Modeling and suppression of respiration‐related physiological noise in echo‐planar functional magnetic resonance imaging using global and one‐dimensional navigator echo correction
Bibliographic record
Abstract
A major source of noise in functional magnetic resonance imaging (fMRI) arises from modulations in the local magnetic field in the head due to motion of the subject's chest through the respiratory cycle, and this physiologic noise can nullify the gains in statistical power expected by the use of higher magnetic fields for fMRI. In particular, fMRI data acquired using echo-planar imaging (EPI) are very sensitive to these spatially and temporally varying respiration-induced frequency offsets. In this study, accurate 3D magnetic field maps in the head were measured and used to determine the frequency offsets at the two extremes of the respiratory cycle. From these maps, spatially dependent frequency variations from about -1.0 Hz to +1.5 Hz were measured in the brain through the respiratory cycle. Simulations of a typical axial EPI fMRI experiment acquired in the presence of this measured field variation were performed, demonstrating regional image intensity variations between 1 and 5% in single pixel time series. The inadequacy of either global or 1D navigator echo corrections to measure and suppress respiratory-induced noise in fMRI time series is demonstrated. The nature of the spatial variations observed suggests that 2D approaches should be considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".