White matter abnormalities revealed by DTI correlate with interictal grey matter FDG‐PET metabolism in focal childhood epilepsies
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 it