A Pilot Study: Extraction of a Neural Network and Feature Extraction of Generation and Reduction Mechanisms Due to Acute Stress
Bibliographic record
Abstract
This study aimed to compare the functional connectivity (FC) assessed during acute stress and recovery after stress using the Montreal imaging stress task (MIST) in adults in their 20s and 30s with Korean Perceived Stress Scale (PSS) scores between 15 and 19 points inclusive. Four seed networks, including the salience network, default mode network, frontoparietal network, and dorsal attention network, were specified to extract the results. Healthy male and female adults who were required to make an effort to relieve stress were exposed to acute stress tasks, and the most common FCs were observed in the salience network, default mode network, and frontoparietal network during the stress and recovery phases. Compared to the stress phase, the increased effect size was significantly different in the recovery phase. In the stress phase, characteristically common FCs were observed in the dorsal attention network. During the recovery period, Salience network (Anterior Insula, R) and Salience network (anterior cingulate cortex, ACC)/Salience network (rostral prefrontal cortex, RPFC), Salience network (AInsula) and Salience network (RPFC), and Default Mode network (posterior cingulate) cortex, PCC) and fronto-parietal network (lateral prefrontal cortex, LPFC) FC were characteristically observed.
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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".