Topographic movie of intracranial ictal high-frequency oscillations with seizure semiology: Epileptic network in Jacksonian seizures
Bibliographic record
Abstract
PURPOSE: We developed a technique to produce images of dynamic changes in ictal high-frequency oscillations (HFOs) >40 Hz recorded on subdural electroencephalography (EEG) that are time-locked to the ictal EEG and ictal semiology video. We applied this technique to Jacksonian seizures to demonstrate ictal HFO propagation along the homunculus in the primary sensory-motor cortex to visualize the underlying epileptic network. METHODS: We analyzed intracranial ictal EEGs from two patients with intractable Jacksonian seizures who underwent epilepsy surgery. We calculated the degrees of increase in amplitude within 40-80, 80-200, and 200-300 Hz frequency bands compared to the interictal period and converted them into topographic movies projected onto the brain surface picture. We combined these data with the ictal EEGs and video of the patient demonstrating ictal semiology. KEY FINDINGS: The ictal HFOs began in the sensory cortex and appeared concomitantly with the sensory aura. They then propagated to the motor cortex at the same time that focal motor symptoms evolved. As the seizure progressed, the ictal HFOs spread or reverberated in the rolandic region. However, even when the seizure became secondarily generalized, the ictal HFOs were confined to the rolandic region. In both cases, there was increased amplitude of higher frequency bands during seizure initiation compared to seizure progression. SIGNIFICANCE: This combined movie showed the ictal HFO propagation corresponding to the ictal semiology in Jacksonian seizures and revealed the epileptic network involved in seizure initiation and progression. This method may advance understanding of neural network activities relating to clinical seizure generation and propagation.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".