Disrupted information flow in resting-state in adolescents with sports related concussion
Bibliographic record
Abstract
ABSTRACT Children and youth are at a greater risk of concussions than adults, and once injured, take longer to recover. A key feature of concussion is a diffuse increase in functional connectivity; yet it remains unclear how changes in functional connectivity relate to the patterns of information flow within resting state networks following concussion and how these relate to brain function. We applied a data-driven measure of directed effective brain connectivity to compare the patterns of information flow in healthy adolescents and adolescents with subacute concussion during the resting state condition. Data from 32 healthy adolescents (mean age =16 years) and 24 concussed adolescents (mean age = 13.8 years) with subacute concussion (< 3 months post injury) took part in the study. Five minutes of resting state data EEG were collected while participants sat quietly with their eyes closed. We applied the Kleeman-Liang information flow rate to measure the transfer of information between the EEG time series of each individual at different source locations, and therefore between different brain regions. Based on the ensemble means of the magnitude of normalized information flow rate, our analysis shows that information flow in the healthy adolescents is characterized by a predominantly (L) lateralized pattern with bidirectional information flow between frontal regions, between frontal and central/temporal regions and between parietal and occipital regions. In contrast, adolescents with concussion show distinct differences in information flow marked by a more symmetrical pattern with connections evenly distributed across the entire brain, increased information flow in the posterior regions of the brain and the emergence of bidirectional, inter-hemispheric connections between the left and right temporal regions of the brain. We also find that the statistical distribution of the normalized information flow rates in each group (control and concussed) is significantly different. Our results are the first to describe altered patterns of information flow in adolescents with concussion as well as differences in the statistical distribution of information flow rate. We hypothesize that the observed changes in information flow in the concussed group are a consequence of the brain injury and indicate functional reorganization of resting state networks.
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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".