Interictal coupling of <scp>HFO</scp>s and slow oscillations predicts the seizure‐onset pattern in mesiotemporal lobe epilepsy
Bibliographic record
Abstract
OBJECTIVE: Low-voltage fast activity (LVF) and low-frequency high-amplitude periodic spiking (PS) are the two most common seizure-onset patterns in mesiotemporal lobe epilepsy, with different underlying mechanisms, pathology, and postsurgical outcome. The present work aims to investigate whether specific coupling patterns of high-frequency oscillations (HFOs >80 Hz) and low-frequency waves in the interictal period may distinguish these two patterns, and also seizure-onset zone (SOZ) from non-SOZ as a secondary aim. METHODS: We used intracranial electroencephalography (iEEG) data (during non-rapid eye movement [NREM] sleep) of 18 patients with either LVF or PS seizure-onset patterns. We investigated the interaction between HFOs (ripples: 80-250 Hz and fast ripples: >250 Hz) and slow oscillations (slow-delta, delta, and theta waves). We compared classic features (amplitude, duration, frequency, and power) and phase of coupling between HFOs and slower oscillations inside and outside the SOZ. We then used these features to classify HFOs and subsequently patients into LVF and PS groups. RESULTS: Ripples in the LVF group had significantly longer duration, lower frequency, and higher amplitude than in the PS group. The phase of slow oscillations at which HFOs occur is different between the LVF and PS HFOs (LVF, mostly at the peak or the transition of peak to trough; PS, mostly during the transition of trough to peak). HFOs associated with theta waves best discriminate seizure-onset patterns. The coupling phase improves the classification of HFOs and patients to either LVF or PS groups, and also the classification of HFOs in SOZ and non-SOZ. SIGNIFICANCE: The phase of coupling of HFOs and low-frequency waves may help to not only identify the SOZ, but also to classify patients with different types of seizure-onset patterns. It likely reflects that different disease processes are involved in these patterns during the interictal period.
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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".