Protocol for synchronized wireless fiber photometry and video recordings in rodents during behavior
Bibliographic record
Abstract
Fiber photometry technique allows investigation of in vivo neural activity during behavior allowing understanding of brain-behavior relationship. Here, we provide a protocol for synchronized wireless fiber photometry and video recordings in rodents during behavior. We explain the detailed steps for stereotaxic virus injection, optic fiber cannula implantation, setup for synchronized fiber photometry and behavioral recording, and analysis of photometry data. These protocol steps can be adapted for various animal models, photometry, and behavioral recording systems. For complete details on the use and execution of this protocol, please refer to Tamboli et al. 1 and Amalyan et al. 2 • Step-by-step instructions for viral injections and optic fiber cannula implantation • Detailed setup and calibration guidelines for the wireless fiber photometry system • Configuration for synchronization of fiber photometry and behavioral video recordings • Comprehensive guidance on fiber photometry recordings, data extraction, and analysis Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Fiber photometry technique allows investigation of in vivo neural activity during behavior allowing understanding of brain-behavior relationship. Here, we provide a protocol for synchronized wireless fiber photometry and video recordings in rodents during behavior. We explain the detailed steps for stereotaxic virus injection, optic fiber cannula implantation, setup for synchronized fiber photometry and behavioral recording, and analysis of photometry data. These protocol steps can be adapted for various animal models, photometry, and behavioral recording systems.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.060 | 0.029 |
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".