Electrocorticographic Temporal Alteration Mapping: A Clinical Technique for Mapping the Motor Cortex with Movement-Related Cortical Potentials
Bibliographic record
Abstract
We propose electrocorticographic temporal alteration mapping (ETAM) for motor cortex mapping by utilizing movement-related cortical potentials (MRCPs) within the low-frequency band [0.05-3] Hz. This MRCP waveform-based temporal domain approach was compared with the state-of-the-art electrocorticographic frequency alteration mapping (EFAM), which is based on frequency spectrum dynamics. Five patients (two epilepsy cases and three tumor cases) were enrolled in the study. Each patient underwent intraoperative direct electrocortical stimulation (DECS) procedure for motor cortex localization. Moreover, the patients were required to perform simple brisk wrist extension task during awake craniotomy surgery. Cross-validation results showed that the proposed ETAM method had high sensitivity (81.8%) and specificity (94.3%) in identifying sites which exhibited positive DECS motor responses. Moreover, although the sensitivity of the ETAM and EFAM approaches was not significantly different, ETAM had greater specificity compared with EFAM (94.3 vs. 86.1%). These results indicate that for the intraoperative functional brain mapping, ETAM is a promising novel approach for motor cortex localization with the potential to reduce the need for cortical electrical stimulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".