Repeated seizures lead to altered skilled behaviour and are associated with more highly efficacious excitatory synapses
Bibliographic record
Abstract
People with epilepsy have a high incidence of interictal behavioural problems that appear to be related to the location of their seizure focus. This study investigated a novel test of the hypotheses that repeated seizures result in behavioural deficits and altered performance during the interictal state, and that those behaviours are related to the presence of more highly efficacious excitatory synapses. We tested these hypotheses by first repeatedly eliciting seizures with electric current through indwelling electrodes in the corpus callosum at the level of the caudal forelimb area of sensorimotor neocortex in the rat. We then assessed learned skilled behaviours that primarily utilize the forelimbs on tasks that are sensitive to the functional integrity of that structure. We observed both behavioural deficits and altered kinematic performance in rats that experienced repeated neocortical seizures relative to an electrode-implanted control group. From a separate set of rats, tissue was prepared for quantification of thickness and excitatory synaptic subtypes from neocortical layer V. We observed significantly increased numbers of perforated synapses that make their connections directly onto the dendritic shaft at 3 weeks following the last seizure. Altered reaching behaviours are likely due to neural reorganization in the neocortex including more efficacious synapses.
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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".