Evidence map›Paper›PMID 38688529›Full record

ReviewJournal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism2024

Time-domain methods for quantifying dynamic cerebral blood flow autoregulation: Review and recommendations. A white paper from the Cerebrovascular Research Network (CARNet).

Kyriaki Kostoglou, Felipe Bello-Robles, Patrice Brassard, Max Chacon, Jurgen Ahr Claassen, Marek Czosnyka, Jan-Willem Elting, Kun Hu, Lawrence Labrecque, Jia Liu and 7 more

Abstract readReviewConsensus Statement
In one paragraph

Review in Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Observational
  3. Automated estimation of the logistic curve model of cerebral COJournal of clinical monitoring and computing · 2026
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  4. Review
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  13. Observational
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  20. The effect of hypercapnia on the directional sensitivity of dynamic cerebral autoregulation and the influence of age and sex.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Kyriaki KostoglouDepartment of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada.ORCID 0000-0002-3513-9286
Felipe Bello-RoblesDepartamento de Ingeniería Informática, Universidad de Santiago de Chile, Santiago, Chile.
Patrice BrassardDepartment of Kinesiology, Faculty of Medicine, Université Laval, Quebec, QC, Canada.ORCID 0000-0002-6254-5044
Max ChaconDepartamento de Ingeniería Informática, Universidad de Santiago de Chile, Santiago, Chile.
Jurgen Ahr ClaassenDepartment of Geriatrics, Radboud University Medical Center, Research Institute for Medical Innovation and Donders Institute, Nijmegen, The Netherlands.ORCID 0000-0002-1778-8151
Marek CzosnykaDepartment of Clinical Neurosciences, Neurosurgery Department, University of Cambridge, Cambridge, UK.
Jan-Willem EltingDepartment of Neurology and Clinical Neurophysiology, University Medical Center Groningen, Groningen, The Netherlands.
Kun HuMedical Biodynamics Program, Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Lawrence LabrecqueDepartment of Kinesiology, Faculty of Medicine, Université Laval, Quebec, QC, Canada.
Jia LiuLaboratory for Engineering and Scientific Computing, Institute of Advanced Computing and Digital Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
Vasilis Z MarmarelisDepartment Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
Stephen J PayneInstitute of Applied Mechanics, National Taiwan University, Taipei, Taiwan.ORCID 0000-0003-1156-2810
Dae Cheol ShinDepartment Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
David SimpsonInstitute of Sound and Vibration Research, University of Southampton, Southampton, UK.ORCID 0000-0001-9072-5088
Jonathan SmirlCerebrovascular Concussion Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada.ORCID 0000-0003-1054-0038
Ronney B PaneraiCerebral Haemodynamics in Ageing and Stroke Medicine (CHiASM), Department of Cardiovascular Sciences, University of Leicester, Leicester, UK.
Georgios D MitsisDepartment of Bioengineering, McGill University, Montreal, QC, Canada.ORCID 0000-0001-9975-5128

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cerebral Autoregulation (CA) is an important physiological mechanism stabilizing cerebral blood flow (CBF) in response to changes in cerebral perfusion pressure (CPP). By maintaining an adequate, relatively constant supply of blood flow, CA plays a critical role in brain function. Quantifying CA under different physiological and pathological states is crucial for understanding its implications. This knowledge may serve as a foundation for informed clinical decision-making, particularly in cases where CA may become impaired. The quantification of CA functionality typically involves constructing models that capture the relationship between CPP (or arterial blood pressure) and experimental measures of CBF. Besides describing normal CA function, these models provide a means to detect possible deviations from the latter. In this context, a recent white paper from the Cerebrovascular Research Network focused on Transfer Function Analysis (TFA), which obtains frequency domain estimates of dynamic CA. In the present paper, we consider the use of time-domain techniques as an alternative approach. Due to their increased flexibility, time-domain methods enable the mitigation of measurement/physiological noise and the incorporation of nonlinearities and time variations in CA dynamics. Here, we provide practical recommendations and guidelines to support researchers and clinicians in effectively utilizing these techniques to study CA.

Indexed as

Cerebrovascular CirculationHomeostasisAnimalsBrainHumansCARNetcerebral autoregulationcerebral blood flowtime-domain methodswhite paper

Identifiers

PMID38688529
PMCPMC11418733

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.