White matter microstructural organisation of interhemispheric pathways predicts different stages of bimanual coordination learning in young and older adults
Bibliographic record
Abstract
The ability to learn new motor skills is crucial for activities of daily living, especially in older adults. Previous work in younger adults has indicated fast and slow stages for motor learning that were associated with changes in functional interactions within and between brain hemispheres. However, the impact of the structural scaffolds of these functional interactions on different stages of motor learning remains elusive. Using diffusion-weighted imaging and probabilistic constrained spherical deconvolution-based tractography, we reconstructed transcallosal white matter pathways between the left and right primary motor cortices (M1-M1), left dorsal premotor cortex and right primary motor cortex (LPMd-RM1) and right dorsal premotor cortex and left primary motor cortex (RPMd-LM1) in younger and older adults trained in a set of bimanual coordination tasks. We used fractional anisotropy (FA) to assess microstructural organisation of the reconstructed white matter pathways. Older adults showed lower behavioural performance than younger adults and improved their performance more in the fast but less in the slow stage of learning. Linear mixed models predicted that individuals with higher FA of M1-M1 pathways improve more in the fast but less in the slow stage of bimanual learning. Individuals with higher FA of RPMd-LM1 improve more in the slow but less in the fast stage of bimanual learning. These predictions did not differ significantly between younger and older adults suggesting that, in both younger and older adults, the M1-M1 and RPMd-LM1 pathways are important for the fast and slow stage of bimanual learning, respectively.
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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 | 0.001 | 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".