Presurgical hyperconnectivity of the ablation volume is associated with seizure-freedom after magnetic resonance-guided laser interstitial thermal therapy
Bibliographic record
Abstract
PURPOSE: Magnetic Resonance-guided Laser Interstitial Thermal Therapy (MRgLITT) is an emerging minimally-invasive alternative to resective surgery for medically-intractable epilepsy. The precise lesioning effect produced by MRgLITT supplies opportunities to glean insights into epileptogenic regions and their interactions with functional brain networks. In this exploratory analysis, we sought to characterize associations between MRgLITT ablation zones and large-scale brain networks that portended seizure outcome using resting-state fMRI. METHODS: Presurgical fMRI and intraoperatively volumetric structural imaging were obtained, from which the ablation volume was segmented. The network properties of the ablation volume within the brain's large-scale brain networks were characterized using graph theory and compared between children who were and were not rendered seizure-free. RESULTS: Of the seventeen included children, five achieved seizure freedom following MRgLITT. Greater functional connectivity of the ablation volume to canonical resting-state networks was associated with seizure-freedom (p < 0.05, FDR-corrected). The ablated volume in children who subsequently became seizure-free following MRgLITT had significantly greater strength, and eigenvector centrality within the large-scale brain network. CONCLUSIONS: These findings provide novel insights into the interaction between epileptogenic cortex and large-scale brain networks. The association between ablation volume and resting-state networks may supply novel avenues for presurgical planning and patient stratification.
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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".