White matter abnormalities associate with type and localization of focal epileptogenic lesions
Bibliographic record
Abstract
OBJECTIVE: To evaluate white matter (WM) integrity of distinct groups of patients with antiepileptic drug (AED)-resistant localization-related epilepsies. METHODS: We used diffusion tensor imaging (DTI) fiber-tractography and voxel-based morphometry (VBM) to investigate differences of WM micro- and macrostructural integrity in patients with different drug-resistant localization-related epilepsies: 17 with temporal lobe epilepsy with magnetic resonance imaging (MRI) signs of hippocampal sclerosis (TLE-HS), 17 with TLE and normal MRI (TLE-NL), 14 with frontal lobe epilepsy and subtle MRI signs of focal cortical dysplasia (FLE-FCD), and 112 healthy controls. We performed fiber-tractography using a semiautomatic deterministic method to yield average fractional anisotropy (FA), axial (AD), and radial (RD) diffusivity ipsilateral and contralateral to the epileptogenic zone of the following tracts based on their functional and anatomic relevance: body of fornix (BoF), body of cingulum (BoC), inferior frontal occipital (IFO), and uncinate fasciculi (UF). In addition, we performed VBM of the WM maps to assess macrostructural integrity differences among groups. RESULTS: TLE-HS had ipsilateral and contralateral decreased FA and increased RD for all tracts. VBM showed WM alterations mainly in the ipsilateral parahippocampal region and contralateral superior temporal gyrus. FLE-FCD showed bilateral FA decreases only in the BoC and ipsilateral RD increases also in the BoC. VBM showed WM reduction mainly in the ipsilateral precuneus and posterior and anterior cingulum. No significant WM alterations were found in the TLE-NL in DTI or VBM analysis. SIGNIFICANCE: WM abnormalities differ in distinct AED-resistant localization-related epilepsies. The diverse distribution of the WM damage in these patients suggests that the localization of the epileptic networks may play a role in the WM burden. However, the distinct degree of this damage, more accentuated in TLE-HS, also suggests that the underlying cause of the epilepsy is probably an additional factor to explain this WM damage.
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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".