Relationships between the epileptic focus and hand area in central epilepsy: combining dipole models and anatomical landmarks
Bibliographic record
Abstract
OBJECT: When considering resection of epileptic generators near the central sulcus, it is essential to define the spatial relationship between the epileptic generator and the primary sensorimotor hand area. In this study, the authors assessed the accuracy of dipole modeling of electroencephalographic spikes and median nerve somatosensory evoked potentials (SSEPs) in defining this relationship preoperatively and noninvasively. METHODS: Epileptic spikes and SSEPs in patients with focal central area epilepsy were represented by dipole models coregistered onto global magnetic resonance images. In patients who underwent surgery, spike dipoles were also compared with findings of electrocorticography (ECoG) and with the resection area. To improve the accuracy of the dipole models, anatomical landmarks of the hand area were used to assess the error in SSEP dipole location, and this error measure was used to correct the location of spike dipoles. Five patients with central epilepsy were studied, three of whom underwent ECoG-guided surgical resections. The location of SSEP dipoles correlated well with anatomical landmarks of the primary sensory hand area. The relative position of the spike and SSEP dipoles correlated well with the patients' ictal symptoms, ECoG findings, and the location of the epileptic focus (as defined by the resection cavity in patients who became seizure free postoperatively). Corrected spike dipoles were located even closer to the resection cavity. CONCLUSIONS: The calculation of the relative location of spike and SSEP dipoles is a simple noninvasive method of determining the relationship between the primary hand area and an epileptic focus in the central area. The spatial resolution of this technique can be further improved using easily identifiable anatomical landmarks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".