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Record W4401894643 · doi:10.1002/hbm.26811

Uncovering the hidden effects of repetitive subconcussive head impact exposure: A mega‐analytic approach characterizing seasonal brain microstructural changes in contact and collision sports athletes

2024· review· en· W4401894643 on OpenAlexaff
Anna Kwiatkowski, Carmen Weidler, Ute Habel, Nicole S. Coverdale, Adnan A. Hirad, Kathryn Y. Manning, Alexander Rauscher, Jeffrey J. Bazarian, Douglas J. Cook, David K.B. Li, Bradford Z. Mahon, Ravi S. Menon, Jack Taunton, Kathrin Reetz, Sandro Romanzetti, Charlotte Huppertz

Bibliographic record

VenueHuman Brain Mapping · 2024
Typereview
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsWestern UniversityUniversity of British Columbia HospitalAlberta Children's HospitalUniversity of CalgaryUniversity of British ColumbiaQueen's University
FundersRWTH Aachen University
KeywordsVoxelConcussionWhite matterDiffusion MRIGrey matterPsychologyPoison controlMedicineMagnetic resonance imagingInjury preventionRadiology

Abstract

fetched live from OpenAlex

Repetitive subconcussive head impacts (RSHI) are believed to induce sub-clinical brain injuries, potentially resulting in cumulative, long-term brain alterations. This study explores patterns of longitudinal brain white matter changes across sports with RSHI-exposure. A systematic literature search identified 22 datasets with longitudinal diffusion magnetic resonance imaging data. Four datasets were centrally pooled to perform uniform quality control and data preprocessing. A total of 131 non-concussed active athletes (American football, rugby, ice hockey; mean age: 20.06 ± 2.06 years) with baseline and post-season data were included. Nonparametric permutation inference (one-sample t tests, one-sided) was applied to analyze the difference maps of multiple diffusion parameters. The analyses revealed widespread lateralized patterns of sports-season-related increases and decreases in mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) across spatially distinct white matter regions. Increases were shown across one MD-cluster (3195 voxels; mean change: 2.34%), one AD-cluster (5740 voxels; mean change: 1.75%), and three RD-clusters (817 total voxels; mean change: 3.11 to 4.70%). Decreases were shown across two MD-clusters (1637 total voxels; mean change: -1.43 to -1.48%), two RD-clusters (1240 total voxels; mean change: -1.92 to -1.93%), and one AD-cluster (724 voxels; mean change: -1.28%). The resulting pattern implies the presence of strain-induced injuries in central and brainstem regions, with comparatively milder physical exercise-induced effects across frontal and superior regions of the left hemisphere, which need further investigation. This article highlights key considerations that need to be addressed in future work to enhance our understanding of the nature of observed white matter changes, improve the comparability of findings across studies, and promote data pooling initiatives to allow more detailed investigations (e.g., exploring sex- and sport-specific effects).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.006
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0090.005
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.061
GPT teacher head0.373
Teacher spread0.312 · 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 designMeta-analysis
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

Citations6
Published2024
Admission routes1
Has abstractyes

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