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Record W4286587044 · doi:10.1101/2022.07.21.22277627

Changes in brain structure and function in a multisport cohort of retired female and male athletes, many years after suffering a concussion. The ICHIRF-BRAIN Study

2022· preprint· en· W4286587044 on OpenAlexaff
Michael S. Turner, Antonio Belli, Rudolph J. Castellani, Paul McCrory

Bibliographic record

VenuemedRxiv · 2022
Typepreprint
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsTraffic Injury Research Foundation
Fundersnot available
KeywordsMiddle frontal gyrusSupramarginal gyrusConcussionPsychologyVoxel-based morphometryNeuroscienceGrey matterSuperior frontal gyrusLingual gyrusMiddle temporal gyrusMedicineCognitionPoison controlWhite matterMagnetic resonance imagingInjury preventionFunctional magnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

Abstract Mild traumatic brain injury is widely regarded as a misnomer: it is globally a major cause of disability and is hypothesized as a potential causal factor in subsequent neurodegeneration. Commonly arising in sport, mounting evidence of varying degrees of cognitive impairment in retired athletes exposed to repeated concussions motivates close examination of its cumulative effects on the brain. Studying a cohort of 125 retired athletes with a mean of 11 reported concussions and 36 matched controls with none, here we evaluated whole-brain volumetric and subcortical morphological effects with Bayesian regression models and functional connectivity effects with network-based statistics. Estimates of potential cognitive impact were derived from meta-analytic functional mapping based on 13,459 imaging studies. Across the array of brain structural and functional effects identified, regions significantly lower in volume in the concussed group included, in order of greatest effect size, the middle frontal gyrus, hippocampus, supramarginal gyrus, temporal pole, and inferior frontal gyrus. Conversely, brain regions significantly larger within the athlete group included, in order of greatest effect size, the hippocampal and collateral sulcus, middle occipital gyrus, medial orbital gyrus, caudate nucleus, lateral orbital gyrus, and medial segment to the postcentral gyrus (all significant with 95% Bayesian credible interval). Subcortical morphology analysis corroborated these findings, revealing a significant, age-independent relationship between inward deformation of the hippocampus and the number of concussions sustained (corrected- p <0.0001). Functional connectivity analyses revealed a distinct brain network with significantly increased edge strength in the athlete cohort comprising 150 nodes and 400 edges (corrected- p =0.02), with the highest degree nodes including the pre-central and post-central gyri and right insula. The functional communities of the greatest eigenvector centralities corresponded to motor domains. Numerous edges of this network strengthened in athletes were significantly weakened with increasing bouts of concussion, which included disengagement of the frontal pole, superior frontal, and middle frontal gyri ( p =0.04). Aligned to meta-analytic neuroimaging data, the observed changes suggest possible functional enhancement within the motor, sensory, coordination, balance, and visual processing domains in athletes, attenuated by concussive head injury with a negative impact on memory and language. That such changes are observed many years after retirement from impact sport suggests strong repetition effects and/or underpinning genetic selection factors. These findings suggest that engagement in sport may benefit the brain across numerous domains, but also highlights the potentially damaging effects of concussive head injury.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.036
GPT teacher head0.314
Teacher spread0.278 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2022
Admission routes1
Has abstractyes

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