Increased High‐frequency Oscillations Precede in vitro Low‐Mg<sup>2+</sup> Seizures
Bibliographic record
Abstract
PURPOSE: High-frequency oscillations (HFOs) in the range of > or = 80 Hz have been recorded in neocortical and hippocampal brain structures in vitro and in vivo and have been associated with physiologic and epileptiform neuronal population activity. Frequencies in the fast-ripple range (> 200 Hz) are believed to be exclusive to epileptiform activity and have been recorded in vitro, in vivo, and in epilepsy patients. Although the presence of HFOs is well characterized, their temporal evolution in the context of transition to seizure activity is not well understood. METHODS: With an in vitro low-magnesium model of spontaneous seizures, we obtained extracellular field recordings (hippocampal regions CA1 and CA3) of interictal, preictal, and ictal activity. Recordings were subjected to power-frequency analysis, in time, by using a local multiscale Fourier transform. The power spectrum was computed continuously and was quantified for each epileptiform discharge into four frequency ranges spanning subripple, ripple, and two fast-ripple frequency bands. RESULTS: A statistically significant increasing trend was observed in the subripple (0-100 Hz), ripple (100-200 Hz), and fast-ripple 1 (200-300 Hz) frequency bands during the epoch corresponding to the transition to seizure (preictal to ictal). CONCLUSIONS: Temporal patterns of HFOs during epileptiform activity are indicative of dynamic changes in network behavior, and their characterization may offer insights into pathophysiologic processes underlying seizure initiation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".