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Record W2090608123 · doi:10.3389/fphys.2014.00049

Effects of aging on the association between cerebrovascular responses to visual stimulation, hypercapnia and arterial stiffness

2014· article· en· W2090608123 on OpenAlexafffund
Daniela Flück, Andrew E. Beaudin, Craig D. Steinback, Gopukumar Kumarpillai, Nandavar Shobha, Cheryl R. McCreary, Stefano Peca, Eric E. Smith, Marc J. Poulin

Bibliographic record

VenueFrontiers in Physiology · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsLibin Cardiovascular Institute of AlbertaFoothills Medical CentreAlberta Health ServicesUniversity of Calgary
FundersCanadian Institutes of Health ResearchCanadian Stroke NetworkNatural Sciences and Engineering Research Council of CanadaAlzheimer SocietyHeart and Stroke Foundation of Canada
KeywordsArterial stiffnessHypercapniaMedicineCerebral blood flowMiddle cerebral arteryCardiologyTranscranial DopplerCerebral autoregulationInternal medicineCerebral arteriesAnesthesiaNeurovascular bundleHemodynamicsBlood pressureBlood flowAcidosisSurgeryIschemiaAutoregulation

Abstract

fetched live from OpenAlex

Aging is associated with decreased vascular compliance and diminished neurovascular- and hypercapnia-evoked cerebral blood flow (CBF) responses. However, the interplay between arterial stiffness and reduced CBF responses is poorly understood. It was hypothesized that increased cerebral arterial stiffness is associated with reduced evoked responses to both, a flashing checkerboard visual stimulation (i.e., neurovascular coupling), and hypercapnia. To test this hypothesis, 20 older (64 ± 8 year; mean ± SD) and 10 young (30 ± 5 year) subjects underwent a visual stimulation (VS) and a hypercapnic test. Blood velocity through the posterior (PCA) and middle cerebral (MCA) arteries was measured concurrently using transcranial Doppler ultrasound (TCD). Cerebral and systemic vascular stiffness were calculated from the cerebral blood velocity and systemic blood pressure waveforms, respectively. Cerebrovascular (MCA: young = 76 ± 15%, older = 98 ± 19%, p = 0.004; PCA: young = 80 ± 16%, older = 106 ± 17%, p < 0.001) and systemic (young = 59 ± 9% and older = 80 ± 9%, p < 0.001) augmentation indices (AI) were higher in the older group. CBF responses to VS (PCA: p < 0.026) and hypercapnia (PCA: p = 0.018; MCA: p = 0.042) were lower in the older group. A curvilinear model fitted to cerebral AI and age showed AI increases until ~60 years of age, after which the increase levels off (PCA: R (2) = 0.45, p < 0.001; MCA: R (2) = 0.31, p < 0.001). Finally, MCA, but not PCA, hypercapnic reactivity was inversely related to cerebral AI (MCA: R (2) = 0.28, p = 0.002; PCA: R (2) = 0.10, p = 0.104). A similar inverse relationship was not observed with the PCA blood flow response to VS (R (2) = 0.06, p = 0.174). In conclusion, older subjects had reduced neurovascular- and hypercapnia-mediated CBF responses. Furthermore, lower hypercapnia-mediated blood flow responses through the MCA were associated with increased vascular stiffness. These findings suggest the reduced hypercapnia-evoked CBF responses through the MCA, in older individuals may be secondary to vascular stiffening.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.000
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 head0.007
GPT teacher head0.266
Teacher spread0.259 · 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

Citations96
Published2014
Admission routes2
Has abstractyes

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