Cortical thickness analysis in temporal lobe epilepsy
Bibliographic record
Abstract
OBJECTIVE: To assess the reproducibility of neocortical atrophy and its clinical significance across the spectrum of temporal lobe epilepsy (TLE), in particular with respect to postsurgical outcome. METHODS: MRI-based cortical thickness measurement was obtained in 105 patients. A total of 58 had hippocampal atrophy on magnetic resonance volumetry (TLE-HA) and 47 had normal hippocampal volumes (TLE-NV). Twenty-seven patients had repeated scans with a mean interval of 28 months. Patients were compared to 48 age- and sex-matched healthy controls. We used linear models to assess cortical thinning and the effect of seizure control after surgery. Reproducibility of finding cortical atrophy was statistically evaluated using bootstrap simulations. RESULTS: Cross-sectional and longitudinal analyses revealed highly similar topology and rates of neocortical thinning in both TLE groups, predominantly in frontocentral, temporal, and cingulate regions. Bootstrap methods showed that at least 20 subjects per group were necessary to reliably observe these patterns of atrophy in TLE. Moreover, power analysis showed that even with sample sizes of 80 subjects per group, differences in thickness between TLE-HA and TLE-NV would be marginal. With respect to postsurgical outcome, we found an association between residual seizures and atrophy in temporopolar and insular cortices in TLE-HA, and in the posterior quadrant in TLE-NV. CONCLUSION: We demonstrated with a high degree of confidence that static and dynamic effects of epilepsy impact similarly the neocortex of patients with hippocampal atrophy and patients with normal hippocampal volumes. On the contrary, areas predicting unfavorable postsurgical outcome were distinct, suggesting different configurations of epileptogenic networks in these 2 groups.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".