Postacquisition suppression of large‐vessel BOLD signals in high‐resolution fMRI
Bibliographic record
Abstract
Large-vessel BOLD contamination is a serious impediment to localization of neural activity in high-resolution fMRI studies. A new method is presented which estimates and removes the fraction of BOLD signal that arises from oriented vessels, such as cerebral and pial veins in a voxel, by measuring their influence on the phase angle of the complex valued fMRI time series. A maximum likelihood estimator based on a linear least-squares fit of the BOLD signal phase to the BOLD signal magnitude in a voxel is shown to efficiently suppress the BOLD effect from these larger veins, whose activation is not well colocalized with the neural response. In high-resolution in vivo fMRI data at 4 T, it is estimated that the method is sensitive to the phase changes in the cerebral, larger intracortical, and pial veins. The technique requires no special pulse sequence modifications or acquisition strategies, and is computationally fast and intrinsically robust.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".