Epilepsy surgery: recent advances in brain mapping, neuroimaging and surgical procedures.
Bibliographic record
Abstract
The general principle of epilepsy surgery is to achieve seizure freedom without causing any neurological deficit that would outweigh the clinical benefit. To achieve this, the epileptogenic zone, which is the part of the brain responsible for seizure generation, as well as the anatomic location of the eloquent cortex must be precisely identified in order to spare those functions during excision of the epileptogenic tissue. Major technical advances over the last decade have continuously contributed to increase our ability to map the brain and identify these critical areas. These technologies and innovations that can be routinely used today include non-invasive studies such as magnetoencephalography (MEG), functional MRI (fMRI), simultaneous EEG-fMRI, and nuclear medicine based methods like PET and SPECT as well as invasive studies through chronically implanted electrodes. Electrodes can be either placed subdurally via burr holes and craniotomies or within the brain parenchima via frame-based and frameless stereotactic methods. Apart from a continuous change in these insertion techniques, the most valuable advances here include recordings on high frequency bandwidth (100-600 Hz EEG) that are capable to delineate high-frequency oscillations (HFOs). These HFOs have been recognized as a biomarker for epileptogenic tissue. All of these technical advances have made epilepsy surgery a truly multidisciplinary field and surgeons have to be able to understand and interpret all of the gathered data. Moreover, this development has influenced surgical approaches and techniques and epilepsy surgery today includes a wide variety of procedures. These can be subdivided into resective, disconnective and neuromodulation procedures and vary from a small, targeted lesionectomy to disconnection/resection of one entire hemisphere. This review will give an overview of the available surgical techniques today and will focus on how the technical advances enable us to map the brain and delineate the critical areas.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".