Mesial temporal sclerosis is linked with more widespread white matter changes in temporal lobe epilepsy
Bibliographic record
Abstract
Temporal lobe epilepsy patients with unilateral mesial temporal sclerosis (TLE + uMTS) have been demonstrated to have extensive white matter abnormalities both ipsilateral and contralateral to the seizure onset zone. However, comparatively less is known about the white matter integrity of TLE patients without MTS (non-lesional TLE, nl-TLE). The purpose of the study was to investigate the diffusion properties of thirteen major white matter tracts in patients with TLE + uMTS and nl-TLE. Diffusion tensor imaging (DTI) was performed on 23 TLE + uMTS (15 left MTS and 8 right MTS), 15 nl-TLE and 21 controls. Thirteen tracts were delineated by tractography and their diffusion parameters compared for the two TLE groups relative to controls, with left and right hemispheres combined per tract. A subgroup analysis investigated left and right MTS separately. Compared to controls, reduced anisotropy was detected in ten tracts for TLE + uMTS, but only the parahippocampal cingulum and tapetum for nl-TLE. Right MTS subgroup showed reduced anisotropy in 7 tracts bilaterally (3 limbic, 3 association, 1 projection) and 2 tracts ipsilaterally (1 association, 1 projection) and the body of the corpus callosum whereas the left MTS subgroup showed reduced anisotropy in 4 tracts bilaterally (2 limbic, 1 association, 1 projection) and 2 tracts ipsilaterally (1 limbic, 1 association). Diffusion abnormalities in tracts were observed within and beyond the temporal lobe in TLE + uMTS and were more widespread than in nl-TLE. Patients with right MTS had more extensive, bilateral abnormalities in comparison to left MTS. These findings suggest different dysfunctional networks in TLE patients with and without MTS.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".