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Blood biomarkers are associated with brain function and blood flow following sport concussion

2018· article· en· W2789449947 on OpenAlexafffund
Alex P. Di Battista, Nathan W. Churchill, Tom A. Schweizer, Shawn G. Rhind, Doug Richards, Andrew Baker, Michael G. Hutchison

Bibliographic record

VenueJournal of Neuroimmunology · 2018
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsSt. Michael's HospitalUniversity of TorontoDefence Research and Development Canada
FundersCanadian Institute for Military and Veteran Health ResearchHealth Research
KeywordsConcussionMedicineCerebral blood flowBrain functionBlood flowPhysical medicine and rehabilitationInternal medicineNeurosciencePsychologyPoison controlInjury preventionEmergency medicine

Abstract

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BACKGROUND: Secondary injury pathophysiology after sport-related concussion (SRC) is poorly understood. Blood biomarkers may be a useful tool for characterizing these processes, yet there are limitations in their application as a single modality. Combining blood biomarker analysis with advanced neuroimaging may help validate their continued utility in brain injury research by elucidating important secondary injury mechanisms. Hence, the purpose of this study was to evaluate co-modulation between peripheral blood biomarkers and advanced functional brain imaging after SRC. METHODS: Forty-three university level athletes from 7 sports were recruited (16 recently concussed athletes; 15 healthy athletes with no prior history of concussion; 12 healthy athletes with a history of concussion). Seven blood biomarkers were evaluated: s100B, total tau (T-tau), von Willebrand factor (vWF), brain derived neurotrophic factor (BDNF), peroxiredoxin (PRDX)-6, monocyte chemoattractant protein (MCP)-1 and -4. Resting-state functional MRI was employed to assess global neural connectivity (Gconn), and arterial spin labelling was used to evaluate cerebral blood flow (CBF). We tested for concurrent alterations in blood biomarkers and MRI measures of brain function between athlete groups using a non-parametric, bootstrapped resampling framework. RESULTS: Compared to healthy athletes, recently concussed athletes showed greater concurrent alterations in several peripheral blood biomarker and MRI measures: a decrease in T-Tau and Gconn, a decrease in T-Tau and CBF, a decrease in Gconn with elevated PRDX-6, a decrease in CBF with elevated PRDX-6, and a decrease in Gconn with elevated MCP-4. In addition, compared to healthy athletes with no concussion history, healthy athletes with a history of concussion displayed greater concurrent alterations in blood biomarkers and Gconn; lower GConn covaried with higher blood levels of s100B and MCP-4. CONCLUSION: We identified robust relationships between peripheral blood biomarkers and MRI measures in both recently concussed athletes and healthy athletes with a history of concussion. The results from this combinatorial approach further support that human concussion is associated with inflammation, oxidative stress, and cellular damage, and that physiological perturbations may extend chronically beyond recovery. Finally, our results support the continued implementation of blood biomarkers as a tool to investigate brain injury, particularly in a multimodal framework.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.030
GPT teacher head0.291
Teacher spread0.261 · 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".

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Citations37
Published2018
Admission routes2
Has abstractno

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