Multitask Language Mapping to Visualize the Spatial Configuration of Polyfunctional Language Cortex
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Direct electrical stimulation (DES) remains the gold standard for identifying eloquent language cortex. However, most protocols rely on single-task mapping, typically visual naming, potentially overlooking modality-specific or distributed language areas. The aim of this study was to define the spatial extent of polyfunctional language cortex using multitask DES integrated with the Yale Brain Atlas (YBA) and to supplement this with population-based structural connectivity data. METHODS: resolution), normalized for amperage and sampling frequency, and aggregated across patients. Intertask correlations were assessed using Kendall rank coefficients. Structural connectivity between language-involved parcels was evaluated using deterministic tractography from diffusion data of 1,065 healthy participants. Analyses were conducted using Python 3.9.13. RESULTS: = 0.11). DISCUSSION: Multitask DES anchored to the YBA provides an anatomically precise, reproducible platform for language mapping. A focused task set enhances efficiency without sacrificing sensitivity. Integration with structural connectivity offers network-level insights. Limitations include modest sample size, variable electrode coverage with undersampling of the anterior language areas, and patient population with epilepsy, which may affect generalizability. This multimodal framework informs surgical planning and advances our understanding of human language representation.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".