P.036 Exploring changes in functional connectivity after a first unprovoked seizure: an fMRI resting state and movie-driven data study
Bibliographic record
Abstract
Background: A single unprovoked seizure occurs in up to 10% of patients, but does not necessarily develop into epilepsy. It is unclear what determines the susceptibility to develop epilepsy. Although brain network changes have been ascertained in people with epilepsy, this field has not been studied in single seizure patients. Methods: Using 7T resting-state fMRI scanning, and co-registration watching a movie for naturalistic analysis of functional connectivity (Fc). Whole brain, Fc and Brodmann areas were analyzed using phase similarity measures and graph theory. Results: Ten patients with a single unprovoked seizure and fourteen age-and sex-matched healthy controls were recruited. Baseline characteristics were similar. Fc at baseline had no differences between groups. Movie-driven analysis did not show a significant difference overall regions but we observed significant differences in default mode and Visual association cortex as well as Dorsal posterior cingulate cortex (Dorsal PCC). Conclusions: Although no network connectivity differences were found between patients and controls, when movie-driven data was analyzed, differences were seen when comparing patients in the default mode network, visual association cortex, and dorsal posterior cingulate.
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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) | 0.006 | 0.001 |
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".