E.5 Large scale network changes immediately after Magnetic Resonance Imaging-Guided Laser Interstitial Thermal Therapy (MRgLITT) for hypothalamic hamartoma
Bibliographic record
Abstract
Background: Hypothalamic hamartomas (HH) are a challenging cause of seizure in children, partly because the neural circuitry involved in ictogenesis is incompletely understood. We review our institutions’ use of magnetic resonance imaging-guided laser interstitial thermal therapy (MRgLITT) to treat hypothalamic hamartoma (HH) with resting-state fMRI performed immediately before and after ablation. Methods: Seed-based whole brain connectivity to thalamic regions of interest was performed immediately pre- and post- MRgLITT. Multivariable generalized linear models were used to correlate resting-state data with seizure outcomes. Results: Eight patients underwent MRgLITT treatments for HH, with a mean follow up of 29 months. Four patients (50%) were seizure free at 12 months and two (25%) had a significant improvement in seizure frequency. We identified reduced thalamocortical connectivity involving the anterior cingulate and posterior parietal regions, consistent with disconnection of the mammillothalamic tract and interruption of Papez circuit. Large-scale thalamocortical connectivity changes were driven by children who subsequently became seizure free. Conclusions: Disconnection of the mammillothalamic tract and interruption of thalamic circuitry in patients undergoing MRgLITT for HH appears to be associated with improved seizure outcomes. The ability to assess network changes immediately post- MRgLITT could enable operative adjustments to be made mid-procedure to optimize seizure outcome in real time.
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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) | 0.002 | 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".