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Record W4312094332 · doi:10.1093/brain/awac485

The biomechanical signature of loss of consciousness: computational modelling of elite athlete head injuries

2022· review· en· W4312094332 on OpenAlexaff
Karl Zimmerman, Janie Cournoyer, Helen Lai, Samuel B. Snider, David Fischer, Simon Kemp, Clara Karton, T. Blaine Hoshizaki, Mazdak Ghajari, David Sharp

Bibliographic record

VenueBrain · 2022
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsImpactOntario Neurotrauma FoundationUniversity of Ottawa
FundersUK Dementia Research InstituteImperial College LondonNational Institute for Health and Care ResearchWellcome Trust
KeywordsConcussionConsciousnessPhysical medicine and rehabilitationFootballPoison controlPsychologyInjury preventionMedicineNeuroscienceMedical emergencyHistory

Abstract

fetched live from OpenAlex

Sports related head injuries can cause transient neurological events including loss of consciousness and dystonic posturing. However, it is unknown why head impacts that appear similar produce distinct neurological effects. The biomechanical effect of impacts can be estimated using computational models of strain within the brain. Here, we investigate the strain and strain rates produced by professional American football impacts that led to loss of consciousness, posturing or no neurological signs. We reviewed 1280 National Football League American football games and selected cases where the team's medical personnel made a diagnosis of concussion. Videos were then analysed for signs of neurological events. We identified 20 head impacts that showed clear video signs of loss of consciousness and 21 showing clear abnormal posturing. Forty-one control impacts were selected where there was no observable evidence of neurological signs, resulting in 82 videos of impacts for analysis. Video analysis was used to guide physical reconstructions of these impacts, allowing us to estimate the impact kinematics. These were then used as input to a detailed 3D high-fidelity finite element model of brain injury biomechanics to estimate strain and strain rate within the brain. We tested the hypotheses that impacts producing loss of consciousness would be associated with the highest biomechanical forces, that loss of consciousness would be associated with high forces in brainstem nuclei involved in arousal and that dystonic posturing would be associated with high forces in motor regions. Impacts leading to loss of consciousness compared to controls produced higher head acceleration (linear acceleration; 81.5 g ± 39.8 versus 47.9 ± 21.4; P = 0.004, rotational acceleration; 5.9 krad/s2 ± 2.4 versus 3.5 ± 1.6; P < 0.001) and in voxel-wise analysis produced larger brain deformation in many brain regions, including parts of the brainstem and cerebellum. Dystonic posturing was also associated with higher deformation compared to controls, with brain deformation observed in cortical regions that included the motor cortex. Loss of consciousness was specifically associated with higher strain rates in brainstem regions implicated in maintenance of consciousness, including following correction for the overall severity of impact. These included brainstem nuclei including the locus coeruleus, dorsal raphé and parabrachial complex. The results show that in head impacts producing loss of consciousness, brain deformation is disproportionately seen in brainstem regions containing nuclei involved in arousal, suggesting that head impacts produce loss of consciousness through a biomechanical effect on key brainstem nuclei involved in the maintenance of consciousness.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.148
GPT teacher head0.401
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreReview

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".

Quick stats

Citations32
Published2022
Admission routes1
Has abstractyes

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