White matter abnormalities revealed by DTI correlate with interictal grey matter FDG‐PET metabolism in focal childhood epilepsies
Bibliographic record
Abstract
For patients with focal epilepsy scheduled for surgery, including MRI-negative cases, (18)FDG-PET was shown to disclose hypometabolism in the seizure onset zone. However, it is not clear whether grey matter hypometabolism is informative of the integrity of the surrounding white matter cerebral tissue. In order to study the relationship between metabolism of the seizure onset zone grey matter and the integrity of the surrounding white matter measured by diffusion tensor imaging (DTI), we performed a monocentric prospective study (from 2006 to 2009) in 15 children with pharmacoresistant focal epilepsy, suitable for interictal (18)FDG-PET, T1-, T2-, FLAIR sequence MRI and DTI. Children had either positive or negative MRI (eight with symptomatic and seven with cryptogenic epilepsies, respectively). Seven children subsequently underwent surgery. Standardised uptake values of grey matter PET metabolism were compared with DTI indices (fractional anisotropy [FA], apparent diffusion coefficient [ADC], parallel diffusion coefficient [PDC], and transverse diffusion coefficient [TDC]) in grey matter within the seizure onset zone and adjacent white matter, using regions of interest automatically drawn from individual sulcal and gyral parcellation. Hypometabolism correlated positively with white matter ADC, PDC, and TDC, and negatively with white matter FA. In the cryptogenic group of children, hypometabolism correlated positively with white matter ADC. Our results demonstrate a relationship between abnormalities of grey matter metabolism in the seizure onset zone and adjacent white matter structural alterations in childhood focal epilepsies, even in cryptogenic epilepsy. This relationship supports the hypothesis that microstructural alterations of the white matter are related to epileptic networks and has potential implications for the evaluation of children with MRI-negative epilepsy.
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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 | 0.002 |
| 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".