The effects of left dorsolateral prefrontal transcranial direct current stimulation on episodic future thinking following acute psychosocial stress
Bibliographic record
Abstract
Introduction Research on stress-related disorders and brain imaging suggests that (acute) stress might impact the capacity to mentally simulate specific episodic future events (EFT) through the effects of cortisol on brain regions supporting this cognitive function, such as the prefrontal cortices. This study aims to examine the mechanisms underlying this link, using transcranial Direct Current Stimulation (tDCS) over the left dorsolateral prefrontal cortex.Methods 60 healthy participants were subjected to the Montreal Imaging Stress Task (MIST), followed by either active or sham tDCS. After stimulation, the EFT task was administered. Salivary cortisol was measured throughout the protocol.Results Higher cortisol AUCi values were linked to less specific episodic future thoughts. Moreover, active tDCS enhanced EFT specificity irrespective of cortisol, especially in high trait ruminators. We did not observe an effect from active tDCS on cortisol AUCi, and equally there was no interaction effect between cortisol AUCi and stimulation condition predictive for EFT specificity.Conclusion Although we did not find evidence for the effects of tDCS on the HPA-system, our data reveal a crucial link between two critical predictors of mental health for the first time, and provide a solution to help rehabilitate EFT deficits.Trial registration: Netherlands National Trial Register identifier: ntr004..
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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 | 0.001 |
| 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".