Reaction time performance is related to brain blood flow during gravitational stress
Bibliographic record
Abstract
Speed of information processing is critical to effective air combat; however, little is known about the effect of gravitational stress on measures of processing speed. To examine, eight young healthy males (26 ± 6 years of age) underwent progressive lower body negative pressure (LBNP) while recording beat‐to‐beat blood pressure (BP), heart rate variability, brain blood flow (MCABF), and while testing simple reaction time (SRT). Baseline values were compared to the last tolerated stage of LBNP (LBNPmax) and the median stage (LBNP50). During LBNP, SRT was significantly slower compared to baseline (Baseline: 280±33, LBNP50: 324±39, LBNPmax: 316±37 ms, P < 0.05). Also, MCABF (Baseline: 65±6, LBNP50: 63±63, LBNPmax: 59±60 cm/s, P < 0.05) and pulsatility index (Baseline: 0.83±0.1, LBNP50: 0.72±0.15, LBNPmax: 0.65±0.1 au, P < 0.05) decreased during LBNPmax. From baseline to LBNP50 a significant correlation existed between changes in pulsatility index and SRT (r = −0.7; P < 0.05). Our data shows that SRT increases during LBNP. Further, it appears that at moderate gravitational stress those with greater reductions in brain blood perfusion demonstrate a more profound negative affect on SRT. At severe LBNP however, SRT does not appear to be related to brain blood flow or indirect markers of sympathovagal tone. This research was supported by the Natural Sciences and Engineering Research Council of Canada.
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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 | 0.002 |
| 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) | 0.003 | 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".