GP.04 Network connectivity following a single unprovoked seizure using 7 Tesla resting-state fMRI
Bibliographic record
Abstract
Background: Predicting epilepsy following a first seizure is difficult. Network abnormalities are observed in patients with epilepsy using resting-state functional MRI (rs-fMRI), which worsen with duration of epilepsy. We use rs-fMRI to identify network abnormalities in patients after a first seizure that can be used as a biomarker to predict development of epilepsy. Methods: Patients after a single, unprovoked seizure and age/sex matched healthy controls underwent 7 Tesla structural and resting-state functional MRI. Data were analyzed using graph theory measures. Patients were followed for development of epilepsy. Results: Nine patients and nine control subjects were analyzed. There were no differences in baseline characteristics. No patients developed epilepsy (average follow-up 3 months). No differences between groups occurred on a whole-brain network level. At a 20% threshold, significant differences occurred in the default mode network (DMN). Patients demonstrated an increased local efficiency (p=0.02) and clustering coefficient (p=0.04), and decreased path length (p=0.02) and betweenness centrality (p=0.02). Conclusions: No whole-brain network changes occur after a single unprovoked seizure. No patient has developed epilepsy suggesting this group does not have network alterations after a single seizure. In the DMN, the alterations noted indicate increased segregation of network function.
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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) | 0.004 | 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".