Graph-Theoretical Analysis Reveals Disrupted Small-World Organization of Cortical Thickness Correlation Networks in Temporal Lobe Epilepsy
Bibliographic record
Abstract
Temporal lobe epilepsy (TLE) is the most common drug-resistant epilepsy in adults. As morphometric studies have shown widespread structural damage in TLE, this condition is often referred to as a system disorder with disrupted structural networks. Studies based on univariate statistical comparisons can only indirectly test such hypothesis. Graph theory provides a new approach to formally analyze large-scale networks. Using graph-theoretical analysis of magnetic resonance imaging-based cortical thickness correlations, we investigated the structural basis of the organization of such networks in 122 TLE patients and 47 age- and sex-matched healthy controls. Networks in patients and controls were characterized by a short path length between anatomical regions and a high degree of clustering, suggestive of a small-world topology. However, compared with controls, patients showed increased path length and clustering, altered distribution of network hubs, and higher vulnerability to targeted attacks, suggesting a reorganization of cortical thickness correlation networks. Longitudinal analysis demonstrated that network alterations intensify over time. Bootstrap simulations showed high reproducibility of network parameters across random subsamplings, indicating that altered network topology in TLE is a consistent finding. Increased network disruption was associated with unfavorable postoperative seizure outcome, implying adverse effects of epileptogenesis on large-scale network organization.
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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 | 0.003 |
| 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".