Functional magnetic resonance imaging of the human brain based on signal enhancement by extravascular protons (SEEP fMRI)
Bibliographic record
Abstract
Functional magnetic resonance imaging (fMRI) studies of the human brain were carried out at 3 Tesla to investigate an fMRI contrast mechanism that does not arise from the blood oxygen-level dependent (BOLD) effect. This contrast mechanism, signal enhancement by extravascular protons (SEEP), involves only proton-density changes and was recently demonstrated to contribute to fMRI signal changes in the spinal cord. In the present study it is hypothesized that SEEP fMRI can be used to identify areas of neuronal activity in the brain with as much sensitivity and precision as can be achieved with BOLD fMRI. A detailed analysis of the areas of activity, signal intensity time courses, and the contrast-to-noise ratio (CNR), is also presented and compared with the BOLD fMRI results. Experiments were carried out with subjects performing a simple finger-touching task, or observing an alternating checkerboard pattern. Data were acquired using a conventional BOLD fMRI method (gradient-echo (GE) EPI, TE = 30 ms), a conventional method with reduced BOLD sensitivity (GE-EPI, TE = 12 ms), and SEEP fMRI (spin-echo (SE) EPI, TE = 22 ms). The results of this study demonstrate that SEEP fMRI may provide better spatial localization of areas of neuronal activity, and a higher CNR than conventional BOLD fMRI, and has the added benefit of lower sensitivity to field inhomogeneities.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".