Bilateral White Matter Diffusion Changes Persist after Epilepsy Surgery
Bibliographic record
Abstract
PURPOSE: Bilateral white matter diffusion tensor imaging (DTI) abnormalities have been reported in patients with temporal lobe epilepsy (TLE) and unilateral mesial temporal sclerosis (MTS), but it is unknown whether these are functional or structural changes. We performed a longitudinal study in patients with unilateral MTS who were seizure-free for 1 year after surgery to determine whether the observed presurgical white matter diffusion abnormalities were reversible. METHODS: Eight TLE patients with unilateral MTS who were seizure-free after anterior temporal resection and 22 healthy subjects were recruited. DTI was performed before surgery and at 1-year follow-up. Tractography and region-of-interest (ROI) analyses were performed in the fornix, cingulum, genu, and splenium of the corpus callosum and external capsules. Diffusion tensor parameters were compared between groups and before and after surgery in the patient group. RESULTS: The fornix, cingulum, and external capsules showed preoperative bilateral abnormal diffusion parameters (i.e., decreased diffusion anisotropy and increased mean and perpendicular diffusivities). The fornix and cingulum ipsilateral to the resected mesial temporal structures showed signs of wallerian degeneration at 1-year follow-up. The contralateral tracts of the fornix, cingulum, and external capsules, as well as the genu of the corpus callosum, failed to show a normalization of their diffusion parameters. CONCLUSIONS: The irreversibility of the white matter DTI abnormalities on seizure freedom suggests underlying structural abnormalities (e.g., axonal/myelin degradation) as opposed to functional changes (e.g., fluid shifts due to seizures) in the white matter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".