abstract
BACKGROUND: Acute neural effects of antidepressant medication on emotion processing biases may provide the foundation on which clinical outcomes are based. Along with effects on positive and negative stimuli, acute effects on neutral stimuli may also relate to antidepressant efficacy, yet these effects are still to be investigated. The present study therefore examined the impact of a single dose of the selective serotonin reuptake inhibitor escitalopram (20 mg) on positive, negative and neutral stimuli using pharmaco-fMRI. METHODS: Within a double-blind, randomized, placebo-controlled crossover design, healthy women completed 2 sessions of treatment administration and fMRI scanning separated by a 1-week washout period. RESULTS: We enrolled 36 women in our study. When participants were administered escitalopram relative to placebo, left amygdala activity was increased and right inferior frontal gyrus (IFG) activity was decreased during presentation of positive pictures (potentiation of positive emotion processing). In contrast, escitalopram was associated with decreased left amygdala and increased right IFG activity during presentation of negative pictures (attenuation of negative emotion processing). In addition, escitalopram decreased right IFG activity during the processing of neutral stimuli, akin to the effects on positive stimuli (decrease in negative appraisal). LIMITATIONS: Although we used a women-only sample to reduce heterogeneity, our results may not generalize to men. Potential unblinding, which was related to the subjective occurrence of side effects, occurred in the study; however, manipulation check analyses demonstrated that results were not impacted. CONCLUSION: These novel findings demonstrate that a single dose of the commonly prescribed escitalopram facilitates a positive information processing bias. These findings provide an important lead for better understanding effects of antidepressant medication.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".