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Record W4289889770 · doi:10.1002/jbio.202200076

Monitoring stimulus‐evoked hemodynamic response during deep brain stimulation with single fiber spectroscopy

2022· article· en· W4289889770 on OpenAlexafffund
Linhui Yu, M. Sohail Noor, Zelma H. T. Kiss, Kartikeya Murari

Bibliographic record

VenueJournal of Biophotonics · 2022
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHemodynamicsStimulationHaemodynamic responseDeep brain stimulationStimulus (psychology)Blood volumeRespiratory rateBiomedical engineeringMedicineHeart rateAnesthesiaInternal medicinePsychologyBlood pressure

Abstract

fetched live from OpenAlex

Deep brain stimulation (DBS) is a revolutionary treatment for movement disorders. Measuring DBS-induced hemodynamic responses may be useful for surgical guidance of DBS electrode implantation as well as to study the mechanism and assess therapeutic effects of DBS. In this study, we evaluated the performance of a single fiber spectroscopic (SFS) system for measuring hemodynamic response in different cortical layers in a DBS animal model. We showed that SFS is capable of measuring minute relative changes in oxygen saturation and blood volume fraction in-vivo at a sampling rate of 22-33 Hz. During stimulation, blood volume fraction increased, while oxygen saturation showed both increases and decreases at different cortical depths across animals. In addition, we showed the potential of using SFS for measuring other physiological parameters, for example, heart rate, and respiratory rate.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.282
Teacher spread0.272 · 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 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

Citations3
Published2022
Admission routes2
Has abstractyes

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