Cortical Excitability in Temporal Lobe Epilepsy with Bilateral Tonic-Clonic Seizures
Bibliographic record
Abstract
OBJECTIVE: We investigated motor cortical excitability (CE) in unilateral temporal lobe epilepsy (TLE) and its relationship to bilateral tonic-clonic seizure (BTCS) using paired-pulse transcranial magnetic stimulation (TMS). METHODS: In this cross-sectional study, we enrolled 46 unilateral TLE patients and 16 age-and sex-matched healthy controls. Resting motor thresholds (RMT); short-interval intracortical inhibition (SICI, GABAA receptor-mediated); facilitation (ICF, glutamatergic-mediated) with interstimulus intervals (ISIs) of 2, 5, 10, and 15 ms; and long-interval intracortical inhibition (LICI, GABAB receptor-mediated) with ISIs of 200-400 ms were measured via paired-pulse TMS. Comparisons were made between controls and patients with TLE, and then among the TLE subgroups (no BTCS, infrequent BTCS and frequent BTCS subgroup). RESULTS: Compared with controls, TLE patients had higher RMT, lower SICI and higher LICI in both hemispheres, and higher ICF in the ipsilateral hemisphere. In patients with frequent BTCS, cortical hyperexcitability in the ipsilateral hemisphere was found in a parameter-dependent manner (SICI decreased at a stimulation interval of 5 ms, and ICF increased at a stimulation interval of 15 ms) compared with patients with infrequent or no BTCS. CONCLUSIONS: Our results demonstrate that motor cortical hyper-excitability in the ipsilateral hemisphere underlies the epileptogenic network of patients with active BTCS, which is more extensive than those with infrequent or no BTCS.
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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) | 0.001 | 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".