Fiber photometry for monitoring cerebral oxygen saturation in freely-moving rodents
Bibliographic record
Abstract
Hemodynamic parameters, such as tissue oxygen saturation and blood volume fraction, are important markers of brain physiology. They are also widely used surrogate markers of electrophysiological activity. Here, we present a single fiber spectroscopic (SFS) system for monitoring cerebral oxygen saturation in localized, non-line-of-sight brain regions in freely-moving rodents. We adapted the implantation ferrule and patch cable design from commercialized optogenetics and fiber photometry systems, enabling stereotaxic fiber implantation, longitudinal tissue access and measurement from freely-moving animals. The optical system delivers and collects light from the brain through a 200 µm-core-diameter, 0.39NA multimode fiber. We robustly measured oxygen saturation from phantoms with different optical properties mimicking brain tissue. In mice, we demonstrated, for the first time, measurements of oxygen saturation from a highly-localized, targeted brain region over 31 days and continuous measurements from a freely-moving animal for over an hour. These results suggest that single fiber spectroscopy has enormous potential for functional brain monitoring and investigating neurovascular coupling in freely-moving animals. In addition, this technique can potentially be combined with fiber photometry systems to correct for hemodynamic artifacts in the fluorescence detection.
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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 | 0.000 |
| 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 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".