Reduced default mode network connectivity in treatment‐resistant idiopathic generalized epilepsy
Bibliographic record
Abstract
PURPOSE: Idiopathic generalized epilepsy (IGE) resistant to treatment is common, but its neuronal correlates are not entirely understood. Therefore, the aim of this study was to examine resting-state default mode network (DMN) functional connectivity in patients with treatment-resistant IGE. METHODS: Treatment resistance was defined as continuing seizures despite an adequate dose of valproic acid (valproate, VPA). Data from 60 epilepsy patients and 38 healthy controls who underwent simultaneous electroencephalography (EEG) and resting-state functional magnetic resonance imaging (fMRI) were included (EEG/fMRI). Independent component analysis (ICA) and dual regression were used to quantify DMN connectivity. Confirmatory analysis using seed-based voxel correlation was performed. KEY FINDINGS: There was a significant reduction of DMN connectivity in patients with treatment-resistant epilepsy when compared to patients who were treatment responsive and healthy controls. Connectivity was negatively correlated with duration of epilepsy. SIGNIFICANCE: Our findings in this large sample of patients with IGE indicate the presence of reduced DMN connectivity in IGE and show that connectivity is further reduced in treatment-resistant epilepsy. DMN connectivity may be useful as a biomarker for treatment resistance.
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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 | 0.000 | 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 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".