Retrospective coregistration of functional magnetic resonance imaging data using external monitoring
Bibliographic record
Abstract
Coregistration is essential for correcting head motion artifacts in functional magnetic resonance imaging (fMRI). Coregistration algorithms typically realign images through optimization of a similarity measure based on voxel signal intensities. However, coregistration can also be performed through external monitoring, whereby a tracking device measures head motion directly and independently of the imaging data. This paper describes development of external monitoring using fMRI-compatible infrared cameras. Three subjects participated in block-design fMRI experiments consisting of bilateral finger tapping alone and tapping combined with visuomotor tracking to produce controlled task-correlated head motion. Functional MRI time-series were coregistered using the external monitoring technique and a known image-based algorithm for comparison. Over various performance characteristics, external monitoring and image-based coregistration exhibited good agreement, in particular reducing signals correlated with millimeter task-correlated motions by 50-100%, with a 5% difference between the two techniques. These results promise future applications and refinements of external monitoring in patient populations where head motion is especially problematic. Possibilities include 3D prospective coregistration during real-time fMRI, coregistration of individual slices, and motion correction in anatomic MRI.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".