MRI analysis in temporal lobe epilepsy: Cortical thinning and white matter disruptions are related to side of seizure onset
Bibliographic record
Abstract
PURPOSE: Past studies reported more widespread structural brain abnormalities in patients with left compared to right temporal lobe epilepsy (TLE), but the profile of these differences remains unknown. This study investigated the relationship between cortical thinning, white matter compromise, epilepsy variables, and the side of seizure onset, in patients with TLE. METHODS: We performed diffusion tensor imaging tractography and cortical thickness analyses of 18 patients with left TLE (LTLE), 18 patients with right TLE (RTLE), and 36 controls. We investigated the relationship among brain structural abnormalities, side of seizure onset, age of seizure onset, and disease duration. KEY FINDINGS: Patients with TLE displayed cortical thinning and white matter compromise, predominately on the side ipsilateral to the seizure onset. Relative to RTLE, patients with LTLE showed more widespread abnormalities, particularly in white matter fiber tracts. Greater compromise in white matter integrity was associated with earlier age of seizure onset, whereas cortical thinning was marginally associated with disease duration. SIGNIFICANCE: These data support previous findings of LTLE showing greater structural compromise than RTLE, and suggest that mechanisms may not be uniform for gray and white matter compromise in patients with LTLE and RTLE. These results may indicate that LTLE is different from RTLE, possibly due to greater vulnerability of the left hemisphere to early injury and the progressive effects of seizures.
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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 | 0.000 |
| 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".