Math skills and microstructure of the middle longitudinal fasciculus: A developmental investigation
Bibliographic record
Abstract
Functional neuroimaging studies have identified distributed brain networks involved in arithmetic problem-solving. However, it is still poorly understood to what extent microstructural properties of the underlying white matter contribute to mathematical performance. We investigate microstructural characteristics of one of the least studied white matter tracts, the bilateral middle longitudinal fasciculus (MdLF), reconstructed from diffusion-weighted MRI data, and their relations with mathematical performance in arithmetic tasks of varying complexity, in 56 individuals aged 10-29 years (22 children: 10-13 years; 20 adolescents: 14-17 years; 14 adults: 18-29 years). We identify group differences in math performance and diffusivity measures. We highlight linear relations with age in left fractional anisotropy and right radial diffusivity, which can serve as developmental markers. Further, we document for the first time that diffusivity values in the right MdLF are significantly lower than in the left MdLF for all groups, suggesting hemispheric asymmetry. Importantly, associations between math performance in the right MdLF favoured easier tasks and in the left MdLF favoured harder tasks. This finding is a deviation from the classic hemisphere dominance hypothesis. We propose that the observed patterns may be explained by the right-left-right hemispheric dominance hypothesis proposed by a theory of cognitive development. Our results provide new insights into the microstructural properties of the MdLF and their role in mathematical ability, with implications for understanding brain-behaviour relations.
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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".