Motion-corrected independent component analysis for robust functional magnetic resonance imaging
Bibliographic record
Abstract
Head movement during fMRI data collection can result in confusing artifacts when estimating task-related brain activity. We propose an improved version of motion-corrected independent component analysis (MCICA), which mitigates the motion effects of the fMRI time-series by maximizing the entropy difference between the observed fMRI data and a nonlinear function of the derived ICA components. Specifically, the improved MCICA algorithm operates on all timepoints, removing the requirement for the existence of enough motionless timepoints in the time-series and the need to detect motion-corrupted timepoints. Simulations demonstrate that MCICA is robust to activity level and the results are more accurate than cubic interpolation, even when the displacement is known. In real data from a motor fMRI experiment, preprocessing the data with MCICA resulted in the emergence of activity in the primary motor and supplementary motor cortices, and the mutual information between all subsequent volumes and the first one was increased.
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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 | 0.001 |
| 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".