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Non-invasive estimation of arterial blood pressure fluctuations using a peripheral photoplethysmograph inside the MRI scanner

2023· article· en· W4389538372 on OpenAlexafffund
Rémi Dagenais, Georgios D. Mitsis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhotoplethysmogramScannerFunctional magnetic resonance imagingSIGNAL (programming language)Magnetic resonance imagingBiomedical engineeringBlood-oxygen-level dependentBlood pressurePhysicsNuclear magnetic resonanceComputer scienceMedicineOpticsInternal medicineComputer visionRadiology

Abstract

fetched live from OpenAlex

The blood-oxygen-level-dependent (BOLD) signal measured by functional magnetic resonance imaging (fMRI) is modulated by neural activity through the neurovascular coupling effect, as well as non-neural factors of physiological origin such as heart rate, respiration, and arterial blood pressure (ABP). While the former two effects have been previously characterized, the modulation of the BOLD signal by ABP fluctuations is still poorly understood. This is largely due to the difficulty of obtaining reliable ABP measurements in the MRI environment. Here, we propose a combined experimental and mathematical modeling framework to estimate ABP fluctuations inside the MRI scanner using photoplethysmography (PPG). Specifically, we used concurrent PPG and ABP measurements obtained outside the scanner to train the mathematical model and applied it to PPG measurements obtained inside the MRI scanner. Our results suggest good agreement between the model-predicted and experimentally measured ABP fluctuations and region specific correlations with the BOLD fluctuations.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.015
GPT teacher head0.306
Teacher spread0.291 · 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 designBench or experimental
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

Citations4
Published2023
Admission routes2
Has abstractyes

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