Hemodynamic Responses to Interictal Epileptiform Discharges in Children with Symptomatic Epilepsy
Bibliographic record
Abstract
PURPOSE: Simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) (EEG-fMRI) recording is a noninvasive tool for investigating epileptogenic networks. Most EEG-fMRI studies in epilepsy have been performed in adults. Childhood epilepsies, however, differ from those in adults due to interactions between epileptogenic and developmental processes. The purpose of this study was to investigate EEG-fMRI in children with lesional epilepsies. METHODS: Thirteen children with symptomatic epilepsy underwent a 20-min EEG-fMRI acquisition at 3 T under sedation-induced sleep. Statistical analysis was performed using the timing of spikes as events, modelled with hemodynamic response functions (HRFs) that peaked at 3, 5, 7, and 9 s after the spike. RESULTS: Each spike type was analyzed separately, resulting in 25 studies. In 84% of the studies, blood oxygenation level-dependent (BOLD) responses were localized in the lesion or brain area presumably generating spikes. Activation (positive BOLD) corresponding with the lesion was seen in 20% and deactivation (negative BOLD) in 52% of the studies. In the area of spike generation, activation was found in 48% of studies and deactivation in 36%. CONCLUSIONS: Despite the necessarily short recording times (20 min), good results could be obtained from the EEG-fMRI recordings, performed in sedated children using a high field scanner and individual HRFs. In contrast to studies in adults, deactivations in the lesion and the irritative zone were more common than activations. The impact of age, sleep, and sedation on the BOLD response might explain these findings, but future studies in children should not disregard the importance of deactivations in relation to the epileptogenic network.
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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 | 0.002 |
| 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".