Functional MRI as a tool to assess vision in dogs: the optimal anesthetic
Bibliographic record
Abstract
Functional magnetic resonance imaging (fMRI) is a recent advance in neuroimaging that provides a picture of brain activity with excellent spatial resolution. Current methods used to evaluate canine vision are poorly standardized and vulnerable to bias. Functional MRI may represent a valuable method of testing vision in dogs if the impacts of anesthesia on fMRI are understood. Six dogs were scanned during visual stimulation, each under three different anesthetic protocols (isoflurane, propofol, fentanyl/midazolam) to address the questions: (1) Can visually evoked fMR signals be reliably recorded in anesthetized dogs? and (2) Which anesthetic agent permits the least suppression of visually induced fMR signal in dogs? This study confirms that visual stimuli reliably elicit neural activity and fMR signal change in anesthetized dogs. No significant differences in images acquired under the three anesthetics were found, and there was no significant relationship between anesthetic dose and brain activity, within the range of doses used in this study. Images obtained during isoflurane anesthesia were more consistent between dogs than those obtained with the other two agents. This reduced variation may reflect the fact that inhalant anesthesia is more easily controlled than intravenous anesthesia under conditions associated with high field fMRI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".