White-matter diffusion abnormalities in temporal-lobe epilepsy with and without mesial temporal sclerosis
Bibliographic record
Abstract
BACKGROUND: Although epilepsy is considered a grey-matter disorder, changes in the underlying brain connectivity have important implications in seizure generation and propagation. Abnormalities in the temporal and extratemporal white matter of patients with temporal-lobe epilepsy (TLE) and mesial temporal sclerosis (MTS) have previously been identified. Patients with TLE but without MTS often show a different course of the disorder and worse surgical outcome than patients with MTS. The purpose of this study was to determine if said white-matter abnormalities are related to the presence of MTS or if they are also present in non-lesional TLE. METHODS: Seventeen patients with TLE and MTS (TLE+uMTS), 13 patients with non-lesional TLE (nl-TLE) and 25 controls were included in the study. Diffusion tensor imaging (DTI) was used to assess tract integrity of the fornix, cingulum, external capsules and the corpus callosum. RESULTS: The white-matter abnormalities seen in the fornix appear to be exclusive to patients with MTS. Although the cingulum showed an abnormally high overall diffusivity in both TLE groups, its anisotropy was decreased only in the TLE+uMTS group in a pattern similar to the fornix. The frontal and temporal components of the corpus callosum, as well as the external capsules, demonstrated reduced anisotropy in TLE regardless of MTS. CONCLUSIONS: While some white-matter bundles are affected equally in both forms of TLE, abnormalities of the bundles directly related to the mesial temporal structures (ie, the fornix and cingulum) appear to be unique to TLE+uMTS.
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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".