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Record W3127428536 · doi:10.3389/fphys.2021.601369

Insights Into Cerebral Tissue-Specific Response to Respiratory Challenges at 7T: Evidence for Combined Blood Flow and CO2-Mediated Effects

2021· article· en· W3127428536 on OpenAlexafffund
Allen A. Champagne, Alex A. Bhogal

Bibliographic record

VenueFrontiers in Physiology · 2021
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsQueen's University
FundersQueen's UniversityNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsMedicineCerebral blood flowStimulus (psychology)CardiologyInternal medicineNeurosciencePsychology

Abstract

fetched live from OpenAlex

Cerebrovascular reactivity (CVR) mapping is finding increasing clinical applications as a non-invasive probe for vascular health. Further analysis extracting temporal delay information from the CVR response provide additional insight that reflect arterial transit time, blood redistribution, and vascular response speed. Untangling these factors can help better understand the (patho)physiology and improve diagnosis/prognosis associated with vascular impairments. Here, we use hypercapnic (HC) and hyperoxic (HO) challenges to gather insight about factors driving temporal delays between gray-matter (GM) and white-matter (WM). Blood Oxygen Level Dependent (BOLD) datasets were acquired at 7T in nine healthy subjects throughout BLOCK- and RAMP-HC paradigms. In a subset of seven participants, a combined HC+HO block, referred as the “BOOST” protocol, was also acquired. Tissue-based differences in Rapid Interpolation at Progressive Time Delays (RIPTiDe) were compared across stimulus to explore dynamic (BLOCK-HC) versus progressive (RAMP-HC) changes in CO 2 , as well as the effect of bolus arrival time on CVR delays (BLOCK-HC versus BOOST). While GM delays were similar between the BLOCK- (21.80 ± 10.17 s) and RAMP-HC (24.29 ± 14.64 s), longer WM lag times were observed during the RAMP-HC (42.66 ± 17.79 s), compared to the BLOCK-HC (34.15 ± 10.72 s), suggesting that the progressive stimulus may predispose WM vasculature to longer delays due to the smaller arterial content of CO 2 delivered to WM tissues, which in turn, decreases intravascular CO 2 gradients modulating CO 2 diffusion into WM tissues. This was supported by a maintained ∼10 s offset in GM (11.66 ± 9.54 s) versus WM (21.40 ± 11.17 s) BOOST-delays with respect to the BLOCK-HC, suggesting that the vasoactive effect of CO 2 remains constant and that shortening of BOOST delays was be driven by blood arrival reflected through the non-vasodilatory HO contrast. These findings support that differences in temporal and magnitude aspects of CVR between vascular networks reflect a component of CO 2 sensitivity, in addition to redistribution and steal blood flow effects. Moreover, these results emphasize that the addition of a BOOST paradigm may provide clinical insights into whether vascular diseases causing changes in CVR do so by way of severe blood flow redistribution effects, alterations in vascular properties associated with CO 2 diffusion, or changes in blood arrival time.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.311
Teacher spread0.285 · 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

Citations9
Published2021
Admission routes2
Has abstractyes

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