Seizure entrainment with polarizing low-frequency electric fields in a chronic animal epilepsy model
Bibliographic record
Abstract
Neural activity can be modulated by applying a polarizing low-frequency (<<100 Hz) electric field (PLEF). Unlike conventional pulsed stimulation, PLEF stimulation has a graded, modulatory effect on neuronal excitability, and permits the simultaneous recording of neuronal activity during stimulation suitable for continuous feedback control. We tested a prototype system that allows for simultaneous PLEF stimulation with minimal recording artifact in a chronic tetanus toxin animal model (rat) of hippocampal epilepsy with spontaneous seizures. Depth electrode local field potentials recorded during seizures revealed a characteristic pattern of field postsynaptic potentials (fPSPs). Sinusoidal voltage-controlled PLEF stimulation (0.5-25 Hz) was applied in open-loop cycles radially across the CA3 of ventral hippocampus. For stimulated seizures, fPSPs were transiently entrained with the PLEF waveform. Statistical significance of entrainment was assessed with Thomson's harmonic F-test, with 45/132 stimulated seizures in four animals individually demonstrating significant entrainment (p < 0.04). Significant entrainment for multiple presentations at the same frequency (p < 0.01) was observed in three of four animals in 42/64 stimulated seizures. This is the first demonstration in chronically implanted freely behaving animals of PLEF modulation of neural activity with simultaneous recording.
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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".