Elevated glioma-related cortical glutamate <i>in vivo</i> is associated with <i>ex vivo</i> interictal discharges in human brain slices
Bibliographic record
Abstract
Abstract Gliomas are the most common type of malignant brain tumour and are frequently associated with seizures. Seizures associated with gliomas are frequently refractory to conventional antiseizure medications and constitute a significant cause of morbidity. There is a clear need for a biomarker that will guide resection of the ictogenic areas during neurosurgical procedures to improve seizure control, limit morbidity and achieve better tumour clearance. Several studies have investigated glutamate in the peri-tumoural region using magnetic resonance spectroscopy (MRS), but findings remain inconclusive regarding the association among elevated glutamate, tumour type and seizure history. Furthermore, it is not known if high cortical glutamate would lead to spontaneous interictal discharges (IIDs) ex vivo. In this prospective cross-sectional observational study of patients undergoing surgery for supratentorial gliomas, pre-operative 1D MRS imaging using an echo time (TE) averaged Point Resolved Spectroscopy (PRESS) sequence was performed to quantify glutamate levels in the peri-tumoural region. Ex vivo human cortical extracellular local field potential (LFP) recordings from the cortical tissue were used to record the presence or absence of spontaneous IIDs. Metabolite data were compared between cortical regions with spontaneous IIDs (n = 6 patients), versus regions with no evidence of spontaneous IIDs (n = 14 patients). Our findings provide new metabolic evidence that peri-tumoural glutamate accumulation is strongly linked to spontaneous IIDs (P = 0.006), reinforcing its role in glioma-associated seizures. The lack of significant differences in choline to creatine and N-acetyl aspartate to creatine ratios between IID and non-IID regions suggests seizure susceptibility is independent of tumour infiltration or neuronal degeneration. These results highlight MRS as a promising non-invasive tool for identifying metabolically active, seizure-prone cortical regions, which could aid in refining surgical planning. However, further validation is required to determine its utility in predicting clinical seizure occurrence and guiding post-operative seizure management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".