Presurgical language mapping in children with epilepsy: Clinical usefulness of functional magnetic resonance imaging for the planning of cortical stimulation
Bibliographic record
Abstract
PURPOSE: Presurgical language mapping in dominant hemisphere epilepsy to evaluate the risk of postoperative deficit is particularly difficult in children. Extraoperative invasive cortical stimulation can show some areas critical to language, but not all of them, due to scarce sampling, poor cooperation, cortical immaturity, or network reorganization, whereas functional magnetic resonance imaging (fMRI) displays entire networks involved in, but not necessarily critical to, language. In a homogeneous series of children with epilepsy, we compared the contributions of language fMRI and depth electrode stimulations to optimize language mapping. METHODS: Eight children (7.5-15.5 years) with left frontal or temporal epilepsy underwent language fMRI and language stimulation with depth electrodes as part of their comprehensive presurgical workup. fMRI data collected during sentence generation were analyzed using statistical parametric mapping (SPM2) (false discovery rate [FDR] p < 0.05). Bipolar stimulations were performed during language production tasks. By coregistering fMRI and postimplantation computed tomography (CT) images, we were able to directly compare the cortical areas identified by both investigations. KEY FINDINGS: fMRI during sentence generation robustly showed activation in the whole perisylvian regions with little reorganization (left hemisphere dominant in 7). Of the 184 electrode contacts tested for language, only 8 were positive (language disruption) in three of the seven patients with periictal language impairment and left language dominance. All of the positive contacts colocalized with an fMRI activated cluster, that is, fMRI did not miss any region critical to language (sensitivity = 100%). However, 54 of the 176 negative contacts were within activated clusters (low specificity). SIGNIFICANCE: In children with epilepsy, the sensitivity of fMRI during sentence generation allows for the detection of all critical regions displayed by cortical stimulation within the large perisylvian language network, but with a low specificity. It is, therefore, useful to optimize the placement of intracranial electrodes when language mapping is necessary. Systematic planning of the electrode placement according to language fMRI maps should increase the yield of extraoperative cortical stimulation, which appears rather low in children when compared to adults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".