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Record W6884641194 · doi:10.11575/prism/38527

In vivo Assessment of Human Brainstem Cerebrovascular Function: A Multi-Inversion Time Pulsed Arterial Spin Labelling Study

2014· other· en· W6884641194 on OpenAlexfundno aff

Bibliographic record

VenueUniversity of Calgary · 2014
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersCardiff UniversityUniversity of OxfordNatural Sciences and Engineering Research Council of CanadaWellcome Trust
KeywordsPerfusionBrainstemHypercapniaPerfusion scanningPositron emission tomographyIn vivoWhite matterCerebral perfusion pressure

Abstract

fetched live from OpenAlex

The brainstem (BS) is involved in critical physiologic processes, including control of cardiovascular and respiratory functions. This study implements a multi-inversion time pulsed arterial spin labelling (MTI PASL) imaging sequence that addresses the challenges of BS imaging and aims to measure normal and elevated BS perfusion in healthy volunteers. An initial experiment was performed to obtain the kinetic curve of the label in the BS and consequently to estimate the label arrival times and tissue perfusion in seven participants. A second experiment estimated the BS cerebral vascular reactivity (CVR) to hypercapnia in 10 participants. Images were acquired with a gradient-echo sequence with two spiral interleaves and short echo time (TE=2.7 ms). Data were analyzed with a two-compartment model, including a tissue and arterial component. In both experiments, perfusion in the BS was significantly lower than in cortical gray matter (repeated measures analysis of variance (RM-ANOVA), P<0.05), which is as expected since the BS consists of gray and white matter, the latter typically showing lower perfusion. The BS CVR found here is comparable to previous reports obtained with positron emission tomography (PET) imaging. Multi-inversion time pulsed ASL in combination with a two-compartment signal model can be used to assess BS perfusion and CVR.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.314
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.012
GPT teacher head0.238
Teacher spread0.225 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2014
Admission routes1
Has abstractyes

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