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Record W2143763767 · doi:10.1364/oe.18.019386

Optimal quantitation of the cerebral hemodynamic response in functional near-infrared spectroscopy

2010· article· en· W2143763767 on OpenAlexaff
Irina Schelkanova, Vladislav Toronov

Bibliographic record

VenueOptics Express · 2010
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsWavelengthNear-infrared spectroscopySpectroscopyBroadbandOpticsInfraredMaterials scienceFunctional near-infrared spectroscopyNuclear magnetic resonanceBiomedical engineeringPhysicsMedicine

Abstract

fetched live from OpenAlex

We have compared cerebral hemodynamic changes measured by near-infrared spectroscopy (NIRS) with simultaneously acquired BOLD fMRI signals during breath hold challenge in humans. The oxy- and deoxyhemoglobin concentration changes were obtained from the same broadband NIRS data using four different quantitation methods. One method used only two wavelengths (690 nm and 830 nm), and three other methods used broadband data with different spectral fitting algorithms. We found that the broadband techniques employing spectral derivatives were significantly superior to the multi-wavelength methods in terms of the correlation with the BOLD signals. In two cases out of six we found that the time courses of the deoxyhemoglobin changes produced by the two-wavelength method were qualitatively inconsistent with the BOLD fMRI signals.

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 score0.300
Threshold uncertainty score0.381

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 integrity0.0000.001
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 head0.012
GPT teacher head0.292
Teacher spread0.281 · 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

Citations12
Published2010
Admission routes1
Has abstractyes

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