Functional connectome contractions in temporal lobe epilepsy: microstructural underpinnings and associations to surgical outcome
Bibliographic record
Abstract
A bstract Temporal lobe epilepsy (TLE) is the most common drug-resistant epilepsy in adults. While commonly related to hippocampal pathology, increasing evidence suggests structural changes beyond the mesiotemporal lobe. Functional anomalies and their link to underlying structural alterations, however, remain incompletely understood. We studied 30 drug-resistant TLE patients and 57 healthy controls using multimodal magnetic resonance imaging analyses. We developed a novel framework that parameterizes functional connectivity distance, consolidating functional and geometric properties of macroscale networks. Compared to controls, TLE showed connectivity distance reductions in temporo-insular and prefrontal networks, suggesting topological segregation of functional networks. Our novel approach furthermore allowed for the testing of morphological and microstructural associations, and revealed that functional connectivity contractions occurred independently from TLE-related cortical atrophy but were mediated by microstructural changes in the underlying white matter. All patients underwent a comparable resective surgery after our study and a regularized supervised machine learning paradigm with 5-fold cross-validation demonstrated that patient-specific functional anomalies predicted post-surgical seizure outcome with 74±8% accuracy, outperforming classifiers operating on clinical and structural imaging features. Our findings suggest connectivity distance contractions as a clinically relevant pathoconnectomic substrate of TLE. Functional topological isolation may represent a microstructurally mediated network mechanism that tilts the balance towards epileptogenesis.
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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 | 0.002 |
| 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".