Resting-State Functional MRI for Determining Language Lateralization in Children with Drug-Resistant Epilepsy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Task-based fMRI is a noninvasive method of determining language dominance; however, not all children can complete language tasks due to age, cognitive/intellectual, or language barriers. Task-free approaches such as resting-state fMRI offer an alternative method. This study evaluated resting-state fMRI for predicting language laterality in children with drug-resistant epilepsy. MATERIALS AND METHODS: A retrospective review of 43 children with drug-resistant epilepsy who had undergone resting-state fMRI and task-based fMRI during presurgical evaluation was conducted. Independent component analysis of resting-state fMRI was used to identify language networks by comparing the independent components with a language network template. Concordance rates in language laterality between resting-state fMRI and each of the 4 task-based fMRI language paradigms (auditory description decision, auditory category, verbal fluency, and silent word generation tasks) were calculated. RESULTS: Concordance ranged from 0.64 (95% CI, 0.48-0.65) to 0.73 (95% CI, 0.58-0.87), depending on the language paradigm, with the highest concordance found for the auditory description decision task. Most (78%-83%) patients identified as left-lateralized on task-based fMRI were correctly classified as left-lateralized on resting-state fMRI. No patients classified as right-lateralized or bilateral on task-based fMRI were correctly classified by resting-state fMRI. CONCLUSIONS: While resting-state fMRI correctly classified most patients who had typical (left) language dominance, its ability to correctly classify patients with atypical (right or bilateral) language dominance was poor. Further study is required before resting-state fMRI can be used clinically for language mapping in the context of epilepsy surgery evaluation in children with drug-resistant epilepsy.
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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 | 0.006 |
| 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".