Idiopathic-generalized epilepsy shows profound white matter diffusion-tensor imaging alterations
Bibliographic record
Abstract
OBJECTIVES: Idiopathic-generalized epilepsy (IGE) is currently considered to be a genetic disease without structural alterations on conventional MRI. However, voxel-based morphometry has shown abnormalities in IGE. Another method to analyze the microstructure of the brain is diffusion-tensor imaging (DTI). We sought to clarify which structural alterations are present in IGE and the most frequent subsyndrome juvenile myoclonic epilepsy (JME). EXPERIMENTAL DESIGN: We studied 25 patients (13 IGE and 12 JME) and 44 healthy controls with DTI. Fractional anisotropy (FA), mean diffusivity (MD), axial and radial diffusivity (AD/RD) were calculated and group differences were analyzed using tract-based spatial statistics (TBSS). Additionally we performed a target-based classification of TBSS results based on the Freesurfer cortical regions. PRINCIPLE OBSERVATIONS: TBSS showed widespread FA reductions as well as MD and RD increases in patients compared to controls. Affected areas were corpus callosum, corticospinal tract, superior and inferior longitudinal fasciculus and supplementary motor regions. No significant differences were found between JME and IGE subgroups. The target-based classification confirmed a particular involvement of the superior frontal gyrus (mesiofrontal area) in IGE/ME. CONCLUSIONS: IGE and JME patients showed clear microstructural alterations in several large white matter tracts. Similar findings have been reported in rodent models of IGE. Previous, region-of-interest-based DTI studies may have under-estimated the spatial extent of structural loss associated with generalized epilepsy. The comparison of clinically defined JME and IGE groups revealed no significant DTI differences in our cohort.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".