Myoclonus cortical generators in Unverricht-Lundborg disease: an electric source imaging study
Bibliographic record
Abstract
Purpose: To localize the cortical generator of myoclonic jerks in progressive myoclonic epilepsy (Unverricht-Lundborg disease) applying the Electrical Source Immaging method (ESI). Method: 3 patients (2 female, 1 male, age: 17–48 yrs) affected by Unverricht- Lundborg disease underwent a 256—channels EEG with concomitant polygraphic recording. Provocative manouvres were conducted during the exam to elicit myoclonic jerks. EEG (electroencephalographic) and EMG (electromyographic) traces were analyzed off line. For the other 2 patients, a back-averaging of the myoclonic EMG activity and corresponding EEG abnormalities was performed. Analysis was conducted off line. A mean of 15 jerk-locked EEG potentials for each patients were averaged, and projected through a LORETA algorithm on an MNI (Montreal Neurological Institute) brain template in order to identify the cortical generator. Result: Jerk-locked EEG potentials were recognizable over the centrofrontal derivations, with a slight lateralization in each single case. ESI elaboration localizes the cortical generator over the anterior premotor frontal cortex (pt 1: Brodman area 6, pt 2: Brodman area 10; pt 3: Brodman area 11), with a lateralization concordant with the EEG potentials (2 right hemisphere, 1 left). Conclusion: ESI is a technique that permits cortical source localization of EEG potentials. Its application to this rare form of epilepsy depicts the important role of the pre-motor and frontal cortex in the myoclonic jerk's generation. To our knowledge, this is the first report describing the cortical source generation from the anterior/premotor cortex. Since only the jerk-locked potential was examined, we can not infer on the involved networks.
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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 | 0.000 |
| Open science | 0.000 | 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".