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Record W3179313498 · doi:10.21037/qims-20-1296

Cerebrovascular reactivity changes in acute concussion: a controlled cohort study

2021· article· en· W3179313498 on OpenAlexaff
Runrun Wang, Julien Poublanc, Adrian P. Crawley, Olivia Sobczyk, Sander C. M. Kneepkens, Larissa McKetton, Charles H. Tator, Renhua Wu, David J. Mikulis

Bibliographic record

VenueQuantitative Imaging in Medicine and Surgery · 2021
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of TorontoToronto Western HospitalUniversity Health Network
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsConcussionMedicineWhite matterProspective cohort studyInternal medicineCardiologyMagnetic resonance imagingPoison controlRadiologyInjury preventionEmergency medicine

Abstract

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Background: Evidence suggests that cerebrovascular reactivity (CVR) increases within the first week after the incidence of concussion, indicating a disruption of normal autoregulation. We sought to extend these findings by investigating the effects of acute concussion on the speed of CVR response and by visualizing global and regional impairments in individual patients with acute concussion. Methods: Twelve patients aged 18–40 years who experienced concussion less than a week before this prospective study were included. Twelve age and sex-matched healthy subjects constituted the control group. In all subjects, CVR was assessed using blood oxygenation level-dependent (BOLD) echo-planar imaging with a 3.0T MRI scanner, in combination with changes in end-tidal partial pressure of CO2 (PETCO2). In each subject, we calculated the CVR amplitude and CVR response time in the gray and white matter using a step and ramp PETCO2 challenge. In addition, a separate group of 39 healthy controls who underwent the same evaluation was used to create atlases with voxel-wise mean and standard deviation of CVR amplitude and CVR response time. This allowed us to convert each metric of the 12 patients with concussion and the 12 healthy controls into z-score maps. These maps were then used to generate and compare z-scores for each of the two groups. Group differences were calculated using an unpaired t-test. Results: All studies were well tolerated without any serious adverse events. Anatomical MRI was normal in all study subjects. No differences in CO2 stimulus and O2 targeting were observed between the two participant groups during BOLD MRI. With regard to the gray matter, the CVR magnitude step (P=0.117) and ramp + 10 (P=0.085) were not significantly different between patients with concussion and healthy controls. However, the tau value was significantly lower in patients with concussion than in the healthy controls (P=0.04). With regard to the white matter, the CVR magnitude step (P=0.003) and ramp + 10 (P=0.031) were significantly higher and the tau value (P=0.024) was significantly shorter in patients with concussion than in healthy controls. After z-score transformation, the z tau value was significantly lower in patients with concussion than in healthy controls (Grey matter P=0.021, White matter P=0.003). Comparison of the three parameters, z ramp + 10, z step, and z tau, between the two groups showed that z step (Grey matter P=0.035, White matter P=0.005) was the most sensitive parameter and that z ramp + 10 (Grey matter P=0.073, White matter P=0.126) was the least sensitive parameter. Conclusions: Concussion is associated with patient-specific abnormalities in BOLD cerebrovascular responsiveness that occur in the setting of normal global CVR. This study demonstrates that the measurement of CVR using BOLD MRI and precise CO2 control is a safe, reliable, reproducible, and clinically useful method for evaluating the state of patients with concussion. It has the potential to be an important tool for assessing the severity and duration of symptoms after concussion.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.020
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.091
GPT teacher head0.396
Teacher spread0.305 · 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 teacher head, 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

Citations5
Published2021
Admission routes1
Has abstractyes

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