Tissue localization during resective epilepsy surgery
Bibliographic record
Abstract
OBJECT: Imaging-guided surgery (IGS) systems are widely used in neurosurgical practice. During epilepsy surgery, the authors routinely use IGS landmarks to localize intracranial electrodes and/or specific brain regions. The authors have developed a technique to coregister these landmarks with pre- and postoperative scans and the Montreal Neurological Institute (MNI) standard space brain MRI to allow 1) localization and identification of tissue anatomy; and 2) identification of Brodmann areas (BAs) of the tissue resected during epilepsy surgery. Tracking tissue in this fashion allows for better correlation of patient outcome to clinical factors, functional neuroimaging findings, and pathological characteristics and molecular studies of resected tissue. METHODS: Tissue samples were collected in 21 patients. Coordinates from intraoperative tissue localization were downloaded from the IGS system and transformed into patient space, as defined by preoperative high-resolution T1-weighted MRI volume. Tissue landmarks in patient space were then transformed into MNI standard space for identification of the BAs of the tissue samples. RESULTS: Anatomical locations of resected tissue were identified from the intraoperative resection landmarks. The BAs were identified for 17 of the 21 patients. The remaining patients had abnormal brain anatomy that could not be meaningfully coregistered with the MNI standard brain without causing extensive distortion. CONCLUSIONS: This coregistration and landmark tracking technique allows localization of tissue that is resected from patients with epilepsy and identification of the BAs for each resected region. The ability to perform tissue localization allows investigators to relate preoperative, intraoperative, and postoperative functional and anatomical brain imaging to better understand patient outcomes, improve patient safety, and aid in research.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".