Near‐infrared spectroscopy as an alternative to the Wada test for language mapping in children, adults and special populations
Bibliographic record
Abstract
The intracarotid amobarbital test (IAT) is the most widely used procedure for pre-surgical evaluation of language lateralization in epileptic patients. However, apart from being invasive, this technique is not applicable in young children or patients who present mental retardation and/or language deficits. Functional magnetic resonance imaging (fMRI) is increasingly employed as a non-invasive alternative. Again, this method is more difficult to use with young children, especially hyperactive ones, since they have to remain motionless during data acquisition. The aim of this study was to determine whether near-infrared spectroscopy (NIRS) can be used as an alternative technique to investigate language lateralization in children and special populations. Unlike Wada test, NIRS is non-invasive, and it is more tolerant to movement artefacts than fMRI. In the present study, NIRS data were acquired in four epileptic children, a 12-year-old boy with pervasive developmental disorder and a 3-year-old, healthy child, as well as three healthy and two epileptic adults, while they performed a verbal fluency task and a control task. When applicable, the results were compared to the subjects' fMRI and/or IAT findings. Clear laterality of speech was obtained in all participants, including the two non-epileptic children, and NIRS results matched fMRI and IAT findings. These results, if replicable in larger samples, are encouraging and suggest that NIRS has the potential to become a viable, non-invasive alternative to IAT and fMRI in the determination of speech lateralization in children and clinical populations that cannot be submitted to more invasive techniques.
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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 | 0.001 |
| 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".