Systematic Review: Anaesthetic Protocols and Management as Confounders in Rodent Blood Oxygen Level Dependent Functional Magnetic Resonance Imaging (BOLD fMRI)–Part A: Effects of Changes in Physiological Parameters
Bibliographic record
Abstract
Background: To understand brain function in health and disease, functional magnetic resonance imaging (fMRI) is widely used in rodent models. Because animals need to be immobilised for image acquisition, fMRI is commonly performed under anaesthesia. The choice of anaesthetic protocols and may affect fMRI readouts, either directly or via changing physiological balance, and thereby threaten the scientific validity of fMRI in rodents. Methods: The present study systematically reviewed the literature investigating the influence of different anaesthesia regimes and changes in physiological parameters as confounders of blood oxygen level dependent (BOLD) fMRI in rats and mice. Four databases were searched, studies selected according to pre-defined criteria, and risk of bias assessed for each study. Results are reported in two separate articles; this part of the review focuses on effects of changes in physiological parameters. Results: A total of 121 publications was included, of which 49 addressed effects of changes in physiological parameters. Risk of bias was high in all included studies. Blood oxygenation (arterial partial pressure of oxygen (paO2)), ventilation (arterial partial pressure of carbon dioxide (paCO2)) and arterial blood pressure affected BOLD fMRI readouts across various experimental paradigms. Conclusions: Blood oxygenation, ventilation and arterial blood pressure should be monitored and maintained at stable physiological levels throughout experiments. Adequate anaesthetic management and monitoring are crucial to obtain scientifically valid, reproducible results from fMRI studies in rodent models.
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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 | 0.015 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".