Investigating presence of motion artifacts in the oxygen saturation signal during in-vivo fiber photometry
Bibliographic record
Abstract
Oxygen saturation (sO2) and blood perfusion in brain tissue have been known to be modulated with cellular activity in the brain. A single fiber system (SFS) has previously been shown to enable sO2 measurements from localized deep brain regions in freely moving animals. Reflectance spectra (RSF) obtained through the SFS can be used to understand changes in blood perfusion and fit to an empirical model to extract sO2. The sO2 extracted is dependent on the shape of RSF and thus relatively resistant to noise as compared to blood perfusion which is dependent on the magnitude of RSF at specific wavelengths. While slow changes in sO2 have been shown to be robust, sources of certain relatively rapid temporal variations observed in the sO2 signal remains unclear. Potential sources could be variations in cellular activity in the brain or noise due to motion artifacts. In this work, we have described the design of new experiments focused to investigate the effects of motion artifacts on RSF and sO2. Computer simulations and mathematical modelling have been used to explain the experimental findings. Results suggest that the motion artifacts mainly arise from the fiber/brain interface and appear to offset RSF. Using the interpretation from a mathematical model, we also propose a motion artifact correction algorithm which can potentially be used for comparison of perfusion signals.
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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) | 0.000 | 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".