Noninvasive Biomarkers for Assessing the Excitatory/Inhibitory Imbalance in Children with Epilepsy
Bibliographic record
Abstract
Epileptic seizures involve a cortical excitation–inhibition imbalance driven by dysfunctional interneurons that contribute to gamma oscillation generation. While impaired gamma oscillations are commonly reported in epilepsy, the dynamics of broadband and narrowband gamma as well as beta oscillations remain underexplored. These oscillations may serve as noninvasive electrophysiological markers associated with altered cortical dynamics, potentially reflecting underlying excitation–inhibition imbalance in epilepsy. Here, we recorded high-density electroencephalography and magnetoencephalography data to investigate visual stimuli-elicited cortical oscillations in 48 neurotypical (20 females) and 49 children (26 females) with epilepsy. We found that epilepsy is characterized by reduced amplitude and prolonged latency of evoked cortical response compared with controls after visual stimulation, with alterations in N1 peaks and M100, M150, and M250 components ( p < 0.05). Additionally, source imaging revealed disrupted oscillatory features in epilepsy patients, including suppressed power, reduced amplitude, and increased latency in evoked and induced beta and gamma oscillations from the visual cortex ( p < 0.05). These alterations were consistent across diverse epilepsy subtypes, including focal, generalized, and nonlesional epilepsy cases. Utilizing these differences, we developed a novel classification model that differentiates individuals with epilepsy from controls with high accuracy, offering potential clinical utility in epilepsy diagnosis. Our results suggest disrupted beta and gamma oscillations may be associated with impaired inhibitory mechanisms and altered cortical dynamics, potentially indicative of an excitation–inhibition imbalance. Our findings highlight the potential of noninvasive electrophysiological biomarkers to capture cortical dynamics possibly influenced by excitation–inhibition imbalance in epilepsy, supporting their use in early diagnosis and disease monitoring.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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".