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Record W4386941434 · doi:10.1016/j.xpro.2023.102586

In vivo functional brain mapping using ultra-high-field fMRI in awake common marmosets

2023· article· en· W4386941434 on OpenAlexafffund
Alessandro Zanini, Audrey Dureux, Azadeh Jafari, Kyle M. Gilbert, Peter Zeman, Miranda Bellyou, Alex Li, Cheryl Vander Tuin, Stefan Everling

Bibliographic record

VenueSTAR Protocols · 2023
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCanada First Research Excellence Fund
KeywordsMarmosetCallithrixNeuroscienceComputer scienceProtocol (science)Functional magnetic resonance imagingNeuroimagingPsychologyMedicinePrimateBiologyPathology

Abstract

fetched live from OpenAlex

The common marmoset (Callithrix jacchus) is gaining attention in the field of cognitive neuroscience. The development of an effective protocol for fMRI data acquisition in awake marmosets is a key factor in developing reliable comparative studies. Here, we describe a protocol to obtain fMRI data in awake marmosets using auditory and visual stimulation. We describe steps for surgical and anesthesia procedures, MRI training, and positioning the marmosets within an MRI-compatible body restraint. We then detail fMRI scanning and preprocessing of functional images. For complete details on the use and execution of this protocol, please refer to Jafari et al. (2023).1

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: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.103
GPT teacher head0.344
Teacher spread0.241 · 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
GenreMethods

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

Citations15
Published2023
Admission routes2
Has abstractyes

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