Characterizing the Effects of Subconcussive Impact Biomechanics on Resting-State Brain Hemodynamics and Functional Connectivity
Bibliographic record
Abstract
Background: The study of impact biomechanics in contact sports has improved our current understanding of concussion mechanisms and the cumulative effects of subconcussive impacts on brain health. Impact exposure is often described by the total insults an athlete sustains or peak magnitude, however, these metrics do not consider underlying properties of the acceleration-time impact profile. It remains unknown whether additional kinematic information can better differentiate impact exposure across positions and session types or characterize subclinical brain changes. Purpose: The objective of this project was to examine potential differences in the biomechanical properties of impacts sustained by collegiate football athletes. These parameters were also used to evaluate changes in functional connectivity and resting perfusion over a season of football. Methods: Helmet accelerometer data were analyzed to characterize subconcussive impact exposure among collegiate football athletes. Impact frequency (per session), peak linear and rotational magnitude, impact duration, area under the acceleration-time curve, impulse, and peak head jerk were used to differentiate mechanical loading events between positional groups, as well as across session types. Resting-state neuroimaging was also used to evaluate the relationship between positional group, impact biomechanics, and concussion history with changes in functional connectivity and resting perfusion in a subset of athletes following subconcussive impact exposure. Results: Biomechanical differences were found in all parameters of interest between session types and positional groups. Several properties of the linear acceleration profile, in addition to rotational velocity, highlighted alterations in regional hemodynamics and functional connectivity within the brain, whereas no such differences were observed using impact count or peak linear acceleration alone. Scaling the functional connectivity data by resting perfusion altered the observed differences in some regions of the brain, highlighting the shared variance that exists between functional network re-organization and perturbations to local physiology following subconcussive impact exposure. Conclusion: These findings indicate that kinematic profile analyses may provide novel insight beyond impact count or peak magnitude that allows for a more complete characterization of impact biomechanics. Altogether, this approach creates a strong paradigm for future studies to examine how these impact parameters relate to injury risk following exposure to repetitive subconcussive head impacts.
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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 | 0.000 | 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) | 0.003 | 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".