Tracking cognitive changes in new‐onset epilepsy: Functional imaging challenges
Bibliographic record
Abstract
Functional imaging has potential for tracking changes in cognition during the onset and evolution of epilepsy. Although the concept of imaging such changes over time is an exciting new direction, feasibility remains an open question. The current article outlines a case example in which functional magnetic resonance imaging (fMRI) and event-related potentials (ERPs) were used to monitor memory changes before and after selective temporal lobe resection. From this example, three key methodologic challenges for new-onset epilepsy are identified and discussed. The first challenge relates to the interpretation of results in regions near epileptogenic tissue. We argue that this is best addressed by collecting information from multiple modalities to test for convergent evidence. The second challenge relates to optimizing the methods for sensitivity to detecting changes. In this case, enhanced imaging methods and a region-of-interest approach provide necessary focus. The third and final challenge relates to the practical difficulties of conducting research in new-onset epilepsy cases. We suggest that greater integration of imaging research within the clinical setting is needed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".