Robotic-Assisted and Image-Guided MRI-Compatible Stereoelectroencephalography
Bibliographic record
Abstract
BACKGROUND: Stereoelectroencephalography has been in regular use at the Montreal Neurological Institute since 1972. The technique has been in constant evolution to incorporate advances in materials, imaging, and robotics technology. MRI-compatible electrodes were introduced in 2007 and robotics in 2011. Here we report on the technique, safety, and advantages of our current method of stereoelectroencephalography implantation. METHODS: We retrospectively reviewed all patients who underwent stereoelectroencephalography by the senior author. Technical, clinical, and radiological complications, and postimplantation outcomes were analyzed. Only patients implanted with MRI-compatible electrodes were included to review MRI abnormalities with electrodes in situ. RESULTS: A total of 53 patients were implanted with 550 electrodes (average=10.4 per patient), for an average duration of 14.6 days. There was no mortality, infection, or new neurologic deficit. Two patients had a superficial screw plunge without clinical consequence. Four patients demonstrated asymptomatic MRI abnormalities (7.54% per patient, or 0.72% per electrode). MRI with electrodes in situ was used for neuronavigation in all 29 who underwent resection and yielded a histopathological diagnosis of focal cortical dysplasia in 15 MRI-negative patients. CONCLUSIONS: The technique of stereoelectroencephalography described here was associated with no clinical morbidity although not without technical complications or radiologic (MRI) abnormalities. We should therefore remain vigilant in refining the technique and minimizing the number of electrodes required to answer a well-developed hypothesis regarding the epileptogenic zone. The use of MRI-compatible electrodes allowed neuronavigation using the images with the electrodes in situ, which was useful to tailor the eventual definitive resection and in localizing MRI-negative lesions.
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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 | 0.000 |
| 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".