Nerve fiber impairment of anterior thalamocortical circuitry in juvenile myoclonic epilepsy
Bibliographic record
Abstract
Background: Juvenile myoclonic epilepsy (JME) is a syndrome of idiopathic generalized epilepsy (IGE) without structural brain abnormalities detectable by MRI or CT. Objective: In the present study, we addressed the question of whether diffusion tensor MRI (DTI) can detect disease-specific white matter (WM) abnormalities in patients with JME. Methods: We performed whole head DTI at 3 T in 10 patients with JME, 8 age-matched patients with cryptogenic partial epilepsy (CPE), and 67 age-matched healthy volunteers. Nerve fiber integrity was compared between the groups on the basis of optimized voxel-by-voxel statistics of fractional anisotropy (FA) maps obtained by DTI (analysis of covariance, categorical factor “group,” covariate “age”). Results: FA was reduced in a WM region associated with the anterior thalamus and prefrontal cortex in patients with JME compared to both control subjects and patients with CPE (p < 0.001). The patients with CPE showed normal values in this particular WM region. The FA reductions in the patients with JME correlated with the frequency of generalized tonic-clonic seizures (Spearman R = 0.54, p = 0.05). No significant correlations were found in the JME sample between FA reduction and the duration of antiepileptic medication. Conclusions: The results support the hypothesis that juvenile myoclonic epilepsy is associated with abnormalities of the thalamocortical network that can be detected by diffusion tensor MRI. CPE = cryptogenic partial epilepsy; DTI = diffusion tensor imaging; EPI = echoplanar imaging; FA = fractional anisotropy; GMC = gray matter concentration; GTCS = generalized tonic-clonic seizures; IGE = idiopathic generalized epilepsy; JME = juvenile myoclonic epilepsy; MNI = Montreal Neurological Institute; ROI = region of interest; VBM = voxel based morphometry; WM = white matter.
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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".