Motor Map Expansion Following Repeated Cortical and Limbic Seizures Is Related to Synaptic Potentiation
Bibliographic record
Abstract
The effect of experimentally induced seizure activity on the functional reorganization of motor maps has not previously been investigated. Furthermore, while the functional reorganization of motor maps has been thought to involve increases in synaptic communication, there has yet to be a test of this hypothesis. Here we show that repeated seizure activity (kindling), that is accompanied by increased synaptic strength within adult rat motor cortex, results in a doubling of the caudal forelimb motor area. We measured neo-cortical evoked potentials in the right hemisphere prior to 25 days of electrical kindling of the medial frontal corpus callosum or amygdala and re-measured them either 1 or 21 days following the last kindling session. Then, using high resolution intracortical microstimulation (ICMS), the caudal forelimb area in the left hemisphere was mapped. This is the first report of any procedure causing a motor representation to double in size. Furthermore, this expansion was related to the enhanced area of a neocortical polysynaptic field potential and not the motor convulsions per se. Moreover, both the motor map and field potential enhancements were persistent in nature and could be driven from either cortical or limbic sites. The data have implications for human populations 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 | 0.000 | 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) | 0.001 | 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".