Relationship between manual dexterity and left–right asymmetry of anatomical and functional properties of corticofugal tracts revealed by T2-weighted brain images
Bibliographic record
Abstract
Abstract The corticofugal tracts (CFT) are key agents of upper limb motor function. Although the tracts form high-intensity regions relative to surrounding tissue in T2-weighted magnetic resonance images (T2WI), the precise relations of signal intensities of the left and right CFT regions to hand function are unknown. Here, we tested the hypothesis that the different signal intensities between the left and right CFT signify clinically important differences of hand motor function. Eleven right-handed and eleven left-handed healthy volunteers participated in the study. Based on horizontal T2WI estimates, we confirmed the relationship between the signal intensity ratios of the peak values of each CFT in the posterior limbs of the internal capsules (right CFT vs. left CFT). The ratios included the asymmetry indices of the hand motor functions, including grip and pinch strength, as well as the target test (TT) that expressed the speed and accuracy of hitting a target ([right-hand score − left-hand score]/[right-hand score + left-hand score]), using simple linear regression. The signal intensity ratios of each CFT structure maintained significant linear relations with the asymmetry index of the speed (R 2 = 0.493, P = 0.0003) and accuracy (R 2 = 0.348, P = 0.004) of the TT. We found no significant association between left and right CFT structures for grip or pinch strengths. The findings are consistent with the hypothesis that the different signal intensities of the left and right CFT images captured by T2WI serve as biological markers that reflect the dominance of manual dexterity.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".