White matter structural bases for predictive tapping synchronization
Bibliographic record
Abstract
Abstract We determined the intersubject association between rhythmic entrainment abilities of human subjects during a synchronization continuation tapping task (SCT) and the macro and microstructural properties of their superficial (SWM) and deep (dWM) white matter. Diffusion-weighted images were obtained from 32 subjects who also performed the SCT with auditory or visual metronomes and five tempos ranging from 550 to 950 ms. We developed a method to determine the fiber density of U-fibers running tangentially to the cortex. Notably, the right audiomotor system showed individual differences in the density of U-fibers that were correlated with the degree of predictive entrainment across subjects. These correlations were selective for the synchronization epoch with auditory metronomes and were specific for tempos around 1.5 Hz. In addition, there was a significant association between predictive rhythmic entrainment and the density and bundle diameter of the corpus callosum (CC), forming a chronotopic map where behavioural correlations of short and long intervals were found with the anterior and posterior portions of the CC. Finally, the fiber bundle cross-section of the arcuate fasciculus, the CC, and the Superior Longitudinal Fasciculus showed a significant correlation with the mean asynchronies of the auditory SCT. These findings suggest that the structural properties of the SWM and dWM in the audiomotor system support the predictive abilities of subjects during rhythmic tapping, where the density of cortical U-fibers are linked to the preferred tapping tempo, and the bundle properties of CC define an interval selective topography that has an anterior posterior gradient.
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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 | 0.001 | 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) | 0.001 | 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".