Vasomodulation of skeletal muscle BOLD signal
Bibliographic record
Abstract
PURPOSE: To evaluate whether the BOLD signal from skeletal muscle can be modulated by exercise and ingestion of vasoactive substances. MATERIALS AND METHODS: The right calf muscles of healthy adult volunteers were imaged using a GE 1.5-Tesla scanner and a gradient-echo sequence with spiral readout. Time-varying changes in the BOLD signal were induced through cyclic phases of normoxia (90 seconds of 20.8% O2) and hyperoxia (45 seconds of 100% O2 at 22 L/minute). Superimposed on this paradigm were pre- and post-exercise regimes, with and without ingestion of caffeine (100 mg) or antihistamine (4 mg chlorpheniramine). The numbers of voxels within slow-twitch (soleus) and fast-twitch (gastrocnemius) muscles that significantly responded to the paradigms were scored and compared using the AFNI software (NIMH). RESULTS: Cycling-inspired O2 produced a corresponding BOLD modulation that increased in magnitude with exercise. Chlorpheniramine significantly (P<0.01) prevented the overall increase in exercise-induced soleus muscle BOLD signal, while caffeine accentuated the increase (P<0.05) in the gastrocnemius relative to control (no vasomodulator) conditions. CONCLUSION: BOLD signal changes with exercise can be modulated by standard doses of chlorpheniramine (antihistamine) and caffeine. We suggest that chlorpheniramine may act detrimentally on slow-twitch muscle contractility, while caffeine appears to improve fast-twitch muscle function.
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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 | 0.000 |
| 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) | 0.001 | 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".