CNSC-97. ELECTROCORTICOGRAPHIC ANALYSIS AND DETECTION OF SPEECH NETWORKS IN GLIOMA-INFILTRATED CORTEX
Bibliographic record
Abstract
Abstract Direct cortical stimulation (DCS) is the clinical gold standard for identifying the functional cortex in the human brain, essential for the safe removal of brain lesions. However, DCS can be time- and resource-intensive and yields only one datapoint on each trial. In contrast, subdural electrocorticography (ECoG) is multidimensional, yielding measures of activity across a cortical region. Further, it is unknown which electro-physiological features reliably predict DCS+ regions in patients with gliomas. Accordingly, defining the electro-physiological properties of DCS-positive cortical regions may facilitate the identification of critical language regions, thereby permitting safe glioma resections in communities without access. Leveraging a prospectively collected multicenter electrophysiologic dataset of DCS-positive language regions spatially matched with a subdural ECoG array, we analyzed regions identified as functionally critical (DCS+) versus functionally non-critical (DCS–) during intraoperative language mapping (81 patients). In IDH-mutant gliomas (36 patients; 39 DCS+, 576 DCS–), DCS+ regions exhibited significantly greater, more coordinated speech-related neural activity in the high gamma frequency range (70-150 Hz), indicating stronger task-related electrophysiological coordination at DCS+ sites. These differences persisted even when pair matching each DCS+ to the closest DCS– site (within the same patient/gyrus). Interestingly, these differences were not apparent in the IDH-wildtype cohort (45 patients; 44 DCS+, 479 DCS–). Further, neuronal high gamma time series from IDH-mutant DCS+ sites demonstrated enhanced encoding and decoding of linguistic and semantic features (using neuronal recordings to predict/classify word-level features). Again, this was not apparent in the IDH-wildtype cohort. Finally, we demonstrate that resting-state classifiers can accurately distinguish DCS+ from DCS– regions in IDH-mutant tumors (>88% accuracy, AUROC of 0.817, sensitivity of 0.80, a specificity of 0.917 in an external validation cohort). This capability has the potential to accelerate DCS mapping by guiding surgeons to priority regions, thereby improving surgical efficiency and patient outcomes. As the task-based and resting-state electrophysiologic distinctions were not observed in IDH-wildtype glioblastomas, this suggests a pathology-specific remodeling of cortical language networks following glioma infiltration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".