Attenuation of brain BOLD response following lipid ingestion
Bibliographic record
Abstract
A great deal of heterogeneity exists in fMRI data. Even within the same subject, results on successive days or scan sessions often differ in the number of significantly activated pixels and/or the intensity of activation. We sought to assess whether controllable physiologic modulators, such as dietary factors, could influence the outcome of fMRI data. A high fat diet, for example, prior to a fMRI scan could change microvascular blood rheologic factors and potentially alter brain blood oxygen-level dependent (BOLD) signal patterns. In healthy adult volunteers, we measured brain BOLD signal during bilateral finger tapping (2 Hz) in the fasted state, and at 40 and 100 minutes post-ingestion of a 235 mL can of Ensure Plus (Ross Labs), alone or supplemented with either 25 cc or 50 cc of canola oil. Both the 25 cc and 50 cc Canola oil treatments produced a significant bilateral decrease in BOLD signal 40 and 100 minutes postprandial. No significant effect was observed with Ensure in the absence of oil. Therefore, to decrease fMRI within and between subject heterogeneity, and thereby increase fMRI statistical power, it is suggested that scanning within 2 hours post high fat ingestion should be avoided. As a corollary, a thorough understanding of a subject's physiological state, prior to an fMRI exam, may reduce the impact of other confounding variables.
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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 | 0.001 |
| 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".