Support vector machines using EEG features of cross-frequency coupling can predict treatment outcome in Mecp2-deficient mice
Bibliographic record
Abstract
Anti-convulsive drug treatments of epilepsy typically produce varied outcomes from one patient to the next, often necessitating patients to go through several anticonvulsive drug trials until an appropriate treatment is found. The focus of this study is to predict treatment outcome using a priori electroencephalogram (EEG) features for a rare genetic model of epilepsy seen in patients with Rett Syndrome. Previous work on Mecp2-deficient mice, exhibiting the symptoms of Rett syndrome, have revealed EEG-based biomarkers that track the pathology well. Specifically the presence of cross-frequency coupling of the delta-like (3-6 Hz) frequency range phase with the fast ripple (400 - 600 Hz) frequency range amplitude in long duration discharges was found to track seizure pathology. Support Vector Machines (SVM) were trained with features generated from phase-amplitude comodulograms and tested on (n=6) Mecp2-deficient mice to predict treatment outcome to Midazolam, a commonly used anti-convulsive drug. Using SVMs it was shown that it is possible to generate a likelihood score to predict treatment outcomes on all of the animal subjects. Identifying the most appropriate treatment a priori would potentially lead to improved treatment outcomes.
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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) | 0.000 | 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) | 0.000 | 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".