Lateralization of the arcuate fasciculus from childhood to adulthood and its relation to cognitive abilities in children
Bibliographic record
Abstract
The arcuate fasciculus is a major white matter tract involved in language processing that has also been repeatedly implicated in intelligence and reasoning tasks. Language in the human brain is lateralized in terms of both function and structure, and while the arcuate fasciculus reflects this asymmetry, its pattern of lateralization is poorly understood in children and adolescents. We used diffusion tensor imaging (DTI) and tractography to examine arcuate fasciculus lateralization in a large (n = 183) group of healthy right-handed volunteers aged 5-30 years; a subset of 68 children aged 5-13 years also underwent cognitive assessments. Fractional anisotropy and number of streamlines of the arcuate fasciculus were both significantly higher in the left hemisphere than the right hemisphere in most subjects, although some subjects (10%) were right lateralized. Age and gender effects on lateralization were not significant. Children receiving cognitive assessments were divided into three groups: a "left-only" group in whom only the left side of the arcuate fasciculus could be tracked, a left-lateralized group, and a right-lateralized group. Scores on the Peabody Picture Vocabulary Test (PPVT) and NEPSY Phonological Processing task differed significantly among groups, with left-only subjects outperforming the right-lateralized group on the PPVT, and the left-lateralized children scoring significantly better than the right-lateralized group on phonological processing. In summary, DTI tractography demonstrates leftward arcuate fasciculus lateralization in children, adolescents, and young adults, and reveals a relationship between structural white matter lateralization and specific cognitive abilities in children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".