Complex and magnitude‐only preprocessing of 2D and 3D BOLD fMRI data at 7 T
Bibliographic record
Abstract
A challenge to ultra high field functional magnetic resonance imaging is the predominance of noise associated with physiological processes unrelated to tasks of interest. This degradation in data quality may be partially reversed using a series of preprocessing algorithms designed to retrospectively estimate and remove the effects of these noise sources. However, such algorithms are routinely validated only in isolation, and thus consideration of their efficacies within realistic preprocessing pipelines and on different data sets is often overlooked. We investigate the application of eight possible combinations of three pseudo-complementary preprocessing algorithms - phase regression, Stockwell transform filtering, and retrospective image correction - to suppress physiological noise in 2D and 3D functional data at 7 T. The performance of each preprocessing pipeline was evaluated using data-driven metrics of reproducibility and prediction. The optimal preprocessing pipeline for both 2D and 3D functional data included phase regression, Stockwell transform filtering, and retrospective image correction. This result supports the hypothesis that a complex preprocessing pipeline is preferable to a magnitude-only pipeline, and suggests that functional magnetic resonance imaging studies should retain complex images and externally monitor subjects' respiratory and cardiac cycles so that these supplementary data may be used to retrospectively reduce noise and enhance overall data quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".