Temporal‐plus epilepsy in children: A connectomic analysis in magnetoencephalography
Bibliographic record
Abstract
OBJECTIVE: Seizure recurrence following surgery for temporal lobe (TL) epilepsy may be related to extratemporal epileptogenic foci, so-called temporal-plus (TL+) epilepsy. Here, we sought to leverage whole brain connectomic profiling in magnetoencephalography (MEG) to identify neural networks indicative of TL+ epilepsy in children. METHODS: Clinical and MEG data were analyzed for 121 children with TL and TL+ epilepsy spanning 20 years at the Hospital for Sick Children. Resting-state connectomes were derived using the weighted phase lag index from neuromagnetic oscillations. Multidimensional associations between patient connectomes, TL versus TL+ epilepsy, seizure freedom, and clinical covariates were performed using a partial least squares (PLS) analysis. Bootstrap resampling statistics were performed to assess statistical significance. RESULTS: A single significant latent variable representing 66% of the variance in the data was identified with significant contributions from extent of epilepsy (TL vs TL+), duration of illness, and underlying etiology. This component was associated with significant bitemporal and frontotemporal connectivity in the theta, alpha, and beta bands. By extracting a brain score, representative of the observed connectivity profile, patients with TL epilepsy were dissociated from those with TL+, independent of their postoperative seizure outcome. SIGNIFICANCE: By analyzing 121 connectomes derived from MEG data using a PLS approach, we find that connectomic profiling could dissociate TL from TL+ epilepsy. These findings may inform patient selection for resective procedures and guide decisions surrounding invasive monitoring.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| 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".