Workshop on Neurobiology of Epilepsy appraisal: New systemic imaging technologies to study the brain in experimental models of epilepsy
Bibliographic record
Abstract
Modern functional neuroimaging provides opportunities to visualize activity of the entire brain, making it an indispensable diagnostic tool for epilepsy. Various forms of noninvasive functional neuroimaging are now also being performed as research tools in animal models of epilepsy and provide opportunities for parallel animal/human investigations into fundamental mechanisms of epilepsy and identification of epilepsy biomarkers. Recent animal studies of epilepsy using positron emission tomography, tractography, and functional magnetic resonance imaging were reviewed. Epilepsy is an abnormal emergent property of disturbances in neuronal networks which, even for epilepsies characterized by focal seizures, involve widely distributed systems, often in both hemispheres. Functional neuroimaging in animal models now provides opportunities to examine neuronal disturbances in the whole brain that underlie generalized and focal seizure generation as well as various types of epileptogenesis. Tremendous advances in understanding the contribution of specific properties of widely distributed neuronal networks to both normal and abnormal human behavior have been provided by current functional neuroimaging methodologies. Successful application of functional neuroimaging of the whole brain in the animal laboratory now permits investigations during epileptogenesis and correlation with deep brain electroencephalography (EEG) activity. With the continuing development of these techniques and analytical methods, the potential for future translational research on epilepsy is enormous. A PowerPoint slide summarizing this article is available for download in the Supporting Information section here.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".