Larger cortical motor maps after seizures
Bibliographic record
Abstract
Expansion of motor maps occurs in both clinical populations with epilepsy and in experimental models of epilepsy when the frontal lobes are involved. We have previously shown that the forelimb area of the motor cortex undergoes extensive enlargement after seizures, although the extent to which many movement representation areas are altered is not clear. Here we hypothesize that movement representations in addition to the forelimb area will be enlarged after cortical seizures. To test our hypotheses, Long Evans Hooded rats received 20 sessions of callosal (or sham) kindling, and then were subjected to intracortical microstimulation to map several movement representations including the jaw, neck, forelimb, hindlimb, trunk and tail. We found significantly larger total map areas of several movement representations, including movements that could be evoked more posterior than they are in control rats. We also show the presence of more multiple movement sites and lower movement thresholds in kindled rats, suggesting that movements not only overlap and share cortical territory after seizures, but become present in formerly non-responsive sites as they become detectable with our intracortical microstimulation methodology. In summary, several motor map areas become larger after seizures, which may contribute to the interictal motor disturbances that have been documented in patients with epilepsy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".