A single dose of escitalopram blunts the neural response in the thalamus and caudate during monetary loss
Bibliographic record
Abstract
Background: Selective serotonin reuptake inhibitors (SSRIs) show acute effects on the neural processes associated with negative affective bias in healthy people and people with depression. However, whether and how SSRIs also affect reward and punishment processing on a similarly rapid time scale remains unclear. Methods: We investigated the effects of an acute and clinically relevant dose (20 mg) of the SSRI escitalopram on brain response during reward and punishment processing in 19 healthy participants. In a doubleblind, placebo-controlled study using functional MRI, participants performed a well-established monetary reward task at 3 time points: at baseline; after receiving placebo or escitalopram; and after receiving placebo or escitalopram following an 8-week washout period. Results: Acute escitalopram administration reduced blood-oxygen-level-dependent (BOLD) response during punishment feedback in the right thalamus (family-wise error corrected [FWE] p = 0.013 at peak level) and the right caudate head (pFWE = 0.011 at peak level) compared to placebo. We did not detect any significant BOLD changes during reward feedback. Limitations: We included only healthy participants, so interpretation of findings are limited to the healthy human brain and require future testing in patient populations. The paradigm we used was based on monetary stimuli, and results may not be generalizable to other forms of reward. Conclusion: Our findings extend theories of rapid SSRI action on the neural processing of rewarding and aversive stimuli and suggest a specific and acute effect of escitalopram in the punishment neurocircuitry.
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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 | 0.000 |
| 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) | 0.002 | 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".