Diffusion tensor imaging abnormalities in photosensitive juvenile myoclonic epilepsy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Multiple structural white matter abnormalities have been described in patients with juvenile myoclonic epilepsy (JME). In the present study, the question of whether microstructural variations exist between the two subgroups of JME, with and without photoparoxysmal responses (PPR positive and negative), was addressed using diffusion tensor imaging. METHODS: A selection of 18 patients (eight PPR positive) from a tertiary epilepsy center diagnosed with JME and 27 healthy controls was studied. The following regions of interest were investigated: the ascending reticular activating system, lateral geniculate nucleus, genu of the internal capsule, ventromedial thalamus and inferior cerebellar peduncle. RESULTS: Widespread white matter microstructural abnormalities in JME and in particular in PPR positive cases were identified. PPR positive patients demonstrated increased fractional anisotropy in the ascending reticular activating system and ventromedial thalamus compared to PPR negative patients and healthy controls. Reduced fractional anisotropy of the lateral geniculate nucleus was observed in the entire JME group compared to healthy controls. CONCLUSIONS: Several microstructural variations between PPR positive and negative JME patients have been identified. Our findings highlight the pivotal role of the thalamus in the pathophysiology of primary generalized seizures and suggest that thalamo-premotor connections are both an essential part of epileptic networks and important in the pathogenesis of photosensitivity.
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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".