Altered resting state EEG microstate dynamics in acute-phase pediatric mild traumatic brain injury.
Bibliographic record
Abstract
Objective: Sport-related concussion presents significant diagnostic and monitoring challenges, especially in youth populations. This study investigates the potential of EEG microstate analysis as a tool for assessing acute-phase brain activity changes in adolescent male athletes following a concussion. We analyzed resting-state EEG data from 32 participants in a between-subjects design, comparing participants with acute concussion (within two weeks of injury) to an age- and sex- matched sample with no reported history of concussion. Methodology: We applied a modified k-means clustering algorithm to group resting-state EEG topographical maps into seven clusters, with each cluster represented by one of the canonical microstate classes (A-G). Average duration, occurrence rate, and time coverage for each microstate were extracted. Results: Statistically significant differences in mean duration, occurrence rate, and time coverage of microstates B and E were observed. Specifically, the mean duration, occurrence and time coverage of microstate E showed a significant decrease in the concussed cohort in comparision to the controls (p < 0.001). In addition, the mean duration, occurrence rate and time coverage was higher in the concussed cohort in comparision with the healthy cohort (p = 0.003). A significant negative linear relationship was found between microstate E and symptom severity (p = 0.006, F = 15.72). Discussion: These results suggest that mild traumatic brain injury may disrupt the dynamic interaction of large-scale brain networks, hinting at potential biomarkers of injury. This study may help to inform future work on objective, brain-based tools for diagnosis and recovery assessment in concussed adolescents. Further research in larger, more diverse populations is necessary to validate these potential biomarkers.
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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".