Spatial localization and time‐dependant changes of electrographic high frequency oscillations in human temporal lobe epilepsy
Bibliographic record
Abstract
PURPOSE: High frequency oscillations (HFOs) >200 Hz are believed to be associated with epileptic processes. The spatial distribution of HFOs and their evolution over time leading up to seizure onset is unknown. Also, recording HFOs through conventional intracranial electrodes is not well established. We therefore wished to determine whether HFOs could be recorded using commercially available depth macroelectrodes. We also examined the spatial distribution and temporal progression of HFOs during the transition to seizure activity. METHODS: Intracranial electroencephalography (EEG) recordings of 19 seizures were obtained from seven patients with temporal lobe epilepsy using commercial depth or subdural electrodes. EEG recordings were analyzed for frequency content in five spectral bands spanning DC-500 Hz. We examined the spatial distribution of the different spectral bands 5 s before and 5 s after seizure onset. Temporal changes in the spectral bands were studied in the 30-s period leading up to seizure onset. RESULTS: Three main observations were made. First, HFOs (100-500 Hz) can be recorded using commercial depth and subdural grid electrodes. Second, HFOs, but not <100 Hz oscillations, were localized to channels of ictal onset (100-200, 400-500 Hz, p < 0.05; 300-400 Hz, p < 0.001). Third, temporal analysis showed increased HFO power for approximately 8 s prior to electrographic onset (p < 0.05). CONCLUSIONS: These results suggest that HFOs can be recorded by depth macroelectrodes. Also, HFOs are localized to the region of primary ictal onset and can exhibit increased power during the transition to seizure. Thus, HFOs likely represent important precursors to 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".