Differences in the spatial and temporal patterns of head motion during MRI of adults and infants
Bibliographic record
Abstract
Abstract Aim Head motion has a profound effect on MRI, and will contaminate comparisons of function or structure between groups that move differently. This work compares adults and infants. Infants might move differently for physical, physiological and cognitive reasons, but so far these differences have not been quantified. Methods The spatial modes and total magnitude of motion in the MRI scanner were measured (N=211). The effects of group (infant vs. adult) and stimulation paradigm (auditory vs. visual) were evaluated. Results Spatial modes of motion were found to be distinct between infant and adult groups. Infants had less anterior-posterior translational motion, but greater motion in other dimensions, often with complex multi-axis patterns. In magnitude distribution, sleeping infants often remained more still than adults, but when movement did occur it was more extreme and abrupt. Two groups of adults presented with different stimulation showed similar shapes of motion. Conclusion The spatial modes and magnitude distribution of motion differed substantially between groups, and must be considered carefully as a confound in comparisons of structure or function. The abruptness and magnitude of movement suggests that for infants relative to adults post-processing strategies such as de-noising are likely to be more effective than prospective motion correction. Key notes Quantified the spatial and temporal distribution of motion during MRI in 211 adults and neonates The different spatial modes in adults and infants were visualized and statistically contrasted The magnitude of motion had “heavier tails” in infants, with more still periods, and more large movements, than adults.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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 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".