MétaCan
Menu
Back to cohort
Record W4363672413 · doi:10.1016/j.celrep.2023.112369

Co-variations of cerebral blood volume and single neurons discharge during resting state and visual cognitive tasks in non-human primates

2023· article· en· W4363672413 on OpenAlexfundno aff
Julien Claron, Matthieu Provansal, Quentin Salardaine, Pierre Tissier, Alexandre Dizeux, Thomas Deffieux, Serge Picaud, Mickaël Tanter, Fabrice Arcizet, Pierre Pouget

Bibliographic record

VenueCell Reports · 2023
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsnot available
FundersDivision of Agriculture and Natural Resources, University of CaliforniaDystonia Medical Research Foundation CanadaAgence Nationale de la Recherche
KeywordsResting state fMRINeuroscienceNeurovascular bundleFunctional connectivityCerebral blood volumeBrain activity and meditationHuman brainUncorrelatedCognitionCerebral blood flowPsychologyElectroencephalographyBiologyMedicineAnatomyCardiologyMathematics

Abstract

fetched live from OpenAlex

To better understand how the brain allows primates to perform various sets of tasks, the ability to simultaneously record neural activity at multiple spatiotemporal scales is challenging but necessary. However, the contribution of single-unit activities (SUAs) to neurovascular activity remains to be fully understood. Here, we combine functional ultrasound imaging of cerebral blood volume (CBV) and SUA recordings in visual and fronto-medial cortices of behaving macaques. We show that SUA provides a significant estimate of the neurovascular response below the typical fMRI spatial resolution of 2mm 3 . Furthermore, our results also show that SUAs and CBV activities are statistically uncorrelated during the resting state but correlate during tasks. These results have important implications for interpreting functional imaging findings while one constructs inferences of SUA during resting state or tasks.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.538

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 head0.007
GPT teacher head0.238
Teacher spread0.231 · 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 designObservational
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

Citations25
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCell ReportsSame topicPhotoacoustic and Ultrasonic ImagingFrench-language works237,207