Assessment of anesthesia on physiological stability and bold signal reliability during visual or acoustic stimulation in the cat
Bibliographic record
Abstract
Abstract Background Neuroimaging methods including fMRI provide powerful tools to observe whole-brain functional networks. This is particularly powerful in animal models, allowing these networks to be probed using complementary methods. However, most animals must be anesthetized for neuroimaging, giving rise to complications resulting from anesthetic effects on the animal’s physiological and neurological functions. For example, an established protocol for feline neuroimaging involves co-administration of ketamine and isoflurane – the latter of which is known to suppress cortical function. New Method Here, we compare this established protocol to alfaxalone, a single-agent anesthetic for functional neuroimaging. We first compare the two in a controlled environment to assess relative safety and to measure physiological stability over an extended time window. We then compare patterns of auditory and visually-evoked activity measured at 7T to assess mean signal strength and between-subjects signal variability. Results in Comparison with Existing Methods We show that alfaxalone results in more stable respiratory rates over the 120 minutes testing period, with evidence of smaller between measurements variability within this time window, when compared to ketamine plus isoflurane. Moreover, we demonstrate that both agents evoke similar mean BOLD signals across animals, but that alfaxalone elicits more consistent BOLD activity in response to sound stimuli across all ROIs observed. Conclusions Alfaxalone is observed to be more physiologically stable, evoking a more consistent BOLD signal across animals than the co-administration of ketamine and isoflurane. Thus, an alfaxalone-based protocol may represent a better approach for neuroimaging in animal models requiring anesthesia.
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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 | 0.000 | 0.001 |
| 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 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".