Dipole Modeling of Epileptic Spikes Can Be Accurate or Misleading
Bibliographic record
Abstract
PURPOSE: We investigated the accuracy and potential for serious error when representing cortical generators of epileptic spikes with the common single-dipole model. Spike generators were realistically simulated with cortical areas of different extents. METHODS: The source was simulated by using a patch that comprised small triangles on the cortical surface, each triangle having an elementary dipole generator with a moment corresponding to real intracerebral fields of spikes. The source-patch covered various clinically important parts of the temporal and frontal lobes, with an area ranging from 6 to 120 cm2. The scalp field was computed for each source-patch by using a realistic head model and was fitted by the single-dipole model to determine the best-fit dipole and the intracerebral distribution of residual variance (RV). Dipole modeling also was performed for the simulated scalp field with additional real EEG background. RESULTS: The RV after fitting a dipole to the scalp field without noise was at most 1.34%. Scalp spikes arising from sources of 6 cm2 were of small amplitude, and the dipoles estimated for these spikes were inconsistent. Extension of the source area was associated with increase of scalp potential amplitude, only very small increase of RV, and increased consistency of the estimated dipoles. When the source was very large, the dipoles clustered at very misleading locations. CONCLUSIONS: Pitfalls in dipole source localization are caused by the procedure of fitting the simplistic dipole model to real cortical sources with spatial extent and complex configuration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".