Relations between White Matter Maturation and Reaction Time in Childhood
Bibliographic record
Abstract
White matter matures with age and is important for the efficient transmission of neuronal signals. Consequently, white matter growth may underlie the development of cognitive processes important for learning, including the speed of information processing. To dissect the relationship between white matter structure and information processing speed, we administered a reaction time task (finger abduction in response to visual cue) to 27 typically developing, right-handed children aged 4 to 13. Magnetoencephalography and Diffusion Tensor Imaging were used to delineate white matter connections implicated in visual-motor information processing. Fractional anisotropy (FA) and radial diffusivity (RD) of the optic radiation in the left hemisphere, and FA and mean diffusivity (MD) of the optic radiation in the right hemisphere changed significantly with age. MD and RD decreased with age in the right inferior fronto-occipital fasciculus, and bilaterally in the cortico-spinal tracts. No age-related changes were evident in the inferior longitudinal fasciculus. FA of the cortico-spinal tract in the left hemisphere and MD of the inferior fronto-occipital fasciculus of the right hemisphere contributed uniquely beyond the effect of age in accounting for reaction time performance of the right hand. Our findings support the role of white matter maturation in the development of information processing speed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".