Laser Ablation of Periventricular Nodular Heterotopia for Medically Refractory Epilepsy
Bibliographic record
Abstract
OBJECTIVE: Periventricular nodular heterotopia (PVNH) is the most common neuronal heterotopia, frequently resulting in pharmaco-resistant epilepsy. Here, we characterize variables that predict good epilepsy outcomes following surgical intervention using stereo-electroencephalography (SEEG) -informed magnetic resonance-guided laser interstitial thermal therapy (MRgLITT). METHODS: A retrospective review of consecutive cases from a single high-volume epilepsy referral center identified patients who underwent SEEG evaluation for PVNH to characterize the intervention and outcomes. RESULTS: Thirty-nine patients underwent SEEG-guided MRgLITT of the seizure onset zone (SoZ) in PVNH and associated epileptic tissue. PVNH and polymicrogyria (PMG) were densely sampled with a mean of 16.5 (SD = 2)/209.4 (SD = 36.9) SEEG probes/recording contacts per patient. Ablation principally targeted just the PVNH and cortex that was abnormal on imaging was ablated (5 patients) only if implicated in the SoZ. Volumetric analyses revealed a high percentage of PVNH SoZ ablation (96.6%, SD = 5.3%) in unilateral and bilateral (92.9%, SD = 7.2%) cases. Mean follow-up duration was 31.4 months (SD = 20.9). Seizure freedom (ILAE 1) was excellent: unilateral PVNH without other imaging abnormalities, 80%; PVNH with mesial temporal sclerosis (MTS) or PMG, 63%; bilateral PVNH, 50%. SoZ ablation percentage significantly impacted surgical outcomes (p < 0.001). INTERPRETATION: PVNH plays a central role in seizure genesis as revealed by dense recordings and selective targeting by LITT. MRgLITT represents a transformative technological advance in PVNH-associated epilepsy with seizure control outcomes consistent with those seen in focal lesional epilepsies. In localized unilateral cases and otherwise normal imaging, PVNH ablation without invasive recordings may be considered, and this approach deserves to be explored further. ANN NEUROL 2024;96:1174-1184.
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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 | 0.001 |
| 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".