Seizure freedom after surgical resection of diffusion‐weighted magnetic resonance imaging abnormalities
Bibliographic record
Abstract
Abstract Objective Successful epilepsy surgery requires accurate localization and removal of the epileptogenic zone. Neuroimaging helps detect structural brain abnormalities to guide surgery, but current clinical practice does not use diffusion‐weighted magnetic resonance imaging (dwMRI). However, previous work has shown that diffusion abnormalities are present in epilepsy and may relate to the epileptogenic zone. Here, we investigate whether surgical resection of diffusion abnormalities relates to postoperative seizure freedom. Methods We investigated the association between surgical resection of diffusion abnormalities and postoperative seizure freedom in 200 individuals with drug‐resistant focal epilepsy using dwMRI. A cohort of 97 healthy controls provided a normative baseline for dwMRI metrics, allowing calculation of voxelwise z ‐scores to identify abnormal clusters in both gray and white matter. Results Surgical resections overlapping with the largest abnormal cluster significantly correlated with sustained seizure freedom at 12 months (83% vs. 55%; ) and over 5 years (). Notably, resecting only a small proportion of the largest cluster was associated with better seizure outcomes than cases with no resection of this cluster (). Furthermore, sparing the largest cluster but resecting other large clusters still improved seizure freedom rates compared to no overlap (). Significance Our results suggest that abnormal clusters, identified using dwMRI, are integral to the epileptogenic network, and even a partial removal of such an abnormal cluster is sufficient to achieve seizure freedom. This study highlights the potential of incorporating dwMRI into presurgical planning to improve outcomes in focal epilepsy by reliably identifying and targeting diffusion abnormalities.
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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".