Impaired Default Mode Network on Resting-State fMRI in Children with Medically Refractory Epilepsy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Resting-state networks including the DMN have been shown to be abnormal in adults with temporal lobe epilepsy. However, little is known about the DMN in children with medically refractory epilepsy. The aim was to determine whether there was a difference in the DMN in children with medically refractory epilepsy relative to controls. MATERIALS AND METHODS: Eleven children with medically refractory epilepsy and 11 age-matched healthy controls underwent resting-state fMRI. IC analysis was used to identify the DMN. A random-effects analysis was performed on the Z-maps of the DMN within each group and between groups. We calculated the temporal correlation coefficients of pairs of ROIs: PCC/PCUN, mPFC, and left and right lateral parietal cortices. The relations between z scores of temporal correlation coefficients of pairs of ROIs and clinical seizure parameters and IQ were assessed. RESULTS: The patients demonstrated decreased DMN connectivity in the PCC/PCUN, bilateral lateral parietal cortex, and anterior and midcingulate relative to controls. There was reduced connectivity between the mPFC-right lateral parietal cortex, the PCC/PCUN-left lateral parietal cortex, and the PCC/PCUN-right lateral parietal cortex pairs of ROIs in patients compared with controls. There were no significant correlations between the z scores of temporal correlation coefficients of the 6 pairs of ROIs in patients and age of seizure onset, duration of epilepsy, number of medications, seizure frequency, and IQ. CONCLUSIONS: We have found reduced connectivity in the DMN in children with medically refractory epilepsy. Further studies are needed to determine whether different seizure types have different effects on the DMN and whether the impaired connectivity is related to cognitive functions subserved by the DMN.
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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".