Evidence-based guidelines for improving network detectability in rodent fMRI
Bibliographic record
Abstract
Abstract Mouse resting-state functional magnetic resonance imaging (rs-fMRI) is an increasingly popular tool for probing brain activity under experimental manipulations; however, there remains considerable variability in data quality throughout the field. There is a need for an accessible set of acquisition guidelines such that a baseline level of data quality can be attained regardless of domain expertise or specialized equipment (i.e. in anesthetized, free-breathing mice). In particular, there is a gap in the literature regarding the interpretation of physiological parameters as markers of anesthetic depth, and ultimately, data quality. To this end, we developed a set of acquisition guidelines after examining whether continuous physiological variables predict network detectability above and beyond categorical external variables (anesthetic dose, session, time) in C57Bl/7 and C3HeB/FeJ mice anesthetized with isoflurane-dexmedetomidine. Standard physiological metrics (respiration rate and heart rate) did not predict network detectability above and beyond anesthetic dose but instead depended strongly on strain and subject, thus we advise against tuning anesthesia based on respiration or heart rate when the goal is obtaining clear resting-state networks. The most important predictor of improved network detectability was a low isoflurane dose of 0.23%, hence we recommend that researchers prioritize piloting the minimal possible isoflurane dose for their mouse model. In summary, our work examines the contributions from sources of variability that impact rs-fMRI data quality and synthesizes the findings into practical guidelines to help experimenters adapt their acquisition protocols and improve data quality.
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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 | 0.161 | 0.447 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".