Neural Mechanisms Underlying the Impact of Psychological Resilience on Psychosocial Stress Responses
Bibliographic record
Abstract
Background: High psychological resilience (HR) could protect individuals from psychosocial stress and thereby make individuals less vulnerable to depression and anxiety; however, the underlying neural mechanism remains to be investigated. Methods: The Montreal Imaging Stress Task (MIST) was administered to participants of 59 healthy individuals with HR and 56 individuals with low psychological resilience (LR) during functional magnetic resonance imaging (fMRI) scanning. Cortisol concentrations and subjective stress levels were collected across the MIST. Repeated measures analyses of variance were conducted to measure the group differences in subjective and cortisol stress responses. Two‐sample t‐tests were conducted to detect the group differences in stress‐related brain activation and functional connectivity (FC). Results: The LR group exhibited an increase in cortisol concentration after the MIST, whereas the HR group exhibited a decrease in cortisol concentration after the MIST. The LR group exhibited higher activation in the left anterior insula and lower FC between the left orbitofrontal cortex (OFC) and the right temporal pole (TP) (all pFWE < 0.05). Mediation analyses revealed that the left anterior insula mediates the relationship between psychological resilience and depression and the left OFC–right TP FC mediates the relationship between psychological resilience and anxiety. Conclusions: Findings highlight that the anterior insula and OFC–TP FC could be the critical neural mechanism underlying the interaction between psychological resilience and psychosocial stress. Moreover, higher anterior insula activation and lower OFC–TP FC could be the crucial neural mechanism of individuals with low psychological resilience developing into depression/anxiety when experiencing daily psychosocial stressors.
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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 | 0.001 |
| 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".