Pregabalin effects on neural response to emotional faces
Bibliographic record
Abstract
Pregabalin has shown promise in the treatment of anxiety disorders. Previous functional magnetic resonance imaging (fMRI) studies indicate agents used to treat anxiety, e.g., SSRIs and benzodiazepines, attenuate amygdala, insula, and medial prefrontal cortex (mPFC) activation during emotional processing. Our prior study has shown that during anticipation of an emotional stimulus, pregabalin attenuates amygdala and insula activation but increases medial PFC activation. In this study, we examined whether, similar to SSRIs and benzodiazepines, pregabalin attenuates amygdala, insula, and medial PFC during emotional face processing. Sixteen healthy volunteers underwent a double-blind within-subjects fMRI study investigating effects of placebo, 50 mg, and 200 mg pregabalin on neural activation during an emotional face-matching task. Linear mixed model analysis revealed that pregabalin dose-dependently attenuated left amygdala activation during fearful face-matching and left anterior insula activation during angry face-matching. The 50 mg dose exhibited more robust effects than the 200 mg dose in the right anterior insula and ventral ACC. Thus, pregabalin shares some similarity to SSRIs and benzodiazepines in attenuating anger and fear-related insula and amygdala activation during emotional face processing. However, there is evidence that a subclinical 50 mg dose of pregabalin produced more robust and widespread effects on neural responses in this paradigm than the more clinically relevant 200 mg dose. Taken together, pregabalin has a slightly different effect on brain activation as it relates to anticipation and emotional face processing, which may account for its unique characteristic as an agent for the treatment of anxiety disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".