Combined impact of gray and superficial white matter abnormalities: Implications for epilepsy surgery
Bibliographic record
Abstract
Abstract Objective Drug‐resistant focal epilepsy is associated with abnormalities in the brain in both gray matter (GM) and superficial white matter (SWM). However, it is unknown if both types of abnormalities are important in supporting seizures. Here, we test if surgical removal of GM and/or SWM abnormalities relates to post‐surgical seizure outcome in people with temporal lobe epilepsy (TLE). Methods We analyzed structural imaging data from 143 patients with TLE (pre‐op diffusion magnetic resonance imaging and pre‐op T1‐weighted MRI) and 97 healthy controls. We calculated GM volume abnormalities and SWM mean diffusivity abnormalities and evaluated if their surgical removal distinguished seizure outcome groups post‐surgically. Results At a group level, GM and SWM abnormalities were most common in the ipsilateral temporal lobe and hippocampus in people with TLE. Analyzing both modalities together, compared to in isolation, improved surgical outcome discrimination (GM area under the curve [AUC] = 0.68, p < 0.01; WM AUC = 0.65, p < 0.01; Union AUC = 0.72, p < 0.01; Concordance AUC = 0.64, p = 0.04). In addition, 100% of people who had all concordant abnormal regions resected had International League Against Epilepsy (ILAE) 1,2 outcomes. Significance Resecting abnormalities in GM or SWM individually affects surgical outcomes but combining both provides clearer patient group distinctions. This approach improves outcome differentiation, showing higher rates of patients living without disabling seizures when all concordant abnormal regions are resected. These findings suggest that regions identified as abnormal from both diffusion‐weighted and T1‐weighted MRI are involved in the epileptogenic network and that resection of both types of abnormalities may enhance the chances of living without disabling seizures.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".