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Record W2040848946 · doi:10.1117/12.808317

Application of a multicompartment dynamical model to multimodal optical imaging for investigating individual cerebrovascular properties

2009· article· en· W2040848946 on OpenAlexaff
Michèle Desjardins, Louis Gagnon, Claudine Gauthier, Rick Hoge, Mathieu Dehaes, Laurence Desjardins-Crépeau, Louis Bherer, Frédéric Lesage

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsUniversité du Québec à MontréalPolytechnique MontréalInstitut Universitaire de Gériatrie de Montréal
Fundersnot available
KeywordsComputer scienceFunctional near-infrared spectroscopyDeoxygenated HemoglobinCerebral blood volumeBiological systemNear-infrared spectroscopyHemodynamicsBlood flowSpectroscopyBlood volumeBiomedical engineeringOpticsHemoglobinNeurosciencePhysicsCardiologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

Biophysical models of hemodynamics provide a tool for quantitative multimodal brain imaging by allowing a deeper understanding of the interplay between neural activity and blood oxygenation, volume and flow responses to stimuli. Multicompartment dynamical models that describe the dynamics and interactions of the vascular and metabolic components of evoked hemodynamic responses have been developed in the literature. In this work, multimodal data using near-infrared spectroscopy (NIRS) and diffuse correlation flowmetry (DCF) is used to estimate total baseline hemoglobin concentration (HBT<sub>0</sub>) in 7 adult subjects. A validation of the model estimate and investigation of the partial volume effect is done by comparing with time-resolved spectroscopy (TRS) measures of absolute HBT<sub>0</sub>. Simultaneous NIRS and DCF measurements during hypercapnia are then performed, but are found to be hardly reproducible. The results raise questions about the feasibility of an all-optical model-based estimation of individual vascular properties.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.272
Teacher spread0.254 · 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 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

Citations2
Published2009
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicOptical Imaging and Spectroscopy TechniquesFrench-language works237,207