Brain Stiffness Correlates With Pathological Tissue in Patients With Drug-Resistant Epilepsy Due to Rasmussen Encephalitis and Focal Cortical Dysplasia
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Complete resection of epileptogenic zone is the single most important determinant of favorable seizure outcomes in resective surgery. However, identifying and resecting this zone is challenging in patients harboring diffuse; MRI-occult malformations of cortical development, such as focal cortical dysplasia; or acquired pathology, such as Rasmussen encephalitis. Intraoperative adjuncts that can aid in identifying the lesion and/or epileptogenic zone can optimize the extent of resection and seizure outcome. We sought to study a novel intraoperative tool, brain tonometer, to measure brain stiffness and correlate with histopathological and radiological findings. METHODS: Brain stiffness was measured at various presumed normal and abnormal areas of the cortex during surgery in 2 patients with drug-resistant epilepsy. These results were correlated with preoperative and intraoperative neuroimaging and histopathology. RESULTS: We found brain stiffness correlated well with the degree of inflammation and cortical disorganization. CONCLUSION: Brain tonometry may help to intraoperatively identify inflammatory brain tissue along with structural and histopathological abnormalities. In select cases, this could potentially allow more tailored resections of the underlying lesion, to ensure complete removal of the epileptogenic lesion and improve the probability of achieving seizure freedom, while sparing normal brain leading to better functional outcomes.
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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 | 0.002 |
| 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".