Functional MRI as a tool to assess vision in dogs: the optimal anesthetic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it