What Stress Does to Your Brain: A Review of Neuroimaging Studies
Bibliographic record
Abstract
OBJECTIVE: Recent neuroimaging studies aimed at investigating effects of psychological stress on the neural activity have used a range of experimental paradigms to elicit an acute stress response. The goal of this review is to, first, summarize results from these studies from a perspective of task design and, second, assess the appropriateness of the different stress tasks used. METHOD: We completed a PubMed search on recent articles that have examined the effects of psychological stress on neural processes in a neuroimaging environment. Selected articles were arranged according to the stress task used in the following categories: script-driven stress stimuli, Stroop colour-word interference task, speech in front of an audience, serial subtraction, and Montreal Imaging Stress Task (MIST). RESULTS: Only studies using serial subtraction or the MIST were able to induce a significant cortisol stress response in their participants. Most consistent findings include decreased activity in orbitofrontal regions in response to stress. Additional findings of note are increases in activity in the frontal lobes, particularly the anterior cingulate cortex, as well as deactivation of the limbic system, particularly the hippocampus. CONCLUSION: Research to date is beginning to outline the involvement of prefrontal and limbic regions in perception and modulation of psychological stress. However, additional research is needed in designing a neuroimaging stress task that will yield a significant cortisol stress response consistently, across populations and laboratories.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".