Exploring the Relationship Between White Matter Tracts and Resting-State Functional Language Lateralization Index
Bibliographic record
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) enables the evaluation of the language network and is particularly useful for measuring language lateralization with minimal participant effort and methodological biases (e.g., no language task execution or selection). Tractography using diffusion MRI (dMRI) provides complementary information on language-associated white matter bundles. Some structural white matter measures of the left or right hemisphere have been related to the functional language lateralization index (LI) and allow a better understanding of this network. This study utilizes tractography to identify white matter structural predictors of LI from a single hemisphere, employing linear regression and random forest models. Rs-fMRI and dMRI data from 618 healthy subjects of the Human Connectome Project were used to link LI to micro- and macro-structural measures of the arcuate fasciculi, the inferior longitudinal fasciculi, the frontal aslant tracts and sections of the corpus callosum. Results suggest a possible relationship between micro- and macro-structural measures of white matter tracts, and functional language lateralization measured in resting-state. However, the identified predictors are not sufficiently representative to be considered proxies for functional language lateralization. In conclusion, both micro- and macro-structural white matter characteristics as well as both left and right hemispheres are important to consider, but are not sufficient on their own, when investigating the relationship between brain structures and functional language lateralization.
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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 | 0.003 |
| 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) | 0.002 | 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".